CHAPTER 4. IMPACTS OF FERAL HORSES ON THE …

Chapter 4. Plateau Exclosure Experiment in Themeda Grassland 85

CHAPTER 4.

IMPACTS OF FERAL HORSES ON THE GROUNDSTOREY VEGETATION OF A TEMPERATE

GRASSY WOODLAND–OPEN FOREST PLATEAU

4.1 INTRODUCTION

The herbaceous layer in forests and woodlands of the Northern Tablelands at the

time of European settlement was dominated by tall, caespitose, warm-season perennial

grasses with herbaceous legumes and trailers such as slender tick-trefoil (Desmodium

varians), variable glycine (Glycine tabacina), false sarsaparilla (Hardenbergia violacea) also

common (Norton 1971; Whalley et al. 1978; Curtis 1989, 2001). Integrated models

developed by Moore (1959, 1962, 1964, 1967, 1973) explain the changes that occurred in

temperate grassy ecosystems in south-eastern Australia and the Northern Tablelands under

settlement (e.g. Whalley et al. 1978; Mack 1989; Landsberg 2000). As ungulate livestock

grazing increases in intensity and duration, there is a shift from tall to shorter or prostrate

grasses, from warm-season perennials to yearlong and cool-season perennials and annuals,

and corresponding with the increasing dominance of annuals, the gradual replacement of

native plants by exotic species (Moore 1970; Lunt 2005). General features of Moore’s model,

including the almost total replacement of the pre-European structure by exotic annual

species, have been confirmed by numerous studies in southern temperate Australia

(Biddiscombe 1956; Moore 1964; Stuwe and Parsons 1977; Lunt 1991; Trémont and McIntyre

1994; Pettit et al. 1995; Prober and Thiele 1995; Lunt 1997a, 1997b; Dorrough et al. 2004),

although regional variation in outcomes has also been detected. Grazing appears to have

had a more subtle impact in temperate–subtropical grassy ecosystems on the Northern

Tablelands and in adjacent subtropical southern Queensland (Lunt 2005). While the impact

of grazing there is consistent with the earlier stages of Moore’s degradation sequence, the

end stage of exotic annual dominance has been recorded less often than in the winter rainfall

areas in southern Australia (Lodge and Roberts 1979; Lodge and Whalley 1983; Tothill and

Mott 1985; McIntyre and Lavorel 1994a, 1994b; Fensham 1998; Fensham and Skull 1999;

McIntyre and Martin 2001; Clarke 2003; Reseigh et al. 2003; McIntyre et al. 2005). The

extent of remnant native vegetation, specific history of management and stocking rates may

have contributed to such differences (Prober 1996; McIntyre and Martin 2001; Dorrough et

al. 2006). However, the persistence of perennials on the Northern Tablelands has historically

been linked to the region’s relatively uniform winter and summer rainfall distribution


(Whalley et al. 1978; Clarke 2003) that sustains native perennial grasses despite heavy

grazing and provides little opportunity for the establishment of winter annuals. Even with

the onset of winter frosts, there is a heavy cover of dried off tussock tillers or grass litter

from the summer growing period that prevents regular reductions in ground cover.

The impact of feral horse grazing in Australian ecosystems is poorly understood

(Prober and Thiele 2007; Dawson 2009b), but the New Zealand experience conforms to

elements of Moore’s model. New Zealand’s high altitude, high rainfall grasslands were also

originally dominated by a few abundant, perennial tall, caespitose species whereas a mix of

adventives and native tussocks is the current norm (Lee 1995). Long-term monitoring of

permanent grassland exclosure plots found that the biomass and stature of all the potentially

taller, palatable grasses were low and the number of individuals significantly declined over

time; short-statured, ‘grazing lawn’ species had greater biomass facilitating a significant

increase in prostrate and unpalatable forbs (Rogers 1989, 1991). The term ‘grazing lawn’ has

specific connotations, especially in the African savannas where the phrase was coined

(McNaughton et al. 1977; McNaughton 1983, 1984; Krook et al. 2007; Waldram et al. 2008).

It refers to the replacement of grazing sensitive species by those species, often with

prostrate or stoloniferous habits, that are able to withstand or compensate for repeated

defoliation, trampling and the concentrated addition of nutrients from animal excreta

(Archibald and Bond 2004; McIntyre and Tongway 2005; McIvor et al. 2005). Under horse

grazing in New Zealand, the recruitment of the regionally important and characteristic red

tussock (Chionochloa pallens) and hard tussock (Festuca novae-zelandiae) was significantly

reduced and silver tussock (Poa colensoi) was evidently grazed out (Rogers 1989, 1991,

1994). The initial shift from a closed tall tussock to a depleted short tussock grassland

accompanied the early era of exploitative pastoralism (O'Connor 1982) but Rogers (1991)

concluded that if horse grazing continued, the hard and red tussock grasslands will continue

to degrade to adventive grasslands or herbfields.

The most common dominant of the original herbaceous layer on the Northern

Tablelands and much of south-eastern Australia was the tall tussock, kangaroo grass

(Themeda australis) (Moore 1970; Whalley et al. 1978). At higher elevations, tussocky poa

(Poa sieberiana) either replaced kangaroo grass or was co-dominant. Wild sorghum (Sarga

leiocladum) and barbed wire grass (Cymbopogon refractus) were also frequently

co-dominant (Lodge and Whalley 1989b). In the absence of disturbance, kangaroo grass can

rapidly senesce and become stagnant in as little as 3–5 years in tropical grasslands and


savannas (Mott and Andrew 1985; Belsky 1992). In other studies, after 10 years, tussock

mortality had significantly increased and the canopy collapsed upon itself, forming a thick

layer of dead thatch over the soil surface (McDougall 1989a; Lunt and Morgan 1999b;

Morgan and Lunt 1999). Kangaroo grass can thus outcompete a variety of herbaceous

species that colonise the inter-tussock space due to the smothering effect of the senescing

litter, with plant diversity declining as a result (Stuwe and Parsons 1977; Fensham and

Kirkpatrick 1989; Trémont 1994; Kirkpatrick and Gilfedder 1995). Light, infrequent grazing by

cattle, or grazing at greater intensity during periods when grasses are dormant can

potentially favour or have no immediate effect on the persistence and productivity of

kangaroo grass itself (Durham 1953; Barnard 1964; Ash and McIvor 1998), but may maintain

or increase total species richness (Trémont 1994; Trémont and McIntyre 1994). Hence, it has

been argued that livestock grazing is not necessarily incompatible with biodiversity

conservation values in grassy eucalypt woodland (Fensham 1998; McIntyre and Martin 2001).

At the other extreme, productivity can be reduced by poor resilience of dominants to

the disturbance regime (Bennett et al. 2002). In a controlled experiment, tussocky poa has

been shown to be highly sensitive to trampling by horses in the context of recreational riding

(Gillieson et al. 1987). Along with kangaroo grass, it does not tolerate frequent

(within-season) defoliation and disappears under heavy continuous grazing (Whalley et al.

1978; Mott and Tothill 1984b; Ash and McIvor 1998). The production of new tillers in

kangaroo grass is impaired under frequent defoliation, resulting in the inadequate

replacement of dead tillers (Coughenour et al. 1985; Danckwerts and Stuart-Hill 1987;

Hodgkinson et al. 1989), possibly assisted by its limited ability to maintain a positive carbon

balance (Mott et al. 1992; Danckwerts 1993). In addition, daughter tillers grow at a high

incident angle and are easily grazed by selective herbivores (Mott et al. 1992). While Rogers

(1994) found that horses significantly reduced tiller weight, length and biomass of hard

tussock, the effect of horses on the recruitment of Australian perennial tussock grasses has

not been investigated. In monsoon tall-grass communities in the Northern Territory,

kangaroo grass was strongly preferred by cattle throughout the year, but particularly so

during the wet season (Andrew 1986; Ash and McIvor 1998). On the Northern Tablelands,

the species is relatively unpalatable to sheep, and to compensate, sheep tend to graze

kangaroo grass in irregular patches (Whalley et al. 1978). Where regrowth remains green

and palatable, the cropped tussock grass patch may be repeatedly grazed, while tall patches

are ignored (Lodge and Whalley 1989b; Adler et al. 2001; McIntyre and Tongway 2005).


Themeda grazing lawns may result, but such lawns are usually temporary as kangaroo grass

decreases with increasing soil nitrogen and is often replaced by more persistent species such

as red grass (Bothriochloa macra), lovegrasses (Eragrostis spp.), wallaby grasses

(Austrodanthonia spp.) and weeping grass (Microlaena stipoides) (Whalley et al. 1978; Lunt

1997b). When livestock are removed, grazing lawn sites may be maintained by marsupial

grazing (Newsome 1975; Jackman 1997). Marsupial grazing, along with fire and the activities

of fossorial mammals, were the major pre-European forms of disturbance in kangaroo grass-

dominated herbaceous vegetation (Lunt and Morgan 2002; Martin 2003). As in African and

American grasslands, it is likely that all three interacted to form a distinct, complex

disturbance regime that shaped patch structure and community diversity at a range of scales

(Collins and Gibson 1990; Belsky 1992; Archibald et al. 2005; Archibald 2008).

Prober et al. (2007) commented that the effects of disturbance on sward structure,

productivity and the species composition of the dominant matrix can lead to more complex

and potentially compensatory interactions between disturbance, vegetation composition and

grassland resilience (i.e. recovery potential). Optimising these interactions provides an

important challenge for conservation management globally (Belsky 1992; Lavorel 1999). This

is certainly applicable to national parks on the Northern Tablelands where little is known

about horse-associated disturbance. Accordingly, experimental exclosures were established

in grassy woodland in GFRNP with the following objectives:

1) to assess the level of modification associated with the long history of livestock grazing

and more recent horse activity; that is, how closely the composition and structure of the

groundstorey vegetation resembled putative pre-European conditions

2) to test the hypotheses that excluding horses would result in (i) greater biomass and a

reduction in species richness variables; (ii) a reduction in bare ground and a concurrent

increase in litter and plant cover variables; and (iii) an increase in the number of tillers

and associated reproductive variables (i.e. number of panicles and spikelets) of the

grazing-sensitive kangaroo grass

3) to determine if excluding horses produced short-term (2.5 years) changes in species

composition, with the specific hypothesis that shorter-statured, grazing-resistant lawns

would become closer in composition to tall, grazing-sensitive, dominant, tussock

vegetation

4) to compare the use of two types of exclosures, one precluding only horses and the other

all herbivores with controls, to ascertain the role of native herbivores (e.g. macropods) in


regulating ground cover and vegetation structure and composition in the absence of

horse grazing.

The term groundstorey vegetation was preferred to herbaceous vegetation as all

vascular plants in the ground layer were sampled, not just herbaceous species, and small

woody plants such as the seedlings of trees and shrubs were included. In the North

American horse impact literature, the term graminoid refers to grass or grass-like plants,

including grasses (Poaceae), sedges (Cyperaceae) and rushes (Juncaceae) and it is used

similarly in this thesis.


4.2 METHODS

4.2.1 Experimental design

The experimental design for this study was developed by Associate Professor Nick

Reid prior to the commencement of the project and the baseline data obtained by Nick Reid

and botanical consultant, Dr. Jodie Reseigh, assisted by University of New England (UNE)

Senior Technical Officer, Jim Fittler. Reid and Fittler (2004) described the methods. Six sites

were selected >1 km from each other in the Paddys Plateau section of Guy Fawkes River

National Park (GFRNP). Three sites (Sites 1, 3 and 5) were positioned near small dams

previously constructed for watering livestock when the land tenure was crown lease for

cattle grazing. The dams always contained water, and when sites were selected, horse tracks

and dung deposits indicated that horses were watering at each dam. All three dams were

located in swales (a depression in otherwise level ground; Perez-Trejo 1994) or grassy

drainage flats to maximise water capture. Swales of similar topography but >1 km from a

water source were selected for the other three sites (Sites 2, 4 and 6). The initial design

targeted the impact of horse grazing close to and distant from water (dams) in temperate

eucalypt woodland with a grassy to shrubby understorey. As discussed in Chapter 3, the

NPWS trapping and removal program became the dominant influence in horse occurrence

and horse removals negated any effect of dams.

Three plots 30 × 30 m assessed to have the same groundstorey biomass were marked

out in woodland at all sites and within 300 m of the dams at Sites 1, 3 and 5. Where possible,

areas with low sapling and shrub cover were avoided to maximise the amount of herbaceous

vegetation in each exclosure. Plots were randomly allocated to one of three treatments per

site: unfenced control (C) with just four corner posts allowing access to all herbivores; horse-

excluded exclosure (H) consisting of a three-strand wire fence prohibiting access by large

herbivores such as horses or cattle, but allowing access to macropods and other herbivores

such as rabbits; and all herbivore-excluded exclosure (A) with a fully enclosed netting fence

1.8-m high with 20 cm spread out flat along the ground and secured with pegs to deter

animals from pushing under the wire. Hereafter, control plots are referred to as ‘controls’;

horse-excluded exclosures as ‘horse exclosures’, and all herbivore-excluded exclosures as

‘complete exclusion’. The exclosures were constructed by field staff from the Dorrigo

Plateau Area office of the NSW National Parks and Wildlife Service (NPWS) during 3–14

May 2004.


4.2.2 Plant biomass

Herbage biomass on each transect was estimated using a hybrid BOTANAL–Adelaide

technique procedure (Tothill et al. 1978; Andrew et al. 1979; Dowling et al. 2005). Two

transects were assessed per plot with ten quadrats per transect. Each 20-m transect was

randomly laid out in the plot and both ends marked with a wooden peg, labelled as the start

or end. Beginning at the 1.0-m mark, a 50 × 50-cm steel quadrat was placed on the ground

such that the tape measure bisected the quadrat into two 25 × 50 cm halves. The quadrat

was assigned a biomass score, which was later converted to estimated biomass as described

below. The procedure was repeated down the length of the transect at 2 m intervals and

repeated for the second transect in each plot. Estimated biomass for the ten quadrats was

summed to obtain a single biomass value for each transect.

Groundstorey plant biomass was estimated using five reference and 19 calibration

quadrats (50 × 50 cm) at Site 1. The reference quadrats were selected to contain 20%, 40%,

60%, 80% and 100% of the maximum biomass of groundstorey vegetation in the study area

and the calibration quadrats were selected to contain a range of biomass levels spread

evenly between 0 and 100%. Baseline biomass data were obtained by Associate Professor

Nick Reid and Jim Fittler on 24–25 June 2004. This was some weeks after the exclosures

were constructed (May) but, due to the winter conditions, groundstorey vegetation had

grown little (Reid and Fittler 2004). The two operators estimated the biomass in each

calibration quadrat independently and then estimated biomass along all transects in plots at

Site 1. Another calibration run was estimated by both operators before progressing to

Sites 6 and 5. After sampling Site 5 towards the end of the first day, another calibration run

at Site 1 was done. A calibration run was done at the start and finish of the second day of

sampling when Sites 4, 3 and 2 were sampled in that order. The method and order of sites

was preserved for all subsequent sampling times except I was the operator. The five

sampling times were: 25–29 June 2005, 4–7 December 2005, 27 February–2 March 2006, 28–

30 June 2006 and 3–5 December 2006. On some occasions due to weather, the number of

sites sampled per day varied. That determined the number of calibration runs, as if sampling

was interrupted, each new day of sampling commenced with another calibration run.

Once all sites were sampled at any one sampling time, the biomass in the calibration

quadrats was harvested to ground level and placed in paper bags. After air drying for

2 weeks, samples were dried for 48 hours (or until sample weight had stabilised) at 55°C and

weighed to the nearest 0.1 g.


The dry weights of the calibration quadrats were regressed against the calibration

scores for each calibration run to produce a set of calibration equations. Calibration

equations were used to estimate the dry weight of the groundstorey vegetation on each

transect. The equation used was from the calibration run that corresponded closest in time

to the transect being estimated. To ensure the 19 calibration quadrats included the range of

dominant graminoid species of varying relative weights (e.g. the soft-leaved, yearlong-green

weeping grass has low dry weights compared to the stalky tussocks of kangaroo grass and

wild sorghum), biomass was assessed after full floristic data had been obtained at each site

and the estimator was familiar with the vegetation on transects in each plot.

4.2.3 Full floristics and cover

Data were collected using the methods of McIntyre et al. (1993) and Reseigh et al.

(2003). Two 6 × 5-m quadrats were pegged out and labelled in each plot. Quadrats were

systematically searched and each species of groundstorey vegetation present recorded. The

quadrat was visually assessed and the percent cover of each species estimated so that the

sum of all species contributions plus the estimated cover of bare ground and litter equalled

100%. Litter comprised leaf, bark, sticks and senesced grass and forb material not attached

to a living plant. Litter (e.g. wind-blown bark) on top of dense patches of plants was not

included.

Baseline data was collected by Jodie Reseigh assisted by Jim Fittler on 24–28

May 2004. All subsequent data were collected by me at five sampling times: 19–

24 June 2005, 29 November–3 December 2005, 24–26 February 2006, 24–27 June 2006 and

28 November–2 December 2006. At the start of the experiment, the tall tussock grass layer

had been altered by chronic ungulate grazing, most recently by horses, and patches of short,

closely-grazed lawns were evident in some sites and plots. In such instances, one quadrat

per plot was placed in a grazing lawn and the other in tall, tussock grass-dominated

vegetation. If grazing lawns were not present in a plot then both quadrats were placed in

tall, tussock grass vegetation.


4.2.4 Taxonomy

Almost all vascular plants in quadrats were identified to species (and subspecies or

variety, as appropriate) according to Wheeler et al. (2002) and Harden (1993a, b, 2000,

2002). Voucher specimens of each taxon were collected and two consultants were employed

to check all identifications and identify unknowns. The primary consultant was Dr Lachlan

Copeland and the secondary consultant was David Carr, National Technical Capacity Manager

for Greening Australia. Specimen vouchers are stored in Ecosystem Management, UNE. Taxa

were assigned to the following categories: (1) herb functional group—F: forb (ferns,

herbaceous dicotyledons), Gs: grass (Poaceae), S: sedge (Cyperaceae) or ST: shrub or tree

that form the overstorey at maturity; (2) origin—N: native or E: exotic; and (3) life cycle—

P: perennial or A: annual and biennial.

Plants identified to genus only were assigned categories if all congeners were in the

same category. Species richness variables were derived for each quadrat: total plant

richness, shrub and tree richness, sedge richness, grass richness, forb richness, exotic

richness and annual richness. Native and perennial richness were not calculated as there was

rarely more than one exotic and annual species per quadrat (and the richness of both was

low) and thus native and perennial richness approximated total richness. Cover variables

corresponded to the species richness variables, for example, total plant cover, annual cover

etc. Exotic cover was not calculated as few exotic species were recorded and cover per

quadrat ranged from 0.5–3.0%.

4.2.5 Kangaroo grass biomass and reproductive variables

Data relating to the seed production of one of the dominant grass species, kangaroo

grass, was collected in the same quadrats as biomass data on 5–10 January 2006 and 15–

19 January 2007. Kangaroo grass has a spatheate inflorescence (Australian Biological

Resources Study 2002) with the potential for each culm to have one or more spatheate

panicles comprised of one or more spikelets. In each quadrat, the following measurements

and reproduction variables were recorded: total number of tussocks, number of culms per

tussock, number of panicles per culm and number of spikelets per panicle. No baseline data

were collected at the commencement of the study by Reid and Fittler (2004) as the

experiment was conceived and executed opportunistically by me, but, baseline kangaroo

grass biomass estimates were available. Estimates of kangaroo grass biomass were obtained

for the same quadrats and sampling times in which seed production variables were counted.


4.2.6 Environmental data

4.2.6.1 Soil collection and laboratory analysis

A soil sample was collected at each plot at the same sampling time as baseline

biomass data. In close proximity to each of the four corner pegs a soil core, 10 cm deep and

6 cm in diameter, was collected and the four cores pooled per plot. In the laboratory, the

soil samples were air-dried and stored in sealed polythene bags out of direct sunlight for

later chemical analysis.

A random subsample was taken from the polythene bag for each plot and rocks and

plant roots removed and placed in two 50 g containers. One of the 50-g subsamples was

gently crushed by hand and sieved to <2 mm, and electrical conductivity (EC) and pHCa

determined at UNE by Dr Matthew Tighe using 1:5 soil to solution extracts in water and

0.01 M CaCl2, respectively. The second 50-g subsample was sent to a commercial laboratory,

Environmental Analysis Laboratory, Southern Cross University Lismore, NSW, for chemical

analysis of topsoil nutrients (total C, N and S via the LECO combustion method, extractable P

via the Olsen method). All samples were analysed on a dry weight basis and dried at 70°C for

48 hours prior to ring mill grinding and analysis.

4.2.6.2 Tree basal area and cover

The percent projective foliage of trees (tree cover) over each of the two

6 × 5-m quadrats per plot was estimated at baseline data collection. Tree diameter at breast

height (DBH; i.e. 1.3 m above the ground) was measured for each species in the quadrats.

Basal area (BA) was calculated for individual trees using an equation based on the formula

for the area of a circle as follows: BA (m2) = π × (DBH/2)2. Individual basal areas were

summed for each quadrat.

4.2.7 Plot dung deposits

The presence of dung of the large grazing herbivores of interest, horses and

macropods, were assessed in all three types of treatment plots or exclosures. As only dung

within 3 m of the plot perimenter was counted the plot-scale area was equal to an area of

0.032 ha. Each plot was searched at the same time as vegetation sampling was undertaken

and all dung raked clear after each sampling time with one exception. Dung was not counted

when baseline data was collected in June 2004. Definitions of macropod and dung deposits

were described previously in Chapter 3 and also expressed as number of deposits/ha.


4.2.8 Statistical analysis

4.2.8.1 Univariate analysis

To avoid pseudoreplication, values from each of the two transects and quadrats

within a plot were averaged to obtain a single value per plot for biomass, all cover and

richness variables, and kangaroo grass biomass and reproductive variables. In this study, for

the reasons presented in Chapter 3, Sections 3.2.1.1 and 3.2.4.1, Site was considered a fixed

factor with six corresponding levels and Treatment was a fixed factor with three levels: C, H

and A. Baseline (June 2004 or T1) biomass, cover and richness variables and kangaroo grass

biomass were analysed using the linear model (lm) function in R (analogous to a two-way

ANOVA) with Treatment and Site as the independent variables (fixed factors) (Crawley

2007b). As transect and quadrats were averaged, the design had no replication (only one

observation per treatment and site combination) and hence there were no degrees of

freedom for inference in a saturated model that included the Treatment × Site interaction,

and an additive model testing only the Treatment and Site main effects was used (Sokal and

Rohlf 1995). The levels of Treatment and Site were consistent and both factors treated as

fixed factors for the analysis of all variables. The lm summary function in R provided a test of

pair-wise comparisons for significant main effects for the fixed factors and multiple

comparisons were obtained by using the re-level function to change the baseline

comparison.

Groundstorey plant biomass and cover and richness variables were measured at six

sampling times, including the collection of baseline data in June 2004 (Time 1 or T1).

Baseline values of variables were subtracted from the values at each subsequent sampling

time to obtain baseline adjusted scores at five times through the experiment. Time (T) was

treated as a fixed factor with five levels: June 2005 (T2), December 2005 (T3), February 2006

(T4), June 2006 (T5) and December 2006 (T6). Repeated measures ANOVA was performed in

R using the aov function where the between-factors (Treatment, Site and Treatment × Site

interaction) and within-factor (Time, Treatment × Time, and Site × Time) sources of variation

were explicit. Without replication, the saturated model including the

Treatment × Site × Time interaction could not be tested. Compound symmetry (CS) in the

variance–covariance matrix is a desirable condition for the F-ratio to follow an F-distribution

in repeated measures designs (Quinn and Keough 2002). Compound symmetry is achieved

when the covariances among all within-group errors pertaining to the same group are equal

(i.e. the variance–covariance structure has a single variance for each level of Time and a


single covariance for each of the pairs of levels of Time) and is the simplest form of serial

correlation structure (Pinheiro and Bates 2000). This is not a necessary condition however,

as it is too restrictive (Quinn and Keough 2002), and dependence in time-series data can be

modelled using alternative variance–covariance matrices. Hence, to test and account for

dependence between sampling times, each plot was coded with a unique number and

models with different variance–covariance structures were compared using the groupedData

function to specify repeated measurement of variables were in the same plot and the gls

function (generalised least squares) to run the models (Chen et al. 2003). The best model

had a significantly smaller AIC value and Log Likelihood score (P < 0.050) when using the

anova function to make multiple comparisons between the competing variance–covariance

structures. Variance–covariance structures compared in addition to compound symmetry

were: (a) unstructured (UN), which assumed that each variance and covariance was unique

(Chen et al. 2003) (i.e. each level of Time had its own variance and each pair of levels of Time

had its own covariance); (b) autoregressive (AR), which recognised that observations that

were closer in time were more correlated than measures that were more distant; and (c)

autoregressive heterogeneous (ARH), which assumed data were equally spaced in time and

the variances changed over time (Box et al. 1994; Wolfinger 1996). Thus, instead of

transforming the data, heterogeneity and covariance considerations were modelled directly.

Significance of main effects and two-way interactions was assessed using the anova

function, and F-values and associated P-values were reported. The gls summary function in R

provided a test of pair-wise comparisons for significant main effects for the fixed factors and

multiple comparisons were obtained by using the re-level function to change the baseline

comparison. When the interaction was significant, the levels of each of the two factors

included in the interaction were combined and recoded and the data treated as a two-way

analysis (i.e. the combined factor as one factor and the remaining third factor not involved in

the interaction as the second factor) using an additive model. The gls summary function was

used to evaluate pair-wise tests of significance treating the combined factor (or interaction)

as a main effect (Crawley 2007b). Since the hypothesis test (t-test) was estimated by

iteration (by dividing the restricted maximum likelihood point estimate by its estimated

asymptotic standard error; Doran and Lockwood 2005) only the P-values were reported for

pair-wise comparisons. The validity of the gls summary function for pair-wise comparisons

was confirmed by comparing model output using planned Helmut contrasts for the main


effects and interactions for biomass, cover of bare ground and total species richness

(Venables and Ripley 2002; Chen et al. 2003).

The best model for biomass, bare ground cover, total plant cover, shrub/tree cover,

grass cover, forb cover, annual cover assumed a CS structure for the variance–covariance

matrix; for litter cover, sedge cover, total species richness, shrub/tree richness, grass

richness, forb richness, exotic richness and annual richness, AR; and for sedge richness, UN.

The same repeated measures gls model and pair-wise summary function was applied to the

change in kangaroo grass relative to baseline, and kangaroo grass reproductive variables and

dung counts except for the following differences. Kangaroo grass biomass was measured at

three sampling times, including the collection of baseline data in June 2004. Baseline values

of kangaroo grass biomass were subtracted from the biomass at each subsequent sampling

time to obtain baseline adjusted scores at two times through the experiment. Time (T) was

treated as a fixed factor with two levels: January 2006 (T2), January 2007 (T3). Baseline data

was not available for kangaroo grass reproductive variables—number of culms, panicles and

spikelets per transect—as they were measured at T2 and T3 only. As there were just two

sampling times, the best model was CS for change in kangaroo grass biomass relative to

baseline and for kangaroo grass reproductive variables.

Dung counts were not obtained at baseline data collection but the total numbers of

horse and macropod dung deposits in plots were counted at the same five post-baseline

sampling times (T2–T6) as groundstorey vegetation variables were monitored. Horse dung

was recorded in controls, hence an additive model with Site and Time (five levels) as fixed

factors was used and the best model was ARH. Macropod dung in controls and horse

exclosures was analysed using the same model and the best model was UN for both analyses.

Horse and macropod dung in plots was also analysed as one dataset using a similar model

where the between-factor was Treatment with three levels: horse dung in controls,

macropod dung in controls and macropod dung in horse exclosures. The response variable

was number of dung deposits and the within-factor was Time. Site was not included in the

model and provided replication for each combination of Treatment and Time and a saturated

model with the interaction term was used. The best variance–covariance structure was

ARH.

All chemical soil variables and the environmental variables, tree BA and tree cover,

were analysed using the same linear model as baseline comparisons with Treatment and Site

as the independent variables. However, as soil and tree measurements were obtained for


each of the two quadrats per plot prior to the imposition of treatments (i.e. baseline), both

replicates and a saturated model including the interaction was used.

All model assumptions were checked with three diagnostic plots: (i) residuals versus

fitted responses; (ii) normal Q–Q plots; and iii) Cook’s distance plot to check for influential

observations or isolated departures from the model (Cox and Snell 1968; Cook and Weisberg

1982; Park and Lee 2004). Data violating the assumptions of ANOVA were transformed. The

normal Q-Q plot and Cook’s distance plot identified an extreme outlier in the sulphur data

and the data point was eliminated from the analysis (Sokal and Rohlf 1995). Baseline

measures of bare ground cover and sedge cover were square-root-transformed, baseline

litter cover log-transformed, and baseline exotic richness log (X + 1)-transformed. Sulphur

was square-root-transformed, EC rank-transformed and tree basal area log-transformed.

4.2.8.2 Multivariate analysis

Multivariate statistical analyses were performed on the quadrat–plant–time cover

abundance matrix and plant incidence (presence/absence) matrix using Primer 6 (Primer-E

Ltd) and permutational multivariate analysis of variance (PERMANOVA) (Anderson 2001;

McArdle and Anderson 2001). To visualise the effect of grazing treatments over time on

groundstorey vegetation composition, nonmetric multi-dimensional scaling (MDS) plots with

centroids were created. Centroids based on Huygens’ theorem (Anderson et al. 2008) were

generated in PERMANOVA for each combination of Treatment and Time (Hashiguchi et al.

2006). In the MDS plots, the proximity of points or centroids reflected the similarity in

community composition of samples (Carson et al. 2007). The degree to which the MDS plots

matched the underlying similarity matrix was shown by the stress (Kruskals stress) (Kruskal

1964; Clarke 1993; Anderson 2005).

The significance of patterns in the MDS plots was tested using PERMANOVA, which

performs distance-based analysis of variance on dissimilarity (distance) measures to test

hypotheses using permutation procedures to calculate P-values (Anderson 2005). The

technique was applied to the full floristics abundance (cover) dataset and, as for the MDS

plot, data were square-root-transformed and analysis used Bray–Curtis dissimilarities.

Type III Sums of Squares (SS) was selected as it tends to be the most conservative and there

was no particular reason to use the other types of SS (Anderson et al. 2008). Although the

three options provided by PERMANOVA to calculate the pseudo-F statistic give very similar

results (Anderson 2005), the ‘permutation of residuals under a reduced model’ method was


used. It empirically gives the best power and most accurate Type I error for complex designs

(multi-factor) under the widest conditions (Anderson and Legendre 1999; Anderson and ter

Braak 2003) and is theoretically the closest to the conceptually exact test (Anderson and

Robinson 2001). When appropriate, pair-wise comparisons were executed using

permutations (pseudo t-statistic) (Anderson et al. 2008).

Quadrats were located in two types of grassland structure, grazing lawns and tussock

grass-dominated quadrats. To test if grazing lawns became more similar in composition to

tussock grass-dominated quadrats over Time in any Treatment, MDS centroids were

generated for the two levels of Structure within each combination of Treatment and Time.

The repeated-measures multi-factor PERMANOVA incorporated the factors Treatment, Site

and Time as previously described and Structure (fixed factor with two levels corresponding to

grazing lawn and tussock grassland). Centroids and PERMANOVA analysis was conducted on

both the cover abundance matrix and incidence matrix.


4.3 RESULTS

4.3.1 Plot dung deposit counts

4.3.1.1 Horse dung deposits

In control plots, the number of horse dung deposits averaged across sampling times

differed between sites (F5,20 = 8.09, P < 0.001). Dung was greater in Sites 4 and 6 than

Sites 1–3 by an average of 123.5–259.3 deposits (P ≤ 0.040) and greater in Site 5 than Site 3

by an average of 92.6 deposits (P = 0.038) (Figure 4.1a). Horse dung in controls progressively

declined at T2, T3 and T4 and the number of horse dung deposits averaged across sites

differed by Time (F4,20 = 11.44, P < 0.001). The mean of 339.5 ± 79.3 deposits in June 2005

(T2) was greater than mean horse deposits in T3–T6 (P < 0.001) (Figure 4.1b). The mean of

200.6 ± 47.6 deposits in December 2005 (T3) was also greater than in T4–T6. Dung did not

differ between T4–T6, ranging from a maximum of 87.4 ± 36.9 deposits in June 2006 (T5) to a

minimum of 61.7 ± 26.4 deposits in December 2006 (T6).

Figure 4.1 Mean (±1 S.E.) number of horse dung deposits/ha in control plots in (a) each Site averaged across sampling times and (b) at each Time averaged across sites. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2006, T6: December 2006; * above a bar column or line marker denotes a significant difference (P < 0.050) as specified.

0

50

100

150

200

250

300

350

400

S1 S2 S3 S4 S5 S6

Dun

g de

posi

ts (n

o./h

a)

S4, S6 > S1–S3S5 > S3

a)

* *

0

50

100

150

200

250

300

350

400

450

Dun

g de

posi

ts (n

o./h

a)

b)

Jun-05 Dec-05 Feb-06 Dec-06Jun-06

T2 > T3–T6*

T3 > T4–T6

*


4.3.1.2 Macropod dung deposits in controls

In control plots, the number of macropod dung deposits averaged across sampling

times differed between sites (F5,20 = 4.04, P = 0.011; Table 4.1). The mean of

759.3 ± 354.4 deposits in Site 1 was greater than mean macropod deposits in Sites 2–6

(P ≤ 0.010) (Figure 4.2a). In Sites 2–6, macropod dung ranged from a mean of

185.2 ± 16.9 deposits in Site 3 to zero deposits as no macropod dung was detected in Sites 5

and 6 for the duration of the experiment. Macropod dung was 3–4.3 times greater in

June 2006 (T5) and December 2006 (T6) (Figure 4.2b), but because of variability between

sites and the response of Site 1, in particular, the Time main effect was not significant

(F4,20 = 0.95, P = 0.457; Table 4.1).

