Species richness and endemism of Australian bryophytes

Species richness and endemism of Australian bryophytes Lisa A Stevenson1,2, Carlos E Gonza´lez-Orozco1, Nunzio Knerr1, D Christine Cargill1,3, Joseph T Miller1 1

Centre for Australian National Biodiversity Research, CSIRO Plant Industry, Canberra, Australia, 2School of Marine and Tropical Biology, James Cook University, Townsville, Qld, Australia, 3Australian National Botanic Gardens, Canberra, ACT, Australia

Although understanding the patterns of diversity is essential for conservation and environmental studies, an understanding of bryophyte distributions in Australia has been limited by the absence of continental-scale maps for patterns of bryophyte diversity. The aim of this study was to identify the patterns of species richness and endemism of hornworts, liverworts, and mosses in Australia. A database of 85 383 georeferenced herbarium records was assembled and aggregated at a grid cell resolution of 0.5u. A one-cell radius neighbourhood analysis was applied to identify the spatial patterns of species richness and endemism. Primary centres of species richness were located on the east of the continent, with the highest number of species occurring in Queensland’s Wet Tropics, the Border Ranges near the Queensland–New South Wales border, the central coast of New South Wales, southern Victoria, and Tasmania. Endemism scores were high in the Wet Tropics, but highly endemic regions were scattered across the continent but not found in arid regions. The spatial patterns of diversity differed among hornworts, liverworts, and mosses, and areas of endemism and species richness did not always overlap. Comparisons with other taxa additionally indicated that areas of bryophyte diversity do not correspond with groups that are currently used as proxies in conservation. Keywords: Australia, Bryophytes, Distribution, Endemism, Richness

Introduction Large-scale studies of species richness and endemism are lacking for the bryophytes. Many investigations are confined to specific taxa within hornworts, liverworts, or mosses rather than the bryophyte community as a whole (Whinam et al., 2003; Ramsay & Cairns, 2004; Preston et al., 2011), and there is a bias in research effort towards the Northern Hemisphere (Turner et al., 2006; Preston et al., 2011). Most studies have been confined to relatively small regions, such as the Central Himalayas in Nepal (Grau et al., 2007) and various British counties (Wigginton, 1995; Rhind, 1999; Bates et al., 2004) and spatial data for bryophyte diversity is still lacking for most countries (Vanderpoorten & Engels, 2002). Callaghan & Aston (2008b) illustrated the possible size of knowledge gaps in spatial studies of bryophytes using a regression model to predict the percentage of unrecorded bryophyte taxa for North and South Lancashire in the UK. The results, being 55% and 66% respectively, suggest that knowledge of Correspondence to: Joseph T Miller, Centre for Australian National Biodiversity Research, CSIRO Plant Industry, GPO Box 1600, Canberra, ACT 2601, Australia. Email: [email protected]

ß British Bryological Society 2012 DOI 10.1179/1743282012Y.0000000004

bryophyte distributions, even in well-surveyed areas, is far from complete. There are currently no published maps of bryophyte species richness and endemism in Australia. Research on patterns of bryophyte diversity in Australia has been primarily restricted to how community structure and environmental variables influence species richness in localized regions, particularly in northern Queensland (Fensham & Streimann, 1997) and Tasmania (Jarman & Kantvilas, 1994; Pharo et al., 2005; Turner & Pharo, 2005; Turner et al., 2006). Other studies, such as those by Poćs & Streimann (2006) and Ramsay & Cairns (2004) have focused on generating species checklists and updating previously published material. Our study is the most comprehensive of its kind to examine the distribution of species richness and endemism in bryophytes at the continental scale. We use geo-referenced data from the Australian Virtual Herbarium and the latest geo-spatial tools to determine: (1) patterns in the distributions of species richness and endemism, and (2) if hornworts, liverworts, and mosses share patterns of diversity. We also test the hypothesis that areas of high endemism and

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species richness overlap. Describing patterns of bryophyte species diversity will facilitate future studies of the evolutionary processes which in combination with environmental and climatic factors drive bryophyte distributions.

