Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change
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{"title"=>"Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change", "type"=>"journal", "authors"=>[{"first_name"=>"Giovanni", "last_name"=>"Rapacciuolo", "scopus_author_id"=>"55200346000"}, {"first_name"=>"David B.", "last_name"=>"Roy", "scopus_author_id"=>"7402439363"}, {"first_name"=>"Simon", "last_name"=>"Gillings", "scopus_author_id"=>"6603620093"}, {"first_name"=>"Richard", "last_name"=>"Fox", "scopus_author_id"=>"14025403600"}, {"first_name"=>"Kevin", "last_name"=>"Walker", "scopus_author_id"=>"7202489069"}, {"first_name"=>"Andy", "last_name"=>"Purvis", "scopus_author_id"=>"36939286100"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365201739", "sgr"=>"84863630919", "issn"=>"19326203", "pmid"=>"22792243", "scopus"=>"2-s2.0-84863630919", "doi"=>"10.1371/journal.pone.0040212", "isbn"=>"1932-6203"}, "id"=>"7a046cb2-88b5-37ad-8d46-8f39fe7af5c0", "abstract"=>"Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records--as assessed using widespread metrics--need not indicate a model's ability to predict the future.", "link"=>"http://www.mendeley.com/research/climatic-associations-british-species-distributions-show-good-transferability-time-low-predictive-ac", "reader_count"=>110, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Researcher"=>37, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Researcher"=>37, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>35, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Environmental Science"=>26, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>72, "Medicine and Dentistry"=>1, "Social Sciences"=>1, "Computer Science"=>1, "Earth and Planetary Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>72}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>26}}, "reader_count_by_country"=>{"Argentina"=>1, "Belgium"=>1, "United States"=>1, "Brazil"=>4, "Italy"=>1, "United Kingdom"=>4, "France"=>1, "Switzerland"=>3, "Portugal"=>1, "Spain"=>3}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/319661", "https://ndownloader.figshare.com/files/319686", "https://ndownloader.figshare.com/files/319718", "https://ndownloader.figshare.com/files/319752", "https://ndownloader.figshare.com/files/319792", "https://ndownloader.figshare.com/files/319838", "https://ndownloader.figshare.com/files/319874", "https://ndownloader.figshare.com/files/319918"], "description"=>"<div><p>Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time – due to their accuracy to predict large areas retained by species – but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records – as assessed using widespread metrics – need not indicate a model’s ability to predict the future.</p> </div>", "links"=>[], "tags"=>["climatic", "associations", "british", "distributions", "transferability", "predictive"], "article_id"=>123103, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.s001", "https://dx.doi.org/10.1371/journal.pone.0040212.s002", "https://dx.doi.org/10.1371/journal.pone.0040212.s003", "https://dx.doi.org/10.1371/journal.pone.0040212.s004", "https://dx.doi.org/10.1371/journal.pone.0040212.s005", "https://dx.doi.org/10.1371/journal.pone.0040212.s006", "https://dx.doi.org/10.1371/journal.pone.0040212.s007", "https://dx.doi.org/10.1371/journal.pone.0040212.s008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Climatic_Associations_of_British_Species_Distributions_Show_Good_Transferability_in_Time_but_Low_Predictive_Accuracy_for_Range_Change/123103", "title"=>"Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-05 00:51:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/614252"], "description"=>"<p>The accuracy of forecasts generated by each modelling framework was measured by mean AUC and reported for each major taxonomic group. Error bars represent ±1 standard error of the mean. The dashed line indicates the rule-of-thumb for good predictions (AUC = 0.8). Abbreviations: ANN  =  artificial neural networks, CTA  =  classification tree analysis, GAM  =  generalised additive models, GBM  =  generalised boosted models, GLM  =  generalised linear models, MARS  =  Multivariate adaptive regression splines, MaxEnt  =  maximum entropy models, Mn(PA)  =  prediction mean from all presence-absence modelling frameworks, RF  =  random forests, SRE  =  surface range envelopes.</p>", "links"=>[], "tags"=>["ecology"], "article_id"=>284745, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_model_forecasts_/284745", "title"=>"Accuracy of model forecasts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-05 01:19:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/614311"], "description"=>"<p>Mean sensitivity and specificity of forecasts generated by each modelling framework for (A) butterflies, (B) plants, (C) birds. Error bars represent ±1 standard error of the mean. The dotted line indicates the condition where mean sensitivity  =  mean specificity.