Table 4.1 Repeated measures multifactor ANOVA of number of macropod dung deposits in control plots and horse exclosures.

Controls Horse exclosures

Source of variation df F P F P

Site 5 4.04 0.011 24.78 <0.001

Time 4 0.95 0.457 2.81 0.053

Residuals 20

Figure 4.2 Mean (±1 S.E.) number of macropod dung deposits/ha in control plots in (a) each Site averaged across sampling times and (b) at each Time averaged across sites. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6; * above a bar column denotes a significant difference (P < 0.050) between that (a) site and other sites and (b) sampling time and other sampling times.

0

200

400

600

800

1000

1200

S1 S2 S3 S4 S5 S6

Dun

g de

posi

ts (n

o./h

a)

a)*

S1 > S2–S6

0

100

200

300

400

500

600

700

Dun

g de

posi

ts (

no./

ha)

b)

Jun-05 Dec-05 Feb-06 Dec-06Jun-06


4.3.1.3 Macropod dung deposits in horse exclosures

In horse exclosures, the number of macropod dung deposits averaged across

sampling times differed between sites (F5,20 = 24.78, P < 0.001; Table 4.1). The mean of

388.9 ± 139.6 deposits in Site 1 was greater than mean macropod deposits in Sites 2–6

(P ≤ 0.030; Figure 4.3a). The mean of 321.0 ± 54.9 deposits in Site 2 and

240.7 ± 35.7 deposits in Site 3 was greater than mean deposits at Sites 4–6 (P ≤ 0.025). No

macropod dung was recorded in horse exclosures in Site 5 throughout the experiment

(Figure 4.3a). In Site 6, two deposits were recorded at the final sampling time. Mean

macropod deposits tended to increase over time in horse exclosures and the Time main

effect was marginally significant (F4,20 = 2.81, P < 0.053; Table 4.1). Macropod dung was 1.7

and 2.5 times greater, respectively, at T3 and T5 than T2 (P ≤ 0.050) and while 2.4 times

greater at T6 than T2, site variability driven by Site 1 and Site 2 precluded a significant result

(P = 0.225; Figure 4.3b).

Figure 4.3 Mean (±1 S.E.) number of macropod dung deposits/ha in horse exclosures in (a) each Site averaged across sampling times and (b) at each Time averaged across sites. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2006, T6: December 2006; * above a bar column or line marker denotes a significant difference (P ≤ 0.050) between that (a) site and other sites and (b) sampling time and other sampling times.

0

100

200

300

400

500

600

S1 S2 S3 S4 S5 S6

Dun

g de

posi

ts (n

o.ha

)

S1 > S2–S6a)

*

*

S2, S3 > S4–S6

*

0

50

100

150

200

250

300

350

400

Dun

g de

posi

ts (n

o./h

a)

b)

Dec-05 Feb-06 Dec-06Jun-06

T2 < T3, T5

*


4.3.1.4 Horse and macropod dung deposits in plots

The Treatment × Time interaction was significant (F8,75 = 2.26, P = 0.032; Table 4.2).

In June 2005, control plots contained an average of 339.5 ± 79.3 horse deposits and

51.4 ± 23.5 macropod deposits, while horse exclosures contained an average of

102.9 ± 50.2 macropod deposits (Figure 4.4). The greater number of horse than macropod

deposits in both controls (P < 0.001) and horse exclosures (P = 0.003) was significant.

June 2005 was the only sampling time when Treatment differences were significant

(Figure 4.4). By December 2005, mean number of macropod deposits in horse exclosures

(174.9 ± 70.7 deposits) were similar to horse deposits in control plots (200.6 ± 47.6 deposits;

P = 0.723), while horse deposits in controls were almost twice the number of macropod

deposits in controls (97.7 ± 44.7 deposits; P = 0.158). Subsequent trends in herbivore dung

were described in the previous three sections.

Table 4.2 Repeated measures multifactor ANOVA of total number of herbivore dung deposits (horse and macropod in controls, macropod in horse exclosures).

Source of variation df F P Treatment 2 1.89 0.157 Time 4 1.36 0.257 Treatment × Time 8 2.26 0.032

Residuals 75

Figure 4.4 Mean (±1 S.E.) number of horse (H) deposits/ha in control plots and mean (±1 S.E.) number of macropod deposits/ha in control (MC) and horse exclosure plots (MH) by Time; * denotes a significant difference in horse and macropod dung deposits by plot type within that sampling time as specified (P < 0.050).

0

100

200

300

400

500

600

700

Dun

g de

posi

ts (

no./

ha)

Horse Macropod-Control Macropod-Horse excluded

June 2005 December 2005 February 2006 June 2006 December 2006

*

H > MC, MH


4.3.2 Biomass

4.3.2.1 Baseline biomass

The Site main effect was significant (F5,10 = 26.62, P < 0.001) at the start of the

experiment in June 2004. Biomass was greatest in Site 1 (mean of 206.0 ± 17.4 g/m2) and

differed from other sites (P ≤ 0.015) except Site 6 (P = 0.061) (Figure 4.5a). Mean biomass

of 173.3 ± 23.2 g/m2 in Site 6 was greater than mean biomass in Sites 3–5 (P ≤ 0.042), and

mean biomass in Sites 2 and 3 was greater than biomass in Sites 4 and 5 (P ≤ 0.003). Thus,

Site 4 (63.0 ± 14.1 g/m2) and Site 5 (76.0 ± 15.5 g/m2) had less biomass than all other sites

(P ≤ 0.003). By chance, mean biomass was greater by 26.5g/m2 and 38.8 g/m2, respectively,

in A and C than H quadrats (F2,10 = 6.63, P = 0.015; Figure 4.5b).

Figure 4.5 Mean (±1 S.E.) biomass at the start of the experiment in June 2004 compared between (a) Site and (b) Treatment. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded; * denotes a significant difference in biomass between sites or treatments as specified (P < 0.050).

4.3.2.2 Changes in biomass in relation to grazing exclusion

After 1 year (June 2005), biomass in C did not differ from baseline but biomass had

increased in both H and A by about 50 g dry weight/m2 (Figure 4.6). By December 2005,

biomass in C remained the same as baseline but the relative increase in biomass in H

compared to baseline had declined by half. By contrast, in A, biomass had increased still

further compared to baseline. By February 2006, biomass increased by similar amounts

relative to the preceding measurements and peaked in all treatments. In June 2006, biomass

in all treatments had decreased to mirror the December 2005 results. In December 2006,

0

50

100

150

200

250

S1 S2 S3 S4 S5 S6

Biom

ass

(g/m

2 )

a) S1 > S2–S5

*

*

S2,S3 > S4,S5

S6 > S3–S5

*

*

0

50

100

150

200

250

C H A

Biom

ass

(g/m

2 )

*

H < C,A

b)


biomass was substantially less than baseline levels for all treatments, having decreased in the

previous 6 months by 74–120 g/m2.

The Treatment × Time interaction (F8,40 =2.58, P = 0.023) was significant (Table 4.3).

After 1 year, the change in biomass in H and A was significantly greater than C where

biomass did not differ from baseline (Figure 4.6). The increment was 61 g/m2 in A

(P = 0.028), and 50 g/m2 in H (P = 0.040). At the three subsequent sampling times, the

increase in biomass in A was greater than that in C (P ≤ 0.007) and H (P ≤ 0.032). The

exception was the final sampling time, December 2006. The reduction in biomass relative to

baseline was less in A than C only (P = 0.002).

Table 4.3 Repeated measures multifactor ANOVA of the difference in biomass relative to baseline.

Source of variation df F P Treatment 2 77.55 <0.001 Site 5 25.59 <0.001 Time 4 133.16 <0.001 Treatment × Site 10 6.84 <0.001 Treatment × Time 8 2.58 0.023 Site × Time 20 2.96 0.002 Residual 40

Figure 4.6 Mean (±1 S.E) change in biomass from baseline (T1) for each Treatment at each Time; * denotes a significant difference in the change in biomass from baseline between treatments at that sampling time as specified (P < 0.050). C: control, H: horse excluded, A: all herbivore excluded.

In comparison to C, the increase in biomass was consistently greater in A and either

greater or comparable relative to baseline in H (Figure 4.7). The Treatment × Site interaction

was significant (Table 4.3) and treatment differences were most pronounced in Sites 1, 4 and

-120

-70

-20

30

80

130

180

230

Chan

ge in

bio

mas

s (g

/m2 )

C H A

A,H > C

*

*

A > H,C

*

A > H,C

*

A > H,C

June 2005 December 2005 February 2006 June 2006

December 2006

*

C < A

C H A


6 and to a lesser extent in Site 5. In Site 1, changes in biomass over time were, on average,

greater than baseline in both A (P < 0.001) and H (P < 0.001) and differed from average

changes in biomass in C, which were less than baseline. The biomass increment in A was

greatest in Site 4 and Site 6, differing from biomass increments in C (P = 0.003) and H

(P = 0.046) in Site 4 and C in Site 6 (P = 0.051). Treatment differences were not significant at

other sites but Site 5 resembled the Site 4 and 6 pattern with the relative increase in biomass

being A > H > C, whereas in Sites 2 and 3 the increase in biomass in C and H were similar.

Figure 4.7 Mean (±1 S.E) change in biomass from baseline for each Treatment in each Site averaged across all times after T1 (i.e. T2–T6 hereafter); * denotes a significant difference in the change in biomass from baseline between treatments within a site as specified (P ≤ 0.051). C: control, H: horse excluded, A: all herbivore excluded.

The Site × Time interaction was significant (Table 4.3). Site 1 consistently either

recorded the smallest increment or largest reduction in biomass from baseline (Figure 4.8)

compared to other sites. Site differences were not significant in June 2005 (T2), but in

December 2005 (T3) the change in biomass from baseline in Site 1 was negative and differed

from the positive changes in biomass at other sites (P ≤ 0.034). The relationship between

Site 1 and Sites 3, 4 and 6 was similar in June 2006 (T5) (P ≤ 0.042) while in December 2006

(T6), Site 1 recorded a greater reduction in biomass than Sites 3, 4 and 5 (P ≤ 0.044). At T3,

Site 3 recorded a greater increase in biomass than Site 5, and at T5, both Sites 3 and 4

registered more biomass relative to baseline than Site 5 (P ≤ 0.028). Biomass had increased

in Site 4 from baseline more than in Sites 1, 2, 3 and 5 in February 2006 (T4) and Sites 2, 3

and 6 at T6 (P < 0.050).

-150

-100

-50

0

50

100

150

200

250

1 2 3 4 5 6

Chan

ge in

bio

mas

s (g

/m2 )

Site

C H A

*

A, H ≠ C

*

A > C, H

*

A > C


Figure 4.8 Mean (±1 S.E) change in biomass from baseline in each Site at each Time; * denotes a significant difference in the change in biomass from baseline between sites within a sampling time as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6.

4.3.2.3 Significant weather in November 2006

The reduction in biomass over the spring growing season to December 2006

compared to baseline winter biomass in June 2004 was uncharacteristic. It was due to

unseasonably late cold weather and frosts through most of November 2006 as a series of

high pressure systems moved from the Southern Ocean south of Western Australia over

north-eastern NSW (BOM 2006b). November also recorded unseasonal snowfalls on the

higher elevation ranges across the Northern Tablelands, including the Guyra–Ebor region

where Paddys Plateau is situated (BOM 2006b). Snowfalls on the Northern Tablelands are

rare in November with Armidale recording snow on the 15–16 November 2006, for the first

time in 41 years (BOM 2006a).

4.3.3 Cover variables: bare ground, litter and plant cover variables

4.3.3.1 Baseline cover of bare ground

The Site main effect was significant (F5,10 = 14.45, P < 0.001) at the start of the

experiment in June 2004. The cover of bare ground was zero in Sites 1 and 5 and <0.2% on

average in Site 2, differing significantly from the greater mean cover recorded in Sites 3, 4

and 6 (P ≤ 0.017; Figure 4.9a). Cover was also greater in Site 4 (6.7 ± 2.2%) than Site 6

(2.2 ± 0.9%; P = 0.020). Mean cover of bare ground ranged from 1.5 ± 1.0% in C to 2.7 ± 1.6%

in H and did not differ between treatments (F2,10 = 0.88, P = 0.445; Figure 4.9b).

-150

-100

-50

0

50

100

150

200

250

300

Chan

ge in

bio

mas

s (g

/m2 )

Site 1 2 3 4 5 6

*

S4 > S(1,2,3,5)

*

S1 ≠ S(2,3,4,5,6)S3 > S5

*

S1 ≠ S(3,4,6)S5 < S3,S4


*

S1 < S(3,4,5)S4 > S(2,3,6)

**

*


Figure 4.9 Mean (±1 S.E.) cover of bare ground at the start of the experiment in June 2004 compared between (a) Site and (b) Treatment. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded; * denotes a significant difference in bare ground cover between sites as specified (P < 0.050). 4.3.3.2 Changes in the cover of bare ground

In the analysis of changes in cover of bare ground from baseline, the Site × Time and

Treatment × Site interactions were significant (Table 4.4). Cover of bare ground in Sites 1, 2

and 5 did not change from baseline for the duration of the experiment and the interaction

was driven by the reduction in bare ground in sites with most bare ground initially (Sites 3

and 4; Figure 4.9 and 4.10). The response of Sites 3 and 6 were similar, with cover less than

baseline by 1.0–2.2% throughout T2–T6 (Figure 4.10). The reduction in bare ground in Site 3

was significantly greater than in Sites 1, 2 and 5 in February 2006 (T4) and marginally

significantly greater in December 2006 (T6) (P ≤ 0.052) and the reduction in bare ground in

Site 4 was greater than in Sites 1, 2, 4 and 6 at the same time (P ≤ 0.039). Bare ground had

already decreased from baseline in Site 4 by T2 (2.5 ± 0.6%) with the difference becoming

more pronounced through time (5.3 ± 1.3%, T6). The reduction in bare ground was greater

in Site 4 than Sites 1, 2 and 5 from December 2005 (T3) until the final sampling time

(P ≤ 0.050). Treatment differences were only significant at Sites 3, 4 and 6 (Figure 4.11).

Relative to baseline, the reduction in bare ground was greater in both A and H than C in Site

4 and 6 (P < 0.001), and greater in A than H and C in Site 3 (P < 0.001).

0

1

2

3

4

5

6

7

8

9

10

S1 S2 S3 S4 S5 S6

Bare

gro

und

cove

r (%

)

S(3,4,6) > S(1,2,5)S4 > S6 *

*

*

a)

0

1

2

3

4

5

6

7

8

9

10

C H A

Bare

gro

und

cove

r (%

)

b)


Table 4.4 Repeated measures multifactor ANOVA for changes in percent cover of bare ground relative to baseline (June 2004).


Figure 4.10 Mean (±1 S.E) change in cover of bare ground from baseline in each Site at each Time; * denotes a significant difference in the change in bare ground from baseline between sites within a sampling time as specified (P ≤ 0.052). C: control, H: horse excluded, A: all herbivore excluded.

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

Chan

ge in

bar

e gr

ound

cov

er (

%)

1 2 3 4 5 6


**

* *

S4 < S(1,2,5)

S4 < S(1,2,5,6)

S3 < S(1,2,5) S4 < S(1,2,5) S4 < S(1,2,5,6)

S3 < S(1,2,5)

SITES:


Figure 4.11 Mean (±1 S.E) change in cover of bare ground from baseline in each Treatment at each Site averaged across T1–T6; * denotes a significant difference in the change in bare ground from baseline between treatments in a site as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. 4.3.3.3 Baseline litter cover

Litter cover did not vary by Site (F5,10 = 1.43, P = 0.296) or Treatment (F2,10 = 2.12,

P = 0.170) at the start of the experiment in June 2004. Mean litter cover ranged from a

minimum of 5.2 ± 1.7% in Site 1 to a maximum of 16.7 ± 2.7% in Site 5, with intermediate

values for Sites 2–4 and Site 6 (Figure 4.12a). Mean litter cover ranged from a minimum of

9.3 ± 3.0% in H to a maximum of 14.7 ± 1.4% in C, with cover in A similar to H (Figure 4.12b).

Figure 4.12 Mean (±1 S.E.) litter cover at the start of the experiment in June 2004 compared between (a) Site and (b) Treatment. S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

-7

-6

-5

-4

-3

-2

-1

0

1

2

1 2 3 4 5 6

Chan

ge in

bar

e gr

ound

cov

er (

%)

Site

C H A

*A < C, H

**

A , H < C

*

*

A, H < C

0

5

10

15

20

25

S1 S2 S3 S4 S5 S6

Litt

er c

over

(%)

a)

0

5

10

15

20

25

C H A

Litt

er co

ver (

%)

b)


4.3.3.4 Changes in litter cover

In the analysis of changes in cover of litter from baseline the Treatment × Time and

Treatment × Site interactions were significant (Table 4.5). By June 2005 (T2), litter cover had

decreased from baseline levels by 3.8–4.1% across all treatments (Figure 4.13). From T2 to

T3, litter cover decreased further, most notably in C (4.1%), then A (2.4%) and H (1.6%). After

T3, litter cover tended to fluctuate by small amounts in all treatments and the reduction in

cover from baseline ranged from 4.5–5.5% in H, 5.4–6.8% in A and 8.4–8.8% in C

(Figure 4.13). Thus, after T2 the reduction in litter cover compared to baseline was greater in

C quadrats than exclosures with the difference between C and H marginally significant in

June 2006 (T5; P = 0.051). Litter cover tended to decline from baseline levels less in H than A

and C, in particular, over T3–T5 due to the response of treatments in Sites 2 and 6 (C v H,

P < 0.001; A v H, P ≤ 0.037; Figure 4.14). Litter declined further from baseline in C than A in

Site 6 (P = 0.033) and Site 3 (P = 0.003) and in H than A in Site 3 (P < 0.001). Site 5 was the

only site where the reduction from baseline was greater in exclosures than C quadrats

(P ≤ 0.054).

Figure 4.13 Mean (±1 S.E) change in litter cover from baseline for each Treatment at each Time; * denotes a significant difference in the change in litter cover from baseline between treatments at that sampling time as specified (P ≤ 0.051). C: control, H: horse excluded, A: all herbivore excluded.

-14

-12

-10

-8

-6

-4

-2

0

Chan

ge in

litt

er c

over

(%)

C H A

*C < H


C H A


Figure 4.14 Mean (±1 S.E) change in litter cover from baseline in each Treatment at each Site averaged across T2–T6; * denotes a significant difference in the change in litter cover from baseline between treatments in a site as specified (P ≤ 0.054). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. Table 4.5 Repeated measures multifactor ANOVA for changes in percent cover of litter relative to baseline (June 2004).


4.3.3.5 Baseline and changes in total plant cover

At the start of the experiment in June 2004, total plant cover did not differ between

Treatment (F2,10 = 2.06, P = 0.179; Figure 4.15b) but the Site main effect was marginally

significant (F5,10 = 3.20, P = 0.055) with greater cover in Site 1 than Site 4 and 5 (P ≤ 0.011)

and in Site 2 than Site 4 (P = 0.048; Figure 4.15a). In the analysis of the change in total plant

cover from baseline, the Treatment × Site interaction and Time main effect was significant

(Table 4.6). In the site with the greatest starting cover (Site 1), cover declined over time from

baseline in A exclosures, differing from H exclosures where cover increased (P = 0.035;

Figure 4.15c). In sites with the least starting cover (Sites 4 and 5), cover increased over time

from baseline in all treatments, although the increase in C quadrats in Site 4 was greater than

H (P = 0.019). The response of treatments in Sites 2 and 6 was similar with a greater increase

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

2

1 2 3 4 5 6

Chan

ge in

litt

er c

over

(%)

Site

C H A

*C, H ≠ A

*

*A , H < C

*

*

C < H, A*

*

C, A < H

* A < H


in cover from baseline in C than A (P ≤ 0.022) and H differing from both C and A as cover did

not change from baseline in H exclosures (P ≤ 0.028). Averaged across all sites and

treatments, the greatest incremental increase in total plant cover from baseline occurred

from June 2005 (T2) to December 2005 (T3) and cover remained greater at T3–T6 than T2

with a small decrease from June 2006 (T5) to December 2006 (T6) (Figure 4.15d) due to cool

spring conditions (Section 4.3.2.3). Cover at T6 only differed from peak cover levels at T3

(P = 0.027).

Figure 4.15 Mean (±1 S.E.) total plant cover at the start of the experiment in June 2004 compared between (a) Site and (b) Treatment, and mean (±1 S.E.) change in total plant cover from baseline in (c) each Treatment at each Site averaged across T2–T6 and (d) over Time; * denotes a significant difference as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. T1: June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2005, T6: December 2006.

0

10

20

30

40

50

60

70

80

90

100

S1 S2 S3 S4 S5 S6

Tota

l pla

nt c

over

(%

)

S1 > S4,S5S2 > S4

a)

* *

0

10

20

30

40

50

60

70

80

90

100

C H A

Tota

l pla

nt c

over

(%

)

b)

-10

-5

0

5

10

15

1 2 3 4 5 6

Chan

ge in

tot

al p

lant

cov

er (

%)

Site

C H A

*

A ≠ H

*

C > H

*

*

C ≠ H, AA ≠ H

**

H, C > A

*

C > H, AA > H

*

c)

0

1

2

3

4

5

6

7

8

9

Ch

ange

in t

ota

l p

lan

t co

ver

(%)

Jun-05 Dec-05 Feb-06 Jun-06 Dec-06

*

T2 < T3–T6

*T3 > T6

d)


Table 4.6 Repeated measures multifactor ANOVA for changes in total plant cover and cover of shrubs and trees, and sedges, relative to baseline (June 2004).

Total Shrub/tree Sedge Source of variation df F P F P F P

Treatment 2 11.34 <0.001 1.34 0.274 6.68 0.003

Site 5 16.92 <0.001 9.51 <0.001 15.89 <0.001

Time 4 6.14 <0.001 2.06 0.105 2.95 0.032

Treatment × Site 10 4.80 <0.001 7.48 <0.001 1.80 0.092

Treatment × Time 8 0.43 0.896 1.17 0.341 0.85 0.568

Site × Time 20 1.05 0.435 0.90 0.586 1.75 0.066

Residual 40

4.3.3.6 Baseline and changes in cover of shrubs and trees

At the start of the experiment in June 2004, cover of shrubs and trees did not differ

between Site (F5,10 = 2.31, P = 0.122) or Treatment (F2,10 = 1.29, P = 0.316; Figure 4.16a andb).

In the analysis of the change in shrub/ tree cover from baseline, the Treatment × Site

interaction was significant (Table 4.6). Shrub/tree cover increased the most from baseline in

all treatments in Site 6, however, the increase was greater in A than C and H (P ≤ 0.041) with

the result repeated in Site 2 (P ≤ 0.049; Figure 4.16c) except the increase was also marginally

significantly greater in H than C (P = 0.052). In Site 4 and 5, cover increased the most from

baseline in H and was marginally significantly different from C and A (P ≤ 0.052). In Site 5,

cover in A exclosures had declined from baseline and also differed from the response in C

quadrats (P < 0.001). Cover in H declined from baseline in Site 1 whereas cover had

increased in C and A, but only C differed from H (P = 0.023).


Figure 4.16 Mean (±1 S.E.) shrub and tree cover at the start of the experiment in June 2004 compared between (a) Site and (b) Treatment, and mean (±1 S.E.) change in shrub and tree cover from baseline in (c) each Treatment at each Site averaged across T2–T6; * denotes a significant difference in the change in shrub and tree cover from baseline between treatments in a site as specified (P ≤ 0.052). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

4.3.3.7 Baseline and changes in cover of sedges

Baseline (T1) cover of sedge plants ranged from a minimum of 0.2 ± 0.2% in Site 2 to

a maximum of 1.0 ± 0.3% in Site 4 and the Site main effect was not significant (F5,10 = 1.52,

P = 0.267). Mean cover at T1 varied by ≤0.25% across treatments and the Treatment main

effect was not significant (F2,10 = 0.37, P = 0.702; Figure 4.17a). However, the Treatment

main effect was significant for change in sedge cover from baseline (Table 4.6). Cover was

less than baseline in C but greater than baseline in H and A (Figure 4.17b). Thus by June 2005

0

1

2

3

4

5

6

S1 S2 S3 S4 S5 S6

Shru

b/tr

ee co

ver

(%)

a)

0

1

2

3

4

5

6

C H A

Shru

b/tr

ee co

ver

(%)

b)

-2

-1

0

1

2

3

4

5

6

1 2 3 4 5 6

Chan

ge in

shr

ub/t

ree

cove

r (%

)

Site

C H A

*C ≠ H

*H > C, A

*

A > H, CH > C *

H ≠ A, CC ≠ A

*

*

A > C, Hc)


(T2), sedge cover was greater in H and A than C on average by 0.42% (P = 0.004) and 0.34%

(P = 0.019), respectively, with the difference maintained throughout T3–T6.

Figure 4.17 Mean (±1 S.E.) cover of sedges (a) at the start of the experiment in June 2004 compared between each Treatment, and (b) mean (±1 S.E.) change in cover of sedges from baseline in each Treatment averaged across T2–T6; * denotes a significant difference in the change in sedge cover from baseline between treatments as specified (P < 0.050). C: control, H: horse excluded, A: all herbivore excluded.

4.3.3.8 Baseline and changes in cover of grasses

Mean baseline grass cover ranged from a minimum of 65.9 ± 2.8% in C to a maximum

of 72.1 ± 4.3% in A, with cover in H similar to A and treatment differences not significant

(F2,10 = 1.98, P = 0.189). However, site differences at the start of the experiment were

significant (F5,10 = 6.74, P = 0.005). Site 5, followed by Site 4 recorded the lowest mean grass

cover and differed from Site 1, 2 and 6 (P ≤ 0.039; Figure 4.18a). Site 5 also differed from

Site 3 (P = 0.034) and Site 3 from Site 1 (P = 0.023), which had the highest mean baseline

cover. The Treatment × Site interaction was significant for change in grass cover from

baseline (Table 4.7). The reduction in grass cover from baseline was greatest in H and

A exclosures in sites with a high starting cover (Sites 1, 2 and 6), differing from the reponse of

C quadrats which either increased in cover (P < 0.001; Site 2) or did not decline from baseline

to the same extent as exclosures (P ≤ 0.052; Sites 1 and 6; Figure 4.18b). In Site 1, the

reduction in cover from baseline was greater in A than H (P = 0.006) and in Site 6, the

reduction was greater in H than A (P = 0.004). Treatment differences in Site 4 differed from

other sites in that cover was greater than baseline in both C and A while less than baseline in

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

C H A

Sedg

e co

ver

(%)

a)

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

C H ACh

ange

in s

edge

cov

er (%

)

**

H, A ≠ Cb)


H (P ≤ 0.006). Grass cover was less than baseline at T2–T6 (Figure 4.18c) and the Time main

effect was significant for changes in grass cover (Table 4.7). The maximum mean reduction in

grass cover from baseline was recorded in February 2006 (T4), differing from all other

sampling times (P ≤ 0.015).

Figure 4.18 Mean (±1 S.E.) grass cover at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in grass cover from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) over Time; * denotes a significant difference as specified (P ≤ 0.052). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. T1: June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2005, T6: December 2006.

0

10

20

30

40

50

60

70

80

90

100

S1 S2 S3 S4 S5 S6

Gra

ss c

ove

r (%

)

S3 < S1

a)

S5 < S3

* *

S4, S5 < S1, S2, S6

*

-20

-15

-10

-5

0

5

10

1 2 3 4 5 6

Chan

ge in

gra

ss c

over

(%)

Site

C H A

C > A, HH > A

C, A ≠ H

*

C ≠ A, H* C > H, A

A > H

b)

**

*

**

-12

-10

-8

-6

-4

-2

0

Chan

ge in

gra

ss c

over

(%)


*

T(2, 3, 5, 6) > T4

c)


Table 4.7 Repeated measures multifactor ANOVA for changes in the cover of grasses, forbs and annual plants relative to baseline (June 2004).

Grass Forb Annual Source of variation df F P F P F P

Treatment 2 18.17 <0.001 1.55 0.224 0.19 0.830

Site 5 18.45 <0.001 6.62 <0.001 24.74 <0.001

Time 4 4.91 0.003 26.70 <0.001 1.36 0.267

Treatment × Site 10 5.11 <0.001 5.96 <0.001 3.45 0.003

Treatment × Time 8 1.89 0.089 0.69 0.698 0.31 0.959

Site × Time 20 0.64 0.859 2.78 0.003 1.56 0.113

Residual 40

4.3.3.9 Baseline and changes in cover of forbs

Baseline level of mean forb cover was slightly greater in Site 5 otherwise cover levels

were similar between sites and the Site main effect was not significant (F5,10 = 2.54, P = 0.098;

Figure 4.19a). Mean baseline forb cover ranged from a minimum of 11.6 ± 1.4% in A to a

maximum of 12.3 ± 1.1% in C and treatment differences were not significant (F2,10 = 0.09,

P = 0.907). In the analysis of the change in forb cover from baseline, the Treatment × Site

and Time × Site interaction was significant (Table 4.7). In all comparisons, forb cover was

greater than baseline (Figure 4.19b and c). Treatment differences between sites were not

consistent. In Site 1, the increase in cover from baseline was greater in A and H than C

(P ≤ 0.034), whereas in Site 3, the increase was greater in H than C and A (P ≤ 0.011;

Figure 4.19b). In Site 6, cover increased the most in H, differing from A only (P = 0.007), and

in Site 5, cover increased from baseline to a greater extent in A than H (P = 0.025). Site 4 was

the only site where the greatest increase in cover from baseline was recorded in C quadrats

and where the increase was greater than in a grazing exclusion treatment (A exclosure;

P = 0.039). Site differences were not significant in June 2005 (T2) and June 2006 (T5) when

the increase in forb cover relative to baseline in sites was less than at other sampling times

(Figure 4.19c). However, the response of Site 3, in particular, differed from most sites at all

other sampling times, explaining the interaction. In December 2005 (T3), the relative

increase in forb cover in Site 3 compared to baseline had declined by about two-thirds,

differing from all other sites where the relative change from baseline was further increases in

forb cover (P ≤ 0.053; Figure 4.19c). This result was repeated in December 2006 (T6) except

the decline relative to the previous sampling time was less and also occurred in Site 1, thus

Site 1 and Site 3 did not differ in their relative increase in cover from baseline at T6


(P = 0.178). From June to December 2005, the increase in forb cover relative to baseline in

Site 5 almost tripled and differed from the smaller increases in Sites 1 and 2 (P ≤ 0.028). The

relative increase from baseline was also greatest in Site 5, followed by Site 4 at the following

sampling time (February 2006), differing from the large relative increase from baseline in

Site 3 (P ≤ 0.052).

Figure 4.19 Mean (±1 S.E.) forb cover at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in grass cover from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) each Site at each sampling time; * denotes a significant difference as specified (P ≤ 0.053). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

0

2

4

6

8

10

12

14

16

18

20

S1 S2 S3 S4 S5 S6

Forb

cove

r (%

)

a)

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6

Chan

ge in

forb

cov

er (%

)

Site

C H A

C < A, HC > A

*

H > A, C*

H > A

b)

*

**A > H

-5

0

5

10

15

20

Chan

ge in

forb

cov

er (%

)

1 2 3 4 5 6


*

S3 < S(1, 2, 4, 5, 6)S5 > S1, S2 S3 < S4, S5

S3 < S(2, 4, 5, 6)

SITES:

c)

*

*


4.3.3.10 Baseline and changes in cover of annual plants

Mean baseline cover of annual plants ranged from a minimum of 0.8 ± 0.3% in A to a

maximum of 1.1 ± 0.3% in C, with cover in H similar to A and treatment differences not

significant (F2,10 = 0.59, P = 0.571). Site differences at the start of the experiment were

significant (F5,10 = 6.84, P = 0.005). Baseline annual plant cover was greater in Site 5 than

other sites (P ≤ 0.013) except Site 4, and greater in Site 4 than Sites 3 and 6 (P = 0.005;

Figure 4.20a). The Treatment × Site interaction was significant for changes in the cover of

annual plants relative to baseline (Table 4.7). In Site 1, the reduction in annual plant cover

from baseline was greater in H than the slight increase from baseline in C (P = 0.007), but did

not differ from the reduction in cover in A (P = 0.084; Figure 4.20b). In Site 3, the relative

decline in annual cover in C and H was greater than the small increase in A quadrats

(P ≤ 0.022). Cover declined from baseline in Site 2 by a similar amount in all treatments.

Conversely, cover increased from baseline in Site 5 in all treatments and in C quadrats only in

Site 6, but treatment differences were not significant (P > 0.050). The relative increase in

annual cover in Site 4, however, was greater in H than A (P = 0.045) but not C (P = 0.149).

Figure 4.20 Mean (±1 S.E.) cover of annual plants at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in annual cover from baseline in (b) each Treatment at each Site averaged across T2–T6; * denotes a significant difference as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

0

0.5

1

1.5

2

2.5

3

S1 S2 S3 S4 S5 S6

An

nu

al p

lan

t co

ver

(%)

S5 > S(1, 2, 3, 6)

a)

*

*S4 > S3, S6

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1 2 3 4 5 6

Ch

ange

in a

nn

ual

pla

nt

cove

r (%

)

Site

C H A

*

H ≠ C

*

C , H ≠ A

*

H > A

b)


4.3.4 Floristic composition

4.3.4.1 Baseline floristic composition

The most widespread species was kangaroo grass, present in all but one quadrat at

the start of the experiment (T1) and in all quadrats after December 2005 (T2) (Table 4.8).

Kangaroo grass had high levels of cover averaged across all quadrats, with maximum cover at

T1 (45.9%; Table 4.8). Wild sorghum (mean cover 6.2–8.6%) was also widespread (present in

81–94% of quadrats) but subordinate to kangaroo grass in terms of cover except in one

quadrat. Tussocky poa, weeping grass and barbed wire grass (Cymbopogon refractus) were

the next most widespread grass species, present in more than half of quadrats with average

cover of 2.6–6.4%. Unlike the other two species, the cover and presence of tussocky poa

tended to increase through the experiment with the minimum cover and percentage of

quadrats in Table 4.8 corresponding to June 2004 and the maximum to the final sampling

time. Tussocky poa was notably absent from Sites 4 and 6 until the species was recorded in a

control quadrat in Site 4 in June 2006 (T5) and again in December 2006 (T6) when it was also

recorded in a control quadrat in Site 6 (Appendix 4). Similarly, the species was initially

present in Site 5 in two quadrats, increasing to three in December 2005 (T3) and five at T6.

