SWAFR biodiversity patterns and new map 1
1 A new phytogeographic map for the Southwest
2 Australian Floristic Region after an exceptional decade
3 of collection and discovery
4 Paul Gioia and Stephen D. Hopper
5 Botanical Journal of the Linnean Society
6 APPENDIX S1. DETAILED METHODS
7 Data were obtained from a snapshot of the Western Australian Herbarium Specimen Database
8 (also available from online portal FloraBase - WA Herbarium, 1998 onwards). Data were
9 processed using ARCGIS 10.1 (ESRI, 2012), and statistical analysis performed using R
10 (R Core Team, 2014) and a number of freely available R packages ('vegan': Oksanen et al., 2013;
11 'ecodist': Goslee & Urban, 2007; 'betapart': Baselga et al., 2013).
12 Data filtering
13 Only vascular plant taxa native to Western Australia were considered. Furthermore, only
14 specimen collections with current species names and a valid geocode were analysed. Both
15 species-level and infraspecific taxa were included in the analysis. To avoid double-counting of
16 terminal taxa within a cell, species-level taxon names were renamed to the typical subspecies
17 where both were present. For example, if both Caladenia flava R.Br. and Caladenia flava R.Br.
18 subsp. flava were recorded in a cell, the Caladenia flava record was renamed to Caladenia flava
19 subsp. flava.
20 Species richness analyses are sensitive to spatial outliers and errors in nomenclature. We
21 applied a methodology (Beard et al., 2000) for identifying and categorising spatial outliers using
22 climatic parameters that was also used successfully by Gioia and Pigott (2000) in a previous SWAFR biodiversity patterns and new map 2
23 study on species richness and endemism. (The Western Australian Herbarium has an ongoing
24 program for identifying and correcting outliers using tools developed from this methodology.)
25 Study area
26 This study initially aspired to analyse patterns of biodiversity across the whole of Western
27 Australia. Collection distribution and intensity were examined by mapping point locations and
28 collections per unit area (see main text, Fig. 3). This preliminary analysis revealed marked data
29 paucity within the Eremaean and Northern Provinces (main text, Fig. 1) and localised nodes of
30 intensity that, on inspection, were artefacts of sampling effort bias. This was particularly so in
31 the Northern Province where inaccessibility makes sampling difficult and expensive, artificially
32 emphasising accessible collecting locations at the expense of others. The study area was
33 therefore restricted to the boundary depicted in Fig. 1, where sampling effort has been
34 considerably greater. To avoid edge effects and provide a rigorous test of the SWAFR boundary,
35 the study area was extended to include a buffer area of km.
75 36 Scale and grid cell size
37 We adopted a grid cell approach for determining sampling units and analysing diversity (see
38 main text, Fig. 1, for cell extent). The choice of cell size should reflect the phenomena being
39 mapped and the scale of investigation (Stoms, 1994). A related study on endemism (Crisp et al.,
40 2001) used a latitude / longitude grid of 1 degree for analysing patterns at a continental scale.
41 With substantially greater data available through WAHERB, and a smaller study area, a better
42 resolution was possible and a cell size of approximately 0.25 x 0.25 degrees was adopted ( km
43 at latitude 32 This matches the sampling unit in several studies of the Greater Cape Floristic27
44 Region of South ˚S). Africa (see Allsopp et al., 2014) rendering our analyses comparable with theirs.
45 To facilitate area and density calculations, and minimise the effects of area distortion (Crisp et
46 al., 2001), an Albers Equal Area projection was adopted. SWAFR biodiversity patterns and new map 3
47 Calculating species richness and endemism
48 Species richness was calculated by counting the number of terminal taxa in each grid cell. For
49 aesthetic reasons, areas with rapidly changing values, such as a step change in richness, were
50 smoothed to change more gradually (using a low pass filter), and symbolised by displaying
51 increasing richness on a white-yellow-red colour ramp. To calculate endemism we first
52 identified taxa restricted to Western Australia (State endemics) by comparing taxon names with
53 checklists from other Australian jurisdictions (data sourced from ALA, 2015). While metrics
54 exist that encapsulate both narrow and broad-scale endemism as a single value (e.g. Crisp et al.,
55 2001), it was informative to evaluate endemism for a range of area thresholds. We therefore
56 generated grid counts for State endemics having area of extent restricted to km2, km2,
57 km2, 10 km2, 20 km2 and 50 km2. 200 400
900 000 000 000 58 Area of extent was calculated by generating an alpha hull (Burgman & Fox, 2003) for each taxon
59 having at least three spatially distinct locations. (It was impractical to model distributions for all
60 8625 native, vascular taxa occurring within the study area using commonly applied techniques
61 such as maximum entropy (Phillips et al., 2006).) Cells with at least 50% of their area
62 overlapping the alpha hull were included in the cell count. Where a hull could not be generated,
63 the cell each point fell in was counted. An advantage of this approach is that area calculation is
64 independent of cell size and not subject to cell edge effects. The method therefore generates a
65 reliable and conservative index reflecting endemism at a range of scales.
