Journal
LANDSCAPE ECOLOGY
Volume 34, Issue 4, Pages 865-878Publisher
SPRINGER
DOI: 10.1007/s10980-019-00811-0
Keywords
African elephant; Conservation planning; Resistance surface; Landscape connectivity; Movement corridors; Step-selection function
Funding
- European Commission under the Erasmus Mundus Joint Doctorate Programme (FONASO)
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ContextResistance-based connectivity models are widely used conservation tools for spatial prioritization and corridor planning, but there are no generally accepted methods and recommendations for validating whether these models accurately predict actual movement routes. Hence, despite growing interest and recognition of the importance of protecting landscape connectivity, the practical utility of predictions derived from connectivity models remains unclear.ObjectivesThe difficulties in validations are mainly related to the unavailability of independent data and lack of appropriate, easily applied statistical frameworks. Here, we present a case study where two independently collected datasets were used to validate resistance-based landscape connectivity models and movement corridors identified by these models.MethodsWe used annual aerial counts to evaluate the connectivity model, and a field survey to assess the performance of predicted corridors. We applied these two independent datasets to validate a previously developed connectivity model for the African elephant (Loxodonta africana) in the Borderland region between Kenya and Tanzania.ResultsThe results of this study confirm that the resistance-based connectivity model is a valid approach for predicting movement corridors for the African elephant. We show that high connectivity values are a strong predictor of the presence of large numbers of the elephants across the years. The probability of observing elephants increased with increasing connectivity values, while accounting for seasonality is an important factor for accurately predicting movements from connectivity models.ConclusionMovement corridors derived from resistance-based connectivity models have a strong predictive power and can be successfully used in spatial conservation prioritization.
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