基于红外相机数据估算浙江省开化县野猪种群数量

陈小南; 田佳; 刘鸣章; 申云逸; 余建平; 刘锋; 申小莉; 李晟

doi:10.16829/j.slxb.150785

兽类学报 >

2023 , Vol. 43 >Issue 5: 523 - 532

DOI: https://doi.org/10.16829/j.slxb.150785

研究论文

基于红外相机数据估算浙江省开化县野猪种群数量

陈小南 ,
田佳 ,
刘鸣章 ,
申云逸 ,
余建平 ,
刘锋 ,
申小莉 ,
李晟

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1 钱江源国家公园管理局, 开化 324300;
2 北京大学生命科学学院, 北京 100871;
3 Department of Electrical Engineering and Computer Science, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
4 浙江省开化县林业局, 开化 324300;
5 中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093

陈小南(1988-)，男，工程师，主要从事保护地管理与野生动物研究；田佳(2000-)，女，博士研究生，主要从事动物生态与保护生物学研究.

收稿日期: 2023-03-08

修回日期: 2023-07-20

网络出版日期: 2023-09-22

基金资助

美丽中国生态文明建设科技工程专项(XDA23080302)；生态环境部生物多样性调查、观测与评估项目(2019HJ2096001006)；钱江源国家公园综合科学考察项目

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Estimating the population size of wild boar (Sus scrofa) in Kaihua County, Zhejiang Province using camera-trapping data

CHEN Xiaonan ,
TIAN Jia ,
LIU Mingzhang ,
SHEN Yunyi ,
YU Jianping ,
LIU Feng ,
SHEN Xiaoli ,
LI Sheng

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1 Qianjiangyuan National Park Administration, Kaihua 324300, China;
2 School of Life Sciences, Peking University, Beijing 100871, China;
3 Department of Electrical Engineering and Computer Science, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;
4 Kaihua Forestry Bureau, Kaihua 324300, China;
5 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Received date: 2023-03-08

Revised date: 2023-07-20

Online published: 2023-09-22

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摘要

野猪(Sus scrofa)具有较强的繁殖力和对多种环境的适应能力，近年来在我国多地出现种群明显增长，因野猪损毁农林作物、伤人而引发的人兽冲突事件频发。为针对野猪的计划性捕猎与种群管理提供科学支持，本研究以浙江省开化县为例，基于红外相机实地调查数据对野猪种群数量进行估算。选用开化县2020年9—10月429个位点的红外相机监测数据，包括有效相机工作日23 690 d，共记录到964次野猪的独立探测。利用Royle-Nichols模型，结合地形、植被、人类影响的环境变量因子和野猪家域面积参数，估计了野猪种群密度及分布。结果显示，研究区内野猪数量为(5548 ±2343)头(mean ±95% C.I.)；在与野猪家域面积大小相当的网格中，野猪的平均种群密度为每平方千米(2.38 ±0.61)头(mean ±SD)；野猪密度与海拔和居民点密度呈负相关关系，与森林覆盖率呈弱正相关关系。基于以上结果，结合实际管理需求，建议研究区域内的野猪捕猎活动选择在其种群密度较高的农田、种植园以及森林与农田交错区域开展。当地主管部门与钱江源国家公园未来应依托系统布设的红外相机监测网络，整合捕猎获取的个体信息记录和其他来源的数据，修正和完善种群动态模型，建立长期种群动态监测机制。

关键词： 野猪; 种群管理; 人兽冲突; 计划性捕猎; 种群密度估计; 红外相机

本文引用格式

陈小南 , 田佳 , 刘鸣章 , 申云逸 , 余建平 , 刘锋 , 申小莉 , 李晟 . 基于红外相机数据估算浙江省开化县野猪种群数量[J]. 兽类学报, 2023 , 43(5) : 523 -532 . DOI: 10.16829/j.slxb.150785

Abstract

Wild boar (Sus scrofa) has high fecundity and strong adaptability to various environments. In recent years, the populations of wild boar in China have been increasing dramatically, causing numerous human-wildlife conflicts primarily due to crop damages and people injuries by the boars. To provide a scientific basis for future planned hunting and population management, we took Kaihua County, Zhejiang Province as an example to estimate the population size of wild boar using camera-trapping data. The camera-trapping data, collected during September and October 2020, contained 964 independent records of wild boar from 429 camera stations with an extensive sampling effort of 23 690 camera days. We used the Royle-Nichols model, combining environmental factors including terrain, vegetation and human impacts and the species’home range parameter, to estimate the population and distribution of wild boars in Kaihua. The results showed that the population size of wild boar within the study area was estimated as 5548 ±2343 (mean ±95% C. I. ), with an average density of 2. 38 ±0. 61 (mean ±SD) ind. /km², which was negatively associated with altitude and resident density, and slightly positively associated with forest coverage. Based on the results, we suggest that wild boar hunting, as a management measure to control its population, in the study area should be conducted in farmland, plantations, and ecotone areas of forest and farmland, where the boar densities are high. In the future, local administrations and the Qianjiangyuan National Park shall integrate data from the systematically designed camera-trapping network, as well as those from hunting records and other sources, into this management framework, so that the population dynamics model of target species can be continuously updated and improved, and a long-term population dynamics monitoring system can be established.

Key words： Wild boar; Population management; Human-wildlife conflict; Planned hunting; Population density estimation; Camera-trapping

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