Establishing the link between habitat selection and animal population dynamics

Matthiopoulos, Jason, Fieberg, John, Aarts, Geert, Beyer, Hawthorne L., Morales, Juan M. and Haydon, Daniel T. (2015) Establishing the link between habitat selection and animal population dynamics. Ecological Monographs, 85 3: 413-436. doi:10.1890/14-2244.1

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ367849_OA.pdf Full text (open access) application/pdf 965.16KB 0

Author Matthiopoulos, Jason
Fieberg, John
Aarts, Geert
Beyer, Hawthorne L.
Morales, Juan M.
Haydon, Daniel T.
Title Establishing the link between habitat selection and animal population dynamics
Journal name Ecological Monographs   Check publisher's open access policy
ISSN 1557-7015
Publication date 2015-08-01
Year available 2015
Sub-type Article (original research)
DOI 10.1890/14-2244.1
Open Access Status DOI
Volume 85
Issue 3
Start page 413
End page 436
Total pages 24
Place of publication Washington, DC United States
Publisher Ecological Society of America
Collection year 2016
Language eng
Abstract Although classical ecological theory (e.g., on ideal free consumers) recognizes the potential effect of population density on the spatial distribution of animals, empirical species distribution models assume that species–habitat relationships remain unchanged across a range of population sizes. Conversely, even though ecological models and experiments have demonstrated the importance of spatial heterogeneity for the rate of population change, we still have no practical method for making the connection between the makeup of real environments, the expected distribution and fitness of their occupants, and the long-term implications of fitness for population growth. Here, we synthesize several conceptual advances into a mathematical framework using a measure of fitness to link habitat availability/selection to (density-dependent) population growth in mobile animal species. A key feature of this approach is that it distinguishes between apparent habitat suitability and the true, underlying contribution of a habitat to fitness, allowing the statistical coefficients of both to be estimated from multiple observation instances of the species in different environments and stages of numerical growth. Hence, it leverages data from both historical population time series and snapshots of species distribution to predict population performance under environmental change. We propose this framework as a foundation for building more realistic connections between a population's use of space and its subsequent dynamics (and hence a contribution to the ongoing efforts to estimate a species' critical habitat and fundamental niche). We therefore detail its associated definitions and simplifying assumptions, because they point to the framework's future extensions. We show how the model can be fit to data on species distributions and population dynamics, using standard statistical methods, and we illustrate its application with an individual-based simulation. When contrasted with nonspatial population models, our approach is better at fitting and predicting population growth rates and carrying capacities. Our approach can be generalized to include a diverse range of biological considerations. We discuss these possible extensions and applications to real data.
Keyword Accessibility
Climate change
Density dependence
Generalized functional response
Generalized linear model
Habitat suitability
Ideal free distribution
Mathematical model
Resource selection function
Species distribution models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Copyright by the Ecological Society of America

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Biological Sciences Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 25 Aug 2015, 01:35:13 EST by System User on behalf of School of Biological Sciences