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I have a formal background in oceanography and employ that to study the effects of climate fluctuations on fish populations and marine ecology more generally. I am presently a lead author of the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) and work package leader of the large EC H2020 project INTAROS, Integrated Arctic Observation System.<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="4500242">View All (11)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="4500242" href="https://www.academia.edu/Documents/in/Fisheries"><div id="js-react-on-rails-context" style="display:none" 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data-cv-filename="CV_Ottersen_February_2016.pdf" data-placement="top" data-toggle="tooltip" href="/GeirOttersen/CurriculumVitae"><button class="ds2-5-text-link ds2-5-text-link--small" style="font-size: 20px; letter-spacing: 0.8px"><span class="ds2-5-text-link__content">CV</span></button></li><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Geir Ottersen</h3></div><div class="js-work-strip profile--work_container" data-work-id="32920612"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920612/Adaptation_of_Express_to_the_Ibm_PC_A_Tool_for_Building_Knowledge_Based_Statistical_Systems_Using_Existing_Packages"><img alt="Research paper thumbnail of Adaptation of 'Express' to the Ibm PC: A Tool for Building Knowledge-Based Statistical Systems Using Existing Packages" class="work-thumbnail" src="https://attachments.academia-assets.com/53055317/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920612/Adaptation_of_Express_to_the_Ibm_PC_A_Tool_for_Building_Knowledge_Based_Statistical_Systems_Using_Existing_Packages">Adaptation of 'Express' to the Ibm PC: A Tool for Building Knowledge-Based Statistical Systems Using Existing Packages</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The program system EXPRESS constitutes a tool for constructing knowledge-based statistical system...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The program system EXPRESS constitutes a tool for constructing knowledge-based statistical systems requiring repeated cycles of statistical analysis on given data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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fish populations facilitated through classification of mechanisms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920611/Inter_regional_comparison_of_climate_effects_on_m_arine_fish_populations_facilitated_through_classification_of_mechanisms">Inter-regional comparison of climate effects on m arine fish populations facilitated through classification of mechanisms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Variations in climate strongly affect the structure and function of m arine ecosystems, but a num...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Variations in climate strongly affect the structure and function of m arine ecosystems, but a number of different mechanisms are at play and their relative importance varies between regions and with time. There are obvious semi-permanent regional differences in how marine populations respond to climate, but there may also be long-term trends either in climate itself or in the response pattern. In some cases single strong climate events may shift an ecosystem from one state to another (e.g., El Niño). To facilitate comparison between different large marine ecosystems we here give an overview of some of the manners in which one can classify how marine fish populations are affected by climate. Responses to climate fluctuations may be bottom-up, top-down or middle-out, immediate or temporally delayed, direct or via an intermediate population of predators, prey or competitors. Climate may invoke a linear or non-linear effect at the population or community level. Ecological effects of the NAO have been classified according to the four major classes: direct effects, indirect effects, integrated effects and translations, which also may be applied to other climate patterns and regions. By using classification schemes a more precise description of the particular properties of the various ecosystems may be possible. This approach enhances the possibility to compare between regions that may differ not only with regards to the relative importance of different climate factors for ecology, but also through dissimilarities in scientific tradition and terminology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920611"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920611"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920611; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920611]").text(description); $(".js-view-count[data-work-id=32920611]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920611; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920611']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920611, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920611]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920611,"title":"Inter-regional comparison of climate effects on m arine fish populations facilitated through classification of mechanisms","translated_title":"","metadata":{"abstract":"Variations in climate strongly affect the structure and function of m arine ecosystems, but a number of different mechanisms are at play and their relative importance varies between regions and with time. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920609/Emergent_properties_of_complex_marine_systems_a_macroecological_perspective"><img alt="Research paper thumbnail of Emergent properties of complex marine systems: a macroecological perspective" class="work-thumbnail" src="https://attachments.academia-assets.com/53055314/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920609/Emergent_properties_of_complex_marine_systems_a_macroecological_perspective">Emergent properties of complex marine systems: a macroecological perspective</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A macroecological view of the ecosystem offers the possibility to integrate information at large ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A macroecological view of the ecosystem offers the possibility to integrate information at large spatial and temporal scales over a variety of complex ecological systems. Marine macroecology can be regarded as a new research agenda aiming to develop new models which can explain the emergent structures and dynamics of complex ecological systems in terms of basic physical and biological principles (Brown 1999). Macroecology theory as a way to describe the emergent properties in terrestrial systems has received relatively little attention in marine ecology. This Theme Section is a collection of articles that will discuss the importance of macroecological and complexity theory, in a very broad context, to untangling patterns that underlie the relationships between species abundance and other biotic and abiotic factors linking organismal biology, population dynamics, community ecology, food web structure, biodiversity, and behavioral ecology, to ecosystem structure and function. This macroecological view of different processes underlying the dynamics of marine ecosystems extends the general theory of macroecology and allometric scaling, developed mainly for terrestrial systems (Brown 1995, West et al. 1997, to a marine context as recently proposed by Li (2002). Ultimately we need to understand by first principles, from organism organization to ecosystem organization (Reynolds 2001), the basic common ecological rules that generate the variability and patterns that we observe across scales. LITERATURE CITED Brown JH (1995) Macroecology. Chicago University Press, Chicago Brown JH (1999) Macroecology: progress and prospect. Oikos</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b4a9d9a7f11ae12eedaf4f733017abe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055314,"asset_id":32920609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055314/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920609]").text(description); $(".js-view-count[data-work-id=32920609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920609, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0b4a9d9a7f11ae12eedaf4f733017abe" } } $('.js-work-strip[data-work-id=32920609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920609,"title":"Emergent properties of complex marine systems: a macroecological perspective","translated_title":"","metadata":{"ai_title_tag":"Understanding Emergent Marine Ecosystem Properties through Macroecology","grobid_abstract":"A macroecological view of the ecosystem offers the possibility to integrate information at large spatial and temporal scales over a variety of complex ecological systems. 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This macroecological view of different processes underlying the dynamics of marine ecosystems extends the general theory of macroecology and allometric scaling, developed mainly for terrestrial systems (Brown 1995, West et al. 1997, to a marine context as recently proposed by Li (2002). Ultimately we need to understand by first principles, from organism organization to ecosystem organization (Reynolds 2001), the basic common ecological rules that generate the variability and patterns that we observe across scales. LITERATURE CITED Brown JH (1995) Macroecology. Chicago University Press, Chicago Brown JH (1999) Macroecology: progress and prospect. