Predictive model for sustaining biodiversity in tropical countryside

Proc Natl Acad Sci U S A. 2011 Sep 27;108(39):16313-6. doi: 10.1073/pnas.1111687108. Epub 2011 Sep 12.

Abstract

Growing demand for food, fuel, and fiber is driving the intensification and expansion of agricultural land through a corresponding displacement of native woodland, savanna, and shrubland. In the wake of this displacement, it is clear that farmland can support biodiversity through preservation of important ecosystem elements at a fine scale. However, how much biodiversity can be sustained and with what tradeoffs for production are open questions. Using a well-studied tropical ecosystem in Costa Rica, we develop an empirically based model for quantifying the "wildlife-friendliness" of farmland for native birds. Some 80% of the 166 mist-netted species depend on fine-scale countryside forest elements (≤ 60-m-wide clusters of trees, typically of variable length and width) that weave through farmland along hilltops, valleys, rivers, roads, and property borders. Our model predicts with ∼75% accuracy the bird community composition of any part of the landscape. We find conservation value in small (≤ 20 m wide) clusters of trees and somewhat larger (≤ 60 m wide) forest remnants to provide substantial support for biodiversity beyond the borders of tropical forest reserves. Within the study area, forest elements on farms nearly double the effective size of the local forest reserve, providing seminatural habitats for bird species typically associated with the forest. Our findings provide a basis for estimating and sustaining biodiversity in farming systems through managing fine-scale ecosystem elements and, more broadly, informing ecosystem service analyses, biodiversity action plans, and regional land use strategies.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Biodiversity*
  • Birds / classification*
  • Costa Rica
  • Models, Theoretical*
  • Remote Sensing Technology
  • Tropical Climate*