Cover was <5.5% in Sites 4–6, whereas cover tended to be greater in Sites 1–3, peaking at

35% in one quadrat in June 2004 (Appendix 4). Paddock lovegrass (Eragrostis leptostachya)

was the widespread grass with the lowest maximum average cover, which was comparable

with the average cover of the common legumes (e.g. slender tick-trefoil Desmodium varians).

While percentage cover did not exhibit any clear trends, the proportion of quadrats in which

paddock lovegrass was present tended to decline over time with the minimum (27.8%)

recorded at the final sampling time. The species was concentrated in Sites 4 and 6 and the

proportion of quadrats containing the species was greatest (50%) in June 2004 (T1).

Slender tick-trefoil was the most widespread and abundant forb, followed by variable

glycine (Glycine tabacina) and kidney weed (Dichondra repens) (Table 4.8). The interstitial

herb layer was more diverse than the grass layer, and the majority of forb species in June

2004 tended to be localised and occur in quadrats at just a few sites. For example, austral

bugle (Ajuga australis) was abundant in Site 5 and daisy-leaf goodenia (Goodenia bellidifolia)

in Site 3 (Appendix 4). Over time, several forb species became widespread and more

abundant (e.g. stinkweed Opercularia diaphylla, small St. John’s wort Hypericum gramineum,

blue flax-lily Dianella revoluta, wattle mat-rush Lomandra filiformis subsp. filiformis, Senecio

sp. E., and zornia Zornia dyctiocarpa in Appendix 4).


Table 4.8 The minimum and maximum average cover of the most abundant and widespread grasses and forbs, and the minimum and maximum proportion of quadrats in which these species occured. The percent cover of each species was averaged across all quadrats for each sampling time, as was the percentage of quadrats containing the species. There were six averages corresponding to the six sampling times from June 2004 to December 2006 inclusive.

Cover (% ±1 S.E.) Sample frequency (%) Latin name Common name Min Max Min Max Grasses Themeda australis Kangaroo grass 35.3 ± 2.3 45.9 ± 3.3 97.2 100.0 Sarga leiocladum Wild sorghum 6.2 ± 1.3 8.6 ± 1.2 80.6 94.4 Poa sieberiana Tussocky poa 4.5 ± 1.3 6.4 ± 1.2 47.2 63.9 Microlaena stipoides Weeping grass 4.0 ± 1.3 4.7 ± 1.5 66.7 83.3 Cymbopogon refractus Barbed wire grass 2.6 ± 0.4 3.4 ± 0.6 66.7 77.8 Eragrostis leptostachya Paddock lovegrass 1.7 ± 0.9 2.7 ± 1.3 27.8 50.0 Forbs Desmodium varians Slender tick-trefoil 0.9 ± 0.1 2.6 ± 0.2 80.6 91.7 Glycine tabacina Variable glycine 0.5 ± 0.1 2.4 ± 0.2 50.0 80.6 Dichondra repens Kidney weed 0.9 ± 0.1 2.3 ± 0.3 69.4 77.8 Senecio sp. E 0.6 ± 0.1 1.3 ± 0.1 50.0 77.8 Lomandra filiformis subsp. filiformis Wattle mat-rush 0.7 ± 0.1 1.0 ± 0.1 38.9 66.7 Dianella revoluta Blue flax-lily 0.4 ± 0.1 1.0 ± 0.2 58.3 77.8

4.3.4.2 Treatment effects on floristic composition over time

The MDS of plant cover data showed that the relative position of C, H and A quadrats

was maintained at each sampling time throughout the experiment (Figure 4.21). The

centroid of the C quadrats grouped at the top of the ordination diagram, A quadrats in the

centre and H quadrats at the bottom of the ordination diagram. At the baseline sampling

time (T1), treatment group centroids were equidistant from each other along the vertical axis

and that equidistant relationship was largely retained at other sampling times. Consistent

with the MDS plot, the effect of grazing exclusion on the floristic composition of

groundstorey vegetation, as represented by the PERMANOVA Treatment × Time interaction,

was not significant (F10,108 = 0.54, P = 1.000; Table 4.9). The effect of Time was significant

(F5,108 = 6.09, P = 0.001). The June 2004 (C1, H1, A1) and June 2005 treatment groups (C2,

H2, A2) were most closely aligned with the June 2006 groups (C5, H5, A5); as were the two

early summer sampling times of December 2005 (C3, H3, A3) and December 2006 (C6, H6,

A6; Figure 4.21). The single late summer sampling time of February 2006 (C4, H4, A4)

separated to the far left of ordination space, closer to the early summer treatment group,

than the winter groups.


Figure 4.21 Nonmetric MDS of plant cover data comparing the similarity in composition of groundstorey vegetation in treatments at different sampling times. Symbols for treatment centroids were: C: controls, H: horse excluded, A: all herbivore excluded and sampling times: 1: June 2004, 2: June 2005, 3: December 2005, 4: February 2006, 5: June 2006, and 6: December 2006.

Table 4.9 Multifactor PERMANOVA of the cover abundance and species incidence similarity matrices to test for changes in species composition between Treatments over Time and by Site.

Cover Indicence Source of variation df MS F P MS F P Treatment 2 5482.3 7.18 0.001 5121.2 6.29 0.001 Site 5 21731.0 28.46 0.001 25472.0 31.28 0.001 Time 5 4649.9 6.09 0.001 4854.3 5.96 0.001 Treatment × Site 10 5429.2 7.11 0.001 5715.5 7.02 0.001 Treatment × Time 10 415.9 0.54 1.000 452.5 0.56 0.998 Site × Time 25 722.3 0.95 0.718 728.9 0.90 0.862 Treatment × Site × Time 50 299.2 0.39 1.000 303.2 0.37 1.000

Residual 108 763.5 814.3 C, H and A centroids in the species incidence MDS did not separate into distinct

groups like the abundance data but the same equidistant relationship between each

treatment was maintained for the duration of the experiment (Figure 4.22). The trajectory of

change through time was consistent across treatments since the PERMANOVA

Treatment × Time interaction was not significant (F10,108 = 0.56, P = 0.998; Table 4.9). The

seasonal effect in the species incidence MDS was similar to the cover abundance MDS with

winter samples to the left of the plot and summer samples to the right (Figure 4.22).

C1

H1

A1

C2

H2

A2

C3

H3

A3

C4

H4

A4

C5

H5

A5

C6

H6

A6

2D Stress: 0.11


Figure 4.22 Nonmetric MDS plot, based on species incidence data, comparing the similarity in composition of groundstorey vegetation in treatments at different sampling times. Notation as for Figure 4.21.

4.3.4.3 Baseline grazing lawn composition

In June 2004, ten of the 36, 6 × 5-m quadrats had a grazing lawn structure

(Table 4.10). The majority (60%) of lawn quadrats were kangaroos grass grazing lawns with

tussocky poa, wild sorghum or barbed wire grass as subsidiary species (Table 4.10). Three of

the remaining lawn quadrats were weeping grass and one paddock lovegrass grazing lawns.

Horse exclosures had just two grazing lawns, one dominated by kangaroo grass, the other by

weeping grass. Controls and complete exclusion had four grazing lawns. In controls, two

were kangaroo grass lawns, one weeping grass lawn and one paddock lovegrass lawn.

Complete exclusion had three kangaroo grass and one weeping grass grazing lawn.

C1

H1A1

C2

H2A2

C3

H3

A3

C4

H4

A4

C5

H5

A5C6

H6

A6

2D Stress: 0.14

Chap

ter

4. P

late

au E

xclo

sure

Exp

erim

ent i

n Th

emed

a G

rass

land

125

Tabl

e 4.

10 P

erce

nt c

over

of d

omin

ant g

rass

spe

cies

in g

razi

ng la

wn

quad

rats

in Ju

ne 2

004.

The

dom

inan

t spe

cies

in e

ach

law

n is

hig

hlig

hted

.

Latin

nam

e Co

mm

on n

ame

1C

1H

1A

3C

4C

4A

5A

6C

6H

6A

Them

eda

aust

ralis

Ka

ngar

oo g

rass

30

65

70

50

4

7 35

0

15

60

Poa

sieb

eria

na

Tuss

ocky

poa

20

0

11

0 0

0 3

0 0

0 Sa

rga

leio

clad

um

Wild

sor

ghum

12

4

2 0

0 0

10

1 7

3 Cy

mbo

pogo

n re

frac

tus

Barb

ed w

ire

gras

s 0

7 0

4 0

2 10

1

1 4

Both

rioch

loa

mac

ra

Red

gras

s 0

0 0

0 0

3 0

1 0

0 M

icro

laen

a st

ipoi

des

Wee

ping

gra

ss

2 1

1 1

2 40

2

50

45

2 Er

agro

stis

lept

osta

chya

Pa

ddoc

k lo

vegr

ass

0 0

0 1

40

2 0

5 1

1

Tabl

e 4.

11 M

ultif

acto

r PE

RMA

NO

VA o

f th

e co

ver

abun

danc

e an

d sp

ecie

s in

cide

nce

sim

ilari

ty m

atri

ces

to t

est

for

chan

ges

in g

rass

land

str

uctu

re

com

posi

tion

betw

een

Trea

tmen

ts o

ver

Tim

e an

d by

Site

.

Cove

r In

cide

nce

Sour

ce o

f var

iatio

n df

MS

F P

M

S F

P Tr

eatm

ent

2 4

966.

1 7

.44

0.00

1 4

966.

1 1.

35

0.27

5 Si

te

5 15

857.

0 23

.75

0.00

1 15

857.

0 3.

51

0.00

3 Ti

me

5 2

093.

4 3

.14

0.00

1 2

093.

4 7.

80

0.00

2 St

ruct

ure

1 5

536.

2 8

.29

0.00

1 5

536.

2 8.

29

0.00

1 Tr

eatm

ent ×

Sit

e 10

4

592.

7 6

.88

0.00

1 4

592.

7 1.

05

0.45

6 Tr

eatm

ent ×

Tim

e 10

339

.9

0.5

1 0.

999

3

39.9

1.

40

0.09

1 Tr

eatm

ent ×

Str

uctu

re

2 3

124.

3 4

.68

0.00

1 3

124.

3 4.

68

0.00

1 Si

te ×

Tim

e 25

550

.4

0.8

2 0.

911

5

50.4

2.

22

0.00

1 Si

te ×

Str

uctu

re

4 3

048.

0 4

.57

0.00

1 3

048.

0 4.

57

0.00

1 Ti

me

× St

ruct

ure

5

239

.6

0.3

6 0.

998

2

39.6

0.

36

0.99

9 Tr

eatm

ent ×

Sit

e ×

Tim

e 50

288

.5

0.4

3 1.

000

2

88.5

1.

57

0.01

4 Tr

eatm

ent ×

Sit

e ×

Stru

ctur

e 3

334

8.9

5.0

2 0.

001

334

8.9

5.02

0.

001

Trea

tmen

t × T

ime

× St

ruct

ure

10

2

83.9

0

.43

1.00

0

283

.9

0.43

1.

000

Site

× T

ime

× St

ruct

ure

20

2

66.2

0

.40

1.00

0

266

.2

0.40

1.

000

Trea

tmen

t × S

ite

× Ti

me

× St

ruct

ure

15

2

51.0

0

.38

1.00

0

251.

0 0.

38

1.00

0

Resi

dual

48

667

.6

667.

55


4.3.4.4 Changes in grassland structure in treatments over time

Grazing lawns did not become closer in ordination space to tussock grass-dominated

quadrats with time as no interaction including Structure and Time was significant

(Table 4.11). The only significant three-way interaction including Structure for both the plant

cover abundance and incidence matrices was the Treatment × Site × Structure interaction

(Table 4.11). The PERMANOVA output and MDS plot was identical for both data matrices,

hence only the cover abundance MDS is presented in Figure 4.23. The position of

GL centroids relative to TG centroids reflected the difference in composition of grazing lawn

quadrats described in the previous section and Table 4.9. In Sites 1, 3 and 5, grazing lawns

were either dominanted by kangaroo grass or by kangaroo grass in conjunction with wild

sorgum, tussocky poa or barbed wire grass (Table 4.9). These species were the most

widespread tussock grasses when cover was averaged over all quadrats at baseline

(Section 4.3.4.1; Table 4.8), therefore it was not suprising that TG and GL centroids did not

separate into distinct groups in Sites 1, 3 and 5. Conversely, GL centroids separated to the

far left bottom corner from TG centroids in Site 4 as grazing lawns were dominated by either

weeping grass or paddock lovegrass (Table 4.9) and the cumulative baseline cover of the

aforementioned four tussock grasses was <10%. Likewise, the Site 6 control-grazing lawn

dominated by weeping grass (Table 4.9) was closer to the Site 4 GL centroids than the Site 6

TG centroids whereas the kangaroo grass-dominated centroid was adjacent to the Site 6

TG centroids (Figure 4.23). The Site 6 horse exclosure-GL centroid was intermediate with

respect to the control-GL centroid and TG centroids because although it was dominated by

weeping grass, the cumulative cover of the four tussock grasses at baseline was 23%.


Figure 4.23 Nonmetric MDS plot based on cover abundance data comparing the similarity in composition of tussock grass-dominated (TG) and grazing lawn (GL) dominated quadrats by treatment and site. 1: Site 1, 2: Site 2, 3: Site 3, 4: Site 4, 5: Site 5, 6: Site 6. C: control,

H: horse excluded, A: all herbivore excluded. C-1 represented Site 1 controls etc. C-1,

H-1, A-1, C-2, H-2, A-2, C-3, H-3, A-3, C-4, H-4, A-4, C-5, H-5,

A-5, C-6, H-6, A-6. 4.3.5 Species richness variables

In June 2004, 98 vascular plant taxa were recorded in the baseline data: 89 (90.8%)

were native, six were exotic (6.1%) and three (3.1%) were of uncertain status because they

could not be identified to species. Of the total, 63 were forbs, 17 were grasses, 15 were

shrubs or trees, and three were sedges; 88 were perennial, seven were annual and three

were of uncertain status. From June 2005 to December 2006, an additional 26 plant taxa

were recorded: 14 were forbs, seven were grasses, three were shrubs or trees and two were

sedges (Appendix 4). All taxa were perennial with the exception of one annual forb and one

grass species of uncertain status. No additional exotic species were recorded, thus exotic

richness as a proportion of total species recorded over all sampling times was as low as 4.8%

(see Appendix 4).

TGGL

GL

TG

TG GL

TGTG

TG

GL TG

TG TGTG

GL

TG

GL

TG

TGTG

GL

TG

GL TG

GLTG

TG

GL

2D Stress: 0.12


4.3.5.1 Baseline and changes in total species richness

At the beginning of the experiment in June 2004, the Site main effect was significant

(F5,10 = 3.41, P = 0.047) with greater total species richness in Site 5 by chance than other sites

(P ≤ 0.052) except Site 4 (P = 0.099; Figure 4.24a). Mean total species richness at baseline

ranged from a minimum of 20.3 ± 2.4 species in A to a maximum of 21.8 ± 1.7 species in H

and did not differ between treatments (F2,10 = 0.35, P = 0.712). The Treatment × Site

interaction and Time main effect were significant for the change in total species richness

from baseline (Table 4.12). Treatment differences were not consistent between sites with

the increase in total richness from baseline greater in exclosures than C quadrats in Site 1

(P ≤ 0.020), greater in A and C than H in Site 2 (P < 0.001), and greater in H and C than A in

Site 3 (P ≤ 0.013; Figure 4.24b). In Site 4, total richness declined relative to baseline in A and

differed from C and H (P ≤ 0.017), which increased moreso in C than H (P = 0.002). Total

richness averaged across all quadrats had increased from baseline by June 2005 (T2) and

tended to increase through time except for the decline in the relative increase from T4 to T5

(Figure 4.24c). Total species richness was less in June 2006 (T5) than February 2006 (T4) or

December 2006 (T6; P < 0.001).


Figure 4.24 Mean (±1 S.E.) total species richness at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in total species richness from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) over Time; * denotes a significant difference (P ≤ 0.052). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. T1: June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2005, T6: December 2006. Table 4.12 Repeated measures multifactor ANOVA for changes in the richness of total plant species, shrubs and trees, and sedges relative to baseline (June 2004).

Total Shrub/tree Sedge

Source of variation df F P F P F P

Treatment 2 0.83 0.444 1.78 0.181 8.57 <0.001

Site 5 9.67 <0.001 3.06 0.019 37.92 <0.001

Time 4 8.11 <0.001 1.84 0.141 3.00 0.029

Treatment × Site 10 8.31 <0.001 6.18 <0.001 4.48 <0.001

Treatment × Time 8 0.81 0.597 1.32 0.261 1.16 0.345

Site × Time 20 1.75 0.065 1.18 0.323 1.65 0.088

Residual 40

0

5

10

15

20

25

30

S1 S2 S3 S4 S5 S6

Tota

l sp

ecie

s ri

chn

ess

S5 > S(1, 2, 3, 6)*

a)

-4

-2

0

2

4

6

8

10

1 2 3 4 5 6Ch

ange

in t

otal

spe

cies

ric

hnes

s Site

C H A

*

A, H > C

*

H ≠ A, CC ≠ A

*

* C , A > H

*

*

H, C > A*

*

b)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Chan

ge in

tot

al s

peci

es r

ichn

ess


*

T5 < T4, T6

c)


4.3.5.2 Baseline and changes in shrub and tree richness

Species richness of shrubs and trees did not vary by Site (F5,10 = 1.99, P = 0.165) or

Treatment (F2,10 = 1.66, P = 0.239) at the start of the experiment in June 2004. Mean

shrub/tree richness ranged from a minimum of 1.8 ± 0.4 species in Site 1 to a maximum of

3.7 ± 0.8 species in Site 2 and from 2.4 ± 0.1 species in C to 3.3 ± 0.4 species in H, with A

similar to C. The Treatment × Site interaction was significant for the change in shrub/tree

richness from baseline (Table 4.12). Shrub/tree richness was greater than baseline in

C quadrats in Site 1, differing from the reductions relative to baseline in A and H (P ≤ 0.015),

which also differed due to the greater reduction in H (P = 0.005; Figure 4.25). In Site 2,

richness declined relative to baseline in all treatments but moreso in exclosures than

C quadrats (P ≤ 0.042). In Site 4, the relative increase in richness in H was greater than the

relative increase in C, with no change from baseline in A (P ≤ 0.042). In Site 5, the relative

increase in H differed from the relative reductions in A and C (P ≤ 0.015), the reduction in A

being greater than C (P < 0.001). Conversely, in Site 3 the relative reduction in H differed

from the increases relative to baseline in A and C (P < 0.001) while in Site 6 the relative

increase in A differed from the reductions in shrub/tree richness in C and H (P ≤ 0.005).

Figure 4.25 Mean (±1 S.E.) change in shrub/tree richness from baseline in each Treatment at each Site averaged across T2–T6; * denotes a significant difference between treatments at a site as specified (P < 0.050). C: control, H: horse excluded, A: all herbivore excluded.

-3

-2

-1

0

1

2

3

1 2 3 4 5 6

Chan

ge in

shr

ub/t

ree

rich

ness

Site

C H A

*

C ≠ H, AH < A *

H > A, C

*

*

C > A, H

*

H ≠ A, C *

H ≠ A, CA < C

*

A ≠ C, H


4.3.5.3 Baseline and changes in sedge richness

Richness of sedges did not vary by Site (F5,10 = 1.47, P = 0.281) or Treatment

(F2,10 = 0.79, P = 0.481) at the start of the experiment in June 2004. Mean baseline sedge

richness ranged from a minimum of 0.2 ± 0.2 species in Site 2 to a maximum of

0.8 ± 0.2 species in Sites 3 and 4 and from 0.4 ± 0.2 species in A and C to 0.7 ± 0.2 species in

H. The Treatment × Site interaction and Time main effect were significant for the change in

sedge richness from baseline (Table 4.12). The number of sedge species increased relative to

baseline in Sites 1 and 6 in all treatments but in Site 1, the relative increase was greater in A

than C (P = 0.013; Figure 4.26a). In Sites 2 and 5, richness was greater than baseline in H and

differed from the relative reductions in A (P = 0.042), and C and A (P < 0.001), respectively.

Conversely, sedge richness was less in H relative to baseline in Site 3, differing from the

smaller reduction in A (P = 0.016). Averaged across all quadrats, sedges had increased in

number relative to baseline by June 2005 (T2) but declined relative to baseline thereafter

until December 2006 (T6), with the greatest reduction in June 2006 differing from the

relative increase in June 2005 (P = 0.037; Figure 4.26b).

Figure 4.26 Mean (±1 S.E.) change in sedge richness from baseline in (a) each Treatment at each Site averaged across T2–T6 and (c) over Time; * denotes a significant difference as specified (P < 0.050). C: control, H: horse excluded, A: all herbivore excluded. T1: June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2006, T6: December 2006.

-1

-0.5

0

0.5

1

1.5

2

1 2 3 4 5 6

Chan

ge in

sed

ge r

ichn

ess

Site

C H A

A > C

*

*

H ≠ A

*

H ≠ A

a)

*

H ≠ A, C

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Ch

ange

in s

ed

ge r

ich

ne

ss


*

T2 ≠ T5

b)


4.3.5.4 Baseline and changes in grass richness

Mean baseline grass richness ranged from a minimum of 5.0 ± 0.6 species in Site 1 to

a maximum of 7.2 ± 1.2 species in Site 4 (F5,10 = 0.73, P = 0.615) and from 6.0 ± 0.9 species in

A to 6.4 ± 0.4 species in H (F2,10 = 0.12, P = 0.887). Changes in grass richness relative to

baseline varied only with Time (Table 4.13) with reductions in richness in December 2005

(T3) and February 2006 (T4) greater than the reduction in richness recorded in June 2005 (T2;

P ≤ 0.047; Figure 4.27). Richness at T4 had also declined from baseline to a greater extent

than in June 2006 (T5).

Figure 4.27 Mean (±1 S.E.) change in grass richness from baseline over Time; * denotes a significant difference between sampling times as specified (P < 0.050). June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2006, T6: December 2006.

Table 4.13 Repeated measures multifactor ANOVA for changes in the richness of grasses and forbs relative to baseline (June 2004).

Grass Forb Source of variation df F P F P

Treatment 2 2.22 0.122 7.89 0.001

Site 5 0.60 0.699 13.28 <0.001

Time 4 2.80 0.039 22.32 <0.001

Treatment × Site 10 1.78 0.097 11.68 <0.001

Treatment × Time 8 1.34 0.251 0.82 0.591

Site × Time 20 0.58 0.903 2.94 0.002

Residual 40

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

Chan

ge in

gra

ss r

ichn

ess


*

T3, T4 < T2T4 < T5

*


4.3.5.5 Baseline and changes in forb richness

Forb richness did not differ between treatments (F2,10 = 0.03, P = 0.973) at the start of

the experiment in June 2004 but the Site main effect was almost significant (F2,10 = 2.97,

P = 0.065) due to the greater richness in Site 5 than other sites (P ≤ 0.036) except Site 4

(Figure 4.28a). In the analysis of the change in forb species richness from baseline, the

Treatment × Site and Time × Site interactions were significant (Table 4.13). Forb richness was

equal to or greater than baseline in all comparisons (Figures 4.28b and c). The relative

increase from baseline was greater in H than both C and A in Sites 3, 4 and 6 (P ≤ 0.003) but

less in H than C and A in Sites 2 and 5 (P ≤ 0.046; Figure 4.28b). The increase in C in Site 3

was also greater than A and exclosures only recorded a greater increase in richness from

baseline compared to C quadrats in Site 1. The greatest increases in richness from baseline

through time were recorded in Sites 1 and 2 and the least in Sites 4 and 5, with the response

of Site 3 the most variable between sampling times (Figure 4.28c). Thus, in June 2005 (T2)

the relative increase was greater in Site 2 than Site 5 (P = 0.051) and greater in Site 1 than

Sites 4 and 5 in February 2006 (T4; P ≤ 0.051). Site 4 in June 2006 was the only instance

where richness declined from baseline, differing from the relative increases in Sites 1–3

(P ≤ 0.051). Site 3 deviated from the response of other sites in December 2005 (T3) when

richness declined from the relative increase in T2, but only differed from Site 2 (P = 0.035).


Figure 4.28 Mean (±1 S.E.) forb richness at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in total species richness from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) each Site at each sampling time; * denotes a significant difference as specified (P ≤ 0.051). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. T1: June 2004, T2: June 2005, T3: December 2005, T4: February 2006, T5: June 2005, T6: December 2006.

0

2

4

6

8

10

12

14

16

18

20

S1 S2 S3 S4 S5 S6

Forb

ric

hnes

sS5 > S(1, 2, 3, 6)

*

a)

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6Ch

ange

in fo

rb r

ichn

ess

Site

C H A

H,A > C H > A, CC > A

*

A, C > H *

C, A > H

b)

*

*

*

H > A, C

*

H > C, A

*

-4

-2

0

2

4

6

8

10

12

Chan

ge in

forb

ric

hnes

s

1 2 3 4 5 6


*S3 < S2

S5 < S2

S4, S5 < S1

SITES:

c)

*

*

*

S4 < S(1, 2, 3)


4.3.5.6 Baseline and changes in exotic and annual richness

When baseline data was collected, farmer’s friend (Bidens pilosa) was the most

widespread exotic forb, present in seven quadrats; followed by the only exotic annual grass,

finger grass (Digitaria ternata), present in three quadrats (Reid and Fittler 2004). The only

other exotic annual species was the forb, common centaury (Centaurium erythraea) in one

quadrat and the remaining exotic forbs were fleabane (Conyza bonariensis), greater plantain

(Plantago major), and common dandelion (Taraxacum officinale). By the last two sampling

times, common centaury and greater plantain were no longer present in any quadrats and

just four exotic species persisted, with exotic richness as a proportion of all species recorded

during the experiment as low as 3.2% at T5 and T6.

Treatments did not differ in exotic richness at baseline (F2,10 = 0.42, P = 0.671) but the

Site main effect was significant (F2,10 = 12.56, P < 0.001) because no exotic species were

recorded in Sites 1–3 through the experiment (Figure 4.29a). This result contributed in part

to the significant Time × Site interaction for changes in exotic richness relative to baseline

(Table 4.14). Site 6 recorded the greatest reduction in exotic species richness relative to

baseline, the reduction becoming more pronounced through time and differing from the

response of Sites 1–3 from T3 to T6 (P ≤ 0.019; Figure 4.29c). The response of Site 5 was less

consistent through time, declining in richness relative to baseline and not differing from

Site 6 at T2–T4. However, as richness continued to deline in Site 6 at T5, the relative

reduction in Site 5 was less and comparable to richness levels at T3, with significant

differences between sites at T5 and T6 (P ≤ 0.019). Similarly, exotic richness in Site 4

declined relative to baseline until after T3, not differing from baseline at T5 and differing

from Site 6 from T3 to T6 (P ≤ 0.019). Treatment response was also inconsistent in Sites 4–6

(Figure 4.29b). In Site 4, the relative decline in richness was greater in A than C (P = 0.047)

and also greater than the minor relative increase in H (P = 0.008). The result was repeated in

Site 6 (P < 0.001) except richness declined relative to baseline in H. Exotic richness declined

in all treatments to a similar extent in Site 5.


Figure 4.29 Mean (±1 S.E.) richness of exotic species at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) changes in exotic species richness from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) each Site at each sampling time; * denotes a significant difference as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

S1 S2 S3 S4 S5 S6

Exo

tic

ric

hn

ess S4–S6 > S1–S3

*

a)

-1.5

-1

-0.5

0

0.5

1

1 2 3 4 5 6Ch

ange

in e

xoti

c ri

chne

ssSite

C H A

A ≠ H, C

*

A < C, H

b)

*

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

Chan

ge in

exo

tic

rich

ness

1 2 3 4 5 6


*S4–S6 ≠ S1–S3

S5, S6 ≠ S1–S4

S6 ≠ S1–S5

SITES:

c)

*

*

*

S6 ≠ S1–S5


Table 4.14 Repeated measures multifactor ANOVA of the change in exotic and annual richness relative to June 2004.

Exotic Annual Source of variation df F P F P

Treatment 2 15.67 <0.001 0.35 0.709

Site 5 27.52 <0.001 7.58 <0.001

Time 4 2.51 0.057 0.59 0.672

Treatment × Site 10 7.31 <0.001 2.01 0.058

Treatment × Time 8 0.29 0.964 0.76 0.636

Site × Time 20 2.41 0.009 1.69 0.078

Residual 40

The remaining six of the eight annual species recorded in total were all native forbs.

Baseline annual richness did not differ between treatments (F2,10 = 0.59, P = 0.571). The Site

main effect was significant at baseline (F2,10 = 12.56, P < 0.001) and for change in annual

richness relative to baseline (Table 4.14). In June 2004, annual richness was greater in Site 5

than other sites except Site 4 (P ≤ 0.013) as was the reduction in richness relative to

June 2004 (P ≤ 0.048; Figure 4.30). Likewise, baseline annual richness was greater in Site 4

than Sites 3 and 6 (P = 0.005) and the relative reduction greater in Site 4 than Sites 1–3

(P ≤ 0.007).

Figure 4.30 Mean (±1 S.E.) richness of annual plants at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) changes in annual species richness from baseline between (b) Sites averaged across T2–T6; * denotes a significant difference between sites as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6.

0

0.5

1

1.5

2

2.5

S1 S2 S3 S4 S5 S6

Ann

ual r

ichn

ess

S5 > S1–S3, S6S4 > S3, S6 *

a)

*

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

S1 S2 S3 S4 S5 S6

Chan

ge in

ann

ual

rich

ness

S4, S5 ≠ S1–S3S5 < S6

*

b)



4.3.6.1 Kangaroo grass biomass

At the beginning of the experiment in June 2004, kangaroo grass biomass ranged

from 55.1 ± 17.9 g/m2 in A to 85.6 ± 23.7 g/m2 in C and did not differ between treatments

(F2,10 = 2.43, P = 0.138). Site differences were significant (F5,10 = 6.67, P = 0.006) as Site 1, with

the greatest biomass, differed from Sites 2 and 4 and Site 5, with the least mean biomass

(P ≤ 0.030; Figure 4.31a). The result in Sites 3 and 6 was similar to Site 1 (P ≤ 0.053) except

these sites did not differ in biomass at baseline from Site 2 in which biomass was also greater

than Site 5 (P = 0.045). In the analysis of the change in kangaroo grass biomass from

baseline, the Treatment × Site interaction was significant (Table 4.15). In Sites 1 and 4,

kangaroo grass biomass increased from baseline in A, which differed from the relative

declines in C and H (P ≤ 0.004) with biomass decreasing further in C than H (P ≤ 0.006;

Figure 4.31b). Biomass did not increase from baseline in C quadrats for the duration of the

experiment, thus the relative increases in A and H in Site 5 differed from C (P ≤ 0.013). In

Site 6, biomass was equivalent to baseline levels in C and A, contrasting with the relative

decrease in H (P ≤ 0.018). Biomass averaged across all quadrats did not differ from baseline

in January 2006 as opposed to the decline recorded 1 year later, explaining the significant

Time main effect (Table 4.15;Figure 4.31c).


Figure 4.31 Mean (±1 S.E.) biomass of kangaroo grass at the start of the experiment in June 2004 compared between (a) Site, and mean (±1 S.E.) change in kangaroo grass biomass from baseline in (b) each Treatment at each Site averaged across T2–T6 and (c) over Time; * denotes a significant difference as specified (P ≤ 0.053). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded. Table 4.15 Repeated measures multifactor ANOVA of the change in kangaroo grass biomass relative to June 2004.


0

20

40

60

80

100

120

140

160

S1 S2 S3 S4 S5 S6

Kna

garo

o g

rass

bio

mas

s (g

/m2)

S1 > S(2, 4, 5)

*a)

*

S3 > S4, S5

*S2 > S5

*

S6 > S4, S5

-150

-100

-50

0

50

100

1 2 3 4 5 6

Chan

ge in

kan

garo

o gr

ass

biom

ass

(g/m

2 )

Site

C H A

A ≠ C, HC < H

*

A ≠ C, HC < H

A, H ≠ C

b)

*

*

*

H ≠ C, A

*

*

-50

-40

-30

-20

-10

0

10

20

January 2006 January 2007

Chan

ge in

kan

garo

o gr

ass

biom

ass

(g/m

2 )

*

c)


4.3.6.2 Culms, panicles and spikelets

Trends were similar for total culms, panicles and spikelets and the Treatment and Site

main effects were significant (Table 4.16). The Time main effect was significant for culms and

panicles (Table 4.16). When the results of pair-wise tests were the same for total culms,

panicles and spikelets, all three are reported collectively using the term ‘reproductive

variables’ (Figure 4.32). Reproductive variables values were greater in A than both C and H

(P ≤ 0.022; Figures 4.32a–c). Sites 6, 4, 3 and 1 recorded high mean values for reproductive

variables and Sites 2 and 5 low mean values, pair-wise tests differing between variables

(Figures 4.32d–f). Mean total culms and panicles were greater in Sites 3 and 6 than Site 5

(P ≤ 0.052), and greater in Site 6 than Site 2 (P ≤ 0.021; Figures 4.32d and e). Total culms

were also greater in Site 4 than Sites 5 and 2 (P ≤ 0.042) and total panicles greater in Site 3

than Site 2 (P = 0.050). Mean total spikelets were greater in Site 6 than Sites 1, 2 and 5

(P ≤ 0.010; Figure 4.32f). Mean reproductive variable values were greater in January 2006

than January 2007 although pair-wise tests were only significant for total culms and panicles

(P ≤ 0.009; Figures 4.32g–i).


