Predicting when climate-driven phenotypic change affects population dynamics

McLean, Nina, Lawson, Callum R., Leech, David I., and van de Pol, Martijn (2016) Predicting when climate-driven phenotypic change affects population dynamics. Ecology Letters, 19 (6). pp. 595-608.

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Abstract

Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower-level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life-history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully-parameterized population models, new insights can be gained; differences among species in the impacts of climate-driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data-deficient species.

Item ID: 69640
Item Type: Article (Research - C1)
ISSN: 1461-0248
Keywords: Birds, Climate change, Comparative, Demographic rates, Functional traits, Phenology, Population dynamics, Species responses, Trait
Copyright Information: © 2016 John Wiley & Sons Ltd/CNRS.
Funders: Australian Research Council (ARC)
Projects and Grants: ARC FT120100204
Date Deposited: 19 Oct 2021 02:59
FoR Codes: 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics @ 50%
31 BIOLOGICAL SCIENCES > 3103 Ecology > 310307 Population ecology @ 50%
SEO Codes: 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1905 Understanding climate change > 190501 Climate change models @ 100%
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