Data from: Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

It is common to use multiple 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 multi-method models permit rigorous comparison of method-specific detection parameters associated with different methods, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate 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 upon 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 multi-method analyses 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.在开展野生动物调查时，通常会采用多种方法，以比较不同方法的效能或成本效益、整合不同方法提供的差异化信息，或是评估特定方法的偏差与误分类误差。现有多方法模型能够严格对比不同方法对应的特定检测参数，可估计假阳性检测概率等额外参数，并优化出现率或丰度的估计结果，但该类模型均假设各独立方法的检测结果彼此独立。当方法成对部署或同时近距离布置时，这一假设便难以成立——而这种部署方式属于常见实践，既可降低实施多方法所需的额外工作量，也能减少不同方法的特定检测参数间的差异被其他环境因素混淆的风险。我们构建了允许不同方法检测结果间存在协方差的占用（occupancy）模型与空间捕获-再捕获（spatial capture-recapture）模型，通过模拟将新模型与假设检测独立的模型的估计器性能进行对比，并基于成对部署的远程相机、毛发捕获法与雪地追踪法开展的美洲貂（Martes americana）调查案例，提供了实证应用。模拟结果显示，当方法独立检测个体的前提不成立时，现有假设独立的模型会产生有偏的参数估计结果，且显著低估估计的不确定性；而我们重构的模型对方法独立或存在协方差的两种情况均具有稳健性。实证结果表明，远程相机与雪地追踪法检测现存美洲貂的概率相当，但雪地追踪法会产生假阳性的美洲貂检测结果，若不加以校正，可能会对分布估计造成显著偏差。远程相机相较于被动毛发捕获法，更易检测到美洲貂个体。无法通过摄影区分个体性别似乎并未对相机密度估计产生负向偏差；相反，毛发捕获法似乎会在个体间引发检测竞争，这可能是负向偏差的来源之一。我们的模型重构拓展了可稳健开展多方法分析的场景范围，实证结果也证明，采用多种野外调查方法能够增强对目标生态参数的推断能力，并提升我们对调查方法采样这些参数的可靠程度的理解。