In modeling species distributions and population dynamics, spatially-interpolated climatic data are often used as proxies for real, on-the-ground measurements. For shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite-derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high-resolution global maps of air-LSWT offsets, corresponding uncertainty measures and derived LSWT-based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature-based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab-measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically-meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.

中文翻译：

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全球湖泊表面水温图揭示了生态预测中空气对水替代的缺陷

在对物种分布和种群动态进行建模时，空间插值气候数据通常用作真实实地测量的代理。对于浅水淡水系统，这种做法可能会有问题，因为用于地表水的插值是从测量气温的地面传感器网络生成的。因此，使用这些可能会使物种的环境耐受性或种群预测的统计估计产生偏差——尤其是在 pleustonic 和 epilimnetic 生物中。使用包含数百万每日卫星导出的湖面水温 (LSWT) 的全球数据库，我训练了机器学习模型来校正空气和 LSWT 之间的对应关系，作为大气和地形预测因子的函数，从而创建月度高-空气 LSWT 偏移的分辨率全球地图，相应的不确定性措施和派生的基于 LSWT 的生物气候层，供科学界使用。然后，我比较了这些 LSWT 层和基于气温的层在种群动态和生态位模型 (ENM) 中的性能。虽然普遍较高，但气温和 LSWT 之间的对应关系非常多变，并且通常是非线性的，具体取决于空间环境。这些 LSWT 预测能够更好地捕捉两种常见水生植物物种的模拟种群动态和地理分布。此外，使用 LSWT 预测器训练的 ENM 模型更准确地捕获了实验室测量的热响应曲线。我的结论是，当用于人口预测和环境生态位建模时，这些预测的 LSWT 温度比原始空气​​温度表现更好，并且应该被从业者用来获得更具生物学意义的结果。这些全球 LSWT 预测和相应的误差估计以及生物气候层已在永久档案中免费提供给所有研究人员。