Murn Campbell, Holloway Graham J
Hawk Conservancy Trust, Andover, Hampshire SP11 8DY, UK; School of Biological Sciences, University of Reading, Berkshire RG6 6AS, UK.
School of Biological Sciences , University of Reading , Berkshire RG6 6AS , UK.
R Soc Open Sci. 2016 Oct 12;3(10):160368. doi: 10.1098/rsos.160368. eCollection 2016 Oct.
Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9-20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.
低密度出现的物种可能难以被发现，如果没有得到妥善考虑，不完美的检测将导致占用率的估计不准确。了解检测概率的变化来源以及如何对其进行管理是监测的关键部分。我们采用基于似然性的建模方法，利用已知占用区域（繁殖领地）内一种低密度且难以捉摸的猛禽（白头兀鹫）的目击数据，来计算检测概率及其影响因素。由于已知占用率为100%，我们将模型占用参数固定为1.0，并专注于识别检测概率的变化来源。利用359次领地访问的检测历史，我们在29个候选模型中评估了9个协变量。支持度最高的模型表明，调查期间的观察者速度，与诸如一年中的时间和在领地内的停留时间等时间协变量相结合，对检测概率的影响最大。平均检测概率为0.207（标准误0.033），基于此，要在95%置信度内确定繁殖区域没有白头兀鹫所需的平均访问次数为13次（95%置信区间：9 - 20）。地形和栖息地协变量对最佳模型贡献不大，对检测概率影响较小。我们强调，某些物种的低检测概率意味着，在占用模型中只强调栖息地协变量可能会导致虚假结果，而这些模型没有纳入时间成分。虽然检测概率的变化很复杂，且受时间和空间尺度效应的影响，但时间协变量可以而且应该作为稳健调查方法的一部分加以控制。我们的结果强调了在占用研究中考虑检测概率的重要性，特别是在对诸如猛禽这类分布广泛且密度低的物种进行存在/不存在研究时。
