Do soil and climate properties drive biogeography of the Australian ? Author(s): Emily Prentice, Nunzio Knerr, Alexander N. Schmidt-Lebuhn, Carlos E. González-Orozco, Elisabeth N. Bui, Shawn Laffan and Joseph T. Miller Source: and Soil, Vol. 417, No. 1/2 (August 2017), pp. 317-329 Published by: Springer Stable URL: https://www.jstor.org/stable/26651557 Accessed: 08-07-2021 08:21 UTC

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This content downloaded from 86.59.13.237 on Thu, 08 Jul 2021 08:21:46 UTC All use subject to https://about.jstor.org/terms Plant Soil (2017) 417:317-329 (|) CrossMark DOI 10.1007/s 11104-017-3261-6

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Do soil and climate properties drive biogeography of the Australian proteaceae?

Emily Prentice • Nunzio Knerr • Alexander N. Schmidt-Lebuhn • Carlos E. Gonzälez-Orozco • Elisabeth N. Bui • Shawn Laffan • Joseph T. Miller

Received: 30 September 2016 /Accepted: 19 April 2017 /Published online: 28 April 2017 © Springer International Publishing Switzerland (outside the USA) 2017

Abstract species-level spatial biodiversity patterns of the Aims The Proteaceae are a diverse family of approxi Australian Proteaceae has been conducted. The aim of mately 80 genera and 1700 species with a mostly this study is to identify and examine patterns of distri southern-hemisphere distribution. While distributional bution, diversity and endemism for the Proteaceae (at patterns of various subsets of the Proteaceae have been family, genera and species levels) of continental studied, no quantitative continental-scale study of Australia and to investigate the environmental drivers for the observed patterns. Methods Using 151,899 herbarium records for 1179 Australian Proteaceae species, we investigate taxon richness, endemism, and compositional turnover along Responsible Editor: François Teste. with climatic and soil correlates. Electronic supplementary material The online version of this Results Species richness and endemism was highest in article (doi:10.1007/sl 1104-017-3261-6) contains supplementary the Southwest phytogeographical region, as well as the material, which is available to authorized users. Atherton and Southeastern subregions. richness E. Prentice • N. Knerr • A. N. Schmidt-Lebuhn was highest in the Northeastern and Atherton subre National Research Collections Australia, CSIRO National gions. Highest species turnover occured in the Facilities and Collections, GPO Box 1700, Canberra, ACT 2601, Southwestern region and the Southeastern subregion Australia while lowest species turnover occured in the Northern, e-mail: [email protected] Northern Desert and Eremaean regions. Over the entire C. E. Gonzâlez-Orozco continent, soil geochemistry and climate explain 37% of Corporaciôn Colombiana de Investigaciön Agropecuaria, the variation in species turnover; however, in areas of Corpoica, La Libertad, km 17 Via, Puerto Lôpez, Meta, Colombia high species richness, they account for >75% of the E. N. Bui variation in species turnover. CSIRO Land and Water, GPO Box 1700, Canberra, ACT 2601, Conclusions These results suggest that the biogeo Australia graphic patterns of the Proteaceae are impacted by cli

S. Laffan mate and soils, where Proteaceae specialization has filled novel environmental niches associated with low Centre for Ecosystem Science, School of Biological Earth and Environmental Sciences, University of New South Wales, nutrient and low water availability soils, particularly in Sydney 2052, Australia southwestern Australia.

J. T. Miller Office of International Science and Engineering, National Science Keywords Species turnover - Phytogeography • Foundation, Arlington, Virginia 22230, USA Proteaceae • Soil geochemistry

