Detection Histories for Hemlock Woolly Adelgid Infestations at Cadwell Forest in Pelham MA 2008

Monitoring programs increasingly are used to document the spread of invasive species in the hope of detecting and eradicating low-density infestations before they become established. However, interobserver variation in the detection and correct identification of low-density populations of invasive species remains largely unexplored. In this study, we compare the abilities of volunteer and experienced individuals to detect low-density populations of an actively spreading invasive species and we explore how interobserver variation can bias estimates of the proportion of sites infested derived from occupancy models that allow for both false negative and false positive (misclassification) errors. We found that experienced individuals detected small infestations at sites where volunteers failed to find infestations. However, occupancy models erroneously suggested that experienced observers had a higher probability of falsely detecting the species as present than did volunteers. This unexpected finding is an artifact of the modeling framework and results from a failure of volunteers to detect low-density infestations rather than from false positive errors by experienced observers. Our findings reveal a potential issue with site occupancy models that can arise when volunteer and experienced observers are used together in surveys.

监测项目正日益被用于记录外来入侵物种的扩散态势，以期在低密度入侵种群定殖前及时发现并根除它们。然而，在低密度入侵物种种群的检测与准确识别环节中，观察者间的差异仍未得到充分探索。

本研究中，我们对比了志愿者观测者与资深观测者对正在扩散的外来入侵物种低密度种群的检测能力，并探究了观察者间差异会如何对同时考虑假阴性（false negative）与假阳性（false positive，误分类）误差的占用模型（occupancy models）所估算的入侵位点占比产生偏倚。

研究结果显示，在志愿者观测者未能发现入侵种群的位点中，资深观测者成功检测到了低密度入侵种群。但占用模型却错误地显示，相较于志愿者观测者，资深观测者误将非入侵位点判定为入侵位点的概率更高。

这一与预期相悖的结果实则是建模框架的人为产物，其根源在于志愿者观测者未能检测到低密度入侵种群，而非资深观测者出现假阳性误判。

本研究结果揭示了在野外调查中同时使用志愿者与资深观测者时，位点占用模型可能存在的潜在问题。