Detection/non-detection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often subject to imperfect detection. Recent model development has focused on accounting for both false positive and false negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false positive error in detection/non-detection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false positive error following the site confirmation or observation confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) in Wisconsin, USA reinforce concerns that biases induced by false positive error can also distort spatial predictions often used to guide decision-making. Model sensitivity to false positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

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物种检测/非检测数据中假阳性错误的基于广义模型的解决方案

对估计物种分布、丰度和物候学感兴趣的生态学家广泛收集检测/非检测数据，并且经常受到不完善的检测。鉴于有证据表明错误分类在许多采样协议中很常见，因此最近的模型开发集中在考虑假阳性和假阴性错误。然而，迄今为止，基于模型的误报解决方案在很大程度上解决了占用率估计问题。我们描述了一个广义模型结构，允许调查人员在广泛的生态参数和模型类别中解释检测/非检测数据中的误报，并证明先前开发的基于模型的解决方案是广义模型的特殊情况。模拟结果表明，即使只有 5-10% 的检测是误报，忽略误报误差的丰度和迁移到达时间估计值也会表现出严重的 (20-70%) 相对偏差。当假阳性检测更可能发生在站点或不太可能发生真阳性检测的场合时，偏差会增加。在现场确认或观察确认设计之后考虑误报错误的模型通常会大大减少偏差，即使很少有检测被确认为真或误报，或者误报错误的过程模型被错误指定。来自威斯康星州灰狐 (Urocyon cinereoargenteus) 的实证示例的结果，美国更加担心由误报错误引起的偏差也会扭曲通常用于指导决策的空间预测。模型对误报错误的敏感性远远超出了占用估计，但令人鼓舞的是，为占用估计开发的基于模型的解决方案在生态研究中广泛使用的一系列模型中具有推广性和有效性。