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Border Ranges Biodiversity Management Plan: defining plant functional groups for

use in resource-limited multi-species recovery implementation scenarios

FLORA REPORT PREPARED FOR NSW DEC BY

Robert Kooyman and Maurizio Rossetto

National Herbarium of NSW, Botanic Gardens Trust, Mrs Macquaries Rd, Sydney NSW

2000, Australia.

June 2007 Abstract

This report provides an overview of the development of a bioregional approach to

biodiversity assessment and management that uses trait-based plant functional groups as a

basis for multi-species recovery planning. Multi-variate methods were used to extract and

test emergent groups, and additional information fields related to species life history and

distributional data were added to develop a biodiversity assessment matrix in spreadsheet

format (Appendix 1). Tests of phylogenetic independence were undertaken and showed

that phylogeny significantly affects the clustering of character states for nearly all the

traits studied. Data rich samples were used to test the methods in one (rainforest)

community type, and several species from one of our emergent groups were chosen from

that sample to provide an example of the function of the biodiversity assessment tool.

Relating emergent trait-based plant functional groups to habitat was found to be the most

informative approach for the development of management recommendations and

recovery planning related to landscape scale threat / risk categories. Appendix 2 provides

a list of species representing the various groups that have been identified as the focus for

additional research and information gathering. Appendix 3 provides species level

information related to defining the realised niche (reflecting species distribution in

relation to environmental variables) for twenty-nine species from the data rich sample

that occur on the BRBMP list.

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Table of Contents

Project Brief ........................................................................................................................ 3 Introduction......................................................................................................................... 4 Methods............................................................................................................................... 8

Study area and data compilation ..................................................................................... 8 Trait Information............................................................................................................. 9 Habitat Types and Initial Threat Assessment ................................................................. 9 Data analysis methods for the target species ................................................................ 10

Results............................................................................................................................... 12

Identifying appropriate trait-based groups.................................................................... 12 BRBMP Project Listed Species .................................................................................... 13 Description of trait-based groups obtained from cluster analysis................................. 14

Discussion ......................................................................................................................... 15

Multi-species planning based on trait-related groups: conceptual framework ............. 15 References......................................................................................................................... 21 Appendix 1 - Biodiversity Assessment Matrix .................................................................. 34 Appendix 2 – Group priorities for research..................................................................... 37 Appendix 3 – Data Rich Community Example (SNMVF) ................................................ 40 Introduction....................................................................................................................... 40 Methods............................................................................................................................. 40

Data analysis methods for the data rich community example ...................................... 40 Trait relationships for rare and threatened taxa in the SNVF sample........................... 41

Results............................................................................................................................... 42 Discussion ......................................................................................................................... 43

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Project Brief

• Develop a transferable conceptual approach to guide rare and threatened plant

biodiversity management planning for use at bio-regional scales.

• Using the available and accessible data develop a (parsimonious) trait-based

approach defining plant functional groups that reflect biological / ecological /

evolutionary and habitat factors.

• Provide a spreadsheet based biodiversity assessment matrix that, once relevant

data has been added, can integrate group and species level landscape-scale threats

to help define RISK-based assessment categories that inform management

priorities and the strategic allocation of resources.

• Provide a broad and fine-scale data rich (species richness / abundance /

environmental variables) and threatened species rich ecosystem-based example to

allow exploratory analyses and testing of methods.

• Provide guidance for future implementation needs and develop preliminary

examples using taxa for which data is available.

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Introduction

The study of the distribution of plants and their relative abundances in variable

landscapes is central to the development of conservation planning. Determining how

species make a living and how they interact with other species and environmental

variables in their shared habitats is an important area of research linked to community

ecology. Plant life history traits provide insights into both evolutionary and ecological

processes and are regarded as a critically important area of research in plant science.

From a conservation perspective, functional groups based on life-history characters have

the potential to bring together taxa that are likely to respond to selective processes,

environmental threats and potential management actions in similar ways. They also

provide insights into the mechanisms behind species responses to land-use change

(Verheyen et al., 2003; Kolb and Diekmann, 2005).

The focus of recovery planning in response to Australian state and federal threatened

species legislation has, until recently, predominantly been on the development of single-

species recovery plans that developed strategies for threat abatement and population

preservation. However, the allocated resources available for the development of such

plans has often been insufficient and exhausted before many of the identified recovery

actions could be implemented. In response to this, there is increasing interest in

developing management and recovery planning efforts that are focused on securing

multiple species outcomes at bioregional scales. The aim of such plans is to identify

threatening processes, and coordinate and prioritise recovery efforts across landscape,

community, site, and species levels while simultaneously responding to the requirement

for cost-effectiveness.

The need for effective approaches to guide the use of scarce resources for the

conservation of rare and threatened species is well established and acknowledged as of

broad interest to conservation scientists, managers and practitioners. Using plant

functional traits to group species for research and management has attracted considerable

research interest over many years (Cornelissen et al., 2003), and has resulted in a number

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of plant strategy schemes being developed (eg., Grime 1977; Gitay and Noble 1997;

Westoby 1998; Westoby et al., 2002).

The idea that plant species with different qualitative and quantitative traits can be more

successful in different parts of the landscape is certainly not new (for example Schimper,

1903). It is also well known that plant species functional traits vary both along

environmental gradients and within communities of species under similar conditions. The

shift in trait frequencies along gradients, the mix of traits at a site, and trait correlations,

can all provide important insights into community and species level vegetation processes,

and species present day ecological competence. In that context, the physical environment

can then be considered as filtering the kinds of species that can succeed at a given site (by

providing the framework within which species interact), but not as the final determinant

of the range of trait values present at a site (Westoby and Wright 2006).

Partial reviews of the history of development and efficacy of biodiversity recovery

planning at various scales and in different contexts have been undertaken (for example,

Harding et al., 2001; Hecht and Parkin, 2001; Abbitt and Scott, 2001; Clarke et al., 2002;

Moore and Wooller, 2004; Roberge and Angelstam, 2004; Male and Bean, 2005). It is

not our intention to compare or provide detailed descriptions of the various approaches to

recovery planning, from the species to the systems level (refer to McNeely, 2006)

however, the applicability of other recovery planning approaches to this case has been

explored. It was concluded that:

Reverting to a compilation of single species population viability analyses (PVA) for all

taxa in the list was considered inconsistent with the agency-defined multi-species

planning objectives and unachievable within the project time frame or (more importantly)

with the available resources.

Focusing on, or prioritising species on the basis of rarity (or population size) was found

to be unsuitable (in this case), as it was previously shown that the local species with the

smallest populations are not necessarily the most threatened (for example research in the

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study area on Eidothea hardeniana; Rossetto and Kooyman, 2005; and Uromyrtus

australis; Kooyman, 2005).

The surrogate biodiversity assessment approach uses environmental (abiotic) surrogates

to model and predict species diversity across whole landscapes (Faith, 2003; Faith et al.,

2004; but see Araújo et al., 2004). The methods presented here are predominantly

focused on a subset of listed rare taxa in the study area only, and (except for the data rich

example) do not reflect research into biodiversity patterns in assemblages at landscape

scales. However, in this approach we do use species level preferred habitat as a secondary

filtering process within the trait-based groupings, to assist the intended threat / risk

assessment process (Appendix 1).

The ‘umbrella’ species approach assumes that survival and recovery of the chosen taxa

reflects favourably on all co-occurring taxa (see Roberge and Angelstam, 2004;

Lambeck, 1997). Ecologists generally agree that it is difficult to determine which species

are the most significant (or informative) in different ecosystems. From a strictly

functional perspective, species matter to the extent that their traits interact to maintain

ecosystem function. Thus, the ‘umbrella’ species concept may not always be useful, or

the most informative, as appropriate ‘umbrella’ species are not necessarily to be found on

lists of recognised rare or threatened taxa. The exception may be in cases where multi-

species approaches based on unrestricted focal species selection procedures from whole

of biota are used (Roberge and Angelstam, 2004).

Comparison of rare and common taxa based on phylogenetic contrasts is another means

to develop species groups for conservation assessment (for example, Farnsworth 2007),

but such comparisons were not undertaken as part of this study. However, phylogenetic

independent contrasts were undertaken to test for phylogenetic influence on trait groups

within the species included in the project, and to test the usefulness of this approach for

grouping species.

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Here we aim to identify trait-based functional groups that provide a useful starting point

for assembling and prioritising species as a means to explore the development of broader

management options within a constrained (resource and time) scenario. The objective is

to respond to legislative requirements and to the format required by local conservation

agencies for multi-species biodiversity management and recovery plans, and hopefully to

a widely acknowledged need for conservation actions in resource and time constrained

management scenarios (Bawa et al., 2004; McNeely, 2006).

