Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

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, c...

Full description

Bibliographic Details
Main Authors: Adam M Wilson, Walter Jetz
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-03-01
Series:PLoS Biology
Online Access:http://europepmc.org/articles/PMC4816575?pdf=render
id doaj-24cd8ca3663945b595e3153f6e886bd7
record_format Article
spelling doaj-24cd8ca3663945b595e3153f6e886bd72021-07-02T10:07:56ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852016-03-01143e100241510.1371/journal.pbio.1002415Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.Adam M WilsonWalter JetzCloud 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.http://europepmc.org/articles/PMC4816575?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Adam M Wilson
Walter Jetz
spellingShingle Adam M Wilson
Walter Jetz
Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
PLoS Biology
author_facet Adam M Wilson
Walter Jetz
author_sort Adam M Wilson
title Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
title_short Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
title_full Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
title_fullStr Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
title_full_unstemmed Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.
title_sort remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions.
publisher Public Library of Science (PLoS)
series PLoS Biology
issn 1544-9173
1545-7885
publishDate 2016-03-01
description 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.
url http://europepmc.org/articles/PMC4816575?pdf=render
work_keys_str_mv AT adammwilson remotelysensedhighresolutionglobalclouddynamicsforpredictingecosystemandbiodiversitydistributions
AT walterjetz remotelysensedhighresolutionglobalclouddynamicsforpredictingecosystemandbiodiversitydistributions
_version_ 1721332311758209024