Effectiveness of Biological Surrogates for Predicting Patterns of Marine Biodiversity: A Global Meta-Analysis
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{"title"=>"Effectiveness of biological surrogates for predicting patterns of marine biodiversity: A global meta-analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Camille", "last_name"=>"Mellin", "scopus_author_id"=>"24598228500"}, {"first_name"=>"Steve", "last_name"=>"Delean", "scopus_author_id"=>"6602210687"}, {"first_name"=>"Julian", "last_name"=>"Caley", "scopus_author_id"=>"42461039300"}, {"first_name"=>"Graham", "last_name"=>"Edgar", "scopus_author_id"=>"7005803258"}, {"first_name"=>"Mark", "last_name"=>"Meekan", "scopus_author_id"=>"6701849401"}, {"first_name"=>"Roland", "last_name"=>"Pitcher", "scopus_author_id"=>"7006475680"}, {"first_name"=>"Rachel", "last_name"=>"Przeslawski", "scopus_author_id"=>"8927320900"}, {"first_name"=>"Alan", "last_name"=>"Williams", "scopus_author_id"=>"8274985300"}, {"first_name"=>"Corey", "last_name"=>"Bradshaw", "scopus_author_id"=>"7102130752"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203", "pmid"=>"21695119", "scopus"=>"2-s2.0-79958798535", "doi"=>"10.1371/journal.pone.0020141", "sgr"=>"79958798535", "pui"=>"361952762"}, "id"=>"34a8062c-8e26-34e8-8c1e-1af44ea40150", "abstract"=>"The use of biological surrogates as proxies for biodiversity patterns is gaining popularity, particularly in marine systems where field surveys can be expensive and species richness high. Yet, uncertainty regarding their applicability remains because of inconsistency of definitions, a lack of standard methods for estimating effectiveness, and variable spatial scales considered. We present a Bayesian meta-analysis of the effectiveness of biological surrogates in marine ecosystems. Surrogate effectiveness was defined both as the proportion of surrogacy tests where predictions based on surrogates were better than random (i.e., low probability of making a Type I error; P) and as the predictability of targets using surrogates (R(2)). A total of 264 published surrogacy tests combined with prior probabilities elicited from eight international experts demonstrated that the habitat, spatial scale, type of surrogate and statistical method used all influenced surrogate effectiveness, at least according to either P or R(2). The type of surrogate used (higher-taxa, cross-taxa or subset taxa) was the best predictor of P, with the higher-taxa surrogates outperforming all others. The marine habitat was the best predictor of R(2), with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a <10-km spatial scale, in low-complexity marine habitats such as soft bottoms, and using multivariate-based methods. Comparisons with terrestrial studies in terms of the methods used to study surrogates revealed that marine applications still ignore some problems with several widely used statistical approaches to surrogacy. Our study provides a benchmark for the reliable use of biological surrogates in marine ecosystems, and highlights directions for future development of biological surrogates in predicting biodiversity.", "link"=>"http://www.mendeley.com/research/effectiveness-biological-surrogates-predicting-patterns-marine-biodiversity-global-metaanalysis", "reader_count"=>153, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>48, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>8, "Student > Master"=>26, "Other"=>7, "Student > Bachelor"=>10, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>48, "Student > Doctoral Student"=>3, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/766186"], "description"=>"<p>Prior distribution (circles), posterior distribution given an uninformative prior (analogous to the likelihood; squares) and posterior distribution given an informative prior (diamonds). Error bars depict the standard deviation of the prior or posterior. Asterisks indicate the best model according to the deviance information criterion. Factors include the marine habitat (<i>Habitat</i>), spatial scale (<i>Scale</i>), the statistical method used to assess surrogate performance (<i>Method</i>) and the type of surrogate (<i>Type</i>).</p>", "links"=>[], "tags"=>["defined", "predictability", "targets"], "article_id"=>436544, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Surrogate_effectiveness_defined_by_R_2_the_predictability_of_targets_using_surrogates_/436544", "title"=>"Surrogate effectiveness defined by <i>R<sup>2</sup></i>, the predictability of targets using surrogates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:49:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/766035"], "description"=>"<p>(A) Higher-taxa, where a taxon (or taxa) at a higher taxonomic level acts as surrogates for taxa at lower levels, (B) cross-taxa surrogates, where a taxon (or taxa) acts as a surrogate for another taxon (or taxa) at the same taxonomic level, and (C) subset-taxa surrogates, where a particular taxon (or taxa) acts as a surrogate for the entire target community. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020141#pone.0020141.s002\" target=\"_blank\">Table S2</a> for referenced examples of each type of biological surrogate.</p>", "links"=>[], "tags"=>["types", "surrogates", "targets"], "article_id"=>436396, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_different_types_of_biological_surrogates_red_and_their_targets_green_/436396", "title"=>"The different types of biological surrogates (red) and their targets (green).