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Genetics |
2 National Herbarium of NSW, Botanic Gardens Trust, Mrs Macquaries Road, Sydney, NSW 2000, Australia 3 Department of Biological Sciences, Macquarie University, NSW 2109, Australia 4 School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia
Received for publication 14 September 2007. Accepted for publication 3 February 2008.
ABSTRACT
Despite their narrow distribution, Australian rainforests still contain considerable levels of diversity and include many ancient, but often rare, lineages. Very little is known about the general biology of rainforest species, yet their long-term management depends on a better understanding of the main factors leading to rarity. For instance, are they highly endemic taxa, at the early stages of expansion, nearing the end of a period of decline, or persisting at low numbers over the long term? In this study we combine molecular, environmental, and ecological data to identify the factors responsible for the narrow distribution of a paleoendemic rainforest tree: Elaeocarpus sedentarius (Elaeocarpaceae). Between-population and between-generation comparisons of genetic diversity across all known populations of E. sedentarius show evidence of mutation–drift equilibrium rather than evidence of a recent bottleneck. Similarly, floristic and environmental data negate the hypothesis of rarity as a consequence of highly specialized habitat requirements. Instead, genetic structure and the available ecological data support the hypothesis of dispersal limitation as the main cause of endemism and that the species may have attained genetic equilibrium without realizing its full niche potential. We suggest that these factors are likely to explain narrow endemism in a broader range of taxa.
Key Words: dispersal • endemism • Elaeocarpaceae • Elaeocarpus • genetic structure • microsatellites • persistence • rarity
The progressive aridification of the Australian continent during the last 30 million years has considerably reduced the amount of habitat suitable to broadleafed (mesic) vegetation, as well as the number of lineages found within it (Hill, 2004
; Martin, 2006). Yet, despite currently representing <1% of the Australian land surface, rainforests have preserved many ancient lineages and still contain considerable levels of biodiversity and endemism (Adam, 1992). This floristic conservatism suggests the presence of localized pockets of relative environmental stability (Greenwood and Christophel, 2005), but also points to the adaptive potential of some lineages that successfully survived the evolutionary sifting caused by severe climatic cycles. However, while some taxa adapted to the new conditions or dispersed to more suitable locations, others became extinct or only persisted in marginal portions of their former range.
Narrow endemics are often the focus of much concern because of their perceived vulnerability to anthropogenic changes. Highly localized species could have experienced one of three different types of evolutionary history: being at the early stages of expansion, nearing the end of a period of decline, or persisting at low numbers over the long term. Each of these three scenarios would involve a different combination and timing of historical processes such as bottlenecks, long-term environmental trends, anthropogenic disturbance, and barriers to dispersal or gene flow. Each scenario would also involve interactions between different life-history traits (such as those related to dispersal) and specialized habitat requirements.
Identifying factors that influence species richness, species dynamics, and species population structure in community assemblages is of major interest for tropical forest ecology (for example, Hubbell, 1979, 2001; Tilman and Pacala, 1993; Clark et al., 1999; Webb and Peart, 2000; Harms et al., 2001; Phillips et al., 2003). This interest has led to a significant expansion of large-scale, long-term, plot-based research (Losos and Leigh, 2004). Niche differentiation, dispersal and recruitment limitations, and density-dependent factors have all been identified as important contributors to the maintenance of high diversity in tropical forests (Wright, 2002). Neutral (Hubbell 2001; Hu et al., 2006) and nonneutral (McGill et al., 2006; Westoby and Wright, 2006) theoretical approaches have been put forward to unravel the complexities of community assembly, with emphasis often being placed on the role of biological trade-offs between colonization ability and persistence (Tilman, 1994), and seed size and dispersal (Westoby et al., 1996).