66 We compared results with a previous similar study (Hopper & Gioia, 2004), noting changes in
67 sampling effort and species richness with the benefit of eleven years additional collecting.
68 Essential differences in methods between this study and the previous one include more
69 stringent data validation, resulting in fewer collections available for analysis in some cells, and
70 the use of a more appropriate metric for calculating site dissimilarities that is more tolerant of
71 species richness variation. SWAFR biodiversity patterns and new map 4
72 Sampling effort bias
73 A relationship was evident between sampling effort and species richness. To quantify this we
74 generated a correlation matrix (Pearson’s product moment), comparing sampling effort with
75 species richness and endemism at a range of scales (P values were calculated manually).
76 We evaluated methods for adjusting sampling effort to enable a legitimate comparison of
77 richness or endemism between cells. Techniques include rarefaction, extrapolation and spatial
78 filtering. Rarefaction can be used to interpolate lower values of richness (Gotelli & Colwell,
79 2001), but assumes random spatial dispersion, adequate sample size and consistent sampling
80 methodology (Gotelli & Colwell, 2001; Collins & Simberloff, 2009; Küper et al., 2006; Tobler et
81 al., 2007) and application to collections data is problematic (Tobler et al., 2007). Techniques also
82 exist for extrapolating to higher richness values (Colwell & Coddington, 1994; Melo et al., 2003),
83 but these are generally used for calculating total richness and require a large number of sample
84 points and uniform sampling. An alternative, and simpler, technique is spatial filtering
85 (Fourcade et al., 2014; Kramer-Schadt et al., 2013; Boria et al., 2014). However, sampling effort
86 extremes existed within WAHERB that were not adequately dealt with by any of the above
87 methods.
88 To further explore sampling effort variation, a species accumulation curve was generated for
89 each cell in the study area. Asymptotic convergence was only noticeable in cells with at least
90 1200 collections (75 out of 748 cells, i.e. only 10% of study area), these occurring mostly in
91 coastal areas or near population centres. We also explored the distribution of cell record count
92 values. Fig. 2 (main text) shows that half the grid cells in the study area contained less than 300
93 collections, 85% had less than 1000, and a few had between 5000 and 10 000. Given these
94 extremes, there was no obvious or realistic value to adjust cell numbers towards .
95 Despite shortcomings, we maintained the rarefaction approach for this study to enable
96 comparison of results with a previous similar study (Hopper & Gioia, 2004), recognising (a) it
97 would at best moderate the effects of extreme localised sampling, (b) this approach might SWAFR biodiversity patterns and new map 5
98 prevent masking of smaller, localised maxima that would otherwise be obscured by much larger
99 maxima that were artefacts of intense sampling, and (c) cells with insufficient collections would
100 remain unchanged. We felt this approach was better than presenting analyses with no
101 moderation of sampling extremes, though cautious interpretation would be required.
102 Individual-based rarefaction curves are generated by accumulating species from successive,
103 randomly drawn individuals, repeating the exercise many times and averaging the results
104 (Gotelli & Colwell, 2001). If richness is to be estimated for a specific subsample size n, a faster
105 method is to calculate the number of species from n randomly drawn individuals per cell, then
106 repeating the subsampling exercise many times and calculating the mean richness over all
107 subsamples. We adopted a target subsample size of 500 collections per cell through a trial and
108 error process, a trade-off between maximising the number of cells where rarefaction might have
109 any benefit, while retaining sufficient information within each cell to display discernible pattern.
110 We created 100 subsample datasets, with a maximum of 500 individuals per cell randomly
111 drawn from the corresponding cell in the original dataset, noting that for cells containing
112 numbers near or below this value, the same individuals would be drawn in every subsample.
