Data from: Evaluating citizen versus professional data for modeling distributions of a rare squirrel

1) To realize the potential of citizens to contribute to conservation efforts through the acquisition of data for broad-scale species distribution models, scientists need to understand and minimize the influences of commonly observed sample selection bias on model performance. Yet evaluating these data with independent, planned surveys is rare, even though such evaluation is necessary for understanding and applying data to conservation decisions. 2) We used the state-listed fox squirrel (Sciurus niger) in Florida, USA, to interpret the performance of models created with opportunistic observations from citizens and professionals by validating models with independent, planned surveys. 3) Data from both citizens and professionals showed sample selection bias with more observations within 50 m of a road. While these groups showed similar sample selection bias in reference to roads, there were clear differences in the spatial coverage of the groups, with citizens observing fox squirrels more frequently in developed areas. 4) Based on predictions at planned field surveys sites, models developed from citizens generally performed similarly to those developed with data collected by professionals. Accounting for potential sample selection bias in models, either through the use of covariates or via aggregating data into home range size grids, provided only slight increases in model performance. 5) Applications: Despite sample selection biases, over a broad spatial scale opportunistic citizen data provided reliable predictions and estimates of habitat relationships needed to advance conservation efforts. Our results suggest that the use of professionals may not be needed in volunteer programs used to determine the distribution of species of conservation interest across broad spatial scales.1) 为充分发挥公民通过采集数据参与大规模物种分布模型构建、助力物种保护工作的潜力，科研人员需理解并尽可能削弱常见样本选择偏差对模型性能的负面影响。然而，尽管此类评估对于理解数据并将其应用于保护决策至关重要，但借助独立计划性调查对这类数据开展评估的研究仍较为稀缺。 2) 本研究以美国佛罗里达州州列保护物种狐松鼠（Sciurus niger）为研究对象，通过独立计划性调查对模型进行验证，以此解析利用公民与专业人员的偶发观测数据构建的物种分布模型的性能表现。 3) 来自公民与专业人员的观测数据均呈现样本选择偏差特征：距离道路50米范围内的观测记录占比更高。尽管两类群体在与道路相关的样本选择偏差上表现相似，但二者的空间覆盖范围存在显著差异——公民观测到的狐松鼠在开发区域出现的频率更高。 4) 基于计划性野外调查点位的预测结果显示，利用公民数据构建的模型整体性能与专业人员采集数据构建的模型不相上下。在模型中校正潜在样本选择偏差（通过引入协变量或将数据聚合至家域尺度网格）仅能小幅提升模型性能。 5) 应用场景：尽管存在样本选择偏差，但在大空间尺度下，偶发的公民观测数据仍可提供可靠的预测结果与栖息地关联估算，为物种保护工作的推进提供支撑。本研究结果表明，在旨在明确具有保护价值物种在大空间尺度上分布的志愿项目中，未必需要专业人员参与数据采集。