Integrated models combine multiple data types within a unified analysis to estimate species abundance and covariate effects. By sharing biological parameters, integrated models improve the accuracy and precision of estimates compared to separate analyses of individual datasets. We developed an integrated point process model to combine presence-only and distance sampling data for estimation of spatially-explicit abundance patterns. Simulations across a range of parameter values demonstrate that our model can recover estimates of biological covariates, but parameter accuracy and precision varied with the quantity of each data type. We applied our model to a case study of black-backed jackals in the Masai Mara National Reserve, Kenya, to examine effects of spatially varying covariates on jackal abundance patterns. The model revealed that jackals were positively affected by anthropogenic disturbance on the landscape, with highest abundance estimated along the Reserve border near human activity. We found minimal effects of landscape cover, lion density, and distance to water source, suggesting that human use of the Reserve may be the biggest driver of jackal abundance patterns. Our integrated model expands the scope of ecological inference by taking advantage of widely available presence-only data, while simultaneously leveraging richer, but typically limited, distance sampling data.

中文翻译：

---

整合距离采样和仅存在数据来估计物种丰度

集成模型在统一分析中结合了多种数据类型，以估计物种丰度和协变量效应。通过共享生物参数，与单独分析单个数据集相比，集成模型提高了估计的准确性和精确度。我们开发了一个集成的点过程模型，以结合仅存在和距离采样数据来估计空间显式的丰度模式。对一系列参数值的模拟表明，我们的模型可以恢复生物协变量的估计值，但参数准确性和精度随每种数据类型的数量而变化。我们将我们的模型应用于肯尼亚马赛马拉国家保护区黑背豺的案例研究，以检查空间变化的协变量对豺狼丰度模式的影响。该模型显示，胡狼受到景观中人为干扰的积极影响，估计在人类活动附近的保护区边界沿线的丰度最高。我们发现景观覆盖、狮子密度和到水源的距离的影响最小，这表明人类对保护区的使用可能是胡狼丰度模式的最大驱动因素。我们的集成模型通过利用广泛可用的仅存在数据扩展生态推断的范围，同时利用更丰富但通常有限的距离采样数据。表明人类对保护区的使用可能是胡狼数量模式的最大驱动因素。我们的集成模型通过利用广泛可用的仅存在数据扩展生态推断的范围，同时利用更丰富但通常有限的距离采样数据。表明人类对保护区的使用可能是胡狼数量模式的最大驱动因素。我们的集成模型通过利用广泛可用的仅存在数据扩展生态推断的范围，同时利用更丰富但通常有限的距离采样数据。