Abstract Crop raiding by wildlife poses major threats to both wildlife conservation and human well‐being in agroecosystems worldwide. These threats are particularly acute in many parts of Africa, where crop raiders include globally threatened megafauna such as elephants, and where smallholder agriculture is a primary source of human livelihood. One framework for understanding herbivore feeding behaviour, the forage‐maturation hypothesis, predicts that herbivores should align their movements with intermediate forage biomass (i.e., peak green‐up); this phenomenon is known as “surfing the green wave.” Crop‐raiding elephants, however, often consume not just foliage, but also fruits and tubers (e.g., maize and potatoes), which generally mature after seasonal peaks in photosynthetic activity. Thus, although elephants have been reported to surf the green wave in natural habitats, they may utilize a different strategy in cultivated landscapes by selecting crops that are “browning down.”We sought to understand the factors that underpin movement of elephants into agricultural landscapes.In Mozambique's Gorongosa National Park, we used movement data from GPS‐collared elephants, together with precipitation records, remotely sensed estimates of landscape greenness (NDVI), DNA‐based diet analysis, measurements of plant nutritional quality and survey‐based metrics of crop availability to understand spatiotemporal variation in elephant crop‐raiding behaviour.Elephants tracked peak NDVI while foraging inside the Park. During the dry season, however, when NDVI within the Park declined and availability of mature crops was high, crop raiding increased dramatically, and elephants consistently selected crop plants that were browning down while foraging in cultivated landscapes. Crops contained significantly higher digestible energy than wild food plants, but comparable (and sometimes lower) levels of digestible protein, suggesting that this foraging strategy maximized energy rather than protein intake.Our study is the first to combine GPS tracking data with high‐resolution diet analysis and community‐based reporting of crop availability to reveal fine‐scale plasticity in foraging behaviour of elephants at the human–wildlife interface. Our results extend the forage‐maturation hypothesis by showing that elephants surf waves of plant brown‐down in cultivated landscapes. These findings can aid efforts to reduce human–elephant conflict by enabling wildlife managers to prioritize mitigation actions in time and space with limited resources.
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Synthesizing Remote Sensing and Biophysical Measures to Evaluate Human–wildlife Conflicts: The Case of Wild Boar Crop Raiding in Rural China
Crop raiding by wild boars is a growing problem worldwide with potentially damaging consequences for rural dwellers’ cooperation with conservation policies. Still, limited resources inhibit continuous monitoring, and there is uncertainty about the relationship between the biophysical realities of crop raiding and humans’ perceptions and responses. By integrating data from camera traps, remote sensors, and household surveys, this study establishes an empirical model of wild boar population density that can be applied to multiple years to estimate changes in distribution over time. It also correlates historical estimates of boar population distribution with human-reported trends to support the model’s validity and assess local perceptions of crop raiding. Although the model proved useful in coniferous and bamboo forests, it is less useful in mixed broadleaf, evergreen broadleaf, and deciduous forests. Results also show alignment between perceptions of crop raiding and actual boar populations, corroborating farmers’ perceptions which are increasingly dismissed as a less reliable source of information in human–wildlife conflict research. The modeling techniques demonstrated here may provide conservation practitioners with a cost-effective way to maintain up-to-date estimates of the spatial distribution of wild boar and resultant crop raiding.
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- Award ID(s):
- 1826839
- PAR ID:
- 10172439
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 12
- Issue:
- 4
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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