Biotic Interactions in the Face of Climate Change: A Comparison of Three Modelling Approaches
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{"title"=>"Biotic Interactions in the Face of Climate Change: A Comparison of Three Modelling Approaches", "type"=>"journal", "authors"=>[{"first_name"=>"Anja", "last_name"=>"Jaeschke", "scopus_author_id"=>"36621052700"}, {"first_name"=>"Torsten", "last_name"=>"Bittner", "scopus_author_id"=>"36620782600"}, {"first_name"=>"Anke", "last_name"=>"Jentsch", "scopus_author_id"=>"6602883636"}, {"first_name"=>"Björn", "last_name"=>"Reineking", "scopus_author_id"=>"14042169400"}, {"first_name"=>"Helmut", "last_name"=>"Schlumprecht", "scopus_author_id"=>"6507221896"}, {"first_name"=>"Carl", "last_name"=>"Beierkuhnlein", "scopus_author_id"=>"6603051853"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"23236505", "doi"=>"10.1371/journal.pone.0051472", "sgr"=>"84870818617", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84870818617", "issn"=>"19326203", "pui"=>"366234582"}, "id"=>"8436f777-0fce-31fa-9a56-1180da2f40b4", "abstract"=>"Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between Aeshna viridis and its egg-laying plant Stratiotes aloides in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1) We separately modelled each species based on climatic information, and intersected the future range overlap ('overlap approach'). (2) We modelled the potential future distribution of A. viridis with the projected occurrence probability of S. aloides as further predictor in addition to climate ('explanatory variable approach'). (3) We calibrated the model of A. viridis in the current range of S. aloides and multiplied the future occurrence probabilities of both species ('reference area approach'). Subsequently, all approaches were compared to a single species model of A. viridis without interactions. All approaches projected a range expansion for A. viridis. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower) than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of the 'reference area approach' as this method allows a separation of the effect of climate and occurrence of host plant.", "link"=>"http://www.mendeley.com/research/biotic-interactions-face-climate-change-comparison-three-modelling-approaches-2", "reader_count"=>71, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>5, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>5, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Environmental Science"=>19, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>47, "Social Sciences"=>1, "Earth and Planetary Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Social Sciences"=>{"Social Sciences"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>47}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>19}}, "reader_count_by_country"=>{"Canada"=>1, "Sweden"=>1, "Czech Republic"=>1, "United States"=>1, "Tanzania"=>1, "Brazil"=>1, "South Africa"=>1, "Mexico"=>1, "Germany"=>1, "Spain"=>2, "India"=>1, "Costa Rica"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/530337"], "description"=>"<p>A) ‘Overlap approach’: modelling both species separately and intersecting the future range overlap. B) ‘Explanatory variable approach’: modelling the dependent species with the essential species as additional explanatory variable. C) ‘Reference area approach’: modelling the dependent species in the range of the essential species and multiplication of the occurrence probabilities of both species. Species 1: dependent species (here: <i>Aeshna viridis</i>), Species 2: essential species (here: <i>Stratiotes aloides</i>), Climate [Sp2]: restriction of climatic reference area of species 1 to the current distribution of species 2.</p>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "applied", "approaches", "biotic"], "article_id"=>200805, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0051472.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conceptual_framework_of_the_three_applied_approaches_for_modelling_biotic_interactions_/200805", "title"=>"Conceptual framework of the three applied approaches for modelling biotic interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-06 00:13:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/530229"], "description"=>"<p>Current distribution of A) <i>Aeshna viridis</i> and B) <i>Stratiotes aloides</i> in Europe <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051472#pone.0051472-EIONET1\" target=\"_blank\">[21]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051472#pone.0051472-Hultn1\" target=\"_blank\">[22]</a>.</p>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "europe"], "article_id"=>200694, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0051472.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Current_distribution_of_A_Aeshna_viridis_and_B_Stratiotes_aloides_in_Europe_21_22_/200694", "title"=>"Current distribution of A) <i>Aeshna viridis</i> and B) <i>Stratiotes aloides</i> in Europe [21], [22].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-06 00:11:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/530651"], "description"=>"<p>As the ‘overlap approach’ represents the intersection of both species’ projected occurrences the AUC, threshold values, omission and commission rates of the single species modelling without interaction are shown.</p><p>The occurrence threshold is equivalent to the prevalence of the model-building data.