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Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains

Lester,RE, Close,PG, Barton,JL, Pope,AJ and Brown,SC 2014, Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains, Global change biology, vol. 20, no. 11, pp. 3471-3481, doi: 10.1111/gcb.12634.

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Title Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains
Author(s) Lester,REORCID iD for Lester,RE orcid.org/0000-0003-2682-6495
Close,PG
Barton,JLORCID iD for Barton,JL orcid.org/0000-0002-8216-3756
Pope,AJ
Brown,SC
Journal name Global change biology
Volume number 20
Issue number 11
Start page 3471
End page 3481
Publisher Wiley-Blackwell Publishing
Place of publication Chichester, England
Publication date 2014-11
ISSN 1354-1013
1365-2486
Keyword(s) analogy
climate change response
ecological modelling
ergodic
estuary
gradient studies
Science & Technology
Life Sciences & Biomedicine
Biodiversity Conservation
Ecology
Environmental Sciences
Biodiversity & Conservation
Environmental Sciences & Ecology
GLOBAL CHANGE
COMMUNITIES
AUSTRALIA
ESTUARIES
DYNAMICS
MODELS
IMPACT
FUTURE
WORLD
Summary Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.
Language eng
DOI 10.1111/gcb.12634
Field of Research 050101 Ecological Impacts of Climate Change
050104 Landscape Ecology
050209 Natural Resource Management
060205 Marine and Estuarine Ecology (incl Marine Ichthyology)
040608 Surfacewater Hydrology
Socio Economic Objective 960301 Climate Change Adaptation Measures
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Wiley-Blackwell Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070597

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