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Introduction Lambers et al. 2010). Species richness has been shown to increase with soil age; specifically, species richness Biogeography, the study of distributional patterns of increases as phosphorus levels decrease (Wardle et al. biodiversity in geographical space, provides essen 2004; Laliberté et al. 2013), suggesting that phosphorus tial insights for focusing conservation efforts limitation might drive community assemblages on poor (Lamont and Connell 1996). The biogeography of soils (Venterink 2011). Proteaceae have exceptional a taxon is determined largely by the biology and adaptions for aridity and poor nutrient soils (Lamont evolutionary history of the organism and the effects 1993): most proteaceous genera except (and of the physical environment upon its range (Cox and other members of the tribe Persoonieae) form proteoid Moore 1976). Quantifying this has traditionally in root clusters (Zuniga-Feest et al. 2014; Lambers et al. volved descriptive statistics such as richness, abun 2015). These root structures have high surface areas dance and the approximation of total ranges. In which aids in water absorption and they produce a triage-based conservation efforts, emphasis has often short-chain carboxylic acid exudate that allows uptake been placed upon identifying areas of high ende of occluded phosphorus normally unavailable to mism; these are areas in which the rarest and most (Lambers et al. 2006). Proteaceae also resorb phospho threatened taxa are found. Unique combinations of rus from senescing organs, thus once taken up phospho historical range restriction and contraction, as well rus remains within the living plant tissues (Lambers as speciation, diversification and persistence of par et al. 2010). Leaf longevity and delayed greening are ticular lineages can result in areas which are irre additional traits that allow Proteaceae to survive in placeable in conservation terms (Rosauer et al. phosphorus limited soil (Lambers et al. 2015). These 2009). These locations are particularly vulnerable mechanisms allow the Proteaceae to be one of the few because even small-scale threats can significantly families of flowering plants not to have mycorrhizal impact range-restricted assemblages of taxa symbiosis. (Cadotte and Davies 2010). The current climate of Australia varies dramati The Proteaceae are a family comprising approximate cally from north to south and east to west. The ly 80 genera and 1700 species with a mostly southern northern mesic areas, especially the northwest, are hemisphere distribution. Australia has the world's strongly influenced by monsoonal rains. Central greatest diversity of Proteaceae (Myerscough et al. Australia is characterised by deserts, with semi 2000), including iconic genera such as , deserts closer to the coastal areas. The southern , Telopea and . Within Australia, parts of Australia are temperate. South-western the family is present across a wide range of habitats, Australia has a Mediterranean climate due to the with many groups displaying remarkable adaptations to influence of hot continental air currents and cool the Australian environment (Johnson and Briggs 1975; winds from the Indian Ocean. These climatic char Lamont and Markey 1995; Hill 1998; Mast and Givnish acteristics make Australia a climatically diverse con 2002; He et al. 2011; Keeley et al. 2012). tinent, which favours species diversification. The The Australian Proteaceae species do not comprise a Proteaceae are distributed in all of these climatic single monophyletic lineage (Sauquet et al. 2009; regions. Onstein et al. 2016) but rather are interspersed with Although significant patterns of Proteaceae distribu Proteaceae taxa from many other parts of the southern tion have previously been studied (e.g. Johnson and hemisphere, as well as south Asia. There are several Briggs 1975; Lamont and Connell 1996; Thiele and species rich genera endemic to Australia (Fig. 1 ) includ Ladiges 1996; Mast and Givnish 2002) and functional ing Banksia, Grevillea (including ), , traits have formed the basis for exploration of the evo Persoonia, and , although mono lution of taxa in particular regions (e.g. Jordan et al. phyly has not been tested for all genera 2005,2008; He et al. 2011 ; Mast et al. 2012), a species Proteaceae species diversity is concentrated in south level quantitative assessment of the biogeography of the west Western Australia, which is a climatically buffered, Australian Proteaceae has not yet been undertaken. The old, highly weathered landscape characterised by low aim of this study is to identify and examine patterns of soil nutrient quality, including low phosphorus soils distribution, diversity and endemism for the Proteaceae (Hopper 1979, 2009; Hopper and Gioia 2004; of continental Australia and to investigate the

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Two species per genus Monotypic genera genera 350350 300300 250250 200200 Megahertzia 150150 Nothorites

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Fig. 1 Histogram of the number of species across of Proteaceae northern generaAustralia and neighbouring New Guinea, and present in Australia (data from Sauquet etGrevillea, al. 2009). of * whichindicates five species are found in New Guinea, New genus is not endemic to Australia. Exceptions Caledonia, are Indonesia , and Sulawesi Banksia and Bleasdalia, for which a single species is distributed

