Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, Maine, 04469-5755, USA.
Maine Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Orono, Maine, 04469-5755, USA.
Ecol Appl. 2017 Oct;27(7):2031-2047. doi: 10.1002/eap.1587. Epub 2017 Sep 5.
It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.
在实施野生动物调查时，通常会使用多种野外采样方法来比较方法的效果或成本效率，整合来自不同方法的不同信息，或评估特定方法的偏差和误分类错误。现有的结合多个野外方法或采样设备信息的模型可以严格比较特定方法的检测参数，能够估计假阳性检测概率等额外参数，并改进出现或丰度估计，但前提是单独的采样方法彼此独立地产生检测结果。如果方法是配对的或同时近距离部署，那么这种假设就不太可靠，这种常见的做法会减少实施多种方法所需的额外努力，并降低因特定方法检测参数差异而导致的混淆的风险其他环境因素。我们开发了占有和空间捕获-再捕获模型，这些模型允许不同方法产生的检测结果之间存在协方差，通过模拟比较新模型和假设独立的模型的估计器性能，并提供基于使用配对的远程摄像机、毛发捕捉和雪地追踪的美洲貂调查的实证应用。模拟结果表明，当违反这种假设时，假设方法独立地检测生物体会产生有偏差的参数估计值，并大大低估估计不确定性，而我们重新制定的模型对方法独立性或协方差具有鲁棒性。实证结果表明，远程摄像机和雪地追踪检测当前貂的可能性相似，但雪地追踪也产生了假阳性貂的检测，这些检测如果不加以纠正，可能会严重偏分布估计。远程摄像机比被动毛发捕捉更容易检测到貂个体。无法通过摄影来区分个体性别似乎不会导致相机密度估计值产生负偏差；相反，毛发捕捉似乎在个体之间产生了检测竞争，这可能是负偏差的一个来源。我们的模型重新制定扩大了可以稳健使用包含多个信息源的分析的情况范围，我们的实证结果表明，使用多种野外方法可以增强对感兴趣的生态参数的推断，并更好地了解调查方法如何可靠地对这些参数进行采样。
