A multi-method approach to delineate and validate migratory corridors
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Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models.
We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania.
We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type.
Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use.
The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information.
KeywordsConnectivity Connochaetes taurinus Circuit theory Cost distance Land-use planning Least Cost Path Analysis Maxent Migration Resource selection functions Wildebeest
This research was conducted with permission from Tanzania Commission for Science and Technology (COSTECH), Tanzania Wildlife Research Institute, and villages of Selela, Engaruka, and Mbaash, under COSTECH permits 2014-53-ER-90-172 and 2015-22-ER-90-172, and Wildlife Division permit NO.HA/403/563/0l/104. DEL and MLB received funding from Fulbright U.S. Scholar Program, ERM Group Foundation, Columbus Zoo, Rufford Foundation, and TRIAS. TAM received funds for GPS collaring from Wildlife Conservation Society Tarangire Elephant Project, Tembo Foundation, and Earthwatch Institute.
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