environmental drivers for the observed Records from patterns. naturalised or cultivatedFor populations out example, do taxon richness, endemism side of theirand native species ranges, asturn well as records of speci over patterns of Proteaceae match mensthat outside of ofthe continental flora Australia in were deleted fol general? Can patterns of species turnover, lowing Flora ofat Australia local (1995,and 1999, 2000). Missing continental scales, be explained by or patterns incorrect spatial of co-ordinates climate were either excluded or and soil geochemistry turnover, especially corrected where phosphorus adequate information was available. It levels? was not necessary to eliminate duplicate records from the dataset, as our diversity metrics are not affected by abundance. A total of 151,899 records remained at Methods completion of the data-cleaning processes, representing 88% of the original AVH dataset. Spatial data We used R (R Development Core Team 2013) to consolidate the cleaned dataset and transform our data All collections data for the Proteaceae family from points into an Albers 1994 equal-area conic projection, Australia's Virtual Herbarium (CHAH 2013 a) were which minimises spatial distortion and facilitates accurate downloaded, totalling 172,747 individual records for dispersion of latitude/longitude data points within grid 47 genera and 1179 species occurring within Australia. cells. Analyses were generated for the entire dataset at the Methods follow those of Gonzâlez-Orozco et al. (2013, species and genus levels as well as for major clades 2014a, b). Using Google Refine v.2.5, each record was including: Banskia (173 species, 20,524 records), checked for taxonomic and spatial accuracy. Only re Grevillea/Hakea (517 species, 73,370 records), and cords with species-level identification were aggregated Persoonia ( 104 species, 11,027 records). Additional anal into the dataset for analysis, and records were corrected ysis of less species-rich Proteaceae genera (, where taxonomic classification had been superseded , Isopogon, Petrophile, Synaphea and following the Australian Plant Census (CHAH 2013b). Macadamia) are included in the supplementary data.

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Spatial analyses Jaccard and Sorensen. Consistent with other turnover metrics, the numeric scale is from 0 (all taxa are shared) All spatial analyses were undertaken using Biodiverse to 1 (no taxa are shared). v0.19 (LafFan et al. 2010). Following Gonzalez-Orozco A WPGMA agglomerative cluster analysis of the et al. (2013, 2014a, b) a grid cell size of 100 x 100 km ßsim turnover matrix was used to generate a cluster was found to provide the best resolution for the purposes dendrogram in Biodiverse. Dissimilarity clustering al of this study, being robust enough for manipulation of a gorithms have topological biases (Dapporto et al. 2013) large dataset and to identify continental-scale patterns, and to reduce biases the cluster analysis included a tie while also reducing the prevalence of sampling artefacts. breaker condition (Gonzâlez-Orozco et al. 2013, 2014a, Redundancy values were calculated to assess sampling b) such that, when more than one pair of sub-clusters adequacy for each cell. Redundancy is defined as one had the same turnover score, the pair which had the minus the ratio of species to the number of collections highest CWE score was selected for merging. This and ranges from 0 to 1, where a value of zero indicates approach increases the endemicity of clusters and guar only one sample per species in a cell. The Proteaceae antees replication of the results over multiple analyses. returned good redundancy values at the species level in grid cells throughout most of the continent. Only 54 Environmental correlates (6.2%) cells had no redundancy and these were mostly along continental edges and in the species-poor arid We also investigated correlation of several environmen interior. 12.8% of the cells had redundancy values be tal variables with the geographic distribution of compo tween 10%—50%, 80.9% had redundancy values above nents of the Proteaceae. We investigated five geochem 50% and 48.5% had redundancy values above 75%. ical (electrical conductivity [EC], calcium [Ca], phos The analyses, which included species richness (SR), phorus [P], pH and silicon [Si]) and five climatic vari genus richness (GR), weighted endemism (WE), and ables (mean evaporation, mean radiation, mean precip corrected weighted endemism (CWE), were conducted itation, maximum temperature and minimum tempera in Biodiverse for both the species and genus levels ture) that were considered most relevant in previous (Table 1; Crisp et al. 2001). We plotted biodiversity work (Bui et al. 2014). The geochemistry data were patterns of 1) all species of Proteaceae, 2) all species derived from the National Geochemical Survey of of species-rich genera, and 3) all data at the genus level. Australia (NGSA) which measured concentrations of To examine changes in community composition from 68 elements at 1315 georeferenced point measurements one location to another we calculated a matrix of species across Australia at two depth intervals (TOS, top of level compositional turnover values between each pair sample, 0-10 cm, and BOS, bottom of sample, 60 of grid cells using Simpson's Beta (ßsim) and plotted the 80 cm) (de Caritat and Cooper 2011). Bui et al. (2014) resultant turnover surfaces for selected grid cells, ßsim identified high correlations between the two soil depths was used because it corrects for species richness differ so we only used TOS in the present study. Exponential ences between samples, unlike other metrics such as variogram models were fitted using the geoR package (Ribeiro and Diggle 2001) in the R software (R Development Core Team 2013). Ordinary kriging was Table 1 Diversity metrics calculated in biodiverse. In these anal then used to predict the elemental concentrations for the yses a single sample isalOOkmxlOOkm cell TOS interval on a 1 km grid. The five climatic layers are Diversity Metric Description from Williams et al. (2010a, b).