113 This approach to randomisation is therefore only effective where there are significantly larger
114 numbers of collections than the target 500 (see Fig. S1.1 for where this occurs, typically near the
115 coastal region and populated areas).
116
117 Figure S1.1. Sampling effort categorised by number of collections in subsampling pool SWAFR biodiversity patterns and new map 6
118 Calculating species richness and endemism using subsampled data
119 Richness and endemism calculated from subsampled data are indices, not absolute values. An
120 Adjusted Richness Index was calculated by averaging richness for each cell over the 100
121 subsamples. The same process was used to calculate the Adjusted Endemism Index. However,
122 given the extensive computing time required to generate hulls for each taxon in each subsample,
123 endemism was averaged over 10 subsamples rather than 100.
124 Delineating floristic provinces and districts
125 We delineated provinces and districts by classifying and ordinating data in each of the 100
126 subsamples. We deemed quarter-degree grid cells as sites, conservatively selecting sites
127 containing at least 50 collections and with at least 50% of the site on land. Singletons (species
128 occurring in only one site) were excluded from analysis.
129 Our methodology drew from the frameworks described by Kreft and Jetz (2010) and Kulbicki et
130 al. (2013). For each subsample, site species assemblages were compared and a dissimilarity
131 matrix generated using βsim (i.e. Simpson's Diversity Index, Baselga, 2010; Baselga et al., 2013),
132 chosen due to its insensitivity to species richness variation. After repeating this process for each
133 subsample we required a method for combining the 100 dissimilarity matrices to produce a
134 single consensus classification. Approaches to the consensus classification problem, such as
135 maximum parsimony and maximum likelihood have been applied in phylogeny and cladistics
136 (e.g. Swofford, 2003; Wiley et al., 1991). Their application in this study required further research
137 and, because of other constraints, were considered out of scope. After considering a number of
138 approaches, we opted for a method that placed the least assumptions on data. The dissimilarity
139 for the same site pair within each subsample was averaged across the 100 subsamples to
140 produce a single value, then compiled with all other site pair dissimilarities to produce a mean
141 dissimilarity matrix.
142 The dissimilarity matrices from each subsample and the mean dissimilarity matrix were
143 classified using a standard UPGMA clustering approach ('agnes' function; R Core Team, 2014). SWAFR biodiversity patterns and new map 7
144 Matrices were also ordinated using non-metric multidimensional scaling ('isoMDS' function;
145 R Core Team, 2014).
146 To validate classifications we calculated the co-phenetic correlation coefficient, which provides a
147 measure of the extent to which information from the dissimilarity index has been preserved in
148 the classification. We also attempted to generate robustness or confidence values for each
149 branch, using techniques initially applied to phylogenetic trees, but now available for more
150 generic use. These include PVCLUST (multistep bootstrap sampling - Suzuki & Shimodaira, 2006)
151 and SIMPROF (significant cluster analysis - Whitaker & Christman, 2014). However, neither
152 package was able to generate results for our dataset size (505 sites and 7000 species per
153 subsample), even for small numbers of bootstraps or simulations. >
154 In the absence of alternative methods for evaluating clustering reliability and variability, we
155 compared dissimilarities between subsamples using a Mantel test, which performs a Pearson
156 correlation between two dissimilarity matrices (We used the 'ecodist' package - Goslee & Urban,
157 2007, which is substantially faster than its equivalent in 'vegan'; Oksanen et al., 2013). We
158 transformed that correlation to a dissimilarity and repeated the comparison between all
159 subsample combinations to produce what is effectively a dissimilarity matrix of dissimilarity
160 matrices. We classified and ordinated that matrix to identify any non-random relationships
161 between subsamples. Initially, the dendrogram was split at the 16 group level (see Appendix S3,
162 Figs S3.5 through S3.7 for classification, ordination and site locations). This grouping was
163 adjusted to remove singleton branches (occurring primarily in data-poor cells) by merging into
164 adjacent groups. Additionally, some groups were further split to reflect well-defined geographic
165 separation and / or historical precedent. Finally, we visually compared the classified and
166 spatially mapped groups for each subsample dataset to assess the persistence of groups across
167 data subsamples and variability in their higher level classification. SWAFR biodiversity patterns and new map 8
168 Naming of and statistics for floristic provinces and districts
169 We have recognised southwest Australia as a floristic region rather than a botanical province to
170 correct and update Diels’ (1906) nomenclature so that it conforms with modern global
171 phytogeographic classifications (Cox, 2001). In broad terms floristic regions are characterised
172 by having some endemic families, regions by having some endemic genera and districts by
173 having some endemic species. From this perspective, the SWAFR is directly comparable to the
174 Greater Cape Floristic Region of South Africa, the latter traditionally treated as a Kingdom (Diels,
175 1906), but now recognised as a Region in global biogeographic schemes (Cox, 2001).
