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Occupancy modeling species-environment relationships with non-ignorable survey designs

机译：与非无知调查设计的占用物种 - 环境关系

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Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non-probability surveys are combined or unequal selection probabilities are used, the design may be non-ignorable. We outline the use of pseudo-maximum likelihood estimation for site-occupancy models to account for such non-ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo-likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non-detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudomaximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species-environment relationships with non-ignorable sampling designs was biased, whereas the pseudo-likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non-probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increa
机译：支持关于环境梯度的物种发生模式推断的统计模型是生态和保护生物学的基础。常见的隐式假设是采样设计是忽略的，不需要正式地占分析。分析师假设数据代表所需的人口和统计建模所需。但是，如果使用来自概率和非概率调查的数据集或使用不等的选择概率，则设计可能是非忽略的。我们概述了现场占用模型的伪最大似然估计的使用，以考虑此类不可忽略的调查设计。该估计方法通过适当加权伪似然方程来占调查设计。在我们的经验示例中，遗留和更新的随机选择的地点为蝙蝠进行了调查，以通过持续的全国范围内的计划弥合历史悠久的全国范围内。我们提供了一个使用蝙蝠声学检测/非检测数据的工作示例，并展示分析师如何诊断他们的设计是否无知。使用模拟我们评估了我们的方法是否可用于建模由站点组成的数据集，该网站贡献的概率设计之外。 PSeudomax最大似然估计与某些蝙蝠物种的通常最大似然占用估计不同。使用仿真，我们显示了与非忽略采样设计的物种环境关系的最大似然估计被偏置，而伪可能性估计器是无偏见的设计。然而，在我们的模拟研究中，由大部分遗留或非概率站点组成的设计导致标准错误的估计问题。这些问题可能是由小样本大小（5％或10％的采样强度和四个重访）混淆的高度可变权重的结果。来自多个来源的聚合数据集逻辑上支持更大的样本大小和潜在的增量

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