Habitat conversion to farmland has increased human‐wildlife interactions, which often lead to conflict, injury or death for people and animals. Understanding the behavioural and landscape drivers of human‐wildlife conflict is critical for managing wildlife populations. Staging behaviour prior to crop incursions has been described across multiple taxa and offers potential utility in managing conflict, but few quantitative assessments of staging have been undertaken. Animal movement data can provide valuable, fine‐scale information on such behaviour with opportunities for application to real‐time management for conflict prediction. We developed an approach to assess the efficacy of six widely used metrics of animal movement to identify staging behaviour prior to agricultural incursions. We applied this approach to GPS data from 55 African elephants in the Serengeti‐Mara ecosystem and found tortuosity and HMM‐derived behavioural states to be the most effective for identifying staging events. We then assessed temporal patterns of defined staging at daily and seasonal scales and explored environmental and anthropogenic drivers of staging from spatial generalized logistic mixed models. Finally, we tested the viability of combining movement and simple spatial metrics to predict crop incursions based on GPS data. Our approach identified staging behaviour that appeared to be driven largely by human activity and diurnal availability of protective cover from forest, riverine vegetation, and topography. Staging also varied substantially by season. Tortuosity and behavioural state metrics identified different staging strategies with distinct spatial distributions and anthropogenic drivers, and appeared to be linked to the juxtaposition between protected and cultivated lands. Tortuosity‐based staging combined with distance‐to‐agriculture produced promising results for pre‐event prediction of crop incursion.
- Award ID(s):
- 1764269
- NSF-PAR ID:
- 10328858
- Editor(s):
- Faisal, Aldo A
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1009672
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1009672
