A PREDICTIVE MODEL OF EDGE EFFECTS

Authors

  • Leslie Ries,

    1. Northern Arizona University, Department of Biological Sciences and Center for Environmental Sciences and Education, Flagstaff, Arizona 86011 USA
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    •  Present address: Department of Biology, University of Maryland, College Park, Maryland 20742 USA. E-mail: lries@umd.edu

  • Thomas D. Sisk

    1. Northern Arizona University, Department of Biological Sciences and Center for Environmental Sciences and Education, Flagstaff, Arizona 86011 USA
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Abstract

Edge effects are among the most extensively studied ecological phenomena, yet we lack a general, predictive framework to understand the patterns and variability observed. We present a conceptual model, based on resource distribution, that predicts whether organismal abundances near edges are expected to increase, decrease, or remain unchanged for any species at any edge type. Predictions are based on whether resources are found predominantly in one habitat (decreased abundance in preferred habitat, increase in non-preferred), divided between habitats (predicts an increase near both edges), spread equally among habitats (predicts a neutral edge response), or concentrated along the edge (increase). There are several implications of this model that can explain much of the variability reported in the edge literature. For instance, our model predicts that a species may show positive, negative, and neutral responses, depending on the edge type encountered, which explains some intraspecific variability observed in the literature. In addition, any predictable change in resource use (for example, by region or season) may explain temporal or spatial variability in responses even for the same species at the same edge type. We offer a preliminary test of our model by making predictions for 52 bird species from three published studies of abundance responses near forest edges. Predictions are based solely on general information about each species' habitat associations and resource use. Our model correctly predicted the direction of 25 out of 29 observed edge responses, although it tended to under-predict increases and over-predict decreases. This model is important because it helps make sense of a largely descriptive literature and allows future studies to be carried out under a predictive framework.

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