bioRxivZoologyPreprint
Mapping human-macaque encounter potential by integrating visitor-use and macaque-use intensity in Kamikochi, Japan
Yosuke OtaniiD, Ayaka TsuchihashiiD, Satoru Sugita, Hiromichi FukuiiD, Yusuke Shikano, Takuya MatsumotoiD
Abstract
Managing visitor-wildlife interactions in high-use protected areas requires identifying not only where visitors concentrate but also where visitor use overlaps with wildlife space use. We estimated relative human- macaque encounter potential in Kamikochi, Chubu Sangaku National Park, Japan, where Japanese macaques (Macaca fuscata) and visitors use the same frontcountry landscape. We integrated Mobile Spatial Statistics derived from mobile phone network data, trail-camera counts, and direct-follow location data from macaques along a frontcountry-backcountry gradient. Broad-scale 500-m grid estimates of daytime visitor presence for the 2023 and 2024 open seasons were allocated to 50-m trail sections and adjusted using counts from 10 trail cameras. Automated video counts based on YOLO11 and ByteTrack showed high agreement with manual counts of 100 validation videos (MAE = 0.10 persons/video, MAPE = 1.26%, exact agreement rate = 92.0%). Visitor-use and macaque-use data were converted into kernel density surfaces, and encounter potential was calculated as their normalized product. High encounter potential was concentrated in the central frontcountry: 87.1% of areas with index values [≥] 0.9 fell within the frontcountry and 12.9% within the backcountry. Areas with high visitor use or high macaque use alone did not necessarily show high encounter potential when the two distributions were spatially offset. These results show that spatial overlap provides management information not captured by visitor counts alone. Relative encounter potential can help prioritize patrols, visitor messaging, viewing management, and other interventions in protected areas where wildlife does not consistently avoid heavily used visitor spaces.
- organism: non-human primate
- assay: imaging
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 0
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