Seagrass as pasture for seacows: Landscape-level dugong...

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Estuarine, Coastal and Shelf Science xx (2006) 1e16www.elsevier.com/locate/ecss

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Seagrass as pasture for seacows: Landscape-leveldugong habitat evaluation

James K. Sheppard a,b,*, Ivan R. Lawler a,b, Helene Marsh a,b

a School of Tropical Environment Studies and Geography, James Cook University, Townsville 4811, Australiab CRC Reef Research Centre, PO Box 772, Townsville 4810, Australia

Received 6 February 2006; accepted 19 July 2006

Abstract

A 24 km2 seagrass meadow in Hervey Bay, Queensland, Australia, was confirmed as important dugong habitat by a satellite tracking study.Marine videography, near-infrared spectroscopy (NIRS) and geographic information systems (GIS) were used to survey, analyse and map sea-grass species composition, nutrient profile and patch structure at high resolution (200 m). Five species of seagrass covered 91% of the total hab-itat area. The total above and below-ground seagrass biomass was estimated to be 222.7 � 19.6 t dry-weight. Halodule uninervis dominated thepasture (81.8%, 162.2 t), followed by Halophila ovalis (35.3%, 16.5 t), Zostera capricorni (15.9%, 22.2 t), Halophila spinulosa (14.5%, 21.9 t),and traces of Halodule pinifolia. Because seagrass distributions overlapped, their combined percentage totalled >100% of the survey area. Theseagrass formed a continuous meadow of varying density. Abiotic variables explained relatively little of the spatial patterns in the seagrass. Forall seagrass species, the above-ground component (shoots and leaves) possessed greater total nitrogen than the below-ground component (rootsand rhizomes), which possessed greater total starch. Because of the relatively low intraspecific variation in nutrient composition, nutrients wereconcentrated according to seagrass biomass density. Halodule uninervis was the most nutritious seagrass species because of its superior whole-plant nitrogen (1.28 � 0.05% DW) and starch (6.42 � 0.50 DW %) content. Halodule uninervis formed large, clustered patches of dense biomassacross the pasture and thus nitrogen and starch were concentrated where H. uninervis was prevalent. This seagrass meadow appears to be utilisedwell below its potential dugong carrying capacity. The survey and analytical techniques used enabled rapid, economical and accurate quantifi-cation and characterisation of seagrass habitat at scales relevant to a large forager.� 2006 Published by Elsevier Ltd.

Keywords: seagrass; dugong; habitat; nutritional ecology; GIS; patchiness

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UNCO1. Introduction

The spatial distribution of quality food plants profoundlyinfluences the movement patterns and foraging behaviours oflarge, terrestrial, mammalian grazers, which in turn imposepatterns on the landscape (for reviews see Senft et al., 1987;Coughenour, 1991; Fryxell, 1991; Huntly, 1991; Gross et al.,1995; Bailey et al., 1996; Hulme, 1996; Augustine andMcNaughton, 1998; Belovsky et al., 1999; Wilmshurst et al.,

* Corresponding author. School of TESAG, James Cook University, Towns-

ville 4811, Australia.

E-mail address: [email protected] (J.K. Sheppard).

0272-7714/$ - see front matter � 2006 Published by Elsevier Ltd.

doi:10.1016/j.ecss.2006.07.006

Please cite this article as: James K. Sheppard, et al., Seagrass as pasture for sea

Science (2006), doi:10.1016/j.ecss.2006.07.006.

1999; Van der Wal et al., 2000). The nutritional ecology ofthe dugong has many similarities with that of large, terrestrial,mammalian grazers. Like other free-ranging grazers, dugongsmust optimise their nutrient intake by selecting quality foodplants from within pasture foodscapes (sensu Gordon et al.,1996) of lower quality herbage.

Dugongs are seagrass specialists, feeding almost exclu-sively on phanerograms of the families Potamogetonaceaeand Hydrocharitaceae (Kingdon, 1971; Heinsohn and Birch,1972; Husar, 1978; Marsh et al., 1982; Preen, 1995a). Seagrasscommunities have many similarities to terrestrial grasses, ex-hibiting spatial heterogeneity (patchiness) at a range of scales,as both continuous and fragmented landscapes interspersedwith non-vegetated substrate (Lobo et al., 1998; Bell et al.,

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cows: Landscape-level dugong habitat evaluation, Estuarine, Coastal and Shelf

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mailto:[email protected]

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2 J.K. Sheppard et al. / Estuarine, Coastal and Shelf Science xx (2006) 1e16

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1999; Turner et al., 1999; Camarero et al., 2000; Robbins andBell, 2000; Shiyomi et al., 2001; Hovel et al., 2002; Frederik-sen et al., 2004; Mutanga et al., 2004). Dugong grazing pres-sure is believed to influence the structure and function of manyseagrass communities (de Iongh et al., 1995; Preen, 1995b;Aragones and Marsh, 2000; Masini et al., 2001; Aragoneset al., unpublished). However, the influence of seagrass foodquality on dugong grazing patterns and nutritional ecology ispoorly understood.

The nutritional requirements of dugongs are also largelyunknown. There have been few published studies into the nu-tritional aspects of the seagrass meadows targeted by dugongs(but see Lanyon, 1991; de Iongh et al., 1995; Yamamuro et al.,2004; Aragones et al., in press). Several authors suggest thatdugongs prefer lower seral stage or pioneer species such asHalodule uninervis and Halophila ovalis, and that their diet se-lection is governed by the nutrient and structural compositionof the food plants (Gohar, 1957; Heinsohn and Birch, 1972;Lipkin, 1975; Johnstone and Hudson, 1981; Marsh et al.,1982; de Iongh et al., 1995; Preen, 1995a). However, it is un-certain whether the ideal measure of dugong food qualityshould be based on the concentration of desirable elementsin the food (e.g. protein, energy) or the absence of less desir-able elements (e.g. fibre).

Defining key habitats is one of the most important topicsof sirenian research (Lefebvre et al., 2000). Habitat selectionby dugongs is beginning to receive greater attention by man-agers and ecologists, but a predictive model is lacking. A ho-listic understanding of the nutritional ecology of a large,mammalian grazer requires knowledge of the interactive dy-namics between the animal (e.g., metabolic demands, func-tional response, movement behaviours) and its food plants(e.g., nutrient profile, patchiness, and community composi-tion) across domains of scale. Dugongs appear to prefer

Please cite this article as: James K. Sheppard, et al., Seagrass as pasture for seac


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some seagrass pastures and avoid others and presumablymake foraging decisions at a range of scales (Anderson,1982, 1998; Preen, 1995b; Sheppard, in press). Hence, under-standing the spatial dynamics of seagrass communities isessential for predicting patterns of habitat use by selectivelyfeeding dugongs and for the effective management ofseagrass resources.

