Infusing considerations of trophic dependencies into species distribution modelling
Anne M Trainor1, Oswald J Schmitz
1School of Forestry and Environmental Studies, Yale University, New Haven, CT, 06511, USA; The Nature Conservancy, Arlington, VA, 22203, USA.
Ecology Letters
|September 25, 2014
Summary
This study introduces a new framework to improve predictions of species' geographical distributions by better accounting for species interactions and environmental factors. It offers a more spatially refined understanding of habitat suitability for species.
Area of Science:
- Ecology
- Biogeography
- Ecological Modeling
Background:
- Modern species distribution models (SDMs) accurately describe species-environment relationships but inadequately represent species interdependencies.
- Current methods often assume homogeneous species interactions across space, contradicting empirical evidence and limiting predictive accuracy for habitat suitability.
- Accurate prediction of species distribution and abundance requires understanding complex species-environment and species-species interactions.
Purpose of the Study:
- To introduce an integrated framework enhancing quantitative predictions of species geographical distributions.
- To improve the characterization of species interdependency within ecological analyses.
- To provide a more spatially refined understanding of species distribution sensitive to environmental nuances.
Main Methods:
- Integration of principles from consumer-resource analyses, resource selection theory, and species distribution modeling.
- Development of a novel framework to explicitly model spatially varying species interactions.
- Application of the framework using a case study of lynx and snowshoe hare interactions.
Main Results:
- The proposed framework offers a more accurate and spatially refined prediction of species distributions.
- Demonstrated improved understanding of how environmental attributes influence the location and strength of species interactions.
- Highlighted the limitations of current SDMs in capturing the heterogeneity of species interdependencies.
Conclusions:
- The integrated framework significantly enhances the predictive power of species distribution models by incorporating spatially explicit species interactions.
- Ecological analyses must account for the spatial heterogeneity of species interactions for accurate habitat suitability assessments.
- This approach provides a robust tool for understanding and predicting species distributions in complex ecological systems.
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