Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions
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{"title"=>"Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions", "type"=>"journal", "authors"=>[{"first_name"=>"Adam M.", "last_name"=>"Wilson", "scopus_author_id"=>"26533580600"}, {"first_name"=>"Walter", "last_name"=>"Jetz", "scopus_author_id"=>"6602506334"}], "year"=>2016, "source"=>"PLoS Biology", "identifiers"=>{"pmid"=>"27031693", "doi"=>"10.1371/journal.pbio.1002415", "sgr"=>"84962167828", "isbn"=>"10.1371/journal.pbio.1002415", "scopus"=>"2-s2.0-84962167828", "issn"=>"15457885", "pui"=>"609389380"}, "id"=>"2d46208c-48ed-3a37-920f-f5ece0af0d41", "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.", "link"=>"http://www.mendeley.com/research/remotely-sensed-highresolution-global-cloud-dynamics-predicting-ecosystem-biodiversity-distributions", "reader_count"=>187, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>53, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>43, "Student > Master"=>21, "Other"=>14, "Student > Bachelor"=>18, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>53, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>43, "Student > Master"=>21, "Other"=>14, "Student > Bachelor"=>18, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>9}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>12, "Environmental Science"=>70, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>70, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Computer Science"=>4, "Earth and Planetary Sciences"=>25, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>25}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>70}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>70}}, "reader_count_by_country"=>{"Colombia"=>3, "United States"=>5, "Japan"=>1, "United Kingdom"=>2, "Portugal"=>1, "Spain"=>3, "India"=>1, "Sweden"=>1, "Belgium"=>1, "Brazil"=>2, "Italy"=>1, "South Africa"=>1, "Chile"=>1, "France"=>1, "Australia"=>1, "Germany"=>2}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4907803"], "description"=>"<p><b>A</b>. Mean annual cloud frequency (%) over 2000–2014. <b>B</b> Inter-annual variability in cloud frequency (mean of 12 monthly standard deviations). <b>C</b>. Spatial variability (standard deviation of mean annual cloud frequency within a one-degree, ≈110 km, circular moving window). <b>D</b> Intra-annual variability in cloud frequency (standard deviation of 12 monthly mean cloud frequencies). Grey indicates the (<b>A)</b> median global cloud frequency (51%) and (<b>B,D</b>) median inter-annual variability (11%), blues indicate areas with below-median values, and reds indicate areas with higher-than-median values. Data are available only for MODIS land tiles, resulting in missing data in black tiles over oceans. For further exploration see <a href=\"http://earthenv.org/cloud\" target=\"_blank\">http://earthenv.org/cloud</a> and for download see <a href=\"http://doi.org/10.6084/m9.figshare.1531955\" target=\"_blank\">http://doi.org/10.6084/m9.figshare.1531955</a>.</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150193, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Global_1_km_cloud_metrics_/3150193", "title"=>"Global 1 km cloud metrics.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907704", "https://ndownloader.figshare.com/files/4907785", "https://ndownloader.figshare.com/files/4907776", "https://ndownloader.figshare.com/files/4907764", "https://ndownloader.figshare.com/files/4907761", "https://ndownloader.figshare.com/files/4907755", "https://ndownloader.figshare.com/files/4907746", "https://ndownloader.figshare.com/files/4907734", "https://ndownloader.figshare.com/files/4907728", "https://ndownloader.figshare.com/files/4907713", "https://ndownloader.figshare.com/files/4907707", "https://ndownloader.figshare.com/files/4907788"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150166, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>["https://dx.doi.org/10.1371/journal.pbio.1002415.s001", "https://dx.doi.org/10.1371/journal.pbio.1002415.s011", "https://dx.doi.org/10.1371/journal.pbio.1002415.s010", "https://dx.doi.org/10.1371/journal.pbio.1002415.s009", "https://dx.doi.org/10.1371/journal.pbio.1002415.s008", "https://dx.doi.org/10.1371/journal.pbio.1002415.s007", "https://dx.doi.org/10.1371/journal.pbio.1002415.s006", "https://dx.doi.org/10.1371/journal.pbio.1002415.s005", "https://dx.doi.org/10.1371/journal.pbio.1002415.s004", "https://dx.doi.org/10.1371/journal.pbio.1002415.s003", "https://dx.doi.org/10.1371/journal.pbio.1002415.s002", "https://dx.doi.org/10.1371/journal.pbio.1002415.s012"], "stats"=>{"downloads"=>13, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Remotely_Sensed_High_Resolution_Global_Cloud_Dynamics_for_Predicting_Ecosystem_and_Biodiversity_Distributions/3150166", "title"=>"Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions", "pos_in_sequence"=>1, "defined_type"=>4, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907887"], "description"=>"<p>Evaluation of species distribution models using interpolated precipitation or cloud product for the montane woodcreeper (<i>Lepidocolaptes lacrymiger</i>) and king protea (<i>Protea cynaroides</i>).</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150229, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Evaluation_of_species_distribution_models_using_interpolated_precipitation_or_cloud_product_for_the_montane_woodcreeper_i_Lepidocolaptes_lacrymiger_i_and_king_protea_i_Protea_cynaroides_i_/3150229", "title"=>"Evaluation of species distribution models using interpolated precipitation or cloud product for the montane woodcreeper (<i>Lepidocolaptes lacrymiger</i>) and king protea (<i>Protea cynaroides</i>).", "pos_in_sequence"=>7, "defined_type"=>3, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907839"], "description"=>"<p><b>A</b>. Geographic locations of minima in cloud dynamics using colors defined in <b>C–E</b>. <b>B</b>. Inset showing detail (red square in <b>A</b>) over East African Biodiversity Hotspots. <b>C–E.