Figure 4.32 Mean (±1 S.E.) total culms, panicles and spiklets per plot compared between (a–c) Treatment, (d–f) Site and (g–i) Time; * denotes a significant difference as specified (P ≤ 0.052). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

0

20

40

60

80

100

120

C H A

Tota

l cu

lms/

m2

*A > C, Ha)

0

50

100

150

200

250

300

350

400

C H A

Tota

l pan

icle

s/m

2

*A > C, Hb)

0

500

1000

1500

C H A

Tota

l sp

ike

lets

/m2

*A > C, Hc)

0

20

40

60

80

100

120

S1 S2 S3 S4 S5 S6

Tota

l cu

lms/

m2

S4, S6 > S2

*

d)

*

S5 < S(3, 4, 6)

0

50

100

150

200

250

300

350

400

S1 S2 S3 S4 S5 S6

Tota

l pan

icle

s/m

2

S3, S6 > S2

*

e)

*

S5 < S3, S6

0

500

1000

1500

2000

S1 S2 S3 S4 S5 S6

Tota

l sp

ike

lets

/m2

f)

*

S6 > S1, S2, S5

0

20

40

60

80

100

120

Jan-06 Jan-07

Tota

l cul

ms/

m2

*

g)

0

50

100

150

200

250

300

350

400

Jan-06 Jan-07

Tota

l pan

icle

s/m

2

*

h)

0

500

1000

1500

2000

Jan-06 Jan-07

Tota

l spi

kele

ts/m

2

i)


Table 4.16 Repeated measure multifactor ANOVA for kangaroo grass reproductive variables.

Culms Panicles Spikelets Source of variation df F P F P F P Treatment 2 10.94 0.003 15.48 <0.001 8.88 0.006 Site 5 3.61 0.039 3.76 0.036 3.33 0.049 Time 1 7.59 0.020 7.92 0.018 2.58 0.139 Treatment × Site 10 1.52 0.260 1.58 0.241 1.04 0.478 Treatment × Time 2 1.28 0.321 1.32 0.309 0.45 0.649 Site × Time 5 0.72 0.622 0.56 0.727 1.28 0.345 Residual 10

4.3.7 Soil and environmental variables

4.3.7.1 Top soil chemistry

The Site main effect was significant for EC, pHCa, sulphur and carbon (Tables 4.17 and

4.18). The lowest mean EC, pHCa and carbon values were recorded in Site 3, differing from all

other sites in EC (P ≤ 0.025), all sites except Site 1 in pHCa (P ≤ 0.011), and only Site 1 in

carbon (P < 0.001; Figures 4.33a, b and d). Carbon was greater in Site 1 than other sites

(P ≤ 0.023), which did not differ (Figure 4.33d). Mean EC was greatest and most variable in

Site 5 because of two values ≥160 μS/cm, but EC was only significantly greater in Site 6 than

Sites 2–4 (P ≤ 0.044; Figure 4.33a). Mean pHCa was the highest in Sites 6, 2 and 5, differing

from Site 1 (P ≤ 0.004) in addition to Site 3 (Figure 4.33b). Mean pHCa was also higher in

Sites 5 and 6 than Site 4 (P ≤ 0.048). The highest mean sulphur values were recorded in Site 1

followed by Site 3, with both sites differing from Sites 2, 5 and 6 (P ≤ 0.031; Figure 4.33c).

Site 4 had a relatively high sulphur value but only differed from Site 5 (P = 0.008).

Total carbon was greater in C than A (P = 0.027) and while H was more similar to A, C

did not differ from H (P = 0.089; Figure 4.33e). The Treatment × Site interaction was

significant for total nitrogen and phosphorus (Olsen) due to the response of treatments in

Sites 4–6 (Table 4.18). Mean nitrogen was notably high in C plots in Site 4 although nitrogen

was greater in both C (P < 0.001) and A (P = 0.042) than H (Figure 4.33f). Similarly, the

highest mean nitrogen level was recorded in C plots in Site 6, differing from nitrogen levels

which were similar in exclosures (P ≤ 0.003). Phosphorus (Olsen) in Site 5 was greater in C

than H (P = 0.005) and in Site 6, phosphorus (Olsen) was greater in H than C and A (P ≤ 0.031;

Figure 4.33g).


Figure 4.33 (a–g) Mean (±1 S.E.) values for chemical top soil variables in June 2004; * denotes a significant difference as specified (P < 0.050). S1: Site 1, S2: Site 2, S3: Site 3, S4: Site 4, S5: Site 5, S6: Site 6. C: control, H: horse excluded, A: all herbivore excluded.

0

20

40

60

80

100

120

140

S1 S2 S3 S4 S5 S6

EC (μ

S/cm

)

a)

*

S3 < S(1, 2, 4, 5, 6)S2, S4, < S6

**

0

1

2

3

4

5

6

S1 S2 S3 S4 S5 S6

pH

Ca

b)

*

S1, S3 < S(2, 5, 6)S3 < S4 < S5, S6

* *

0

20

40

60

80

100

120

S1 S2 S3 S4 S5 S6

Sulp

hur

(mg/

kg)

c)

*

S1, S3 > S(2, 5, 6)

*

*S4 > S5

0

10000

20000

30000

40000

50000

60000

1 2 3 4 5 6

Carb

on (

mg/

kg)

d) S1 > S2–S6

*

0

10000

20000

30000

40000

50000

60000

C H A

Carb

on (

mg/

kg)

e)

C > A

*

0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6

Nit

rog

en

(m

g/

kg

)

SITE

C H Af)

*C > A, H

*

H < A, C

0

5

10

15

20

25

30

1 2 3 4 5 6

Ph

osp

ho

rus

(Ols

en

mg/

kg)

SITE

C H A

*

C > H

*

H > A ,C

g)


Table 4.17 Multifactor ANOVA of EC, pHCa and total sulphur in the topsoil.

Source of df EC pHCa Total sulphur variation F P F P df F P

Treatment 2 0.61 0.555 0.94 0.410 2 0.64 0.538 Site 5 5.99 0.002 11.27 <0.001 5 5.64 0.003 Treatment × Site 10 1.24 0.330 1.28 0.311 10 1.15 0.382 Residual 18 17

Table 4.18 Multifactor ANOVA of total carbon, total nitrogen and phosphorus (Olsen) in the topsoil.

Source of df Total carbon Total nitrogen Phosphorus

(Olsen) variation F P F P F P Treatment 2 3.39 0.054 8.30 0.003 0.75 0.485 Site 5 3.73 0.018 7.80 <0.001 15.48 <0.001 Treatment × Site 10 1.42 0.248 2.43 0.048 2.39 0.051 Residual 18

4.3.7.2 Tree basal area and cover

The baseline basal area and cover of trees did not vary between treatments

(Figures 4.34a and b) but the Site main effect was significant for both basal area and tree

cover (Table 4.19). The relative ranking of sites from greatest to least basal area was

6 > 1 > 2, 5 > 3 > 4 and for tree cover 1 > 2, 5 > 3, 6 > 4 (Figures 4.34c and d). However, basal

area was only significantly less in Site 4 than other sites (P ≤ 0.030) and tree cover

significantly greater in Site 1 than other sites (P ≤ 0.040). Tree cover was also less in Site 4

than Sites 2 and 5 (P ≤ 0.015).

Table 4.19 Multifactor ANOVA for tree basal area and percent cover.

Basal area Tree cover Source of variation df F P F P Treatment 2 0.99 0.389 0.24 0.788 Site 5 4.34 0.009 6.30 0.002 Treatment × Site 10 0.54 0.068 1.19 0.358 Residuals 18


Figure 4.34 Mean (±1 S.E.) values for baseline tree basal area by (a) Treatment and (c) Site and for percent tree cover by (b) Treatment and (d) Site; * denotes a significant difference (P < 0.050) between treatments or sites as specified. C: control, H: horse excluded, A: all herbivore excluded.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

C H A

Tree

bas

al a

rea

(m2 )

a)

0

10

20

30

40

50

60

70

80

C H A

Tree

cove

r (%

)

b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

S1 S2 S3 S4 S5 S6

Tree

bas

al a

rea

(m2 )

S4 < S(1, 2, 3, 5, 6)

*

c)

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6

Tree

cove

r (%

)

S1 > S2–S6

*

d)

*

S4 < S2, S5


4.4 DISCUSSION

The outcomes of the exclosure experiment on Paddys Plateau were determined by

the density-dependent nature of feral horse impacts (Duncan 1992; English 2001b),

ecosystem resistance and interspecific competition between horses and macropods.

Although management (horse capture and removal) and the social behaviour and foraging

ecology of feral horses were important qualifiers in assessing density-dependent impacts, the

number of animals was a critical factor in this manipulative experiment, as elsewhere (Albon

et al. 2007; Hoshimo et al. 2009). The removal of 158 horses between the baseline

measurements in June 2004 and the first monitoring in June 2005 and removal of a further

83 horses over the course of the experiment reduced the chances of detecting treatment

effects due to the presence of horses over time, especially as the experiment progressed and

horses continued to be removed. Rather, the experiment became an examination of the

competitive effects of horses on macropods and provided insight into the potential role of

native herbivores in regulating plant communities after the removal of horses.

4.4.1 Biomass and exclosure dung counts

Excluding horses resulted in an increase in biomass 1 year after commencement of

the experiment (June 2005), when dung counts and the trapping program indicated that

horses were still present on Paddys Plateau and were more abundant than at subsequent

sampling times. In June 2005 (T2), both grazing exclusion treatments had greater biomass

(50–60 g/m2) than controls despite baseline biomass being less in horse exclosures than

controls and complete exclusion. Conversely, the mean number of horse dung deposits in

control plots (339.5 ± 79.3 deposits/ha) was greater than macropod dung deposits in control

plots (51.4 ± 23.5 deposits/ha) and horse exclosures (102.9 ± 50.2 deposits/ha). Horse dung

was present in all control plots and macropod dung limited to plots in Sites 1–3. Catch-per-

unit-effort (CPUE) values had not yet declined and macropod dung was still rare on district-

scale dung transects (Chapter 2). From December 2005 (T3) to June 2006 (T5), biomass was

greater only under complete exclusion compared to both controls and horse exclosures.

Horse dung declined from June 2005 (T2) to December 2005 (T3) and although not

significantly greater, mean horse deposits in controls were twice the number of deposits

recorded for macropods in controls. As 31 horses were trapped during that period, horses

may have consumed the biomass in controls and macropods the biomass in horse exclosures

prior to December 2005. From December 2005 (T3) to February 2006 (T4), horse dung in


control plots declined to 77.2 ± 42.0 deposits/ha and remained at that level for the duration

of the experiment, indicating the presence of residual low levels of horse grazing at some

sites. The Site main effect for total number of horse deposits in control plots suggested that

residual horse activity was concentrated in Sites 4–6, a result that was congruent with the

evidence of greater horse grazing pressure around Boban Hut. Mean horse deposits

averaged across all sampling times were greater in Sites 4 and 6 than Sites 1–3 and greater in

Site 5 than Site 3. The Site × Time interaction was precluded from dung analyses because of

a lack of replication. However, in June 2006 (T5), zero horse deposits were recorded in

Sites 1–3 and in December 2006 (T6), just a single horse deposit was counted in the Site 2

control plot as opposed to totals of between 2 and 7 horse deposits in all control plots in

Sites 4–6 at both sampling times.

The response of macropod dung was not as statistically robust as horse dung due to

greater variability between sites, but macropod dung in both control and horse exclosure

plots tended to increase as horse dung declined. Consistent with the response of horse dung

previously described and the inverse relationship detected between horse and macropod

dung on district-scale dung transects (Chapter 2), the trend was pronounced at the final two

sampling times (Time main effects) and in Sites 1–3 (Site main effects). Macropod dung had

progressively increased from a mean of ≤102.9 ± 50.2 deposit/ha across both control and

horse exclosure plots in June 2005 (T2) to between ≤257.2 ± 102.6 and

≤365.2 ± 282.2 deposits/ha in June and December 2006 (T5, T6). Site 1 was influential, with

greater macropod dung deposits than all other sites in both controls and horse exclosures.

Macropods were also relatively active in Sites 2 and 3, with greater dung deposits than Sites

4–6. No macropod dung was detected in Site 5 plots throughout the experiment and none

in Site 6 controls. Biomass did not differ between sites in June 2005 (T2) when horses were

relatively abundant, but by December 2005 (T3), the response of Site 1 differed from all

other sites. It was the only site to record a reduction in biomass from baseline while Site 5

recorded the smallest increase in biomass relative to baseline. This pattern also supported

the plot scale dung counts in this study and the district scale dung transects results

(Chapter 2). Eastern grey kangaroos were most active in the initial stages of the experiment

in and around Site 1 and 2 and horses in Site 5 around Boban Hut. Eastern grey kangaroos

progressively colonised Sites 2 and 3 with residual horse grazing pressure greatest in Sites 5

and 6, and Site 4 until the last two sampling times. Sites 3, 4 and 6 periodically recorded the

greatest incremental increase in biomass, possibly reflecting the transition from low horse


grazing pressure to low macropod grazing pressure when horse grazing pressure had

previously been high and macropod pressure subsequently increased. It may also reflect

fluctuations in the number of horses around Boban Hut due to the trapping program

(Chapter 2). It would appear, however, that after December 2005 (T3), the majority of

biomass in both controls and horse exclosures was consumed by eastern grey kangaroos.

In the temperate region of their range, eastern grey kangaroos are the largest native

mammal and one of the most prominent consumers of biomass (Fletcher 2006), especially in

kangaroo grass-dominated grasslands where they graze selectively and maintain uniformly

short grass swards (Neave and Tanton 1989; Neave 1991). The grass species with dominant

cover both in exclosure plots and dung transects in this study (e.g. kangaroo grass, barbed

wire grass, wild sorghum, weeping grass and tussocky poa) have been recorded as either

favoured species or common in the diet of eastern grey kangaroos in temperate grasslands

(Taylor 1980, 1983; Fletcher 2006). With progressive horse removals, the exclosure

experiment demonstrated the role of macropods in controlling biomass accumulation in

kangaroo grass-dominated grasslands and provided further evidence of horses affecting the

availability and use of forage resources by macropods.

Alternative explanations to macropod grazing to explain the biomass patterns

through time, such as horse exclosures had lower productivity than complete exclusion

quadrats, were not supported by environmental variables. Trees in temperate and tropical

savannas can reduce the productivity (biomass) of dominant grasses through competition for

light, water and nutrients (Beale 1973; Walker and Noy-Meir 1982; McMurtrie and Wolf

1983; Belsky 1994; Mordelet and Menaut 1995) and enhance the spatial and temporal

heterogeneity in the availability of such resources, often favouring and leading to an increase

in richness and cover of subsidiary forbs and non-tussocky grasses (Prober et al. 2002;

Graham et al. 2004). Prober et al. (2002) reported a lower combined abundance of kangaroo

grass and tussocky poa beneath trees, driven by kangaroo grass as C4 grasses are more likely

to be disadvantaged by lower light and temperature conditions beneath trees (Moore 1993;

Chilcott et al. 1997). When rainfall is not limiting, nitrogen may be the primary factor limiting

grassland productivity (Fisher 1974; Pastor et al. 1984; Tilman 1984), with carbon often

strongly correlated with nitrogen in kangaroo grass-dominated grasslands (Barrett and Burke

2000; Schultz 2007). Treatments did not differ in tree basal area or tree cover. In the

significant Treatment main effect, total carbon was greater in controls than exclosures and

total nitrogen was only less in horse exclosures than complete exclusion in Site 4. Peaks in


both soil nutrients were related to the presence of grazing lawns. Grazing lawns have higher

rates of dung and urine deposition relative to tall grasslands (Day and Detling 1990; Archer

and Smeins 1991) and generally have a higher nitrogen content (Karki et al. 2000; Person et

al. 2003).


As for plant biomass in the previous section, the removal of horses generated

interesting results for kangaroo grass reproductive variables, which were also best explained

by eastern grey kangaroo activity and kangaroo grass biomass. As discussed previously, after

December 2005 (T3), dung counts indicated that eastern grey kangaroos were progressively

more active in control and horse exclosures. Thus, only the Treatment main effect was

significant for all three reproductive variables monitored in January 2006 (after 1.5 years)

and January 2007 (after 2.5 years). As for general plant biomass, the mean number of

culms, panicles and spikelets was greater under complete exclusion than in both controls and

horse exclosures. General plant biomass was less than baseline in all treatments

(Treatment × Time interaction) and sites (the Treatment × Site interaction) in December 2006

due to unseasonably late cold weather, frosts and record snowfalls through most of

November 2006. Such climatic conditions would also explain the significant Time effects

whereby kangaroo grass biomass, culms and panicles were all less in January 2007 than

January 2006. Baseline kangaroo grass differed by Site, but not Treatment, and the

Treatment × Site interaction was significant for changes in biomass relative to baseline.

Treatment differences were pronounced in Site 1. Kangaroo grass biomass increased relative

to baseline under complete exclusion and declined in controls to a greater extent than horse

exclosures due to high macropod activity in both controls (a total of 57 and 48 deposits/plot,

respectively) and horse exclosures (a total of 19 and 27 deposits/plot, respectively) in

June 2006 and December 2006. The biomass and dung response in Site 1 was not reflected

to the same extent in the Site main effect for reproductive variables, although spikelets were

less in Site 1 than Site 6 and comparable to Sites 2 and 5. The apparent discrepancy can most

likely be attributed to site differences in baseline kangaroo grass biomass, which was

greatest in Site 1, differing from Sites 5, 4 and 2 with the least baseline biomass. Low

kangaroo grass biomass in June 2004 in Sites 4 and 5 was likely due to high horse grazing

pressure before the trapping program began. Likewise, the increase in kangaroo grass

biomass from baseline in both grazing exclusion treatments in Site 5, compared to the


reduction in biomass in controls, was due to residual horse grazing pressure around Boban

Hut. Thus, low mean values for kangaroo grass reproductive variables in Site 5, which were

less than in Sites 3, 4 and 6, were likely due to historical cattle and horse grazing having

induced compositional shifts where the dominance of kangaroo grass was reduced, and the

effect maintained by horses throughout this experiment. Sites 4 and 6, in addition to Site 3,

recorded the highest mean values for reproductive variables, which were greater than Site 5.

Mean number of spikelets were also greater in Site 6 than Site 1. Horse dung had declined

and macropod dung increased in Site 4 to essentially the same number of deposits as Site 3

in both June and December 2006 (a total of 5–8 deposits/plot). Both Site 3 and 6 had greater

baseline kangaroo grass biomass than Sites 4 and 5. Starting biomass and lower macropod

dung counts possibly accounted for the minor differences in the response of kangaroo grass

reproductive variables between Site 1 and Sites 3, 4 and 6. However, it appeared that

changes in kangaroo grass biomass and reproductive variables differed between complete

exclusion and both controls and horse exclosures mostly due to eastern grey kangaroo

grazing pressure in Sites 1–4 and Site 6, while horses may have influenced the response of

Site 5. Allcock and Hik (2004) found that excluding kangaroos (including eastern grey

kangaroos), moreso than sheep or rabbits, led to a significant increase in the survival of

kangaroo grass seedlings in white box woodland. When abundant, kangaroo grass is highly

preferred by eastern grey kangaroos and consumed in half of all feeding bouts compared to

11% for the next preferred species (Taylor 1980). As for general plant biomass, there is

sufficient evidence to conclude that eastern grey kangaroos were the primary herbivore that

interacted with site specific characteristics to influence kangaroo grass biomass and

reproductive ecology in the absence of feral horses.

4.4.3 Cover variables

Akin to biomass, excluding horses was associated with a significant reduction in the

cover of bare ground relative to baseline after 1 year. The significant Site × Time interaction

partly reflected the Site main effect for baseline cover of bare ground. At the start of the

experiment, the cover of bare ground ranged from a mean of 2.2–6.7% across Sites 3, 4 and 6

with the higest cover value recorded in Site 4, differing from Site 6. Bare ground was greater

in all three sites than Sites 1, 2 and 5 with ≤0.2% cover. Thus, bare ground did not change

from baseline in Sites 1, 2 and 5 but had declined from baseline in Sites 3 and 6, and Site 4, in

particular, by June 2005 (T2). The lower baseline cover of bare ground in Sites 3 and 6 than


Site 4 meant that after the initial reduction prior to June 2005 bare ground cover was close to

zero and hence fluctuated little after June and December 2005. The significant

Treatment × Site interaction showed that bare ground increased relative to baseline in

controls, and differed from the relative reductions in both grazing exclusion treatments in

Site 6. Treatment differences were repeated in Site 4 and to a lesser extent in Site 3. Bare

ground declined relative to baseline in both grazing exclusion treatments (4.8–5.7%) in Site 4

to levels approximating the site mean at baseline (6.7%), differing from the minor decline in

controls (2.3%). In Site 3 the trend was similar but the reduction relative to baseline was

greater under complete exclusion than controls and horse exclosures. Horses, when more

abundant prior to June 2005, either maintained baseline levels or prevented bare ground

from declining to the extent possible in the absence of horses as demonstrated by the

grazing exclusion treatments. The Treatment × Time interaction was not significant but as

bare ground cover declined further through time after June 2005 in Site 4, the reduction in

bare ground in the control quadrats at this site was possibly linked to the additional removal

of horses at later sampling times. Unlike biomass, macropods did not have the same impact

as horses in maintaining bare ground as the grazing exclusion treatments did not differ in

their reduction in bare ground cover in Sites 4 and 6, and bare ground cover tended to be

less in horse exclosures than controls in Site 3. This supports the common assumption that

grazing activities of introduced hoofed ungulates lead to increased soil erosion in comparison

to macropods for reasons presented in Chapter 6 (Bennett 1999; Grigg 2002).

The removal of horses confounded comparisons between manipulative treatments

over time for litter cover and most plant cover variables. However, the effect of excluding

horses was small but significant (Treatment effect) on the cover of sedges in the first year of

the experiment. Sedge cover increased relative to baseline in both horse exclosures and

complete exclusion, differing from the relative decline in controls. Litter cover declined

relative to baseline in all treatments by 3.8–4.1% prior to June 2005 and by a further 1.6–

4.1% from June to December 2005 (Treatment × Time interaction), varying little for the

remainder of the experiment. Conversely, total plant cover averaged across all quadrats

(Time main effect) increased relative to baseline by an initial 2.3% and by a further 4.6% over

the same periods and cover was greater than in June 2005 from December 2005 onward.

The response of litter and plant cover were concentrated in the first (June 2005) and second

(December 2005) sampling times following the removal of the greatest number of horses

(158 compared to 83 during December 2005–December 2006). Treatment responses in each


site were almost the mirror opposite of each other for litter and total plant cover confirming

the reduction in litter cover mostly corresponded to an increase in total plant cover. For

example, in Site 6, mean total plant and litter cover did not change from baseline whereas

the decrease in litter cover in controls (10.1%) and complete exclusion (5.6%) was similar to

the increase in total plant cover in both treatment (9.7% and 4.4%, respectively). Although

not a manipulative test, the general replacement of litter and bare ground cover in all

treatments by plant cover in the first 18 months of the experiment may have been in

response to the shifts from high to moderate to low horse grazing pressure with time as the

trapping program continued through the experiment. That is, horses were sufficiently

abundant to remove a significant portion of biomass and maintain bare ground, but not so

abundant as to over-graze and uproot vegetation, preventing plant establishment and

growth.

The incremental reductions in litter cover after June 2005 to February 2006 also

tended to be greater in controls than both grazing exclusion treatments and controls differed

from horse exclosures in June 2006. Litter cover was less at the final sampling time than

June 2005 in controls only. It was possible that disturbance associated with residual light,

intermittent horse grazing in addition to macropod grazing in controls prevented excess litter

accumulation and stimulated plant foliar production, root growth and total plant cover, as

demonstrated in other grazing studies (Vallentine 1990; Popolizio et al. 1994; Manske 1998,

2000, 2004). In high rainfall tall-grassland, excess litter can retard growth in the spring,

prevent seedling establishment and decrease forage production whereas the removal of

excess litter by light grazing exposes plants to light and warmer temperatures, increasing

herbage yield and cover (Weaver and Fitzpatrick 1934; Laycock 1994). Increases in light

intensity induce root respiration and nutrient uptake and above-ground regulation of litter

influences soil organisms (Briske and Richards 1995; Bardgett et al. 1998; Manske 1998).

Germination of the dominant tussock grass in this study, kangaroo grass, is poor under leaf

litter (McDougall 1989b) and in the absence of ungulate disturbance, plant production in

exclosures showed signs of becoming idle. Light, intermittant grazing and natural site

variation may also explain the Treatment × Site interaction for grass, shrub/tree and forb

cover. For example, in Sites 1, 2 and 6, the grazing exclusion treatments differed from

controls. The reduction in grass cover was either less than in exclosures or cover did not

change from baseline in controls. Conversely, either shrub and tree or forb cover increased

relative to baseline to a greater extent in exclosures by about the same amount as grass


cover declined for the same comparison (i.e. treatment–site combination). Thus, grass

dominance had been maintained by horses prior to June 2004 and with the reduction in

grazing pressure, grasses yielded to forbs, shrubs and trees, and sedges with the change less

evident in controls than exclosures due to residual horse grazing pressure. Grazing

optimisation theory predicts that certain levels of plant herbivory can lead to

over-compensatory growth and initial increases in productivity (McNaughton 1979; Hilbert et

al. 1981; Dyer et al. 1986; Frank and McNaughton 1992), particularly in combination with

rainfall in non-equilibrium grazing systems (Oba et al. 2000). While the hypothesis has been

the topic of much debate (Belsky et al. 1993; Painter and Belsky 1993; Jaremo et al. 1996), it

is not uncommon for the productivity and cover of perennial sedges and grasses, including

kangaroo grass, to be higher in grazed versus ungrazed treatments (Hyder et al. 1975;

McNaughton 1983; Hik and Jefferies 1990). At the same time forbs often display evidence of

anti-grazing optimisation (e.g. Otieno et al. 2009). In addition, Oesterheld and McNaughton

(1988) have reported overcompensation in kangaroo grass clones from tall grassland regions

of the Serengeti National Park in response to simulated defoliation (clipping). Some of the

dominant perennial graminoids in the Pryor Mountain Wild Horse Range (PMWHR) were able

to withstand even heavy levels of grazing by horses via morphological adaptations which led

to compensatory growth with adequate rainfall (Fahnestock 1998; Fahnestock and Detling

2000). The contrasting response of grasses and forbs in this study to residual horse grazing is

consistent with the Northern Tablelands grazing record, where native perennial grasses have

been found to be more resistant than native forbs to continuous grazing at low pressures

(Reseigh et al. 2003; Reseigh 2004).

4.4.4 Floristic composition

Despite the long history of livestock grazing and recent horse activity, the Paddys

Land section of GFRNP represented a large area of temperate grassy woodland with

relatively unmodified, groundstorey vegetation that matched putative pre-European

conditions (Whalley et al. 1978; Curtis 1989, 2001; Reid and Fittler 2004). Typical of natural

grasslands and despite the woodland overstorey, the vegetation was dominated by a few

abundant, matrix forming grasses (Collins and Gibson 1990). Kangaroo grass was notably

dominant, present in all quadrats with a cover of 35.3–45.9% per sampling time when

averaged over all quadrats. The second most abundant and widespread species, wild

sorghum was present in 80.6–94.4% of quadrats but with a comparative cover of only 6.2–


8.6%. Tussocky poa and barbed wire grass were the major subsidiary tussocky grass species

in addition to wild sorghum, as was weeping grass, the non-tussocky grass mentioned in early

accounts (Gardner 1854 cited in Whalley et al. 1978). The legumes, slender tick-trefoil and

variable glycine, were the dominant forbs, as was kidney weed, with cover approximating

that of paddock lovegrass. None of the stages in Moore’s degradation sequence was evident

as the majority of grass species were either warm-season perennials or year-long greens,

with few cool-season perennials (common wheat grass Elymus scaber and short-hair plume

grass Dichelachne micrantha). Finger grass, a warm-season annual, was the only exotic

grass recorded in quadrats and was present in Site 4 but cover did not exceed 3% per

quadrat. The cover of the five exotic forbs recorded in total was lower with ≤1.5% per

quadrat. Exotic richness was low at baseline monitoring when it accounted for 6.1% of total

species richness. As no additional exotic species were recorded, overall exotic richness was

lower still (4.5%) before the declines in exotic richness were factored in.

The exclosure experiment did not detect any significant effect of grazing exclusion on

plant species composition. The distance and relative position of treatment centroids for both

cover abundance and incidence data changed in unison in response to season. Similarly,

grazing lawns did not become more similar in composition to the tussock grass-dominated

quadrats. The Treatment × Site × Structure PERMANOVA interactions for both data matrices

only confirmed baseline differences in composition between grazing lawns dominated by

kangaroo grass, weeping grass or paddock lovegrass. The maintenance of grazing lawn

composition and structure over 2.5 years was not surprising as grazing lawns were generally

little modified at the start of the experiment, as in 60% of lawns kangaroo grass had not been

replaced by species whose relative abundance increases with grazing such as red grass,

slender rat’s tail grass, lovegrasses (Eragrostis spp.), wallaby grasses (Austrodanthonia spp.)

and weeping grass (Lodge and Whalley 1989b; Lodge et al. 1990). In addition, feral horses

maintain grazing lawns whatever their densities (Menard et al. 2002), and grazing lawns are

also a feature of kangaroo grass-dominated grasslands grazed by eastern grey kangaroos

(Newsome 1975; Senft et al. 1987; Fletcher 2006). However, as in the case of the New

Zealand silver tussock (Poa colensoi), tussocky poa had potentially been grazed out from sites

with higher grazing pressure (Site 4–6) (Reid and Fittler 2004), where it was notably absent at

the start of the experiment. With the removal of horses, tussocky poa became more

prevalent in general. Total cover increased from 4.5 ± 1.3% per quadrat at baseline to

6.4 ± 1.2% at the final sampling time and the proportion of quadrats in which it was present


increased from 47.2% to 63.9%. After 2.5 years the species had also appeared in Sites 4 and

6 and in all but one quadrat in Site 5. While cattle may have been instrumental in the initial

disappearance of tussocky poa, recent horse activity appeared to have prevented

regeneration of the species.

Exclusion studies overseas that did not find a significant effect of horse grazing on

composition concluded that exclosures were not in place long enough (1–2 years) or horse

densities were low (Detling 1998; Seliskar 2003). Exclosures were also in environments (e.g.

island foredunes and semi-arid steppe) where vegetation is naturally sparse and dynamic,

and the effects of horse grazing were overshadowed by between-site or inter-annual

variation in rainfall (Detling 1998; Fahnestock 1998; Seliskar 2003). In an exclosure study in

the Australian Alps, excluding horses did not have a striking effect on vegetation composition

after 5 years (Prober and Thiele 2007). The authors attributed the result to low horse grazing

pressure after the distribution and density of horses appeared to be affected by major

wildfires (Chapter 1), as well as natural local site variation and exclosure breeches. In this

study, the aforementioned minor effects of excluding horses evident in the MDS plots after

1.5 years and the trends for tussocky poa were consistent with biomass and bare ground

results. All confirm the importance of the horse removal program and resultant low residual

grazing levels in determining the outcomes of this study. If horses had not been removed,

length of time since exclusion may have been highly influential, coupled with site variation.

4.4.5 Plant species richness variables

The Treatment × Time interaction was not significant for any richness measure.

Interpretating the significant Treatment × Site interaction for total species richness,

shrub/tree richness, sedge richness, forb richness and exotic richness in relation to the effect

of feral horses prior to June 2005 was complicated. The change in richness measures relative

to baseline were best explained by composite interactions between biomass, tree cover, soil

nutrients (Chilcott et al. 1997; Jackson and Ash 1998; Gibbs et al. 1999; Yates et al. 2000;

Lunt and Morgan 2001; Prober et al. 2002) and other richness measures operating at the site

or quadrat scale. Small scale plant composition and structure in grasslands are notoriously

complex and highly dynamic in both space and time (Morgan 1998d and references therein;

Loreau et al. 2001). In addition to the modulating effects of the disturbance regime, site

characteristics and resource availability on interspecific plant competition, individualistic

species’ responses and the number of available species in the soil seedbank ('species pool


concept', e.g. Zobel 1992, 1997) and broader environmental and climatic conditions are just a

few of the factors influencing vegetation dynamics directly (Herben et al. 1993; van der

Maarel and Sykes 1993; Hobbs and Mooney 1995; Morgan 1998a, d; Grace 1999; Amorim

and Batalha 2008). All of these factors likely applied in this study. Suffice to say that

treatment responses were rarely consistent across all sites and that the following results

warrant further discussion.

Removal and exclusion of feral horses in conjunction with an increase in biomass led

to a reduction in exotic species richness relative to baseline in the first year of the

experiment. Exotic species were only encountered at sites with more intense localised

grazing pressure (Sites 4–6) and the reduction in exotic richness was greater under complete

exclusion than both other treatments in Sites 4 and 6. It is likely that exotic species were

outcompeted by the dominant tussock grass vegetation as the increase in biomass relative to

baseline was also significantly greater under complete exclusion at those same sites. Sites 4

and 5 had similarly low baseline biomass but the relative increase in Site 5 was less in all

treatments. Baseline levels of mean phosphorus (Olsen) were moderate to high (17.8–

26.6 mg/kg; Dorrough et al. 2006) in all Site 5 treatment quadrats and greater in horse

exclosures (22.8 mg/kg) than other treatments (7.9–11.8 mg/kg) in Site 6. Extractable

phosphorus leaches slowly and can be positively correlated with both the number and cover

of exotic species and negatively correlated with the number and cover of natives in pastoral

and remnant grassy woodlands (Morgan 1998c; Allcock 2002; Dorrough et al. 2006).

However, the establishment and growth of some native forbs and grasses, such as slender

tick-treefoil, variable glycine, tough scurf-pea (Cullen tenax) and common tussock grass (Poa

labillardieri; a closely related species to tussocky poa) increase in response to phosphorus

addition and also nitrogen in the case of common tussock grass (Begg 1963; Fisher 1972;

Groves et al. 1973; Fisher et al. 1974). Higher baseline values of phosphorus across all

treatments in Site 5 may have contributed to the site’s greater baseline total richness driven

by forb richness and interacted with biomass to nullify the effect of complete exclusion on

exotic richness. Different grazing management regimes (i.e. combinations of nil to high levels

of grazing, cultivation and fertiliser application) in Northern Tableland pastures were found

to have no effect on species richness when available phosphorus was >25 mg/kg (Reseigh et

al. 2003; Reseigh 2004). High phosphorus may have also contributed to the lesser decline in

exotic richness and greater relative increase in forb richness in horse exclosures in Site 6.


