Data from: Edge effects and geometric constraints: a landscape-level empirical test
Cite this dataset
Ribeiro, Suzy E.; Prevedello, Jayme A.; Delciellos, Ana Cláudia; Vieira, Marcus Vinicius (2016). Data from: Edge effects and geometric constraints: a landscape-level empirical test [Dataset]. Dryad. https://doi.org/10.5061/dryad.72c92
Edge effects are pervasive in landscapes yet their causal mechanisms are still poorly understood. Traditionally, edge effects have been attributed to differences in habitat quality along the edge-interior gradient of habitat patches, under the assumption that no edge effects would occur if habitat quality was uniform. This assumption was questioned recently after the recognition that geometric constraints tend to reduce population abundances near the edges of habitat patches, the so-called “geometric edge effect” (GEE). Here we present the first empirical, landscape-level evaluation of the importance of the GEE in shaping abundance patterns in fragmented landscapes. Using a dataset on the distribution of small mammals across 18 forest fragments, we assessed whether the incorporation of the GEE into the analysis changes the interpretation of edge effects and the degree to which predictions based on the GEE match observed responses. Quantitative predictions were generated for each fragment using simulations that took into account home range, density and matrix use for each species. The incorporation of the GEE into the analysis changed substantially the interpretation of overall observed edge responses at the landscape scale. Observed abundances alone would lead to the conclusion that the small mammals as a group have no consistent preference for forest edges or interiors, and that the black-eared opossum Didelphis aurita (a numerically dominant species in the community) has on average a preference for forest interiors. In contrast, incorporation of the GEE suggested that the small mammal community as a whole has a preference for forest edges, whereas D. aurita has no preference for forest edges or interiors. Unexplained variance in edge responses was reduced by the incorporation of GEE, but remained large, varying greatly on a fragment-by-fragment basis. This study demonstrates how to model and incorporate the GEE in analyses of edge effects, and that this incorporation is necessary to properly interpret edge effects in landscapes. It also suggests that geometric constraints alone are unlikely to explain the variability in edge responses of a same species among different areas, highlighting the need to incorporate other ecological factors into explanatory models of edge effects.