</p>", "links"=>[], "tags"=>["specificity"], "article_id"=>284806, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_sensitivity_versus_mean_specificity_of_model_forecasts_/284806", "title"=>"Mean sensitivity versus mean specificity of model forecasts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-05 01:20:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/614393"], "description"=>"<p>Accuracy of predicted changes in occupancy between t<sub>1</sub> and t<sub>2</sub> as a function of species’ observed proportional range change between t<sub>1</sub> and t<sub>2</sub>: (A) histogram of the frequency of species’ proportional range change values; (B) correct classification rate across stable grid squares (i.e., those that have remained either occupied or unoccupied between time periods; CCR<sub>stable</sub>) as a function of observed proportional range change, overall and for each modelling technique; (C) correct classification rate across changed grid squares (i.e., those that have changed occupancy status between time periods; CCR<sub>changed</sub>) as a function of observed proportional range change, overall and for each modelling technique. Functions were fitted using generalised additive models (GAM; using a cubic spline smoother with 4 degrees of freedom). This analysis was limited to species experiencing a proportional change between −100% and +100% (i.e., 85% of all species), due to the very high influence of the few species whose range more than doubled. The dashed line in panels (B) and (C) represents the value of CCR expected from a random guess (i.e., CCR  = 0.5).</p>", "links"=>[], "tags"=>["forecasted", "changes"], "article_id"=>284893, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_forecasted_changes_in_species_occupancy_/284893", "title"=>"Accuracy of forecasted changes in species occupancy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-05 01:21:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/614456"], "description"=>"<p>Dates and sources of the distribution records used.</p>", "links"=>[], "tags"=>["sources", "records"], "article_id"=>284951, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dates_and_sources_of_the_distribution_records_used_/284951", "title"=>"Dates and sources of the distribution records used.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-05 01:22:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/614484"], "description"=>"<p>Reported values are the Spearman’s ρ coefficients of observed versus predicted range size in t<sub>2</sub> (range size column) and observed versus predicted change in range size between time periods (range change column) for each modelling framework and major taxonomic group modelled. Stars indicate the significance level of correlations: <sup>*</sup>  =  p<0.05; <sup>**</sup>  =  p<0.01; <sup>***</sup>  =  p<0.001.</p>", "links"=>[], "tags"=>["coefficients", "observed"], "article_id"=>284974, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_coefficients_of_observed_versus_predicted_range_size_and_range_change_for_model_forecasts_/284974", "title"=>"Correlation coefficients of observed versus predicted range size and range change for model forecasts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-05 01:22:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/614507"], "description"=>"<p>Prediction accuracy was measured for each species by AUC, sensitivity, and specificity of the entire range in t<sub>2</sub>, as well as the correct classification rate of grid squares that have remained occupied or unoccupied (CCR<sub>stable</sub>) and the correct classification rate of grid squares that have changed occupancy status between time periods (CCR<sub>changed</sub>). Values represent the total number (and proportion of the total sample) of species for which each technique performed best. Proportions may exceed 100% of the sample as several species were equally well-predicted by more than one technique.</p>", "links"=>[], "tags"=>["generated"], "article_id"=>285005, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_species_for_which_each_modelling_framework_generated_the_most_accurate_forecasts_/285005", "title"=>"Number of species for which each modelling framework generated the most accurate forecasts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-05 01:23:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/614539"], "description"=>"<p>The values reported are the results of a variance components analysis of the linear mixed-effects (LME) models investigating the factors affecting the accuracy of forecasts. AUC, sensitivity, specificity of the entire range, as well as the correct classification rate of grid squares that have remained occupied or unoccupied (CCR<sub>stable</sub>), and the correct classification rate of grid squares that have changed occupancy status between time periods (CCR<sub>changed</sub>) were modelled as a function of the following random effects: modelling framework (n = 10), major taxonomic group (n = 3) and species (n = 1823). The ratio between the variance explained by each random effect and null variance (expressed as a percentage) is reported for each random effect in each model.</p>", "links"=>[], "tags"=>["taxonomic", "methodological"], "article_id"=>285035, "categories"=>["Ecology"], "users"=>["Giovanni Rapacciuolo", "David B. Roy", "Simon Gillings", "Richard Fox", "Kevin Walker", "Andy Purvis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040212.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_effect_of_taxonomic_and_methodological_variation_on_accuracy_of_forecasts_/285035", "title"=>"Relative effect of taxonomic and methodological variation on accuracy of forecasts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-05 01:23:55"}

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Relative Metric

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