While groundstorey biomass was greater in exclosures than controls in June 2005, an

increase in species richness relative to baseline was only greater (or reduction less) in

controls than both grazing exclusion treatments for shrub and tree richness in Sites 1 and 2

(Treatment × Site interaction). Hence, the hypothesis that plant richness would decrease

under grazing exclusion due to competitive exclusion was only supported for exotic richness.

For total species richness, sedge richness and forb species richness, increases in richness

relative to baseline were greater (or if a reduction relative to baseline, less) in one or both

grazing exclusion treatments than controls in numerous sites, but richness was only greater

in controls than just one exclusion treatment in fewer sites. The lack of evidence for

competitive exclusion is to be expected given that the greatest experimental biomass

accumulation was 391 g/m2 under complete exclusion in Site 4 at February 2006. Declines in

species richness in temperate kangaroo grass-dominated grasslands have been associated

with long-term biomass accumulation >500 g/m2 over periods of 5–15 years (Lunt and

Morgan 1999a; Schultz 2007).

4.4.6 Resistance to grazing

The resistance of kangaroo grass-dominated grassland on Paddys Plateau to changes

in composition and exotic annual invasion was likely due to a combination of the density-

dependent nature of feral horse impacts (English 2001b) and the effects of climate (Whalley

et al. 1978) and natural disturbance regimes (e.g. fire) on the dominant perennial tussock

vegetation. The densities associated with feral horse impact studies were discussed in detail

in Chapter 1. The adverse impacts of horses on the red and hard tussock grasslands in New

Zealand (Rogers 1991, 1994), for example, were associated with long-term higher horse

densities (upper and lower 95% confidence limits (CL) = 0.044–0.070 horses/ha and 0.076–

0.114 horses/ha, Linklater et al. 2001) than estimated for Bobs Creek and Pargo Creek

catchments in GFRNP (upper and lower 95% CL = 0.021–0.034 horses/ha and 0.035–

0.057 horses/ha, Vernes et al. 2009). While no estimates are available for Paddys Plateau,

the density of horses is thought to be much lower than Bobs and Pargo Creek (Chapter 2).

As suggested by Moore and others (Moore 1970; Landsberg et al. 2000; McIntyre and

Martin 2001) the temperate–subtropical grassland on Paddys Plateau appeared less

susceptible to ungulate grazing than temperate native grassy vegetation in southern

Australian, possibly due to differences in pre-adaptations among local species assemblages

and climatic influences (e.g. rainfall seasonality). In addition to such factors and consistent


with recent studies emphasising the importance of past grazing management regimes

(McIntyre et al. 2003; Dorrough et al. 2006), the traditional practice of graziers (Heritage

Working Party 2002a) and park management to burn regularly at low intensity to promote

green pasture regrowth for stock and reduce wildfire fuel loads may have also assisted in

keeping the dominant tussock matrix intact, while reducing the long-term competitive

exclusion effect of biomass on forb diversity. Prober et al. (2007) found that preventing

grazing by native and feral herbivores had a less substantial effect on sward composition and

structure in kangaroo grass-dominated grassland than burning. Repeat burning over long

periods of time also tends to reduce soil nutrients, particularly nitrogen (Adams et al. 1994;

Ojima et al. 1994; Marrs 2002). Burning (in spring) has also been shown to lead to a

substantial decline in annual grasses (Prober et al. 2004). The maintenance of a mixed

perennial tussock dominance and functional plant diversity enhanced the resistance of

Paddys Plateau to invasion by nitrophilic exotics (Walker et al. 1999; Hector et al. 2001;

Kennedy et al. 2002). Kangaroo grass is a key species due to the rapid rate at which it can

reduce levels of soil nitrate (Prober et al. 2005).

4.4.7 Conclusion

The effect of excluding horses was significant for several variables during the first

year of the experiment when most horses were progressively removed from the Plateau.

This study demonstrated that horses consumed a significant portion of biomass and

maintained existing levels of bare ground, probably created by historical horse grazing.

Horses were also associated with a reduction in plant cover and concurrent increase in litter

cover in addition to bare ground, a reduction in sedge cover and an increase in exotic species

richness. After 18 months and with progressive horse removals, macropods responded by

colonising sites and consuming most of the groundstorey and kangaroo grass biomass, thus

regulating the reproductive and competitive effects of the dominant tussock grass, kangaroo

grass, and maintaining a heterogeneous grassland structure in the form of grazing lawns.

Macropod activity, however, did not prevent reductions in bare ground and the increase in

sedge cover relative to baseline. With the possible exception of shrub and tree richness,

excluding horses either maintained or increased sedge, forb, grass and total species richness

which approximated native richness. Analysis of baseline floristic composition suggested

that Paddys Plateau section of GFRNP had resisted over 100 years of livestock and horse

grazing with a relatively unmodified, groundstorey vegetation that matched putative


pre-European conditions. As the groundstorey vegetation was floristically intact, this in

conjunction with horse removals precluded any significant change in composition through

the course of the experiment. However, horses may have caused minor retrogressive shifts

in composition that were simply not detected in this study because grazing pressure was not

maintained. The general increase in plant cover and richness measures through time under

residual low horse grazing pressure and the gradual establishment of tussocky poa in sites

with greater historical horse grazing pressure (Sites 4–6), where it was absent at the start of

the experiment, support this assessment. Residual horse grazing in combination with

macropod grazing in controls also stimulated grass production and the minor disturbance of

the litter layer promoted a greater reduction in litter cover and concurrent increase in total

plant cover. Given the biomass results in this chapter and the displacement of macropods by

horses demonstrated in Chapter 2, it is likely macropods will continue to expand their use of

Paddys Plateau in the absence of horses and stimulate primary production. Kangaroo grass-

dominated grasslands are prone to becoming idle in the long-term (10–15 years) absence of

disturbance, at least in fertile sites, and may benefit from prescribed burning in the future,

since regular low-intensity burns appear to have contributed to the resistance of the

groundstorey vegetation.

Chapter 5. Riparian Grazing Exclusion Experiment in Gorge Country 160

CHAPTER 5.

IMPACT OF FERAL HORSES ON THE GROUNDSTOREY VEGETATION OF GRASSY RIPARIAN

FLATS IN GORGE COUNTRY

5.1 INTRODUCTION

Historically, riparian areas were valued from an animal production point of view for

their high fertility and potential to provide food, shade and water for domestic livestock

(Stoddart and Smith 1955; Belsky et al. 1999). The exploitation of such resources by

improper livestock use has contributed to the degradation and elimination of riparian

systems on a global scale, and several reviews have described these processes in detail

(Kauffman and Krueger 1984; Fleischner 1994; Trimble and Mendel 1995; Robertson 1997;

Belsky et al. 1999; Agouridis et al. 2005). The adverse effect of past grazing management

regimes on Australian river and tributary systems has also been documented (Walker 1993;

Robertson and Rowling 2000; Jansen and Robertson 2001). Riparian areas are preferentially

selected and heavily grazed by unregulated or feral populations of ungulates, including feral

horses (Roath and Krueger 1982; Ganskopp and Vavra 1986; Rogers 1994; Crane et al. 1997b;

Wockner et al. 2003).

Exclosures prohibiting grazing are a preferred method to obtain knowledge about

past and present degradational pathways associated with a single species or a particular

management practice, such as livestock grazing (Sarr 1995). Exclosure research assumes that

post-exclusion responses are the reverse of those caused by livestock and therefore

represent a test of the grazing effect on plant communities (Sarr 2002). The traditional

‘Clementsian’ model of succession, in which vegetation changes are bidirectional towards

and away from a single stable climax community, is central to this rationale (Clements 1936;

Pettit and Froend 2001). The predictive power of early indices of range condition in North

America, which classified plant species as ‘increasers’ or ‘decreasers’ with grazing pressure,

and many disturbance–diversity models (including the intermediate disturbance hypothesis),

is based on a symmetrical and hence reversible response to grazing (Dyksterhuis 1949; Grime

1973; Sarr 2002; Lunt et al. 2007a). Situations where vegetation recovers vigour and

composition rapidly in the reverse direction once livestock stress is removed, are evidence of

this ‘rubber band’ model of stress and recovery (Lake et al. 2007). Productive sites in tall

grass prairies, moist sedge meadows and degraded watersheds in western rangelands in

North America often show this response if the primary cause of degradation is livestock


herbivory and if feedback loops between streams and their floodplains have not been

disrupted by soil compaction, stream entrenchment or gully erosion (Schulz and Leininger

1990; Green and Kauffman 1995; Sarr 1995; Sarr et al. 1996; Dobkin et al. 1998)

Upon exclusion of feral horses, plant communities have shown recovery trajectories

consistent with the rubber band model, for example, in island salt marsh, foredune, and

sagebrush steppe plant communities in North America (Furbish and Albano 1994; Beever and

Brussard 2000a; Dolan 2002). Exclosure studies have thus been an integral part of ecological

research into the impacts of feral horses on riparian and grassland vegetation. Most studies

have documented dramatic increases in plant height and cover and greater below and

aboveground biomass in exclosures, after as little as 2 years in some locations (Turner 1987,

1988; Hay and Wells 1991; Duncan 1992; Beever and Brussard 2000a; Seliskar 2003). Shifts

in floristic composition away from short-statured, exotic, and annual forb and grass-

dominated communities towards native, palatable perennial grass and sedge-dominated

communities following rest from horses also occurred in these and other studies (Rogers

1991; Furbish and Albano 1994; Fahnestock and Detling 1999b; Buerger et al. 2005).

Disturbance–diversity models, in which the effect of a disturbance on diversity is

modulated by the intensity of the disturbance (Grime 1973), have been re-evaluated for

managing grassy woodland remnants. Based on the intermediate disturbance hypothesis

and other non-equilibrium theories of plant species co-existence, diversity is expected to be

low under heavy grazing due to the physiological intolerance of many species to frequent

defoliation, and low in the absence of grazing due to competitive exclusion by dominant light

competitive species (Connell and Slatyer 1977; Connell 1978; Huston 1979; Huston and

DeAngelis 1994). Light or intermediate levels of grazing can reduce the competitive

exclusion effects of palatable, dominant or tall-statured species and open up the

inter-tussock space as a niche for herbaceous or shorter species, thereby increasing species

co-existence (Belsky 1992; Gough and Grace 1998). The intermediate disturbance

hypothesis, like many diversity–disturbance or diversity–grazing models does not distinguish

between native, perennial, exotic or annual species (Huston 1979; Milchunas et al. 1988;

Milchunas and Lauenroth 1993; Olff and Ritchie 1998). Peaks in total species richness at

intermediate levels of grazing have been reported (Trémont 1994; Trémont and McIntyre

1994; McIntyre and Lavorel 1994a; Fensham 1998). However, often richness does not

significantly differ from no or low levels of grazing or is driven by the exotic component

(Pettit et al. 1995; Prober and Thiele 1995; Clarke 2003; Dorrough et al. 2004). Few studies


have found tentative support for peaks in native species richness (McIntyre and Lavorel

1994a; Kirkpatrick and Gilfedder 1995; Fensham 1998).

Reductions in species richness in ungrazed exclosures have been reported more often

(Rogers 1991; Duncan 1992; Rogers 1994; Fahnestock 1998; Loucougaray et al. 2004) than

increases in richness (Beever and Brussard 2000a) for feral horses. However, the number of

studies are small compared to the international livestock grazing literature, where the effects

of excluding cattle and sheep are inconsistent (Kauffman and Krueger 1984; Naiman 1994;

Reeves and Champion 2004). Species richness, as commonly reported, does not discriminate

between plant origin (native versus exotic) and life cycle (perennial versus annual). Hence,

riparian plant diversity is often inflated by invasion of exotic or upland species in the

presence of livestock disturbance, and total species richness may decline with exclusion as

the system recovers and a few, native tall-statured graminoids resume dominance (Belsky et

al. 1999; Prober and Thiele 2007). In Australia, there is stronger, consistent evidence for the

ecological impacts of grazing to increase with grazing intensity and for exotic invasions to

have been accompanied by a loss of native species (Moore 1970; Robinson and Dowling

1976; Groves and Burdon 1986; Morcom 1990; Abensperg-Traun et al. 1996). Thus plant

origin has been suggested as a useful trait for identifying grazing resistance or resilience

(Allcock and Hik 2003), with natives considered more sensitive because they evolved in the

absence of, and thus were not adapted to, ungulate grazing (Fox and Fox 1986). Mechanisms

to explain native substitution by exotics under ungulate grazing include a general intolerance

of native species to the physical effects of defoliation, slow re-colonisation due to breeding

systems and lack of large, persistent, soil seed banks, and an inability to outcompete exotic

species (McIntyre and Lavorel 1994a; Lunt et al. 2007b). Dorrough et al. (2004) found that

some natives resisted grazing but that under intense grazing, perennials tended to decrease

and annuals increase, which were more often native and exotic, respectively. They

suggested a combination of longevity or life form and origin for predicting grazing response.

These and other plant traits such as growth form, location of the apical meristem and

reproduction strategies have also been investigated to predict the impact of invading species

(Trémont 1994; McIntyre et al. 1995). In grazed Queensland sub-tropical pastures, a diverse

range of native species remain dominant under grazing but exhibit significant changes in

life form composition (McIntyre and Lavorel 2001; McIntyre et al. 2005). The same traits,

and palatability to stock, have been used to classify a number of Australian taxa as increaser,


decreaser and tolerant to livestock grazing (Lodge and Whalley 1989b; McIntyre and Lavorel

1994a; Yates and Hobbs 1997).

As noted above, simple restoration techniques that involve the removal of

ungulates in Australia and protection of the site from further disturbance (i.e. fencing)

assume Clementsian succession (Lamb 1994; Spooner et al. 2002). It is policy for the NSW

National Parks and Wildlife Service (NPWS) to remove livestock from national parks and

fence the boundary adjacent to pastoral land (DECCW 2001). By reducing or eliminating

grazing, the expectation is that plant populations will regenerate and native plant diversity

will increase. Exclosure studies associated with restoration projects have had variable

outcomes on conservation values, such as structural complexity, tree and shrub recruitment,

species richness, and understorey cover and composition, as reviewed by Lunt et al. (2007a).

Climate, time since exclusion, level of site degradation prior to exclusion and spatial scale of

sampling and landscape context are all important considerations in explaining different

outcomes (Yates and Hobbs 1997; Lunt et al. 2007a). Predictive models and frameworks

have been developed for Australian grasslands to address when, under what circumstances,

and in what ecosystems the removal of grazing is likely to have positive, negative, neutral or

uncertain impacts on diversity and composition of native plants (Moore 1964, 1970;

McIntyre and Lavorel 2001; Lunt et al. 2007a). A model devised for a grazing exclusion

experiment in a riparian forest in south-eastern Australia, incorporated two factors, site

productivity and degree of past degradation (Lunt et al. 2007b) (Table 5.1). The model

predicts that grazing exclusion is likely to lead to positive conservation outcomes (e.g. high

native richness and cover, and low exotic cover) in intact, historically ungrazed sites of low

productivity and to negative outcomes (e.g. high exotic cover and low native richness and

cover) in degraded, historically grazed sites (Lunt et al. 2007b).

The aim of the present experiment was to assess the effects of exclusion of horse

grazing on the biomass, vegetation cover, richness and composition of the grassy

understorey in riparian woodland in GFRNP over 2 years. All that was known a priori was

that the study sites were in a fertile riparian zone. The hypothesised outcomes of grazing

exclusion for high productivity sites were large increases in biomass of large, grazing-

sensitive, herbaceous species and a concurrent reduction in small-scale plant diversity due to

competitive exclusion (Table 5.1). Whether native or exotic species would dominate cover

and richness, and what changes would occur in native species composition were difficult to

predict as the initial site condition and degree of floristic degradation were unknown. The


impact of horses on the grassy understorey is inferred from post-exclusion responses, which

also provide information on how riparian flats should be managed if horses are eliminated

from the Park, as is the policy of NSW NPWS.

Table 5.1 Two-factor model adapted from Lunt et al. (2007b) to predict the outcome of grazing exclusion on groundstorey herbaceous vegetation depending on opposing levels of site degradation and productivity.

Initial site condition or degree of degradation of herbaceous vegetation

Low High

Low

• Natives dominate • Natives and exotics co-dominate • Minor increase in biomass • Minor increase in biomass • Minor change in exotic biomass • Possible minor increase in small-scale

productivity • Possible minor increase in small-scale richness of low-biomass species richness of low-biomass species • Limited increase in native richness due to propagule constraints

High

• Natives dominate • Exotics dominate • Major increase in biomass • Major increase in biomass • Potential increases in large exotic • Decline in small-scale plant richness,

productivity species especially low-biomass species due to • Decline in small-scale plant richness, competitive exclusion especially low-biomass species due to • Negligible increase in native diversity due to competitive exclusion competition and propagule constraints


5.2 METHODS

5.2.1 Experimental design

The design developed for this experiment targeted the impact of horse grazing on

grassy riparian flats. Ten sites were chosen >500 m apart along the banks of Bobs Creek, a

tributary of the Sara River. Bobs Creek has permanent water except under extreme drought

conditions. Sites were small (<12 × 100 m), narrow, grassy flats without tree or shrub cover

adjacent to woodland hillslopes and spurs (Figure 5.1). Horses were previously sighted on

these creek flats and horse tracks, dung and wallows suggested that horses grazed these

sites semi-continuously (Figure 5.2).

Figure 5.1 Typical riparian flat with greater length following the path of the tributary than width; the flats extend upslope into woodland.


Figure 5.2 Linear horse tracks, and bare ground associated with reduction in plant cover and the wallowing behaviour of horses in dirt and sand on a riparian flat.

In each site, three plots, 6 × 6 m, were marked out in sections of the flats visually

assessed to have similar biomass. Large patches of bare ground on the flats, typically

associated with dust wallows (Figure 5.2), were avoided when plots were selected in favour

of vegetated patches. Plots were randomly allocated to one of three treatments per site

with the same design and abbreviation of terms as Chapter 4: controls (C) were unfenced

with just four corner posts (Figure 5.3); (2) horse excluded (H) exclosures had a three-strand

wire fence prohibiting free access to large herbivores such as horses or cattle but were

accessible to macropods and other small herbivores such as rabbits (‘horse exclosures’)

(Figure 5.4); and (3) exclosures that excluded all herbivores (A) had a fully enclosed netting

fence 1.8-m high secured to the ground so animals could not push under it (‘complete

Horse Track

Dust wallow


exclusion’) (Figures 5.5). A small flap in the A-exclosures allowed for sampling (Figure 5.11).

In each of the three plots, a 2 × 2-m quadrat was marked out using wooden pegs. The

quadrat was located in the centre to create a 2-m buffer around the quadrat in case horses

put their heads through the wire strands in the H exclosures. Exclosures were constructed by

NSW NPWS field staff on 1 June 2006. Baseline vegetation measurements were obtained

from 6–11 June 2006 (T1). Quadrats were re-sampled on 16–22 April 2007 (T2), 15–

22 November 2007 (T3), 23–28 February 2008 (T4) and 26 June–10 July 2008 (T5). In

April 2007, all horse exclosures were found to be damaged and quadrats grazed by horses

and were not sampled at that time. H exclosures at Sites 4–9 were reconstructed and

reinforced on 6–8 June 2007, but not at Sites 1–3 or Site 10 where H-exclosure sampling was

discontinued. Attempts were made to sample in consecutive seasons but access to sites was

limited by weather conditions, with several floods prohibiting access in late 2006, and by the

outbreak of the equine influenza virus from July 2007 to March 2008.

Figure 5.3 White wooden pegs and tape measure representing the 2 × 2 m-quadrat within the 6 × 6-m control plot marked by the black star picket corner posts.


Figure 5.4 Example of horse-excluded exclosure design in foreground and horses grazing in the background.

Figure 5.5 Construction and design of all herbivore-excluded exclosure.


5.2.2 Plant biomass

Herbage biomass for each experimental quadrat was estimated using the BOTANAL

(Tothill et al. 1978) procedure. The following components of data collection were the same

as in Chapter 4: biomass estimation, selection of reference and calibration quadrats,

harvesting of biomass samples for drying and weighing, and conversion of biomass scores

into estimated biomass.

The method differed from Chapter 4 due to the use of a 2 x 2-m quadrat rather than

a 20-m transect. A tape measure was strung around the four corner pegs to define the

quadrat. The entire area was sampled with a 50 × 50-cm steel subquadrat (16 subquadrats

per quadrat). Estimated biomass for the 16 subquadrats was summed to obtain a single

average biomass value for the quadrat.

5.2.3 Full floristics and cover variables

Data was collected using the methods employed in Chapter 4, except the single

2 × 2-m quadrat per plot was systematically searched and each species of groundstorey

vegetation present recorded. The percent cover of each species was estimated so that the

sum of all species’ contributions plus the estimated cover of bare ground and litter equalled

100%.

5.2.4 Taxonomy

Specimens were collected and identified using the methods in Chapter 4, with the

following modifications and additions. All species not identified in the field were

photographed and specimens identified by Drs Lachlan Copeland and Wal Whalley (Honorary

Fellow in Botany, UNE). All specimens were identified to species, subspecies and variety if

possible. Two species were identified to genus only (Oenothera and Swainsona) as they were

limited to one or two quadrats at one or two sampling times and thus appropriate material

was not available for species level determination (McIntyre et al. 1993).

A number of growth characteristics and life-history attributes were used to describe

species: (1) herb type—F: forb (ferns, herbaceous dicotyledons), Gs: grass (Poaceae), S: sedge

(Cyperaceae); (2) seasonal growth patterns—W: warm season, C: cool season, Y: yearlong

green. This attribute was applied to members of the Poaceae only in combination with life

cycle to produce the categories: warm-season perennial, cool-season perennial, yearlong-

green perennial, warm-season annual, cool-season annual; (3) origin—N: native, or E: exotic;


(4) life cycle—P: perennial, or A: annual and biennial; (5) growth form as described by

Trémont (1994) and summarised in Table 5.2; and (6) vegetation reproduction—

V: vegetative, PV: partially vegetative, or NV: non-vegetative, distinguishing species that

were capable of vegetative spread by stolons or rhizomes.

Life-history attributes for forbs were obtained where possible from Trémont (1994)

and complemented by other Australian plant trait studies (Lunt and Morgan 1999a; Dorrough

et al. 2004; McIntyre et al. 2005), the literature (Harden 1993a, 2000, 2002), consultation

with Dr Copeland, and field observations and specimens. Life-history attributes for some

grasses and sedges were also obtained from Trémont (1994), but final assignment of

attributes and seasonal growth patterns was checked by Dr Wal Whalley with reference to

Jacobs et al. (2008), Lodge and Whalley (1989b), Lodge et al. (1990) and field observations.

In assignment of growth form, Dr Whalley distinguished mat-forming species that root at

multiple individual nodes from either above-ground stolons or below-ground rhizomes (e.g.

common couch Cynodon dactylon) from species (usually tufted) that can take on a prostrate

growing form resembling a mat when grazed but only root from the central node (e.g. goose

grass Eleusine tristachya).

Table 5.2 Growth form codes and descriptions adapted from Trémont (1994).

Code Growth form Description Er Erect Upright central stem and variously orientated lateral branches Ts Tussock Diameter of the rooted base approximately equal to or greater

than plant height Tf Tufted Diameter of the rooted base smaller in diameter than plant height,

and from which leaves or petioles directly emerge R Rosette All leaves arise from a central rootstock (only basal rossette

leaves) PR Partial Basal rosette leaves and a central upright stem on which some

rosette leaves and all flowers and fruits arise Mt Mat-forming Adjacent and decumbent non-stoloniferous stems or leaves

or massed stems emerging directly from the soil, each with a leaf Tr Trailers Including herbaceous climbers, scramblers and twiners with decumbent stems not capable of supporting the plant itself


Several species diversity and cover variables were calculated for each quadrat and

analysed separately: total species richness, annual species richness, perennial species

richness, native species richness, exotic species richness, proportion of native species, and

cover of native and annual species. The proportion of native species per quadrat was

expressed as a percentage and calculated by dividing native richness (N) × 100 by total

species richness (T). Cover of native and annual species was calculated by first subtracting

the cover of bare ground and litter to obtain a value for total vegetation cover for each

individual quadrat, then rescaling the summed covers for each class (native and annual) to

100% total vegetation cover. While only native and annual cover were analysed and

presented, they sum to 100% vegetation cover with their respective counterparts of exotic

and perennial cover. Thus, the reverse trends could be inferred for exotic and perennial

cover.

5.2.5 Dung transects and visual observations

The presence of herbivore dung was assessed at the plot, site and district scale. At

the plot scale, the entire 6 × 6-m plot (0.0036 ha) was searched at the same time as

vegetation sampling was undertaken and all dung raked clear after each sampling time. Site

and district scale were described previously in Chapter 2.

5.2.6 Statistical analysis

5.2.6.1 Univariate analyses

The experimental design included the factors Treatment with three levels (C, H, and

A) and Time with five sampling times (T1–T5). The dataset was incomplete, however, due to

the destruction of horse exclosures by horses at several sampling times. For statistical

validity, this necessitated multiple analyses that maximised replication using different

portions of the dataset. Analysis 1 (A1) examined the response of control and complete

exclusion plots only, making use of all ten sites at all five sampling times. Analysis 2 (A2)

incorporated the valid horse exclosure data so only six replicates (Sites 4–9) of all three

treatments at four sampling times (T1, T3–T5) were included. The exception was analysis 2

of biomass. At the final sampling time, full floristic composition was recorded at all sites first,

and then 5 days were spent checking regional dung transects. During that time, three horse

exclosures (Sites 7–9) were damaged and grazed. Analysis 2 for biomass thus included six

replicates for the C and A treatment at all four times, and six replicates for the H treatment

until the final sampling time (T5), when only three replicates were available.


Sites were blocks with Treatment levels randomly allocated in each block and Time

was the repeated measures factor. As Site was random and Treatment and Time fixed, the

linear mixed-effects (lme) function, recommended for modelling grouped data including

repeated-measures and blocked designs (Pinheiro and Bates 2000), was used for all variable

responses. This experiment in Bobs Creek required a different statistical model from the the

grazing exclusion experiment on Paddys Plateau (Chapter 4) because Site was considered a

fixed factor on the Plateau due to hypothesised differences in horse grazing pressure. In

Bobs Creek, horses appeared to use all ten riparian flats to a similar extent and Site was not a

factor of interest. The linear mixed-effects approach provided a direct test of significance

for the within-block factors of Treatment and Time and the interaction between the two by

using the Sites plot value (between block) as replicates. It also modelled the sources of

variability associated with Site as a random factor as determined from exploratory plots. In

the majority of cases, only a random effect attributed to Site was appropriate but, in some

instances, a Site by Time or Site by Treatment random interaction better reflected the

sources of variability. Models were checked for fit by generating confidence intervals on

variance–covariance components. Occasionally, the random Site by Time interaction led to

non-convergence and just Site as a random effect was used. All statistical assumptions

regarding normality and independence of errors (Pinheiro and Bates 2000 and Chapter 3)

were assessed with the appropriate residual and diagnostic plots complemented by the

Shapiro–Wilks test. Data violating the assumptions were transformed. Observations from

different Treatment levels within a block were independent as no sequential correlation

between Sites was detected in plots of the autocorrelation function (Maindonald and Braun

2007).

The response variables were biomass, cover of bare ground, total richness, annual

richness, perennial richness, native richness, exotic richness, percentage of native species,

cover of native species, cover of annual species, and 14 of the 15 important species identified

by SIMPER. Transformations were required for the following response variables. Biomass in

A1 was log-transformed and A2 square-root-transformed. Bare ground in A1 and A2, and

exotic richness in A2 were log(X + 1)-transformed. Annual cover was square-root-

transformed in A1 and A2.

The methods employed in Chapter 3 were used to test the significance of main

effects and interactions, and to obtain pair-wise comparisons. The validity of the lme

summary function for pair-wise comparisons was confirmed by comparing output to the


model using linear contrasts for the main effects and interactions for the response of two

variables (biomass and total species richness) (Chen et al. 2003). All models were fitted using

restricted maximum likelihood estimation (REML; Fox 2002; Maindonald and Braun 2007) as

REML avoids or reduces bias by correcting the maximum-likelihood (ML) estimator for

degrees of freedom (see Pinheiro and Bates 2000 for details).

In two instances, alternate models were used. Although REML used by the lme

function is generally not sensitive to imbalanced designs, the three replicates at the final

sampling time for analysis 2 of biomass were inadequate and a linear model was used for the

fixed Treatment and Time effects. Similarly, for cover of one species, slender tick-trefoil

(Desmodium varians), the frequency of zeros in the dataset meant that normality could not

be achieved with transformations and a generalised linear model with a Poisson error

distribution and log-link function was used (Warton 2005).

5.2.6.2 Multivariate analyses

Multivariate statistical analyses were performed on the quadrat–plant–time cover

abundance matrix and plant incidence (presence/absence) matrix for each sampling time

using Primer 6 (Primer-E Ltd) and permutational multivariate analysis of variance

(PERMANOVA) (Anderson 2001; McArdle and Anderson 2001) as in Chapter 4. Centroids

were generated for each level of Treatment at each level of Time (representing a ‘cluster’)

(Hashiguchi et al. 2006) and represented the mean of all the attributes (species cover values)

that defined that cluster (e.g. controls at Time 1, C-1) (Fraschetti et al. 2001). Non-metric

multi-dimensional scaling (MDS) was used to ordinate both the quadrat–plant–time species

cover matrix and plant incidence matrix. The repeated measures PERMANOVA for both

matrices incorporated the following factors and allowed the test for main effects and

treatment interactions: (1) Treatment (fixed factor with three levels: C, H and A), (2) Time

(fixed factor with five levels corresponding to the five sampling times T1–T5), and (3) Site

(random factor with ten levels corresponding to the ten sites). As all data from intact

exclosures were used and horse exclosures were periodically damaged, the experimental

design was unbalanced. All ten sites for all five sampling times were included for C and A,

whereas the number of replicates for H were as follows: T1, ten sites; T2, zero sites; T3, six

sites; T4, six sites; and T5, six sites. Analyses with both Type III and Type I Sums of Squares

(SS) were run and the degree of imbalance in the design was modest as the same conclusions

were drawn when the analysis was run with each type (Anderson et al. 2008). Type III SS is


recommended for unbalanced designs, however, and was the type used. When appropriate,

pair-wise comparisons were executed using permutations (pseudo-t statistic) (Anderson et

al. 2008).

SIMPER analysis was conducted on the plant incidence matrix at selective sampling

times to determine the species that contributed to significant treatment differences in

richness measures. SIMPER analysis was conducted on the complete quadrat–cover–time

matrix after MDS ordination and PERMANOVA analysis to identify important species

contributing to dissimilarity between treatments. Important species were the first ten

species whose combined or cumulative percentage contribution to between-group average

dissimilarity approximated 50% at a minimum of one sampling time (e.g. Tuya and Haroun

2006). The MDS utilising the complete quadrat–cover–time matrix was used to generate

bubble plots for individual species to complement univariate analyses of important species

and overlayed on the centroid MDS plot of the same matrix.


5.3 RESULTS

5.3.1 Plot scale dung deposits

Only horse dung was recorded in 6 × 6-m control plots and no dung was recorded in

any intact exclosures, indicating that only horses regularly utilised the controls for grazing,

and macropods, lagomorphs and cattle did not utilise horse exclosures. Before treatments

were imposed in June 2006, there was no significant difference between the C, H and A

treatments in the proportion of plots containing horse dung (Figure 5.6a). From T2–T5, 70–

80% of controls contained horse dung, whereas after June 2006, none was recorded in intact

H and A exclosures. Horses were thus successfully prevented from using exclosures when

operational, while continuing to use controls. The violated horse exclosures (HV in

Figure 5.6a) were also sampled throughout the experiment. At T2, all ten horse exclosures

were damaged. Seven of those contained horse dung. At T5, a further three of the

remaining six H exclosures were damaged and all three contained horse dung. Horse hair

was often found caught on the wire of damaged exclosures, with hoof prints in plots, and

with the presence of horse dung, confirmed that horses caused the damage.

The number of horse dung deposits in the C, H and A treatments, and the HV

treatment, was analysed and the Treatment × Time interaction was significant (F8,114 = 3.77,

P < 0.001). Mean number of horse deposits did not differ between treatments at the start of

the experiment (Figure 5.6b). At subsequent sampling times (T2–T5), horse dung was greater

in controls than complete exclusion (P < 0.050), as dung in C did not vary from baseline levels

of dung (P > 0.050), whereas dung in A declined to zero deposits after T1 (P < 0.001) and

remained there for the duration of the experiment (Figure 5.6b). The pattern for A–C was

repeated for H–C, as dung in H also declined to zero deposits after T1 (P = 0.002).

Conversely, the mean number of deposits in destroyed horse exclosures (HV) did not differ

from that in controls at T2 or T5 (Figure 5.6b).


Figure 5.6 (a) Proportion (±1 S.E.) of treatment plots containing horse dung, and (b) mean (±1 S.E.) number of horse dung deposits/ha in treatment plots. C: control, H: horse excluded, HV: horse excluded exclosures violated, AE: all herbivore excluded. Sample size varied in both graphs: for C and A, n =10; for H, n = 10 in June 2006 and n = 6 from November 2007 onward; for HV, n = 10 in April 2007 and n = 3 in June 2008; * denotes a significant difference between the treatments indicated from pair-wise tests (P < 0.050).

5.3.2 Biomass

5.3.2.1 Analysis 1

For biomass and richness measures, exclosure results at successive sampling times

represented the effect of time since grazing exclusion as well as season, while control results

reflected just the seasonal effect.