Materials and Methods Spatial data We extracted all bryophyte records for hornworts, liverworts, and mosses from the Australian Virtual Herbarium, a database containing geo-referenced data collected by the Australian herbaria (CHAH, 2010). The taxonomic names on the records were updated to align with the classification of the Australian Plant Census (CHAH, 2010; Goffinet & Shaw, 2008; Klazenga & Stremann, 2002; McCarthy, 2003). Varieties and sub-species were included at the species level. Thirty per cent of the records were either not identified to species level, did not have spatial coordinates (latitude and longitude), or fell outside of continental Australia and were excluded from the dataset. The final dataset consisted of 85 383 records and included 214 records of 14 hornwort species, 13 248 records of 686 liverwort species, and 71 921 records of 1324 moss species. Despite having a large number of records per group, which allows a robust statistical sample, it is unrealistic to avoid the sampling bias caused by the unevenness of collections across Australia as a continent. Under-sampling in remote areas and edge effects are other issues that are commonly encountered in spatial analyses (Laffan & Crisp, 2003) and potentially affect this study.

Species richness and endemism All spatial analyses were conducted using Biodiverse 0.15 (Laffan et al., 2010) at a grid cell size of 0.5u60.5u (degrees). The selection of 0.5u grid cells was recommended by Bickford et al. (2004) as reducing sampling errors common to herbarium records while maintaining the spatial variation of the data. A neighbourhood analysis with a one-cell radius was selected to account for sampling errors. This window analysis smoothes the unevenness of the collection records across the local landscape (Laffan & Crisp, 2003). All species found within the moving cell were used to calculate species richness and endemism. Species richness is the total count number of species within each grid cell. Endemism was assessed using corrected weighted endemism (CWE). CWE is the total count of species within each grid cell and is weighted inversely to the number of cells the species occurs (see equations (1)–(3)). This produces a value for weighted endemism (WE), which is then divided by the number of species in the grid cell for CWE (Bickford et al., 2004). CWE has been demonstrated to be an equivalent to beta diversity (Laffan & Crisp, 2003).

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Richness~ WE~

X t[T

1

X

r =Rt t[T t

CWE~WE=Richness

(1) (2) (3)

Where number of species per grid cell is Richness (equation (1)), WE (equation (2)) is weighted endemism with richness and CWE (equation (3)) is corrected weighted endemism. WE is calculated for each moving window by summing, for each taxon t in the set of taxa T in the neighbourhood, the number of cells in the neighbourhood containing taxon t (the local range, rt) divided by its range (Rt, the number of cells in the dataset in which it is found). CWE can be interpreted as an average percentage of the species in the overall dataset restricted to that cell (Bickford et al., 2004), i.e. a value of 0.1 indicates that on average 10% of the species occurring in that cell are restricted to that cell. For this study, true hotspots could not be defined per se because of the sampling gaps and associated biases. Instead the grid cells with the highest species numbers or endemism scores were considered as centres of diversity. Our working definition of a ‘hotspot’ thus follows that of Crisp et al. (2001), where hotspots refer to areas with higher endemism or species richness than expected in comparison to the surrounding landscape. A disadvantage to using CWE is that it can be susceptible to errors associated with isolated grid cells with low numbers of collections. These grid cells could be interpreted as an under-sampling error, which results in mapping ‘false hotspots’. We therefore applied a redundancy test in Biodiverse to assess the reliability of the grid cells identified as hotspots of CWE (calculated as equation (4)). Redundancy measures ranged between 0 and 1. Values close to 1 represent grid cells that have been well-sampled and are likely to represent true centres of endemism. Values close to zero indicate no redundancy and possible under-sampling in the grid cell, which could correspond to false centres of endemism. Redundancy~1{richness=sum of the sample counts (4)

For example, if a grid cells contains two samples and one species, then it would have a redundancy of 0.5. In contrast, if a grid cell has only one sample and one species, then redundancy is 0.