Richness Total numbernumber ofof taxataxa in a sample. Statistical analyses of environmental correlates Weighted Sum of the fractions of each taxon's Endemism (WE) i) range range found found in in a asi sample. We investigated the relative roles of climatic and geo Corrected Weighted :d WE WE divided divided byby specispecies richness. Endemism chemical variables in accounting for the variation in (CWE) Proteaceae species turnover (Legendre 2008). The Simpson's Beta Rate of turnover between the assemblages ßsim matrix was used as the response matrix with in two samples, correcting for species climate and geochemistry as explanatoiy variables in richness differences between them. the 'varpart' function in the R package vegan (Borcard

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et al. 2011; Oksanen et al. 2012). This procedure esti responsible for the majority of arid land species of mates the proportion of variance explained by each set Proteaceae. Persoonia has highest species richness in of environmental variables when controlling for the the southeast with highest endemism in the southwest. other, the proportion of variance jointly explained by Species richness and CWE for less species-rich both, and the proportion of variance that remains unex Proteaceae genera (Adenanthos, Conospermum, plained by either set (residuals). Isopogon, Petrophile, Synaphea and Macadamia) are To identify which individual environmental variables also highest in the southwest and southeast, with provided explanatory power for a given area the forward Conospermum also having high CWE in inland south selection procedure ('forward.sel' function in the R east Queensland (SI Fig. 1). packfor package, Dray et al. 2007) was applied. This procedure uses a permutation test to select the explana Species turnover patterns tory variable with the highest R2 using two stopping criteria: alpha level of 0.05 and maximum global model Species turnover among all species of the Proteaceae adjusted R2. varied based on geographic location. Low levels of species turnover are found in the tropical north of the country, and species turnover is also very low Results within the arid zone of Australia (Fig. 4a, b), sug gesting that the few taxa located there have broader Richness and endemism ranges on average. The highest levels of species turnover are seen in southwest Western Australia Species richness was highest in the Southwest and the coastal regions of south-eastern Australia Australian Phytogeographical region (Austral (Fig. 4c, d) suggesting that the large number of Bioregionalisation Atlas (ABA) phytogeographical no species have small distributions on average. menclature; see Ebach et al. 2013,2015), the wet tropics We identified four well-defined biogeographic re of northern Queensland and the southern portion of the gions by clustering of grid cells based on the species New South Wales Coast (Fig. 2a). By contrast, genus turnover (Fig. 5). Overall these community composi richness was highest in the wet tropics of Queensland tions revealed clear parallels with similar analyses of but lower within the Southwest Australian phytogeo modern phytogeographical and historical terrestrial re graphical region (Fig. 2b). gions of the Australia sub-realm (see Fig. 1 in Gonzâlez Species endemism (CWE) was highest within the Orozco et al. 2014b; Ebach et al. 2013, 2015) and southwest, the New South Wales Coast and the wet turnover regions similar to those of Acacia (Gonzâlez tropics of Queensland, though there were additional Orozco et al. 2013) and Eucalypts (Gonzâlez-Orozco 'hotspots' evident in the Kakadu region of Northern et al. 2014a). The main difference between Proteaceae Territory, the Kimberley and Shark Bay regions of regions and the others is that the Northern phytogeo Western Australia, Kangaroo Island (South Australia) graphical region is distinctively larger toward the south and scattered hotspots in coastal locations around the east extent, suggesting the presence of dominant arid continent (Fig. 2c). At the genus level the wet tropics of clades such as Grevillea/Hakea. Queensland contain remarkably high CWE for The main differences from the results of Gonzâlez Proteaceae, with only low values seen elsewhere in the Orozco et al. (2013,2014a, b) is that there is no bound continent (Fig. 2c). ary between the Northern Desert and Eremaean ABA The spatial distributions of species richness and regions in the Proteaceae species turnover patterns. CWE for individual genera of the Proteaceae reflect Also, we recognize a split in the Proteaceae patterns of similar patterns to that of the family as a whole. the Euronotian phytoregion into two clearly demarcated Species richness is high for Banksia in the southwest, regions; one containing the Victorian, Tasmanian, with the highest endemism found in a single cell con Adelaide and Eyre Peninsula subregions; and the other taining one endemic Banksia species (Fig. 3). The containing the Southeastern subregion of the Grevillea/Hakea clade has high species richness and Euronotian. CWE in both the southwest and southeast and is the The family splits into four major community types, only clade with a continental distribution. This clade is with members of Grevillea/Hakea appearing to

