Péter Szandra A, Gallo Travis, Mullinax Jennifer, Roess Amira, Palomo-Munoz Gabriela, Anderson Taylor
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, 22030, USA.
Department of Environmental Science and Technology, University of Maryland, College Park, MD, 20742, USA.
Sci Rep. 2025 May 28;15(1):18588. doi: 10.1038/s41598-025-03577-5.
Human expansion into wildlife habitats has increased the need to understand human-wildlife interactions, necessitating interdisciplinary approaches to assess zoonotic disease transmission risks and public health impacts. This study integrated fine-grained human foot traffic data with hourly GPS data from 38 white-tailed deer (Odocoileus virginianus), a species linked to SARS-CoV-2, brucella, and chronic wasting disease, in Howard County, Maryland. We explored spatial and temporal overlap between human and deer activity over 24 months (2018-2019) across a hexagonal tessellation with metrics like hourly popularity and visit counts. Negative binomial models were fitted to the visit counts of each deer and humans per tessellation area, using landscape features as predictors. A separate deer-only model included commercial human activity as another predictor. Spatial analysis showed deer and humans sharing spaces in the study area, with results indicating deer using more populated residential areas and areas with commercial activity. Temporal analysis showed deer avoiding commercial spaces during daytime but using them in late evening and early morning. These findings highlight the complex space use between species and the importance of integrating detailed human mobility and animal movement data when managing wildlife-human conflict and zoonotic disease transmission, particularly in urban areas with a high probability of deer-human interactions.
人类向野生动物栖息地的扩张增加了理解人类与野生动物相互作用的必要性，这就需要采用跨学科方法来评估人畜共患病传播风险和对公共卫生的影响。本研究将细粒度的人类行人流量数据与来自马里兰州霍华德县38只白尾鹿（弗吉尼亚鹿）的每小时GPS数据相结合，白尾鹿是一种与SARS-CoV-2、布鲁氏菌病和慢性消耗病有关的物种。我们通过六边形网格划分，利用每小时人气和访问量等指标，探索了24个月（2018 - 2019年）内人类和鹿活动的时空重叠情况。对每个六边形区域内每只鹿和人类的访问量拟合负二项式模型，将景观特征作为预测因子。一个单独的仅针对鹿的模型将人类商业活动作为另一个预测因子。空间分析表明，鹿和人类在研究区域共享空间，结果显示鹿利用人口较多的居民区和有商业活动的区域。时间分析表明，鹿在白天避开商业区域，但在傍晚和清晨使用这些区域。这些发现凸显了物种间复杂的空间利用情况，以及在管理野生动物与人类冲突和人畜共患病传播时，整合详细的人类流动性和动物移动数据的重要性，特别是在鹿与人类互动可能性高的城市地区。
