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Within each sub-region, potential ecosystem greenspots were identified, at a range of thresholds, based on an index derived from: the mean and coefficient of variance (COV) of fPAR over the 10-year time series; the minimum mean annual fPAR; and the COV of the 12 values of mean monthly fPAR. We calculated and mapped the greenspot index using the following percentiles: 10 th , 25 th , 50 th , 75 th , 90 th and 95 th . The 10 th greenspot percentile class represent those 250 m cells which over the time series had the highest and most constant fpar values. The 95 th greenspot percentile class corresponds with those grid cells with the lowest and most variable fpar values. We then intersected the greenspot index map with the boundaries of protected areas that comprise the Australian National Reserve System (DSEWPaC 2010) and calculated the area and percentage of each greenspot percentile class that falls within a protected area. These statistics were calculated for each IBRA sub-region and then aggregated for all of monsoonal Australia. 6.4 Results and outputs Figure 58 shows the Interim Biogeographic Regionalisation for Australia (IBRA) regions that were used to define the boundary of monsoonal Australia for this study. As noted above, the greenspot index was calculated for each of the IBRA sub-bioregions. Figure 59 shows a map of greenspot index percentile classes for monsoonal Australia and and Table 6 provides a statistical summary of the results. These values are based on the aggregation of the sub-regional analyses. Table 6 also provides estimates of the area and percentage of each greenspot percentile class found within the protected areas of the National Reserve System. Table 8 shows these statistics for one subregion, Gulf Fall & Uplands. Table 7 provides a statistical summary of the results. Figure 61: The Australian bioregions (IBRA) selected for the greenspot analysis of monsoonal Australia. 98 Climate change refugia for terrestrial biodiversity
These values are based on the aggregation of the sub-regional analyses. Table 7 also provides estimates of the area and percentage of each greenspot percentile class found within the protected areas of the National Reserve System. Table 8 shows these statistics for one sub-region, Gulf Fall and Uplands. Figure 62: This map shows the results of the greenspot analysis for monsoonal Australia. The greenspot index is mapped in percentiles —10% corresponds with those 250 m resolution grid cells that have the relatively highest and most stable values of fPAR for each of the IBRA bioregions in Figure 58. Table 8: Greenspot spatial statistics for monsoonal Australia as defined in Figure 58 and Figure 59. The total area of monsoonal Australia as defined in Figure 58 is 139 332 046 ha. The total area of monsoonal Australia in protected areas is 15 155 499 ha. This is 10.9% of monsoonal Australia. A B C D E 10 6 177 600 990 976 4.4 0.7 25 12 345 089 1 599 564 8.9 1.1 50 29 057 644 3 507 979 20.9 2.5 75 39 720 164 4 488 210 28.5 3.2 90 30 203 223 2 796 058 21.7 2.0 95 Key: 10 577 584 861 372 7.6 0.6 A – Greenspot percentile class B – Total area of greenspot percentile class in monsoonal Australia (ha) C – Area of greenspot percentile class located within protected areas (ha) D – Percentage of greenspot percentile class in monsoonal Australia E – Percentage of greenspot percentile class located in protected areas in monsoonal Australia Climate change refugia for terrestrial biodiversity 99
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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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highest RCP reported in the SRES (A
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3.3.4 Species data The study genera
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AUC score is negatively correlated
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Finally, we assess climate change t
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Territory, the Great Australian Big
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Figure 5: The 10 th percentile of l
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Figure 8: The absolute predicted ch
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3.4.5 Distance measures At a given
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the continent, the required movemen
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Figure 16: An example of a species
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Figure 18: Species richness for eac
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Figure 20: The number of immigrants
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Figure 22 (cont.): The ‘Immigrant
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Figure 24 (cont.): The immigrants a
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Figure 26 (cont.): The areas with t
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3.4.7.6 Areas of stability Understa
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including the Nullarbor Regional Re
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Table 2: The different classes of r
Page 60 and 61: most of the continent by 2085 that
Page 62 and 63: 3.7 Gaps and future research The ne
Page 64 and 65: individual species) will respond to
Page 66 and 67: 4.3.2 Topographically adjusted clim
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Page 70 and 71: Table 1: Biological groups compiled
Page 72 and 73: Invoking space-for-time substitutio
Page 74 and 75: expected to be considerably less ex
Page 76 and 77: is, the refugial analysis itself, f
Page 78 and 79: Figure 40: Refugial potential based
Page 80 and 81: Figure 43: Enlarged portion (Kimber
Page 82 and 83: Figure 46: Enlarged portion (Sydney
Page 84 and 85: Figure 48: Inclusion of areas of hi
Page 86 and 87: Figure 51: Refugial potential in th
Page 88 and 89: 4.6 Gaps and future research The GD
Page 90 and 91: 5.3 Research activities and methods
Page 92 and 93: time periods to a distance calculat
Page 94 and 95: proportion of this suitability foun
Page 96 and 97: (a) Figure 54: Stability of the cli
Page 98 and 99: Figure 55: Climate and habitat suit
Page 100 and 101: Mountains in Queensland, the Eungel
Page 102 and 103: Figure 58: Areas of endemism in the
Page 104 and 105: Northern Tablellands Figure 60: Gla
Page 106 and 107: 5.6 Discussion 5.6.1 Endemism in re
Page 108 and 109: (back to 120 kya compared to LGM in
Page 112 and 113: Table 9: Greenspot statistics for o
Page 114 and 115: drought micro-refuges, then targete
Page 116 and 117: The iterative recalculation of all
Page 118 and 119: Figure 64: Zonation conservation pr
Page 120 and 121: 7.6 Gaps and future research The co
Page 122 and 123: Figure 66: The refugia areas result
Page 124 and 125: 8.1.3 Overall lessons from the diff
Page 126 and 127: 9. GAPS AND FUTURE RESEARCH DIRECTI
Page 128 and 129: REFERENCES Ackerly, D. D., S. R. Lo
Page 130 and 131: Couper, P. J., B. Hamley, and C. J.
Page 132 and 133: Winton, A. T. Wittenberg, F. Zeng,
Page 134 and 135: A. Utteridge, J. E. Watkins, R. Wil
Page 136 and 137: R Development Core Team. 2011. R: A
Page 138 and 139: Tingley, M. W., W. B. Monahan, S. R
Page 140 and 141: APPENDIX 1. CLIMATE SCENARIOS AND B
Page 142 and 143: Table A1-4: Eighteen Global Climate
Page 144 and 145: Table A1-5: Thirty-year climate cov
Page 146 and 147: Figure A2-68. Change in temperature
Page 148 and 149: Figure A2-70. Proportionate change
Page 150 and 151: Figure A2-72. The distance an organ
Page 152 and 153: Figure A2-74. The areas for which t
Page 154 and 155: APPENDIX 3. SPECIES DISTRIBUTION MO
Page 156 and 157: APPENDIX 4. PROJECTED SPECIES RICHN
Page 158 and 159: 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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