Biodiversity Science ›› 2019, Vol. 27 ›› Issue (3): 249-256.doi: 10.17520/biods.2018195

• Special Feature: Analysis of Wildlife Camera-Trapping Data • Previous Article     Next Article

Using occupancy models in wildlife camera-trapping monitoring and the study case

Xiao Wenhong1, Shu Zufei2, Chen Lijun1, Yao Wutao3, Ma Yong3, Zhang Yingming2, Xiao Zhishu1, 4, *()   

  1. 1 State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101
    2 Guangdong Chebaling National Nature Reserve Administration Bureau, Shaoguan, Guangdong 512500
    3 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094
    4 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2018-07-16 Accepted:2018-12-19 Online:2019-03-20
  • Xiao Zhishu

Occupancy models have the advantage of accounting for imperfect detection, they have simple and flexible data requirements as we all high economic efficiency. For these reasons, occupancy models have been increasingly applied in wildlife research. In this paper, we introduced the basic principles and use of the occupancy models. Using camera trap data we outlined the operational procedures and considerations from field investigation to data analysis. Using the masked palm civet (Paguma larvata) in the Guangdong Chebaling National Nature Reserve as an example we demonstrate the basic steps of how estimate occupancy and detect probability. Finally, according to occupancy model research from recent years, we summarize use trends and main limitations in the research.

Key words: camera trap, wildlife, data analysis, occupancy models, detectability, masked palm civet

Table 1

Summary of model selection results for masked palm civet occupancy in the Guangdong Chebaling National Nature Reserve, showing estimated occupancy probability (ψ) and detectability (p) for the models with ΔAIC ≤ 2"

AIC weight
Ψ p
p(海拔范围)ψ(海拔范围) p(elevation range)ψ(elevation range) 4 267.61 0 0.30 0.55 0.13
p(海拔范围, EVI)ψ (海拔范围) p(elevation range, EVI)ψ(elevation range) 5 268.99 1.38 0.15 0.55 0.12
p(海拔范围)ψ(海拔范围, EVI) p(elevation range)ψ(elevation range, EVI) 5 269.11 1.51 0.14 0.58 0.13
模型平均 Model average 0.56 0.13

Table 2

Covariates influencing masked palm civet occupancy and detectability according to β-coefficients and associated standard errors (SE)"

模型成分 Model component 协变量 Covariates 估计值 Estimate 标准误 SE P
占域 Occupancy 截距 Intercept 0.38 0.85 0.66
海拔范围 Elevation range -1.82 1.02 0.07
EVI 0.06 0.23 0.78
探测 Detection 截距 Intercept -2.45 0.26 < 0.001
海拔范围 Elevation range 1.29 0.30 < 0.001
EVI 0.04 0.12 0.75

Fig. 1

The effect of covariates (elevation range, EVI) on masked palm civet occupancy probability and detectability under top models. EVI, Enhanced vegetation index."

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