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生态学杂志 ›› 2026, Vol. 45 ›› Issue (3): 1042-1056.doi: 10.13292/j.1000-4890.202603.034

• 技术与方法 • 上一篇    

基于红外相机信息的野猪栖息地季节性分布特征与人类活动的关联性研究

陈阳阳1,王成1*,陈逸飞1,雍凡2,丁晶晶3,钱梦珍1,龚官清1,张紫涵1,李婧1   

  1. 1安徽农业大学经济管理学院, 合肥 230036; 2生态环境部南京环境科学研究所, 南京 210042; 3江苏省林业科学研究院, 南京 211100)

  • 出版日期:2026-03-10 发布日期:2026-09-01

Correlation between seasonal distribution characteristics of Sus scrofa habitat and human activities based on infrared camera information.

CHEN Yangyang1, WANG Cheng1*, CHEN Yifei1, YONG Fan2, DING Jingjing3, QIAN Mengzhen1, GONG Guanqing1, ZHANG Zihan1, LI Jing1   

  1. (1School of Economics and Management, Anhui Agricultural University, Hefei 230036, China; 2Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China; 3Jiangsu Academy of Forestry, Nanjing 211100, China).

  • Online:2026-03-10 Published:2026-09-01

摘要: 近年来随着生态环境保护理念深入人心,野猪(Sus scrofa)生存环境不断改善,种群数量快速增加。然而,由于大型捕食者的缺失,野猪种群数量快速膨胀,并带来人猪冲突事件的发生,但目前对于二者在冲突空间的季节性关联规律尚不明确。本研究选取浙江省25个区县展开红外相机持续监测,并借助样点样线调查,获取了2019—2022年总计852个野猪出现点位数据;随后,进一步构建反映自然及人为两大要素的13个栖息地识别指标,运用MaxEnt模型剖析野猪适宜栖息地季节性分布模式并探讨其影响因素;继而采用人类足迹指数法和空间自相关分析,开展二者关联性分析。结果表明:(1)春季野猪潜在栖息地面积为9184.2 km2,主要分布于丽水市、温州市泰顺县;夏季野猪适生区主要分布在衢州市开化县、金华市磐安县,中、高适生区面积总计峰值达18300.93 km2;秋季潜在栖息地面积为7957.06 km2,中、高适生区主要分布在衢州市开化县、丽水市龙泉市等;冬季野猪非适生区面积上升至73859.72 km2。(2)海拔、年平均降水量和距农田距离是影响野猪潜在栖息地分布的主导环境因子,次要因子为人口密度与植被覆盖度。研究区人类活动强度空间分布呈现“北高南低、东高西低、沿海高内陆低”的特征。(3)春(0.87%)、夏(0.99%)两季H-H型占比均低于秋季(1.62%)和冬季(1.06%),表明春、夏两季二者空间重叠度相对秋、冬季较为缓和,秋季是人类活动空间与野猪生存空间高度重叠时期。


关键词: 野猪栖息地, MaxEnt模型, 人类活动强度, 关联性分析

Abstract: With the concept of ecological environment protection deeply ingrained in society, the habitat of wild boars (Sus scrofa) has continuously ameliorated in recent years. Consequently, the populations of wild boars are increasing at a rapid pace. Due to the absence of large predators, the rapid expansion of wild boar populations has induced the occurrence of human-wild boar conflicts. Nevertheless, the research regarding the seasonality of human-wild boar interactions within conflict zones remains insufficient. In this study, we conduct continuous monitoring via infrared cameras in 25 districts/counties of Zhejiang Province. With the assistance of sampling point and sampling line surveys, a total of 852 wild boar location data were recorded from 2019 to 2022. A total of 13 habitat identification indicators reflecting both natural and anthropogenic factors were constructed, and the MaxEnt model was applied to analyze the seasonal distribution pattern of suitable habitat for wild boar and to explore its influencing factors. Eventually, the Human Footprint Index Method and spatial autocorrelation analysis were used to uncover the hotspots of human-wild boar conflicts. The results showed that: (1) The potential habitat area spanned 9184.2 km2 in spring, primarily distributed in Lishui City and Taishun County (Wenzhou City). In summer, suitable habitats for wild boars were mainly concentrated in Kaihua County (Quzhou City) and Pan’an County (Jinhua City), with a total area of the moderately and highly suitable habitats reaching a peak of 18300.93 km2. Autumn witnessed a reduction of potential habitats to 7957.06 km2, with moderately and highly suitable habitats located in Kaihua County (Quzhou City) and Longquan City (Lishui City). There was a significant expansion of non-suitable habitat areas in winter, reaching 73859.72 km2. (2) Altitude, average annual precipitation, and distance to farmland were the dominant factors influencing the distribution of potential wild boar habitats, with secondary factors being human population density and vegetation coverage. The spatial distribution of human activity intensity in the study area was characterized by “high in the north, east and along the coast, and low in the south, west, and inland”. (3) The proportion of H-H clusters was lower in spring (0.87%) and summer (0.99%) compared to autumn (1.62%) and winter (1.06%), suggesting a relatively less pronounced spatial overlap in spring and summer. Autumn was the period with the high overlapping between human activity space and the living space of wild boars.


Key words: wild boar habitat, MaxEnt model, human activity intensity, correlation analysis