Point counts (PCs) are widely used in biodiversity surveys, but despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability produces biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data and capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables separate estimation of the availability and perceptibility components of detectability. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new IDS() function in the R package unmarked. Extant citizen-science data generally lack adjustments for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine distance sampling with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.
翻译:在生物多样性调查中广泛使用点数(PCs),但尽管有许多优势,简单的个人计算机存在若干问题:可探测性和丰度是未知的;在可探测性方面系统性的时空差异产生偏差推论,而未知的调查区则阻碍正式的密度估计和向地貌水平的扩大;我们采用综合的远距离取样模型,将远距离取样(DS)与简单的个人计算机或检测/检测(DND)数据结合起来,并利用每个数据类型的强点和弱点;IDS模型的关键是将简单的个人计算机和DND数据视为观察同一基本密度过程的潜在DS调查的集合;这可以对所有数据类型分别进行测算,同时产生不同的共变影响;我们采用综合的远程取样模型,将远程取样和检测的DDS数据分开,从而使得对可探测的可用性和可感知性成分进行单独估计;IDS模型协调各数据集之间的空间和时间错错配,并解决上述简单的个人计算机和DD数据的问题;为了适应 IDS模型,我们提供JAGS代码和新的DS数据组潜在组合,从而将这种远程数据组合纳入远程数据采集系统,因此缺少这些通用数据。