The biomass response of C and A progressively diverged with time as biomass in A

increased by substantial amounts while C fluctuated little (Figure 5.7a). The

Treatment × Time interaction was significant (F4,81 = 4.93, P < 0.001; Table 5.3). At the

beginning of the experiment, biomass in C and A quadrats averaged 132 g/m2 and 125 g/m2,

respectively, and did not differ (P = 0.802). Highly significant (P < 0.001) treatment

differences occurred at all subsequent sampling times, with A biomass greater than

C biomass by a factor of 2.7 at T2 (April 2007), 3.6 at T3 (November 2007), 21.0 at T4

(February 2008) and 9.4 at T5 (June 2008). Biomass in C quadrats differed from baseline

biomass when it decreased to 86 g/m2 at T2 (P = 0.030) and 45 g/m2 at T4 (P < 0.001).

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Figure 5.7 (a) Mean (±1 S.E.) biomass for analysis 1; * denotes a significant difference in biomass between A and C from pair-wise tests (n = 10; P < 0.050). (b) Mean (±1 S.E.) biomass for analysis 2; * denotes a significant difference in biomass between Treatments, as specified (n = 6; P < 0.050). C: control, H1: baseline horse excluded values, H: horse excluded after exclosures reset, A: all herbivore excluded, HR: horse excluded reset at 1 year.

Table 5.3 Repeated-measures linear mixed-effects model output for analysis 1 (A1) of biomass and linear model output for analysis 2 (A2) of biomass.

Biomass A1 Biomass A2 Source of variation df F P df F P Treatment 1 34.75 < 0.001 2 46.53 < 0.001 Time 4 105.70 0.001 3 15.84 < 0.001 Treatment × Time 4 4.93 < 0.001 6 7.98 < 0.001

Residual 81 57

Empirical measurements of changes in height of vegetation and structural complexity

were not recorded, but Figures 5.8–5.11 provide a visual comparison of treatments. Low-

biomass, short-statured grazing lawns were more common than taller patches of ungrazed or

lightly grazed vegetation in controls and over most of the riparian flats. Figures 5.8 and 5.10

were examples of the typical condition of riparian flats. Some sites at some sampling times

had no tall patches and others a greater proportion of tall patches. Small tall patches

(Figure 5.8) were commonly associated with decayed dung piles and dry, senescing tussocks,

and large tall patches (Figure 5.10) with unpalatable mature woody sub-shrubs, such as

fleabane (Conyza bonariensis) and paddy’s lucerne (Sida rhombifolia). Along with biomass

accumulation, vegetation height increased under complete exclusion unlike controls.

Figure 5.9 is a typical A exclosure at November 2007 (T3) after 17 months exclusion with

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vegetation height at one-third of the 1.8-m high exclosure and graminoids dominant.

Figure 5.11 is a typical A exclosure at the final sampling time after 2 years exclusion with

vegetation height more than half that of the 1.8-m exclosure and graminoids dominant, but

with several mature sub-shrubs evident.

Figure 5.8 Typical height of vegetation and biomass accumulation in controls in November 2007.

Figure 5.9 Typical height of vegetation and biomass accumulation under complete exclusion in November 2007 (T3). The relative height of vegetation outside the exclosure was evident in the foreground.


Figure 5.10 Example of a control plot at the final sampling time, June 2008, with patches of bare ground and a larger patch of ungrazed vegetation just outside the plot.

Figure 5.11 Typical height of vegetation and biomass accumulation under complete exclusion at the final sampling time, June 2008.


5.3.2.2 Analysis 2

Biomass in horse exclosures increased over time but due to the baseline biomass

being reset to June 2007 after exclosures were repaired, biomass did not accumulate at the

same rate or to the same extent as in complete exclusion; the Treatment × Time interaction

was significant (F6,57 = 7.98, P < 0.001; Table 5.3). At the start of the experiment (T1), the

biomass of 125 g/m2 in H quadrats did not differ from biomass in C or A quadrats

(Figure 5.7b). At T4, biomass in A and H quadrats was 25.2 and 13.8 times greater than C

(P < 0.001), respectively (Figure 5.7b). At T5, biomass in A (P < 0.001) and H (P = 0.009) was

13.8 and 5.7 times greater than C, respectively, and biomass in A was 2.5 times greater than

H (P = 0.012; Figure 5.7b).

Figure 5.12 Typical height of vegetation and biomass accumulation in horse exclosures in November 2007.

Horse exclosures were repaired in June 2007 and after less than 6 months exclusion,

biomass had accrued in the 2 × 2-m quadrats by November 2007. However, edge effects in

the form of a short grazed perimeter due to horses extending their heads through the wire

strands and grazing within the plot were detected in up to half of horse exclosures (e.g.

Figure 5.12). Periodically, edge effects would intrude into the 2 × 2-m quadrat. Edge effects

were not apparent at all sites, and in particular not at the sites with a greater proportion of

unpalatable, mature sub-shrubs (Figure 5.13). Biomass accumulation in horse exclosures was

influenced by the destruction of exclosures, edge effects, floristic composition and site

fertility.


Figure 5.13 Example of vegetation height and biomass accumulation in a horse exclosure with a large proportion of sub-shrubs, such as fleabane, in June 2008.

5.3.3 Richness measures

5.3.3.1 Baseline richness measures

The numbers of species of different types of herb, origin and life cycle were averaged

across all quadrats at T1. Forbs and grasses predominated with <1 species of sedge per

quadrat on average (Table 5.4). Native richness was almost twice that of exotic richness and

perennial richness four times greater than annual richness (Table 5.4).

Table 5.4 Mean (±1 S.E.) number of species of forb type, origin and life cycle for the herbaceous groundstorey vegetation on the riparian flats at the start of the experiment (T1).

Richness Herb type Grass (Gs) 6.1 ± 0.3

Sedge (S) 0.8 ± 0.1 Forb (F) 8.1 ± 0.4

Origin Native (N) 9.9 ± 0.5 Exotic (E) 5.1 ± 0.3

Life cycle Perennial (P) 12.0 ± 0.5 Annual (A) 3.0 ± 0.3


5.3.3.2 Changes in species richness and analysis 1

The Treatment × Time interaction was significant in all univariate analyses of richness

measures and proportion of native species (Table 5.5). C and A did not differ at the start of

the experiment (T1 or baseline) in any of these analyses with one exception: exotic richness

was greater in A than C at T1 (Figure 5.14i). Total richness increased in A from T1 to T2 but

declined during T3–T5 and was less than T1 at T5 (Figure 5.14a). In C, richness increased

from T2 to T3 and declined at T4 and T5. Total species richness was greater in C than A at T3

(P = 0.036) and T4 (P = 0.001).

Annual richness decreased from T1 to T2 in A and C and continued to decline during

T3–T5 in A (Figure 5.14c). At T3, richness had increased in C and was greater than richness at

T1, and while richness declined at T4 and T5, it did not differ from T1 at the final two

sampling times. Annual richness was greater in C than A at T3–T5 (P ≤0.003). Perennial

richness increased from T1 to T2 in A and C and was also greater than T1 at T2–T4 in C only

(Figure 5.14e). However, perennial richness in C decreased after T4 and did not differ at T5

from T1. Richness in A declined during T3–T5 but did not differ from T1 at the final three

sampling times. Perennial richness was greater in C than A at T4 (P = 0.001).

Native richness increased in A from T1 to T2 and was greater at T2 and T3 than T1,

but did not differ from T1 at the final two sampling times (Figure 5.14g). Native richness was

greater in A than C at T2 and only differed from baseline richness in controls at T5, when it

was less than T1. Exotic richness declined after T2 in A and was less than T1 at T3–T5

(Figure 5.14i). Conversely, exotic richness increased in C after T2 and was greater at T3 and

T4 than T1. Exotic richness was greater in C than A at T3–T5 (P < 0.001).

The response of percentage of native species in A reflected the progressive decline in

exotic richness in A compared to C where natives declined marginally over time (T5<T1,

P = 0.022) (Figure 5.14k). Percentage of natives increased from T1 to T2 in A and continued

to increase to T5. Percentage of natives was greater in A than C at T3–T5 (P < 0.001).


Table 5.5 Repeated-measures linear mixed-effects model for measures of groundstorey herbaceous richness and proportion of native to exotic species (% Native). A1: analysis 1, A2: analysis 2, Treat: Treatment.

Source df Total A1 Annual A1 df Total A2 Annual A2 of variation F P F P F P F P

Treatment 1 1.61 0.208 13.07 < 0.001 2 2.35 0.105 0.90 0.395 Time 4 13.54 < 0.001 11.09 < 0.001 3 21.00 < 0.001 15.62 < 0.001 Treat × Time 4 6.87 < 0.001 6.93 < 0.001 6 1.22 0.310 7.80 0.121 Residual 81 55

df Perennial A1 Exotic A1 df Perennial A2 Exotic A2

F P F P F P F P Treatment 1 0.19 0.660 24.97 < 0.001 2 3.09 0.054 3.39 0.041 Time 4 10.72 < 0.001 7.78 < 0.001 3 12.19 < 0.001 10.55 < 0.001 Treat × Time 4 3.22 0.017 11.55 < 0.001 6 0.81 0.569 3.54 0.005 Residual 81 55

df Native A1 % Native A1 df Native A2 % Native A2

F P F P F P F P Treatment 1 4.57 0.036 34.07 < 0.001 2 6.46 0.003 7.67 0.001 Time 4 9.88 < 0.001 4.29 0.003 3 12.98 < 0.001 4.73 0.005 Treat × Time 4 2.55 0.045 10.65 < 0.001 6 0.48 0.821 2.81 0.019 Residual 81 55


Figure 5.14 Mean (±1 S.E.) species richness of groundstorey vegetation for analysis 1(A1) and analysis 2 (A2). Notation as in Figures 5.6 and 5.7. Treatment and Time main effects have a single notation specifying pair-wise differences (P < 0.050). Treatment × Time interactions have notation for each treatment specifying the relationship of T2–T5 to T1 and * specifying a difference between treatments at that sampling time. For analysis 1 the * denotes a significant difference between A and C (n = 10; P < 0.050) and for analysis 2, additional notation specifies which treatments differ (n = 6; P < 0.050). C: control, H1: base line horse excluded values, H: horse excluded after exclosures reset, A: all herbivore excluded, HR: horse excluded reset at 1 year. T1: June 2006, T2: April 2007, T3: November 2007, T4: February 2008, T5: June 2008.

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Figure 5.14 continued.

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5.3.3.3 Changes in species richness and analysis 2

Only the Time main effect was significant in univariate analyses of total and annual

richness (Table 5.5). Total richness was greatest at T3 (spring 2007), followed by T4 (summer

2008), T1 (winter 2006) and T5 (winter 2008) (Figure 5.14b). Annual richness was greater at

T1 and T2 than T4 and T5 (Figure 5.14d). Both Time and Treatment main effects were

significant for perennial and native richness. Both richness measures were greater at T3 and

T4 than T4 and T5 and greater in H than C, although native richness was also greater in H

than A (Figure 5.14f and h). The Treatment × Time interaction was significant for exotic

richness and percentage of native species (Table 5.5). Exotic richness in H was less than A at

T1 but increased to be intermediate between A and C at T3 and T4. In particular, exotic

richness declined to be less in A than both C and H at T3–T5 (Figure 5.14j). Conversely,

percentage of native species was greater in H than A at T1 but similar in all three treatments

at T3 (Figure 5.14l). However, at T4, A and H (P ≤ 0.004) had a greater percentage of natives

than C, while at T5, native percentage was greater in A than H and greater in both grazing

exclusion treatments than C (P ≤ 0.013). The percentage of natives in H at T1 did not differ

from that in H at T3–T5 (P > 0.050).

5.3.3.4 SIMPER analysis of native richness treatment effects

After 10 months exclusion (T2), native species richness was 2.9 species greater in A

than C. The two native species present in C and absent in A, a daisy, Solenogyne belliodes,

and Eucalyptus seedling, were not present at any sampling time under complete exclusion as

previously discussed. An additional 19 native species were present in A and not C at T2, all of

which were perennial, 16 were forbs and three were grasses (Table 5.6).

Seven species appeared for the first time in A at T2, and were not present in C at any

sampling time, suggesting they may have responded to the release from horse grazing

(Table 5.6). Two twining species, variable glycine (Glycine tabacina) and large tick-trefoil

(Desmodium brachypodium) are palatable legumes, and wonga wonga vine (Pandorea

pandorana) is also a twiner. The remaining species were the two grasses, windmill grass

(Chloris truncata) and forest hedgehog grass (Echinopogon ovatus), the small-statured forb,

stinkweed (Opercularia diphylla) and the partial rosette forb, austral bugle (Ajuga australis).

Of these seven species, large tick-trefoil, windmill grass, stinkweed and austral bugle were

not recorded in A quadrats again, whereas variable glycine, wonga wonga vine and forest

hedgehog grass persisted, that is, were present in at least one A quadrat at one sampling


time after T2. The remaining 12 species appearing first in A at T2 were also present in C

either at T1 or T3–T5. Two species, a small-statured forb, shade plantain (Plantago debilis)

and a climber, Swainsona sp. did not persist in A after T2, but only Swainsona sp., persisted

in C after T2. Of the 19 native perennial species present in A and not C at T2, the three

graminoids were warm-season grasses, and one tufted and one tussock species persisted in A

after T2. All three mat-forming forbs persisted in A. Creeping mint and native wandering jew

(Commelina cyanea), in particular, increased in frequency and cover, while six of the eight

twining forbs also persisted. One of the two partial rosette species, brown dock, and one of

the three taller-statured erect species, native blue bell, were detected again in A after T2.

Table 5.6 SIMPER output for the species incidence matrix comparing native species present in controls (C) but absent from all herbivore excluded (A) treatment at T1 or T2, and native species present in A but absent from C at T1 or T2. Species present or absent in A and C at one or more sampling times after T2 (i.e. T3–T5) were noted. Average abundance is for the treatment where the species was present at T2. Y: present and N: absent. T1: June 2006, T2: April 2007.

C A C A A C Average

Latin name Common name T2 T2 T1 T1 after

T2 after

T2 abundance (% cover)

Eucalyptus sp. Y N Y N N Y 0.1 Solenogyne bellioides A daisy Y N Y N N Y 0.1 Geranium solanderi Native geranium N Y Y Y Y Y 0.4 Glycine tabacina Variable glycine N Y N N Y N 0.3 Rumex brownii Brown dock N Y N N Y Y 0.3

Commelina cyanea Native wandering jew N Y N N Y Y 0.2

Desmodium Large tick-trefoil N Y N N N N 0.2 brachypodium Panicum effusum Hairy panic N Y Y Y Y N 0.2 Swainsona sp. N Y Y N N Y 0.2 Chloris truncata Windmill grass N Y N N N N 0.2 Pratia purpurascens White root N Y Y Y Y Y 0.2 Opercularia diphylla Stinkweed N Y N N N N 0.2 Wahlenbergia gracilis Native blue bell N Y N N Y Y 0.2 Echinopogon ovatus Forest hedgehog N Y N N Y N 0.1 Einadia nutans Climbing saltbush N Y N N Y Y 0.1 var. nutans Urtica incisa Stinging nettle N Y Y Y Y Y 0.1 Mentha satureioides Creeping mint N Y N N Y Y 0.1 Plantago debilis Shade plantain N Y Y N N N 0.1 Ajuga australis Austral bugle N Y N N N N 0.1 Pandorea pandorana Wonga wonga vine N Y N N Y N 0.1 Stephania japonica Snake vine N Y N N Y Y 0.1


5.3.3.5 SIMPER analysis of total species richness treatment effects

In November 2007 (T3), total species richness was greater in controls than complete

exclusion. SIMPER analysis (Table 5.7) identified nine species present in C but not A, and six

species present in A but not C at T3, corresponding to the difference of 2.2 species in mean

total richness between C and A. The six species present in A but not C were native perennial

grasses and forbs. The forbs were predominantly mat-forming species, with one twiner and

one small-statured, erect species. The first three species in C were native perennial species.

The most notable difference from A quadrats was the presence of a daisy (Solenogyne

bellioides) with an average abundance of 0.4% (Table 5.7). Other species exclusive to C at T3

had an average abundance of ≤0.2% (Table 5.7). Of the four native perennial species in C,

Swainsona sp. may be considered a loss as it was present in two A quadrats at T2 but not

recorded in A thereafter. The solenogyne daisy, Eucalyptus seedling and blady grass

(Imperata cylindrica), however, were not present in A at T1 or T2, and their presence in

C quadrats was probably due to random differences in plots selected for different

treatments. Three of the remaining species in C, but not A, were exotic annuals and one was

an exotic, perennial forb, khaki weed (Alternanthera pungens), capable of forming a low

mat-like covering by vegetative reproduction. The two exotic, annual grasses were either

short-statured, tufted species (i.e. goose grass, grows to 40 cm in height) or a stoloniferous,

tufted species that adopt to a prostrate growth form when grazed (i.e. liverseed grass

Urochloa panicoides) (Jacobs et al. 2008). The exotic, annual forb, scarlet pimpernel

(Anagallis arvensis), has a partial rosette growth form. Khaki weed, goose grass and

liverseed grass were losses from A as they were present in five, four and five A quadrats,

respectively, in T1 and T2 and were not recorded in A again after T2. Scarlet pimpernel was

never recorded in A quadrats.


Table 5.7 SIMPER output for the species incidence matrix comparing species present in controls (C) but absent from all herbivore excluded (A) treatment at T3, and species present in A but absent from C at T3. Y: present and N: absent. T3: November 2007; * denotes exotic species.

Contribution Cumulative Average to average average

abundance dissimilarity dissimilarity Latin name Common name C A (% cover) (%) (%) Solenogyne bellioides A daisy Y N 0.4 2.2 66.9 Eucalyptus sp. Y N 0.2 1.1 90.5 Swainsona sp. Y N 0.2 1.0 92.5 Oenothera sp. Y N 0.1 0.6 94.7 Anagallis arvensis *Scarlet pimpernel Y N 0.1 0.6 95.3 Urochloa panicoides *Liverseed grass Y N 0.1 0.6 95.8 Eleusine tristachya *Goose grass Y N 0.1 0.6 96.4 Imperata cylindrica Blady grass Y N 0.1 0.5 96.9 Alternanthera pungens *Khaki weed Y N 0.1 0.5 99.5

Echinopogon ovatus Forest hedgehog grass N Y 0.2 1.3 88.2

Mentha satureioides Creeping mint N Y 0.2 1.1 89.3 Pandorea pandorana Wonga wonga vine N Y 0.1 0.6 94.1

Dichelachne micrantha Short-hair plumegrass N Y 0.1 0.5 98.5

Centella asiatica Pennywort N Y 0.1 0.5 99.0 Hypericum gramineum Small St. John's wort N Y 0.1 0.5 100.0

In February 2008 (T4), total species richness was greater in controls than complete

exclusion. SIMPER analysis in Table 5.8 identified ten species present in C but not A, and six

species present in A but not C, at T4, corresponding to the difference in mean total species

richness of 4.1 species between C and A.

In A, the exotic, perennial sub-shrub, veined verbena (Verbena rigida), had the

greatest average abundance (0.3%; Table 5.8). The remaining five species present in A at T3

but not C included two native perennial grasses, one tufted and one a tussock. The

remaining three native perennial forbs were the mat-forming pennywort (Centella asiatica)

and two twiners, wonga wonga vine (Pandorea pandorana) and variable glycine (Glycine

tabacina).

Of the ten species in C, the three native plants included a Eucalyptus seedling and

one annual and one perennial short-statured forb. Two of the remaining seven exotic

species were perennial sub-shrubs and one tufted warm-season perennial grass. The two

other warm-season exotic grasses were annual species with either a tufted or mat-forming

growth form. The two exotic forbs were either a partial rosette or mat-forming perennial


plant. The first two species in C were exotic plants with relatively high cover (average

abundance of 0.7% and 0.6%). The first species was the sub-shrub, paddy’s lucerne (Sida

rhombifolia), which was present in ten A quadrats across T1–T3, and while absent at T4, was

recorded in two A quadrats in T5. The second was the partial rosette forb, ribwort, present

in 15 A quadrats across T1 and T2, and one A quadrat at T3 and T5. The remaining species

were less influential to composition, with relatively low cover (average abundance 0.1–0.3%).

Khaki weed, the solenogyne, goose grass, liverseed grass and Eucalyptus sp. were discussed

in relation to T3. Noogoora burr (Xanthium occidentale) was present in one quadrat in C, and

its presence was possibly an artefact of plot selection. Jersey cudweed (Pseudognaphalium

luteoalbum) was recorded in several quadrats at different times in C, but in just the one

quadrat in A at T3. Jersey cudweed may have been excluded from A as it was not recorded at

T4 and T5, but the species was naturally sparse in the A treatment compared to C.

Conclusions were similar for crowsfoot grass (Eleusine indica) present in one A quadrat at T2

and T5.

Table 5.8 SIMPER output for the species incidence matrix comparing species present in controls (C) but absent from all herbivore excluded (A) treatment at T4 and species present in A but absent from C at T4. Y: present and N: absent. T4: February 2008; * denotes exotic species.

Contribution Cumulative Average to average average

abundance dissimilarity dissimilarity Latin name Common name C A (% cover) (%) (%) Sida rhombifolia *Paddy's lucerne Y N 0.7 4.2 8.6 Plantago lanceolata *Ribwort Y N 0.6 3.6 16.1 Alternanthera pungens *Khaki weed Y N 0.3 1.7 75.7 Pseudognaphalium Jersey cudweed Y N 0.2 1.2 87.4 luteoalbum Solenogyne bellioides A daisy Y N 0.2 1.2 90.9 Eleusine tristachya *Goose grass Y N 0.2 1.2 92.0 Xanthium occidentale *Noogoora burr Y N 0.1 0.6 96.6 Urochloa panicoides *Liverseed grass Y N 0.1 0.6 97.2 Eleusine indica *Crowsfoot grass Y N 0.1 0.6 98.9 Eucalyptus sp. Y N 0.1 0.6 99.5 Verbena rigida *Veined verbena N Y 0.3 1.9 74.0 Echinopogon ovatus Forest hedgehog N Y 0.2 1.4 85.0

grass Centella asiatica Pennywort N Y 0.2 1.2 89.7 Panicum effusum Hairy panic N Y 0.2 1.1 93.1 Pandorea pandorana Wonga wonga vine N Y 0.1 0.7 95.9 Glycine tabacina Variable glycine N Y 0.1 0.5 100.0


5.3.4 Cover variables

5.3.4.1 Baseline cover measures

The percent cover of bare ground, and of plants of different herbaceous type, origin

and life cycle categories was averaged across all quadrats at T1. Bare ground averaged

3.6 ± 0.5%, while plant cover was dominated by grasses, with more than twice the cover of

forbs and 12 times the cover of sedges (Table 5.9). Native and perennial species dominated

with 72.2 ± 3.4% and 84.0 ± 2.3% cover, respectively.

Table 5.9 Mean (±1 S.E.) cover of plants of different growth form, origin and life cycle for the herbaceous groundstorey vegetation on the riparian flats at the start of the experiment (T1).

Cover (%) Herb type Grass (Gs) 66.6 ± 1.3

Sedges (S) 5.3 ± 1.2 Forb (F) 28.7 ± 1.7

Origin Native (N) 72.0 ± 3.4 Exotic (E) 28.0 ± 3.2

Life cycle Perennial (P) 84.0 ± 2.3 Annual (A) 16.1 ± 2.3

5.3.4.2 Changes in the cover of bare ground and analysis 1

At the beginning of the experiment (T1), the cover of bare ground averaged 3.7–4.1%

in C and A quadrats (Figure 5.15a) with a maximum of 8.0–10.0%. Cover of bare ground in C

remained similar at subsequent sampling times but decreased in A, as indicated by the

significant Treatment × Time interaction (F4,81 = 5.85, P < 0.001; Table 5.10). By T2,

10 months after the experiment was established, bare ground cover in A was 0.8% and less

than that in C (P = 0.009). By T3, cover of bare ground was 0.0% in A and significantly less

than in C (P < 0.001), remaining thus for the duration of the experiment.

Table 5.10 Repeated-measures linear mixed-effects model output for bare ground cover. A1: analysis, A2: analysis 2.

Bare ground A1 Bare ground A2 Source of variation df F P df F P Treatment 1 105.70 < 0.001 2 29.83 < 0.001 Time 4 4.93 0.001 3 9.09 < 0.001 Treatment × Time 4 5.85 < 0.001 6 2.67 0.024

Residual 81 55


5.3.4.3 Changes in the cover of bare ground and analysis 2

The trends and interpretation of statistical output for C and A quadrats were the

same as for analysis 1 and the response of H quadrats the same as A, except that bare

ground had declined to 0.0% after less than 6 months grazing exclusion. The

Treatment × Time interaction was significant (F6,55 = 2.67, P = 0.024; Table 5.10). At the

beginning of the experiment, the cover of bare ground in H was the same as in A and C

(~2.8%), declining in H and A to 0.0% from T3 onward (Figure 5.15b). Bare ground in H, like

A, was less than C at other sampling times: T3 (P = 0.005), T4 (P = 0.004), and T5 (P = 0.027).

Figure 5.15 (a) Mean (±1 S.E.) cover of bare ground for analysis 1; * denotes a significant difference in bare ground between A and C at that sampling time (n = 10; P < 0.050). (b) Mean (±1 S.E.) cover of bare ground for analysis 2; * denotes a significant difference in bare ground between C, and the other two Treatments, A and H, which did not differ, at that sampling time (n = 6 at each Time except June 2008 when n = 3; P < 0.050). C: control, H1: baseline horse excluded values, H: horse excluded after exclosures reset, A: all herbivore excluded, HR: horse excluded reset at 1 year. 5.3.4.4 Changes in the cover of annual and native plants and analysis 1

Native cover in control and complete exclusion quadrats varied little through time

and neither the Treatment × Time interaction nor the main effects were significant

(Figure 5.16a; Table 5.11). The response of annual cover yielded a significant

Treatment × Time interaction (F4,81 = 3.42, P = 0.012; Table 5.11), C and A not differing until

they diverged after T2. Annual cover declined from baseline cover in A (P = 0.007) and C

(P = 0.004) at T2, and remained at low levels in A from T3–T5 (P < 0.001) (Figure 5.16b).

Annual cover in C returned to baseline levels at T3 (P = 0.414) and remained at similar levels

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at T4 and T5 (P > 0.050). Annual cover was 8.9% greater in controls than complete exclusion

quadrats at T4 (P = 0.018) and 9.3% greater in controls at T5 (P = 0.015).

5.3.4.5 Changes in the cover of annual and native plants and analysis 2

Trends in the response of native cover in H quadrats did not differ from A quadrats,

and native cover in all treatments varied little through time; neither the Treatment × Time

interaction nor the main effects were significant (Table 5.11; Figure 5.16c). Similarly, trends

in the response of annual cover in H quadrats did not differ from C and A quadrats and

neither the Treatment main effect, nor the Treatment × Time interaction was significant

(Table 5.11). The Time main effect was significant (Table 5.11; Figure 5.16d), annual cover

decreasing in all treatments during the June 2006 to November 2007 period, increasing

thereafter, more so in C, than H and A. Annual cover was greater at T1 than at T3 and T4.

Table 5.11 Repeated-measures linear mixed-effects model output for cover of native and annual species. A1: analysis, A2: analysis 2.

Source of variation df Native A1 Annual A1 df Native A2 Annual A2

F P F P F P F P Treatment 1 1.09 0.301 4.44 0.038 2 1.75 0.183 0.94 0.397 Time 4 1.08 0.371 4.62 0.002 3 1.04 0.383 11.38 < 0.001 Treatment × Time 4 1.38 0.248 3.42 0.012 6 0.40 0.874 0.97 0.456 Residual 81 55


Figure 5.16 Mean (±1 S.E.) cover per treatment group of groundstorey vegetation for analysis 1 (a) and (b) and analysis 2 (c) and (d). Note different scales on y-axis. For analysis 1, * denotes a significant difference between A and C (n = 10; P < 0.050) based on the significant Treatment × Time interaction. For analysis 2, Time main effects are specified by T1: June 2006, T3: November 2007, and T4: February 2008. C: control, H1: base line horse excluded values, H: horse excluded after exclosures reset, A: all herbivore excluded, HR: horse excluded reset at 1 year.

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5.3.5 Full floristics: composition

5.3.5.1 Baseline floristic composition

The cover of all species at T1 was averaged and the ten species with the greatest

cover presented in Table 5.12. The cumulative mean percentage cover of ten species was

70.3%. At the start of the experiment, weeping grass (Microlaena stipoides) had at least

twice the cover of other species except for paddock lovegrass (Eragrostis leptostachya)

(Table 5.12). Five of the ten species were warm-season perennial grasses including one

species of exotic grass (paspalum Paspalum dilatatum), one was a sedge (knob sedge Carex

inversa), and the only forb was ribwort (Plantago lanceolata), an exotic species with a partial

rosette growth form. Two annual species were warm-season exotic grasses, summer grass

(Digitaria sanguinalis) and goose grass (Eleusine tristachya), with a cumulative contribution

to cover of 9.2%. The two rhizomatous and stoloniferous mat-forming grasses (common

couch Cynodon dactylon and summer grass) were ranked fourth and fifth with a cumulative

cover of 12.9%, followed by knob sedge and paspalum. Species that spread and grow by

vegetative reproduction totalled 23.1% of plant cover.

Several species not included in the top ten most cover-abundant species had similar

cover to goose grass, the tenth species. All species featuring in later analyses and their

life-history attributes are presented in Table 5.12 as a reference guide and to avoid

duplication, even if they did not contribute to ‘important’ cover at T1.


Table 5.12 Mean (±1 S.E.) cover of the ten species above the line with the greatest contribution to total plant cover on the riparian flats at the start of the experiment (T1). Additional species below the line featured in SIMPER analyses. Gs: grass, S: sedge, F: forb, Y: year-long green, W: warm-season, E: exotic, N: native, A: annual, P: perennial, Tf: tufted, Mt: mat-forming, Ts: tussock, PR: partial rosette, R: rosette, Er: erect, Tr: twiner, NV: non-vegetative reproduction, V: vegetative reproduction, PV: partially vegetative reproduction; * denotes exotic species.

Life-history attributes Common name Latin name Cover (%) Gs, Y, P, N, Tf, NV Weeping grass Microlaena stipoides 17.3 ± 3.5 Gs, W, P, N, Tf, NV Paddock lovegrass Eragrostis leptostachya 10.2 ± 1.9 Gs, W, P, N, Tf, NV Red grass Bothriochloa macra 8.2 ± 1.5 Gs, W, P, N, Mt, V Common couch Cynodon dactylon 6.8 ± 1.9 Gs, W, A, E, Mt, V *Summer grass Digitaria sanguinalis 6.1 ± 2.2 S, W, P, N, Tf, V Knob sedge Carex inversa 5.2 ± 1.2 Gs, W, P, E, Ts, V *Paspalum Paspalum dilatatum 5.0 ± 0.9 Gs, W, P, N, Tf, NV Slender rat's tail grass Sporobolus creber 4.7 ± 1.1 F, E, P, PR, NV *Ribwort Plantago lanceolata 3.7 ± 0.6 Gs, W, A, E, Tf, NV *Goose grass Eleusine tristachya 3.1 ± 1.1 F, N, P, Mt, V Creeping oxalis Oxalis exilis 2.7 ± 0.4 F, E, A, PR, NV *Spiked cudweed Gamochaeta spicata 2.5 ± 0.4

F, N, P, Er, NV Pink tongues Rostellularia adscendens 2.1 ± 0.5 F, E, A, Er, NV *Fleabane Conyza bonariensis 1.8 ± 0.4 Gs, W, N, P, Tf, NV Hairy panic Panicum effusum 1.8 ± 0.4 F, N, P, Tr, NV Stinging nettle Urtica incisa 1.8 ± 1.0 F, E, P, Tf, V *White clover Trifolium repens 1.7 ± 0.6 F, N, P, Mt, V Kidney weed Dichondra repens 1.6 ± 0.4 F, E, P, Mt, V *Khaki weed Alternanthera pungens 1.5 ± 0.7 F, N, P, Tr, NV Slender tick-trefoil Desmodium varians 1.3 ± 0.5 F, E, A, Er, NV *Farmer's friend Bidens pilosa 1.2 ± 0.4 F, N, P, Mt, V Pennywort Centella asiatica 1.2 ± 0.4 Gs, W, N, P, Tf, NV Brown’s lovegrass Eragrostis brownii 1.2 ± 0.7 F, E, A, Er, NV *Common centaury Centaurium erythraea 1.0 ± 0.4 F, N, P, Tr, PV Native geranium Geranium solanderi 1.0 ± 0.5 F, N, P, Er, V Rock fern Cheilanthes sieberi 0.6 ± 0.3 F, N, P, Tf, NV Shade plantain Plantago debilis 0.6 ± 0.5 F, N, P, Mt, V White root Pratia purpurascens 0.6 ± 0.2 Gs, W, A, E, Tf, NV *Liverseed grass Urochloa panicoides 0.5 ± 0.1 F, E, P, Er NV *Paddy's lucerne Sida rhombifolia 0.5 ± 0.2 F, E, A, Er, NV *Slender celery Cyclospermum leptophyllum 0.5 ± 0.2 S, W, P, N, Tf, NV Slender flat-sedge Cyperus gracilis 0.5 ± 0.2 F, N, P, PR, NV Brown dock Rumex brownii 0.4 ± 0.3 F, N, P, R, NV A daisy Solenogyne bellioides 0.3 ± 0.2 F, E, P, Mt, NV *Chilean whitlow wort Paronychia brasiliana 0.3 ± 0.1 F, N, P, Tr, PV Climbing saltbush Einadia nutans var. nutans 0.3 ± 0.3 F, E, P, PR, NV *Common dandelion Taraxacum officinale 0.3 ± 0.2 F, N, P, Er, NV Small St.John's wort Hypericum gramineum 0.2 ± 0.1


Table 5.12 continued. Species featured in SIMPER analyses. Gs: grass, S: sedge, F: forb, Y: year-long green, W: warm-season, E: exotic, N: native, A: annual, P: perennial, Tf: tufted, Mt: mat-forming, Ts: tussock, PR: partial rosette, R: rosette, Er: erect, Tr: twiner, NV: non-vegetative reproduction, V: vegetative reproduction, PV: partially vegetative reproduction; denotes exotic species.

