Habitat Heterogeneity Determines Climate Impact on Zooplankton Community Structure and Dynamics
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{"title"=>"Habitat heterogeneity determines climate impact on zooplankton community structure and dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Saskia A.", "last_name"=>"Otto", "scopus_author_id"=>"55308767400"}, {"first_name"=>"Rabea", "last_name"=>"Diekmann", "scopus_author_id"=>"16635524800"}, {"first_name"=>"Juha", "last_name"=>"Flinkman", "scopus_author_id"=>"6602290017"}, {"first_name"=>"Georgs", "last_name"=>"Kornilovs", "scopus_author_id"=>"6506033992"}, {"first_name"=>"Christian", "last_name"=>"Möllmann", "scopus_author_id"=>"6603955209"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"372749838", "doi"=>"10.1371/journal.pone.0090875", "sgr"=>"84897524413", "scopus"=>"2-s2.0-84897524413", "pmid"=>"24614110"}, "id"=>"064389c6-ad6d-3012-bd51-5dd3f63524fb", "abstract"=>"Understanding and predicting species distribution in space and time and consequently community structure and dynamics is an important issue in ecology, and particularly in climate change research. A crucial factor determining the composition and dynamics of animal populations is habitat heterogeneity, i.e., the number of structural elements in a given locality. In the marine pelagic environment habitat heterogeneity is represented by the distribution of physical oceanographic parameters such as temperature, salinity and oxygen that are closely linked to atmospheric conditions. Little attention has been given, however, to the role of habitat heterogeneity in modulating the response of animal communities to external climate forcing. Here we investigate the long-term dynamics of Acartia spp., Temora longicornis, and Pseudocalanus acuspes, three dominant zooplankton species inhabiting different pelagic habitats in the Central Baltic Sea (CBS). We use the three copepods as indicator species for changes in the CBS zooplankton community and apply non-linear statistical modeling techniques to compare spatial population trends and to identify their drivers. We demonstrate that effects of climate variability and change depend strongly on species-specific habitat utilization, being more direct and pronounced at the upper water layer. We propose that the differential functional response to climate-related drivers in relation to strong habitat segregation is due to alterations of the species' environmental niches. We stress the importance of understanding how anticipated climate change will affect ecological niches and habitats in order to project spatio-temporal changes in species abundance and distribution.", "link"=>"http://www.mendeley.com/research/habitat-heterogeneity-determines-climate-impact-zooplankton-community-structure-dynamics-2", "reader_count"=>46, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>12, "Student > Bachelor"=>6, "Lecturer > Senior Lecturer"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>12, "Student > Bachelor"=>6, "Lecturer > Senior Lecturer"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Environmental Science"=>16, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>20, "Physics and Astronomy"=>1, "Chemistry"=>1, "Social Sciences"=>1, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>16}}, "reader_count_by_country"=>{"Latvia"=>1, "Finland"=>1, "Brazil"=>1, "Portugal"=>1, "Germany"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1413206"], "description"=>"<p>Partial plots of significant covariates in the final spring and summer GAMs are presented for each basin separately or together depending on the significance and model performance. Values on the y-axis indicate the effect that the term on the x-axis has on the biomass anomaly. The solid lines indicate the smoothed (non-) parametric trend, shaded areas indicate the pointwise 95% CI.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods"], "article_id"=>956400, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090875.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_model_results_of_Acartia_spp_T_longicornis_and_P_acuspes_/956400", "title"=>"Statistical model results of <i>Acartia</i> spp., <i>T. longicornis</i>, and <i>P. acuspes</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-10 02:47:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1413207"], "description"=>"<p>Adjusted R<sup>2</sup>, estimated degrees of freedom (edf), significance (P-value), and individual explained deviance (excluding other significant effects) of the various covariates are provided. The covariate that explains most is indicated in bold. Note that for the <i>Acartia</i> spp. spring model the BSI and temperature smoother are presented for each basin separately but explained deviance is given for all three combined.</p><p>Covariates: BSI = Baltic Sea Index, T = Temperature, S = Salinity, PI = Predation Index.</p><p>Basins: BB = Bornholm Basin, GD = Gdansk Deep, GB = Gotland Basin.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods", "generalized", "additive", "species-specific", "responses", "predation", "hydro-climatic", "drivers"], "article_id"=>956401, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090875.t002", "stats"=>{"downloads"=>7, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_final_Generalized_Additive_Models_of_species_specific_responses_to_predation_and_hydro_climatic_drivers_in_spring_and_summer_/956401", "title"=>"Summary of final Generalized Additive Models of species-specific responses to predation and hydro-climatic drivers in spring and summer.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-10 02:47:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1413209"], "description"=>"<p>Generalized Additive Models (GAM) with a single year-smoother <i>f</i>(Year) for the entire Central Baltic Sea and basin-specific smoothers <i>f</i><sub>basin</sub>(Year) using the F-ratio tests for each species and season. The residual deviance and the residual degrees of freedom (df) are given for each model together with the reduction in deviance and the change in df's, the F-statistic and its probability value. P-values<0.05 (in bold) indicate a better performance of the more complex GAM with basin-specific smoothers.