Predicting and mitigating future biodiversity loss using long-term ecological proxies

Damien A. Fordham (1), H. Resit Akcakaya (2), John Alroy (3), Frédérik Saltre (1), Tom M. Wigley (1), Barry W. Brook (4)

1 The Environment Institute and School of Biological Sciences, The University of Adelaide, SA 5005, Australia

2Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York 11794, USA

3Department of Biological Sciences, Macquarie University, NSW 2109, Australia.

4School of Biological Sciences, Private Bag 55, University of Tasmania, Hobart, TAS, 7001, Australia

The incorporation of retrospective knowledge from long-term ecological proxies into strategies for mitigating future biodiversity loss has been far too limited in scope. Consequently, conservation policy continues to be guided by forecasts of species’ responses to global change that often lack a robust accounting of the biases and uncertainties in these forecasts. Likewise, on-ground efforts to manage ecological outcomes in the face of global change are failing to benefit fully from strong inferences of long-term changes in biodiversity patterns. Recent advances in geochronological dating, paleoclimate reconstructions and molecular techniques for inferring population dynamics provide exciting new prospects for using information on biotic responses to past climate change, such as during the near-time geological past of the Quaternary. Such opportunities include using fossils and species genes to identify ecological traits that have made some species more (or less) prone to regional and range-wide extinction in the past, to test the validity of threatened-species assessment approaches, and to pinpoint habitats that support the stability of ecosystems in the face of shifting climates. These long-term retrospective analyses are key to establishing a more robust framework for predicting the likely effect of future shifts in climate and other environmental change on biodiversity over the coming decades to centuries, and targeting conservation management resources most effectively.

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