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/766556"], "description"=>"<p>Methods used are grouped into three categories: congruence of univariate biodiversity metrics assesses whether surrogate biodiversity is spatially correlated with target biodiversity; congruence of multivariate biodiversity metrics evaluates whether pairs of sites showing the highest similarity in surrogate assemblages also show the highest similarity in target assemblages, and; representation uses site-selection algorithms to assess whether a network of areas selected to maximize the number of surrogate taxa also maximises the number of target taxa and whether this number is greater than expected by chance. Test ID refers to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020141#pone.0020141.s002\" target=\"_blank\">Table S2</a>.</p>", "links"=>[], "tags"=>["methods", "biodiversity", "metrics", "surrogacy"], "article_id"=>436922, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_methods_and_biodiversity_metrics_used_in_marine_biological_surrogacy_studies_/436922", "title"=>"Statistical methods and biodiversity metrics used in marine biological surrogacy studies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:55:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/766512"], "description"=>"<p>With <i>sd</i> = standard deviation, <i>n</i> = number of tests, <i>n</i><sub>tot</sub> = total number of tests.</p>", "links"=>[], "tags"=>["surrogate", "predictive", "taxonomic", "steps"], "article_id"=>436872, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.t003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Higher_taxa_surrogate_predictive_power_R_2_as_a_function_of_the_number_of_taxonomic_steps_between_the_surrogate_and_the_target_/436872", "title"=>"Higher-taxa surrogate predictive power (<i>R</i><sup>2</sup>) as a function of the number of taxonomic steps between the surrogate and the target.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:54:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/766467"], "description"=>"<p>The diagonal indicates the total number of tests in each factor level including, for <i>Habitat</i>, TR: tropical reefs, TE: temperate reefs, SO: soft bottoms; for <i>Type</i>, CT: cross-taxa surrogate, HT: higher-taxa surrogate, ST: subset-taxa surrogate; for <i>Scale</i>, L: >100 km, M: 10–100 km, S: <10 km; and for <i>Method</i>, UC: univariate congruence, MC: multivariate congruence, RP: representation.</p>", "links"=>[], "tags"=>["ecology"], "article_id"=>436835, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_tabulations_of_the_number_of_tests_for_each_combination_of_factor_levels_/436835", "title"=>"Cross-tabulations of the number of tests for each combination of factor levels.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:53:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/766346"], "description"=>"<p>Posterior distributions of <i>R</i><sup>2</sup> (i.e. the surrogate predictive power) are given according to the marine habitat (<i>Habitat</i>), spatial scale (<i>Scale</i>), the statistical method used to assess surrogate performance (<i>Method</i>) and the type of surrogate (<i>Type</i>). Asterisks indicate models outperforming the null model. A Gaussian distribution with the mean and standard deviation of the posterior distribution was used to approximate posterior distributions.</p>", "links"=>[], "tags"=>["distributions", "informative", "surrogate", "defined"], "article_id"=>436706, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.g005", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_distributions_given_an_informative_prior_of_surrogate_effectiveness_defined_as_R_2_/436706", "title"=>"Posterior distributions (given an informative prior) of surrogate effectiveness defined as <i>R</i><sup>2</sup>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:51:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/766431"], "description"=>"<p>Factors include the type of surrogate (type), the statistical method used to assess surrogate performance (method), the marine habitat (habitat), the spatial scale (scale) and the sample size (n). Models are ranked by increasing DIC.</p>", "links"=>[], "tags"=>["criterion", "models", "surrogate", "defined", "concluding", "predictions", "non-random", "predictive"], "article_id"=>436782, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.t004", "stats"=>{"downloads"=>6, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deviance_information_criterion_DIC_for_models_of_surrogate_effectiveness_defined_as_the_proportion_of_tests_P_concluding_that_surrogate_predictions_are_non_random_and_as_the_surrogate_predictive_power_R_2_/436782", "title"=>"Deviance information criterion (DIC) for models of surrogate effectiveness defined as the proportion of tests (P) concluding that surrogate predictions are non-random and as the surrogate predictive power (R<sup>2</sup>).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-14 01:53:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/766262"], "description"=>"<p>Posterior distributions of <i>P</i> (i.e. the proportion of tests concluding that surrogate predictions are non-random) are given according to the marine habitat (<i>Habitat</i>), spatial scale (<i>Scale</i>), the statistical method used to assess surrogate performance (<i>Method</i>) and the type of surrogate (<i>Type</i>). Asterisks indicate models outperforming the null model. A Gaussian distribution with the mean and standard deviation of the posterior distribution was used to approximate posterior distributions.