Information is required on the composition and configura-tion of the seagrass meadows that dugongs target. The sam-pling intensity required to identify and understand the driversand scales of dugong foraging decisions has been prohibi-tively expensive for both field and laboratory componentsof the research. However, rapid economical techniques haverecently been developed with improvements in remote sens-ing, near infrared spectroscopy and GIS (e.g. Long et al.,1994; Foley et al., 1998; Robbins and Bell, 2000; Kellyet al., 2001; Pasqualini et al., 2001; Ierodiaconou andLaurenson, 2002; Kendrick et al., 2002; Lawler et al., inpress). The aim of this study was to use these techniquesto quantify, at a resolution appropriate to foraging decisionsmade by individual animals, the composition and configura-tion of the seagrass community in a pasture used intensivelyby dugongs.

2. Methods

2.1. Study site

2.1.1. LocationThe study area was located in Hervey Bay, Queensland,

Australia, where seagrass meadows cover 2300 km2, makingHervey Bay one of the largest areas of seagrass on the easternAustralian seaboard (McKenzie et al., 2000) (Fig. 1). Since1988, various aerial surveys have estimated that Hervey Bay

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Fig. 1. Study site and survey location (circled) off the Burrum coast in Hervey Bay, Queensland, Australia.

ows: Landscape-level dugong habitat evaluation, Estuarine, Coastal and Shelf

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supports a population of between about 600 and 2250 dugongs(Marsh et al., 1990, 1996; Marsh and Lawler, 2001). Conse-quently, Hervey Bay is designated as a site of high dugongconservation significance and is the largest Dugong ProtectionArea on the Queensland coast (Marsh, 2000).

The dugong population in Hervey Bay fluctuates dramati-cally, especially following periods of seagrass dieback as a re-sult of environmental perturbation (Preen and Marsh, 1995;McKenzie et al., 2000; Marsh and Lawler, 2001). Althoughdugongs occur throughout Hervey Bay, a significant concen-tration consistently occurs around the south west coast, nearBurrum Heads where this survey was conducted (25�11.4 0S,152�36.6 0E) (Fig. 1) (Preen and Marsh, 1995; Marsh et al.,1996; Marsh and Lawler, 2001). Although the Burrum Headsdugong population is small relative to the entire Hervey Baypopulation, it is large relative to the small area of the BurrumHeads seagrass habitat (24 km2). Additionally, dugongs can befound at Burrum Heads consistently throughout the year (asopposed to the infrequent use by dugongs of other seagrassmeadows in Hervey Bay). Hence, the site is recognised as im-portant dugong habitat.

2.1.2. General physiographyThe low depositional coast directly south of the Burrum

River channel, where the seagrass meadows occur, is charac-terised by a nearshore terrace of 1e2 km wide intertidalsand flats. The terrace shelters the beach from wave actionand is backed by a 3.5 m frontal ridge/dune system. Man-groves colonise the foreshore at the south-western end of thebeach. The entrance to the Burrum River has formed a largeebb tide delta, supplied by littoral drift from the north. Themaximum tide range is 4.2 m (Pioerwicz, 1997) and themean tide velocity is 0.14e0.17 m/s at ebb and flood tides re-spectively (Queensland Beach Protection Authority, 1989).

2.2. Sampling design

A sampling grid was generated within the survey areato cover the Burrum Heads habitat most frequented by forag-ing dugongs (Sheppard et al., in press). Fixed kernel homeranges were generated using the Animal Movement Analyst

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Fig. 2. (A) Survey perimeter and sampling grid



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Extension (Hooge and Eichenlaub, 1997) for the ArcViewv3.3 GIS program (Environmental Systems Research Institute,Inc., Redlands, CA) for eight dugongs that exhibited high sitefidelity to the Burrum Heads region. Each animal was trackedusing PTT/GPS telemetry for an average of 3 months fromJuly and November 2003. Home range kernels were computedfrom the location data with a least-squares cross-validationsmoothing function. Outlines of the 95% kernels were thenoverlaid to create a single, roughly rectangular survey area, as-sumed to represent available seagrass habitat for all dugongsin the region (perimeter 23.3 km, area 23.8 km2) (Fig. 2a).

Remote sensing (satellite and aerial imagery) is of limitedefficacy in evaluating the biomass and species compositionof tropical seagrasses occurring at low density (Long et al.,1994). Remote sensing is further limited in deep or turbidwaters (Hyland et al., 1989; Pasqualini et al., 2001). Accord-ingly, we used a 554-point sampling grid to estimate seagrasscover within the survey area. The grid was generated using theSpatial Analyst extension for ArcView (EnvironmentalSystems Research Institute, Inc., Redlands, CA). Samplingpoints were systematically spaced every 200 m across thesurvey area, a suitable resolution for detecting local scale(<10 km2) spatial variation in seagrass meadows (L. McKen-zie, personal communication) (Fig. 2a). Seagrass species andbiomass were estimated visually for all sites (see below).Samples of seagrass material were taken from 89 sites (28intertidal; 61 subtidal) for nutrient analysis. Seven sites,400e1600 m external to the sampling grid, were haphazardlyselected (Fig. 2b) and sampled to compare seagrasses targetedby dugongs with those less used.

2.3. Field methods and survey equipment

Sampling was conducted during July 2004 while up to ninedugongs were being tracked using the area as outlined above.Intertidal sites were accessed on foot using a handheld GPS.Subtidal sites were located from a vessel using a marineGPS/sonar plotter and sampled using a portable marine videocamera. Five replicate 0.25 � 0.25 m quadrats were haphaz-ardly sampled from each site. Species composition and cover

of 557 points. (B) Nutrient sampling points.

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were recorded by one experienced investigator from above-ground estimates to eliminate inter-observer bias.

Linear ratio estimations of percent seagrass cover relativeto the substratum were calculated following the protocol ofLanyon and Marsh (1995) to facilitate ease of interpolation us-ing GIS. The low biomass and short standing crop height oftropical seagrasses, and the low current speeds, wave energyand generally clear water of the study site did not obfuscateactual cover values, negating the concerns of Mumby et al.(1997). Following Zieman and Wetzel (1980), we definedbiomass as total above- and below-ground plant material(see below for method of estimation) and standing crop asthe above-ground component only.

The marine video camera was an Applied Microvideo, IncModel-10 (http://www.appliedmicrovideo.com/). To reduce therisk of blurred images resulting from horizontal camera move-ment, the video camera was mounted on a height-adjustable,stainless steel 0.25� 0.25 m videoquadrat frame following Pre-skitt et al. (2004). The camera was centred 30 cm directly abovethe quadrat at a standardised height that fitted the quadrat to theimage frame. This arrangement facilitated rapid estimation ofseagrass species composition and percentage cover estimates;the high-definition digitised video frames approximated still pho-tographs. The camera was haphazardly lowered to the seafloor ateach sampling point. Tidal currents slowly drifted the boat(<5 cm/s) so that the quadrat was lowered onto a different sea-grass patch for each replicate. Total drift from the sample sitenever exceeded 30 m ( �5 m GPS error). Time to obtain cover es-timates from each subtidal sampling point averaged 5 min. Theseven sites external to the survey area were sampled by driftingwithout power or direction for 15 min with the entire videogridassembly (camera and frame) suspended <5 cm above thebenthos.

