Using Species Distribution Models to Predict Suitable Habitat for Threatened Plant Species of Southern Ontario
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Surveys are required to locate previously unknown occurrences of threatened species. To optimize efficiency of species-at-risk surveys, I used species distribution models for 22 rare plants throughout southern Ontario to predict the best possible survey locations among individual 1-ha pixels. For each pixel, I weighted model outputs by accuracy and species rarity to create an efficiency value. Based on efficiency values, I conducted field surveys in multi-species cells, "MSC" (areas with high predicted efficiency for multiple species) and in single species cells, "SSC" (areas with high probability for only one species) to determine the utility of multi-species survey optimization. MSC were over two times more likely to have at least one threatened plant species discovered than SSC. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. This technique can help direct surveys to more efficiently find threatened species occurrences.
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Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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