176 In naming floristic provinces and districts, we applied a rule of priority akin to that used in
177 taxonomy – areas matching previously named provinces and districts retained the original
178 name. New names were coined only where there was no prior recognition of the floristic area
179 identified by our analyses.
180 Based on our new regional province and district boundaries, statistics on species richness and
181 endemism were derived.
182 REFERENCES
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204 Crisp, M.D., Laffan, S., Linder, H.P. & Monro, A. 2001. Endemism in the Australian flora. Journal of 205 Biogeography, 28: 183-198. 206 Diels, L. 1906. Die Pflanzenwelt von West-Australien südlich des Wendekreises. In Vegetation der Erde 7. 207 Engelmann, Leipzig. 208 ESRI 2012. ArcGIS Desktop Version 10.1. Environmental Systems Research Institute, Redlands, California. 209 Fourcade, Y., Engler, J.O., Rödder, D. & Secondi, J. 2014. Mapping Species Distributions with MAXENT Using 210 a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for 211 Correcting Sampling Bias. PLoS ONE, 9(5): 1-13. 212 Gioia, P. & Pigott, J.P. 2000. Biodiversity assessment: a case study in predicting richness from the potential 213 distributions of plant species in the forests of south-western Australia. Journal of Biogeography, 27(5): 214 1065-1078. 215 Goslee, S.C. & Urban, D.L. 2007. The ecodist package for dissimilarity-based analysis of ecological data. 216 Journal of Statistical Software, 22(7): 1-19. 217 Gotelli, N.J. & Colwell, R.K. 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and 218 comparison of species richness. Ecology Letters, 4: 379-391. 219 Hopper, S.D. & Gioia, P. 2004. The Southwest Australian Floristic Region: Evolution and conservation of a 220 global hot spot of biodiversity. Annual Review of Ecology, Evolution, and Systematics, 35: 623-650. 221 Kramer-Schadt, S. et al. 2013. The importance of correcting for sampling bias in MaxEnt species 222 distribution models. Diversity & Distributions, 19(11): 1366-1379. 223 Kreft, H. & Jetz, W. 2010. A framework for delineating biogeographical regions based on species 224 distributions. Journal of Biogeography, 37(11): 2029-2053. 225 Kulbicki, M., Parravicini, V., Bellwood, D.R., Arias-Gonzàlez, E., Chabanet, P., Floeter, S.R., Friedlander, A., 226 McPherson, J., Myers, R.E., Vigliola, L. & Mouillot, D. 2013. Global Biogeography of Reef Fishes: A 227 Hierarchical Quantitative Delineation of Regions. PLoS ONE, 8(12): e81847. 228 Küper, W., Sommer, J.H., Lovett, J.C. & Barthlott, W. 2006. Deficiency in African plant distribution data – 229 missing pieces of the puzzle. Botanical Journal of the Linnean Society, 150: 355–368. 230 Melo, A.S., Pereira, R.A.S., Santos, A.J., Shepherd, G.J., Machado, G., Medeiros, H.F. & Sawaya, R.J. 2003. 231 Comparing species richness among assemblages using sample units: why not use extrapolation 232 methods to standardize different sample sizes? Oikos, 101(2): 398-410. 233 Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B., Simpson, G.L., Solymos, P., 234 Henry, M., Stevens, H. & Wagner, H. 2013. vegan: Community Ecology Package. R package version 2.0- 235 10. 236 Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006. Maximum entropy modeling of species geographic 237 distributions. Ecological Modelling, 190: 231–259. 238 R Core Team 2014. R: A language and environment for statistical computing. R Foundation for Statistical 239 Computing. Vienna, Austria. Available from: http://www.R-project.org. 240 Stoms, D.M. 1994. Scale dependence of species richness maps. Professional Geographer, 46(3): 346-358. 241 Suzuki, R. & Shimodaira, H. 2006. Pvclust: an R package for assessing the uncertainty in hierarchical 242 clustering. Bioinformatics, 22(12): 1540-1542. 243 Swofford, D.L. 2003. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. 