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920607"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920607/Predictability_of_Barents_Sea_temperature"><img alt="Research paper thumbnail of Predictability of Barents Sea temperature" class="work-thumbnail" src="https://attachments.academia-assets.com/53055348/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920607/Predictability_of_Barents_Sea_temperature">Predictability of Barents Sea temperature</a></div><div class="wp-workCard_item"><span>Fisheries Oceanography</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the North Atlantic has increased during the last decade. To allow this information to be used in ®sheries management, some forecast of the environment is important. Predictions of temperature in the Arcto-boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as in¯uencing the temperature of the Barents Sea. These include components of suggested periodic nature, large-scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto-and cross-correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic¯uctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A ®rstorder autoregressive model, including modelled atmospherically driven volume¯ux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8602883521b662d52b2ae6c963bb3610" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055348,"asset_id":32920607,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055348/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920607"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920607"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920607; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920607]").text(description); $(".js-view-count[data-work-id=32920607]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920607; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920607']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920607, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8602883521b662d52b2ae6c963bb3610" } } $('.js-work-strip[data-work-id=32920607]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920607,"title":"Predictability of Barents Sea temperature","translated_title":"","metadata":{"ai_title_tag":"Forecasting Barents Sea Temperature Influences and Predictability","grobid_abstract":"Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the North Atlantic has increased during the last decade. 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The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. 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To allow this information to be used in ®sheries management, some forecast of the environment is important. Predictions of temperature in the Arcto-boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as in¯uencing the temperature of the Barents Sea. These include components of suggested periodic nature, large-scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto-and cross-correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic¯uctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A ®rstorder autoregressive model, including modelled atmospherically driven volume¯ux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[{"id":53055348,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055348/thumbnails/1.jpg","file_name":"Predictability_of_Barents_Sea_temperatur20170509-20550-1au5plj.pdf","download_url":"https://www.academia.edu/attachments/53055348/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predictability_of_Barents_Sea_temperatur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055348/Predictability_of_Barents_Sea_temperatur20170509-20550-1au5plj-libre.pdf?1494329530=\u0026response-content-disposition=attachment%3B+filename%3DPredictability_of_Barents_Sea_temperatur.pdf\u0026Expires=1734214762\u0026Signature=d8GfHvZnjyFje5k6q~N4NnXtjcJNlL6UfnTzMVzZrRhFHs8qoE4nO9lLYAPHOJHzUcBhkz2BjM~Mntsgd7qpwBoKm-6Ms1qieBOBjvwKtXhHcUc1dCDLIWfWwHUm5A05p29uxNaWjTe113bqEdTsy~xt-IowSMWDEp25btyTJ-uNqV0Q~G91lU39qTetrHGJ7e8M6hzRLw92LlgaKRMlK4KBuyKQDONTje9sbf5AM-rQ6tBVoeAc5WrM3tmRsmv86V1AmiGmQGJxCQIf27z3g0JiCNuvifAEUTjvH3~0nmEo4p5uhnLT9wKrSd~FO5Hunk9KN1aYx1wpDfclMSPCcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"},{"id":100087,"name":"Fisheries Oceanography","url":"https://www.academia.edu/Documents/in/Fisheries_Oceanography"},{"id":170652,"name":"Fisheries Sciences","url":"https://www.academia.edu/Documents/in/Fisheries_Sciences"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920606"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920606/The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation"><img alt="Research paper thumbnail of The response of terrestrial ecosystems to climate variability associated with the North Atlantic Oscillation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920606/The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation">The response of terrestrial ecosystems to climate variability associated with the North Atlantic Oscillation</a></div><div class="wp-workCard_item"><span>Geophysical Monograph Series</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920606"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920606"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920606; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920606]").text(description); $(".js-view-count[data-work-id=32920606]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920606; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920606']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920606, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920606]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920606,"title":"The response of terrestrial ecosystems to climate variability associated with the North Atlantic Oscillation","translated_title":"","metadata":{"abstract":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Geophysical Monograph Series"},"translated_abstract":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","internal_url":"https://www.academia.edu/32920606/The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation","translated_internal_url":"","created_at":"2017-05-09T04:26:41.792-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":44265,"name":"North Atlantic Oscillation","url":"https://www.academia.edu/Documents/in/North_Atlantic_Oscillation"},{"id":54961,"name":"Growth","url":"https://www.academia.edu/Documents/in/Growth"},{"id":373754,"name":"Ecosystem","url":"https://www.academia.edu/Documents/in/Ecosystem"},{"id":411513,"name":"Geophysical","url":"https://www.academia.edu/Documents/in/Geophysical"},{"id":630543,"name":"Tree Ring","url":"https://www.academia.edu/Documents/in/Tree_Ring"},{"id":779767,"name":"Nonlinearity","url":"https://www.academia.edu/Documents/in/Nonlinearity"},{"id":908385,"name":"Long Term","url":"https://www.academia.edu/Documents/in/Long_Term"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="21375120"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/21375120/Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea"><img alt="Research paper thumbnail of Spatial patterns of age-0 cod survival in the Barents Sea" class="work-thumbnail" src="https://attachments.academia-assets.com/41840506/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/21375120/Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea">Spatial patterns of age-0 cod survival in the Barents Sea</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uib.academia.edu/GjertDings%C3%B8r">Gjert Dingsør</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/HaraldGj%C3%B8s%C3%A6ter">Harald Gjøsæter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://havforskningsinstituttet.academia.edu/GeirOttersen">Geir Ottersen</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of po...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of population variability of marine fish stocks. Factors affecting recruitment dynamics can change over both temporal and spatial scales. It follows that at the same time, over the entire range of distribution of a single population, different individuals may experience different level of environmental forcing and survival, which may not be well represented by average conditions throughout the entire distribution range. In this study we focus on the spatial pattern and its relative sources of variability in the survival of the Arcto-Norwegian cod (Gadus morhua L.) from the age-0 to the age-1 stage. This is a delicate phase of the cod pre-recruitment dynamics, as individuals are confronted with a suite of survival challenges, such as settlement, pre-winter body condition, growth, and predation avoidance. During the over 20 years analyzed , we found that on average age-0 cod experience lower survival in the areas north of the Norwegian coastline, from about 71 to 75 degree of latitude north and about 20 to 35 degree of longitude east. However, in coastal areas, immediately north of the Norwegian coastline, age-0 cod experience greater survival. Within the studied area, the average survival of age-0 cod is significantly greater during years with low adult cod and high capelin abundance, and high Arctic Oscillation. In addition, when capelin abundance is high, age-0 cod experience better survival particularly near the Norwegian coastline. Based on these results it appears that within the sampled grid the observed geographic patterns of age-0 cod survival is affected by the predation from adult cod in relation to the availability and distribution of capelin (Mallotus villosus), the alternative and preferred prey of adult cod. Climate can affect the spatial survival of age-0 cod by both affecting the distribution of their predators (e.g., adult cod) and the distribution and availability of zooplankton prey.