The combination of molecular, environmental, and ecological data can facilitate the investigation of the biotic and abiotic circumstances leading to rarity and narrow endemism in rainforest tree species and can identify some of the major factors influencing the distribution of genes, individuals, and species. A better appreciation of these factors should in turn assist the development of long-term species and vegetation management strategies that can directly take into account predictive modeling of climatic and ecological variables. By using integrated data analysis approaches, we aim to provide insights into the relationship between evolutionary history and processes, present-day ecological competence (refer to Westoby, 2006) and biodiversity, and the likely management implications at the species and ecological community levels (Rossetto and Kooyman, 2005).
To this end, we investigated a narrowly distributed rainforest tree Elaeocarpus sedentarius Maynard & Crayn (Elaeocarpaceae). Elaeocarpus sedentarius is restricted to the core (Floyd, 1990) rainforest area of the Mt. Warning caldera in northern New South Wales (NNSW; Australia) and is found at only five sites included within two main populations, Koonyum and Nightcap (Fig. 1). A phylogenetic treatment of Elaeocarpaceae suggests that this species occupies a basal position in the genus (Crayn et al., 2006). Interestingly, the distinguishing combination of unique morphological features in this species are also found in a morphologically near-identical species in Papua New Guinea, E. blepharoceras Schltr. (Maynard et al., in press), suggesting that this lineage might once have been considerably more widespread. Thus, taxonomy and systematics exclude the scenario of a recently evolved species but still permit the scenarios of stable persistence or recent decline as testable hypotheses.
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Because a recent bottleneck was not identified as a direct cause of current rarity, and populations were shown to be at equilibrium within a narrow geographic range, we then investigated the likely influence of distributional limitations. More specifically, we quantified the possible links between the distribution of E. sedentarius and specific environmental features (site by floristics) to assess the likelihood that this rainforest tree is restricted by highly specialized habitat requirements. We also used a suite of genetic methods to quantify population structure and combined this information with ecological evidence to assess if distributional potential is (or has been) constrained by dispersal limitations (because metapopulation stability is dependent upon a balance between local extinction and recolonization potential; refer to Hubbell, 2001).
The findings from this and other local studies were used to explore some of the potential processes leading to rarity and endemism within highly diverse rainforest floras and to develop specific conservation and management strategies for E. sedentarius.
MATERIALS AND METHODS
Study species
The genus Elaeocarpus L. is the largest genus in Elaeocarpaceae and mostly comprises rainforest trees. Elaeocarps generally produce small white flowers that are thought to be adapted to buzz-pollination (Matthews and Endress, 2002) and fleshy fruits that are attractive to a range of large frugivorous birds and bats (Barker and Vestjens, 1989). Around 30 species are currently present on the Australian continent, but the fossil record shows extensive local diversification in the early Oligocene (when rainforests were widespread throughout the continent) with a number of fossil lineages bearing close morphological similarity to extant taxa (Dettmann and Clifford, 2001). With the overall aridification of the continent intensifying in the last 10 million years (My), the distribution of fossil elaeocarps eventually contracted to what are now rainforest remnants along the eastern coast of Australia (Kershaw et al., 2005).
Elaeocarpus sedentarius is associated with rhyolitic soils in warm temperate rainforest or with the ecotone between rainforest and wet sclerophyll forest. It is a medium-sized tree that can grow to >30 m in height, with reddish-brown fissured bark and small buttresses on larger stems. Flowers are white and fruits (drupes) are dark blue, 19–28 mm in diameter with a rounded, triangular shape and have a fibrous pseudoendocarp but lack a fleshy mesocarp (unlike other local common elaeocarps that have fleshy mesocarps).
Sampling, DNA extraction and genotyping
Sampling was aimed at obtaining a suitable account of the genetic diversity across the entire distribution of the species, while proportionally representing current age cohorts. Comparing genetic data across whole populations as well as between age cohorts enabled us to evaluate the relative influence of historic and contemporary events. Material suitable for genetic analysis was collected from every accessible individual at the smaller sites, while representative samples were collected from the largest sites. Total genomic DNA was extracted from leaf or bark of E. sedentarius using DNeasy 96 plant kits (Qiagen, Hilden, Germany). Seven simple sequence repeat (SSR) loci (Scu07Eg, Scu20Eg, Scu22Eg, Scu27Eg, Scu31Eg, Scu32Eg, Scu33Eg) originally developed for Eleaocarpus grandis F. Muell. (Jones et al., 2002; Rossetto et al., 2004b) were used following the original PCR conditions.
