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The scaling of geographic ranges: implications for species distribution models
Authors:Charles B. Yackulic  Joshua R. Ginsberg
Affiliation:1.U.S. Geological Survey, Southwest Biological Science Center,Grand Canyon Monitoring and Research Center,Flagstaff,USA;2.Cary Institute of Ecosystem Studies,Millbrook,USA
Abstract:

Context

The geographic ranges of many species are responding to ongoing environmental change. Processes operating at different levels of biological organization, with corresponding spatial extents and grains and temporal rates, interact with the evolving configuration of environmental conditions to determine range dynamics.

Objectives

To synthesize understanding of scales and scaling, including relevant biological levels of organization, focusing on the processes that mediate species-environment relationships and the models used to make inferences about species distributions.

Methods

We review concepts related to the scaling of geographic ranges and implications for the most commonly used analytic methods, using simple simulations to illustrate important issues.

Results

Many processes lead to species distributions being dependent on environmental conditions within sites and within a neighborhood. Studies with large extents and fine grains can cut across several levels of biological organization (individual, within-population, and metapopulation processes) complicating interpretation. Many geographic ranges are not in dynamic equilibrium, but common models used for inference assume equilibrium. Interspecific interactions shape species distributions at multiple scales, and arguments for ignoring species interactions also assume equilibrium.

Conclusions

There is a need for timely science to inform policy and management decisions; however, we must also strive to provide predictions that best reflect our understanding of ecological systems. Species distributions evolve through time and reflect responses to environmental conditions that are mediated through individual and population processes. Species distribution models that reflect this understanding, and explicitly model dynamics, are likely to give more accurate predictions.
Keywords:
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