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods", "f-ratio", "comparing", "species-", "season-specific", "trends", "modeled", "basins", "basin"], "article_id"=>956402, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090875.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_F_ratio_tests_comparing_species_and_season_specific_long_term_trends_modeled_across_basins_or_for_each_basin_separately_/956402", "title"=>"Summary of the F-ratio tests comparing species- and season-specific long-term trends modeled across basins or for each basin separately.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-10 02:47:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1413213", "https://ndownloader.figshare.com/files/1413214", "https://ndownloader.figshare.com/files/1413215", "https://ndownloader.figshare.com/files/1413216", "https://ndownloader.figshare.com/files/1413217", "https://ndownloader.figshare.com/files/1413218"], "description"=>"<div><p>Understanding and predicting species distribution in space and time and consequently community structure and dynamics is an important issue in ecology, and particularly in climate change research. A crucial factor determining the composition and dynamics of animal populations is habitat heterogeneity, i.e., the number of structural elements in a given locality. In the marine pelagic environment habitat heterogeneity is represented by the distribution of physical oceanographic parameters such as temperature, salinity and oxygen that are closely linked to atmospheric conditions. Little attention has been given, however, to the role of habitat heterogeneity in modulating the response of animal communities to external climate forcing. Here we investigate the long-term dynamics of <i>Acartia</i> spp., <i>Temora longicornis</i>, and <i>Pseudocalanus acuspes</i>, three dominant zooplankton species inhabiting different pelagic habitats in the Central Baltic Sea (CBS). We use the three copepods as indicator species for changes in the CBS zooplankton community and apply non-linear statistical modeling techniques to compare spatial population trends and to identify their drivers. We demonstrate that effects of climate variability and change depend strongly on species-specific habitat utilization, being more direct and pronounced at the upper water layer. We propose that the differential functional response to climate-related drivers in relation to strong habitat segregation is due to alterations of the species’ environmental niches. We stress the importance of understanding how anticipated climate change will affect ecological niches and habitats in order to project spatio-temporal changes in species abundance and distribution.</p></div>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods", "heterogeneity", "determines", "Zooplankton"], "article_id"=>956407, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0090875.s001", "https://dx.doi.org/10.1371/journal.pone.0090875.s002", "https://dx.doi.org/10.1371/journal.pone.0090875.s003", "https://dx.doi.org/10.1371/journal.pone.0090875.s004", "https://dx.doi.org/10.1371/journal.pone.0090875.s005", "https://dx.doi.org/10.1371/journal.pone.0090875.s006"], "stats"=>{"downloads"=>11, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Habitat_Heterogeneity_Determines_Climate_Impact_on_Zooplankton_Community_Structure_and_Dynamics_/956407", "title"=>"Habitat Heterogeneity Determines Climate Impact on Zooplankton Community Structure and Dynamics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-10 02:47:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1413203"], "description"=>"<p>(A) Map of the Central Baltic Sea with its three basins (BB = Bornholm Basin, GD = Gdansk Deep, GB = Gotland Basin) including the sampling station of the different datasets. (B) Schematic vertical profile of the hydrology together with the copepod (1 = <i>Acartia</i> spp., 2 = <i>Temora longicornis</i>, 3 = <i>Pseudocalanus acuspes</i>) and predator distribution (4). (C) Horizontal profile of the hydrology in August 2001, i.e., the gradient in temperature and salinity at different water depths. The respective depth ranges were chosen based on the species most affected by these parameters in our analysis.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods"], "article_id"=>956397, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090875.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_area_/956397", "title"=>"Study area.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-10 02:47:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1413204"], "description"=>"<p>Spring and summer biomass anomalies of <i>Acartia</i> spp. (green boxes), significantly differed between the Bornholm Basin (BB), the Gdansk Deep (GD), and the Gotland Basin (GB), while trends for <i>Temora longicornis</i> (red boxes), and <i>Pseudocalanus acuspes</i> (blue boxes) were not basin-specific and rather consistent within the entire Central Baltic Sea (CBS) region. Open circles (BB), triangles (GD) or crosses (GB) represent the observed values in each basin, while the continuous lines indicate the predicted trends from the GAM based on basin-specific smoothers or a single smoother fore the entire CBS region. The shaded areas indicate the pointwise 95% CI.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "ecology", "Community Ecology", "community structure", "Niche construction", "Species interactions", "Ecological environments", "Marine environments", "Ecological metrics", "Biomass (ecology)", "Marine ecology", "Spatial and landscape ecology", "Marine biology", "Population biology", "Population Dynamics", "Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "statistics", "Biostatistics", "Statistical methods", "trends", "performing", "generalized", "additive"], "article_id"=>956398, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Saskia A. Otto", "Rabea Diekmann", "Juha Flinkman", "Georgs Kornilovs", "Christian Möllmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090875.g002", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_and_predicted_long_term_trends_based_on_the_best_performing_Generalized_Additive_Model_GAM_/956398", "title"=>"Observed and predicted long-term trends based on the best performing Generalized Additive Model (GAM).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-10 02:47:06"}

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