Genetic diversity
We measured genetic variation between single sites and between populations using three levels of generalized diversity measures: allelic richness; heterozygosity and related statistics; and information indices (Sherwin et al., 2006). Allelic distributions and unique alleles were measured, and to avoid bias caused by uneven sampling (Leberg, 2002), a standardized estimate of allelic richness independent of sample size (El Mousadik and Petit, 1996) was calculated using the program FSTAT 2.9.3 (Goudet, 1995). Measures of expected (He) and observed heterozygosity (Ho), and the inbreeding coefficient (f) (Weir and Cockerham, 1984) were calculated using FSTAT 2.9.3 with the probability of f being greater than zero determined after 10 000 permutations, and 95% confidence interval determined after 15 000 bootstraps. Permutation tests (10 000 permutations) on samples weighted by size to eliminate bias were also used to compare the statistics of various groups of individuals (FSTAT 2.9.3). Hardy–Weinberg and linkage equilibriums were assessed with the program GENEPOP 3.2a (Raymond and Rousset, 1995) using the exact test, and significance levels were determined after 500 batches of 5000 iterations each. Sequential Bonferroni corrections were applied to relevant significance tests (Rice, 1989).
We also used Shannon's information index SH because Sherwin et al. (2006) showed that it can provide a valuable additional measure of genetic diversity, with greater sensitivity to rare alleles and relatively low error. We calculated within-population information using the average over loci of SHA = 
–pi•log2pi, where pi is the proportion of allele i (using the Microsoft [Redmond, Washington, USA] Excel spreadsheet "sH_sHua" at website http://www.bees.unsw.edu.au/school/staff/sherwin/sherwinresearch.html; Sherwin et al., 2006).
Recent bottleneck or equilibrium?
The program BOTTLENECK (Piry et al., 1999) was used to investigate whether the rarity of E. sedentarius was likely to be a direct consequence of a recent bottleneck. This program uses Sign and Wilcoxon's signed rank tests to assess if populations are outside mutation–drift equilibrium and if the excess of heterozygosity expected after a bottleneck is significant (Cornuet and Luikart, 1996). With the assumption of the stepwise mutation model (SMM, suitable to the expected mutation behavior of microsatellites), we tested for significant departure from equilibrium at each main population. To illustrate the results of this test, we calculated the expected heterozygosity at mutation–drift equilibrium for the SMM, using He = 1 – 1/(1 + 2Neµ)–0.5, where Ne is the effective size and µ is the mutation rate (Kimura and Ohta, 1975). We performed this analysis at three mutation rates (10–2, 10–3, 10–4), and two Ne values, using the adult population size (70 and 85 in Koonyum and Nightcap, respectively) and 10% of the adult population size (Frankham, 1995). We then investigated whether the information value SHA of each population was consistent with mutation-drift equilibrium under the SMM, using the same range of mutation rates and population sizes as for the parallel investigation of heterozygosity. Expected values for SHA (E SH) were calculated by
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= Neµ Sherwin et al. (2006), using the program "compute_sH" at website http://www.bees.unsw.edu.au/school/staff/sherwin/sherwinresearch.html.
Between-population dispersal: Evidence from gene flow and genetic structure
We used a suite of methods to analyze population structure: the Fst analogue of Weir and Cockerham (1984), the Shannon information equivalent SHUA (Excel spreadsheet sH_sHua; Sherwin et al., 2006), Bayesian clustering (STRUCTURE; Pritchard et al., 2000), and AMOVA (Excoffier et al., 1992). Each method has its strengths that will be described later. We measured population subdivision using the Fst estimate of Weir and Cockerham (1984) to assess overall and pairwise values across all sites. Significance was assessed by comparison to 95% and 99% confidence intervals acquired by 5000 bootstrap permutations (FSTAT 2.9.3; Goudet, 1995). Pairwise Fst values were converted to estimates of dispersal between populations using the assumption of island-model dispersal, and equilibrium, so that Fst= 1/(4 Nem +1), where m is the proportion of the population that disperses each generation. Because this latter equation assumes that Ne is the same for both populations, we used an average value (Ne = 77).