A Van Veen steel benthic grab was used to confirm thespecies composition of seagrass viewed on the video monitor,collect subtidal samples for nutrient analysis, and assess sedi-ment type. Voucher specimens of seagrass were collected fortaxonomic verification where necessary. Sediments wereinitially visually classified in situ into broad categories(mud, fine sand, coarse sand, gravel, shells). Sediment grainsize was later formally determined from samples by wet siev-ing and microscopy (Murdoch et al., 1997). Approximately50 g wet weight of total seagrass biomass was harvestedfrom each nutrient sample site for laboratory analysis. Sam-ples were washed in seawater and the epiphytes removed.The samples were then hand sorted by species into leaf(above-ground) and rhizome/root (below-ground) fractionsand air-dried to a constant weight at 60 �C for 72 h.

To minimise the confounding influence of species composi-tion, three replicate, monospecific, intertidal quadrats each of5, 25, 50 and 100% cover were selected and harvested of totalbiomass. These values were used to calibrate the relationshipbetween percentage cover and above and below ground sea-grass biomass following Mellors (1991) and Mumby et al.(1997). Species differences in terms of standing crop, leafsize and below-ground biomass can confuse cover and bio-mass estimations (Mellors, 1991; Mumby et al., 1997).



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Therefore, monospecific replicates were taken for eachseagrass species found in the study site. Biomass estimateswere expressed as grams of dry weight per metre square (gDW/m2). A linear regression was calculated for the relation-ship between the observer’s cover estimates and the actualharvested cover value. This curve was used to calculateabove-ground biomass for all estimates. Because dugongsextract the entire plant during feeding (Heinsohn and Birch,1972; Marsh et al., 1982; Marshall et al., 2003), above andbelow seagrass components were combined into a whole-plantvalue for each species from ratios derived from the calibrationsample set (n ¼ 4 species � 12).

2.4. Nutrient analysis

Overall, 282 samples were analysed for a range of chemicalconstituents. Near infrared reflectance spectroscopy (NIRS)was used to analyse the nutrient content of seagrass samplesfollowing methods described by Lawler et al. (in press).This technique was chosen for its ability to identify the com-position of organic samples in a rapid, cost-effective and re-peatable manner (Shenk and Westerhaus, 1993, 1994).Calibration equations were developed to describe the relation-ships between NIRS spectra and their matching nutrient values(Shenk and Westerhaus, 1991), derived from the followinglaboratory methods: (1) total nitrogen (N) colorimetricallyby the salicylate-hypochlorite method of Baethgen and Alley(1989); (2) total starch using the thermostable a-amylase andamyloglucosidase method of McCleary et al. (1997); (3) in vitrodry matter digestibility (IVDMD) using pepsin-cellulase in vitrodigestibility; (4) neutral detergent fibre (NDF) using the Fossfibre cap system Fibretec 2021 (6 place); (5) total organicmatter (OM) by straight ashing, (6) water-soluble carbohydrate(WSC) by ethanol extraction, and (7) lignin using the ANKOMsulphuric acid/acetone extraction system.

2.5. Point pattern spatial analysis

Exploratory analyses of the spatial association and disper-sion characteristics of the sample point data were conductedusing ArcGIS (ESRI Inc.). Moran’s I statistic (Moran, 1948,1950; Sokal and Oden, 1978; Legendre and Fortin, 1989; An-selin, 1996) and nearest neighbour analysis (Clark and Evans,1954) were used to test for the spatial randomness of the dis-tribution of each seagrass species. Moran’s I is a measure ofspatial autocorrelation (feature similarity) based on the covari-ance of sample values and their proximity. A Moran’s indexvalue near þ1.0 indicates clustering; an index value near�1.0 indicates dispersion, with zero being the expected valuefor no spatial autocorrelation (Cliff and Ord, 1981; Upton andFingleton, 1985). A z-score for Moran’s I was also calculatedto assess whether or not the observed spatial association wasstatistically significant when compared with the range ofvalues for a particular confidence level. A high positive z-scoreat a sampling point indicated that the surrounding samplingpoint values were similar (either high or low), indicatinga cluster.


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2.5.1. Spatial analysis of surface patternsRaster surfaces of bathymetry, seagrass cover and nutrient

profiles were generated from the point data using a GIS.Tropical seagrass meadows can be effectively interpolatedinto a two-dimensional surface using point data because sea-grass forms continuous patches with gradual gradients andvery low (<5 cm) canopy height. Several interpolation tech-niques were tested for their ability to generate biologically re-alistic coverages. IDW, spline and kriging techniques werecompared, with kriging producing the most precise output.Kriging, which recently has been used to successfully mapterrestrial grasslands (e.g. Atkinson, 1996; Cannavacciuoloaet al., 1998; Feng et al., 2004), is an optimal technique forlinear unbiased estimation and interpolation (reviewed byRoyle et al., 1981; Oliver and Webster, 1990; Atkinson,1996).

A Kriging estimator in ArcGIS (v.9, 2004, ESRI Inc.) wasused to generate fixed radius ordinary surfaces. The most ap-propriate kriging method was selected by generating seagrassinterpolations from a re-substituted sample grid that was ran-domly thinned by 25 and 50% and selecting the method thatproduced a surface similar in structural detail to the full sam-pling grid. Ordinary circular kriging produced the best surfacefrom the re-substituted data and the best fit in terms ofAkaike’s and Bayesian Information Criteria (Akaike, 1973;Atkinson, 1996) and was selected for all subsequentinterpolations.

Water depths at each subtidal sample point were matchedwith the tide height at the time the sample was recorded andadjusted to give the water depth at mean sea level (MSL)and high and low water spring tides (MHWS, MLWS). Thesecorrected depth values were interpolated via kriging to createa bathymetric surface for the survey area that was delineatedinto intertidal and subtidal fractions.

Once the seagrass meadow interpolations were generated,the Patch Analyst extension (Rempel and Carr, 2003) for Arc-View (ESRI Inc.) was used to analyse the spatial structure ofthe distribution of each seagrass species. This extension incor-porates FRAGSTATS v2.0 (McGarigal and Marks, 1994),a spatial pattern analysis program for quantifying landscapestructure. A patch was defined as contiguous 100 m2 grid cellsof the same (mean percentage cover) value of the same speciesof seagrass. Mean nearest neighbour distances were alsocalculated between patches of similar cover.

A measure of habitat quality was calculated using the meanmass of total nitrogen and starch available per m2 across theBurrum Heads pasture for each seagrass species. Biomassvalues were obtained from the kriging interpolations of theseagrass patches at a 100 m2 resolution, which were then con-verted into nutrient masses using the following equation foreach species separately:

N2 ¼ ððB=AÞ � SÞ �N1

where N2 is the mean nutrient % per m2 across the pasture, B isthe total seagrass species biomass, A is the total area of sea-grass species cover, S is the percent of the pasture covered



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by seagrass species, and N1 is the level of whole-plant nutrient(mean %).

The minimum and maximum mean nutrient masses per m2

found within the Burrum Heads pasture were also calculated toidentify the range of nutritional qualities available to foragingdugongs. Nutrient masses for each species were combined toprovide an estimate of mean nutrient g/m2 at each samplepoint. The points were then interpolated into coverages of ni-trogen and starch mass to create a 100 m2 resolution map ofdefinitive habitat quality. This map enabled identification ofthe 100 m2 patches of seagrass/nutrients that represented high-est nutritional return for an animal seeking to maximise intakeof total nitrogen and starch from foraging effort. All error es-timates are given as � standard error.