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8cc6577acc476d1d41d65a182e4413c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41840506,"asset_id":21375120,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41840506/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="21375120"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="21375120"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 21375120; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=21375120]").text(description); $(".js-view-count[data-work-id=21375120]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 21375120; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='21375120']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 21375120, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8cc6577acc476d1d41d65a182e4413c0" } } $('.js-work-strip[data-work-id=21375120]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":21375120,"title":"Spatial patterns of age-0 cod survival in the Barents Sea","translated_title":"","metadata":{"grobid_abstract":"Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of population variability of marine fish stocks. 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Factors affecting recruitment dynamics can change over both temporal and spatial scales. It follows that at the same time, over the entire range of distribution of a single population, different individuals may experience different level of environmental forcing and survival, which may not be well represented by average conditions throughout the entire distribution range. In this study we focus on the spatial pattern and its relative sources of variability in the survival of the Arcto-Norwegian cod (Gadus morhua L.) from the age-0 to the age-1 stage. This is a delicate phase of the cod pre-recruitment dynamics, as individuals are confronted with a suite of survival challenges, such as settlement, pre-winter body condition, growth, and predation avoidance. During the over 20 years analyzed , we found that on average age-0 cod experience lower survival in the areas north of the Norwegian coastline, from about 71 to 75 degree of latitude north and about 20 to 35 degree of longitude east. However, in coastal areas, immediately north of the Norwegian coastline, age-0 cod experience greater survival. Within the studied area, the average survival of age-0 cod is significantly greater during years with low adult cod and high capelin abundance, and high Arctic Oscillation. In addition, when capelin abundance is high, age-0 cod experience better survival particularly near the Norwegian coastline. Based on these results it appears that within the sampled grid the observed geographic patterns of age-0 cod survival is affected by the predation from adult cod in relation to the availability and distribution of capelin (Mallotus villosus), the alternative and preferred prey of adult cod. Climate can affect the spatial survival of age-0 cod by both affecting the distribution of their predators (e.g., adult cod) and the distribution and availability of zooplankton prey.","owner":{"id":42274834,"first_name":"Gjert","middle_initials":null,"last_name":"Dingsør","page_name":"GjertDingsør","domain_name":"uib","created_at":"2016-01-28T07:51:38.487-08:00","display_name":"Gjert Dingsør","url":"https://uib.academia.edu/GjertDings%C3%B8r"},"attachments":[{"id":41840506,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41840506/thumbnails/1.jpg","file_name":"Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3.pdf","download_url":"https://www.academia.edu/attachments/41840506/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spatial_patterns_of_age_0_cod_survival_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41840506/Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3-libre.pdf?1454319506=\u0026response-content-disposition=attachment%3B+filename%3DSpatial_patterns_of_age_0_cod_survival_i.pdf\u0026Expires=1734214762\u0026Signature=S4Lz~336a-zlpN0wMzfTIl8JOidbrjrTOx1FZ3F8sw6DhROVNvWIm3f796W7l~RHmJ-ojC7ihAp66754AXtOziSEspgo-Z9YPYkXzUhlK-cPBMV5SjGsL4QUgjNklvpZ-9CtwbuBTi3BCIx5HCnOYpouW01xKTAxes~lzq4DQXq6cjJUPmwM~vEB~6C2QpwgJkY~9hjK~UvQhDIswc7WjaIi~Wmh2UZEMAMehvqsIeZYx6umyyHEjXcTrbNvMIB9XiMmLOrkr7iGUJ9qGOmgBcInCrkX0W6MpkcwWOqZholoxGux6h67mnWaFKVihYAzRRj~9BHFkLQ8BctriUCQ6g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920605"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod"><img alt="Research paper thumbnail of Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod" class="work-thumbnail" src="https://attachments.academia-assets.com/53055347/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod">Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The world&#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is hea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The world&#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b0ce5d52a40d28595c4628e2d6f35dd1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055347,"asset_id":32920605,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055347/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920605"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920605"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920605; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920605]").text(description); $(".js-view-count[data-work-id=32920605]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920605; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920605']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920605, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b0ce5d52a40d28595c4628e2d6f35dd1" } } $('.js-work-strip[data-work-id=32920605]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920605,"title":"Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod","translated_title":"","metadata":{"abstract":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ..."},"translated_abstract":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ...","internal_url":"https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod","translated_internal_url":"","created_at":"2017-05-09T04:26:41.484-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":53055347,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055347/thumbnails/1.jpg","file_name":"Climatic_effects_filtered_through_the_fo20170509-20547-1e2luwj.pdf","download_url":"https://www.academia.edu/attachments/53055347/download_file?st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Climatic_effects_filtered_through_the_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055347/Climatic_effects_filtered_through_the_fo20170509-20547-1e2luwj-libre.pdf?1494329528=\u0026response-content-disposition=attachment%3B+filename%3DClimatic_effects_filtered_through_the_fo.pdf\u0026Expires=1734214762\u0026Signature=OSvkc80ouoz7MInkn2NlMWgXHmxw7cj8G179NyrCcSQmgfY1zrSFVXUP5IGitsetZjt-2wYmw4TJFvZOejBZkJVR8gaeehVO5DnYTmBZU2TjlAZMTZFu7FTlYjI4Mva3dw08Iz72V2DGVU88BcRhTSXLit9N2e08raNBWKs0tHXewiS-R5J6121YdT4OO6CKcRluiTtBD1IOCI1LLv~SNvKhloSJjdl7onVxQ0NmnOxdxTsUeXsXDI8vvqxPP3WWomzZR2pYbDLwvmtk5a7pPYclCnlLcXi1SH7fDvMsLYC6wtNY13VY0PyIqa4w-AKFpPvHwKAMEv2RtJicY~54ZQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod","translated_slug":"","page_count":6,"language":"en","content_type":"Work","summary":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. 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Hjort’s insights and how they stand the test of time" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920603/Standing_on_the_shoulders_of_Hjort_or_in_his_shadow_Hjort_s_insights_and_how_they_stand_the_test_of_time">Standing on the shoulders of Hjort - or in his shadow? Hjort’s insights and how they stand the test of time</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920603"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920603"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920603; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920603]").text(description); $(".js-view-count[data-work-id=32920603]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920603; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920603']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920603, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920603]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920603,"title":"Standing on the shoulders of Hjort - or in his shadow? Hjort’s insights and how they stand the test of time","translated_title":"","metadata":{},"translated_abstract":null,"internal_url":"https://www.academia.edu/32920603/Standing_on_the_shoulders_of_Hjort_or_in_his_shadow_Hjort_s_insights_and_how_they_stand_the_test_of_time","translated_internal_url":"","created_at":"2017-05-09T04:26:41.365-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Standing_on_the_shoulders_of_Hjort_or_in_his_shadow_Hjort_s_insights_and_how_they_stand_the_test_of_time","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920602"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920602/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_"><img alt="Research paper thumbnail of Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920602/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_">Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)</a></div><div class="wp-workCard_item"><span>Progress In Oceanography</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920598"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920598/Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_"><img alt="Research paper thumbnail of Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920598/Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_">Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)</a></div><div class="wp-workCard_item"><span>Progress in Oceanography</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to man...