Williams-Climate-change-refugia-for-terrestrial-biodiversity_0

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expected to be considerably less extensive in the surrounding landscape than it is a present, indicating good potential for this location to serve as a refugium for biota associated with this particular environment. Applying the same logic to the other labelled locations, location A (a relatively high outcrop, or sheltered topographic position in the middle of a flat plain) and location D (at mid-elevation in highly dissected terrain) exhibit moderate levels of refugial potential (but less than location E), whereas location C has relatively low refugial potential (but more than location B). Figure 37: Diagrammatic representation of the shifting relationship between geographical space and biotically scaled environmental space under climate change. For ease of explanation, both geographical space and environmental space have been simplified to a single dimension each. The geographical dimension can be thought of as a straight-line transect across a landscape, and the environmental dimension can be thought of as a biotically scaled temperature gradient. The labelled locations (A to E) are discussed in the text. The concentric ellipses centred on each of these locations depict decreasing levels of compositional similarity with increasing distance from a location in biotically scaled environmental space, and decreasing likelihood of dispersal with increasing distance in geographical space. The calculation of refugial potential was carried out using the MPI/ OpenMP hybrid implementation of the Muru GDM model, developed by Tom Harwood and Maciej Golebiewski at CSIRO. Utilising 120 to 160 parallel processes, the required CPU time of 344 hours (for each combination of biological group, climate scenario, and dispersal distance, across the entire continent at 250 m resolution) was reduced to a more manageable 3.6 hours, subject to availability of the required number of nodes on the CSIRO High Performance Computing cluster. 4.4 Results and outputs 4.4.1 Fitted compositional-turnover models Models of compositional turnover were developed for 15 different taxonomic (biological) groups across continental Australia using 9-second gridded predictor data. 62 Climate change refugia for terrestrial biodiversity
Models of compositional turnover in vascular plants were also developed using floristic survey data for three regions: all of NSW (3-second resolution), south-east NSW (3second resolution), and the Tingle Mosaic in south-west Western Australia (1-second resolution) (Table 4). Environmental predictors overall explained substantially more of compositional-turnover patterns (estimated from the sum of coefficients fitted to each predictor) than did geographic distance in all models except that for amphibians, where geographic distance was marginally more important (see details in APPENDIX 7. Compositional turnover modelling). As expected, climate variables were the strongest predictors of compositional turnover, particularly among the continental models, compared with regolith (soil and related proxies) and landform variables. Regolith was proportionally more important for most plant groups, especially the nitrogen-fixing Fabales and for the local model of the Tingle Mosaic. All models included one or more of the topographically adjusted climate variables. The continental models incorporated the index of topographic-moisture (EAA) derived from the 9-second MODIS actual evapo-transpiration dataset. This variable was amongst the most important climatic predictors along with continental patterns of summer to winter rainfall seasonality (PTS1), maximum precipitation deficit (WDI) and minimum temperature regimes (TXI, TNX) (see APPENDIX 6. Environmental variables used in GDM modelling for definitions of variables). Table 2: Fitted compositional-turnover (GDM) models. Continental models were applied to the taxonomic subgroups using presence data (first 15 groups listed) and regional models were applied to presence–absence survey data of vascular plants (last three groups listed). Taxonomic group % deviance explained Summed coefficients # predictors Class Mammalia (mammals) 32.40 9.96 20 Class Aves (birds) 24.79 4.60 11 Class Reptilia (reptiles) 37.07 13.30 18 Class Amphibia (amphibians) 45.33 19.50 19 Order Apocrita (wasps and bees) 20.14 16.48 16 Order Coleoptera (beetles) 22.57 13.80 19 Order Aranae (spiders) 20.91 18.40 20 Order Asparagales (lilies and onions) 21.67 17.92 17 Order Asterales (daisies) 37.83 26.51 18 Order Fabales (peas) 37.62 20.12 22 Division Gymnospermae (fruitless seed plants) 35.17 24.47 20 Order Myrtales 29.09 19.59 19 Order Poales (grasses and sedges) 48.17 25.94 19 Order Proteales 42.57 35.06 20 Kingdom Fungi 49.64 34.12 20 NSW plant survey1 45.78 14.00 20 Southeast NSW plant surveys2 33.82 10.59 17 Tingle Mosaic plant surveys1 26.27 6.75 14 1. Vascular plants – ferns and allies, gymnosperms and angiosperms 2. Trees capable of reaching the canopy (includes gymnosperms and some angiosperms) 4.4.2 Refugial potential – Continental 9-second analyses Due to the considerable computation time required to run each of the continental refugial potential analyses (section 4.3.8), the full analytical process was completed for only four of the 15 biological groups: Order Proteales (banksias, grevilleas etc.), Order Fabales (peas, including acacias), reptiles and amphibians. However, all required inputs for the remaining biological groups have been prepared, including all projected environmental variables, biotically scaled at 250 m resolution, using the fitted GDMs for these groups. Potential therefore exists to undertake the final stage of processing; that Climate change refugia for terrestrial biodiversity 63