3. Results

3.1. Physiography

The hydrodynamic regime was generally consistent acrossthe study area because the extensive intertidal flats minimisewave and current disturbance. The only evidence of wave ac-tion influencing patch structure was an absence of seagrassacross some of the interface zones between the inter andsub-tidal regions along the southern shoreline where the inter-tidal flats narrowed. Intertidal and subtidal fractions of the sur-vey area at MLWS comprised 693.7 (29.2%) and 1682.2(70.8%) hectares respectively (Fig. 3).

Sediments were fine (0.06e0.25 mm) to medium grained(0.35e0.5 mm), with sand on the intertidal flats finer thanthe subtidal sands. The sediment mud component across thearea was low (6.1%) and generally confined to the deepestpart of the survey area; a 150 hectare, 7.4 m deep (mean sealevel) trough at the southern end, adjacent to the Beelbi Creekchannel (152.66�E;e25.22�S). This trough extended from700 m off the shoreline to at least the edge of the surveyarea (Fig. 3). Shells and shell fragments were distributed pri-marily around the seaward edge of the littoral zone and com-prised only 3.9% of the study area (although one intertidal sitehad 60% mean shell cover).

3.1.1. Species composition of the seagrass pastureFive species of seagrass were recorded at the survey site:

Halodule uninervis (narrow leaf), Halodule pinifolia, Halo-phila ovalis, Halophila spinulosa and Zostera capricorni (nar-row and broad leaf). All of these species were of smallmorphology with a canopy height generally <5 cm. Kriginginterpolations of the point grid created accurate and biologi-cally realistic surfaces of seagrass cover from the raw quadratdata (Fig. 4). Within the survey area, the seagrass formed con-tinuous meadows of varying densities with spatial distributionsshowing coherent patterns (Fig. 5). Patches containing H. un-inervis and H. ovalis often overlapped, whereas patches con-taining Z. capricorni, and to a lesser extent H. spinulosa,were generally monospecific. Halodule pinifolia occurred intrace amounts (<5% cover) at the southern intertidal end ofthe survey site (152.64�Ee25.21�S). Halodule pinifolia was



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Fig. 3. The bathymetry surface of the survey area interpolated using Kriging from the tide-adjusted depths at each sampling point. Depth is in metres below mean

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found incidentally outside of any sampling point and thus wasnot included in any interpolation or analysis. No seagrass wasfound at any of the sampling sites external to the survey area.

The patterns of seagrass species association were describedby principal component analysis. The first three loadings ac-counted for 96.0% of the spatial variation of the seagrasses.The fourth component explained only 3.98% of the varianceand was discounted. The first three components demonstratedsignificant association between Halodule uninervis and Halo-phila ovalis. A significant separation occurred between thisspecies association and Zostera capricorni, and to a lesser ex-tent Halophila spinulosa. The distributions of H. spinulosa andZ. capricorni only were significantly positively associated withwater depth. Neither water depth nor sediment grain sizeinfluenced the distribution of H. uninervis and H. ovalis. Be-cause H. spinulosa was restricted to deep water it was foundprimarily in medium-grained sands. Zostera capricorni wasfound in the muddy sediments of the deep water trough andthe fine-grained (0.06e0.25 mm) sand of the intertidal flats(Fig. 5c).

Ninety-one percent of the 2376 hectare survey area wascovered in seagrass as follows: Halodule uninervis(1944.2 hectares, 81.8% of the total survey area), Halophilaovalis (839.3 hectares, 35.3% of the total survey area). Zosteracapricorni (377.7 hectares, 15.9% of the total survey area) andHalophila spinulosa (337.9 hectares, 14.5% of the total surveyarea) (Table 1). These percentages represent the total area cov-ered by each seagrass species regardless of patch cover levelsor biomass. Because seagrass distributions overlapped, theircombined percentage totalled >100% of the survey area.



EHalophila spinulosa was virtually absent from the intertidalzone, being found predominantly in deep water subtidal sites(>4 m mean sea level) (Fig. 5d).

The site of densest seagrass cover occurred across the edgeof a approximately rectangular, 240 hectare ridge of shallowwater (<2 m MLWS) dominated by Halodule uninervis andHalophila ovalis (Fig. 5a,b). This ridge extended diagonally108� from the coast, beginning at the edge of the intertidalzone and continuing out at least 4 km to the subtidal edge ofthe survey perimeter. The southern edge bordered the deep wa-ter trough. The highest mean cover value for any sample pointin this area was 36 � 6.78% for H. uninervis.

Filamentous green epiphytic microalgae were found at40.6% of sample sites. Relative algal abundance was generallylow (mean 5%), and found principally across the northern halfof the survey area close to the Burrum River (152.65�Ee25.18�S). Because algal abundance varied dramatically oversmall time scales (4e5 days), algal cover was not interpolatedor spatially analysed. Macroalgae (Caulerpa spp.) were foundat only two dispersed deepwater subtidal sites (>4 m MLWS).Small, solitary bryozoans were found at three sites.

The total combined estimated above and below-ground sea-grass biomass was 222.7 � 19.6 tonnes dry-weight. The totalintertidal biomass component of seagrass was 35.3 tonnes,the total subtidal biomass 187.5 tonnes, made up as follows:(1) Halodule uninervis (162.2 tonnes total; 24.1 intertidal;138.1 subtidal), (2) Zostera capricorni (22.1 tonnes total; 7.3intertidal; 14.9 subtidal), (3) Halophila spinulosa (21.9 tonnestotal; 0 intertidal; 21.9 subtidal) and (4) Halophila ovalis(16.5 tonnes total; 4 intertidal; 12.5 subtidal) (Table 1).



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685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741

742743744745746747748749750751752753754755756757758759760761762763764765766767768769770771772773774775776777778779780781782783784785786787788789790791792793794795796797


UNCORRECT

0 10 15 20 25 30 35 40 45 500

200

400

600

800

1000

1200

1400 H. uninervis H. ovalis H. spinulosa Z. capricorni

0 10 15

0

1E5

2E5

3E5

4E5

5E5

6E5

Num

ber o

f obs

erva

tions

1 2 9 10 11 12 13 14Cover %

0

20000

40000

60000

80000

1E5

1.2E5

A

B

C

5

5

3 4 5 6 7 8

Fig. 4. Frequency plots of seagrass cover % for (A) the raw quadrat data from

the survey point grid, (B) the Kriged interpolated surfaces comprised of 10/m2

grid cells of contiguous seagrass species type plotted on the same scale and

(C) the Kriged surface data at full resolution. Note how the interpolated

data in (C) is more spread out across the range with a lower maximum cover

% than the raw data in (A). This is a function of the kriging process as it

spreads the values across a surface during the interpolation.