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920598"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920598"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920598; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920598]").text(description); $(".js-view-count[data-work-id=32920598]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920598; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920598']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920598, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920598]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920598,"title":"Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)","translated_title":"","metadata":{"abstract":"ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. 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We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920597"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920597/Climate_forcing_food_web_structure_and_community_dynamics_in_pelagic_marine_ecosystems"><img alt="Research paper thumbnail of Climate forcing, food web structure, and community dynamics in pelagic marine ecosystems" class="work-thumbnail" src="https://attachments.academia-assets.com/53055357/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920597/Climate_forcing_food_web_structure_and_community_dynamics_in_pelagic_marine_ecosystems">Climate forcing, food web structure, and community dynamics in pelagic marine ecosystems</a></div><div class="wp-workCard_item"><span>Aquatic Food Webs</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="354d4f75da4df623ac33231440c34f7d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055357,"asset_id":32920597,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055357/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920597"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920597"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920597; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920596"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_"><img alt="Research paper thumbnail of Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_">Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)</a></div><div class="wp-workCard_item"><span>Progress in Oceanography</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920596]").text(description); $(".js-view-count[data-work-id=32920596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920596, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920596,"title":"Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)","translated_title":"","metadata":{"abstract":"ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Progress in Oceanography"},"translated_abstract":"ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.","internal_url":"https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_","translated_internal_url":"","created_at":"2017-05-09T04:26:40.557-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920595"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920595/The_Responses_of_Fish_Populations_to_Ocean_Climate_Fluctuations"><img alt="Research paper thumbnail of The Responses of Fish Populations to Ocean Climate Fluctuations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920595/The_Responses_of_Fish_Populations_to_Ocean_Climate_Fluctuations">The Responses of Fish Populations to Ocean Climate Fluctuations</a></div><div class="wp-workCard_item"><span>Marine Ecosystems and Climate Variation</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920595"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920595"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920595; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920594/Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey"><img alt="Research paper thumbnail of Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey" class="work-thumbnail" src="https://attachments.academia-assets.com/53055352/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920594/Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey">Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two factors determine whether pollution is likely to affect a population indirectly through loss ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. We here apply a statistical recruitment model for Northeast Arctic cod to evaluate the sensitivity of cod cohorts to loss of zooplankton prey, for example following an oil spill. The calculations show that cod cohorts are highly sensitive to possible zooplankton biomass reductions in the distribution area of the cod larvae, and point to a need for more knowledge about oileffects on zooplankton. Our study illustrates how knowledge about population dynamics may guide which indirect effects to consider in environmental impact studies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f2b1126de7c64c8304a83b17802fb49" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055352,"asset_id":32920594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055352/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920594]").text(description); $(".js-view-count[data-work-id=32920594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920594, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0f2b1126de7c64c8304a83b17802fb49" } } $('.js-work-strip[data-work-id=32920594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920594,"title":"Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey","translated_title":"","metadata":{"grobid_abstract":"Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. 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Biological sciences / The Royal Society</span><span>, Jan 22, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Interactions within and between species complicate quantification of climate effects, by causing ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Interactions within and between species complicate quantification of climate effects, by causing indirect, often delayed, effects of climate fluctuations and compensation of mortality. Here we identify direct and indirect climate effects by analysing unique Russian time-series data from the Norwegian Sea-Barents Sea ecosystem on the first life stages of cod, capelin, herring and haddock, their predators, competitors and zooplanktonic prey. By analysing growth and survival from one life stage to the next (eggs-larvae-juveniles-recruits), we find evidence for both bottom-up, direct and top-down effects of climate. Ambient zooplankton biomass predicts survival of all species, whereas ambient temperature mainly affects survival through effects on growth. In warm years, all species experienced improved growth and feeding conditions. Cohorts born following a warm year will, however, experience increased predation and competition because of increased densities of subadult cod and herring, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="96f87194ee0486d3e4e5a965a1d98f44" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":43001706,"asset_id":15646226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/43001706/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="15646226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="15646226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15646226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=15646226]").text(description); $(".js-view-count[data-work-id=15646226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 15646226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='15646226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 15646226, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "96f87194ee0486d3e4e5a965a1d98f44" } } $('.js-work-strip[data-work-id=15646226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":15646226,"title":"Direct and indirect climate forcing in a multi-species marine system","translated_title":"","metadata":{"abstract":"Interactions within and between species complicate quantification of climate effects, by causing indirect, often delayed, effects of climate fluctuations and compensation of mortality. Here we identify direct and indirect climate effects by analysing unique Russian time-series data from the Norwegian Sea-Barents Sea ecosystem on the first life stages