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Population dynamics can be more important than physiological limits for determining range shifts under climate change
Damien A Fordham1, Camille Mellin, Bayden D Russell
1The Environment Institute, School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia.
Global Change Biology
|August 3, 2013
Summary
Climate change impacts on abalone species are complex. Integrating physiological and metapopulation dynamics provides more accurate predictions of range and abundance than traditional ecological niche models (ENM).
Area of Science:
- Ecology
- Climate Change Biology
- Marine Biology
Background:
- Species' climate change responses depend on physiology, biotic interactions, and dispersal.
- Abalone fisheries face challenges from climate change, necessitating accurate predictive models.
Purpose of the Study:
- To assess the importance of integrating physiological limits, metapopulation dynamics, and exploitation in predicting abalone range and abundance under climate change.
- To compare predictions from traditional ecological niche models (ENM) with those incorporating demographic and physiological factors.
Main Methods:
- Utilized blacklip (Haliotis rubra) and greenlip (Haliotis laevigata) abalone as case studies.
- Employed models that simultaneously account for demographic processes, physiological responses to climate, and metapopulation dynamics.
- Compared model outputs with traditional correlative ecological niche models (ENM).
Main Results:
- ENMs predict increased abalone abundance and stable range under climate change.
- Integrated models show different future estimates of area of occupancy (AOO) and abundance.
- Blacklip abalone unlikely to expand range due to climate mortality and metapopulation interactions; greenlip abalone may increase in abundance despite AOO contraction.
Conclusions:
- Predicting species' climate change responses requires physiological data and metapopulation models.
- ENM predictions using habitat area as an extinction risk proxy may be unreliable due to non-linear population-area relationships.
- Accurate species distribution modeling necessitates understanding complex interactions beyond simple climate correlations.
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