244 Sinauer Associates, Sunderland, Massachusetts. Available from: http://paup.csit.fsu.edu/. Last 245 accessed on 3rd July, 2015. 246 Tobler, M., Honorio, E., Janovec, J. & Reynel, C. 2007. Implications of collection patterns of botanical 247 specimens on their usefulness for conservation planning: an example of two neotropical plant families 248 (Moraceae and Myristicaceae) in Peru. Biodiversity and Conservation, 16: 659-677. 249 WA Herbarium 1998 onwards. FloraBase - the Western Australian flora. Department of Parks and Wildlife, 250 Perth. Available from: https://florabase.dpaw.wa.gov.au. Last accessed on 15/05/2015. 251 Whitaker, D. & Christman, M. 2014. clustsig: Significant Cluster Analysis. R package version 1.1 Available 252 from: http://CRAN.R-project.org/package=clustsig. 253 Wiley, E.O., Siegel-Causey, D., Brooks, D.R. & Funk, V.A. 1991. The Compleat Cladist: A Primer of 254 Phylogenetic Procedures. Special Publication No. 19. University of Kansas Museum of Natural History. 255 256 SWAFR biodiversity patterns and new map 1
1 A new phytogeographic map for the Southwest
2 Australian Floristic Region after an exceptional decade
3 of collection and discovery
4 Paul Gioia & Stephen D. Hopper
5 Botanical Journal of the Linnean Society
6 APPENDIX S2. SPECIES RICHNESS AND ENDEMISM BASED ON RAW DATA
7 Species richness based on raw data
8
9 Figure S2.2. Species richness based on raw (i.e. un-subsampled) counts of native, vascular plants within quarter
10 degree grid cells, smoothed for aesthetic display and mapped onto a white – yellow – red colour ramp. SWAFR biodiversity patterns and new map 2
11 Species endemism based on raw data
12
13 Figure S2.3. Species endemism at a range of scales based on raw (i.e. un-subsampled) counts of native, vascular plants
14 within quarter degree grid cells, smoothed for aesthetic display and mapped onto a white – yellow – red colour ramp. SWAFR biodiversity patterns and new map 3
15 Table S2.1. Correlation (Pearson’s product moment) between sampling effort, species richness and endemism
16 SE E200 E400 E900 E10000 E20000 E50000
17 E200 0.73
18 E400 0.76 0.98
19 E900 0.78 0.93 0.98
20 E10000 0.78 0.75 0.81 0.87
21 E20000 0.77 0.71 0.77 0.83 0.98
22 E50000 0.75 0.66 0.71 0.76 0.91 0.96
23 SR 0.88 0.74 0.76 0.79 0.84 0.87 0.89
24 sampling effort, endemism ( km2), endemism ( km2), endemism
25 (SE = km2), E200 = endemismrange ( < 200 10, E400 = km2), range endemism< 400 ( E900 = 20 km2),
26 range < 900 endemism ( E10000 = 50 km2), 0.0range for all< comparisons000 E20000 = range < 000
E50000 = range < 000 P = 27 SWAFR biodiversity patterns and new map 1
1 A new phytogeographic map for the Southwest
2 Australian Floristic Region after an exceptional decade
3 of collection and discovery
4 Paul Gioia & Stephen D. Hopper
5 Botanical Journal of the Linnean Society
6 APPENDIX S3. FURTHER DETAILS OF THE SOUTH WEST AUSTRALIAN FLORISTIC REGION,
7 INCLUDING CLASSIFICATION AND ORDINATION DIAGRAMS, PROVINCE AND DISTRICT
8 DESCRIPTION, COMPARATIVE REGIONAL STATISTICS AND DETAILED MAPPING
9 SWAFR regional statistics
10 Regional statistics at region, province and district level were produced for the SWAFR depicted
11 in the main text, Fig. 10, as well as statistics for previous or alternative regionalisations. Table
12 S3.2 shows a comparison of records and taxa within each regionalisation, while Table S3.3
13 shows the largest families and genera within the SWAFR. More detailed statistics broken down
14 by province and district are provided in a range of reports, available at
15 https://naturemap.dpaw.wa.gov.au/resources/gh/index.html, together with high resolution
16 maps. SWAFR biodiversity patterns and new map 2
17 Table S3.2. Comparative statistics of the SWAFR based on previous or alternative regionalisations, based on
18 collections within current analysis.