798



EDPROOF

3.2. Biophysical configuration of the seagrass pasture

Moran’s I and nearest neighbour spatial statistics indicatedthat the physical structure of the Burrum Heads seagrass pas-ture varied considerably by species. The pasture was com-prised primarily of low biomass patches of seagrass incomparison to the overall range of patch cover values. Highdensity patches of Halodule uninervis, the most prevalent spe-cies, were significantly smaller than low density patches.However, this was not true for the less prevalent Halophilaovalis and Zostera capricorni. Thus, relative to their totalcover and patch frequency, these seagrasses occurred more of-ten as patches of dense cover than did H. uninervis (althoughtotal patch cover value, size and frequency for these speciesnever exceeded those of H. uninervis (Fig. 6d).

Density values for Halodule uninervis, Halophila ovalis andZostera capricorni patches were significantly negatively corre-lated with the total area of the survey region possessing thatvalue, indicating that the pasture was mostly covered withlow density patches of these species (H. uninervis: r2 ¼ 0.84,p ¼ <0.001, df ¼ 985; H. ovalis: r2 ¼ 0.70, p ¼ 0.01, df ¼290; Z. capricorni: r2 ¼ 0.70, p ¼ 0.04, df ¼ 92). Correspond-ingly, patch density values for H. uninervis were also nega-tively correlated with patch size, so that low density patchesof H. uninervis occupied large areas whereas high densitypatches were generally small (r2 ¼ 0.66, p ¼ <0.001,df ¼ 985). Seagrass patch density was not significantly corre-lated with patch size for H. ovalis (r2 ¼ 0.32, t ¼ �1.69,p ¼ 0.14, df ¼ 290) or Z. capricorni (r2 ¼ 0.24, t ¼ �1.11,p ¼ 0.33, df ¼ 92).

Moran’s I and nearest neighbour statistics also indicatedthat patterns of seagrass distribution and abundance varied ac-cording to species. For instance, Moran’s I statistics for Halod-ule uninervis (index ¼ 0.04, z-score ¼ 12.8) and Halophilaovalis (index ¼ 0.06, z-score ¼ 10.9) indicated that their dis-tributions were significantly highly clustered. Zostera capri-corni was randomly distributed throughout the study area(index ¼ �0.01, z-score ¼ 0.3), while Halophila spinulosawas mildly clustered (index ¼ 0.01, z-score ¼ 1.4). These spa-tial patterns can be seen in the plot of mean nearest neighboursfor patch mean cover values (Fig. 6a). For example, all of thenearest neighbour distances for H. uninervis patches werecomparatively close, reflecting its clustered distribution. Incontrast, nearest neighbour distances for Z. capricorni patcheswere highly variable as a consequence of its disperseddistribution.

3.3. Nutrient analysis

3.3.1. Within plantsFor all species, the above-ground seagrass component

(shoots and leaves) possessed greater total nitrogen and neutraldetergent fibre than the below-ground component (roots andrhizomes). The below-ground component possessed greater to-tal starch and water soluble carbohydrates than the above-ground component (Table 2). Extrapolating these nutrient



+ MODEL

799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855

856857858859860861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895896897898899900901902903904905906907908909910911912


UNCORRECTEDPROOF

Fig

.5

.K

rig

ing

inte

rpo

lati

on

so

fth

em

ean

per

cen

tco

vera

ges

of

each

seag

rass

spec

ies

atth

est

ud

ysi

tein

the

con

tex

to

f1

mb

athy

met

ryco

nto

urs

:(A

)H

alod

ule

unin

ervi

s;(B

)H

alop

hila

ova

lis;

(C)

Zo

ster

a

capr

icor

ni;

(D)

Hal

ophi

lasp

inul

osa.

Please cite this article as: James K. Sheppard, et al., Seagrass as pasture for seacows: Landscape-level dugong habitat evaluation, Estuarine, Coastal and Shelf



+ MODEL

913914915916917918919920921922923924925926927928929930931932933934935936937938939940941942943944945946947948949950951952953954955956957958959960961962963964965966967968969

970971972973974975976977978


Table 1

Cover and biomass attributes of seagrass species found within the survey area at Burrum Heads. h, hectares; t, tonnes dry-weight; patch, contiguous 100 m2 grid

cells of the same (mean percentage) cover value

Species Total cover

(h)

Intertidal

cover (h)

Subtidal

cover (h)

% survey

area

Number of

patches

Total

biomass (t)

Std err Intertidal

biomass (t)

Std err

Halodule uninervis 1944.2 563.6 1380.6 81.8 987 162.20 6.13 24.06 0.94

Halophila ovalis 839.3 259.4 579.9 35.7 291 16.51 3.03 3.95 0.35

Zostera capricorni 377.7 162.4 215.5 16.2 93 22.15 4.64 7.26 0.01

Halophila spinulosa 337.9 0 337.9 13.8 66 21.86 5.82 0 0

979980981982983984985986987988989990991992993994995

ratios into total seagrass dry-weight biomass, the survey areacontained 2.72 tonnes of nitrogen and 11.79 tonnes of starch.

3.3.2. Between speciesThere was significant variation between the four seagrass

species for: (1) total starch (df ¼ 3, F ¼ 41.51, p ¼ <0.001),(2) nitrogen (df ¼ 3, F ¼ 36.05, p ¼ <0.001), (3) organic mat-ter (df ¼ 3, F ¼ 5.98, p ¼ 0.001), (4) lignin (df ¼ 3,F ¼ 38.97, p ¼ <0.001), and (5) neutral detergent fibre(df ¼ 3, F ¼ 24.61, p ¼ <0.001) (Fig. 7, Table 2). The great-est variation in inter-species nutrients occurred with totalstarch. The mean whole-plant starch content of Halodule unin-ervis was 6.42 � 0.50%, compared with 0.75 � 0.24% forHalophila ovalis, 4.22 � 0.33% for Zostera capricorni, and0.70% � 0.15 for Halophila spinulosa. A Tukey HSD posthoc test indicated the largest significance difference in starch



DPROOF

occurred between H. uninervis and H. spinulosa (mean differ-ence ¼ 5.72 � 0.57, p¼<0.001) and H. uninervis and H. ova-lis (mean difference ¼ 5.41 � 0.70, p ¼ <0.001). Halophilaspinulosa and H. ovalis starch levels were not statistically dif-ferent (mean difference ¼ 0.30 � 0.76%, p ¼ 0.978). Halod-ule uninervis had higher whole-plant nitrogen content(1.28 � 0.05%) than H. ovalis (1.21 � 0.08%), although thisdifference was not significant (mean ¼ 0.12 � 0.08%,p ¼ 0.494). The greatest significant difference in nitrogen con-tent occurred between the nitrogen rich H. uninervis and nitro-gen poor Z. capricorni (mean ¼ 0.51 � 0.052%, p ¼ <0.001).

