PDF(Pubmed)
Abstract：
Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy-detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time-to-detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time-to-first detection conditional on occupancy in relation to local factors, using modified interval-censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time-to-detection model provided unbiased parameter estimates despite interval-censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P-values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval-censored time-to-detection model provides a practical solution to model occupancy-detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.
摘要：
控制不完美的检测对于开发物种分布模型（SDM）很重要。可以使用基于检测物种所需时间的占用检测模型来解决此问题，但是当检测时间不准确时，这会受到阻碍。这里,我们扩展了检测时间模型来处理以时间间隔记录的检测，并使用流鱼分布建模的案例研究来说明该方法。我们在葡萄牙东北部的一个地中海流域收集了六种鱼类的电捕捞样本。基于贝叶斯分层框架，我们对河道中存在水的概率进行了建模，物种占据的概率取决于水的存在，与环境和空间变量有关。我们还对以占用为条件的首次检测时间与局部因素的关系进行了建模，使用改进的间隔删失指数生存模型。从模型得出的占用概率的后验分布用于生成物种分布图。仿真表明，尽管进行了间隔审查，但修改后的检测时间模型仍提供了无偏的参数估计。检测率的空间变化有主要受深度影响的趋势，在较小程度上，流宽度。物种占用率一直受到流订单的影响，高程,和年降水量。贝叶斯P值和AUC表明所有模型都具有足够的拟合度和较高的判别能力，分别。预测占用概率的映射显示大多数物种广泛分布，但支流和上游的不确定性普遍较高。当在时间间隔中记录检测时，间隔删失时间检测模型提供了一种实用的解决方案，用于对占用检测进行建模。此建模框架可用于开发SDM，同时控制检测率的变化，因为它使用简单的数据，可以很容易地由野外生态学家收集。