The methods and example described below emerged as a consequence of our process of

exploration of a range of options, and analysis tools, to address what we consider an

important question and challenge in biodiversity management. That is: what to do when

confronted with the problem of managing numerous threatened species in a high

biodiversity bioregion when few data and resources are available? Important distinctions

from previous approaches are the constraint of the rare species list (that is, no contrasts or

comparisons with more common taxa were undertaken) and the use of trait-based

clustering methods to identify species groups within that list, thus avoiding a-priori

sorting on the basis of potentially arbitrary or conflicting ‘expert opinion’. We establish a

starting point for prioritising management actions based on plant groups that reflect

biological and ecological aspects of species life histories and provide a preliminary

example of how to use groupings within a management context. However, it is

acknowledged, that for such approaches to succeed, conservation objectives need to be

clearly defined (Nicholson and Possingham, 2006) and managing multiple species at

multiple scales will always present unique and context dependent challenges

(Meentemeyer and Box, 1987; Fischer et al., 2004).

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Methods

Study area and data compilation

The study area for the plan presents a demanding array of challenges for conservation

planning at the bioregional multi-species level. It straddles the Southeast Queensland and

Northern New South Wales border, is geographically complex, bisected by a political

boundary, and has (across that boundary) numerous shared but differentially rare species

and habitats, including many restricted endemics (Fig. 1 and Appendix 1). The area also

represents an important subtropical centre of endemism and diversity (Webb and Tracey,

1981; Webb et al., 1984) with representatives of both tropical and temperate floras

(Burbidge, 1960), and over 50 endemic genera and more than 200 species occurring at

their southern or northern limits (McDonald and Elsol, 1984).

The high numbers of rare and threatened species in the study area belong to a broad range

of lineages, with generally very little known about their biology and ecology. This

reflects a pattern that has been identified more broadly in Australia, and in many places

around the world (see for example, Root et al., 2003). These taxa occupy habitats that

vary from larger protected areas, to fairly well protected but isolated remnants, to small

remnants in poor condition within mostly cleared land. The varying levels of landscape

disturbance and degradation, and potential / extant threats combine to create a complex

mosaic of risk.

A final list including 159 rare, threatened (scheduled) and of concern (unscheduled) plant

species to be included within the biodiversity management plan was determined by the

relevant government conservation agencies and BRBMP vegetation committee. Many of

these species are local endemic and by definition rare, and generally their life histories

and distribution patterns are poorly known with little published information / data

available. Because of the restriction in available ecological information and the lack of

time and resources to significantly increase our knowledge, we selected a range of study

traits for which information was (reasonably) easily accessible and that are recognised

more broadly as important indicators of species and functional group level performance

(Westoby and Wright, 2006).

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Trait Information

Trait information was extracted from published information sources provided as species

taxonomic descriptions (for example, Floyd, 1989; Harden, 2001), herbarium specimens

and databases, species recovery plans and other sources, and added in spreadsheet format

to develop a matrix of 159 species by ten (ranked) traits. The traits chosen included those

associated with life history strategies, dispersal distances, reproductive biology, energy

balance, hydraulic architecture, and persistence potential. The specific traits for which

information was compiled were seed size, fruit size, fruit type, dispersal mode, breeding

system, pollination mechanism, leaf size, wood density, resprouting / clonality, and

maximum height. Five traits were subsequently removed. The rationale for doing so

reflected the fact that fruit size was correlated with seed size, sex (breeding) system and

pollination data were found to be unsuitable for analysis (binary form only or incomplete

information), and wood density and maximum height were confounded (in this case)

because of the inclusion of (non-woody) herbs, epiphytes and other life forms. Despite

this reduction, the five remaining traits represent important features of plant ecological

strategies (Westoby et al., 2002; Ackerly, 2004). Trait selection, in this case, also reflects

the availability of species-level information, and the likelihood of their being informative

(see Rossetto and Kooyman, 2005; Westoby and Wright, 2006) relative to threats. Trait

rankings follow Rossetto and Kooyman (2005), and wherever possible were based on

measured data from published floras.

Habitat Types and Initial Threat Assessment

A second matrix was developed that included six habitat types, and determined species

level altitudinal distribution categories (lowland only; upland only; and combination of

both). In the study area the lowlands and some mid-altitude forest habitats have been

most affected by human-impacts, while the uplands are largely intact. Subsequently, a

threat assessment team associated with the project determined that species in the upland

cool (less disturbed and better reserved forest areas) may be subject to different threats

and (perhaps) a lower risk category (setting aside climate change; refer to Westoby and

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Burgman, 2006) than lowland species in the agricultural and human-development prone

landscape matrix.

Species were allocated to six habitat types that reflect the vegetation mapping criteria of

the project, and represent the broad community types. For convenience, and to be

consistent with the terminology used in Queensland, the structural and physiognomic rain

forest typology of Webb (1978) that uses life form diversity and structural complexity

with leaf size terminology to characterise types was used. 1 = Microphyll Vine Fern

Forest (MVFF), for example Nothofagus dominated forest often referred to as Cool

Temperate rainforest; 2 = Simple Notophyll – Microphyll Vine Forest (SNVF-SNMVF),

for example Warm Temperate rainforest dominated by Ceratopetalum; 3 = Complex –

Notophyll Vine Forest (CNVF-NVF), lowland to upland Sub-tropical rainforest); 4 =

Araucarian Notophyll – Microphyll Vine Forest (ANVF-ANMVF) representing the drier

vine forests ± Araucarian emergents; 5 = Eucalypt dominated Wet and Drier Sclerophyll

forest types; 6 = rocky outcrop, cliff, mountain-top, habitat specialists.

Data analysis methods for the target species

Multivariate analysis methods are based on the recommended methods detailed in Clarke

and Worley (2006) for different data types; including linearly ranked (trait) data. The

species by trait data were normalised and then classified by grouping similar taxa using a

simple numerical hierarchical agglomerative clustering process, and the Gower

association measure in the Primer v6 package. Similarity profile permutation tests

(Simprof) were used to test the groupings. Similarity among quadrats/sites was further

investigated through non-metric multidimensional scaling (nMDS) ordination using the

underlying resemblance matrix as input. This provides a direct representation of the

underlying classification in ordination space. Principal component analysis (PCA) was

subsequently used to visualise and examine the position of group members in component

space relative to the influence of trait variables (as axes). The Euclidean distance measure

was used in the PCA analyses.

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The relative merits of the various distance and association measures used in multivariate

analysis are discussed in more detail in Clarke et al. (2006a,b); Clarke and Gorley (2006).

In the case of the data rich (SNVF) example cited below that included site by species by

abundance by environmental data, the Bray-Curtis distance measure was used for

classification and ordination of sites.

In all cases additional analyses were undertaken to test the initial results. These included

ANOSIM permutation tests (for the R statistic), and a modified MRT (multivariate

regression tree) analysis (De’ath, 2002) referred to as the Linkage Tree procedure in the

PRIMER v6 package (Clarke and Gorley, 2006; Clarke et al., 2006c; and see Clarke and

Warwick, 2001). The outputs from these analyses provided additional opportunities to

interrogate and test the pattern of relationships of the species to life history traits and trait

combinations, and to groupings and habitats. The assessment matrix developed uses the

(Simprof tested) Primer groups generated by cluster analysis (classification) and shown in

the nMDS ordination.

Phylogenetic independent contrast tests

In order to identify potential management groups we also explored the co-distribution of

traits among taxa. However, the study did not set out to test evolutionary hypotheses

relating to phenotypic evolution or lineage diversification, or to use phylogenetic

diversity at community or habitat levels as a potential measure of priority, or as a means

to measure achievement (sensu Barker, 2002). Nevertheless, we did investigate the

impact of phylogeny on the distribution of the chosen character states (traits) across the

rare and threatened flora included (in the project) in order to provide preliminary

background information for future studies on the evolutionary emergence of the selected

traits and the evolutionary and biodiversity dynamics of the local flora.

To that end, a super-tree including all the BRBMP rare and threatened plant genera as

terminal taxa was developed using Phylomatic (Webb and Donoghue, 2005). This is an

automated process that builds a hypothetical tree based on the most recently published

phylogenetic data. Genera rather than single species were used because species-level

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phylogenies are too poorly understood to produce a useful tree. Intra-generic

relationships were not always resolved and as a result clades for which insufficient

information existed were presented as polytomies (soft), and unknown branch length

were given unit length. The multi-state traits were traced over the tree using MacClade

4.03 (Maddison and Maddison, 2001). Phylogenetic conservatism, or clustering of traits,

was examined for each trait by randomly reshuffling character states among the taxa 1000

times (Chazdon et al., 2003; Gross, 2005). Using character tracing, the actual number of

steps for each character was compared to those obtained across the 1000 randomisations.