Because of the relatively low intraspecific variation in nu-trient composition, nutrients were concentrated or dispersedaccording to seagrass biomass (Table 3, Figs. 8 and 9). Theonly detectable influence of site on nutrient distribution wastidal exposure. Intertidal Halophila ovalis possessed a higher

996997998999000001002003004005006007008009010011012013014015016017018019020021022023024025026

UNCORRECTE

0 2 8 10 12 14-20

020406080

100120140160180200220

Mea

n ne

ares

t nei

ghbo

ur (m

)

0 2 10 12 14

0

1

2

3

4

5

6

7

Mea

n pa

tch

size

(hec

tare

s)

0 10 12 14Mean cover %

0

20

40

60

80

100

120

140

160

Num

ber o

f pat

ches

0 4 8 10 12 14Mean cover %

0

50

100

150

200

250

300

350

400

450

Tota

l are

a (h

ecta

res)

H. uninervisH. ovalisZ. capricorniH. spinulosa

C D

A B

4 6 4 6 8

628642

Fig. 6. Relationships between percent mean patch cover values for the four species of seagrass found within the survey area and: (A) patch mean nearest neighbour;

(B) mean patch size; (C) number of patches; and (D) total area of the patch. A patch was defined as contiguous 100 m2 grid cells of the same (mean percentage

cover) value of the same species of seagrass.

111111111111111111111111111



+ MODEL

102710281029103010311032103310341035103610371038103910401041104210431044104510461047104810491050105110521053105410551056105710581059106010611062106310641065106610671068106910701071107210731074107510761077107810791080108110821083

108410851086108710881089109010911092109310941095109610971098109911001101110211031104110511061107110811091110111111121113111411151116111711181119112011211122112311241125112611271128112911301131113211331134113511361137113811391140


UNCORRECT

Tab

le2

Mea

n%

nu

trie

nt

valu

esfo

rab

ove

and

bel

owg

rou

ndp

lant

frac

tio

ns

of

each

spec

ies

of

seag

rass

atB

urru

mH

ead

s.N

,to

tal

nit

rog

en;

WS

C,

wat

erso

lub

leca

rbo

hy

drat

es;

OM

,o

rgan

icm

atte

r;N

DF,

neu

tral

det

erg

ent

fib

re;

IVD

MD

,in

-vit

rod

ry-m

atte

rd

iges

tib

ilit

y

Sp

ecie

sP

lan

t

com

po

nen

t

N(m

ean

)N

(Hal

odul

e

Nra

tio)

Sta

rch

(mea

n)

Sta

rch

(Hal

odul

e

star

chra

tio

)

WS

C

(mea

n)

OM

(mea

n)

ND

F

(mea

n)

Lig

nin

(mea

n)

IVD

MD

(mea

n)

Hal

odul

eun

iner

vis

Ab

ove-

gro

un

d1

.99

0.0

51

.00

0.9

90

.11

1.0

02

.21

0.3

14

7.9

81

.62

52

.50

1.6

12

0.1

60

.46

71

.74

1.0

1

Bel

ow-g

rou

nd

0.9

00

.03

1.0

08

.76

0.5

01

.00

2.8

30

.23

45

.16

1.2

95

1.4

10

.85

18

.85

0.4

06

6.1

10

.89

To

tal

pla

nt1

.28

0.0

51

.00

6.4

20

.50

1.0

02

.52

0.2

34

5.9

01

.30

51

.60

1.0

71

9.1

70

.37

67

.48

0.7

9

Hal

ophi

laov

alis

Ab

ove-

gro

un

d1

.64

0.0

80

.82

0.3

70

.14

0.3

71

.47

0.4

24

4.0

33

.43

51

.14

1.3

61

6.5

31

.05

67

.51

3.8

9

Bel

ow-g

rou

nd

0.7

80

.04

0.8

70

.87

0.3

10

.10

2.5

40

.42

38

.07

2.3

54

6.5

31

.79

15

.08

1.0

86

7.9

92

.55

To

tal

pla

nt1

.21

0.0

80

.95

0.7

50

.24

0.1

22

.14

0.3

74

0.6

11

.94

48

.86

1.6

91

5.7

30

.96

67

.43

2.7

6

Zos

tera

capr

icor

niA

bov

e-g

rou

nd

1.6

10

.05

0.8

11

.00

0.1

41

.01

2.4

30

.24

49

.89

1.4

44

8.4

31

.14

18

.37

0.4

07

1.1

61

.07

Bel

ow-g

rou

nd

0.6

60

.03

0.7

33

.82

0.3

30

.44

2.3

40

.21

39

.02

1.4

64

3.0

30

.81

13

.19

0.4

66

5.7

31

.05

To

tal

pla

nt0

.82

0.0

40

.64

4.2

20

.33

0.6

62

.20

0.1

94

1.0

31

.27

44

.04

0.7

51

4.3

20

.45

66

.85

0.9

1

Hal

ophi

la

spin

ulos

a

Ab

ove-

gro

un

d1

.17

0.0

50

.59

0.6

30

.17

0.6

41

.29

0.4

04

1.2

53

.29

52

.81

1.2

81

3.9

90

.60

63

.64

2.6

2

Bel

ow-g

rou

nd

0.6

60

.03

0.7

30

.74

0.2

00

.08

2.0

90

.37

37

.87

2.8

45

0.0

91

.51

13

.92

0.8

36

7.2

02

.27

To

tal

pla

nt0

.88

0.0

40

.69

0.7

00

.15

0.1

11

.97

0.3

73

8.6

42

.34

50

.83

1.0

61

3.5

40

.54

63

.93

2.2

6



EDPROOF

mean IVDMD than subtidal plants (difference ¼ 18.5%,F ¼ 14.69, p ¼ 0.003). Similarly, mean total starch was signif-icantly higher for intertidal Zostera capricorni than subtidalZ. capricorni (difference ¼ 3.4%, F ¼ 26.57, p ¼ < 0.001).

4. Discussion

An understanding of the ecological factors that determinethe value of habitat patches to large grazing herbivores is ofboth practical and theoretical importance. The marine videog-raphy technique presented here, combined with NIRS andGIS, provided a rapid, economical and accurate assessmentof seagrass characteristics across a relatively large area.Near infrared spectroscopy has been shown in previous studiesto offer considerable savings over conventional laboratoryanalyses (Foley et al., 1998; Andre and Lawler, 2003; Ara-gones et al., in press; Lawler et al., in press). Additionally, vid-eography enabled us to sample subtidally without the need forscuba and its associated time and financial costs (Long et al.,1994; Mumby et al., 1997). This approach enabled the speciescomposition, nutrient profile and patch structure of a seagrasspasture used intensively by dugongs (Sheppard et al., in press)to be accurately quantified. The study was conducted at scalesrelevant to a foraging dugong and at a level where local mon-itoring and management activities can be effective.

4.1. Characterising the Burrum Heads seagrass pasture

4.1.1. Abiotic influences on seagrass distributionAbiotic variables explained relatively little of the spatial

patterns in the seagrass. The predominantly subtidal distribu-tion of Halophila spinulosa was consistent with its status asa deep-water species (Anderson, 1994; Lee Long et al.,1996). The ridge bathymetric feature (Fig. 3) was a favourablesite for Halodule uninervis probably because it was constantlycovered with at least 1.5 m of calm water. The deepwatertrough immediately adjacent to the ridge was dominated byfine mud sediments and dense, monospecific beds of Zosteracapricorni. Zostera capricorni may have been competitivelyexcluded from the ridge by faster growing, pioneer H. uniner-vis and the high dugong grazing pressure associated with thissite (Sheppard, unpublished; see also Preen, 1995). Con-versely, Z. capricorni may have excluded H. uninervis fromthe deepwater trough through a superior ability to cope withlower light intensities or to compete in the absence of distur-bance by wave action or dugong foraging (Abal and Dennison,1996; Inglis, 2000; Carruthers et al., 2002). The influence ofsite factors other than tidal exposure on the spatial distributionof seagrass nutrients was undetectable. Seagrass nutrient qual-ity was not influenced by proximity to the increased sedimenttransport (and presumed nutrient loading) associated with ei-ther the Burrum River to the north or Beelbi Creek to the south.