19 Regionalisation Area (ha) Records Native taxa1 Naturalised taxa Endemics
20 Current2 34,106,803 398,971 8,379 1,068 3,911
21 Hopper & Gioia (2004)3 29,954,654 385,847 8,122 1,065 3,632
22 IBRA4 29,851,921 386,911 8,133 1,068 3,663
23 1Vascular plant taxa, outliers and errors removed
24 2Updated SWAFR (main text, Fig. 10)
25 3Hopper and Gioia (2004) line work applied to current data
26 4The collection of IBRA 6.1 bioregions most closely approximating the original definition of the Southwest Province by Beard (1980);
27 line work applied to current data
28 Table S3.3. Largest vascular plant families and genera in the SWAFR (main text, Fig. 10)
29 Families Genera
30 Myrtaceae (1436 species/subspecies) Acacia (Mimosaceae; 530)
31 Fabaceae (1156) Eucalyptus (Myrtaceae; 380)
32 Proteaceae (914) Grevillea (Proteaceae; 252)
33 Orchidaceae (422) Stylidium (Stylidiaceae; 218)
34 Ericaceae (362) Leucopogon (Ericaceae; 209)
35 Asteraceae (330) Banksia (Proteaceae; 207)
36 Cyperaceae (262) Melaleuca (Myrtaceae; 194)
37 Goodeniaceae (231) Caladenia (Orchidaceae; 178)
38 Stylidiaceae (227) Verticordia (Myrtaceae; 140)
39 Malvaceae (196) Eremophila (Scrophulariaceae; 123)
40 SWAFR biodiversity patterns and new map 3
41 Historical bioregionalisations of the SWAFR
42
43 Figure S3.4. Historical bioregionalisations of the SWAFR (a) Diels’ Southwest Botanical Province and Districts (Diels,
44 1906), (b) biogeographic regions within Southwest Botanical Province, from Beard (1980), (c) Hopper’s (1979)
45 rainfall zones, (d) Provinces and districts from Hopper & Gioia’s (2004) Southwest Australian Floristic Region, (e)
46 southwest bioregions from Interim Biogeographic Regionalisation of Australia (DOE, 2012: IBRA does not implement
47 a hierarchical aggregation – displayed bioregions approximate Beard’s concept of Southwest Botanical Province), and
48 (f) phytogeographical regions (González-Orozco et al., 2014). SWAFR biodiversity patterns and new map 4
49 Derivation of names and description of SWAFR floristic Provinces and Districts
50 Kalbarri Floristic Province
51 Named for Kalbarri National Park and the township of Kalbarri, at the mouth of the Murchison
52 River. This Nanda Aboriginal word is reputed to be a man’s name, or the name of a type of seed
53 (Murray & Goodchild, 2003). The name for the precise town locality is Wurdimarlu, but Kalbarri
54 National Park extends over a much greater area either side of the Lower Murchison River, and is
55 renowned for its diverse kwongkan flora rich in endemics of the District.
56 1. Shark Bay District – for the famous Bay named by William Dampier in 1699
57 2. Nanda District – for the local Aboriginal people
58 Bibbulmun Floristic Province
59 For a local Noongar Aboriginal dialect centred on the Black Point-Walpole region of highest
60 rainfall in the SWAFR. The name is also used for all people of the Noongar Nation, and has been
61 applied to the Bibbulmun Track, running some 1000 km through forested country from Perth to
62 Albany (Bibbulmun Track, 2016). From bibbi/piip = woman’s breast, nipple, mother’s milk (von
63 Brandenstein, 1988) meaning land of many breasts or women, a bountiful country, or land of
64 many granite outcrops that are rounded and emergent from surrounding terrain, resembling
65 breasts (Nannup, 2008).
66 3. Lesueur District – for Charles Lesueur (1778–1846) artist on the Baudin expedition
67 (Hopper, 2004), the first Europeans to see and name the flat-topped lateritic mesa Mt
68 Lesueur, for which Lesueur National Park is named. Yued Noongars know the small mesa
69 as Koomba Chiller (big wing, or height), or Kada Koomba (big hill, Rooney, 2011).
70 4. Jarrah District – for the jarrah (or jerrail, tyieral) tree (Eucalyptus marginata Sm.), also
71 meaning north in some Noongar dialects (von Brandenstein, 1988). Jarrah is a common
72 hardwood commercially logged, widespread across the District.