Shannon's information index of population subdivision SHUA (Sherwin et al., 2006) provides a robust estimation of genetic exchange over a wider range of dispersal values than Fst and its analogues. This method is also insensitive to effects that perturb Fst estimates (Hedrick, 2005) and can incorporate the effect of unequal numbers of individuals in the populations. Measures of between-population variation SHUA were calculated in two ways: unweighted for relative population size (analogous to all other measures of between population variation) and weighted for adult population size using the approach of Sherwin et al. (2006). From there, using eq. 10b of Sherwin et al. (2006), we obtained an estimate of dispersal between the two populations (also using an average value of Ne = 77).
To investigate the possible presence of genetically differentiated groups of individuals in the absence of preliminary information on group boundaries, we used the Bayesian clustering method described by Pritchard et al. (2000) and implemented in STUCTURE. The model assumes the existence of K clusters (the real number being unknown) and uses the allelic frequencies at each locus to assign individuals to these clusters through a Markov chain Monte Carlo (MCMC) probabilistic approach. The aim was to identify the smallest value for K that captured the major structure in the data. After a preliminary test aimed at finding a suitable range for K and the optimal burn-in period, we tested K from 1 to 10 through five independent runs on the data set. All runs were based on 106 MCMC iterations after a burn-in period of 2 x 105 iterations without prior information on the locality of origin of the individuals sampled. The admixture frequency model was run under the assumption of correlated allele frequencies to improve clustering of closely related populations (Falush et al., 2003). Each group identified was also analyzed separately to see if there was further undetected structure. The optimal number of clusters was verified using the 
K statistical approach suggested by Evanno et al. (2005). Analysis of molecular variance (AMOVA; Excoffier et al., 1992) was used to quantify variance components (using the program GENALEX 6; Peakall and Smouse, 2006), and the significance of the genetic subdivisions identified by the Bayesian test.
Evidence of habitat specificity and availability
To investigate the possible links between the distribution of E. sedentarius and specific environmental features, we used multivariate analyses of response (site by floristics) and environmental variables. Table 1 provides a summary of the environmental variables used in each analysis. The sample of 97 (50 x 20 m) quadrats includes all locations with E. sedentarius and 79 controls representing a landscape scale sample of all potential habitat for the species in the study area (the southern slopes of the Nightcap Range). Forest type mapping derived from air photo interpretation (State Forests of New South Wales, SFNSW n.d.) was used to identify the full extent and distribution of suitable vegetation associations within the study area. Figure 2 shows the location of all plot samples and the general location of plots with E. sedentarius. The plot samples include the simple notophyll–microphyll vine forests (SNVF-SNMVF) dominated by species in the family Cunoniaceae and often referred to as warm temperate rainforest, and areas of overlap with the adjacent wet sclerophyll forests dominated by eucalypt species. Extensive walked traverses through all adjacent areas (regardless of forest type) were undertaken to confirm the extent of available habitat and the species distribution. The target rain forest type is patchily distributed in a mosaic of forest types across the study area. Plot samples were randomly located within the identified and confirmed distribution of the rain forest type(s) representing the known habitat for the species. Full floristics and cover abundance measures were used to identify and evaluate habitat parameters for E. sedentarius across its known distribution. All known E. sedentarius sites occur within the same climatic range and on similar soils/substrates.