</p>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "occurrence", "thresholds", "applied"], "article_id"=>201129, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0051472.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_performance_and_occurrence_thresholds_of_the_applied_approaches_/201129", "title"=>"Model performance and occurrence thresholds of the applied approaches.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-06 00:18:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/530435"], "description"=>"<p>A) <i>A. viridis</i> without interaction, occurrence threshold: 0.02, AUC: 0.93. B) Overlapping area of the potential future distributions of <i>A. viridis</i> and <i>S. aloides</i>, occurrence threshold: 0.02 (<i>A. viridis</i>), 0.35 (<i>S. aloides</i>). AUC: 0.93 (<i>A. viridis</i>), 0.94 (<i>S. aloides</i>). C) Considering the modelled occurrence probability of <i>S. aloides</i> in Europe as additional explanatory variable beside climate. Occurrence threshold: 0.02. AUC: 0.92; D) Potential future distribution of <i>A. viridis</i> in Europe applying the ‘reference area approach’. The model for <i>A. viridis</i> was calibrated within the distribution area of <i>S. aloides</i>. The modelled future occurrence probabilities of both species were multiplied. Occurrence threshold: 0.05. AUC: 0.88. All modelling results are based on an ensemble modelling with nine model algorithms with the climate model HadCM3 and the emission scenario A2 for the time period 2021–50.</p>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "distributions", "europe", "assuming", "unlimited"], "article_id"=>200905, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0051472.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_potential_future_distributions_of_Aeshna_viridis_in_Europe_assuming_unlimited_dispersal_/200905", "title"=>"Projected potential future distributions of <i>Aeshna viridis</i> in Europe assuming unlimited dispersal.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-06 00:15:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/530560"], "description"=>"<p>Losses and gains are shown for the three applied biotic interaction approaches depending on the two most range-influencing climatic variables (out of six variables; variable importance measured by BIOMOD). Climate model: HadCM3, emission scenario: A2, time period: 2021–50. The vegetation period ranges from March until September.</p>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "losses", "gains"], "article_id"=>201038, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0051472.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Projected_future_losses_and_gains_of_the_current_distribution_of_Aeshna_viridis_in_Europe_/201038", "title"=>"Projected future losses and gains of the current distribution of <i>Aeshna viridis</i> in Europe.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-06 00:17:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/287129", "https://ndownloader.figshare.com/files/287186", "https://ndownloader.figshare.com/files/287323"], "description"=>"<div><p>Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between <em>Aeshna viridis</em> and its egg-laying plant <em>Stratiotes aloides</em> in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1) We separately modelled each species based on climatic information, and intersected the future range overlap (‘overlap approach’). (2) We modelled the potential future distribution of <em>A. viridis</em> with the projected occurrence probability of <em>S. aloides</em> as further predictor in addition to climate (‘explanatory variable approach’). (3) We calibrated the model of <em>A. viridis</em> in the current range of <em>S. aloides</em> and multiplied the future occurrence probabilities of both species (‘reference area approach’). Subsequently, all approaches were compared to a single species model of <em>A. viridis</em> without interactions. All approaches projected a range expansion for <em>A. viridis</em>. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower) than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of the ‘reference area approach’ as this method allows a separation of the effect of climate and occurrence of host plant.</p> </div>", "links"=>[], "tags"=>["ecology", "Community Ecology", "Species interactions", "biodiversity", "biogeography", "Conservation science", "Global change ecology", "Macroecology", "Zoology", "Entomology", "Atmospheric science", "Climatology", "climate change", "Climate modeling", "biotic", "interactions", "approaches"], "article_id"=>116581, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Anja Jaeschke", "Torsten Bittner", "Anke Jentsch", "Björn Reineking", "Helmut Schlumprecht", "Carl Beierkuhnlein"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0051472.s001", "https://dx.doi.org/10.1371/journal.pone.0051472.s002", "https://dx.doi.org/10.1371/journal.pone.0051472.s003"], "stats"=>{"downloads"=>7, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Biotic_Interactions_in_the_Face_of_Climate_Change_A_Comparison_of_Three_Modelling_Approaches__/116581", "title"=>"Biotic Interactions in the Face of Climate Change: A Comparison of Three Modelling Approaches", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-12-06 01:49:41"}

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Relative Metric

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