Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species-habitat relationships and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species-habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, data fusion of the two data sources can increase spatial coverage, which can reduce parameter uncertainty and make predictions more accurate, and therefore, can be used for species distribution modeling. We developed a model-based approach for data fusion of DS and CR data. Our modeling approach accounts for two common missing data issues: 1) missing individuals that are missing not at random (MNAR) and 2) partially missing location information. Using a simulation experiment, we evaluated the performance of our modeling approach and compared it to existing approaches that use ad-hoc methods to account for missing data issues. Our results show that our approach provides unbiased parameter estimates with increased efficiency compared to the existing approaches. We demonstrated our approach using data collected for Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA.
翻译:在生态、生物地理学和野生生物管理方面,越来越多地使用物种分布模型(SDM)来了解物种与栖息地的关系以及不同时空的丰度。远程采样和抓捕(CR)是广泛收集的两类数据类型,以了解物种与栖息地的关系和丰度;但是,由于缺乏空间覆盖范围,这些数据很少用于SDM(SDM)中。但是,两个数据来源的数据聚合可以扩大空间覆盖范围,从而降低参数不确定性,使预测更加准确,因此,可以用于物种分布模型。我们为DS和CR数据的数据融合开发了基于模型的方法。我们的模型方法说明了两个共同缺失的数据问题:(1) 失踪的人并非随机失踪(MNAR),和(2) 部分位置信息缺失。我们利用模拟实验,评估了我们的模型方法的性能,并将其与现有方法进行比较。我们的结果表明,我们的方法提供了不偏向的参数估计,与现有方法相比,我们展示了我们的方法,我们使用了在美国东北部的Sirkinsarrows(Ammorams sasranasnanaas-Aast)收集的数据。我们展示了我们的方法。