If the actual number of steps ranked within the lowest 5% of those obtained in the

reshuffled trees, the character was considered as significantly phylogenetically clustered.

Results

Identifying appropriate trait-based groups

Figure 2 shows the nMDS Primer emergent groups (G1-G5) based on the five selected

traits for all species. Consistent with the Simprof tested dendrogram (not presented) this

shows the life history trait groupings and identifies the relationship (similarity /

dissimilarity) of trait groups. Some boundary overlap is evident in several groups, and

there are several outliers (notably in G3). Figures 3 (nMDS) and 4 (PCA) show the

distribution of species in ordination space relative to the influence of broad habitat types

(1-6). Clustering relative to the interaction of traits (see also Figs. 5a-e) and habitats is

evident, particularly in relation to seed size, fruit type and dispersal, and re-sprouting

(refer to Rossetto and Kooyman, 2005). The additional analyses undertaken (including

Simprof tests of the clustering analysis groups, and Regression Tree Analysis, in the

Primer package) confirmed the relative strength of factors influencing the clustering,

provided statistical tests of the results at a group level (Table 1), and (in the case of the

Simprof tested clustering) provided the comparative group column for the multi-species

assessment matrix (Appendix 1).

The distribution of species in ordination space relative to the influence of species

altitudinal range shows considerable overlap with little or no clustering relative to the

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selected traits in relation to species distributions along the altitudinal gradient. The

altitudinal ordinations are (therefore) not presented.

Figures 5a-e (nMDS) show the distribution of trait influence across species and species

groups in ordination space. When viewed in tandem with the habitat ordinations they

provide insight into the patterns of distribution of traits in habitats (refer to Appendix 1

for more detail). They confirm the influence of seed size, dispersal mode, and the

influence of re-sprouting / clonality (as persistence).

The phylogenetic conservatism test showed that phylogeny significantly affects the

clustering of character states for nearly all the traits studied, with the exception of

resprouting. However, we found that phylogenetic groups were not as useful as functional

trait groups because species in the same family or genera often did not share the same

combination of life history traits necessary to inform and develop threat / risk

assessments at group and habitat level(s).

BRBMP Project Listed Species

The species included in the BRBMP are predominantly rainforest species however the list

includes taxa from outside the rainforest habitat and represents at least four rainforest

habitat types. The range of life forms is also wide, with trees, shrubs, herbs, sedges,

orchids, ferns and epiphytes represented. Plant functional traits are known to vary along

environmental gradients (reflecting both within and between habitat variation), and

within communities of species occupying habitats with similar conditions. In this case the

focus was on potential species level trait variation in relation to habitat variation and

species altitudinal distributions, and these were the key factors used to determine and test

emergent trait-based functional groups. Potential or identified threat and risk categories in

the final plan reflect (and are influenced by) both landscape position (upland or lowland)

and subsequent susceptibility to threatening processes, and are related to species life

forms, life history traits, and life cycle aspects reflected in the groups.

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Description of trait-based groups obtained from cluster analysis

Figure 2 (nMDS) provides an overview of the relationship of trait-groups (G1-5), while

Appendix 1 provides the list of species in each group (column heading - G) as part of the

multi-species assessment matrix. Group descriptions (1-5) are provided below to assist

interpretation.

Group 1: Species in this group have large fruits, are often dispersal limited (dispersal

modes include gravity and/or rodents; refer to Rossetto and Kooyman, 2005), are mostly

large canopy to medium to small persistent trees with the capacity to resprout, and are

mature phase shade tolerant rainforest species. An outlier in the group (extreme right side

of group) is the large herb Doryanthes palmeri (rocky outcrop specialist with big leaves

and big fruits), but habitat based sub-grouping resolves this. Most of these species are

persistent and consequently potentially resistant to a range of threats (Rossetto and

Kooyman, 2005). Some are found only within the existing reserve system (for example

Eidothea hardeniana and Elaeocarpus sedentarius ms.), while others are more vulnerable

to anthropogenic threats on private land (notably the two Macadamia species).

Group 2: Species in this group are mostly small seeded herbs, sedges, shrubs and trees.

The group is split almost in half (left and right) between rainforest habitats (2-4), and Wet

Sclerophyll (5) and Cliff and Rock Outcrop (6) habitat specialists. Habitat provides a

robust secondary explanation for the allocation of species in the sub-groups, relative to

potential management actions. The cliff-top and rocky outcrop habitat group is dominated

by shrubs and herbs; the wet sclerophyll habitat group includes sedges and shrubs; while

the rainforest habitat group includes a variety of life forms (herbs, vines, a sedge, shrubs,

and trees). A number of the latter group occur on the lowlands and are threatened by a

variety of factors. The high altitude rocky outcrop group has several species that are

potentially at risk from tourism activities and infrastructure (for example the cliff-top

herb Euphrasia bella).

Group 3: Species in this group are mostly ferns, orchids, and epiphytes (representing

distinct life form groupings) with mostly very small seeds. The group does include

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several woody plants including one Gymnosperm (Callitris baileyi) and one Asteraceae

shrub (Cassinia collina). The outliers include several cliff top herbs and two sedges. The

rocky outcrop (cliff) specialists from higher altitudes could (therefore) be grouped with

those from the previous group for management convenience, though there is some

variation on the basis of traits related to dispersal.

Group 4: The species in this group are mostly woody vines, trees, shrubs and parasitic

mistletoes, with the two sub-groupings (interestingly sharing a similar mix of life forms)

representing a split in species habitat preference for moist rainforest habitats (2-3) and the

drier vine forest (4) and other habitats (5-6).

Group 5: The species in this group are mostly persistent trees and shrubs including

clonal and re-sprouting species, with most species from the rainforest habitat types (1-4),

and a few shrubs from the wet sclerophyll and rocky outcrop habitats.

Discussion

Multi-species planning based on trait-related groups: conceptual framework

Trait-based grouping has two main advantages. First, by cross-referencing to relevant

taxa within the same group, it facilitates the delivery of preliminary recovery actions even

for threatened species for which little ecological information is available. Second, it

enables improved prioritisation of the taxa in need of further research (eg. representative

taxa from poorly researched groups) and identifies the type of data needed (refer to

Appendix 1). For many species, detailed data on distribution, population size, population

dynamics and demographics, population biology and ecology (inclusive of genetic

aspects), habitat aspects inclusive of environmental gradients, and community species

richness and relative abundances are absent. In this case, the assessment matrix has

identified the knowledge gaps, and a separate implementation process will fill those gaps

through time relative to nominated Recovery Plan criteria, targets, and time-lines. As the

assessment matrix, and group models, are populated with additional data through time,

specific management based responses to identified (landscape scale) threats can be

developed, tested, and modelled at the species and multi-species levels (both by group

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and habitat/community; Appendix 1). Furthermore, once the process has started and the

model is developed, adding new species to the system is relatively simple.

The accumulation of species level information for selected taxa will target representative

and potentially informative species within the identified groups. It is intended that

information accumulation at the species level should also inform the functional group and

multi-species levels. Research results may then be more quickly available, informative,

and broadly applicable than current species level approaches allow. This progressive

implementation approach theoretically enables management actions to be taken on an

increasing number of taxa as new information becomes available.

The approach suggested here offers a balance between the need for urgent action, both

on-ground and in terms of the requirements and timelines associated with threatened

species legislation, and the requirement for a sound scientific background on which to

base biodiversity management and recovery planning. The trait-based framework for the

multi-species planning approach proposed allows for a targeted and progressive

accumulation of species level data (biological, ecological and evolutionary) through time

without compromising the capacity of agencies and authorities to undertake initial

threat/risk management analyses based on available information. That is, in contrast to a

resource constrained species-level scenario, the group-based approach allows agencies to

identify and fill significant knowledge gaps more quickly and evenly, and as a

consequence theoretically provides the opportunity to take appropriate conservation

action in a reduced time-frame across a larger number of taxa in a habitat.

