, Volume 156, Issue 1, pp 75–86 | Cite as

Butterfly edge effects are predicted by a simple model in a complex landscape

Population Ecology - Original Paper


Edge responses have been studied for decades and form a critical component of our understanding of how organisms respond to landscape structure and habitat fragmentation. Until recently, however, the lack of a general, conceptual framework has made it difficult to make sense of the patterns and variability reported in the edge literature. We present a test of an edge effects model which predicts that organisms should avoid edges with less-preferred habitat, show increased abundance near edges with preferred habitat or habitat containing complementary resources, and show no response to edges with similar-quality habitat that offers only supplementary resources. We tested the predictions of this model against observations of the edge responses of 15 butterfly species at 12 different edge types within a complex, desert riparian landscape. Observations matched model predictions more than would be expected by chance for the 211 species/edge combinations tested over 3 years of study. In cases where positive or negative edge responses were predicted, observed responses matched those predictions 70% of the time. While the model tends to underpredict neutral results, it was rare that an observed edge response contradicted that predicted by the model. This study also supported the two primary ecological mechanisms underlying the model, although not equally. We detected a positive relationship between habitat preferences and the slope of the observed edge response, suggesting that this basic life history trait underlies edge effects and influences their magnitude. Empirical evidence also suggested the presence of complementary resources underlies positive edge responses, but only when completely confined to the adjacent habitat. This multi-species test of a general edge effects model at multiple edge types shows that resource-based mechanisms can explain many edge responses and that a modest knowledge of life history attributes and resource availability is sufficient for predicting and understanding many edge responses in complex landscapes.


Ecotone Ecological boundary Fragmentation Habitat edge Predictive model 



Access, support, accommodations, and aerial photographs were provided by the San Pedro River Field Office of the Bureau of Land Management. The Semi-Arid Surface-Land Atmosphere (SALSA) program provided GIS coverages and other support. Sheridan Stone and the Ft. Huachuca wildlife office helped us initiate this research and provided helpful feedback at critical stages during its development. Arriana Brand, James Battin, and Barry Noon were our collaborators and contributed substantially to the overall design of this project. Thanks to our many field assistants, including Laura Williams, Ryan Vandermoor, Janine McCabe, Kerrith McCay, Greg Breed, Danielle Denneny, Jennifer Marks, Marke Ambard, Pedro, and Devin Biggs. Laura Williams was responsible for most of our plant identifications, with help from Liz Makings and Ken Bagstaad. Rich Bailowitz, Doug Danforth. and Sandy Upson helped with butterfly identifications. Statistical assistance was provided by Rob Fletcher, Ken Burnham, James Battin, Brent Burch, and Phil Gibbs. Helpful comments on the manuscript were made by Bill Fagan, Nick Haddad, Thomas Whitham, Mike Kearsley, Matthew Loeser, Kiisa Nishikawa, James Battin, Rob Fletcher and several anonymous reviewers. Funding was provided by SERDP, Project CS–1100. Experiments complied with current US law.

Supplementary material

442_2008_976_MOESM1_ESM.doc (132 kb)
(DOC 132 kb)
442_2008_976_MOESM2_ESM.doc (244 kb)
(DOC 244 kb)


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Biological SciencesNorthern Arizona UniversityFlagstaffUSA
  2. 2.Center for Environmental Sciences and EducationNorthern Arizona UniversityFlagstaffUSA
  3. 3.Merriam-Powell Center for Environmental ResearchNorthern Arizona UniversityFlagstaffUSA
  4. 4.Department of BiologyUniversity of MarylandCollege ParkUSA

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