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Fig.Fig. 2 Diversity 2 metrics Diversity for the metrics for Species the Genu$ Genus r'°!3^ISmpbi 0 6060 121^18^^121 182 242242 00 7 7 14^^TS^^z9 14 22 29 Proteaceae,Proteaceae, mapped using mapped using 100100 km xkm 100 km x grid 100 cells, akm grid cells, a SpeciesSpecies richness; richness; b Genus b Genus /" richness;richness; c Species CWE;c Species d CWE; d Genus CWE. CWE is calculated as per Table 1, and is represented on a scale from 0 (taxa within that grid cell occur in all other grid cells) to 1 (all taxa within that grid i cell occur nowhere else). The \ - i value approaches a limit of zero as taxa are increasingly widespread ' (a)(a) (b)(b)

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dominate community composition within the arid zone changes in solar radiation best correlated with the spe (red cells Fig. 5a). This area's zone can be subdivided cies turnover patterns in the arid Eremaean region, max east to west into subregions (blue cells in Fig. 5b), and imum temperature in the Southwest, Euronotian and the southeast can be divided near the New South Wales Northern phytoregions and minimum temperature in - Victoria border. Banksia and Persoonia show a coastal the southeast (Supplementary Tables 1 and 2). Solar north, southeast and southwest pattern (Fig. 3). radiation was noted as a factor in the evolution of leaf Our results indicate that distribution patterns within traits in Proteaceae (Jordan et al. 2005; Jordan et al. GrevillealHakea (SI Fig. 2) are similar to distribution 2008), thus the results of this study support previous patterns for the broader Proteaceae (Fig. 5b). findings. Precipitation was the highest correlate with Clustering of Banksia and Persoonia species by spe species turnover in the Eremaean, Southwest and cies turnover also indicates several well-defined geo Northern regions. pH, Si and P were the geochemical graphic clusters in the south west of the continent, the variables showing the highest correlation with species wet tropics of Queensland, and the New South Wales turnover on these broader regional scales. coast. (SI Fig. 2). Areas containing different levels of species richness were investigated to assess the geochemical and cli Correlation with climate and geochemistry mate variables that explain species turnover in these areas of high and low species diversity. Temperature Geochemistry is varied across the four broad Proteaceae and pH were the main explanatory variables for areas regions, which correspond to the following ABA areas: of low species richness (Supplementary Tables 1 and 1) Southwestern; 2) Northern; 3) Eremaean/Northern 2). In areas of highest species richness in the southeast, Desert phytogeographical regions; and 4) the species turnover was best explained by precipitation Euronotian subregion. For Proteaceae as a family, and soil P, while the areas of highest species richness

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CwroQtedWtCorrected W( Fig. 3 Diversity metrics (SR left a0 i618 3i a 46 Ba 0 00 0-28 0 00 0 oso28 FSÔ and CWE right) for Banksia, Grevillia & Hakea, and Banksia Persoonia mapped using 100x100km grid cells. Patterns are similar data for other genera (SI Fig. 1)

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in the southwest were explained by radiation Within genera, and the soil and climatic variables explain évapotranspiration, and the soil geochemistry from variables36% of variance in Hakea/Grevillea to 64% of the pH and Si. species turnover variance in Banksia and Petrophile. Both geochemistry and climate variables correlate Within particular clades, soil geochemistry played a with the beta diversity of the Proteaceae as a whole larger role in explaining species turnover. Phosphorus and among various subgroups within it. Over the entire was the best explanatory variable for species turnover continent, soil geochemistry and climate explain 37% of patterns in Petrophile and Synaphea and the second the variation in species turnover. However, in areas of highest explanatory variable in Banksia, Hakea/ high species richness (S > 29), these two explanatory Grevillea, and Isopogon (Supplementary Table 1). In groupings explain over 75% of the variation in species the eastern species-rich region, soil geochemistry alone turnover (Fig. 6); and in areas of moderate species accounts for approximately 35% of the species turnover, richness (13 < S < 29), these two explanatory groupings much more than climate alone (Fig. 6); sodium and soil explain 70% of the variation in species turnover (Fig. 6). salinity play a major role (Supplementary Table 1).