Implementing threatened flora management strategies

We suggest that data reflecting trait-based functional group composition in relation to

community composition and environmental gradients provide the means to develop

improved management approaches and responses to identified threats. Put simply, the

relationship of biologically and ecologically derived groups to environmental gradients,

combined with a measure of the relative susceptibility of species in those groups to

threats, can enable more informed extrapolation to data-poor taxa. We believe that this

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may be the ‘core’ information required to effectively implement biodiversity

management plans using a multi-species approach. Unlike other surrogate type

approaches, it provides information that links management to all species. Further to this,

the approach works well even with artificial and heterogeneous species lists, because

consulting the assessment matrix immediately provides information on relevant within-

group distinctions (see example below).

We propose a three-stage approach for the development of multi-species recovery plans

based on the use of trait-based groups developed from a limited database and relying on

limited resources.

1. The first stage involves the accumulation of existing species level information based

(as a minimum) on the traits used here, and reflecting the dispersal and persistence

components of species life histories. In drier habitats this would include factors such

as soil seed reserve and on-plant seed storage, as examples.

2. The second stage determines trait-based functional groupings on the basis of that

information, compiled either as ranked or continuously measured trait data, by using

the multivariate clustering methods recommended (and tested) in this study. This

information is then added to the assessment matrix provided in Appendix 1 that

identifies the life history trait-based groups and further sorts the groups by habitat

types. The matrix also provides an overview of available information related to

conservation status, level of threat, and other factors that can inform conservation

management. The information in the assessment matrix, in combination with threat

susceptibility ranking, will determine species and group level priority based on risk of

decline and extinction.

3. The third stage identifies knowledge gaps and targets selected taxa representative of

each group for additional data collection through time. The additional information

that needs to be collected for representative species from each functional group

during the implementation phase of the multi-species biodiversity management plan

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includes data related to species distributions, population structure, population size,

population demographics, genetic diversity and structure, reproductive biology and

dispersal. This is the information that will help predict possible group responses to

identified threats.

Developing multi-species recovery strategies: a preliminary example

As a preliminary example, a number of the species in Group 1 (including Endiandra

introrsa, Eidothea hardeniana and Elaeocarpus sedentarius ms.) occur within SNVF-

SNMVF, and have almost the whole of their distributional extent represented therein.

Two of these species (E. hardeniana and E. sedentarius ms.) have been studied in

considerable detail and adequate life history and habitat (community) information is

available (refer to Appendix 1) to use them in a representative group example. The SNVF

species occur in larger areas of reserved ‘natural’ habitat, and have very small population

sizes, patchy and limited distribution, are known to be dispersal limited, and have limited

capacity for both population expansion and/or response to disturbance events that further

reduce population sizes. The group also includes two Macadamia species that have a

similar combination of life history traits but occur predominantly in lowland sub-tropical

rainforest (CNVF) habitats that are threatened by agricultural activities and development

proposals. Dispersal of all these species is based predominantly on gravity, and rodent

species that are known seed predators and have limited home ranges.

All the woody species in the group are relatively persistent (capable of resprouting)

mature phase species with relatively slow turnover (and growth) rates. This suggests that

despite small population size and subsequent heightened susceptibility to stochastic

events, the combination of in-situ persistence and storage can mitigate the influence of

unfavourable periods of time (or events) for reproduction (Higgins et al., 2000; Bond and

Midgley, 2001; Rossetto and Kooyman, 2005). In this case, only persistent or extreme

factors (such as total removal of available habitat by, for example, land clearing for

development or identified risk factors such as pollen pollution by commercial orchard

grown Macadamia) will remove such species completely.

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An illustrative example of generalised management options for Group 1 species

1. Highland / protected populations (species):

Translocation: unlikely to be necessary in populations that are within protected areas

as these are protected and even small populations can contain sufficient diversity to

be viable through mechanisms including long-term persistence of genetic individuals

and preferentially outcrossed breeding systems (for example, Eidothea hardeniana,

Elaeocarpus sedentarius, Endiandra introrsa, E. globosa, Niemeyera whitei, and

likely Hicksbeachia pinnatifolia). While it is acknowledged that low genetic

variability is likely in species with excessive reliance on vegetative reproduction (for

example, the lowland species Elaeocarpus williamsianus, and Davidsonia johnsonii,

both present in Group 5) the species in Group 1 show no tendency to clonality despite

several species having small population sizes (for example, Eidothea hardeniana). In

cases where translocation might be regarded as necessary care should to be taken with

selection of material as molecular studies have shown that significant genetic spatial

autocorrelation can result in considerable between-population genetic structure in

large-fruited species, especially in the presence of local landscape barriers (for

example, Eidothea hardeniana, Elaeocarpus sedentarius).

Actions: As a result of this improved understanding, conservation interventions based

on maximising within-species diversity need to be done with awareness of landscape

and habitat features that can act as ‘natural’ barriers to gene flow and protect localised

within species diversity.

2. Lowland populations (species):

Translocation: could be necessary in lowlands where fragmentation is an issue, and

where single individuals, or very small populations are found within remnants (for

example, Endiandra floydii, Floydia praealta, Pouteria eerwah, Neisosperma

poweri). The main difference with the previous group is that these small populations

are likely to be a consequence of anthropogenic activity, rather than being at

equilibrium. Species in the genus Macadamia have been identified as being

vulnerable to pollen limitation (Pisanu, 2001), and the two lowland Macadamia

20

species in Group 1 must be regarded as vulnerable to pollen pollution from

commercial orchard grown Macadamia cultivars, particularly when natural sources

are not available (i.e. within small populations).

Buffer zone: In the context of these lowland species it might be essential to the

survival of smaller natural and translocated populations to be protected from factors

such as pollen pollution (for Macadamia), and disturbance and climatic events and

variables, by appropriate scale buffers.

Habitat expansion: The small scale of some of the isolated remnants and remnant

populations of some of these lowland species suggests that it may be important to

both increase the population size and within species diversity of the populations and

the size of the area of forest habitat, to secure a future for the species.

Management approaches for the species within the group are therefore similar in the same

habitats but differ between habitats according to relative threat susceptibility. This shows

that the trait-based groupings, in combination with habitat and distributional information,

can provide a workable and effective approach to define, develop, and deliver appropriate

conservation management actions. This remains contingent on adequate ecological and

biological information being available for representative species within groups, and in

relation to habitats and distribution relative to landscape threats.

Acknowledgements

This project was partly funded by the New South Wales Department of Environment and

Conservation, Andrew Hall, and Rainforest Rescue. We acknowledge the support of the

BRBMP team from NSW DEC, Paul Houlder for providing the map and Chris Allen,

Doug Benson, Caroline Gross and Bob Makinson for providing comments on earlier

drafts of the manuscript.

21

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Figure 1: Map of the study area. Figure 2: Non-metric Multidimensional Scaling (nMDS) ordination showing 159 species by 5 traits. Simprof tested trait-groups (1-5) are shown (G1-G5). Trait groups represent those influenced by seed size (larger)/ fruit type and the dispersal dimension; and the largely independent traits of resprouting (persistence) and leaf size. Stress in ordination: 0.13. Figure 3: Non-metric Multidimensional Scaling (nMDS) ordination showing 159 species by 5 traits and habitat a-priori group distribution(s). A-priori groups 1-4 (rainforest) with 1=MVFF; 2=SNMVF; 3=CNVF; 4=ANMVF; and groups 5-6 (non-rainforest), with 5=Eucalypt forest (wet and drier sclerophyll); 6=rocky/cliffs, habitat specialists. The ordination shows the relationship of trait groups to habitat types. Refer to Figs. 2 and 7A-E. Stress in ordination: 0.13. Figure 4: PCA (principal component analysis) ordination showing 159 species by 5 traits by 6 habitats. Some overlap between habitat groups is evident. Consistent with the nMDS analyses, these represent trait groups influenced (primarily) by seed size/fruit type and the dispersal dimension; resprouting (persistence); and leaf size. A-priori groups 1-4 (rainforest) with 1=MVFF; 2=SNMVF; 3=CNVF; 4=ANMVF; and groups 5-6 (non-rainforest), with 5=Eucalypt forest (wet and drier sclerophyll); 6=rocky/cliffs, habitat specialists. Figure 5: Non-metric Multidimensional Scaling (nMDS) ordination showing 159 species by 5 traits by altitudinal group distribution(s); (1=lowland; 2=upland and lowland; 3=upland). Species are distributed in the ordination relative to seed size (larger)/ fruit type and the dispersal dimension; and resprouting (persistence). Refer to Figs 7A-E. The ordination shows that the selected traits and trait combinations are distributed across the range of species representing altitudinal distribution groups and confirms that, in this case, altitude has only a minor influence on trait variation. Stress in ordination: 0.13. Figure 6: PCA (principal component analysis) ordination showing 159 species by 5 traits by 3 altitudinal groups. Considerable overlap across the altitudinal distribution groups is evident. This confirms that habitat types are the most influential variable and altitude has only a minor influence on trait variation (in this case). 1=lowland; 2=upland and lowland; 3=upland. Figures 7 (a-e): Non-metric Multidimensional Scaling (nMDS) ordination (bubble-plots) showing 159 species by five traits (PRIMER v6); in relation to the prevalence and distribution of the correlated traits of dispersal mode / fruit type and seed size (a-c); and the largely independent variables of re-sprout (d) and leaf size (e). 7a. Dispersal Mode: 1 represents frugivore dispersed; 2 wind dispersed, 3 mammal; 4 ant (secondary); and 5 gravity. 7b Seed Size: 1 represents the smallest seeds and 5 the largest.7c Fruit Type: 1 represents fleshy fruits; 2 coloured arils; 3 non fleshy (dry); 4 spores; and 5 other.7d Re-sprout (persistence): 1 represents no resprouting; 2 intermediate disturbance response only; and 3 yes (as mature phase stem replacement; refer to Rossetto and Kooyman 2005).7e Leaf Size: 1 represents smallest (nanophyll); 2 microphyll; 3 notophyll; 4 mesophyll; and 5 macrophyll.

