Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks

Diversity

Thomas J. Stohlgren³  Sunil Kumar¹  David T. Barnett² 

[1] 1499 NESB, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; E-Mails:;National Ecological Observatory Network (NEON), Inc., 1685 38th Street, Suite 100, Boulder, CO 80301, USA; E-Mail:;U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Avenue, Building C, Fort Collins, CO 80526, USA

关键词: environmental variation;    species-environmental matching models;    species distribution models;    Maxent;    optimal sampling schemes;   

DOI  :  10.3390/d3020252

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.