Statistical analysis We compared the similarities in patterns of species richness and endemism among hornworts, liverworts, and mosses using GraphPad Prism 5 (Motulsky, 2007). The analysis was performed by species at any given grid cell. Differences in patterns of species richness and endemism at the species level among

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Figure 1 Maps of Australian hornworts with species richness on left column and endemism on the right.

hornworts, liverworts, and mosses were assessed with a Kruskal–Wallis one-way analysis of variance. Differences in patterns of species richness and endemism between locations were assessed with a Friedman’s test with Dunn’s multiple comparison post hoc tests. There were 1147 paired points available for the Friedman’s test. Spearman’s rank correlations were used to assess the relationship between overall endemism and species richness for the dataset.

Results Species richness The highest scores of species richness were: four for the hornworts in the Sydney central coast region and southern Victoria, 135 for the liverworts in the Wet Tropics, and 335 for the mosses in the Wet Tropics of Queensland (Figures 1–3). The larger values for species richness were concentrated along the east coast of Australia, reflecting the preferences of bryophytes for environments with high availability of water. Overall, the highest values of species richness occurred in the Wet Tropics and the Border Ranges near the Queensland–New South Wales border, followed by the Blue Mountains on the central coast of New South Wales, southern Victoria, and south-western Tasmania.

Comparison of species richness among the three groups The results of the statistical tests indicated that there were significant differences in the distributions of

Figure 2 Maps of Australian liverworts with species richness on top and endemism on the bottom.

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Figure 3 Maps of Australian mosses with species richness on top and endemism on the bottom.

species richness among the hornworts, liverworts, and mosses across Australia (Kruskal–Wallis, H5179.8, P,0.0001; Table 1) (Friedman’s, Q51798, P,0.0001; Table 2). There were some areas of overlap for species richness among hornworts, liverworts, and mosses in the Wet Tropics, the Sydney coast, southern Victoria, and Tasmania.

Endemism The highest score of endemism (CWE) was: one (1) for the hornworts in south-western Western Australia, the Wet Tropics, and Tasmania, one (1) for the liverworts in south-western Western Australia, the Wet Tropics, and southeast Queensland, and one (1) for the mosses in eastern South Australia (Figures 1–3). The grid cells with the highest endemism score can be deceptive because they may represent single collections of highly localized species. For example, Table 1 Kruskal Wallis test of differences in patterns of species richness and endemism among hornworts, liverworts and moss species

P value Kruskal–Wallis statistic (H) Degrees of freedom

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Species richness

Endemism

,0.0001 179.8 2

,0.0001 128.2 2

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the two circled grid cells located in Western Australia (Figure 1B) each contain a single hornwort species (Anthoceros tuberosus Taylor and Phaeoceros tuberosus Taylor) not found elsewhere. The redundancy measure, which is a ratio between richness and the sum of the sample counts per grid cell, is a way to explain inconsistent patterns of endemism due to undersampling. The grid cells in Western Australia highlighted in Figure 1B had redundancy values of zero, whereas the grid cells in the Wet Tropics and western Tasmania had higher redundancy values, 0.5 out of a possible value of 1.0. Additionally, we ran the analyses by grouping all species at the genus level. The grid cells with low species-level redundancy were not high in endemism at the genus level (data not shown). The Wet Tropics, the Border Ranges of Queensland and New South Wales and the border region of New South Wales and Victoria in the Great Dividing Range are therefore stronger candidates for true centres of hornwort endemism. The grid cell with the highest CWE score for the liverworts was in the Northern Territory (Figure 2B) and had a redundancy value of 0 which suggests that the area is poorly sampled. In contrast, the grid cells in Western Australia, the Border Ranges of Queensland and New South Wales had a redundancy greater than 0.5, which suggests better species richness sampling and true centres of endemism. Analysis at the generic level confirmed this result. Mosses were sampled more consistently and had higher redundancy scores. The broad areas with the highest endemism are the Wet Tropics of Queensland, the Monsoon Tropics of the Northern Territory and Kimberley, the Border Ranges of Queensland and New South Wales, the border region of New South Wales and Victoria in the Great Dividing Range and Tasmania. Analysis at the generic level confirmed this result with an expanded range of endemism in the Victorian mountains.