26

Fig 1.

27

Fig. 2

Fig. 3

28

Fig. 4

Fig. 5

29

Fig. 6

30

7a. Dispersal Mode 1 represents frugivore dispersed; 2 wind dispersed, 3 mammal; 4 ant (secondary); and 5 gravity.

7b. Seed Size 1 represents the smallest seeds and 5 the largest.

31

7c. Fruit Type 1 represents fleshy fruits; 2 coloured arils; 3 non fleshy (dry); 4 spores; and 5 other.

7d. Re-sprout (persistence) 1 represents no resprouting; 2 intermediate disturbance response only; and 3 yes (as mature

phase stem replacement; refer to Rossetto and Kooyman 2005).

32

7e. Leaf Size 1 represents smallest (nanophyll); 2 microphyll; 3 notophyll; 4 mesophyll; and 5 macrophyll.

33

Table 1: Results of LINKTREE (classification and regression tree analysis) Simprof test in PRIMER v6 (1000 permutations) showing major splits (A-G) in link-tree dendrogram (not presented) by R-value (non-parametric measure of multivariate distance) and B% (absolute measure of group differences) by most significant trait(s).

Group R B% Trait

A 0.63 86.2 dispersal mode

B 0.73 67.3 dispersal mode

C 0.70 48.6 resprout

D 0.69 30.6 seed size E 0.88 24.8 leaf size

F 0.84 81.1 seed size

G 0.72 65.7 dispersal mode

Table 2: Results from the trait clustering tests, showing the actual number of steps, range of steps in randomized trees, and proportion of trees with a number of steps lower or equal to the actual number.

Trait Observed steps Range of steps in 1000

randomisations

Significance

Seed size 24 24-42 0.001 Fruit type 25 25-42 0.001 Dispersal 26 26-46 0.001 Leaf size 32 30-45 0.006 Resprouting 23 20-33 0.6

34

Appendix 1 - Border Ranges Biodiversity Assessment Matrix List of species included in the BRBMP ordered according to the placement within the five trait-based groups (G). Additional available ecological and environmental information useful for decision-making relating to threat management and conservation is also listed (H: habitat type; LF: life form; AD: altitudinal distribution; PS: population size). The final group of columns lists the availability (Y: data available; -: data not available; ip: research in progress) of ecological data relating to major areas of scientific research useful to the conservation and management of species (PSt: population structure; DS: demographic structure; LP: landscape patchiness; DE: distributional extent; FO: frequency of occurrence in habitat; GD: genetic diversity; GS: genetic structure; BM: breeding mechanisms). Other symbols represent: H (habitats 1 to 6); LF (t:tree; st:small tree; s:shrub; v:vine; h:herb; se:sedge; f:fern; fa:fern ally; e:epiphyte; p:parasitic mistletoe; go:ground orchid); AD (1:lowland only; 2:lowland and upland; 3:upland); PS (s:small; m:medium; l:large).

Species Family G H LF AD PS PSt DS LP DE FO GD GS BM

Eidothea hardeniana Proteaceae 1 2 t 3 s Y Y Y Y Y Y Y ip

Elaeocarpus sedentarius Elaeocarpaceae 1 2 t 3 s Y Y Y Y Y Y Y ip

Endiandra globosa Lauraceae 1 2 t 2 l - - - - - - - -

Endiandra introrsa Lauraceae 1 2 t 3 l Y Y Y Y Y - - -

Niemeyera whitei Sapotaceae 1 2 t 2 l - - Y Y - - - -

Corynocarpus rupestris subsp. rupestris Corynocarpaceae 1 3 t 2 s - - - - - - - -