4.1.2. Biomass estimates of total available seagrassThe overall seagrass biomass within the Burrum pasture

was low: 269 of the 554 sample points (48.6%) containedless than 5% mean seagrass cover. Total estimated seagrass



+ MODEL

114111421143114411451146114711481149115011511152115311541155115611571158115911601161116211631164116511661167116811691170117111721173117411751176117711781179118011811182118311841185118611871188118911901191119211931194119511961197

1198119912001201120212031204120512061207120812091210121112121213121412151216121712181219122012211222122312241225122612271228122912301231123212331234123512361237123812391240124112421243


NCORRECTEDPROOF

STARCHWSCN

H. ovalis

IntertidalSubtidal

-2

0

2

4

6

8

Dry

-mat

ter %

H. spinulosa

IntertidalSubtidal

H. uninervis

IntertidalSubtidal

Z. capricorni

IntertidalSubtidal

OMIVMDLIGNDF

H. ovalis

IntertidalSubtidal

10

20

30

40

50

60

70

80

90

Dry

-mat

ter %

H. spinulosa

IntertidalSubtidal

H. uninervis

IntertidalSubtidal

Z. capricorni

IntertidalSubtidal

Fig. 7. Mean whole-plant % dry matter biomass nutrient and fibre profiles separated into intertidal and subtidal fractions with standard errors. WSC, water soluble

carbohydrates; N, nitrogen; OM, organic matter; IVMDM, in-vitro dry matter digestibility; Lig, lignin; NDF, neutral detergent fibre.

244245246247248249250251252253254

Ubiomass (10.48 � 0.90 tonnes DW/km2) was much lower thanthe 57.05 � 15.1 tonnes DW/km2 recorded by Long et al.(1994) using similar techniques in a 37.6 km2 Zostera capri-corni dominated site in Moreton Bay, 400 km to the south.This variation could be due to species differences alone. How-ever, the Burrum estimate is also lower than the 150.58 tonnesDW/km2 � 6.62 recorded by Preen (1993) in an 11.76 km2

Moreton Bay seagrass meadow comprised of Halophila ovalis,Halodule uninervis and Z. capricorni, again reflecting relativespecies composition.



The variation between these biomass estimates may be com-pounded by methodological and seasonal differences (e.g., thestratification method of Long et al. (1994) vs. the tract-methodof Preen (1993)). The Burrum pasture was not sampled acrossa seasonal range. However, seagrass growth rates within theBurrum pasture for 2003e2005 were obtained from rhizometagging (Bite, personal communication) and Seagrass Watchmonitoring data (http://www.seagrasswatch.org). FactorialANOVA showed significant growth differences betweenspecies, year and season (Wilks ¼ 0.85, F ¼ 19.6, effect

11111111111


http://www.seagrasswatch.org


+ MODEL

125512561257125812591260126112621263126412651266126712681269127012711272127312741275127612771278127912801281128212831284128512861287128812891290129112921293129412951296129712981299130013011302130313041305130613071308130913101311

13121313131413151316131713181319132013211322


Table 3

Mean total whole-plant biomass and mean total nitrogen and starch in dry-weight grams per square metre across the Burrum Heads survey area. Values are cal-

culated from the kriging interpolations of seagrass patches, with a patch defined as contiguous 100 m2 grid cells of the same (mean percentage cover) value of the

same species of seagrass. For example, the maximum nitrogen concentration value represents the weight in grams of that nutrient per m2 across a 100 m2 patch of

the highest cover of that seagrass within the survey area. DW, dry-weight

Mean total biomass

(g DW/m2)

Mean N

(g DW/m2)

Mean starch

(g DW/m2)

Halodule uninervis 6.827 0.26 0.087 0.0117 0.438 0.1059

Halophila ovalis 0.695 0.13 0.008 0.0011 0.005 0.0015

Zostera capricorni 0.932 0.13 0.008 0.0014 0.039 0.0144

Halophila spinulosa 0.935 0.13 0.008 0.0014 0.007 0.0073

Combined 9.39 0.65 0.111 0.0156 0.489 0.1291

13231324132513261327132813291330133113321333133413351336133713381339134013411342134313441345
E
CT

df ¼ 15, error df ¼ 4723.7, p ¼ 0.00). Hence, intersite biomassdifferences between these studies may be caused by: (1) thegreater overall biomass possessed by extensive, mature,Zostera-dominated meadows of Moreton Bay comparedwith the young, heavily grazed Halodule-dominated beds offBurrum Heads (Preen and Marsh, 1995; Sheppard, unpub-lished); (2) methodological differences; and (3) seasonaleffects.

4.1.3. Seagrass patch structureExploratory spatial statistics and interpolations of seagrass

cover indicated clear interspecies biomass differences. Halod-ule uninervis had the highest overall patch cover and biomassvalues for a seagrass species in the survey area, reflecting itsubiquity across the inter and subtidal zones. Halodule uniner-vis formed large, clustered patches that often overlapped withHalophila ovalis, the second most prevalent species. Range-sharing between H. uninervis and H. ovalis reflects their sim-ilar ecological roles as fast growing pioneer seagrasses, partic-ularly in areas characterised by high dugong grazing pressurewhere ‘cultivation grazing’ may be occurring (Preen, 1995b).



EDPROOF

4.2. Evaluation of the Burrum Heads pasture asdugong habitat

The nutritional profiles of the four tropical seagrasses wesampled are generally consistent with previous studies of thesame species, e.g. (Lanyon, 1991; de Iongh et al., 1995; Ara-gones, 1996; Udy and Dennison, 1997; Mellors, 2003; Ya-mamuro et al., 2004; Yamamuro and Chirapart, 2005).Based on the nutritional requirements of other mammaliangrazers, the nutritional variation found in these seagrasseswas significant enough to assume a basis for dugong diet se-lectivity (see Lanyon, 1991). For example, horses (a hindgutfermenter like sirenians) require a diet containing 8% crudeprotein (1.28% N) for maintenance and 10% (1.6% N) forwork (irrespective of intake rate as a result of constraintson gut capacity) (National Research Council, 1989). Hence,food plant nitrogen content below a threshold of 1% is as-sumed to be nutritionally inadequate and an optimal foragershould select herbage with higher values. It is uncertainwhether energy or nitrogen content is the most important nu-tritional currency for dugongs.

13461347134813491350135113521353135413551356135713581359136013611362136313641365136613671368

UNCORR

Fig. 8. Maps of (A) spatial distribution and abundance of nitrogen and (B) associated standard error. Nutrient masses are represented as g DW per m2 which were

calculated from the combined biomasses and nutrient profiles of the four seagrass species present in the survey area.