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Fig. 44 MapsMaps of of Proteaceae Proteaceae High species turnoverturnover (Simpson's (Simpson's ß) ß) across Australia relative to four index cells (plotted in grey). The colour gradient represents high dissimilarity in orange and red, and values closer to 1 indicating no shared taxa between the cells. Very high similarity (large proportion of shared taxa), indicated by scores closer to 0, is depicted in light blue

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Discussion Atherton, Southeastern subregions and the Southwestern region contain particularly high numbers The distribution patterns of taxa form biogeographical of Proteaceae species. Crisp et al. (2001) found the wet phenomena that can help explain their evolutionary tropics of Queensland to contain the highest floristic histories (Ebach et al. 2003). Accurately quantifying species richness in the country, with particularly high these patterns depends on sampling effort, taxonomic values found between Cape York and Eungella. Similar and geographic scale and the metrics chosen to under patterns were seen for mosses, hornworts and liverworts take the analyses. Our results have illustrated that there (Stevenson et al. 2012). For the Proteaceae this is a are multiple components to assessing biodiversity, region and of high generic richness, though species richness commonly-used metrics such as richness and endemism is highest within the Southwestern phytoregion are extended by the incorporation of species turnover (Johnson and Briggs 1975). The biological richness of and clustering methods in geographical space. the Southwestern phytoregion has been well One issue inherent in continental-scale datasets for documented (e.g. Burbidge 1960; Hopper and Gioia the Australian flora is the 'roadmap effect', where sam 2004; Sauquet et al. 2009; Gonzâlez-Orozco et al. pling is concentrated in regions accessible by road 2011; Gibson et al. 2012; Thornhill et al. 2016). (Kadmon et al. 2003). Though this can be problematic Although phylogenetic diversity was not investigated for the data integrity of some groups, the Proteaceae in this study, if the generic level data is used as a returned good redundancy values (80.9% of the grid surrogate for phylogeny then the area of highest phylo cells had redundancy values above 50%) at the species genetic diversity would be found in the Wet Tropics. level in grid cells throughout most of the country, indi Future phylogenetic analyses need to test this cating that any sampling bias should have only a limited hypothesis. influence on these analyses. The biological richness of the Southwestern Our work confirms the hypothesis that taxon richness phytoregion has been well-documented (e.g. Burbidge and endemism patterns of Proteaceae match that of the 1960; Hopper and Gioia 2004; Sauquet et al. 2009; flora in general and confirm previous analyses of Gonzâlez-Orozco et al. 2011; Gibson et al. 2012; Johnson and Briggs (1975). Areas of highest species Thornhill et al. 2016). In the Southwestern phytoregion, richness for the Proteaceae align with those identified environmental changes are thought to have resulted in for the Australian flora generally (e.g. Crisp et al. 2001 ; the extinction of some families and genera, while Gonzâlez-Orozco et al. 2013; Thornhill et al. 2016). The prompting explosive speciation within pre-adapted

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Fig. 55 PhytogeographicPhytogeographic regions regions of AustralianAustralian Proteaceae Proteaceae species species based onon WPGMAWPGMA clustering clustering of of Simpson's ß.ß. The The color color of of the the map map regions correspondcorrespond to to color color regions onon thethe dendrograms, dendrograms, a a Map andand dendrogramdendrogram colored colored with thethe fourfour major major clusters clusters and and b b seven clusters.clusters. The The dendrogram dendrogram terminals representrepresent species species composition contained contained within within the the 876 individualindividual grid grid cells cells on on the the map, groupedgrouped together together by by compositional similarity similarity (amount (amount of shared taxa)

