title: Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
abstract: Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.
authors.affiliation: Department of Geography, University at Buffalo, Wilkeson Quad, Buffalo, New York, United States of America.|Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Adam M
authors.initials: AM
authors.lastname: Wilson
authors.affiliation: Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America.|Department of Life Sciences, Imperial College London, Silwood Park, Ascot, United Kingdom.
authors.forename: Walter
authors.initials: W
authors.lastname: Jetz
mesh_terms: D000818:Animals; D044822:Biodiversity; D002980:Climate; D004463:Ecology / Q000379:methods*; D065928:Forests; D058998:Remote Sensing Technology; D014887:Weather