+ MODEL

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ROOF

Fig. 9. Maps of (A) spatial distribution and abundance of starch and (B) associated standard error. Nutrient masses are represented as g DW per m2 which were

calculated from the combined biomasses and nutrient profiles of the four seagrass species present in the survey area.

447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482

UNCORRECT

The preponderance of starch in the below-ground compo-nent of these tropical species confirms previous findings thatseagrass rhizomes provide a rich source of starch (Birch,1975; Masini, 1983; Lanyon, 1991; Mellors, 2003; Aragoneset al., in press). There was more than eight times more starchin the below-ground component of Halodule uninervis thanabove-ground. Whole-plant H. uninervis also possessed morethan eight times as much starch as Halophila ovalis or Halo-phila spinulosa. Halodule uninervis is consistently present indugong stomach content samples and the seagrass meadowsdugongs target (Marsh et al., 1982; de Iongh et al., 1995;Preen, 1995b; Aragones and Marsh, 2000; Masini et al.,2001; Andre and Lawler, 2003). We observed that the majorityof dugong feeding trails in the Burrum Heads habitat crossedpatches of H. uninervis. Hence, dugongs may be actively se-lecting H. uninervis over other seagrasses (Sheppard, unpub-lished) on the basis of its elevated starch content alone,a finding which supports the hypothesis of energy intake-ratemaximising (Westoby, 1974; Belovsky, 1978; Stephens andKrebs, 1986).

The nitrogen content of the leaves of all species of seagrassstudied here was within the range previously reported for trop-ical seagrasses (1.8% N DW, cf. Duarte, 1990). Seagrassescontain low nitrogen levels (<2% DM) relative to most terres-trial grasses (Birch, 1975; Duarte, 1990). The mean total nitro-gen levels recorded here were less than the 2.4% recorded inthe analysis of vegetation consumed by free-ranging cattle(Wallis de Vries and Schippers, 1994). Consequently, dugongsmay not easily meet their nitrogen requirements (Lanyon,1991), particularly during reproduction.

4.2.1. Rankings of the nutritional quality of seagrassBecause dugongs remove up to 91% of the seagrass biomass

during feeding (Heinsohn et al., 1977; Anderson and Birtles,1978; Preen, 1993), they are not as affected by nutrient variation



EDP

between plant tissues as many terrestrial herbivores (although,see Anderson, 1998). Therefore, quality rankings of seagrassspecies can be expressed from best to worst, determined bythe availability of the limiting nutrients and digestibility.

Halodule uninervis was the most nutritious seagrass speciesfound within the Burrum Heads pasture, with the highest con-centration of whole-plant nitrogen (1.28 � 0.05%) and starch(6.42 � 0.50%) content. The relatively poor nutritional qualityof Halophila ovalis, particularly its low starch content(0.75 � 0.24%), has been demonstrated by our data as wellas by Mellors (2003), Yamamuro and Chirapart (2005), Yama-muro et al. (2004), and Aragones et al., unpublished).

Zostera capricorni possessed higher total starch content(4.22 � 0.33%) than H. ovalis and similar digestibility. Inour study, Halophila ovalis was nutritionally superior toZ. capricorni only in its nitrogen content (1.21 � 0.08%compared with 0.82 � 0.04%). However, this difference maybe enough to drive dietary selection for H. ovalis if nitrogendifferences at this resolution are nutritionally important.

Our results agree with those of Lanyon (1991) in rankingHalophila spinulosa as the most fibrous and least digestibleseagrass. Halophila spinulosa is apparently seldom selectedas forage by dugongs, and our results support previous theoriessuggesting fibre as a major deterrent to dugong foraging (e.g.Heinsohn and Birch, 1972; Lipkin, 1975; Wake, 1975; Lan-yon, 1991; Preen, 1995a). Our data differ from Lanyon(1991) in demonstrating that Halodule uninervis has a higherNDF content than Zostera capricorni and Halophila ovalis.However, despite this variation in fibre content, H. uninervis,Z. capricorni and H. ovalis all showed similar IVDMD(df ¼ 3, F ¼ 0.93, p ¼ 0.43). Therefore, since IVDMD isa more functional measure of dugong food quality than NDFand because digestibility for H. uninervis, Z. capricorni andH. ovalis was similar, seagrass nutritional quality can be mea-sured by starch and nitrogen alone.

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UNCORRECT

In summary, depending on whether dugongs are energy ornitrogen limited, the nutritional quality of the four seagrassspecies available to foraging dugongs within the BurrumHeads pasture were ranked as follows:

e Energy-limited: Halodule uninervis < Zostera capricor-ni < Halophila ovalis < Halophila spinulosa

e Protein-limited: Halodule uninervis < Halophila ovalis <Zostera capricorni < Halophila spinulosa

4.2.2. Base carrying capacity of the Burrum Heads pasturePreen (1993) estimated the average seagrass dietary intake

of a dugong to be 3.22 kg DW per day. Assuming that the car-rying capacity of a seagrass pasture to support a dugong pop-ulation is a linear function of its biomass, the Burrum Headspasture could support 152 dugongs grazing both Halophilaovalis and Halodule uninervis at 3.22 kg DW per day fora year, even in the absence of seagrass growth. We have ob-served no more than 80 animals at this site during any singleday during aerial surveys and dugong tagging with the aid ofspotter aircraft from 2002 to 2004 (Marsh and Lawler, 2001;Sheppard et al., in press). The average number of dugongsat this site has been around 10e30. Hence, the Burrum Headsseagrass pasture currently appears to be considerably ‘under-stocked’; possibly a reflection of historical population decline(see Daley, 2005; Marsh et al., 2005) or a foraging strategy toprevent overgrazing.

5. Summary and conclusions

We posit that selection of the Burrum Heads site as dugonghabitat is predicated on the characteristics of the seagrassesthat predominate there. The survey area was dominated by ex-tensive meadows of fast-growing Halodule uninervis config-ured into highly clustered patches of relatively densebiomass. Although starch and nitrogen are concentrated inmorphologically separate components of the seagrass plant,dugongs consume the entire plant, making it difficult to provewhether they are energy or protein rate maximising. Haloduleuninervis was the most overall nutritious seagrass speciesfound within the dugong habitat hotspot, followed by: Zosteracapricorni if starch is the most important limiting nutrient, orHalophila ovalis if nitrogen is. The seagrass food resourcecomprising the Burrum Heads dugong pasture appears to beutilised well below its potential carrying capacity, reflectingdugong foraging strategies or historical population decline.

Uncited references

Hobbs, 1996; Marsh et al., 1984

Acknowledgements

We are grateful for the assistance of Rachel Maitland,Rachel Groom and QPWS/EPA staff with fieldwork and



EDPROOF

Juanita Bite and Rachel Groom with laboratory analyses. Wil-liam Foley of ANU provided invaluable advice with NIRS andstatistical procedures. We also thank Iain Gordon of CSIRO,Len McKenzie of QDPI&F and Alana Grech of JCU for theirhelpful reviews of the manuscript.

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