Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks
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 (453,4...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-05-01
|
| Series: | Diversity |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-2818/3/2/252/ |
| _version_ | 1798041778953125888 |
|---|---|
| author | Paul H. Evangelista David T. Barnett Thomas J. Stohlgren Sunil Kumar |
| author_facet | Paul H. Evangelista David T. Barnett Thomas J. Stohlgren Sunil Kumar |
| author_sort | Paul H. Evangelista |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:26:22Z |
| format | Article |
| id | doaj.art-31c27c185b554677bb9923a53c590a4a |
| institution | Directory Open Access Journal |
| issn | 1424-2818 |
| language | English |
| last_indexed | 2024-04-11T22:26:22Z |
| publishDate | 2011-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diversity |
| spelling | doaj.art-31c27c185b554677bb9923a53c590a4a2022-12-22T03:59:38ZengMDPI AGDiversity1424-28182011-05-013225226110.3390/d3020252Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring NetworksPaul H. EvangelistaDavid T. BarnettThomas J. StohlgrenSunil KumarEnvironmental 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.http://www.mdpi.com/1424-2818/3/2/252/environmental variationspecies-environmental matching modelsspecies distribution modelsMaxentoptimal sampling schemes |
| spellingShingle | Paul H. Evangelista David T. Barnett Thomas J. Stohlgren Sunil Kumar Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks Diversity environmental variation species-environmental matching models species distribution models Maxent optimal sampling schemes |
| title | Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks |
| title_full | Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks |
| title_fullStr | Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks |
| title_full_unstemmed | Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks |
| title_short | Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks |
| title_sort | using maximum entropy modeling for optimal selection of sampling sites for monitoring networks |
| topic | environmental variation species-environmental matching models species distribution models Maxent optimal sampling schemes |
| url | http://www.mdpi.com/1424-2818/3/2/252/ |
| work_keys_str_mv | AT paulhevangelista usingmaximumentropymodelingforoptimalselectionofsamplingsitesformonitoringnetworks AT davidtbarnett usingmaximumentropymodelingforoptimalselectionofsamplingsitesformonitoringnetworks AT thomasjstohlgren usingmaximumentropymodelingforoptimalselectionofsamplingsitesformonitoringnetworks AT sunilkumar usingmaximumentropymodelingforoptimalselectionofsamplingsitesformonitoringnetworks |