A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?
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{"title"=>"A geographic mosaic of climate change impacts on terrestrial vegetation: Which areas are most at risk?", "type"=>"journal", "authors"=>[{"first_name"=>"David D.", "last_name"=>"Ackerly", "scopus_author_id"=>"7004214819"}, {"first_name"=>"William K.", "last_name"=>"Cornwell", "scopus_author_id"=>"15028126900"}, {"first_name"=>"Stuart B.", "last_name"=>"Weiss", "scopus_author_id"=>"7403678244"}, {"first_name"=>"Lorraine E.", "last_name"=>"Flint", "scopus_author_id"=>"7103361010"}, {"first_name"=>"Alan L.", "last_name"=>"Flint", "scopus_author_id"=>"7201816357"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"605407824", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0130629", "scopus"=>"2-s2.0-84938513242", "pmid"=>"26115485", "sgr"=>"84938513242"}, "id"=>"358f9982-b36c-39f6-8d3c-8b71bca009e2", "abstract"=>"Changes in climate projected for the 21st century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21st century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.", "link"=>"http://www.mendeley.com/research/geographic-mosaic-climate-change-impacts-terrestrial-vegetation-areas-most-risk", "reader_count"=>98, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>3, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>2, "Student > Master"=>13, "Other"=>3, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Environmental Science"=>34, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>46, "Medicine and Dentistry"=>1, "Arts and Humanities"=>2, "Social Sciences"=>2, "Earth and Planetary Sciences"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>46}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>34}, "Arts and Humanities"=>{"Arts and Humanities"=>2}}, "reader_count_by_country"=>{"United States"=>5, "Germany"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2152732"], "description"=>"<p>Top row shows the full model with seven predictors, four climatic factors (JJA, DJF, CWD, PPT) and three fixed factors that did not vary under future climate scenarios (Soil depth, Wind speed, March radiation), with 36 parameters (linear, quadratic, 2-way interaction) for each of 21 (n-1) vegetation types. BIC value for the full model is 577096. Next seven rows show models with one factor at a time removed, sorted by decreasing ∆BIC. Last two rows show models with only the climate factors, or only the fixed factors. Average max probability is the mean value of the highest probability vegetation type in each pixel. Proportion correct is the proportion of pixels in which the maximum probability type was equal to the observed type.</p><p>Summary of model fit measures.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466048, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.t002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_model_fit_measures_/1466048", "title"=>"Summary of model fit measures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152712"], "description"=>"<p>Thirty year climatic means vs. mean annual temperature for the historic (1951–1980, red) and recent (1981–2010, blue) periods and 54 possible futures (black) based on 18 different model/forcing scenarios and three time periods (2010–2039, 2040–2069, 2070–2099). See values in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s012\" target=\"_blank\">S3 Table</a>.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466028, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Historical_and_projected_climate_means_/1466028", "title"=>"Historical and projected climate means.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152735", "https://ndownloader.figshare.com/files/2152736", "https://ndownloader.figshare.com/files/2152737", "https://ndownloader.figshare.com/files/2152738", "https://ndownloader.figshare.com/files/2152739", "https://ndownloader.figshare.com/files/2152740", "https://ndownloader.figshare.com/files/2152741", "https://ndownloader.figshare.com/files/2152742", "https://ndownloader.figshare.com/files/2152743", "https://ndownloader.figshare.com/files/2152744", "https://ndownloader.figshare.com/files/2152745", "https://ndownloader.figshare.com/files/2152746", "https://ndownloader.figshare.com/files/2152747", "https://ndownloader.figshare.com/files/2152748"], "description"=>"<div><p>Changes in climate projected for the 21<sup>st</sup> century are expected to trigger widespread and pervasive biotic impacts. Forecasting these changes and their implications for ecosystem services is a major research goal. Much of the research on biotic responses to climate change has focused on either projected shifts in individual species distributions or broad-scale changes in biome distributions. Here, we introduce a novel application of multinomial logistic regression as a powerful approach to model vegetation distributions and potential responses to 21<sup>st</sup> century climate change. We modeled the distribution of 22 major vegetation types, most defined by a single dominant woody species, across the San Francisco Bay Area. Predictor variables included climate and topographic variables. The novel aspect of our model is the output: a vector of relative probabilities for each vegetation type in each location within the study domain. The model was then projected for 54 future climate scenarios, spanning a representative range of temperature and precipitation projections from the CMIP3 and CMIP5 ensembles. We found that sensitivity of vegetation to climate change is highly heterogeneous across the region. Surprisingly, sensitivity to climate change is higher closer to the coast, on lower insolation, north-facing slopes and in areas of higher precipitation. While such sites may provide refugia for mesic and cool-adapted vegetation in the face of a warming climate, the model suggests they will still be highly dynamic and relatively sensitive to climate-driven vegetation transitions. The greater sensitivity of moist and low insolation sites is an unexpected outcome that challenges views on the location and stability of climate refugia. Projections provide a foundation for conservation planning and land management, and highlight the need for a greater understanding of the mechanisms and time scales of potential climate-driven vegetation transitions.