genera (Beard et al. 2000). Our results suggest compar Species richness, however, is only one component of atively lower genus richness in the Southwestern quantifying biodiversity and does not provide insights phytoregion compared with the wet tropics, while spe into the rarity and compositional structure of the taxa cies richness in the Southwestern phytoregion greatly within a given location. Our CWE results demonstrate exceeds that of other centres of Proteaceae diversity. The that some areas may not be high in species richness, yet high species richness found in the Southwestern contain taxa with very restricted ranges. The centres of phytoregion as well as the New South Wales coast endemism for the Proteaceae correspond with those support previous findings that overall, the Proteaceae found by Crisp et al. (2001) for the Australian flora is represented most abundantly by species in the generally: the Southwestern phytoregion; the Border sclerophyllous forests, woodlands and heaths of eastern Ranges between New South Wales and Queensland; and south-western Australia (Thiele and Ladiges 1996; the wet tropics of Queensland; and Kangaroo Island. Myerscough et al. 2000). The major exceptions to this are the two centres of

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100* Fig. 66 AdjustedAdjusted R2 R2 from from analyses toto partition partition variance variance in in beta diversitydiversity due due to to climatic climatic and and 8t»mi geochemical variables variables alone, alone, and and variation explainedexplained jointly jointly by by 60*60% climate andand geochemistry. geochemistry. ■Itreslduats Kiadusls Residual isis thethe variation variation in in beta beta 40%40% ■■ c£n»t« & toils 8 toils diversity thatthat remains remains unexplained. Tables Tables 3 3and and 4 4show show •Heimat« cSmaw which individualindividual environmental environmental JO*ÎO* • «oil«soil* variables areare most most significant significant in in explaining patternspatterns in in beta beta 0% diversity / / / / / jT A J // jf/ /✓ / J*/ / X/ / y

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endemism found for the Proteaceae within the Kakadu due to aridity per se, but rather to the instability of the and Kimberley regions. Centres of endemism for the desert environment, which acts as a selection agent Proteaceae also appear to be near-coastal; again, inagainst the survival of endemic species (Crisp et al. alignment with patterns found for the Australian flora 2001, 2004; McGowran et al. 2004; Byrne et al. 2008). generally (Crisp et al. 2001). Quite possibly, the The influence of Grevillea!Hakea upon overall dis Pleistocene expansions of the central desert, thought to tribution patterns is perhaps unsurprising; with over 370 have driven patterns of endemism for the Australian flora species, Grevillea!Hakea is by far the most species-rich as a whole (Crisp et al. 2001), are also driving the broad genus of the Australian Proteaceae. Consequently, the patterns of endemism identified in the Proteaceae. patterns of Grevillea!Hakea distributions found in the At the continental scale, our matrix cluster results are cluster analysis closely mirror those of the entire congruent with the proposal by Laffan and Crisp (2003) Proteaceae turnover. It therefore appears that Grevillea that species turnover is intensely focused around centres species dominate the distribution patterns of the of endemism. The Southwestern phytoregion and the Australian Proteaceae. wet tropics of Queensland exhibit exceptionally high The results confirm the hypothesis that beta diversity species turnover and also contain more endemic species, patterns of Proteaceae species may be determined by the whereas the arid zone is a large region of exceptionally same broad-scale climate and geochemistry factors de low species turnover, largely dominated by a limited lineating compositional patterns for the Australian flora number of Grevillea and Hakea species. The paucity generally. However, given the Proteaceae's well-known of endemics within the interior of Australia may not bemorphological adaptions to low nutrient soils, it is