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) was used for all multivariate analyses. 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 (1–6), where 1 = cover <5% of site and rare (overall); 2 = cover <5% of site and common (overall), 3 = cover 6–20% of site, 4 = cover 21–50% of site, 5 = cover 51–75% of site, and 6 = cover 76–100% of site. Transformation (or weighting) of species abundance measures prior to classification (clustering analysis) was not 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 are described in Table 1. Transformation of variables included arc sin transformation for aspect and normalization of the other environmental variables for ordinations. The Spearman rank coefficient was used for the subsequent tests of results that included global tests and multivariate regression tree analyses (Clarke and Gorley, 2006). The floristic data were classified by grouping similar plots using a simple numerical hierarchical agglomerative clustering process and the Bray–Curtis association measure. Similarity profile permutation tests (SIMPROF) were used to test the groupings. Similarity among quadrats/sites was further investigated through nonmetric multidimensional scaling (nMDS) ordination using the underlying resemblance matrix as input. Principal component analysis (PCA) was also used to examine the position of group members in component space relative to the influence of environmental variables represented as axes in the ordinations. The Euclidean distance measure was used in the PCA. The relative merits of the various distance (and association) measures used in multivariate analysis are discussed in detail in Clarke et al. (2006b).
RESULTS
Genetic diversity
The seven SSR loci used amplified 35 alleles across E. sedentarius, ranging from 10 (Scu07Eg) to two (Scu22Eg, Scu31Eg) per locus. Only three of 35 locus–site combinations produced monomorphic patterns. Such patterns could have been caused by the presence of null alleles. However, the satisfactory transmission patterns obtained in preliminary progeny trials suggest that null alleles were not a significant concern across these loci. Genotypic disequilibrium was measured for all locus pairings, and none of these was significant after sequential Bonferroni corrections. Allelic and gene diversities were low at all sites (Table 2; mean A = 3.5; mean He = 0.406; mean Ho = 0.420). Of the 35 locus–site combinations four departed from Hardy–Weinberg equilibrium, two representing significant excess of heterozygosity and two significant deficit. None of the inbreeding coefficient values across the locus–site combinations were significant after sequential Bonferroni corrections. The mean within-site f values ranged from 0.006 to –0.128 and were not significant (Table 2).
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). Under the tests and mutation models considered, neither of the two populations (Nightcap and Koonyum) departed significantly from mutation–drift equilibrium, suggesting that E. sedentarius has not been recently affected by a bottleneck. Estimates of expected heterozygosity at mutation–drift equilibrium were made using three mutation rates (10–2, 10–3, 10–4), and two Ne values, adult population (70 and 85) and 10% of adult population (7 and 8.5). The lower Ne produced unrealistically low estimates of heterozygosity for all mutation rates. The estimates using all adults are shown in Table 3 and are in agreement with the measured expected heterozygosity, suggesting that population variation is consistent with stepwise mutation– drift equilibrium. The SH values obtained using adult population size as Ne estimator and mutation rates of µ = 10–2, 10–3, 10–4 produced values close to the actual SH or within approximately one standard deviation of it (Table 3). As a result, we can reasonably say that, to reach equilibrium for heterozygosity and SH, these two populations have spent sufficient time at an effective population size similar to the current adult population.
Finally, cross-generational differences in expected heterozygosity and inbreeding coefficient were minimal (and not significant) in both Nightcap and Koonyum populations (Table 3). Furthermore, allelic frequencies and numbers are also consistent between age cohorts in both populations. Recently bottlenecked populations that are not at equilibrium would be expected to display a pattern of declining diversity within the juvenile cohorts.
Between-population dispersal: Evidence from gene flow and genetic structure
The overall measure of genetic divergence between E. sedentarius populations was Fst = 0.302. Average pairwise Fst values between Koonyum and the other four sites were 0.345 and were higher than the average pairwise values between the four sites on the Nightcap Range (Fst = 0.127). Using the average of the two adult population sizes (77.5) as the estimate of effective population size, Fst was converted to an estimate of the percentage dispersing between populations per generation. The dispersal estimate was m = 0.77% (or m = 7.7% if the effective size was 10% of the adult population size). The Shannon index also showed high differentiation between populations (SHUA = 0.537), and from SHUA the dispersal rate between Nightcap and Koonyum was m = 0.06% (or m = 0.04% if the effective size is 10% of the adult population).