</p></div>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466051, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0130629.s001", "https://dx.doi.org/10.1371/journal.pone.0130629.s002", "https://dx.doi.org/10.1371/journal.pone.0130629.s003", "https://dx.doi.org/10.1371/journal.pone.0130629.s004", "https://dx.doi.org/10.1371/journal.pone.0130629.s005", "https://dx.doi.org/10.1371/journal.pone.0130629.s006", "https://dx.doi.org/10.1371/journal.pone.0130629.s007", "https://dx.doi.org/10.1371/journal.pone.0130629.s008", "https://dx.doi.org/10.1371/journal.pone.0130629.s009", "https://dx.doi.org/10.1371/journal.pone.0130629.s010", "https://dx.doi.org/10.1371/journal.pone.0130629.s011", "https://dx.doi.org/10.1371/journal.pone.0130629.s012", "https://dx.doi.org/10.1371/journal.pone.0130629.s013", "https://dx.doi.org/10.1371/journal.pone.0130629.s014"], "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Geographic_Mosaic_of_Climate_Change_Impacts_on_Terrestrial_Vegetation_Which_Areas_Are_Most_at_Risk_/1466051", "title"=>"A Geographic Mosaic of Climate Change Impacts on Terrestrial Vegetation: Which Areas Are Most at Risk?", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152720"], "description"=>"<p>Illustration of Bray-Curtis distances between baseline and future vegetation vectors (similar to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g003\" target=\"_blank\">Fig 3</a>) for two selected pixels across our model domain. The slope of this relationship was used as a measure of the sensitivity of projected vegetation change in relation to climate, with mean annual temperature as a proxy for changes in JJA, DJF, and CWD. Much of the scatter around each regression line represents additional effects of PPT. Red illustrates a site with high sensitivity (slope = 0.298) and blue a site with low sensitivity (slope = 0.104).</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466036, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g007", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_vegetation_change_at_selected_sites_/1466036", "title"=>"Projected vegetation change at selected sites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152717"], "description"=>"<p>Changes in relative abundance of four vegetation types, plotted relative to mean annual temperature (MAT) of each of the climate scenarios. Colors indicate change in precipitation (see legend in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g004\" target=\"_blank\">Fig 4</a>). See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s006\" target=\"_blank\">S6 Fig</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s014\" target=\"_blank\">S5 Table</a> for results for all 22 vegetation types.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466033, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g005", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_change_in_selected_vegetation_types_/1466033", "title"=>"Projected change in selected vegetation types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152715"], "description"=>"<p>Bray-Curtis distance between future and baseline vegetation distributions, based on frequency distributions illustrated in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g003\" target=\"_blank\">Fig 3</a>, in relation to mean annual temperature of future climates. Colors indicate change in precipitation under future climates (red = negative, blue = positive).</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466031, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g004", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Magnitude_of_projected_vegetation_change_/1466031", "title"=>"Magnitude of projected vegetation change.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152729"], "description"=>"<p>Scatterplots of sensitivity of vegetation to climate change (based on slopes of Bray-Curtis distances, as shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g007\" target=\"_blank\">Fig 7</a>) vs. four landscape factors: a) DJF minimum temperature; b) precipitation; c) wind speed; d) equinox solar radiation. Light blue lines show partial regression slopes from multiple regression on these four factors.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466045, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g010", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlates_of_projected_sensitivity_of_vegetation_to_climate_change_/1466045", "title"=>"Correlates of projected sensitivity of vegetation to climate change.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152714"], "description"=>"<p>Relative frequency of 22 vegetation types across the San Francisco Bay Area, parameterized for the historical baseline period and then projected for alternative climates based on the recent climate (1981–2010) and 54 possible futures. Future climates are arranged in order of increased warming in mean annual temperature from bottom to top. Changes in MAT and PPT, averaged across the region for each climate scenario, are shown on the left.