Comparison of endemism among the three groups There were significant differences in the distributions of endemism for all comparisons of the hornworts, liverworts, and mosses across Australia (Kruskal– Wallis, H5128.2, P,0.0001; Table 1) (Friedman’s, Q51019, P,0.0001; Table 2). The only areas of strong overlap for endemism among the three lineages occurred in the Wet Tropics and Tasmania.

Comparison of species richness and endemism values There was a positive correlation between values for endemism and species richness for bryophytes in general (Spearman’s, r250.4947, P,0.0001). Dunn’s multiple comparison post-hoc tests indicated differences

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among all of the bryophyte groups for both endemism and species richness (Table 2). The largest differences occurred between the hornworts and mosses, and the smallest differences occurred between the hornworts and liverworts in both cases. The results of Dunn’s post-hoc tests reflected the sampling difference among the bryophyte lineages. The difference between the number of species of hornworts, which have fewer species in both Australia and worldwide, compared to the liverworts and mosses (Klazenga & Stremann, 2002; Goffinet & Shaw, 2008) was particularly evident (Table 2).

Discussion Species richness and endemism This study represents the first attempt to map and describe the distribution patterns of bryophytes on a continental scale in Australia. Our analysis identified six primary regions of high species richness in the Australian bryophytes. The main regions of bryophyte richness common to all three groups were located in the Wet Tropics, the Queensland–New South Wales Border Ranges, and south-western Tasmania. Other regions of elevated species richness included southern Victoria, the Monsoon Tropics, and the central coast north of Sydney. The distribution of the centres of species richness in mesic environments along the east coast of Australia supports a strong correlation between bryophyte species richness and moisture availability (Fensham & Streimann, 1997; Turner et al., 2006; Callaghan & Aston 2008a, b). However, the similarities between the species richness patterns of hornworts, liverworts, and mosses also suggest that the different lineages may be controlled by similar environmental niche properties. Patterns of endemism are more difficult to interpret. The grid cells with the highest corrected weighted endemism scores are scattered, suggesting that both sampling effects and uneven distribution of range restricted species, without a clear geographical pattern. Endemism hotspots are important due to the implication that narrowly endemic species are usually rare and at a higher risk of extinction (Myers et al., 2000; Crisp et al. 2001; Laffan & Crisp, 2003). If individual grid cells with the highest endemism scores


(CWE51.00) are interpreted as true hotspots of endemism, we can suggest the presence of eight endemism hotspots. These areas consisted of four grid cells for hornworts, three for liverworts, and one for mosses, and appeared to be independent of each other. However, it may be possible that the endemism values for these grid cells reflect sampling artefacts, under-sampling, or taxonomic misidentification rather than true centres of endemism. South-western Western Australia, which is recognized as a global diversity hotspot (Myers et al., 2000; Brooks et al., 2006) is not a region of high species richness or endemism for bryophytes. Interestingly, the only region of overlap for areas of high endemism among hornwort, liverworts, and mosses occurred in the Wet Tropics, an area that is well known for its long evolutionary history and biodiversity (Ramsay & Cairns, 2004). In this case, we could suggest the Wet Tropics as a true centre of endemism for bryophytes as well as an area of high species richness in general (Myers et al., 2000) Similar to the results for the distribution of species richness, patterns of endemism differed among the three bryophyte lineages. The differences have been hypothesized as being indicative of historical vicariance and dispersal events followed by speciation, and unique combinations of limiting environmental variables (Laffan & Crisp, 2003; Bickford et al., 2004). Studies in sub-Saharan Africa suggested that centres of endemism are correlated with climatic stability (Linder, 2001). It has been suggested that historical climatic events such as the Pleistocene glaciations were an important influence in determining patterns of endemism in Australia (Crisp et al., 2001). Based on comparisons with previous studies of other plant families (Crisp et al., 2001; Laffan & Crisp, 2003), and considering the effects of spatial analysis at different cell sizes, we could suggest that the centres of species richness identified for bryophytes correspond with the broad geographical regions identified for other plant groups. However, it would be premature to state that they occur in exactly the same areas. In terms of endemism, only three of the eight grid cells with the highest scores for bryophyte endemism occurred in an endemism centre