Bayesian clustering as performed by STRUCTURE identified significant differentiation between the individuals located within the two main populations (K = 2; Fig. 3A). Although higher K produced higher posterior probability values, the K statistic supported K = 2 as being the most likely subdivision (K2 = 295; K3 = 193 and gradually lower for higher K). At K = 2, average coefficient of membership was 0.94 and 0.97 for individuals from Koonyum and Nightcap, respectively. In fact, only four individuals sampled from Koonyum had coefficients lower than 0.5 (potentially suggesting the occurrence of limited between-population gene flow) and none of the individuals from the Nightcap populations had coefficients lower than 0.8. Analyzing the data from the adult and juvenile cohorts separately did not change the outcome, with K = 2 still the most supported outcome (Fig. 3B, C).
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Evidence of habitat specificity and availability
The classification of plots by full floristics (not presented) shows that the species habitat can be referred to as SNVF-SNMVF (simple notophyll to simple notophyll–microphyll vine forest), sometimes referred to as warm-temperate rainforest. In the study area, this forest community is dominated by trees in the family Cunoniaceae, with the canopy tree Ceratopetalum apetalum generally the most abundant. Other canopy trees come from families including Lauraceae, Myrtaceae, Elaeocarpaceae, Rutaceae, Burseraceae, Sapindaceae, and Proteaceae. The results of the multivariate analyses presented in the ordination diagram (Fig. 4) show that for the 97-plot data set the most influential environmental variables were the correlated variables of disturbance and fire history, and soil depth and topographic position, and then the independent variable of altitude. Table 4 provides the PCA outputs as eigenvalues, showing the variation explained by PC axes (1–7) and the coefficients in the linear combinations of variables making up PC axes 1 and 2. The overlap of samples in the nMDS ordination demonstrates little difference between the Nightcap and Koonyum sites relative to current environmental gradients, apart from the influence of fire and disturbance. Additional tests of the results were undertaken and included 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 in PRIMER v6 (Clarke and Gorley, 2006). Though not presented here, the outputs from these analyses provided additional opportunities to visualize, interrogate, and test the pattern of relationships of the chosen environmental (abiotic) variables to assemblage patterns (floristic variation) in the data. They confirmed the patterns described and that considerable areas of disturbed and undisturbed habitat suitable for but not currently occupied by E. sedentarius occurs in close proximity to extant sites.
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Recent bottleneck and habitat constraints are not the main causes of rarity
As expected from a comparison between common and rare species, genetic diversity is lower in Elaeocarpus sedentarius (He = 0.406) than in another local but more widespread elaeocarp, E. grandis (He = 0.568; Rossetto et al., 2004b). Two main evolutionary scenarios were tested in this study to explain the restricted distribution of E. sedentarius and the associated loss of diversity. Current rarity could be a direct consequence of a recent bottleneck caused by anthropogenic disturbance, or alternatively, narrow endemism could be associated with the gradual, long-term environmental change linked to the aridification of the continent. The outcome of this latter scenario could result in equilibrium within a narrow distribution.
Recent anthropogenic disturbance is unlikely because the majority of the current distribution area of E. sedentarius was not subjected to the heavy clear-fell logging and land clearing that took place during the last century across much of the local lowland rainforests. Furthermore, the significant departure from mutation–drift equilibrium expected from a recent bottleneck was not detected in the two remaining populations. Table 3 shows no evidence of the loss of diversity within juvenile cohorts that would be expected within populations recently affected by habitat fragmentation and demographic decline (Rossetto et al., 2004b). In fact, both populations are at equilibrium for SHA and He, and do not vary significantly in allele number and frequencies or in genetic diversity and inbreeding coefficient across age cohorts. As a result, rarity is unlikely to have been caused by a recent bottleneck. Instead, a more parsimonious explanation for the restricted distribution of this ancient and once more widespread lineage is that of stability within small populations for numerous generations (and considerably longer than the 100 or so years of local anthropogenic pressure). The fact that seed-based recruitment is successful at all sites (as suggested by the substantial number of juveniles representing >50% and >75% of individuals at Koonyum and Nightcap, respectively) and the majority of individuals originate from outcrossed mating events (Table 3) further supports an equilibrium scenario.
