Modelling species distribution from camera trap by‐catch using a scale‐optimized occupancy approach
Modelling species distribution from camera trap by‐catch using a scale‐optimized occupancy approach
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DOI：
10.1002/rse2.207
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发表时间：
2021-05
影响因子：
5.5
通讯作者：
Jolien Wevers;N. Beenaerts;J. Casaer;F. Zimmermann;T. Artois;J. Fattebert
中科院分区：
文献类型：
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作者：
Jolien Wevers;N. Beenaerts;J. Casaer;F. Zimmermann;T. Artois;J. Fattebert
栖息地的选择是强烈的尺度依赖性，推断生物体响应环境变化的特征尺度对于获得可靠的预测是必要的。占用框架经常被用来模拟物种分布的优势占不完善的观察，但占用研究通常不定义的特征尺度的建模变量。我们使用相机陷阱的数据，从冬季野生动物调查在瑞士的汝拉山的一部分，野猪（猪）和梅花鹿（Capreolus capreolus）的模型占用。我们使用了三步方法：（1）首先，我们确定了影响可检测性的因素;（2）其次，我们优化了每个候选解释变量的特征尺度;（3）第三，我们拟合了与土地覆盖、人类存在和地形有关的多变量、多尺度占用模型。野猪占用率主要受海拔2500米内的森林面积的比例之间的相互作用，与1900米内的坚固性和2500米内的森林面积的比例之间的相互作用，以及到城市地区的距离的非显着的额外效果。900 m内的崎岖度和900 m内的开阔景观比例之间的交互作用是主要的影响因素，海拔1500 m内的开阔景观比例和距市区的距离之间的交互作用也不显著。在相机陷阱数据的占用建模中进行尺度优化可以通过结合占用模型的可能性来校正检测偏倚，同时允许推断某些因素影响所研究生物体分布的特征尺度，从而大大提高对物种-环境关系的理解。
Habitat selection is strongly scale‐dependent, and inferring the characteristic scale at which an organism responds to environmental variation is necessary to obtain reliable predictions. The occupancy framework is frequently used to model species distribution with the advantage of accounting for imperfect observation, but occupancy studies typically do not define the characteristic scale of the modelled variables. We used camera trap data from winter wildlife surveys in the Swiss part of the Jura Mountains to model occupancy of wild boar (Sus scrofa) and roe deer (Capreolus capreolus). We used a three‐step approach: (1) first, we identified factors influencing detectability; (2) second, we optimized the characteristic scale of each candidate explanatory variable; and (3) third, we fit multivariable, multiscale occupancy models in relation to land cover, human presence and topography. Wild boar occupancy was mainly influenced by the interaction between elevation within 2500 m and the proportion of forested areas within a 2500 m, with a nonsignificant additional effect of the interaction between ruggedness within 1900 m and the proportion of forested areas within 2500 m as well as the distance to urban areas. Roe deer occupancy was mainly associated with the interaction between ruggedness within 900 m and the proportion of open landscape within 900 m, with an additional nonsignificant effect of the interaction between elevation within 1500 m and the proportion of open landscape within 900 m as well as the distance to urban areas. Incorporating scale optimization in occupancy modelling of camera trap data can greatly improve the understanding of species‐environment relationships by combining the possibility of occupancy models to correct for detection bias and simultaneously allowing to infer the characteristic scale at which certain factors influence the distribution of the organisms studied.