List of species used in analyses.

A key goal in ecology is to develop effective ways to understand species’ distributions in order to facilitate both their study and conservation. Many species distribution modeling analyses have been performed using either structured survey data or unstructured citizen science data; these two pools of data have tradeoffs in terms of data density, spatiotemporal coverage, and accuracy. Recent studies have shown that combining structured and unstructured survey data can improve the accuracy of species distribution models for birds, but most of this work has focused on north temperate bird species, using bird atlas data that are less available in the Tropics. Here, we adapted a data pooling approach from the literature on north temperate bird biology to create distribution models for a selection of secretive suboscine bird species that occur in a highly diverse region of the southwestern Amazon. Our approach combined automated acoustic monitoring detections and eBird citizen science data available for the region as well as a high resolution land cover dataset of the region’s key ecological gradients. The pooled models outperformed models constructed solely with eBird data for predicting fine grain species responses to habitat gradients in intact forest, but also retained information from the citizen science dataset about species occurrence patterns in non-vegetated areas away from intact forest, including those subject to human disturbance. We present this hybrid approach as a flexible and repeatable means to produce inferences that would not easily be achievable using a single data source, and provide recommendations for other researchers seeking to replicate these methods in Amazonia as well as in other tropical regions.

生态学的核心目标之一，是开发有效的方法解析物种分布格局，以助力物种研究与保护工作。既往物种分布建模研究多采用结构化调查数据或非结构化公民科学数据两类数据源，二者在数据密度、时空覆盖范围与精度层面各有优劣。近期研究表明，结合结构化与非结构化调查数据可提升鸟类物种分布模型的精度，但此类研究多聚焦北温带鸟类，且依托的鸟类图集数据在热带地区较为匮乏。本研究借鉴北温带鸟类学领域的数据集整合方法，为分布于亚马孙西南部高度多样区域的若干隐秘性亚鸣禽物种构建分布模型。本研究整合了该区域可用的自动声学监测检出数据、eBird公民科学数据，以及表征区域关键生态梯度的高分辨率土地覆盖数据集。整合模型在预测完整森林生境梯度下的精细尺度物种响应时，性能优于仅使用eBird数据构建的模型；同时保留了公民科学数据集关于远离完整森林的非植被区域（包括受人类干扰的区域）内物种出现格局的信息。本研究提出的这种混合建模方法，可作为一种灵活且可复现的研究手段，生成单一数据源难以实现的推断结论；同时为其他希望在亚马孙及其他热带区域复现此类方法的研究者提供参考建议。