Nevertheless, the distribution of a fertile and actively recruiting population could still be restricted by a shortage of suitable habitat. Although conditions suitable to rainforest vegetation are relatively limited across the continent, the floristic and environmental data suggest that suitable habitat for E. sedentarius is locally abundant (Fig. 4). Habitat searches indicate that areas adjacent to actively recruiting populations are well within the species' environmental niche but have not been successfully colonized. The fact that suitable habitat is available but not occupied, suggests that dispersal might be a factor in need of further consideration, as the presence of physical barriers would hamper the recolonization of extinct sites. In a scenario of limited dispersal, reduced gene flow and increased genetic structure could be expected to increase differentiation between populations.
Gene flow, genetic structure, and life-history reveal the impact of dispersal limitation
The amount of genetic structure documented across E. sedentarius was unexpected in view of its narrow geographic distribution (Fig. 1). In the absence of preliminary information on group boundaries, Bayesian clustering allocated high levels of genetic structure between the individuals representing two distinct populations located on opposing mountain ranges (Nightcap and Koonyum; Fig. 3). The amount of genetic variance partitioned between these two populations situated just over 5 km apart (30%; AMOVA) is greater than that measured between populations of E. angustifolius Blume distributed >1000 km apart (23%), yet lower than that measured between two morphologically differentiated subspecies of E. largiflorens C.T. White separated by a recognized biogeographic barrier (53%; Rossetto et al., 2007). Geographic disjunction alone is unlikely to explain the level of diversification measured between E. sedentarius populations as there is no evidence of IBD. The fact that genetic structure is maintained across generations support the equilibrium scenario and suggests that differentiation is a consequence of longer-term rather than contemporary events.
The very low estimates of dispersal from Fst and SHUA suggest that there is little if any gene flow between the two main populations of E. sedentarius. As in other elaeocarps, gene flow is more reliant on fruit than pollen dispersal (Rossetto et al., 2004b) because pollination is performed by small unspecialized insects seldom capable of long-distance dispersal events (Weber, 1994). However, the fruits of E. sedentarius have important distinguishing features from those of other elaeocarps, despite general similarities in size, shape, and color. These include the presence of a fibrous layer that replaces the characteristic fleshy and nutritious outer mesocarp, and the absence of a hard pseudoendocarp protecting the seed. As a result, E. sedentarius fruits are unlikely to attract the few specialized frugivorous vertebrates that are found in NNSW. Instead, once fallen to the ground, fruits are methodically collected by one of the most active local seed predators, the native bush rat (Rattus fuscipes), and dispersal (away from maternal genets) depends on their failure to consume all seed after fruit removal. Preliminary data comparing tree visitation and fruit harvest in three NNSW elaeocarps (including E. sedentarius) supports this hypothesis (R. Kooyman, unpublished data).
Because bush rats have relatively small home ranges (Robinson, 1987) and the Wilson River and Coopers Creek gorges separate the Nightcap and Koonyum populations (Fig. 1), the accidental dispersal of fruit across such a geographic barrier is improbable and validates the microsatellite-based dispersal rate of 0.06% to 0.77%. Consequently, current dispersal limitations prevent gene flow between neighboring E. sedentarius populations separated by significant landscape barriers, as well as the recolonization of suitable habitat after local extinction events. This shortcoming is likely to be one of the major factors responsible for the currently limited distribution of this paleoendemic rainforest tree. In addition, the influence of the physical barrier and the identified drift–dispersal equilibrium in the species populations is suggestive of the early stages of the random fission model of speciation described by Hubbell (2001). However, we did not explicitly explore the link between macroecology and population genetics in relation to neutral theory, and it remains an area that warrants further consideration and explanation (refer to Hu et al., 2006).
