评估拟合异构数据集的具有自相关的多季节占用模型
Evaluating multi-season occupancy models with autocorrelation fitted to heterogeneous datasets
AI总结：
研究受异构蝴蝶占用数据集启发，评估含时空随机效应的多季节占用模型，发现其对异构数据和协变量重叠有鲁棒性，但存在可识别性问题，受数据间隙挑战，即使数据丰富地区也受影响。
摘要：
利用考虑不完全检测的占用模型预测物种分布在生态学中已很常见。最近，有人提出对空间和时间自相关进行建模以缓解占用数据中缺乏重复的问题，这常导致模型无法识别。然而，此类模型在缺失或单次访问数据占主导的高度异构数据集中的表现仍是个开放问题。受一个异构精细尺度蝴蝶占用数据集的启发，我们评估了一个具有空间和时间随机效应的多季节占用模型对每个站点调查数量的偏态（泊松）分布、占用和检测子模型之间协变量重叠以及观测的时空聚类的性能。结果表明该模型对异构数据和协变量重叠具有鲁棒性。然而，当添加时空间隙时，站点占用偏向于平均占用，而平均占用本身被高估。由于方差和自相关参数的可识别性问题，随机效应无法纠正间隙的影响。对两种蝴蝶物种的占用分析进一步证实了这些结果。总体而言，具有自相关的多季节占用模型对异构数据和协变量重叠具有鲁棒性，但仍存在可识别性问题，并受到严重数据间隙的挑战，即使在数据丰富的地区，数据间隙也会影响预测。
英文摘要：
Predicting species distributions using occupancy models accounting for imperfect detection is now commonplace in ecology. Recently, modeling spatial and temporal autocorrelation was proposed to alleviate the lack of replication in occupancy data, which often prevents model identifiability. However, how such models perform in highly heterogeneous datasets where missing or single-visit data dominates remains an open question. Motivated by a heterogeneous fine-scale butterfly occupancy dataset, we evaluate the performance of a multi-season occupancy model with spatial and temporal random effects to a skewed (Poisson) distribution of the number of surveys per site, overlap of covariates between occupancy and detection submodels, and spatiotemporal clustering of observations. Results showed that the model is robust to heterogeneous data and covariate overlap. However, when spatiotemporal gaps were added, site occupancy was biased towards the average occupancy, itself overestimated. Random effects did not correct the influence of gaps, due to identifiability issues of variance and autocorrelation parameters. Occupancy analysis of two butterfly species further confirmed these results. Overall, multi-season occupancy models with autocorrelation are robust to heterogeneous data and covariate overlap, but still present identifiability issues and are challenged by severe data gaps, which contaminate predictions even in data-rich areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