Table 2 Friedman’s test of differences in patterns of species richness and endemism between locations (grid cells) among hornworts, liverworts and mosses, with a Dunn’s post-hoc comparison indicating where significant (***P,0.001) differences occur

P value Friedman’s statistic (Q) Degrees of freedom Dunn’s multiple comparison test Hornwort versus liverwort Hornwort versus moss Liverwort versus moss

Species richness

Endemism

,0.0001 1798 2 Difference in rank sum 2724*** 21877*** 21153***

,0.0001 1019 2 Difference in rank sum 2956.5*** 21405*** 2448***

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identified by Crisp et al (2001). Among the three main bryophyte centres, the only centres that coincide with Crisp et al.’s diversity centres are Tasmania and the Wet Tropics. Comparisons of centres of species richness and endemism may be important factors in prioritising areas for conservation. The strong Spearman’s correlation between values of endemism and species richness is reflected in the positive relationship between bryophyte species richness and endemism identified in our results, as well as other research (Laffan & Crisp, 2003). Contrary to our expectations, the overlap between areas of high species richness and endemism occurred only in the Wet Tropics and Tasmania. Our results therefore disagree with those of Crisp et al. (2001) and instead support reports that centres of species richness and endemism are not always congruent (Orme et al., 2005; Gonza´lezOrozco et al., 2011). The current focus on species richness to identify sites for conservation may therefore neglect areas of high endemism that are also in need of protection (Brooks et al., 2006). An additional point of interest for conservation purposes is that our results offer only partial support to earlier studies suggesting that centres of diversity for bryophytes are in common with centres of diversity for other taxa (Fensham & Streimann, 1997). They instead agree more strongly with an increasing number of studies that have concluded that the diversity of vascular plants cannot be substituted as indicators for bryophyte diversity (Jarman & Kantvilas, 1994; Pharo et al., 2000, 2005; Chiarucci et al., 2007; Vellak et al., 2010). The wider tolerances of bryophytes to particular environmental variables, such as those related to elevation and temperature, in comparison to larger vascular plants, may be responsible for the lack of congruence (Lee & La Roi, 1979). Metrics that rely on species counts, such as species richness and CWE, are not the only way to discuss spatial biodiversity. While it was beyond the scope of this study, metrics are now available that place species biodiversity in the context of phylogeny. Phylogenetic diversity (Faith, 1992) measures the total branch length of taxa found in a particular area as a proportion of the total phylogenetic tree length. Phylogenetic endemism (Rosauer, 2009) takes this a step further by identifying spatial areas that are uniquely distributed, not shared, on the phylogeny. These methods require fully resolved phylogenetic trees which are not available for the Australian bryophytes. The sampling deficiencies in collection-based studies could be overcome by modelling species distributions rather than solely relying on known collections. However, model-based methods tend to over-estimate

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species numbers (Guisan & Rahbek, 2011), but may point to areas of potentially high but under-collected diversity. These methods have the advantage of linking distributions to environmental parameters. These methods will need to be integrated to further advance our knowledge of bryophyte spatial biodiversity.

Acknowledgements LS wishes to acknowledge the CSIRO Plant Industry Division’s Summer Student Program for assigning a research project to the Centre for Australian National Biodiversity Research, as well as those who provided assistance throughout the project. We would like to thank the curation staff of bryophytes at the Australian National Botanic Gardens in Canberra. Taxonomic Additions and Changes: Nil.

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