Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

Diversity
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Abstract

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks
Series title Diversity
DOI 10.3390/d3020252
Volume 3
Issue 2
Year Published 2011
Language English
Publisher Molecular Diversity Preservation International
Description 10 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Diversity
First page 252
Last page 261