73 5. Muir District – for a Scottish family that pioneered the southern forest region (Muir,
74 2005). Lake Muir is also named for them. SWAFR biodiversity patterns and new map 5
75 6. Narrogin District for the western wheatbelt town of Narrogin, derived from the Noongar
76 word Narroging for a local pool (Murray & Goodchild, 2003).
77 Transitional Rainfall Province
78 From the early floristic regionalisation of the SWAFR proposed by Hopper (1979), as the
79 Transitional Rainfall Zone, receiving 300-600 mm annual rainfall, lying inland, north and east of
80 the forested High Rainfall Zone, adjacent to the Arid Zone.
81 7. Hyden District – for the eastern wheatbelt town of Hyden, famous for its granite outcrops,
82 including Kaatagidj (Wave Rock), an important site for Noongars and their creation
83 cosmology (Nannup, 2008). The town was possibly named for Carl Heiden, a German
84 prospector active in the area in the 1890s (Murray & Goodchild, 2003).
85 8. Merredin District – for the central wheatbelt town of Merredin, on Great Eastern Highway,
86 named from merit, a Najin Nayaki Noongar word for Eucalyptus falcata Turcz., used for
87 making spears (Murray & Goodchild, 2003).
88 9. Wongan District – for the town and lateritic range of Wongan Hills, derived from the
89 Noongar word kwongkan, generally interpreted as sandplain, but specifically meaning
90 ‘where the light hits the sand’, or ‘where spirits are in sand’, from kaan = light or spirit
91 (von Brandenstein, 1988; Hopper, 2014).
92 Southeast Coastal Province
93 A geographical descriptor coined by Hopper and Gioia (2004)
94 10. Fitzgerald-Stirling District – for the two major national parks in the area, named for early
95 Governors of the Swan River Colony, now Western Australia; Charles Fitzgerald (1791–
96 1887) and James Stirling (1791–1865)
97 11. Esperance District – for the coastal town of Esperance, named for the French ship the
98 “Esperance” captained by Captain Huon de Kermadec on the 1791 expedition led by
99 Admiral Antoine D’Entrecasteaux (Hopper, 2003) SWAFR biodiversity patterns and new map 6
100 12. Maalak District – maalak = dense scrub found east of Albany (von Brandenstein, 1988),
101 and also coincides with the bulk of Eucalyptus platypus Hook.'s distribution, known as
102 moort, muert or maalak. It literally means ‘little hand’, alluding to the finger-like buds
103 coming off a flattened peduncle. A local Noongar name is considered respectful and more
104 apposite than using the northwest Victorian Wergaia word mallee (for impenetrable gum
105 scrub or the multi-stemmed Eucalyptus dumosa A.Cunn. which does not occur in the
106 SWAFR). Mallee was adopted by Beard (1980) and DOE (2012) for a wider District
107 concept embracing our Maalak and Boylya Districts.
108 13. Boylya District – boylya = of the rocks (Ron Doc Reynolds, Esperance Noongar elder, pers.
109 comm. 2014) alluding to the prominent granite outcrops of the District. Interestingly, the
110 District straddles the transition from inland Noongar country and adjacent Nadju people’s
111 country, and the word boylya is a mixed composite from the two languages (Noongar
112 boy/puiy = rock, von Brandenstein (1988); Nadju -ia/-ya = vocative case, for a person,
113 animal or thing being addressed, or the determiners of the noun, hence a significant rock
114 that must be addressed spiritually, ceremonially, for food or water or materials for tools
115 and implements, – von Brandenstein (1980)).