Endiandra compressa Lauraceae 1 3 t 1 s - - - - - - - -

Endiandra floydii Lauraceae 1 3 t 1 m - - - - - - - -

Floydia praealta Proteaceae 1 3 t 1 m - - - - - - - -

Hicksbeachia pinnatifolia Proteaceae 1 3 t 2 l - - Y Y - - - -

Macadamia tetraphylla Proteaceae 1 3 st 1 m Y Y Y Y Y Y Y Y

Neisosperma poweri Apocynaceae 1 3 st 1 m - - - - - - - -

Pouteria eerwah Sapotaceae 1 4 t 1 s - - - - - - - -

Macadamia integrifolia Proteaceae 1 4 st 1 m Y Y Y Y Y Y Y Y

Doryanthes palmeri Doryanthaceae 1 6 h 3 m - - Y Y - ip ip -

Acacia orites Fabaceae 2 2 t 3 l - - - - - - - -

Carex hubbardii Cyperaceae 2 2 se 3 m - - - - - - - -

Cassia brewsteri var. marksiana Fabaceae 2 2 t 1 s - - - - - - - -

Olearia heterocarpa Asteraceae 2 2 s 3 m - - - - - - - -

Bosistoa pentacocca var. pentacocca Rutaceae 2 3 t 2 m - - - - - - - -

Bosistoa selwynii Rutaceae 2 3 t 1 s - - - - - - - -

Bosistoa transversa Rutaceae 2 3 t 1 s - - - - - - - -

Desmodium acanthocladum Fabaceae 2 3 s 1 l - - - - - - - -

Harnieria hygrophiloides Acanthaceae 2 3 s 1 m - - - - - - - -

Helmholtzia glaberrima Phylidraceae 2 3 h 3 l - - - - - - - -

Isoglossa eranthemoides Acanthaceae 2 3 h 1 s - - - - - - - -

Senna acclinis Fabaceae 2 3 s 2 s - - - - - - - -

Corchorus cunninghamii Malvaceae 2 4 s 2 s - - Y Y - Y Y -

Rhynchosia acuminatissima Fabaceae 2 4 v 2 s - - - - - - - -

Cyperus semifertilis Cyperaceae 2 5 se 3 m - - - - - - - -

Lepidosperma clipeicola Sapindaceae 2 5 se 3 l - - - - - - - -

Sophora fraseri Fabaceae 2 5 s 2 s - - - - - - - -

Westringia blakeana Lamiaceae 2 5 s 3 l - - - - - - - -

Zieria collina Rutaceae 2 5 s 2 m - - - - - - - -

Zieria southwellii Rutaceae 2 5 s 3 l - - - - - - - -

Argophyllum nullumense Escalloniaceae 2 6 s 2 l - - - - - - - -

Cyperus rupicola Cyperaceae 2 6 se 3 m - - - - - - - -

Euphrasia bella Scrophulariaceae 2 6 h 3 s - - - - - - - -

Gaultheria viridicarpa subsp. merinoensis Ericaceae 2 6 s 3 s - - - - - - - -

Huperzia varia Lycopodiaceae 2 6 fa 3 m - - - - - - - -

Leionema gracile Rutaceae 2 6 s 3 m - - - - - - - -

Wahlenbergia glabra Campanulaceae 2 6 h 3 s - - - - - - - -

Wahlenbergia scopulicola Campanulaceae 2 6 h 3 s - - - - - - - -

Xerochrysum bracteatum subsp. Mt Merino Asteraceae 2 6 h 3 s - - - - - - - -

Lastreopsis silvestris Dryopteridaceae 3 1 f 3 s - - - - - - - -

Bulbophyllum caldericola Orchidaceae 3 2 e 3 m - - - - - - - -

35

Grammitis stenophylla Grammitidaceae 3 2 f 3 m - - - - - - - -

Lindsaea brachypoda Dennstaedtiaceae 3 2 f 3 s - - - - - - - -

Angiopteris evecta Marrattiaceae 3 3 f 1 s - - - - - - - -

Belvisia mucronata Polypodiaceae 3 3 e 2 s - - - - - - - -

Bulbophyllum argyropus Orchidaceae 3 3 e 3 m - - - - - - - -

Bulbophyllum globuliforme Orchidaceae 3 3 e 3 m - - - - - - - -

Clematis fawcettii Ranunculaceae 3 3 v 2 m - - - - - - - -

Crepidomanes vitiense Hymenophyllaceae 3 3 f 3 s - - - - - - - -

Cyathea cunninghamii Cyatheaceae 3 3 f 2 m - - - - - - - -

Dendrobium schneiderae var. schneiderae Orchidaceae 3 3 e 3 s - - - - - - - -

Oberonia complanata Orchidaceae 3 3 go 1 s - - - - - - - -

Peristeranthus hillii Orchidaceae 3 3 e 1 s - - - - - - - -

Psilotum complanatum Psilotaceae 3 3 e 1 s - - - - - - - -

Sarcochilus dilatatus Orchidaceae 3 3 e 2 m - - - - - - - -

Sarcochilus fitzgeraldii Orchidaceae 3 3 e 2 s - - - - - - - -

Sarcochilus hartmannii Orchidaceae 3 3 e 2 s - - - - - - - -

Sarcochilus weinthalii Orchidaceae 3 3 e 3 s - - - - - - - -

Bulbophyllum weinthalii Orchidaceae 3 4 e 3 m - - - - - - - -

Callitris baileyi Cupressaceae 3 5 t 1 s - - - - - - - -

Cassinia collina Asteraceae 3 5 s 3 m - - - - - - - -

Asplenium harmanii Aspleniaceae 3 6 e 3 m - - - - - - - -

Drynaria rigidula Polypodiaceae 3 6 f 2 m - - - - - - - -

Gen.(Aq247974) sp. Mt Merino Asteraceae 3 6 h 3 s - - - - - - - -

Ozothamnus vagans Asteraceae 3 6 s 3 l - - - - - - - -

Podolepis monticola Asteraceae 3 6 h 3 m - - - - - - - -

Acronychia baeuerlenii Rutaceae 4 2 s,st 3 m Y ? Y Y Y - - -

Austrobuxus swainii Euphorbiaceae 4 2 t 3 l Y ? Y Y Y - - -

Helicia ferruginea Proteaceae 4 2 st 3 l Y - Y Y Y - - -

Pandorea baileyana Bignoniaceae 4 2 v 3 l - - - - - - - -

Pararistolochia laheyana Aristolochiaceae 4 2 v 3 l - - Y Y Y - - -

Streptothamnus moorei Salicaceae 4 2 v 3 l Y - Y Y Y - - -

Symplocos baeuerlenii Symplocaceae 4 2 s,st 3 l Y - Y Y Y - - -

Alloxylon pinnatum Proteaceae 4 3 st 3 m Y - Y Y - - - -

Amyema flexialabstra Loranthaceae 4 3 p 1 s Y - Y Y Y - - -

Amyema plicatula Loranthaceae 4 3 p 1 s - - - - - - - -

Archidendron hendersonii Fabaceae 4 3 t 1 m Y - Y Y Y - - -

Archidendron muellerianum Fabaceae 4 3 st 2 l Y - Y Y Y - - -

Baloghia marmorata Euphorbiaceae 4 3 st 1 s Y - Y Y Y - - -

Cordyline congesta Agavaceae 4 3 s 1 m Y - Y Y Y - - -

Cryptocarya foetida Lauraceae 4 3 st 1 m Y - Y Y Y - - -

Cupaniopsis flagelliformis var. australis Sapindaceae 4 3 t 2 m Y - Y Y Y - - -

Cupaniopsis newmanii Sapindaceae 4 3 s,st 2 l Y - Y Y Y - - -

Davidsonia jerseyana Cunoniaceae 4 3 st 1 m Y Y Y Y Y ip ip ip

Dendrocnide moroides Urticaceae 4 3 s,st 2 s - - - - - - - -

Diospyros mabacea Ebenaceae 4 3 s,st 1 s Y - Y Y Y - - -

Diospyros major var. ebenus Ebenaceae 4 3 st 1 s Y - Y Y Y - - -

Diploglottis campbellii Sapindaceae 4 3 t 1 s Y - Y Y Y - - -

Endiandra hayesii Lauraceae 4 3 t 2 m Y - Y Y Y - - -

Endiandra muelleri subsp. bracteata Lauraceae 4 3 t 2 s Y - Y Y Y - - -

Grevillea hilliana Proteaceae 4 3 t 1 m Y - Y Y - - - -

Hypserpa decumbens Menispermaceae 4 3 v 1 m - - - - - - - -

Jasminum jenniae Oleaceae 4 3 v 2 m - - - - - - - -

Lepiderema pulchella Sapindaceae 4 3 se 2 m Y - Y Y Y - - -

Marsdenia coronata Apocynaceae 4 3 v 2 s - - - - - - - -

Marsdenia hemiptera Apocynaceae 4 3 v 1 s - - - - - - - -

Melicope vitiflora Rutaceae 4 3 st 1 s Y - Y Y Y - - -

Owenia cepiodora Meliaceae 4 3 t 2 s Y - Y Y Y - - -

Pararistolochia praevenosa Aristolochiaceae 4 3 v 1 m Y - Y Y Y - - -

36

Parsonsia tenuis Apocynaceae 4 3 v 3 m - - - - - - - -

Randia moorei Rubiaceae 4 3 st 1 m Y - Y Y Y - - -

Syzygium hodgkinsoniae Myrtaceae 4 3 t 2 m Y - Y Y Y - - -

Syzygium moorei Myrtaceae 4 3 t 1 m Y - Y Y Y - - -

Tinospora tinosporoides Menispermaceae 4 3 v 1 l Y - Y Y Y - - -

Acacia bakeri Fabaceae 4 4 t 2 s Y - Y Y - - - -

Brachychiton sp. Ormeau Malvaceae 4 4 t 1 s - - - - - - - -

Cupaniopsis serrata Sapindaceae 4 4 st 1 s - - - - - - - -

Cupaniopsis tomentella Sapindaceae 4 4 st 2 m - - - - - - - -

Marsdenia longiloba Asclepiadaceae 4 4 v 1 s - - - - - - - -

Muellerina myrtifolia Loranthaceae 4 4 p 2 s - - - - - - - -

Tarenna cameronii Rubiaceae 4 4 st 1 s - - - - - - - -

Tinospora smilacina Menispermaceae 4 4 v 2 s - - Y Y - - - -

Turraea pubescens Meliaceae 4 4 s,st 2 s - - - - - - - -

Eucalyptus dunnii Myrtaceae 4 5 t 1 m Y - Y Y - - - -

Solanum limitare Solanaceae 4 5 s 3 s - - - - - - - -

Tylophora woollsii Apocynaceae 4 5 v 2 s Y - Y Y - - - -

Leucopogon sp. Lamington Ericaceae 4 6 s,st 3 m - - - - - - - -

Pittosporum oreillyanum Pittosporaceae 5 1 s 3 m Y - Y Y - - - -

Corokia whiteana Escalloniaceae 5 2 s 3 l Y - Y Y Y - - -

Cryptocarya meisneriana Lauraceae 5 2 s 3 l Y - Y Y Y - - -

Daphnandra tenuipes Monimiaceae 5 2 t 3 l Y - Y Y Y - - -

Lenwebbia prominens Myrtaceae 5 2 st 3 l Y - Y Y Y - - -

Uromyrtus australis Myrtaceae 5 2 st 3 l Y Y Y Y Y - - Y

Acronychia littoralis Rutaceae 5 3 st 1 s Y - Y Y Y - - -

Actephila grandifolia Euphorbiaceae 5 3 s 2 m - - Y Y - - - -

Ardisia bakeri Myrsinaceae 5 3 s 2 m - - - - - - - -

Davidsonia johnsonii Cunoniaceae 5 3 st 1 s Y Y Y Y Y ip ip ip

Elaeocarpus williamsianus Elaeocarpaceae 5 3 t 1 s Y Y Y Y Y Y Y Y

Eucryphia jinksii Cunoniaceae 5 3 t 3 s Y Y Y Y Y - - -

Fontainea australis Euphorbiaceae 5 3 st 2 m - - - - - Y Y -

Fontainea oraria Euphorbiaceae 5 3 st 1 s Y Y Y Y Y Y Y ip

Lenwebbia sp. Main Range Myrtaceae 5 3 st 3 m - - - - - - - -

Mischocarpus lachnocarpus Sapindaceae 5 3 st 2 m - - - - - - - -

Ochrosia moorei Apocynaceae 5 3 st 2 s - - Y Y Y - - -

Phyllanthus microcladus Euphorbiaceae 5 3 s 1 s - - - - - - - -

Quassia sp. Mt Nardi Simaroubaceae 5 3 s 2 m - - Y Y - - - -