</p>", "links"=>[], "tags"=>["distributions", "informative", "surrogate", "defined"], "article_id"=>436619, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Posterior_distributions_given_an_informative_prior_of_surrogate_effectiveness_defined_as_P_/436619", "title"=>"Posterior distributions (given an informative prior) of surrogate effectiveness defined as <i>P</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:50:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/385613", "https://ndownloader.figshare.com/files/385682", "https://ndownloader.figshare.com/files/385794", "https://ndownloader.figshare.com/files/385841"], "description"=>"<div><p>The use of biological surrogates as proxies for biodiversity patterns is gaining popularity, particularly in marine systems where field surveys can be expensive and species richness high. Yet, uncertainty regarding their applicability remains because of inconsistency of definitions, a lack of standard methods for estimating effectiveness, and variable spatial scales considered. We present a Bayesian meta-analysis of the effectiveness of biological surrogates in marine ecosystems. Surrogate effectiveness was defined both as the proportion of surrogacy tests where predictions based on surrogates were better than random (i.e., low probability of making a Type I error; <em>P</em>) and as the predictability of targets using surrogates (<em>R</em><sup>2</sup>). A total of 264 published surrogacy tests combined with prior probabilities elicited from eight international experts demonstrated that the habitat, spatial scale, type of surrogate and statistical method used all influenced surrogate effectiveness, at least according to either <em>P</em> or <em>R</em><sup>2</sup>. The type of surrogate used (higher-taxa, cross-taxa or subset taxa) was the best predictor of <em>P</em>, with the higher-taxa surrogates outperforming all others. The marine habitat was the best predictor of <em>R</em><sup>2</sup>, with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a <10-km spatial scale, in low-complexity marine habitats such as soft bottoms, and using multivariate-based methods. Comparisons with terrestrial studies in terms of the methods used to study surrogates revealed that marine applications still ignore some problems with several widely used statistical approaches to surrogacy. Our study provides a benchmark for the reliable use of biological surrogates in marine ecosystems, and highlights directions for future development of biological surrogates in predicting biodiversity.</p> </div>", "links"=>[], "tags"=>["surrogates", "predicting", "patterns", "meta-analysis"], "article_id"=>136130, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0020141.s001", "https://dx.doi.org/10.1371/journal.pone.0020141.s002", "https://dx.doi.org/10.1371/journal.pone.0020141.s003", "https://dx.doi.org/10.1371/journal.pone.0020141.s004"], "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effectiveness_of_Biological_Surrogates_for_Predicting_Patterns_of_Marine_Biodiversity_A_Global_Meta_Analysis/136130", "title"=>"Effectiveness of Biological Surrogates for Predicting Patterns of Marine Biodiversity: A Global Meta-Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-06-14 01:42:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/766104"], "description"=>"<p>Prior distribution (circles), posterior distribution given an uninformative prior (analogous to the likelihood; squares), and posterior distribution given an informative prior (diamonds). Error bars depict the standard deviation of the prior or posterior. Asterisks indicate the best model according to the deviance information criterion. Factors include the marine habitat (<i>Habitat</i>), spatial scale (<i>Scale</i>), the statistical method used to assess surrogate performance (<i>Method</i>) and the type of surrogate (<i>Type</i>).</p>", "links"=>[], "tags"=>["defined", "concluding", "surrogate", "predictions"], "article_id"=>436470, "categories"=>["Ecology"], "users"=>["Camille Mellin", "Steve Delean", "Julian Caley", "Graham Edgar", "Mark Meekan", "Roland Pitcher", "Rachel Przeslawski", "Alan Williams", "Corey Bradshaw"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0020141.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Surrogate_effectiveness_defined_by_P_the_proportion_of_tests_concluding_that_surrogate_predictions_were_non_random_/436470", "title"=>"Surrogate effectiveness defined by <i>P</i>, the proportion of tests concluding that surrogate predictions were non-random.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-14 01:47:50"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"10"}
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  • {"unique-ip"=>"8", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2014", "month"=>"11"}
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  • {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
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  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"16", "full-text"=>"19", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"6", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"12", "full-text"=>"13", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"2"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
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  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"11", "full-text"=>"5", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"14", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"2", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"10", "full-text"=>"7", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"10", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"15", "full-text"=>"10", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"9", "full-text"=>"3", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"18", "full-text"=>"19", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"16", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"31", "full-text"=>"25", "pdf"=>"11", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"11", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"7", "full-text"=>"3", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"7", "full-text"=>"4", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}

Relative Metric

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