116 SWAFR biodiversity patterns and new map 7
117 Classification and ordination of SWAFR cells at the 16 group level
118
119 Figure S3.5. Classification of SWAFR sites using a 16 group split. See Fig. S3.7 for site locations. SWAFR biodiversity patterns and new map 8
120
121 Figure S3.6. Ordination of SWAFR sites using a 16 group split. See Fig. S3.7 for site locations. Ellipses represent the centre and standard error of each site group. SWAFR biodiversity patterns and new map 9
122
123 Figure S3.7. SWAFR cells symbolised using a 16 group split in Fig. S3.5. Unshaded cells are those not meeting minimum data requirements. SWAFR biodiversity patterns and new map 10
124 Classification and ordination of SWAFR cells using an adjusted grouping
125 Adjustments were made to the L16 classification to reflect natural subgroupings and singleton
126 branches. Group 1 in the L16 classification revealed a well-separated north-south divide with
127 historical precedent (Beard, 1980) that we have named Jarrah and Muir districts. Group 11 in L16
128 similarly contained a clear split running parallel with the coast that we have named as Wongan and
129 Merredin Districts. Singleton branch 4 in L16 was merged with adjacent branch 3 to become Lesueur
130 district, while singleton group 14 was merged with group 13 to form Nanda District and singleton
131 group 15 merged with adjacent group16 to form Shark Bay District. Two groups fell outside the
132 SWAFR, leaving 13 named districts within the SWAFR. SWAFR biodiversity patterns and new map 11
133 SWAFR biodiversity patterns and new map 12
134 Figure S3.8. Classification of SWAFR sites using an adjusted grouping. See Fig. S3.10 for site locations. Classification includes adjustments to 16-group classification to reflect natural
135 subgroupings and manage singleton branches: a well-separated north-south divide with historical precedent (Beard, 1980) divides the Jarrah and Muir districts; a split running parallel with
136 the coast separates the Wongan and Merredin Districts; singletons were merged with adjacent groups to form the Lesueur, Nanda and Shark Bay Districts. SWAFR biodiversity patterns and new map 13
137 SWAFR biodiversity patterns and new map 14
138 Figure S3.9. Ordination of SWAFR sites using an adjusted grouping. Classification includes adjustments to 16-group classification to reflect natural subgroupings and manage singleton
139 branches: a well-separated north-south divide with historical precedent (Beard, 1980) divides the Jarrah and Muir districts; a split running parallel with the coast separates the Wongan and
140 Merredin Districts; singletons were merged with adjacent groups to form the Lesueur, Nanda and Shark Bay Districts. See Fig. S3.10 for site locations. Ellipses represent the centre and
141 standard error of each site group. SWAFR biodiversity patterns and new map 15
142
143 Figure S3.10. SWAFR cells symbolised using an adjusted grouping in Fig. S3.8. Unshaded cells are those not meeting minimum data requirements. SWAFR biodiversity patterns and new map 16
144 Subsample comparison
145
146 (a) Classification
147
148 (b) Ordination
149 Figure S3.11. Classification and ordination of a dissimilarity matrix compiled from Mantel correlations (transformed to
150 dissimilarities) between 100 subsample dissimilarity matrices
151 REFERENCES
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164 Hopper, S.D. 2003. South-western Australia – Cinderella of the world’s temperate floristic regions 1. Curtis’s 165 Botanical Magazine, 20 (2): 101-126. 166 Hopper, S.D. 2004. South-western Australia – Cinderella of the world’s temperate floristic regions 2. Curtis’s 167 Botanical Magazine, 21(2): 132-180. 168 Hopper, S.D. 2014. Sandplain and Kwongkan: historical spellings, meanings, synonyms, geography and definition, 169 pp. 23-33. In Plant Life on the Sandplains in Southwest Australia: a Global Biodiversity Hotspot (ed. H. 170 Lambers). University of Western Australia Publishing, Crawley, WA. 171 Hopper, S.D. & Gioia, P. 2004. The Southwest Australian Floristic Region: Evolution and conservation of a global 172 hot spot of biodiversity. Annual Review of Ecology, Evolution, and Systematics, 35: 623-650. 173 Muir, A. 2005. Settler Footprints from Star Muir family. Pioneers of the South West and Eucla Western Australia 174 1844-2005. Privately published, Perth. 175 Murray, I. & Goodchild, B. 2003. Araluen to Zanthus: A Gazetteer of Perth Suburbs and Western Australian Towns. 176 Fremantle Arts Centre Press and Department of Land Information, Fremantle. 177 Nannup, N. 2008. Carers for everything, pp. 102-114. In Heartsick for Country (eds. S. Morgan, T. Mia & B. 178 Kwaymullina). Fremantle Press, Fremantle. 179 Rooney, B. 2011. The Nyoongar Legacy: the naming of the land and the language of its people. Batchelor Press, 180 Batchelor, Northern Territory. 181 von Brandenstein, C.G. 1980. Ngadjumaja: An Aboriginal language of South-east Western Australia, Innsbrucker 182 Beitrage Zur Kulturwissenschaft Sonderheft 48, Innsbruck. 183 von Brandenstein, C.G. 1988. Nyungar Anew. Pacific Linguistics Series C 99. Australian National University, 184 Canberra. 185 186