Life-history attributes Common name Latin name Cover F, N, P, Mt, V Creeping mint Mentha satureioides 0.1 ± 0.1 Gs, W, E, P, Tf, NV *Crowsfoot grass Eleusine indica 0.1 ± 0.1 F, N, P, Er, NV Native blue bell Wahlenbergia gracilis 0.1 ± 0.1 F, N, P, Er, NV Stinkweed Opercularia diphylla 0.1 ± 0.1 F, E, P, Er, V *Veined verbina Verbina rigida 0.1 ± 0.1 F, N, A, Er, NV Annual bittercress Cardamine paucijuga 0.0 ± 0.0† F, N, P, PR, NV Austral bugle Ajuga australis 0.0 ± 0.0† Gs, Y, N, P, Mt, V Basket grass Oplismenus imbecillis 0.0 ± 0.0† Gs, W, P, N, Ts, V Forest hedgehog grass Echinopogon ovatus 0.0 ± 0.0† F, E, A, Er, NV *Four-leaved allseed Polycarpon tetraphyllum 0.0 ± 0.0†

F, N, A, Er, NV Jersey cudweed Pseudognaphalium luteoalbum 0.0 ± 0.0†

F, N, P, Tr, NV Large tick-trefoil Desmodium brachypodium 0.0 ± 0.0† F, N, P, Mt, NV Native wandering jew Commelina cyanea 0.0 ± 0.0† F, E, P, Er, NV *Noogoora burr Xanthium occidentale 0.0 ± 0.0† F, E, A, Er, V *Scarlet pimpernel Anagallis arvensis 0.0 ± 0.0† F, N, P, Tr, NV Variable glycine Glycina tabacina 0.0 ± 0.0† Gs, W, N, P, Tf, NV Windmill grass Chloris truncata 0.0 ± 0.0† F, N, P, Tr, NV Wonga wonga vine Pandorea pandorana 0.0 ± 0.0† F, N, P, Tr, NV Swainsona sp. 0.0 ± 0.0†

† These species averaged zero cover either due to rounding or they were not detected at T1. 5.3.5.2 Changes in floristic composition due to treatment effects over time

In the nonmetric MDS plot at T1, all treatment centroids (C-1, H-1 and A-1) were

equidistant to each other and positioned in the right of the ordination plot (Figure 5.17).

Control centroids at T2–T5 were positioned at a similar distance to the left of the C-1

centroid. Similarly, the complete exclusion centroid at T2 (A-2) was to the left of the A-1

centroid but A centroids for the last three sampling times were at comparable distances to

the left of A-2. The H-2 centroid was not plotted due to the destruction of all horse

exclosures, however, the response of H centroids that were plotted resembled the response

of A centroids (Figure 5.17). The significant Treatment × Time interaction (F7,51 = 2.54,

P < 0.001) and pair-wise comparisons of treatments at each sampling time in the

PERMANOVA analysis of the quadrat-plant cover matrix reflected the differential response

over time of grazing exclusion treatments (A and H) and controls (Table 5.13 and Table 5.14).

Treatment centroids formed the tightest grouping at T1 (Figure 5.17) and did not

differ in composition (P > 0.050; Table 5.14). By T2, floristic composition differed between C

and A quadrats (P = 0.011) and continued to differ to a greater extent at T3 (P = 0.008)


(Table 5.14). T3 was the first sampling time after horse exclosures were repaired and floristic

composition in H did not differ from C (P = 0.699) or A (P = 0.266). After T3, the differences

in floristic composition between C and A centroids as a result of 17 months exclusion were

preserved (Table 5.14) and centroids for both treatments moved predominantly in a

downward direction, possibly in response to season (Figure 5.17). The H-4 and C-4 centroids

were also marginally significantly different (P = 0.051) and were futher apart than the H-4

and A-4 centroids that did not differ (P = 0.068). The H-5 centroid, however, differed in

composition from both the C-5 (P = 0.023) and A-5 (P = 0.011) centroids. T1 and T5 were

both June seperated by 2 years. In controls, C-5 was closer to C-1 than C-2, C-3 or C-4. In

horse exclosures, the pattern was repeated but the distance between H1 and H5 was twice

that in controls. In complete exclusion quadrats, A-2 was closest to A-1 while A-5 was more

than twice the distance for the same comparison in C.

Figure 5.17 Nonmetric MDS plot comparing the similarity in composition of groundstorey vegetation based on percent cover of species of each treatment at different sampling times. C: control, H : horse excluded, A: all herbivore excluded, 1: June 2006 (T1), 2: April 2007 (T2), 3: November 2007 (T3), 4: November 2007 (T4), 5: June 2008 (T5).

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

2D Stress: 0.14


Table 5.13 PERMANOVA analysis output on groundstorey vegetation composition (percentage cover) data at up to ten sites, for three treatments and five sampling times. P(perm) values were obtained using 9999 random permutations.

Source of variation df MS F P(perm)

Treatment 2 5912.4 3.46 < 0.001 Time 4 6340.6 8.47 < 0.001 Site 9 6846.1 15.39 < 0.001 Treatment × Time 7 1129.2 2.54 < 0.001 Treatment × Site 18 1972.5 4.43 < 0.001 Time × Site 36 780.5 1.75 < 0.001

Residual 51 444.9

Table 5.14 Pair-wise comparisons for the significant Treatment × Time interactions from PERMANOVA analysis. P(perm) values were obtained using 9999 random permutations. C: control, H: horse excluded, A: all herbivore excluded.

Treatment Abundance Incidence

comparisons df P(perm) P(perm) Time 1 C v H 9 0.540 0.451

C v A 9 0.266 0.127 A v H 9 0.417 0.405

Time 2 C v A 9 0.011 0.010

Time 3 C v H 5 0.699 0.720 C v A 9 0.008 0.009 A v H 5 0.266 0.194

Time 4 C v H 5 0.051 0.044 C v A 9 0.003 0.002 A v H 5 0.648 0.868

Time 5 C v H 5 0.023 0.055 C v A 9 0.006 0.016

A v H 5 0.011 0.010

The outcome and interpretation of the PERMANOVA analysis of the quadrat–plant

incidence data was the same as for the percent cover matrix and the Treatment × Time

interaction significant (F7,51 = 2.85, P = 0.001). Changes in the incidence of less abundant

species were more dynamic in controls with centroids moving a greater distance on the

horizontal plane than compositional changes based on percent cover (Figure 5.18). The

relative distance between treatment centroids at different sampling times was similar to the

percent cover MDS diagram and the results of the pair-wise tests were similar (Table 5.14;

Figure 5.18).


Figure 5.18 Nonmetric MDS plot comparing the similarity in composition of groundstorey vegetation based on the incidence of species in each treatment at different sampling times. C: control, H : horse excluded, A: all herbivore excluded, 1: June 2006 (T1), 2: April 2007 (T2), 3: November 2007 (T3), 4: November 2007 (T4), 5: June 2008 (T5). 5.3.5.3 Multivariate analysis: important species contributing to treatment effects

SIMPER analysis on the quadrat–plant cover matrix summarised in Tables 5.15–5.17

identified 15 important species that contributed to the dissimilarity in floristic composition

between treatments at various sampling times. They were common couch, fleabane, kidney

weed, knob sedge, native geranium, paddy’s lucerne, paddock lovegrass, paspalum, ribwort,

red grass, slender tick-trefoil, slender rat’s tail grass, spiked cudweed, summer grass and

weeping grass. Common couch, paddy’s lucerne, paddock lovegrass, spiked cudweed and

summer grass were less abundant in A and H than C at all sampling times (Table 5.15 and

5.16). Comon couch did not contribute to dissimilarity between A and H while paddy’s

lucerne, paddock lovegrass and spiked cudweed were less abundant in A than H at T5

(Table 5.17). Cover of summer grass was greater in A than H at T5. Fleabane and ribwort

were more abundant in A than C at T2, but less abundant in A at T3–T5. Ribwort was also

less abundant in H than C at T4 and T5 and did not contribute to dissimilarity between A and

H. Conversely, cover of fleabane was greater in H than C at T4 and T5 and thus greater in H

than A at T5. Kidney weed tended to be less abundant in A than C, particularly at the last

two sampling times, while its response in H compared to C was conflicted. Cover was less in

H than C at T4 but greater than both C and A at T5. Knob sedge was less abundant in A than

C at T2 but became progressively more abundant in A over T3–T5 and more abundant than H

at T5. The cover of knob sedge was greater in H than C at both sampling times. The

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4

A-4

H-4

C-5

A-5

H-5

2D Stress: 0.13


response of native geranium, paspalum and slender tick-trefoil was similar to that of knob

sedge except cover was also greater in A than C at T2. Slender tick-trefoil also differed in that

cover was greater in H than A at T5. The cover of red grass was less in A than C, except at T3,

but greater in H than C at both sampling times. Red grass contributed the most to the

average dissimilarity between A and H, being less abundant in A. Slender rat’s tail grass was

consistently less abundant in A than C and only less abundant in H than C at T5, when it was

more abundant than in A. In the comparison with controls, the response of weeping grass

was the opposite in the two grazing exclusion treatments. Cover was greater in A than C at

all four sampling times and less in H than C at the two sampling times, and thus greater in A

than H at T5.

Some patterns emerged for the other species, albeit not as consistently as for the

important species, as the remainder were less abundant and more localised. Those species

more abundant in A and H relative to C were the native grass, basket grass, and the native

forbs, brown dock (Rumex browni), creeping mint (Mentha satureioides), white root (Pratia

purpurascens), pennywort, and native blue bell (Wahlenbergia gracilis) (Tables 5.15 and

5.16). The small-statured erect native forbs, pink tongues (Rostellularia adscendens) and

rock fern (Cheilanthes sieberi), were less in A and H than C until T5. Thus, despite the

increase in biomass, native species with a variety of growth forms, including small-statured

forms like erect, partial rosette and mat-forming, persisted throughout the experiment and

for some species, increased in abundance at times in both grazing exclusion treatments. The

exotic, tall-statured, woody sub-shrub, veined verbena, was consistently greater in A than C,

as was farmer’s friend (Bidens pilosa) until the final sampling time. Farmer’s friend exhibited

the same response in H compared to C. Of the remaining exotic forbs, the mat-forming

species, chilean whitlow wort (Paronychia brasiliana) and khaki weed, were less abundant in

A than C, and in H than C for chilean whitlow wort. White clover (Trifolium repens) was less

in A than C at the last two sampling times while it was greater in H than C. The two native

twining herbs, stinging nettle (Urtica incisa) and snake vine (Stephania japonica), were also

greater in A than C, consistent with the native geranium and slender tick-trefoil response,

while the exotic grass, goose grass, was less abundant in A than C.

Chap

ter

5. R

ipar

ian

Gra

zing

Exc

lusi

on E

xper

imen

t in

Gor

ge C

ount

ry 2

02

Tabl

e 5.

15 S

elec

ted

SIM

PER

outp

ut o

n th

e qu

adra

t–pl

ant

cove

r m

atri

x fo

r si

gnifi

cant

PER

MA

NO

VA p

air-

wis

e co

mpa

riso

ns b

etw

een

the

cont

rol (

C) a

nd a

ll he

rbiv

ore

excl

uded

(A

) tr

eatm

ents

. Cu

t of

f fo

r lo

w c

ontr

ibut

ions

was

90%

with

spe

cies

onl

y in

clud

ed if

pre

sent

at

two

or m

ore

sam

plin

g tim

es.

Ave

rage

abu

ndan

ce f

or C

was

su

btra

cted

fro

m a

vera

ge a

bund

ance

for

A, s

o if

posi

tive

, abu

ndan

ce h

ighe

r in

A t

han

C.

The

ten

spec

ies

that

con

trib

uted

mos

t to

bet

wee

n-gr

oup

diss

imila

rity

are

in

bold

typ

e, a

nd fi

gure

s in

par

enth

eses

are

the

ir c

umul

ativ

e co

ntri

butio

n to

the

bet

wee

n-tr

eatm

ent

aver

age

diss

imila

rity

list

ed b

elow

. '—

' ind

icat

es s

peci

es n

ot in

clud

ed

in S

IMPE

R ou

tput

at t

hat t

ime;

* d

enot

es e

xotic

spe

cies

.

Sp

ecie

s

A2–

C2

A3–

C3

A4–

C4

A5–

C5

Tim

e 2

Tim

e 3

Tim

e 4

Tim

e 5

Tim

e 2

Tim

e 3

Tim

e 4

Tim

e 5

Spec

ies

latin

nam

e co

mm

on n

ame

Diff

eren

ce in

ave

rage

abu

ndan

ce

Ave

rage

dis

sim

ilarit

y

Cont

ribut

ion

to a

vera

ge d

issi

mila

rity

(%)

Opl

ism

enus

imbe

cilli

s Ba

sket

gra

ss

0.

04

—

0.

41

0.

38

1.57

—

1.

76

0.97

3.

05

—

2.95

1.

64

Rum

ex b

row

nii

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n do

ck

0.

10

0.

33

0.

20

0.

26

0.49

1.

05

0.95

0.

96

0.96

1.

78

1.59

1.

62

Erag

rost

is b

row

nii

Brow

n's

love

gras

s

0.08

—

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.40

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4 0.

59

—

1.03

0.

66

1.15

—

1.

72

1.12

Pa

rony

chia

bra

silia

na

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lean

whi

tlow

wor

t

–0.5

2

0.03

–0

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–0.3

9 1.

21

1.17

0.

76

0.61

2.

35

1.97

1.

27

1.03

Cy

nodo

n da

ctyl

on

Com

mon

cou

ch

–0.9

9 –2

.01

–1.9

8 –1

.77

2.57

3.

03

3.00

2.

95

4.99

5.

11

5.03

4.

99

Men

tha

satu

reio

ides

Cr

eepi

ng m

int

—

—

0.

53

0.

56

—

—

1.04

0.

92

—

—

1.74

1.

55

Oxa

lis e

xilis

Cr

eepi

ng o

xalis

0.45

–0

.34

–0.5

2

0.20

1.

14

1.27

1.

34

1.28

2.

22

2.15

2.

24

2.17

Bi

dens

pilo

sa

*Far

mer

's fr

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0.61

0.27

0.45

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0.88

0.

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1.70

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1.43

1.

37

Cony

za b

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is

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0.98

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1.55

1.

37

1.69

1.

77

3.00

2.

31

2.83

2.

99

Eleu

sine

tris

tach

ya

*Goo

se g

rass

–0

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—

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9 —

0.

93

—

0.55

—

1.

81

—

0.92

—

A

ltern

anth

era

pung

ens

*Kha

ki w

eed

–0.5

1 —

–0

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—

1.43

—

0.

73

—

2.78

—

1.

22

—

Dic

hond

ra re

pens

Ki

dney

wee

d –0

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0.

10

–0.6

7 –1

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1.34

1.

49

2.41

2.

79

2.61

2.

51

4.04

4.

72

Care

x in

vers

a Kn

ob s

edge

–0

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0.

82

1.

50

1.

85

2.31

2.

67

2.31

3.

08

4.47

4.

50

3.87

5.

21

Ger

aniu

m s

olan

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N

ativ

e ge

rani

um

0.

58

1.

04

1.

12

1.

02

0.86

1.

75

2.07

2.

11

1.66

2.

96

3.47

3.

57

Erag

rost

is le

ptos

tach

ya

Padd

ock

love

gras

s –0

.66

–0.3

0 –0

.53

–0.1

7 2.

07

1.98

2.

65

2.86

4.

02

3.34

4.

44

4.84

Si

da rh

ombi

folia

*P

addy

's lu

cern

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–0.9

0 –1

.58

–1.1

1 1.

61

1.53

2.

31

2.31

3.

13

2.58

3.

87

3.90

Pa

spal

um d

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tum

*P

aspa

lum

0.58

1.18

0.72

0.67

2.

84

3.73

3.

69

3.87

5.

50

6.30

6.

18

6.55

Ro

stel

lula

ria a

dsce

nden

s Pi

nk to

ngue

s –0

.15

–0.4

9 –0

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0.

26

1.08

1.

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1.27

0.

88

2.09

1.

74

2.13

1.

48

Both

rioch

loa

mac

ra

Red

gras

s –0

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0.

53

–0.4

1 –0

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2.94

2.

79

2.60

3.

44

5.71

4.

70

4.36

5.

82

Plan

tago

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0.09

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8 –1

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1.71

1.

96

2.64

3.

45

2.87

3.

32

Chei

lant

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sieb

eri

Rock

fern

–0

.12

–0.3

6 –0

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—

0.56

0.

96

0.68

—

1.

09

1.61

1.

13

—

Spor

obol

us c

rebe

r Sl

ende

r rat

's ta

il gr

ass

–0.0

2 –0

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–0.3

9 –0

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2.14

2.

85

2.20

2.

45

4.16

4.

80

3.69

4.

15

Des

mod

ium

var

ians

Sl

ende

r tic

k-tr

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l

1.04

0.78

0.78

0.58

1.

85

1.70

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59

2.87

2.

87

3.16

G

onoc

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a sp

icat

a *S

pike

d cu

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d –0

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–1.1

3 –1

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3 1.

33

1.67

2.

09

3.12

2.

58

2.83

3.

51

5.28

U

rtic

a in

cisa

St

ingi

ng n

ettle

—

0.23

0.35

—

—

0.

91

0.88

—

—

1.

53

1.47

—

D

igita

ria s

angu

inal

is

*Sum

mer

gra

ss

–0.6

0 –0

.32

–1.2

7 –0

.22

2.80

2.

34

2.93

3.

84

5.43

3.

94

4.91

6.

49

Verb

ina

rigid

a *V

eine

d ve

rbin

a —

0.47

0.62

0.58

—

0.

97

0.92

0.

94

—

1.64

1.

55

1.59

M

icro

laen

a st

ipoi

des

Wee

ping

gra

ss

0.

59

0.

14

0.

80

0.

87

3.56

2.

43

2.71

3.

63

6.90

4.

10

4.55

6.

14

Trifo

lium

repe

ns

*Whi

te c

love

r

0.03

0.02

–0

.28

–0.6

2 0.

81

1.02

0.

76

1.09

1.

57

1.72

1.

27

1.84

Pr

atia

pur

pura

sien

s W

hite

root

—

0.53

0.17

0.45

—

0.

92

0.90

0.

70

—

1.56

1.

51

1.19

(4

7.90

) (4

3.20

) (4

4.94

) (5

4.19

)

51

.54

59.2

6 59

.67

59.1

3


Table 5.16 Selected SIMPER output for significant PERMANOVA pair-wise comparisons between the control (C) and horse excluded (H) treatments. Explanatory information in Figure 5.15 except average abundance of C was subtracted from H so if positive, abundance higher in H than C; * denotes exotic species.

H4–C4 H5–C5 Time 4 Time 5 Time 4 Time 5

Species Difference in Average Contribution to average

Species latin name common name average abundance dissimilarity dissimilarity (%)

Solenogyne bellioides A daisy 0.05 0.18 0.59 0.67 1.13 1.34

Oplismenus imbecillis Basket grass 0.13 — 1.21 — 2.32 —

Rumex brownii Brown dock –0.01 0.11 0.63 0.71 1.21 1.40

Eragrostis brownii Brown's lovegrass –0.23 0.06 1.09 0.89 2.09 1.76

Paronychia brasiliana *Chilean whitlow wort –0.55 –0.04 0.73 0.73 1.39 1.45

Einadia nutans var. nutans Climbing saltbush — –0.58 — 0.88 — 1.75

Centaurium erythraea *Common centaury 0.24 — 0.51 — 0.97 —

Cynodon dactylon Common couch –1.93 –1.67 2.75 2.74 5.25 5.43

Mentha satureioides Creeping mint 0.39 — 0.77 — 1.47 —

Oxalis exilis Creeping oxalis –0.09 –0.31 0.90 1.01 1.72 2.01

Eleusine indica *Crowsfoot grass 0.48 0.38 0.98 0.57 1.88 1.13

Bidens pilosa *Farmer's friend 0.44 –0.35 0.77 0.52 1.47 1.03

Conyza sp. *Fleabane 0.26 1.06 1.35 1.82 2.57 3.60

Eleusine tristachya *Goose grass –0.39 — 0.51 — 0.97 —

Alternanthera pungens *Khaki weed –0.51 — 0.68 — 1.29 —

Dichondra repens Kidney weed –0.26 0.15 2.30 2.31 4.39 4.57

Carex inversa Knob sedge 1.00 0.42 1.73 1.69 3.29 3.34

Wahlenbergia gracilis Native blue bell 0.41 — 0.65 — 1.24 —

Geranium solanderi Native geranium 0.76 0.47 1.42 1.26 2.70 2.50

Eragrostis leptostachya Paddock lovegrass –0.30 –0.11 2.04 2.17 3.90 4.29

Sida rhombifolia *Paddy's lucerne –1.21 –0.78 1.95 1.87 3.71 3.71

Paspalum dilatatum *Paspalum 0.44 0.50 3.14 3.00 5.99 5.96

Centella asiatica Pennywort 0.37 0.43 0.50 0.66 0.95 1.30

Rostellularia adscendens Pink tongues –0.51 — 1.03 — 1.96 —

Bothriochloa macra Red grass 1.63 1.94 2.39 3.03 4.55 6.01

Plantago lanceolata *Ribwort –1.18 –1.20 1.60 1.83 3.04 3.63

Cheilanthes sieberi Rock fern –0.05 0.09 0.90 0.94 1.72 1.85

Sporobolus creber Slender rat's tail grass 0.36 –0.21 1.82 2.04 3.47 4.05

Desmodium varians Slender tick-trefoil 1.45 1.60 2.03 2.48 3.88 4.92

Gonochaeta spicata *Spiked cudweed –1.18 –0.88 1.91 2.37 3.65 4.71

Digitaria sanguinalis *Summer grass –0.34 –0.35 3.03 3.58 5.77 7.10

Microlaena stipoides Weeping grass –0.43 –0.20 2.12 3.16 4.05 6.27

Trifolium repens *White clover 0.29 0.17 1.21 1.74 2.31 3.45

Pratia purpurasiens White root 0.13 0.58 0.77 0.90 1.48 1.78

(45.14) (53.31)

52.42 50.46


Table 5.17 Selected SIMPER output for significant PERMANOVA pair-wise comparisons between the all herbivore excluded (A) and horse excluded (H) treatments. Explanatory information in Figure 5.15 except average abundance of H was subtracted from A so if positive, abundance higher in A than H; * denotes exotic species.

A5 - H5 Time 5 Time 5 Species Difference in Average Contribution to

Species latin name common name average abundance dissimilarity average dissimilarity (%)

Oplismenus imbecillis Basket grass 0.53 0.85 1.64 Rumex brownii Brown dock 0.15 0.96 1.84 Eragrostis brownii Brown's lovegrass –0.20 0.75 1.45 Paronychia brasiliana *Chilean whitlow wort –0.35 0.56 1.09 Mentha satureioides Creeping mint 0.33 1.08 2.08 Oxalis exilis Creeping oxalis 0.51 1.33 2.57 Eleusine indica *Crowsfoot grass –0.26 0.74 1.44 Conyza bonariensis *Fleabane –2.04 3.26 6.28 Dichondra repens Kidney weed –1.33 2.33 4.48 Carex inversa Knob sedge 1.43 2.35 4.53 Geranium solanderi Native geranium 0.55 1.96 3.78 Eragrostis leptostachya Paddock lovegrass –0.06 2.51 4.85 Sida rhombifolia *Paddy's lucerne –0.33 1.36 2.63 Paspalum dilatatum *Paspalum 0.17 3.97 7.66 Centella asiatica Pennywort –0.14 0.90 1.74 Rostellularia adscendens Pink tongues 0.45 0.71 1.37 Bothriochloa macra Red grass –2.18 4.04 7.79 Cheilanthes sieberi Rock fern –0.41 0.66 1.27 Sporobolus creber Slender rat's tail grass –0.20 2.17 4.19 Desmodium varians Slender tick-trefoil –1.02 2.24 4.32 Gonochaeta spicata *Spiked cudweed –1.05 1.66 3.20 Digitaria sanguinalis *Summer grass 0.13 3.49 6.74 Verbina rigida *Veined verbina 0.40 1.09 2.10 Microlaena stipoides Weeping grass 1.07 2.67 5.14 Trifolium repens *White clover –0.79 1.23 2.37 Pratia purpurasiens White root –0.13 1.28 2.47

(55.98) 51.85


5.3.5.4 Univariate analysis: important species contributing to treatment effects

The Treatment × Time interaction was significant in analysis 1 (Table 5.18) and

analysis 2 (Table 5.19) of the percentage cover of nine and eight, respectively, of the

15 important species. Significance levels were P < 0.050 for all but slender tick-trefoil and

kidney weed (P ≤ 0.052), which can be patchily distributed leading to large variation between

sites. Some species were important contributors to cover at some sites, while absent from

others, but were regionally common. Seven species were common to both analyses

(Table 5.18 and 5.19). Treatment differences were not significant at T1 while differences

between treatments determined by pair-wise comparisons at T2–T5 were significant

(P < 0.050) unless stated otherwise.

Table 5.18 Repeated-measures linear mixed-effects model output for percentage cover of important species identified by SIMPER analysis; analysis 1; * denotes an exotic species.

Common *Fleabane Kidney Knob couch weed sedge

Source of variation df F P F P F P F P Treatment 1 3.62 0.061 0.04 0.838 1.72 0.193 11.83 < 0.001 Time 4 0.93 0.450 2.97 0.024 3.24 0.016 2.25 0.071 Treat ment × Time 4 4.59 0.002 6.29 < 0.001 2.17 0.072 4.58 0.002

Residual 81 Native *Paddy's Paddock *Paspalum

geranium lucerne lovegrass Source of variation df F P F P F P F P Treatment 1 6.75 0.011 15.88 < 0.001 0.88 0.419 2.24 0.139 Time 4 2.10 0.088 1.44 0.227 6.54 <0.001 1.43 0.230 Treat ment × Time 4 2.57 0.044 3.98 0.005 0.55 0.765 0.76 0.553

Residual 81 *Ribwort Red grass Slender Slender rat's

tick-trefoil tail grass Source of variation df F P F P Dev. P (Chi) F P Treatment 1 4.71 0.033 0.01 0.939 46.69 < 0.001 2.55 0.114 Time 4 11.39 < 0.001 6.69 < 0.001 23.57 < 0.001 2.12 0.085 Treat ment × Time 4 8.47 < 0.001 0.57 0.687 8.58 0.051 0.23 0.918 Residual 81 90

*Spiked *Summer Weeping cudweed grass grass

Source of variation df F P F P F P Treatment 1 37.05 < 0.001 1.00 0.319 17.54 < 0.001 Time 4 2.73 0.034 1.98 0.106 5.79 < 0.001 Treat ment × Time 4 5.94 < 0.001 1.54 0.198 2.49 0.049

Residual 81


Table 5.19 Repeated-measures linear mixed-effects model output for percentage cover of important species identified by SIMPER analysis; analysis 2; * denotes exotic species.

Common *Fleabane Kidney Knob couch weed sedge

Source of variation df F P F P F P F P Treatment 1 1.96 0.151 9.49 < 0.001 2.64 0.081 3.32 0.043 Time 4 1.35 0.267 1.04 0.381 5.07 0.004 3.14 0.033 Treatment × Time 4 3.25 0.008 5.75 < 0.001 2.24 0.052 2.78 0.019

Residual 81 Native *Paddy's Paddock *Paspalum

geranium lucerne lovegrass Source of variation df F P F P F P F P Treatment 1 3.03 0.057 7.13 0.002 1.79 0.175 1.30 0.281 Time 4 12.23 < 0.001 1.20 0.319 3.55 0.021 1.06 0.372 Treatment × Time 4 4.03 0.002 0.52 0.788 0.58 0.747 0.72 0.638

Residual 81 *Ribwort Red grass Slender Slender rat's

tick-trefoil tail grass Source of variation df F P F P Dev. P (Chi) F P Treatment 1 0.85 0.433 0.09 0.916 2.64 0.081 1.79 0.175 Time 4 18.47 < 0.001 4.12 0.010 5.07 0.004 3.55 0.021 Treatment × Time 4 3.26 0.008 1.83 0.110 2.24 0.052 0.58 0.747

Residual 81 90 *Spiked *Summer Weeping

cudweed grass grass Source of variation df F P F P F P Treatment 1 6.00 0.004 0.44 0.650 2.34 0.105 Time 4 3.69 0.017 1.53 0.218 5.49 0.002 Treatment × Time 4 2.44 0.037 1.17 0.333 0.98 0.446 Residual 81

The following species generally declined in the grazing exclusion treatments, while

cover was either maintained or increased in controls. Bubbles in MDS bubble plots were

smaller and fewer surrounding grazing exclusion centroids positioned to the left of the MDS

diagram. Consistent with the SIMPER analysis, percentage cover of common couch in A was

less than C by T2, levelling out at T3–T5, whereas in H, the same response compared to C

occurred at T4, when cover plateaued at <1.0% (Figure 5.19). Its cover remained constant in

controls, ranging from 6.4–7.4% over T2–T5. Cover of three of the four exotic forbs, ribwort,

spiked cudweed and paddy’s lucerne, exhibited a similarly strong response to grazing

exclusion (Figures 5.20–5.22). In analysis 1, all three species declined to <1.0% and cover was

less in A than C at T3–T5. Trends and results for ribwort and spiked cudweed for H in

analysis 2 were similar to A in analysis 1. The main difference was that spiked cudweed was

only significantly less abundant in H than C at T5. Similarly, for paddy’s lucerne, H was

consistent with the response of A in analysis 1, but, differences between H and C were not


significant. Fleabane fluctuated in all treatments with the exception of T5 when cover

dropped to 1.0% in complete exclusion quadrats (Figure 5.19). Fleabane was the only species

where the two grazing exclusion treatments had disparate responses. Cover peaked in H,

rather than declining, and was greater than A and C at T5, although only the A–H comparison

was significant.

The following species generally increased in the grazing exclusion treatments, while

cover was either maintained or declined in controls. Bubbles in MDS bubble plots were

larger and greater in number surrounding grazing exclusion centroids positioned to the left of

the MDS diagram. Cover of knob sedge did not differ between treatments at T1, and the

response of A and H at subsequent sampling times was similar (Figure 5.20). Knob sedge

progressively increased in A and H, whereas in C, it declined from T3 to T4. Cover was

greater in A and H than C at T4 and T5. The response of native geranium was consistent with

knob sedge, with greater cover in both grazing exclusion treatments compared to controls at

T4 and T5 (Figure 5.20). In analysis 1, slender tick-trefoil increased substantially from T1 to

T2 with greater cover in A than C at T2, T4 and T5, while cover in C changed little over time

(Figure 5.21). The species response in H was consistent with the response in A in analysis 1,

although cover was greater in H than C and A at T4, and only greater than C at T5.

The Treatment × Time interaction was significant in one analysis for two species. The

interaction for weeping grass was significant in analysis 1, however, pair-wise comparisons

indicated this was due to an artifact of design and not an effect of grazing exclusion

(Figure 5.22). Cover was greater in A than C at the beginning of the experiment and

remained greater until the final sampling time. The significant Time main effect in analysis 2

indicated that the species was in general more abundant after T1. The interaction for kidney

weed was significant in analysis 2. Cover of kidney weed increased in controls over time and

was more abundant in general in C, than A and H after T3, although increases tended to be

large and localised to a few sites (Figure 5.19). Cover was only significantly greater in C than

A at the final sampling time, when cover in A had decreased from T4.

Only the Time main effect was significant in both or one analysis for paddock

lovegrass, red grass and slender rat’s tail grass (Table 5.18 and 5.19). Cover of paddock

lovegrass was less at T3 than all other sampling times in analysis 1 and T3 and T4 in analysis 2

(Figure 5.20). Cover was greater at the start of the experiment (T1) than at T3 and T4 in

analysis 2, but cover did not differ between June 2006 and June 2008. Red grass was more

abundant in June 2008 (T5) than June 2006 (T1) and at T5 compared to T3 and T4 in both


analysis 1 and 2. In analysis 1, cover was also greater at T2 than T5. Slender rat’s tail grass

was more abundant at T3 than T1, T4 and T5 in analysis 2 only, mostly because of the high

cover in the horse exclosure treatment. Paspalum consistently contributed 5% or more to

dissimilarity in the A–C and H–C comparisons with average abundance consistenly less in

controls than H and A (Tables 5.15 and 5.16). It also tended to be greater in A than C in

univariate plots (Figure 5.22). However, the Treatment × Time interaction was not significant

(Tables 5.18 and 5.19) because the increase in cover was localised to a few sites.


Figure 5.19 Mean (±1 S.E.) percentage cover of the important species identified by SIMPER. Analysis 1 (A1) bar graphs show cover for controls versus all herbivore-excluded exclosures at all five sampling times (T1–T5); * above the bar denotes the treatment in which cover was significantly greater (n = 10; P < 0.050). Analysis 2 (A2), seperated from A1 by a dotted line, shows cover for the additional treatment, horse-excluded exclosures, versus treatments in A1 with less replication and without T2; * denotes a significant difference between treatments at a sampling time as specified in the notation above the set of treatment bar graphs (n = 6; P < 0.050). Notation in bar graphs without * specifies Time main effect. Treatment main effect are specified by a single statement of how, and which treatments differ and a * above all sets of treatment bar graphs. Species bar graph is accompanied by a species MDS bubble plot overlaid on centroid MDS. Bubble size increases with greater percentage cover and the largest bubble represents a mean of 10% cover per sample for square-transformed data. Number of bubbles indicates relative species incidence, for example, fewer bubbles suggest species distribution is localised and species is present in a few quadrats, and a greater number of bubbles suggests species is relatively widespread or common in quadrats. C: control, H: horse excluded, A: all herbivore excluded. T1: June 2006, T2: April 2007, T3: November 2007, T4: November 2007, T5: June 2008.

