Ecological occupancy modeling has historically relied on high‐quality, low‐quantity designed‐survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high‐quantity, unknown‐quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed‐survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed‐survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed‐survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red‐eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.

中文翻译：

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使用调解分析用机会数据估算物种分布的驱动力

生态占用模型在历史上一直依靠高质量，低数量的设计调查数据进行估计和预测。近年来，高数量，质量未知的机会数据的数量已大大增加。这激发了关于如何最佳地组合这两个数据源以优化推理的研究。对于大型数据集，现有方法可能不可行，或者要求将机会主义数据放置在存在设计调查数据的位置。这些方法绘制了物种的分布图，激发了需要正确评估其分布的协变量效应（例如土地覆盖率）的需求。我们描述了一种使用中介分析概念补充包括其他机会数据的空间估算方法。机会数据介导协变量对设计调查数据响应的影响，将其分解为直接和间接影响。然后，可以通过使中介变量回归协变量来快速估计间接影响的一个组成部分，而其他组成部分则可以通过空间占用模型进行估计。回归步骤允许使用大量机会数据，这些数据可以在没有设计调查数据的位置收集。仿真结果表明，当数据质量合理时，介导方法可以改善相对MSE。但是，当模拟的机会数据与真实的空间过程之间的关联性很差时，仍然首选标准的无中介方法。还开发了该方法的时空扩展，以分析美国东南部落叶林地覆盖对红眼vireo分布的影响，发现包括机会数据并不能带来实质性的改善。与其他数据集成方法一样，使用此方法时机会数据质量仍然是重要的考虑因素。