Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models that analyzed the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced survey effort.

翻译：整合模型是分析受关注保护物种的流行工具。受关注保护物种常由多个实体监测，产生多个数据集。由于时空分辨率低、采样偏差大或观测不确定性高，这些数据集单独可能不足以指导管理决策。整合模型提供了一种方法，可在统一框架内融合多个数据集，从而弥补上述缺陷。传统整合模型用于融合计数数据与存活率、繁殖力和收获量调查，但它们也能融合具有不同时空区域和观测不确定性的生态调查。受独立的地面与空中草原榛鸡调查启发，我们开发了一种整合建模方法，将来自不同观测误差来源的密度估计融合到一个联合框架中，从而提供对时空趋势的共享推断。我们采用贝叶斯马尔可夫混合方法对这些数据建模，并应用多种数据增强策略以提高采样效率。模拟研究表明，相比独立分析调查数据的模型，我们的整合模型提升了预测性能。我们利用整合模型预测未抽样区域的草原榛鸡密度，并通过敏感性分析量化减少调查投入所导致的推断成本。