of cod, capelin, herring and haddock, their predators, competitors and zooplanktonic prey. By analysing growth and survival from one life stage to the next (eggs-larvae-juveniles-recruits), we find evidence for both bottom-up, direct and top-down effects of climate. Ambient zooplankton biomass predicts survival of all species, whereas ambient temperature mainly affects survival through effects on growth. In warm years, all species experienced improved growth and feeding conditions. Cohorts born following a warm year will, however, experience increased predation and competition because of increased densities of subadult cod and herring, ...","publication_date":{"day":22,"month":1,"year":2010,"errors":{}},"publication_name":"Proceedings. Biological sciences / The Royal Society"},"translated_abstract":"Interactions within and between species complicate quantification of climate effects, by causing indirect, often delayed, effects of climate fluctuations and compensation of mortality. Here we identify direct and indirect climate effects by analysing unique Russian time-series data from the Norwegian Sea-Barents Sea ecosystem on the first life stages of cod, capelin, herring and haddock, their predators, competitors and zooplanktonic prey. By analysing growth and survival from one life stage to the next (eggs-larvae-juveniles-recruits), we find evidence for both bottom-up, direct and top-down effects of climate. Ambient zooplankton biomass predicts survival of all species, whereas ambient temperature mainly affects survival through effects on growth. In warm years, all species experienced improved growth and feeding conditions. 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Here we identify direct and indirect climate effects by analysing unique Russian time-series data from the Norwegian Sea-Barents Sea ecosystem on the first life stages of cod, capelin, herring and haddock, their predators, competitors and zooplanktonic prey. By analysing growth and survival from one life stage to the next (eggs-larvae-juveniles-recruits), we find evidence for both bottom-up, direct and top-down effects of climate. Ambient zooplankton biomass predicts survival of all species, whereas ambient temperature mainly affects survival through effects on growth. In warm years, all species experienced improved growth and feeding conditions. Cohorts born following a warm year will, however, experience increased predation and competition because of increased densities of subadult cod and herring, ...","owner":{"id":34829358,"first_name":"Dmitry","middle_initials":null,"last_name":"Lajus","page_name":"DmitryLajus","domain_name":"spbu","created_at":"2015-09-13T05:38:47.813-07:00","display_name":"Dmitry 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</script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="563492" id="papers"><div class="js-work-strip profile--work_container" data-work-id="32920612"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920612/Adaptation_of_Express_to_the_Ibm_PC_A_Tool_for_Building_Knowledge_Based_Statistical_Systems_Using_Existing_Packages"><img alt="Research paper thumbnail of Adaptation of 'Express' to the Ibm PC: A Tool for Building Knowledge-Based Statistical Systems Using Existing Packages" class="work-thumbnail" src="https://attachments.academia-assets.com/53055317/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920612/Adaptation_of_Express_to_the_Ibm_PC_A_Tool_for_Building_Knowledge_Based_Statistical_Systems_Using_Existing_Packages">Adaptation of 'Express' to the Ibm PC: A Tool for Building Knowledge-Based Statistical Systems Using Existing Packages</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The program system EXPRESS constitutes a tool for constructing knowledge-based statistical system...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The program system EXPRESS constitutes a tool for constructing knowledge-based statistical systems requiring repeated cycles of statistical analysis on given data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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fish populations facilitated through classification of mechanisms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920611/Inter_regional_comparison_of_climate_effects_on_m_arine_fish_populations_facilitated_through_classification_of_mechanisms">Inter-regional comparison of climate effects on m arine fish populations facilitated through classification of mechanisms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Variations in climate strongly affect the structure and function of m arine ecosystems, but a num...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Variations in climate strongly affect the structure and function of m arine ecosystems, but a number of different mechanisms are at play and their relative importance varies between regions and with time. There are obvious semi-permanent regional differences in how marine populations respond to climate, but there may also be long-term trends either in climate itself or in the response pattern. In some cases single strong climate events may shift an ecosystem from one state to another (e.g., El Niño). To facilitate comparison between different large marine ecosystems we here give an overview of some of the manners in which one can classify how marine fish populations are affected by climate. Responses to climate fluctuations may be bottom-up, top-down or middle-out, immediate or temporally delayed, direct or via an intermediate population of predators, prey or competitors. Climate may invoke a linear or non-linear effect at the population or community level. Ecological effects of the NAO have been classified according to the four major classes: direct effects, indirect effects, integrated effects and translations, which also may be applied to other climate patterns and regions. By using classification schemes a more precise description of the particular properties of the various ecosystems may be possible. This approach enhances the possibility to compare between regions that may differ not only with regards to the relative importance of different climate factors for ecology, but also through dissimilarities in scientific tradition and terminology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920611"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920611"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920611; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920611]").text(description); $(".js-view-count[data-work-id=32920611]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920611; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920611']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920611, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920611]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920611,"title":"Inter-regional comparison of climate effects on m arine fish populations facilitated through classification of mechanisms","translated_title":"","metadata":{"abstract":"Variations in climate strongly affect the structure and function of m arine ecosystems, but a number of different mechanisms are at play and their relative importance varies between regions and with time. There are obvious semi-permanent regional differences in how marine populations respond to climate, but there may also be long-term trends either in climate itself or in the response pattern. In some cases single strong climate events may shift an ecosystem from one state to another (e.g., El Niño). To facilitate comparison between different large marine ecosystems we here give an overview of some of the manners in which one can classify how marine fish populations are affected by climate. Responses to climate fluctuations may be bottom-up, top-down or middle-out, immediate or temporally delayed, direct or via an intermediate population of predators, prey or competitors. Climate may invoke a linear or non-linear effect at the population or community level. Ecological effects of the NAO have been classified according to the four major classes: direct effects, indirect effects, integrated effects and translations, which also may be applied to other climate patterns and regions. 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To facilitate comparison between different large marine ecosystems we here give an overview of some of the manners in which one can classify how marine fish populations are affected by climate. Responses to climate fluctuations may be bottom-up, top-down or middle-out, immediate or temporally delayed, direct or via an intermediate population of predators, prey or competitors. Climate may invoke a linear or non-linear effect at the population or community level. Ecological effects of the NAO have been classified according to the four major classes: direct effects, indirect effects, integrated effects and translations, which also may be applied to other climate patterns and regions. By using classification schemes a more precise description of the particular properties of the various ecosystems may be possible. This approach enhances the possibility to compare between regions that may differ not only with regards to the relative importance of different climate factors for ecology, but also through dissimilarities in scientific tradition and terminology.","internal_url":"https://www.academia.edu/32920611/Inter_regional_comparison_of_climate_effects_on_m_arine_fish_populations_facilitated_through_classification_of_mechanisms","translated_internal_url":"","created_at":"2017-05-09T04:26:42.525-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Inter_regional_comparison_of_climate_effects_on_m_arine_fish_populations_facilitated_through_classification_of_mechanisms","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Variations in climate strongly affect the structure and function of m arine ecosystems, but a number of different mechanisms are at play and their relative importance varies between regions and with time. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920609"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920609/Emergent_properties_of_complex_marine_systems_a_macroecological_perspective"><img alt="Research paper thumbnail of Emergent properties of complex marine systems: a macroecological perspective" class="work-thumbnail" src="https://attachments.academia-assets.com/53055314/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920609/Emergent_properties_of_complex_marine_systems_a_macroecological_perspective">Emergent properties of complex marine systems: a macroecological perspective</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A macroecological view of the ecosystem offers the possibility to integrate information at large ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A macroecological view of the ecosystem offers the possibility to integrate information at large spatial and temporal scales over a variety of complex ecological systems. Marine macroecology can be regarded as a new research agenda aiming to develop new models which can explain the emergent structures and dynamics of complex ecological systems in terms of basic physical and biological principles (Brown 1999). Macroecology theory as a way to describe the emergent properties in terrestrial systems has received relatively little attention in marine ecology. This Theme Section is a collection of articles that will discuss the importance of macroecological and complexity theory, in a very broad context, to untangling patterns that underlie the relationships between species abundance and other biotic and abiotic factors linking organismal biology, population dynamics, community ecology, food web structure, biodiversity, and behavioral ecology, to ecosystem structure and function. This macroecological view of different processes underlying the dynamics of marine ecosystems extends the general theory of macroecology and allometric scaling, developed mainly for terrestrial systems (Brown 1995, West et al. 1997, to a marine context as recently proposed by Li (2002). Ultimately we need to understand by first principles, from organism organization to ecosystem organization (Reynolds 2001), the basic common ecological rules that generate the variability and patterns that we observe across scales. LITERATURE CITED Brown JH (1995) Macroecology. Chicago University Press, Chicago Brown JH (1999) Macroecology: progress and prospect. Oikos</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b4a9d9a7f11ae12eedaf4f733017abe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055314,"asset_id":32920609,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055314/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920609"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920609"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920609; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920609]").text(description); $(".js-view-count[data-work-id=32920609]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920609; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920609']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920609, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0b4a9d9a7f11ae12eedaf4f733017abe" } } $('.js-work-strip[data-work-id=32920609]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920609,"title":"Emergent properties of complex marine systems: a macroecological perspective","translated_title":"","metadata":{"ai_title_tag":"Understanding Emergent Marine Ecosystem Properties through Macroecology","grobid_abstract":"A macroecological view of the ecosystem offers the possibility to integrate information at large spatial and temporal scales over a variety of complex ecological systems. Marine macroecology can be regarded as a new research agenda aiming to develop new models which can explain the emergent structures and dynamics of complex ecological systems in terms of basic physical and biological principles (Brown 1999). Macroecology theory as a way to describe the emergent properties in terrestrial systems has received relatively little attention in marine ecology. This Theme Section is a collection of articles that will discuss the importance of macroecological and complexity theory, in a very broad context, to untangling patterns that underlie the relationships between species abundance and other biotic and abiotic factors linking organismal biology, population dynamics, community ecology, food web structure, biodiversity, and behavioral ecology, to ecosystem structure and function. This macroecological view of different processes underlying the dynamics of marine ecosystems extends the general theory of macroecology and allometric scaling, developed mainly for terrestrial systems (Brown 1995, West et al. 1997, to a marine context as recently proposed by Li (2002). Ultimately we need to understand by first principles, from organism organization to ecosystem organization (Reynolds 2001), the basic common ecological rules that generate the variability and patterns that we observe across scales. LITERATURE CITED Brown JH (1995) Macroecology. Chicago University Press, Chicago Brown JH (1999) Macroecology: progress and prospect. 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Marine macroecology can be regarded as a new research agenda aiming to develop new models which can explain the emergent structures and dynamics of complex ecological systems in terms of basic physical and biological principles (Brown 1999). Macroecology theory as a way to describe the emergent properties in terrestrial systems has received relatively little attention in marine ecology. This Theme Section is a collection of articles that will discuss the importance of macroecological and complexity theory, in a very broad context, to untangling patterns that underlie the relationships between species abundance and other biotic and abiotic factors linking organismal biology, population dynamics, community ecology, food web structure, biodiversity, and behavioral ecology, to ecosystem structure and function. This macroecological view of different processes underlying the dynamics of marine ecosystems extends the general theory of macroecology and allometric scaling, developed mainly for terrestrial systems (Brown 1995, West et al. 1997, to a marine context as recently proposed by Li (2002). Ultimately we need to understand by first principles, from organism organization to ecosystem organization (Reynolds 2001), the basic common ecological rules that generate the variability and patterns that we observe across scales. LITERATURE CITED Brown JH (1995) Macroecology. Chicago University Press, Chicago Brown JH (1999) Macroecology: progress and prospect. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920607"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920607/Predictability_of_Barents_Sea_temperature"><img alt="Research paper thumbnail of Predictability of Barents Sea temperature" class="work-thumbnail" src="https://attachments.academia-assets.com/53055348/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920607/Predictability_of_Barents_Sea_temperature">Predictability of Barents Sea temperature</a></div><div class="wp-workCard_item"><span>Fisheries Oceanography</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the North Atlantic has increased during the last decade. To allow this information to be used in ®sheries management, some forecast of the environment is important. Predictions of temperature in the Arcto-boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as in¯uencing the temperature of the Barents Sea. These include components of suggested periodic nature, large-scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto-and cross-correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic¯uctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A ®rstorder autoregressive model, including modelled atmospherically driven volume¯ux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8602883521b662d52b2ae6c963bb3610" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055348,"asset_id":32920607,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055348/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920607"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920607"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920607; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920607]").text(description); $(".js-view-count[data-work-id=32920607]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920607; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920607']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920607, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8602883521b662d52b2ae6c963bb3610" } } $('.js-work-strip[data-work-id=32920607]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920607,"title":"Predictability