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This content downloaded from 86.59.13.237 on Thu, 08 Jul 2021 08:21:46 UTC All use subject to https://about.jstor.org/terms Plant Soil (2017)417:317-329 327 surprising that the combined influence of soil geochem as well as some of its larger component clades such as istry and climate are weaker than seen in Acacia (Bui Banksia (Cardillo and Pratt 2013) and Grevillea/Hakea et al. 2014) and eucalypts (Bui et al. 2017). (El-ahmir et al. 2015). An evolutionary study across the In Acacia, soil geochemistry and climate factors ac entire Proteaceae identified selection on leaf adaptions count for 50% of the species turnover pattern continent that allowed environmental niche adaptions in diverging wide and 82% of it in the species rich Southwestern lineages (Onstein et al. 2016). Future studies integrating phytoregion. In eucalypts, soil geochemistry and cli ever increasing phylogenetic samples with climate, soil mate factors account for 46% of the turnover pattern and traits are poised to further reveal the evolutionary continent-wide and 91% of it in the species rich history and biogeography of this classic southern hemi Southwestern phytoregion. This may reflect the differ sphere family. ence in environmental responses between the plant taxa, with Acacia and eucalypts adapting to various pH and Acknowledgements This work was part of EP's participation in a Summer Student program at CSIRO which was sponsored by the salinity niches whereas the Proteaceae, with their spe Grains Research Development Corporation (GRDC) and Bayer cialized roots, are adapting to niches in low nutrient CropScience. This manuscript includes work done by JTM while status and low water availability associated with serving at the National Science Foundation. The views expressed quartz-rich sandy soils. in this paper do not necessarily reflect those of the National Science Foundation or the United States Government. The effect of phosphorus on Proteaceae biogeogra phy may be clade specific. Areas of highest species richness across the family do not correlate as highly to References phosphorus as calcium at medium levels of species richness, and are lower than pH, silicon and sodium at Beard JS, Chapman AR, Gioia P (2000) Species richness and the highest levels of species richness (Supplementary endemism in the western Australian flora. J Biogeogr 27: Table 1). However within all genera except Persoonia, 1257-1268 phosphorus is the geochemical variable with the highest Borcard D, Gillet F, Legendi« P (2011 ) Numerical ecology with R. correlation with species turnover (Supplementary Springer, NY, 306 pp Bui EN, Thornhill AH, Miller JT (2014) Salt- and alkaline Table 2). This putative phylogenetic niche conservation tolerance are linked in Acacia. Biol Lett 10:20140278 and specialized traits of Proteaceae enable them to grow Bui EN, Thornhill AH, Gonzâlez-Orozco CE, Miller JT (2017) in nutrient-impoverished soils, but these traits might Climate, and geochemistry as drivers of eucalypt diversifica also restrict them to oligotrophy regions of the conti tion in Australia Geobiology. doi:10.111 l/gbi.12235 nent. Comparative phylogenetic studies are needed to Buibidge N (1960) The phytogeogiaphy of the Australian region. Austral J Bot 8:75-211 test these hypotheses. Byrne M, Yeates DK, Joseph L, Kearney M, Bowler J, Williams The partitioning of variation among taxa and areas of MJ, Cooper S, Donnellan SC, Keogh JS, Leys R, Melville J, species richness indicated that, while soil and climate Murphy DJ, Porch N, Wyrwoll K-H (2008) Birth of a biome: account for much of the variation, as much of the insights into the assembly and maintenance of the Australian arid zone biota. Mol Ecol 17:4398-4417. doi: 10.1111 variation is left unexplained in the residuals. Although /j. 1365-294X.2008.03899.X Bui et al. (2014) represents one of the best continental Cadotte MW, Davies JT (2010) Rarest of the rare: advances in level soil geochemistry data sets in the world, it is still combining evolutionary distinctiveness and scarcity to in based on interpolation to a 1-km grid from only ~1300 form conservation at biogeographical scales. Divers Distrib 16:376-385 samples. Soil characteristics change dramatically on Cardillo M, Pratt R (2013) Evolution of a hotspot genus: geo much finer geographic scales, although such detail graphic variation in speciation and extinction rates in Banksia would in any case be smoothed by aggregation to the (Proteaceae). BMC Evol Biol 13:1-12 100 km grid cells and only the broader trends are CHAH -Council of Heads of Australasian Herbaria (2013a) Australia's virtual herbarium, (October 2013). Australian retained in the statistical models presented here. Plant Census (http://www.anbg.gov.au/chah/apc/index.html) Other variables that may contribute to the species CHAH -Council of Heads of Australasian Herbaria (2013b) turnover patterns but are not examined in this study Australian Plant Census (http://www.anbg.gov. are phylogeny, adaptive traits and extinction au/chah/apc/index.html). Cox, CB, Moore PD (1976) Biogeography: an ecological and (Sniderman et al. 2013). Phylogenetic studies are in evolutionary approach, John Wiley & Sons creasing our knowledge of the evolutionary history of Crisp MD, Laffan S, Linder HP, Monro A (2001) Endemism in the the Proteaceae (Sauquet et al. 2009; Onstein et al. 2016) Australian flora. J Biogeogr 28(2):183—198

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