0

5

10

15

20

T1 T2 T3 T4 T5 T1 T3 T4 T5

Perc

enta

ge co

ver

Common couch

C H A

A < CA < H

A1 A2

**

* *

A > H

*

*

A < CH < C

A < CH < C

* * C-1H-1

A-1

C-2

A-2

C-3A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

2D Stress: 0.14

0

2

4

6

8

T1 T2 T3 T4 T5 T1 T3 T4 T5

FleabaneA 1 A 2

*

*

A < CA < H

C-1H-1

A-1

C-2

A-2

C-3A-3

H-3

C-4A-4

H-4 C-5A-5

H-5

0

2

4

6

8

10

12

14

16

18

T1 T2 T3 T4 T5 T1 T3 T4 T5

Kidney weedA 1 A 2

*A < C

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4 C-5A-5

H-5


Figure 5.20 Mean (±1 S.E.) percentage cover of the important species identified by SIMPER continued with notation and explantory information provided in Figure 5.19.

0

5

10

15

20

25

T1 T2 T3 T4 T5 T1 T3 T4 T5

Perc

enta

ge co

ver

Knob sedge

C HE AE

A 1 A 2

*

* A > CH > C

*

A > CH > C

*

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4 C-5A-5

H-5

0

2

4

6

8

10

T1 T2 T3 T4 T5 T1 T3 T4 T5

Native geraniumA 1 A 2

* *

*

*A > CH > C

A > CH > C

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

2

4

6

8

T1 T2 T3 T4 T5 T1 T3 T4 T5

Paddy's lucerneA 1 A 2

A < C

*

**

**

* *C-1

H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

5

10

15

20

T1 T2 T3 T4 T5 T1 T3 T4 T5

Paddock lovegrass

A 1 A 2

T1 > T3,T4T3 < T4,T5

T3 < T1,T2,T4,T5

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5


Figure 5.21 Mean (±1 S.E.) percentage cover of the important species identified by SIMPER continued with notation and explantory information provided in Figure 5.19.

0

5

10

15

20

25

30

T1 T2 T3 T4 T5 T1 T3 T4 T5

Perc

enta

ge co

ver

Paspalum

C HE AE

A1 A2

C-1

H-1A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

2

4

6

8

10

T1 T2 T3 T4 T5 T1 T3 T4 T5

RibwortA 1 A 2

* * ** *

*

A < CA < HH < C A < C

H < C A < CH < C

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

5

10

15

20

25

30

T1 T2 T3 T4 T5 T1 T3 T4 T5

Red grassA 1 A 2

T2 > T5 > T1,T3,T4 T5 > T1,T3,T4

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4 C-5A-5

H-5

0

2

4

6

8

10

12

T1 T2 T3 T4 T5 T1 T3 T4 T5

Slender tick-trefoil

*

A 1 A 2

* *

*

*

A < HH > C

H > C

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5


Figure 5.22 Mean (± 1 S.E.) percentage cover of the important species identified by SIMPER continued with notation and explantory information provided in Figure 5.19.

0

5

10

15

20

25

T1 T2 T3 T4 T5 T1 T3 T4 T5

Perc

enta

ge co

ver

Slender rat's tail grass

C HE AE

A 1 A 2

T3 > T1,T4,T5

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

2

4

6

8

10

12

T1 T2 T3 T4 T5 T1 T3 T4 T5

Spiked cudweedA 1 A 2

*

*

*

A < C

*

*

*

A < C

A < CH < C

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

5

10

15

20

25

T1 T2 T3 T4 T5 T1 T3 T4 T5

Summer grassA1 A2

C-1H-1

A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4C-5

A-5

H-5

0

5

10

15

20

25

30

35

T1 T2 T3 T4 T5 T1 T3 T4 T5

Weeping grassA 1 A 2

**

* *T3 > T4,T5 > T1

C-1

H-1A-1

C-2

A-2

C-3

A-3

H-3

C-4A-4

H-4

C-5A-5

H-5


5.4 DISCUSSION

5.4.1 Herbivore dung counts and activity

When exclosures were constructed in June 2006, the number of horse dung deposits

in exclosure areas did not differ between treatments. After June 2006, the number of horse

deposits in both grazing exclusion treatments declined to zero and horse dung was not

recorded thereafter. Horse dung continued to be recorded in 70% or more of the control

plots after June 2006 and mean number of deposits did not differ between sampling times.

Horse dung was present in all destroyed horse exclosures and the average number of

deposits did not differ from controls. Thus, the six horse exclosures repaired and monitored

after June 2007 were possibly grazed by horses to the same extent as controls for up to

1 year before they were repaired. No macropod and cattle dung were present in treatment

plots. According to the NPWS Wildlife Atlas (NSW NPWS 2009a), other herbivores historically

recorded in the vicinity of the exclosure sites were possum species, the brown hare (Lepus

capensis) and the european rabbit (Oryctolagus cuniculus). Brown hare and european rabbit

dung was not observed in treatment plots or on site-scale dung transects during baseline

monitoring, nor was there any evidence of rabbit warrens on the riparian flats. On this basis,

dung of the two lagomorph species was not included in the systematic counts of herbivore

dung on dung transects after June 2007, and none was visible in treatment plots for the

duration of the experiment. Possum dung was found occasionally in, and around a

treatment plot, but mostly in plots adjacent to trees and the diet of the two prevalent

possum species precluded them from having a notable grazing impact on groundstorey

vegetation. The common ring-tail possum (Pseudocheirus peregrines) is folivorous and

common brush-tail possum (Trichosurus vulpecula) predominantly folivorous, but may be

classified as omnivorous as it occasionally consumes fungi and grasses (Crowe and Hume

1997; Shepherd et al. 1997). As discussed previously in Chapter 3, horse dung was common

on site-scale and district-scale dung transects, whereas cattle dung was absent and

macropod dung rare. One brown hare observed on a riparian flat was the only record during

the study whereas horses were observed every day that plots were monitored. Dung counts

and transects and visual sightings suggested horses were the dominant, if not sole, large

herbivore grazing the riparian flats. The comparisons between controls and both grazing

exclusion treatments were therefore attributed to horses.


5.4.2 Biomass and bare ground

Biomass in control quadrats did not increase during the experiment, but declined

compared to T1 in the autumn (April 2007) and spring (February 2008). Conversely, biomass

consistently accumulated under complete exclusion and biomass was greater than in controls

at T2 until the final sampling time. Biomass in horse exclosures also progressively increased

and was greater than controls in the last 7 months of the study. The difference was

significant within 9 months of exclosure repairs, when time since exclusion was reset to

baseline. This was comparable to the response of complete exclusion, where differences

with controls were significant within 10 months of exclusion. Differences in biomass

accumulation between controls and complete exclusion peaked at the end of the summer

growing period with biomass 25 times greater under complete exclusion after 2 years; visual

differences in the height of vegetation were striking (Figures 5.8–5.13). Bare ground did not

vary in controls over the 2 years and was possibly an impact that is in equilibrium with the

current horse pressure in Bobs Creek. Recovery of vegetation cover, however, was

comparatively rapid with no bare ground present in horse exclosures after 6 months and

after 10 months in complete exclusion quadrats. Observations and biomass results indicated

that low-biomass, short-statured grazing lawns were the dominant grassland structure on

riparian flats outside exclosures, dispersed with small patches of tall, senescing tussock

vegetation (Figures 5.3, 5.5, 5.8, 5.10). Feral horses have been associated with the creation

and maintenance of extensive areas of grazing lawns. For example, 6 months after a harem

group was released into a 50-ha area at Lake Pape, Latvia, grazed patches with a lawn-like

grass constituted 22% of total grazing surface area, increasing to 49% after an additional year

(Prieditis 2002; Lamoot et al. 2005; van Wieren and Bakker 2008). Larger patches of tall

ungrazed vegetation often arise from patches of unpalatable species (Duncan 1992) as was

observed in this experiment with fleabane, blackberry (Rubus fruticosus aggreg.) and paddy’s

lucerne.

Biomass and the height of vegetation were greater under complete exclusion than in

horse exclosures, mostly because the horse exclosures were destroyed after baseline data

was obtained and were grazed by horses for an additional 12 months prior to most being

repaired. Horses appeared to continue to graze a few of the horse exclosures that were

repaired and monitored, with edge effects periodically contributing to less biomass. Wire

strands had been bent and lost tension, and vegetation within 1 m of the edge was as short

as the control plots, and occasionally grazed, shorter patches extended into the


2 × 2-m monitoring quadrat. Treatment plots were small in order to fit three treatments into

the one site and to maximise replication with the funds available. Larger treatment plots

20 × 20 m had been trialled in Bobs Creek riparian flats previously, with one plot per flat, but

were found to obscure access to the open flat resulting in the destruction of all plots,

including the fully enclosed, wire mesh exclosures and the death of at least one horse (Schott

2002). The lower rate of increase in biomass in horse exclosures from February 2008 to June

2008 may have also been influenced by the destruction of three exclosures at sites that were

more productive than the three remaining sites.

The horse exclosure treatment was included to differentiate the effect of horses from

other herbivores, yet there was no evidence that macropods, lagomorphs or grazers other

than horses, utilised riparian flats. The complete exclusion treatment was a valid comparison

with heavily grazed controls to test for the effects of horses on plant and cover variables.

The horse exclosure treatment was influenced by the shorter exclusion time that applied

equally to all sites, and the interaction between edge effects, that varied spatially between

sites and temporally within a site. Edge effects and exclusion time did not influence bare

ground, and the significant biomass result confirmed horses grazed and consumed most of

the available forage biomass on riparian grassland flat adjacent to Bobs Creek, resulting in a

loss of plant cover, increased cover of bare ground and modified vegetation structure.

5.4.3 Richness measures

Total richness increased in April 2007 in complete exclusion quadrats due to

increases in native and perennial richness. In controls, only perennial richness increased.

SIMPER analysis identified 19 native perennial species present in complete exclusion

quadrats, but absent in controls, as opposed to just two native perennial species present in

controls, and absent in complete exclusion quadrats. Annual richness declined sharply in

both treatments while exotic richness changed little over the same period, suggesting that

good rainfall or seasonal conditions were not a factor. It appeared native perennials were

responding to the short-term (within 10 months) release from horse grazing. Evidence was

strongest for those species absent from controls for the duration of the study and not

present in complete exclusion quadrats at the start of the experiment. Seven species

exhibited such a response, of which five, austral bugle, forest hedgehog grass, large

tick-trefoil, variable glycine and stinkweed, have been documented as sensitive to

disturbance and decreasers under grazing (Lodge and Whalley 1989b; McIntyre et al. 2003;


Dorrough et al. 2004; Hill et al. 2005). In the case of the legumes, these are palatable and

sensitive to over-grazing (McIntyre et al. 1995; McIntyre et al. 2003). As bare ground or gaps

in cover decreased and soil disturbance and nutrient enrichment ceased—conditions in

which exotic annual herbs are adapted to exploit as ruderal species with high levels of seed

dormancy, short life histories and early flowering (Grimes 1979)—native perennial species

may have outcompeted exotic annual species in the first 10 months. Once released from

horse grazing, their return suggested a ready source of reproductive material either from the

soil seed bank, underground storage organs or seed rain, as in other studies (Pettit et al.

1995; Yates et al. 2000; Pettit and Froend 2001). Other studies have found no effect of

exclusion on native richness, and that the soil seed bank in ungrazed plots was dominated by

a small number of species, both native and exotic, suggesting compositional changes were

constrained by propagule shortages (Lunt 1990a, 1997; Morgan 1998b; Kenny 2003; Lunt et

al. 2007b). These studies were conducted in semi-arid riparian ecosystems and under dense

tree cover or in a kangaroo grass-dominated grassland, and the authors conceded that

low-fertility soils, dry conditions and competition for resources from trees may have

constrained germination and thus richness and composition changes.

After April 2007, exotic and annual richness mirrored seasonal trends in total species

richness in control quadrats, peaking in spring, decreasing slightly in summer, with the

greatest decrease in winter. The role of seasonal variation in the response of individual

treatments and in detecting treatment differences was limited by only one sampling time for

autumn, summer and spring. However, baseline data were collected in winter (June 2006),

as was the final sampling time (June 2008), thus differences after 2 years can be attributed to

treatment responses. Total, exotic, annual and perennial richness in controls did not differ

between June 2006 and June 2008. Conversely, in complete exclusion quadrats, annual

richness had decreased to less than baseline within 10 months and exotic richness within

17 months, and continued to decline thereafter. Thus, richness measures suggested the

reduction in total richness at 2 years in complete exclusion quadrats was due to the response

of exotic and annual plants, as native and perennial richness did not differ between June

2006 and June 2008. With regard to treatment differences, total species richness was

greater in controls than under complete exclusion in November 2007 and February 2008,

when annual and exotic richness was greater in controls. Native richness only differed

between treatments when richness had increased and was greater under complete exclusion

in April 2007. Thus, greater perennial richness in controls in February 2008 was inflated by


exotic species and the greater total species richness in spring (November 2007) and summer

(February 2008) primarily driven by exotic and then annual richness.

The response of horse exclosures did not differ from controls and complete exclusion

for total, annual and perennial richness. Exotic richness was consistently greater in horse

exclosures than in complete exclusion after horse exclosures were reset, but exotic richness

was greater in complete exclusion than other treatments at baseline. Native richness was

greater in horse exclosures than both treatments throughout the experiment, as indicated by

the significant Treatment effect. As edge effects were not systematically assessed in

conjunction with biomass and richness measures, it is difficult to comment on the response

of horse exclosures to most richness measures. However, the percentage of native species

was consistent with the response of complete exclusion quadrats in relation to controls. The

percentage of natives did not vary between sampling times in horse exclosures, but peaked

at 77% in February 2008, and was greater in horse exclosures than controls in the last

5 months of the experiment. In complete exclusion quadrats, the percentage of natives

rapidly increased from a baseline value of 58% to 66% within 10 months, and was greater

than controls thereafter, the percentage increasing to 78% at 2 years. The percentage of

natives in controls did not vary until it decreased to 57% in June 2008, less than June 2006.

Native richness also declined in controls after 2 years.

SIMPER analysis was used to identify the growth forms, origin and life cycle of species

driving the greater total richness in control quadrats in November 2007 and February 2008,

and to evaluate the concern about species diversity declining in the absence of biomass

removal (Olff and Ritchie 1998; Huisman et al. 1999). Biomass accumulation and vegetation

height by November 2007 was sufficient for superior light competitors to smother

smaller-statured or prostrate plants and account for the reduction in total species richness

(Olff and Ritchie 1998; Huisman et al. 1999), as predicted by the two-factor model of

Lunt et al. (2007b). Moderately grazed communities with a long evolutationary history of

grazing often have high diversity because of the mosaic of growth forms, as a heterogeneous

canopy structure leads to multiple modes of regrowth by either horizontal spread or vertical

re-establishment (Milchunas et al. 1988; Milchunas and Lauenroth 1993). Pooled across both

sampling times, 12 of the 19 species present in controls and absent in complete exclusion

were exotic, eight were annual and seven were exotic and annual species. In contrast, under

complete exclusion, 11 of the 12 species unique to that treatment were a mixture of native

perennial grasses and forbs. Thus, exclusion favoured native perennials and horse grazing


exotic annuals, such as scarlet pimpernel, liverseed grass, goose grass, and exotic perennials

such as crowsfoot grass, khaki weed, paddy’s lucerne, and ribwort. Goose grass, liverseed

grass and khaki weed, unique to controls at both sampling times, were present in

approximately half of the complete exclusion quadrats prior to November 2007. Failure to

record these species in November and thereafter implies these species were eliminated by

competitive exclusion, as they persisted or were recorded at subsequent sampling times in

controls. The native perennial forb, Swainsonia sp., and the small-statured, native annual

forb, jersey cudweed, may have also been lost due to competitive exclusion as they were

present in complete exclusion quadrats in the previous sampling times, and were not

recorded again. However, these species were not common in the complete exclusion

treatment, being present in two and one quadrats, respectively, and only Jersey cudweed

was present earlier in controls with greater frequency and persisted. Thus the competitive

exclusion effect on these two species was not as strong as for the previous mentioned exotic

annual species. The remaining three native perennials, including the small-statured daisy

Solenogyne bellioides, were not absent due to competitive exclusion as they were only

recorded in controls and were unique to that treatment in November 2007 as a result of a

random effect of plot selection. Of the seven natives that responded to release from grazing

pressure in April 2007, five species, large tick-trefoil, windmill grass, stinkweed, austral bugle

and Swainsona sp., were not recorded in complete exclusion quadrats again, and possibly

had insufficient time to establish before competitive exclusion set in the following

November 2007. Wonga wonga vine is a vigorous, fast-growing twiner, and forest hedgehog

grass a tall tussock species, with tussocks usually adapted to light competition; this may

explain why these two species persisted after April 2007 (Olff and Ritchie 1998; Huisman et

al. 1999; Urquhart 1999).

The competitive exclusion effect of excluding horse grazing was not a concern from a

regional biodiversity conservation viewpoint as all species excluded were not riparian

specialists and were common in upland areas of GFRNP, such as Paddys Plateau, and in the

general Northern Tablelands region (Trémont 1994; Hunter and Hunter 2003 and references

therein). The majority (13 species) of the 19 native perennial species released from grazing

pressure persisted in complete exclusion quadrats. Consistent with the principles underlying

competitive exclusion and plant traits associated with heavy grazing, short-statured erect

forbs were more common in grazed controls, as small stature is associated with ephemeral,

gap colonising species and may reduce the ability of grazers to detect plants (Moore and


Biddiscombe 1964b; Belsky 1992; Dorrough et al. 2004). When gap formation and light is

reduced and grazing removed, a few small-statured forbs were susceptible to competition

from taller graminoids under complete exclusion, where twiners and climbers were the most

persistent growth form by way of attaching themselves and growing in conjunction with

taller species (Trémont 1994). However, comparisons between controls and both grazing

exclusion treatments in all SIMPER analyses indicated that the less abundant, native

perennial plants that persisted in the absence of horse grazing had a diversity of growth

forms, including erect forbs (e.g, brown dock, native blue bell, pink tongues, rock fern),

mat-forming forbs (e.g. pennywort, creeping mint, native wandering jew, white root) and

tufted grasses (e.g. hairy panic, windmill grass). The effect of horses was consistent with that

reported for low-intensity livestock grazing in Australian grassy woodlands and high-intensity

horse grazing in the Camargue (Duncan 1992; Prober and Thiele 1995; Dorrough et al. 2004).

Horse grazing promoted a suite of grazing-tolerant species, mostly exotics and to a lesser

extent annual plants, to the potential long-term detriment of the persistence of native plants

as native richness and the percentage of native species had declined in control quadrats after

2 years, consistent with the observations of Moore (1962) and Moore and Biddiscombe

(1964b). Exclusion of horses appeared to have the reverse effect of horse grazing in the

short term, with greater native perennial diversity within 10 months and maintenance of

native perennial diversity over 2 years without much reduction in the variety of growth

forms.

5.4.4 Initial level of site degradation and floristic composition

The two-factor model (Lunt et al. 2007b) was not accompanied by guidelines relating

to initial site condition. While the term ‘degraded grassland’ is commonly used, it is rarely

defined beyond a successional shift to weedy non-native plants, perhaps because it is often a

value-laden, subjective concept that varies between different environments (Gibbons and

Freudenberger 2006). In applying the model in less-productive semi-arid riparian grassland,

highly degraded sites had an initial average total plant cover of 47%, and annual species

made up 87% of this cover, and comprised 50% of total species richness while 45% of total

species were exotic (Lunt et al. 2007b). Federal government guidelines for listing threatened

temperate grasslands require a good quality grassland to have 50% or more of the total

vegetation cover comprised of native perennial tussock or tufted grasses, non-grassy weeds

accounting for less than 30% of the total vegetation cover at any time of the year and a


greater proportion of native than exotic species (Austin et al. 2000; DEWHA 2008). Bobs

Creek riparian flats had an initial average total plant cover of more than 95%, native species

comprised 71% of this cover, and perennials 84%. Native richness was twice, and perennial

richness four times, greater than exotic and annual richness, respectively. Cover of grasses

and sedges was 72%, of which 47% approximated the criteria for native perennial tufted

grasses and sedges. Techniques for assessing riparian condition also often consider cover of

grazing-resistant species and frequency of high impact weed species (Jansen and Robertson

2001; Jansen et al. 2004; Gibbons and Freudenberger 2006 and references therein). Several

invasive weeds were present at T1, such as noogoora burr, farmer’s friend, blackberry and

fleabane, but none had obtained dominance, with cover less than 2% on average.

Grazing-resistant grasses dominated baseline cover. Moore (Moore 1970, 1973)

hypothesised that Australian grasslands were characterised by tall, caespitose, warm-season

perennial grasses prior to European settlement. Native graminoids contributing most to

cover (i.e. the ten most abundant species, comprising 70.3% of cover) at T1 were paddock

lovegrass (ranked second most cover-abundant), red grass (third), knob sedge (sixth) and

slender rat’s tail grass (eighth). The persistence of these native tufted species was consistent

with their classification as ‘increasers’ under grazing pressure, although knob sedge can have

a variable response to different grazing intensities (Dyksterhuis 1949; Lodge and Whalley

1989b; McIntyre and Martin 2001; Dorrough et al. 2004). Although all species have a tufted

growth form, they have moderate forage value and exhibit attributes typical of adaptations

to grazing in subhumid grassland communities with a long history of ungulate grazing in the

model of Milchunas et al. (1988). Red grass and paddock lovegrass adopt prostrate growth

habits when heavily grazed, are resistant to trampling and can increase under disturbed soil

conditions and improved soil fertility (Lodge and Whalley 1989b). Paddock lovegrass also

shows increased growth in response to grazing to prevent taller tussocks shading it out (NSW

DPI 2010), while slender rat’s tail grass has mostly hard textured, basal leaves and poorer

nutritive qualities that reduces palatability (NSW DPI 2010). The shift from tall to shorter or

prostrate grasses was the first stage in Moore’s successional sequence (Moore and

Biddiscombe 1964b) and graminoids with a rhizomatous and stoloniferous mat-forming

growth habit, or prostrate growth habit under heavy grazing, accounted for 44.1% of the

cumulative contribution of the first ten species to cover (70.3%). The exceptions were the

forb, ribwort, weeping grass and slender rat’s tail grass, but all ten species (except knob

sedge) were ‘increasers’ under grazing where their response to grazing was known (Lodge


and Whalley 1989b; McIntyre et al. 1995; Dorrough et al. 2004). Mat-forming forbs such as

creeping oxalis, kidney weed, pennywort, basket grass, creeping mint varied in abundance,

but in combination with grasses and gap colonising annuals, may have contributed to soil

surface stability and consistent levels of bare ground in controls over 2 years. The initial level

of site degradation contrasted with the high degradation state of Lunt et al. (2007b) and was

within government guidelines for grassland of relatively good conservation quality.

5.4.5 Changes in cover and composition over time

Incorporating the abundance of grazing-tolerant species, the intitial site condition of

Bobs Creek riparian flats was broadly classified as medium to low degradation, corresponding

more to the low than high degree of initial degradation in the model of Lunt et al. (2007b).

As predicted by the model, native dominance was maintained as exclusion and grazing by

horses had no effect on the cover of native species over 2 years in Bobs Creek. The result of

excluding horses in riparian flush zones in New Zealand was also consistent with the model of

Lunt et al. (2007b). The New Zealand hard tussock (Festuca novae-zelandiae) grasslands

were highly modified prior to experimental manipulation and exclosures excluding feral

horse grazing were physiognomically dominated by adventive grasses in the short term

(5 years), and a mixture of adventives grasses and red tussock (Chionochloa pallens) at the

expense of the desirable hard tussock and prostrate native herbs in the medium term

(15 years) (Rogers 1991, 1994). The model of Lunt et al. (2007b) did not incorporate an

assessment of composition, however, that proved informative in explaining why treatment

differences in floristic composition were driven by a fewer number of abundant or dominant

grasses than forbs.

In all treatments, the persistence of native dominance was partly linked to the

aforementioned grazing-resistant qualities of the abundant native grasses, which may

explain why treatment differences over time were not detected for cover of weeping grass,

red grass, slender rat’s tail grass and paddock lovegrass in univariate analyses. In controls,

the cover of other abundant native graminoids, such as common couch and knob sedge, did

not change throughout the experiment, nor did the cover of abundant exotics, such as

paspalum and summer grass, contributing to the preservation of the native–exotic cover

relationship detected at T1. Similarly, in both grazing exclusion treatments, the reduction in

the cover of common couch and increase in cover of knob sedge may have maintained native

cover. The response was strongest in complete exclusion, with the cover of common couch


declining over the duration of the experiment from 8.3% to 1.1%, and was less than cover in

controls within 10 months of exclusion. In horse exclosures, the species declined from

2.8% to 0.8%, differing from controls within 5 months of exclosure repairs. Common couch

was the only abundant grass to decline under grazing exclusion, as it tends to colonise bare

ground and spread laterally via vegetative reproduction, with a maximum height of 30 cm as

opposed to 70–120 cm for the other native warm-season perennial graminoids and the

exotic perennial grass, paspalum (Kahn and Heard 1997; Jacobs et al. 2008). Conversely,

cover of knob sedge increased in both exclusion treatments from a baseline cover of <5% to

10–15% during the last 4 months of the experiment, when it was also greater than the <5%

cover in controls. Knob sedge tolerates intermediate levels of cattle grazing in temperate

grasslands in southern Australia and in subtropical pastures, but decreases under frequent

grazing (McIntyre et al. 2003; Dorrough et al. 2004).

Knob sedge may be preferentially selected by horses, given the evidence from the

European wetland horse re-introductions. Dominant tall perennial graminoids

(e.g. Phragmites, Spartina alterniflora) that are sensitive to grazing because their apical

meristem is located at elongated internodes (therefore raising the meristems above

ground level), are reduced in height and biomass when horses are re-introduced (Duncan

and D'Herbes 1982; Turner 1987; Wood et al. 1987; Hay and Wells 1991; Furbish and Albano

1994; Maitland and Morgan 1997). Facultative seeder/resprouters persist under heavy

grazing better than obligate seeder species or resprouter species, which have a single

reproductive strategy (Bell et al. 1984; Pettit et al. 1995). The vegetative reproductive

capacity of knob sedge leads to rapid reproduction and enhanced gap filling ability similar to

other Carex species (Carlsson and Callaghan 1990), and may partly explain why this was the

only common and abundant tufted perennial graminoid or grass species to show a significant

positive effect of grazing exclusion. Paspalum is a tall tussock with vegetative reproduction

and also tended to increase under complete exclusion, but the effect was not significant.

Summer grass had greater baseline cover than knob sedge and paspalum, and is capable of

vegetative reproduction, but its cover did not change over time in any treatment. The

species can grow to 70 cm in height, but is an annual grass (Jacobs et al. 2008).

The response of summer grass to exclusion and the failure of cool-season or

warm-season exotic annuals to dominate warm-season native perennial grasses, even under

heavy grazing, contrasts with Moore’s models (Moore 1959, 1967) and with grazed winter-

rainfall temperate woodlands in southern Australia (Hobbs and Huenneke 1992; Pettit et al.


1995). However, it is consistent with subtropical and tropical woodlands in northern

Australia (McIntyre and Lavorel 2001; McIntyre and Martin 2001) and for the New England

Tablelands region (Clarke 2003). Clarke (2003) explained these differences by suggesting that

climate modifies the impacts of grazing by enhancing the competitive advantage of native

perennial grasses (Wilson 1990a; Fensham et al. 1999), thus altering the opportunities for

annuals to outcompete perennials. The perennial life history allows plants to make use of

soil moisture following summer rain in the New England region as well as winter soil

moisture that enables them to maintain longer growing seasons and compete successfully

with annuals that may have higher assimilation rates (Dorrough et al. 2004). In

winter-rainfall woodlands, native perennial species may not be able to survive summer

drought, defoliation and competition from exotic annuals (Clarke 2003).

In terms of forbs, almost all the annual exotics decreased in abundance in response

to grazing exclusion towards the conclusion of the experiment, consistent with the richness

responses. The exception was fleabane in the final winter sampling time, with greater cover

in horse exclosures and controls than complete exclusion. This was not surprising,

considering horse exclosures were 12 months behind complete exclusion and fleabane is a

winter growing species and had a high starting abundance. Spiked cudweed had greater

baseline cover in both grazing exclusion treatments, yet under complete exclusion in

particular, was almost eliminated after 2 years. Spiked cudweed under horse grazing had

obtained local seasonal dominance at some grazed sites with cover up to 30%. The cover of

annual plants declined prior to the first post-baseline sampling time (April 2007), and cover

halved within 2 years in both grazing exclusion treatments. Annual cover also declined in

controls prior to April 2007, but increased thereafter and was greater than complete

exclusion in the last 5 months of the experiment. The declines in cover of fleabane and

spiked cudweed contributed to the reduction in cover of annual plants in both grazing

exclusion treatments, as did the exotic annual grasses, goose grass and summer grass. In the

SIMPER comparisons for significant PERMANOVA pair-wise tests, goose grass and summer

grass were consistently less abundant in grazing exclusion treatments relative to controls,

although differences were not significant in univariate analyses.

Similar results were obtained for the perennial ribwort: both species have the same

small-statured partial rosette growth form and colonise gaps. A further positive result was

the reductions in abundance of fleabane and paddy’s lucerne within 10 months in both

grazing exclusion treatments. Both species are prominent tall weedy sub-shrubs in riparian


zones in GFRNP, as is farmer’s friend, which was also lower in abundance in both grazing

exclusion treatments. Its cover was low in general and it was uncommon and not an

important species. Of the three important native forbs, cover for both of the perennial

climbers or twiners, slender tick-trefoil and native geranium, increased in both grazing

exclusion treatments, while cover of the perennial mat-forming kidney weed increased under

horse grazing. Grazing response patterns based on life-history attributes for the important

species and richness measures were consistent with that found by Trémont (1994) for nearby

Northern Tablelands grassy woodlands after 1 year of relief from grazing.

5.4.6 Conclusions and management implications

The grazing exclusion experiment demonstrated that horse-induced reductions in

biomass, vegetation cover and height facilitated the invasion of annual exotic species to the

detriment of some perennial native species, with consequences for small-scale plant diversity

and composition. The MDS ordinations indicated that most of the compositional changes

and species substitutions that induced significant dissimilarity between grazed and complete

exclusion quadrats occurred within the first 17 months, with little futher shift in the last

3 months of sampling. Not surprisingly, after only just 12 months, horse exclosures were still

in compositional transition. Other horse grassland studies have shown that changes still

occur after 10 years (Duncan 1992; Rogers 1994; Beever and Brussard 2000a). Long-term

monitoring is advantageous in grassland communities as responses to grazing exclusion,

particularly increases in native cover, can be slow and ongoing for up to 25 years after

treatments are imposed (Wimbush and Costin 1979; Bakker and De Vries 1987; Wahren et al.

1994).

Lunt et al. (2007b) predicted that in high productivity environments, even with low

initial site degradation, grazing exclusion can lead to increases in large exotic species.

Conversely, in riparian grassland communities in Bobs Creek, significant increases in cover

were not detected for any exotic grasses under grazing exclusion, while several positive

outcomes were evident. The cover of a native perennial graminoid (knob sedge) and several

forbs increased, cover of annual plants halved and replaced by perennial plants, cover of

several exotic forbs declined or were eliminated, and the diversity of native perennial plants

and growth forms maintained. These results indicate that the removal of horses from Bobs

Creek in the short term would allow the riparian grassland community to largely ‘restore’

itself, resembling the rubber band model of stress and recovery (Page and Beeton 2000; Sarr


2002). Such models based on traditional Clementsian succession predict a single trajectory

towards and away from the ‘original’ compositional state (Clements 1916, 1936), and while

this can be interpreted in this experiment as a move towards pre-horse grazing, there is

difficulty in defining the ‘natural’ state (Hobbs and Norton 1996; Lunt et al. 2007b). The

outcomes of excluding horses in Bobs Creek validated the assessment of the initial level of

site degradation as moderate to low and explained the apparent contradiction with the New

Zealand studies. While the natural state of Bobs Creek riparian grasslands is unknown, the

starting cover of weeping grass was almost twice that of all graminoids. Weeping grass is

hypothesised to have been the dominant non-tussock grass species on the Northern

Tablelands prior to European settlement (Whalley et al. 1978; Lodge and Whalley 1989b),

and if so, may not have been affected by past cattle and horse grazing. Similarly, the

warm-season native perennials (e.g. red grass and paddock lovegrass), co-dominant with

weeping grass in this study, are locally and regionally common and tolerate high fertility soils

and heavy grazing, and may also have been present prior to the introduction of ungulate

grazers. However, it is likely that historical grazing by horses and possibly cattle has had a

filtering effect, selectively eliminating some intolerant species (Spooner et al. 2002; Dorrough

et al. 2004). As knob sedge was the only graminoid to increase in cover in response to

grazing exclusion, knob sedge and other sedges sensitive to heavy or frequent grazing may

have also been more abundant or present in place of exotic species, such as paspalum and

summer grass.

Several reviews have criticised the small sampling scales used in many grazing

exclusion experiments including feral horse studies (Stohlgren et al. 1999; Sarr 2002; Nimmo

and Miller 2007). It is likely that the number and size of exclosures employed in this

experiment were representative of the riparian flats in Bobs Creek, as the cumulative area of

the three exclosures covered >50% of the total riparian flat area at each site as flats were

short and narrow and the spread of exclosures encompassed the section of Bobs Creek

where flats were present. Riparian flats are limited in their spatial extent in the greater area

of GFRNP, and may be an important resource or habitat for native fauna. The results of this

experiment can be applied with some confidence to other riparian flats along tributaries with

similar geomorphologies and floristic composition in GFRNP, such as Pargo Creek, and the

removal of horses from the gorge country considered as a first step towards protecting and

restoring riparian flats in GFRNP. If horses were removed from the gorge country, riparian

systems would require frequent (e.g. seasonal) monitoring to ensure that endogenous


disturbances, changes in climate or natural long-term shifts in competitive relationships did

not favour the proliferation of exotic grasses and sub-shrubs. If that did occur, frequent

burning in spring may reduce soil nitrate levels and favour the re-establishment of native

plants (Prober et al. 2004; Prober et al. 2005; Smallbone et al. 2007; Smallbone et al. 2008).

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