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Climate change refugia for terrestr
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Published by the National Climate C
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3.4.4 Future: current-2085 ........
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APPENDIX 3. Species distribution mo
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Figure 29: A detailed view of the p
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x Climate change refugia for terres
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EXECUTIVE SUMMARY Climate change is
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we will be in a better position to
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1.2.2 Case study 2: Pleistocene sta
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efugia meeting this latter criterio
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2.7 Conclusions Natural systems are
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8.1.3 Overall lessons from the diff
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9. GAPS AND FUTURE RESEARCH DIRECTI
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REFERENCES Ackerly, D. D., S. R. Lo
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Couper, P. J., B. Hamley, and C. J.
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Winton, A. T. Wittenberg, F. Zeng,
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A. Utteridge, J. E. Watkins, R. Wil
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R Development Core Team. 2011. R: A
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Tingley, M. W., W. B. Monahan, S. R
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APPENDIX 1. CLIMATE SCENARIOS AND B
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Table A1-4: Eighteen Global Climate
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Table A1-5: Thirty-year climate cov
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Figure A2-68. Change in temperature
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Figure A2-70. Proportionate change
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Figure A2-72. The distance an organ
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Figure A2-74. The areas for which t
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APPENDIX 3. SPECIES DISTRIBUTION MO
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APPENDIX 4. PROJECTED SPECIES RICHN
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Figure A4-2. The species richness s
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Table of contents ABSTRACT ........
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INTRODUCTION Terrain modifies local
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RESEARCH ACTIVITIES AND METHODS Dat
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Land Cover The Geosciences Australi
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Figure A5-4: Terrain adjusted model
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RESULTS AND OUTPUTS Figures 8 and 9
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Figure A5-10 Channel Country (SW Qu
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REFERENCES ANU Fenner School of Env
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APPENDIX 6. ENVIRONMENTAL VARIABLES
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Group Short name Name Units Source
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Group Short name Name Units Source
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Data Citations Bureau of Rural Scie
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APPENDIX 7. COMPOSITIONAL TURNOVER
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Table A7-9. Vascular plant data app
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to support in house applications an
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Table A7-10. Summary of GDM model f
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Variable Group Predictor variable a
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a) c) Figure A7-78. Continental Aus
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Table A7-15. Overall relative impor
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a) c) Figure A7-92. Eastern Austral
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Table A7-18. Overall relative impor
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a) c) Figure A7-94. Tingle Mosaic s
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References Allen R, Pereira L, Raes
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APPENDIX 8. PLEISTOCENE STABILITY A
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Figure A8-3. Rainforest in the Aust
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Canis lupus dingo CANLUPU MAMM Cani
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Lampropholis mirabilis LAMMIRA REPT
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Stegonotus cucullatus STECUCU REPT
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Figure A10-1. Spatial patterns of v
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