of Barents Sea temperature","translated_title":"","metadata":{"ai_title_tag":"Forecasting Barents Sea Temperature Influences and Predictability","grobid_abstract":"Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the North Atlantic has increased during the last decade. To allow this information to be used in ®sheries management, some forecast of the environment is important. Predictions of temperature in the Arcto-boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as in¯uencing the temperature of the Barents Sea. These include components of suggested periodic nature, large-scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto-and cross-correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic¯uctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. A ®rstorder autoregressive model, including modelled atmospherically driven volume¯ux to the western Barents Sea during the preceding year and the position of the Gulf Stream off the eastern coast of the USA two years earlier, explained 50% of the total historical temperature variability.","publication_name":"Fisheries Oceanography","grobid_abstract_attachment_id":53055348},"translated_abstract":null,"internal_url":"https://www.academia.edu/32920607/Predictability_of_Barents_Sea_temperature","translated_internal_url":"","created_at":"2017-05-09T04:26:41.925-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":53055348,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055348/thumbnails/1.jpg","file_name":"Predictability_of_Barents_Sea_temperatur20170509-20550-1au5plj.pdf","download_url":"https://www.academia.edu/attachments/53055348/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Predictability_of_Barents_Sea_temperatur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055348/Predictability_of_Barents_Sea_temperatur20170509-20550-1au5plj-libre.pdf?1494329530=\u0026response-content-disposition=attachment%3B+filename%3DPredictability_of_Barents_Sea_temperatur.pdf\u0026Expires=1734214762\u0026Signature=d8GfHvZnjyFje5k6q~N4NnXtjcJNlL6UfnTzMVzZrRhFHs8qoE4nO9lLYAPHOJHzUcBhkz2BjM~Mntsgd7qpwBoKm-6Ms1qieBOBjvwKtXhHcUc1dCDLIWfWwHUm5A05p29uxNaWjTe113bqEdTsy~xt-IowSMWDEp25btyTJ-uNqV0Q~G91lU39qTetrHGJ7e8M6hzRLw92LlgaKRMlK4KBuyKQDONTje9sbf5AM-rQ6tBVoeAc5WrM3tmRsmv86V1AmiGmQGJxCQIf27z3g0JiCNuvifAEUTjvH3~0nmEo4p5uhnLT9wKrSd~FO5Hunk9KN1aYx1wpDfclMSPCcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Predictability_of_Barents_Sea_temperature","translated_slug":"","page_count":15,"language":"en","content_type":"Work","summary":"Knowledge of the in¯uence of the physical environment on commercially important ®sh stocks in the North Atlantic has increased during the last decade. To allow this information to be used in ®sheries management, some forecast of the environment is important. Predictions of temperature in the Arcto-boreal Barents Sea have been given for many years, both as subjective opinions of scientists and implicitly in stock assessment assumptions of, e.g., mortality rates. To evaluate an objective statistical forecasting system, we have analysed time series representing mechanisms previously proposed as in¯uencing the temperature of the Barents Sea. These include components of suggested periodic nature, large-scale advective effects, regional processes, and atmospheric teleconnections. The predictability of Barents Sea temperature based on the above mechanisms was evaluated through calculations of auto-and cross-correlations, linear regression, spectral analysis and autoregressive modelling. Forecasts based on periodic¯uctuations in temperature performed poorly. Advection alone did not explain a major part of the variability. The precision of predictions six months ahead varied with season; forecasts from spring to autumn had least uncertainty. 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Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920606"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920606"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920606; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920606]").text(description); $(".js-view-count[data-work-id=32920606]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920606; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920606']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920606, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920606]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920606,"title":"The response of terrestrial ecosystems to climate variability associated with the North Atlantic Oscillation","translated_title":"","metadata":{"abstract":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","publication_date":{"day":null,"month":null,"year":2000,"errors":{}},"publication_name":"Geophysical Monograph Series"},"translated_abstract":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","internal_url":"https://www.academia.edu/32920606/The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation","translated_internal_url":"","created_at":"2017-05-09T04:26:41.792-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_response_of_terrestrial_ecosystems_to_climate_variability_associated_with_the_North_Atlantic_Oscillation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"... Stenseth1, Nigel G. Yoccoz1,2, Geir Ottersen3, and Rolf Langvatn4 ... Furthermore, the increasing focus on global warming and its ecological consequences [eg, Hughes, 2000; McCarty, 2001] provides additional reasons to relate the NAO to ecosystem functioning. ...","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":44265,"name":"North Atlantic Oscillation","url":"https://www.academia.edu/Documents/in/North_Atlantic_Oscillation"},{"id":54961,"name":"Growth","url":"https://www.academia.edu/Documents/in/Growth"},{"id":373754,"name":"Ecosystem","url":"https://www.academia.edu/Documents/in/Ecosystem"},{"id":411513,"name":"Geophysical","url":"https://www.academia.edu/Documents/in/Geophysical"},{"id":630543,"name":"Tree Ring","url":"https://www.academia.edu/Documents/in/Tree_Ring"},{"id":779767,"name":"Nonlinearity","url":"https://www.academia.edu/Documents/in/Nonlinearity"},{"id":908385,"name":"Long Term","url":"https://www.academia.edu/Documents/in/Long_Term"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="21375120"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/21375120/Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea"><img alt="Research paper thumbnail of Spatial patterns of age-0 cod survival in the Barents Sea" class="work-thumbnail" src="https://attachments.academia-assets.com/41840506/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/21375120/Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea">Spatial patterns of age-0 cod survival in the Barents Sea</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uib.academia.edu/GjertDings%C3%B8r">Gjert Dingsør</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/HaraldGj%C3%B8s%C3%A6ter">Harald Gjøsæter</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://havforskningsinstituttet.academia.edu/GeirOttersen">Geir Ottersen</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of po...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of population variability of marine fish stocks. Factors affecting recruitment dynamics can change over both temporal and spatial scales. It follows that at the same time, over the entire range of distribution of a single population, different individuals may experience different level of environmental forcing and survival, which may not be well represented by average conditions throughout the entire distribution range. In this study we focus on the spatial pattern and its relative sources of variability in the survival of the Arcto-Norwegian cod (Gadus morhua L.) from the age-0 to the age-1 stage. This is a delicate phase of the cod pre-recruitment dynamics, as individuals are confronted with a suite of survival challenges, such as settlement, pre-winter body condition, growth, and predation avoidance. During the over 20 years analyzed , we found that on average age-0 cod experience lower survival in the areas north of the Norwegian coastline, from about 71 to 75 degree of latitude north and about 20 to 35 degree of longitude east. However, in coastal areas, immediately north of the Norwegian coastline, age-0 cod experience greater survival. Within the studied area, the average survival of age-0 cod is significantly greater during years with low adult cod and high capelin abundance, and high Arctic Oscillation. In addition, when capelin abundance is high, age-0 cod experience better survival particularly near the Norwegian coastline. Based on these results it appears that within the sampled grid the observed geographic patterns of age-0 cod survival is affected by the predation from adult cod in relation to the availability and distribution of capelin (Mallotus villosus), the alternative and preferred prey of adult cod. Climate can affect the spatial survival of age-0 cod by both affecting the distribution of their predators (e.g., adult cod) and the distribution and availability of zooplankton prey.