Rhodamnia maideniana Myrtaceae 5 3 s,st 1 m - - Y Y - - - -

Uromyrtus lamingtonensis Myrtaceae 5 3 st 3 l ? Y Y Y Y - - -

Wilkiea austroqueenslandica Monimiaceae 5 3 s,st 2 l - - Y Y Y - - -

Xylosma terrae-reginae Salicaceae 5 3 st 1 m - - Y Y - - - -

Acalypha eremorum Euphorbiaceae 5 4 s 1 s - - - - - - - -

Choricarpia subargentea Myrtaceae 5 4 t 1 s Y Y Y Y Y - - -

Citrus australasica Rutaceae 5 4 s,st 1 m - - Y Y - - - -

Coatesia paniculata Rutaceae 5 4 st 1 s - - - - - - - -

Cryptocarya floydii Lauraceae 5 4 st 3 m - - - - - - - -

Fontainea venosa Euphorbiaceae 5 4 st 1 s - - - - - - - -

Gossia fragrantissima Myrtaceae 5 4 st 1 s Y Y Y Y Y - - -

Pouteria cotinifolia var. cotinifolia Sapotaceae 5 4 st 1 s - - - - - - - -

Hibbertia hexandra Dilleniaceae 5 5 s 3 l Y Y Y Y Y - - -

Leionema elatius subsp. beckleri Rutaceae 5 5 s,st 3 l Y - Y Y Y - - -

Myrsine richmondensis Myrsinaceae 5 5 s,st 2 s - - - - - - - -

Pomaderris notata Rhamnaceae 5 5 s 3 m - - - - - - - -

Pimelea umbratica Thymelaeaceae 5 6 s 3 m - - - - - - - -

Plectranthus nitidus Lamiaceae 5 6 l 3 l - - Y Y - - - -

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Appendix 2

Priority integrative research identified by the Assessment Matrix as necessary for the

implementation of the Border Ranges Biodiversity Management Plan

The trait-based conceptual framework for the multi-species planning approach allows for a targeted and

progressive accumulation of species level data (biological, ecological and evolutionary) through time

without compromising the capacity of agencies and authorities to undertake initial threat/risk

management analyses based on available information. In particular, the Assessment Matrix (Appendix

1) clearly identifies priority areas for integrative research implementation in relation to obtaining

relevant demographic, genetic and breeding systems information. Furthermore, two of the trait-related

groups are conspicuous for the near total absence of relevant biological information, which is likely to

hinder any meaningful management process. These are Group 2 (mostly represented by small seeded

herbs, sedges, shrubs, and trees from five out of six of the habitats included within this plan) and Group

3 (including mostly ferns, orchids, and epiphytes with generally very small seeds and related dispersal

modes and capabilities).

Implementation actions should ensure that relevant information on distribution, population size,

population dynamics and demographics, population biology and ecology (inclusive of genetic aspects),

habitat aspects inclusive of environmental gradients, and community species richness and relative

abundances is obtained (and compiled) for selected representative taxa (see priority list below). This

way, the biodiversity assessment tool, and group models, will be populated with the essential additional

data that will identify specific management based responses to threats at the species and ecological

community levels. This is the basis of the progressive implementation approach presented that enables

implementation actions to be taken on an increasing number of taxa as new information becomes

available.

Appendix 3 shows, as a simplified example, part of the community-level information that can be

efficiently obtained through well planned and integrated data gathering. It describes species

distributions and abundance, and species richness in communities in relation to the influence of

environmental gradients. Such data is both interpretative and predictive, particularly in regard to

describing the realised niche, and, when combined with relevant genetic and population dynamics data,

will be essential for the management and restoration of endangered ecological communities.

38

The additional information that needs to be collected for the representative species from each

functional group listed below includes data related to species distributions, population structure,

population size, population demographics, genetic diversity and structure, reproductive biology and

dispersal. Because of recent technical and analytical developments and existing local experience, this

information can be gathered relatively rapidly and efficiently, and will be essential to predict group

responses to identified threats.

Table 1 List of taxa that need to be prioritised for the integrative research approach described above. Existing and available information are listed (PSt: population structure; DS: demographic structure; LP: landscape patchiness; DE: distributional extent; FO: frequency of occurrence in habitat; GD: genetic diversity; GS: genetic structure; BM: breeding mechanisms). * high priority taxa

Species Family PSt DS LP DE FO GD GS BM

Group 1

*Endiandra introrsa Lauraceae y y y y y x x x

*Endiandra floydii Lauraceae x x x x x x x x

*Hicksbeachia pinnatifolia Proteaceae x x y y x x x x

Niemeyera whitei Sapotaceae x x y y x x x x

*Pouteria eerwah Sapotaceae x x y y x x x x

Group 2

Olearia heterocarpa Asteraceae x x x x x x x x

Bosistoa pentacocca var. pentacocca Rutaceae x x x x x x x x

*Bosistoa transversa Rutaceae x x x x x x x x

Corchorus cunninghamii Malvaceae x x y y x y y X

*Rhynchosia acuminatissima Fabaceae x x x x x x x x

*Senna acclinis Fabaceae x x x x x x x x

Sophora fraseri Fabaceae x x x x x x x x

*Harnieria hygrophiloides Acanthaceae x x x x x x x x

Zieria collina Rutaceae x x x x x x x x

Zieria southwellii Rutaceae x x x x x x x x

*Euphrasia bella Scrophulariaceae y x y x x x x x

*Gaultheria viridicarpa subsp.

merinoensis Ericaceae x x y y x x x x

Wahlenbergia glabra Campanulaceae x x x x x x x x

Wahlenbergia scopulicola Campanulaceae x x x x x x x x

Cyperus rupicola Cyperaceae x x x x x x x x

Lepidosperma clipeicola Cyperaceae y x y y y x x x

Group 3

*Bulbophyllum caldericola Orchidaceae x x x x x x x x

*Bulbophyllum globuliforme Orchidaceae x x x x x x x x

*Clematis fawcettii Ranunculaceae x x x x x x x x

Dendrobium schneiderae var.

schneiderae Orchidaceae x x x x x x x x

*Sarcochilus fitzgeraldii Orchidaceae x x x x x x x x

Sarcochilus hartmannii Orchidaceae x x x x x x x x

*Sarcochilus weinthalii Orchidaceae x x x x x x x x

Ozothamnus vagans Asteraceae x x x x x x x x

Podolepis monticola Asteraceae x x x x x x x x

Group 4

Helicia ferruginea Proteaceae y x y y y x x x

*Archidendron hendersonii Fabaceae y x y y y x x x

*Baloghia marmorata Euphorbiaceae y x y y y x x x

*Davidsonia jerseyana Cunoniaceae y y y y y ip ip ip

Diospyros major var. ebenus Ebenaceae y x y y y x x x

Diploglottis campbellii Sapindaceae y x y y y x x x

*Endiandra hayesii Lauraceae y x y y y x x x

*Endiandra muelleri subsp. bracteata Lauraceae y x y y y x x x

Grevillea hilliana Lauraceae y x y y x x x x

39

Melicope vitiflora Rutaceae y x y y y x x x

Owenia cepiodora Meliaceae y x y y y x x x

Syzygium hodgkinsoniae Myrtaceae y x y y y x x x

Syzygium moorei Myrtaceae y x y y y x x x

Tinospora tinosporoides Menispermaceae y x y y y x x x

Tinospora smilacina Menispermaceae y x y y x x x x

*Tylophora woollsii Asclepiadaceae y x y y x x x x

*Marsdenia longiloba Asclepiadaceae x x x x x x x x

Muellerina myrtifolia Loranthaceae x x x x x x x x

Amyema plicatula Loranthaceae x x x x x x x x

Group 5

*Davidsonia johnsonii Cunoniaceae y y y y y ip ip ip

*Uromyrtus australis Myrtaceae y y y y y x x y

Uromyrtus lamingtonensis Myrtaceae x y y y y x x x

*Gossia fragrantissima Myrtaceae y y y y y x x x

Fontainea oraria Euphorbiaceae y y y y y y y y

*Fontainea australis Euphorbiaceae x x x x x y y x

Pouteria cotinifolia var.

cotinifolia Sapotaceae x x x x x x x x

*Phyllanthus microcladus Euphorbiaceae x x x x x x x x

Ardisia bakeri Primulaceae x x x x x x x x

Myrsine richmondensis Primulaceae x x x x x x x x

40

Appendix 3 – Data Rich Community Example (SNMVF)

Introduction

The single data rich community sample included here contains nearly a fifth (20%) of the species listed

in the BRBMP and provides an example of how additional (detailed) information can inform the

management planning process and modelling. Only ‘woody’ species are represented in the sample.