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466030, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeled_frequency_of_vegetation_types_/1466030", "title"=>"Modeled frequency of vegetation types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152710"], "description"=>"<p>Maps of vegetation types, and the four climatic variables used as predictors for modeling the distribution of vegetation (maps of other predictors are shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s001\" target=\"_blank\">S1 Fig</a>). A) Vegetation (see legend at top of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g003\" target=\"_blank\">Fig 3</a>); gray areas on the map are urban and agricultural, or rare vegetation types not included in model. B-E) Climate variables, showing 1951–1980 historic norms. DJF = December, January, February. JJA = June, July, August. Borders delineate ten counties of San Francisco Bay Area.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466026, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_San_Francisco_Bay_Area_vegetation_and_climate_/1466026", "title"=>"San Francisco Bay Area vegetation and climate.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152723"], "description"=>"<p>Variation in exposure vs. sensitivity, showing the much greater range of variation in the latter. The product of the two axes is the measure of potential vulnerability of vegetation in response to climate change, shown by the isoclines.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466039, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g009", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exposure_vs_sensitivity_of_vegetation_to_climate_change_/1466039", "title"=>"Exposure vs. sensitivity of vegetation to climate change.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152722"], "description"=>"<p>Map of sensitivity values, based on regression slopes for Bray-Curtis distances between baseline and future climate vs. mean annual temperature (illustrated in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.g007\" target=\"_blank\">Fig 7</a>).</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466038, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g008", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_sensitivity_of_vegetation_to_climate_change_/1466038", "title"=>"Projected sensitivity of vegetation to climate change.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152718"], "description"=>"<p>Changes in the location and abundance of the 22 vegetation types illustrated for the GFDL-A2-2070-2099 future. a) Change in distance to coast vs. proportional change in abundance (sum of relative frequencies across all pixels); b) change in elevation vs. proportional change in abundance; c) Change in distance to coast vs. change in elevation.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466034, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.g006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_changes_in_vegetation_location_and_abundance_/1466034", "title"=>"Projected changes in vegetation location and abundance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-26 04:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2152731"], "description"=>"<p>List of 22 vegetation types included in this study under the ‘1G’ model, assignment to physiognomic classes, and percent of the study region covered by each type (based on [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.ref029\" target=\"_blank\">29</a>]). Numbers after each vegetation type indicate alphabetical sort order, used in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s003\" target=\"_blank\">S3 Fig</a> See list of dominant taxa in each vegetation type in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#pone.0130629.s010\" target=\"_blank\">S1 Table</a>.</p><p><sup>1</sup>Collectively, these occupy 61% of the terrestrial area in San Francisco Bay Area.</p><p><sup>2</sup>Cool = 2.68%; Moderate = 5.61%; Warm = 18.3%; Hot = 9.33% (for ‘4G’ model, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130629#sec013\" target=\"_blank\">Methods</a>)</p><p>Vegetation types.</p>", "links"=>[], "tags"=>["climate change", "model vegetation distributions", "21 st century", "21 st century climate change", "CMIP 5 ensembles", "climate change impacts", "54 future climate scenarios", "San Francisco Bay Area"], "article_id"=>1466047, "categories"=>["Uncategorised"], "users"=>["David D. Ackerly", "William K. Cornwell", "Stuart B. Weiss", "Lorraine E. Flint", "Alan L. Flint"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130629.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Vegetation_types_/1466047", "title"=>"Vegetation types.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-26 04:09:42"}

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  • {"unique-ip"=>"18", "full-text"=>"13", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"13", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"18", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"21", "full-text"=>"35", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"13", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"12", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"21", "full-text"=>"25", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"14", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"20", "full-text"=>"19", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"20", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"19", "full-text"=>"21", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"20", "full-text"=>"22", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"17", "full-text"=>"15", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"20", "full-text"=>"20", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"20", "full-text"=>"22", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"28", "full-text"=>"36", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"13", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"51", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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