Tourani Mahdieh, Dupont Pierre, Nawaz Muhammad Ali, Bischof Richard
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway.
Department of Animal Sciences, Quaid-i-Azam University, Islamabad, 44000, Pakistan.
Ecology. 2020 Jul;101(7):e03030. doi: 10.1002/ecy.3030. Epub 2020 Apr 6.
Population monitoring data may originate from multiple methods and are often sparse and fraught with incomplete information due to practical and economic constraints. Models that can integrate multiple survey methods and are able to cope with incomplete data may help investigators exploit available information more thoroughly. Here, we developed an integrated spatial capture-recapture (SCR) model to incorporate multiple data sources with imperfect individual identification. We contrast inferences drawn from this model with alternate models incorporating only subsets of the data available. Using extensive simulations and an empirical example of multi-method brown bear (Ursus arctos) monitoring data from northern Pakistan, we quantified the benefits of including multiple sources of information in SCR models in terms of parameter precision and bias. Our multiple observation processes SCR model (MOP) yielded a more complete picture of the underlying processes, reduced bias, and led to more precise parameter estimates. Our results suggest that the greatest gains from integrated SCR models can be expected in situations where detection probability is low, a large proportion of detections is not attributable to individuals, and the degree of overlap between individual home ranges is low.
种群监测数据可能源于多种方法，并且由于实际和经济限制，往往较为稀疏且充斥着不完整信息。能够整合多种调查方法并能处理不完整数据的模型，可能有助于研究人员更全面地利用现有信息。在此，我们开发了一种综合空间捕获-再捕获（SCR）模型，以纳入具有不完美个体识别的多个数据源。我们将此模型得出的推断与仅纳入可用数据子集的替代模型进行对比。通过广泛的模拟以及来自巴基斯坦北部棕熊（Ursus arctos）多方法监测数据的实证示例，我们从参数精度和偏差方面量化了在SCR模型中纳入多个信息源的益处。我们的多观测过程SCR模型（MOP）对潜在过程给出了更完整的描述，减少了偏差，并得出了更精确的参数估计。我们的结果表明，在检测概率较低、很大一部分检测无法归因于个体以及个体家域之间的重叠程度较低的情况下，综合SCR模型有望带来最大收益。