</b> Scatterplot of pixels in <b>A</b> and <b>B,</b> which serves as a color key to the map. Colored pixels indicate locations in the top 10% quantile of mean annual cloud frequency (see <b>D</b> and <b>E</b>) and bottom 10% quantile of intra-annual cloud variability (blues), inter-annual cloud variability (reds), or both (greens). Lines in scatterplot indicate the 10th (and 90th for mean annual) percentiles. For further exploration see <a href=\"http://www.earthenv.org/cloud\" target=\"_blank\">http://www.earthenv.org/cloud</a> and for download see <a href=\"http://doi.org/10.6084/m9.figshare.1531955\" target=\"_blank\">http://doi.org/10.6084/m9.figshare.1531955</a>.</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150214, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Global_hotspots_of_temporal_cloud_cover_constancy_/3150214", "title"=>"Global hotspots of temporal cloud cover constancy.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907854"], "description"=>"<p><b>A–C</b>: Distribution (relative occurrence rate) of tropical montane cloud forests estimated using an inhomogeneous point process model [<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#pbio.1002415.ref047\" target=\"_blank\">47</a>] of 529 cloud forest locations (black points) [<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#pbio.1002415.ref048\" target=\"_blank\">48</a>] with the new cloud metrics and elevation [<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#pbio.1002415.ref007\" target=\"_blank\">7</a>] (see <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#sec007\" target=\"_blank\">Materials and Methods</a> and <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#pbio.1002415.s012\" target=\"_blank\">S4 Table</a> for modeling details). Panels show predictions for (<b>A</b>) South and Central America, (<b>B</b>) Africa, and (<b>C</b>) Southeast Asia/Australia. All panels share the color bar shown in panel <b>C</b>. For further exploration see <a href=\"http://www.earthenv.org/cloud\" target=\"_blank\">http://www.earthenv.org/cloud</a> and for download see <a href=\"http://doi.org/10.6084/m9.figshare.1531955\" target=\"_blank\">http://doi.org/10.6084/m9.figshare.1531955</a>.</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150217, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Tropical_montane_cloud_forest_distribution_/3150217", "title"=>"Tropical montane cloud forest distribution.", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907821"], "description"=>"<p><b>A.</b> Color key illustrating the distribution of global cloud seasonality and concentration. The hue indicates the month of peak cloudiness, while the saturation and value indicate the magnitude of the concentration ranging from 0 (black, all months are equally cloudy) to 100 (all clouds are observed in a single month). <b>B.</b> Global distribution of seasonal cloud concentration with two red boxes indicating the locations of panels <b>C</b> and <b>D.</b> Coastlines shown in white, areas with no data are dark grey. <b>C.</b> Regional plot of northern South America illustrating the transition from June–July–August to December–January–February cloudiness with little seasonality (dark colors) at high elevations. <b>D.</b> Regional plot of southern Africa illustrating the transition from the Mediterranean climate in the southwest to the summer rainfall region in the northeast. Note the incursions of summer clouds and associated rainfall (red colors) along the southern coast. In <b>C</b> and <b>D</b>, red lines indicate ecoregion boundaries [<a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002415#pbio.1002415.ref035\" target=\"_blank\">35</a>]. For further exploration see <a href=\"http://www.earthenv.org/cloud\" target=\"_blank\">http://www.earthenv.org/cloud</a> and for download see <a href=\"http://doi.org/10.6084/m9.figshare.1531955\" target=\"_blank\">http://doi.org/10.6084/m9.figshare.1531955</a>.</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150202, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Seasonal_cloud_concentration_/3150202", "title"=>"Seasonal cloud concentration.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-03-31 07:27:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/4907869"], "description"=>"<p><b>A–F.</b> Evaluation of predictions from species distribution models of (<b>A,B,E,F</b>) montane woodcreeper (<i>Lepidocolaptes lacrymiger</i>, blue symbols) and (<b>C,D,E,F)</b> king protea (<i>Protea cynaroides</i>, red symbols). <b>A,C</b> are estimated probability of presence from species distribution models fit using cloud frequency, while <b>B,D</b> use interpolated precipitation. Insets in <b>A–D</b> show detail from boxed region. Gray points indicate locations with non-detections, while red/blue “+” marks indicate observed presences. <b>E</b>. correlograms of the spatial autocorrelation of the data in <b>A–D,</b> in which solid lines indicate models built with cloud data (<b>A,C</b>) and dashed lines indicate predictions from a model built using interpolated precipitation data (<b>B,D</b>). <b>F</b>. Estimated probability of presence, in which the species has been undetected at locations with at least five trials or observed (colors/lines as in <b>E</b>), box widths proportional to the number of observations. Data available at <a href=\"http://doi.org/10.6084/m9.figshare.1531955\" target=\"_blank\">http://doi.org/10.6084/m9.figshare.1531955</a>.</p>", "links"=>[], "tags"=>["habitat", "dynamic", "species distributions", "Biodiversity Distributions Cloud", "MODIS", "Moderate Resolution Imaging Spectroradiometer", "ecosystem"], "article_id"=>3150220, "categories"=>["Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Ecology", "Astronomical and Space Sciences not elsewhere classified", "Sociology", "Biological Sciences not elsewhere classified", "Inorganic Chemistry"], "users"=>["Adam M. Wilson", "Walter Jetz"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002415.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Evaluation_of_cloud_data_in_species_distribution_models_/3150220", "title"=>"Evaluation of cloud data in species distribution models.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-03-31 07:27:41"}

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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