Rainforest endemism and species diversity: The role of dispersal
The long-term survival of rainforest vegetation in Australia has only been possible through the preservation of stable environmental conditions within core refugia (Kershaw et al., 2005). These have provided important sources of recolonization potential after localized extinction events. However, the size and connectivity of these refugia have had an even greater impact on the rainforest fauna, with the great majority of larger fruit dispersers having been lost from the smaller southern rainforest remnants (such as those included in this study) during the major aridification processes of the Tertiary (Archer et al., 1994).
The findings from this study strongly support the concept of maladaptation through lack of dispersers as an important factor for explaining the localized distribution of rainforest plants with very large or unpalatable fruits. Interestingly though, this study also presents evidence that the frequently accepted hypothesis that the extinction of dispersal organisms always precedes the short-term extinction of the plants relying on them (see Archer et al., 1994 for example) is not necessarily true. We have shown in this and other studies that highly localized rainforest trees can survive for a long time within small populations (Rossetto et al., 2004a; Kooyman, 2005; Rossetto and Kooyman, 2005). The cumulative genetic and ecological evidence from these studies suggests that a balance between localized persistence and divergent potential is an important factor in explaining how biodiversity is maintained within small, geographically isolated but species-rich rainforests.
Our findings also highlight how habitat availability is only one predictor of a species' distributional potential, and current distribution patterns can be significantly influenced by both habitat accessibility and dispersal potential. Thus, the current range of a species with a limited aptitude for expansion is not necessarily at equilibrium with the local availability of suitable habitat. This imbalance between potential and realized niches is likely to be an important factor in explaining the considerable level of localized endemism in highly diverse plant communities that include ancient lineages.
Implications for management and conservation
A comparison between this and previous population-level studies in Elaeocarpus substantiates that broad generalizations related to the management and development of conservation approaches for rainforest trees may be untenable without careful consideration of the interaction between life-history factors, local geography, and the adaptive landscape. Two local species studied and occupying similar habitat, showed either extensive gene flow (in the case of the common E. grandis; Rossetto et al., 2004b) or extensive clonality (in the case of the rare E. williamsianus; Rossetto et al., 2004a), while for a third species from northern Queensland restrictions in establishment were identified as a potential cause for genetic and morphological divergence between populations (Rossetto et al., 2007). None of these scenarios appear to be directly relevant to E. sedentarius.
At the species level, our data suggest that limited dispersal, rather than recent anthropogenic events or habitat constraints, is the more likely cause of the narrow distribution of E. sedentarius. Contrasting directional drift caused by reduced gene flow and loss of diversity, favored the genetic disjunction of two main populations that can be considered as separate conservation units. As the populations are genetically at equilibrium and fertile, there appears to be no need for active management, except for the protection of all sites and surrounding habitat from extreme events that could cause localized extinction (because recolonization from either Koonyum or Nightcap would be unlikely). However, should the need arise, our results also suggest that if suitable habitat is retained, translocation would have a high chance of success.
At the ecological community level, the findings highlight important implications that need to be considered when developing practical management strategies for these and other highly biodiverse areas. Notably, fine-scale factors can differentially affect metapopulation dynamics even among related taxa, and broad floristic measures alone might not be sufficient to detect important areas of diversity. Patterns of genetic differentiation need to be interpreted in relation to specific expectations consistent with life-history factors and how these (in turn) interact with local environmental features. Finally, the study identifies the often vital role of dispersal and the need to retain or enhance dispersal mechanisms when dispersal is of concern and provides further support to the idea that all suitable sites need to be considered during the development of conservation strategies, because even small pockets of habitat can potentially contain and protect valuable diversity.
FOOTNOTES
1 The authors thank J. Hunter for field support, C. Allen for the distribution map, and two anonymous reviewers for their helpful comments. This project was funded by the Australian Research Council (C00107305) and the NSW Department of Environment and Climate Change. 
5 Author for correspondence (e-mail: maurizio.rossetto{at}rbgsyd.nsw.gov.au); phone: +61 2 9231 8337; fax: +61 2 9251 4403
LITERATURE CITED
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