Refer to Table 1 below.

Methods

A data rich example for a single community type (SNVF-SNMVF, inclusive of the Wet Sclerophyll

elements adjacent) from previous research undertaken by us is provided to test the efficacy and

applicability of the methods in situations where data is available to explore species richness,

abundance, and distribution in a community relative to selected traits and environmental variables.

Refer to Rossetto & Kooyman (2005).

Data analysis methods for the data rich community example

The data rich community sample covers 9.2 hectares and is comprised of 92 / 50 x 20m quadrat

samples (with nested 20 x 20m subplots). The quadrat data were entered into a matrix consisting of 92

sites (objects) and 258 species (attributes). The data represents the specific rainforest types (SNVF-

SNMVF) and areas of interaction and overlap with adjacent vegetation communities, and covered the

whole of the available habitat area on the southern flanks of the Mt Warning study area with rhyolite-

derived soils (154 km2).

All vascular plant species that occurred within a plot were identified and recorded to species level.

Species cover codes (modified from Braun-Blanquet 1932) were entered as a cover abundance scale.

No transformation (or weighting) of the species abundance measures was undertaken as the intention

was to preserve as much of the information in the full floristic samples as possible (refer to Clarke et al.

2006a).

The environmental variables used for ordination analyses were derived from the field-collected

environmental data. The relative values and allocated rankings of the environmental variables and

cover codes, along with detailed description of the methods are provided in Rossetto & Kooyman

41

(2005). In this case, transformation of variables included replacement of all measured environmental

variables by ranks for PCA, and the use of the Spearman Rank coefficient for subsequent tests of

results (Global Tests) and multivariate regression tree analyses (Clarke & Gorley 2006).

The floristic data were classified by grouping similar plots using a numerical hierarchical

agglomerative classification process, the Bray-Curtis association measure, and similarity profile

permutation tests (Simprof) of clusters (1000 permutations).

Additional analyses included PCA (principal component analysis), nMDS (non-metric

multidimensional scaling), ANOSIM permutation tests (for the R statistic), the Global BEST match

test, and a modified MRT (multivariate regression tree) analysis (De’ath 2002) referred to as the

Linkage Tree procedure, all in the PRIMER v6 package (Clarke & Gorley 2006; Clarke et al. 2006c;

and see Clarke & Warwick 2001). The outputs from these analyses provided additional opportunities to

interrogate and test the pattern of relationships of the chosen environmental (abiotic) variables to

assemblage patterns (floristic variation) in the data. The Global BEST match test procedure used the

underlying resemblance matrix from the site by species data, Spearman rank correlation, and the

Euclidean resemblance measure for the site by environmental variables data (Clarke & Gorley 2006).

Trait relationships for rare and threatened taxa in the SNVF sample

Trait-based analyses for all the species (258) in this data followed the methods described above in the

methods section for the listed species (159) by traits section. The data rich quadrat sample included a

total of 30 of the 159 listed rare and threatened species in the project. That total included 4 restricted

endemics that occur only in the study area, and a total of 25 species (including the 4 endemics) that

have what we consider an adequate sample of the species distribution(s) and habitat preference(s) to

interpret species level patterns and describe the ‘realised niche’ (Table 1). The results of the

multivariate analyses (as ordinations) for the 30 target species are presented below. In this case the

analsyses were undertaken in the PATN package (Belbin & Collins 2004) and the ordination diagram

(Fig. 1) generated therein.

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Table 1 (Appendix 2) List of (30) BRMSRP listed species in SNVF-SNMVF data rich example. *species with sufficient data to interpret patterns and define the realised niche.

Species *Species data:

sufficient? Habitat (SNVF-SNMVF-WS)

Acacia orites Yes Disturbance related

Acronychia baeuerlenii Yes Mature, rainforest

Archidendron muellerianum Yes Mature, rainforest

Austrobuxus swainii Yes Mature, rainforest

Corokia whiteana Yes Mature, rainforest

Cryptocarya meisneriana Yes Mature, rainforest

Daphnandra tenuipes Yes Mature, rainforest

Eidothea hardeniana Yes Mature, rainforest

Elaeocarpus sedentarius Yes WS edges, rainforest

Endiandra globosa No WS, rainforest

Endiandra hayesii Yes Mature, rainforest

Endiandra introrsa Yes Mature, rainforest

Helicia ferruginea Yes Mature, rainforest

Helmholtzia glaberrima Yes Wet areas, riparian

Hibbertia hexandra Yes WS, rainforest edges

Hicksbeachia pinnatifolia Yes WS edges, rainforest

Leionema elatius subsp. beckleri Yes WS, rainforest (lower canopy)

Lenwebbia prominens Yes Mature, rainforest (wet)

Lepidosperma clipeicola Yes WS, rocky, wet

Melicope vitiflora Yes Lower alt., mature, rainforest

Niemeyera whitei Yes Lower alt., mature, rainforest

Pandorea baileyana Yes Mature, rainforest

Pararistolochia laheyana Yes Mature, rainforest

Quassia sp. Mt Nardi No Low alt., mature, rainforest

Streptothamnus moorei Yes Mature, rainforest

Symplocos baeuerlenii Yes Mature, rainforest

Syzygium hodgkinsoniae No Lower alt., mature, rainforest

Uromyrtus australis Yes Mature, lower rainforest

Wilkiea austroqueenslandica No Mature, rainforest

Zieria southwellii Yes WS, rainforest edge

Results

Figure 1 shows the results of the species (258) by traits (5) ordination (refer to Rossetto & Kooyman

2005). Consistent with the findings of that study, the ordination presented here shows the influence of

the dispersal dimension (inclusive of seed size, fruit type and dispersal mode) and persistence (as

resprouting / clonality).

43

Fig. 1 Constrained ordination of 258 species in relation to five life history traits. Kruskal-Wallis values for the five traits are: fruit type (199.503), dispersal mode (156.1388), seed size (100.3892), resprout (99.4091), leaf size (15.3688). To provide a less congested graph, only the (30) rare and threatened species listed for inclusion in the BRBMP are shown. The influence of the fruit-type / seed-size / dispersal dimension is apparent on the ordination of the rare and threatened species, with most of these positioned towards the larger seed end of the size gradient. Resprouting is the other important trait dimension influencing the ordination of the rare taxa. Overall stress in the ordination (0.1115).

The results of the site by species abundance by environmental variables analyses (nMDS and PCA

ordinations) for the (30) listed species present in the data for this community type are provided below.

The figures show the distribution and abundance of the subset of rare and threatened species in the

community based 92-plot sample relative to the influence of selected environmental variables. This

equates with a presentation of the ‘realised niche’ for 24 of the species with sufficient data.

Discussion

The results of the multivariate analyses of the SNMVF (Warm Temperate Rainforest) of the southern

Mt. Warning caldera area indicates the potential for data rich plot based samples, particularly when

complemented by detailed population genetic studies, to provide important information to support

biodiversity management planning at the species, multi-species, community and habitat (ecosystem)

levels. The real value of this type of data is the obvious additional power it provides for both

interpretation and prediction, particularly in regard to ‘describing’ the realised niche. In the case of

species with large or complete samples of population distributions within the sample, it represents

species occurrence data without the frequently identified problems of commission and omission errors

44

(refer to Rondinini et al. 2006). Most importantly the data describes species distributions and

abundance, and species richness in communities in relation to the influence of environmental gradients.

This has the potential to link both species and trait-based functional groups to gradients, habitats, and

threat responses to improve management responses. We suggest that such information will also be

critical for the endangered ecological community restoration (see for example, Adam 2001) and

management components of the project.

Figures 2-18 below represent species level data showing abundance and distribution relative to environmental variables.

Figs. 2&3 provides an example of four palaeo-endemic species in the data set for which whole of population data is

available (or almost so). Figs. 4-18 provide information for 30 species, with 29 of those listed as BRBMP species.

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49

50

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