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相机陷阱和活动迹象估计野猪密度并得出丰富度指数。

Camera traps and activity signs to estimate wild boar density and derive abundance indices.

机构信息

National Wildlife Management Centre, Animal and Plant Health Agency, York, UK.

Centre for Ecosystems, Society and Biosecurity, Forest Research, Farnham, UK.

出版信息

Pest Manag Sci. 2018 Apr;74(4):853-860. doi: 10.1002/ps.4763. Epub 2017 Dec 12.

Abstract

BACKGROUND

Populations of wild boar and feral pigs are increasing worldwide, in parallel with their significant environmental and economic impact. Reliable methods of monitoring trends and estimating abundance are needed to measure the effects of interventions on population size. The main aims of this study, carried out in five English woodlands were: (i) to compare wild boar abundance indices obtained from camera trap surveys and from activity signs; and (ii) to assess the precision of density estimates in relation to different densities of camera traps. For each woodland, we calculated a passive activity index (PAI) based on camera trap surveys, rooting activity and wild boar trails on transects, and estimated absolute densities based on camera trap surveys.

RESULTS

PAIs obtained using different methods showed similar patterns. We found significant between-year differences in abundance of wild boar using PAIs based on camera trap surveys and on trails on transects, but not on signs of rooting on transects. The density of wild boar from camera trap surveys varied between 0.7 and 7 animals/km . Increasing the density of camera traps above nine per km did not increase the precision of the estimate of wild boar density.

CONCLUSION

PAIs based on number of wild boar trails and on camera trap data appear to be more sensitive to changes in population size than PAIs based on signs of rooting. For wild boar densities similar to those recorded in this study, nine camera traps per km are sufficient to estimate the mean density of wild boar. © 2017 Crown copyright. Pest Management Science © 2017 Society of Chemical Industry.

摘要

背景

野猪和野猪的数量在世界范围内不断增加,与此同时,它们对环境和经济也产生了重大影响。为了衡量干预措施对种群规模的影响,需要可靠的监测趋势和估算丰度的方法。本研究在五个英国林地进行,主要目的是:(i)比较通过相机陷阱调查和活动迹象获得的野猪丰度指数;(ii)评估与不同相机陷阱密度相关的密度估计的精度。对于每个林地,我们根据相机陷阱调查、根活动和在横截线上的野猪踪迹计算了一个被动活动指数(PAI),并根据相机陷阱调查估计了绝对密度。

结果

使用不同方法获得的 PAI 显示出相似的模式。我们发现,使用基于相机陷阱调查和横截线上的踪迹的 PAI 来衡量野猪丰度的年际差异显著,但基于横截线上的根活动迹象来衡量的丰度差异不显著。从相机陷阱调查中获得的野猪密度在 0.7 到 7 只/公里之间变化。将相机陷阱的密度增加到每公里九个以上并不会提高野猪密度估计的精度。

结论

基于野猪踪迹数量和相机陷阱数据的 PAI 似乎比基于根活动迹象的 PAI 对种群规模的变化更敏感。对于类似于本研究中记录的野猪密度,每公里九个相机陷阱足以估计野猪的平均密度。

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