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8cc6577acc476d1d41d65a182e4413c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41840506,"asset_id":21375120,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41840506/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="21375120"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="21375120"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 21375120; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=21375120]").text(description); $(".js-view-count[data-work-id=21375120]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 21375120; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='21375120']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 21375120, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8cc6577acc476d1d41d65a182e4413c0" } } $('.js-work-strip[data-work-id=21375120]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":21375120,"title":"Spatial patterns of age-0 cod survival in the Barents Sea","translated_title":"","metadata":{"grobid_abstract":"Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of population variability of marine fish stocks. 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However, in coastal areas, immediately north of the Norwegian coastline, age-0 cod experience greater survival. Within the studied area, the average survival of age-0 cod is significantly greater during years with low adult cod and high capelin abundance, and high Arctic Oscillation. In addition, when capelin abundance is high, age-0 cod experience better survival particularly near the Norwegian coastline. Based on these results it appears that within the sampled grid the observed geographic patterns of age-0 cod survival is affected by the predation from adult cod in relation to the availability and distribution of capelin (Mallotus villosus), the alternative and preferred prey of adult cod. Climate can affect the spatial survival of age-0 cod by both affecting the distribution of their predators (e.g., adult cod) and the distribution and availability of zooplankton prey.","grobid_abstract_attachment_id":41840506},"translated_abstract":null,"internal_url":"https://www.academia.edu/21375120/Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea","translated_internal_url":"","created_at":"2016-02-01T01:16:17.185-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":42274834,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":14406498,"work_id":21375120,"tagging_user_id":42274834,"tagged_user_id":40781702,"co_author_invite_id":null,"email":"h***d@imr.no","display_order":0,"name":"Harald Gjøsæter","title":"Spatial patterns of age-0 cod survival in the Barents Sea"},{"id":14406505,"work_id":21375120,"tagging_user_id":42274834,"tagged_user_id":4500242,"co_author_invite_id":null,"email":"g***n@ibv.uio.no","affiliation":"Insitute of Marine Research","display_order":4194304,"name":"Geir Ottersen","title":"Spatial patterns of age-0 cod survival in the Barents Sea"}],"downloadable_attachments":[{"id":41840506,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41840506/thumbnails/1.jpg","file_name":"Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3.pdf","download_url":"https://www.academia.edu/attachments/41840506/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spatial_patterns_of_age_0_cod_survival_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41840506/Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3-libre.pdf?1454319506=\u0026response-content-disposition=attachment%3B+filename%3DSpatial_patterns_of_age_0_cod_survival_i.pdf\u0026Expires=1734214762\u0026Signature=S4Lz~336a-zlpN0wMzfTIl8JOidbrjrTOx1FZ3F8sw6DhROVNvWIm3f796W7l~RHmJ-ojC7ihAp66754AXtOziSEspgo-Z9YPYkXzUhlK-cPBMV5SjGsL4QUgjNklvpZ-9CtwbuBTi3BCIx5HCnOYpouW01xKTAxes~lzq4DQXq6cjJUPmwM~vEB~6C2QpwgJkY~9hjK~UvQhDIswc7WjaIi~Wmh2UZEMAMehvqsIeZYx6umyyHEjXcTrbNvMIB9XiMmLOrkr7iGUJ9qGOmgBcInCrkX0W6MpkcwWOqZholoxGux6h67mnWaFKVihYAzRRj~9BHFkLQ8BctriUCQ6g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Spatial_patterns_of_age_0_cod_survival_in_the_Barents_Sea","translated_slug":"","page_count":13,"language":"en","content_type":"Work","summary":"Apart from human harvest, recruitment dynamics is conceivably recognized as the main source of population variability of marine fish stocks. Factors affecting recruitment dynamics can change over both temporal and spatial scales. It follows that at the same time, over the entire range of distribution of a single population, different individuals may experience different level of environmental forcing and survival, which may not be well represented by average conditions throughout the entire distribution range. In this study we focus on the spatial pattern and its relative sources of variability in the survival of the Arcto-Norwegian cod (Gadus morhua L.) from the age-0 to the age-1 stage. This is a delicate phase of the cod pre-recruitment dynamics, as individuals are confronted with a suite of survival challenges, such as settlement, pre-winter body condition, growth, and predation avoidance. During the over 20 years analyzed , we found that on average age-0 cod experience lower survival in the areas north of the Norwegian coastline, from about 71 to 75 degree of latitude north and about 20 to 35 degree of longitude east. However, in coastal areas, immediately north of the Norwegian coastline, age-0 cod experience greater survival. Within the studied area, the average survival of age-0 cod is significantly greater during years with low adult cod and high capelin abundance, and high Arctic Oscillation. In addition, when capelin abundance is high, age-0 cod experience better survival particularly near the Norwegian coastline. Based on these results it appears that within the sampled grid the observed geographic patterns of age-0 cod survival is affected by the predation from adult cod in relation to the availability and distribution of capelin (Mallotus villosus), the alternative and preferred prey of adult cod. Climate can affect the spatial survival of age-0 cod by both affecting the distribution of their predators (e.g., adult cod) and the distribution and availability of zooplankton prey.","owner":{"id":42274834,"first_name":"Gjert","middle_initials":null,"last_name":"Dingsør","page_name":"GjertDingsør","domain_name":"uib","created_at":"2016-01-28T07:51:38.487-08:00","display_name":"Gjert Dingsør","url":"https://uib.academia.edu/GjertDings%C3%B8r"},"attachments":[{"id":41840506,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41840506/thumbnails/1.jpg","file_name":"Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3.pdf","download_url":"https://www.academia.edu/attachments/41840506/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spatial_patterns_of_age_0_cod_survival_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41840506/Spatial_patterns_of_age-0_cod_survival_i20160201-15664-3zlcw3-libre.pdf?1454319506=\u0026response-content-disposition=attachment%3B+filename%3DSpatial_patterns_of_age_0_cod_survival_i.pdf\u0026Expires=1734214762\u0026Signature=S4Lz~336a-zlpN0wMzfTIl8JOidbrjrTOx1FZ3F8sw6DhROVNvWIm3f796W7l~RHmJ-ojC7ihAp66754AXtOziSEspgo-Z9YPYkXzUhlK-cPBMV5SjGsL4QUgjNklvpZ-9CtwbuBTi3BCIx5HCnOYpouW01xKTAxes~lzq4DQXq6cjJUPmwM~vEB~6C2QpwgJkY~9hjK~UvQhDIswc7WjaIi~Wmh2UZEMAMehvqsIeZYx6umyyHEjXcTrbNvMIB9XiMmLOrkr7iGUJ9qGOmgBcInCrkX0W6MpkcwWOqZholoxGux6h67mnWaFKVihYAzRRj~9BHFkLQ8BctriUCQ6g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920605"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod"><img alt="Research paper thumbnail of Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod" class="work-thumbnail" src="https://attachments.academia-assets.com/53055347/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod">Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The world&#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is hea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The world&#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b0ce5d52a40d28595c4628e2d6f35dd1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055347,"asset_id":32920605,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055347/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920605"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920605"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920605; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920605]").text(description); $(".js-view-count[data-work-id=32920605]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920605; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920605']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920605, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b0ce5d52a40d28595c4628e2d6f35dd1" } } $('.js-work-strip[data-work-id=32920605]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920605,"title":"Climatic effects filtered through the food web affect the dynamics of Arcto-Norwegian cod","translated_title":"","metadata":{"abstract":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ..."},"translated_abstract":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. Using data on cod, herring and temperature back to 1921 to verify the model,we find a significant relationship between predictions and data back to the 1950s, but before this, the predicted time-series pattern is not observed ...","internal_url":"https://www.academia.edu/32920605/Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod","translated_internal_url":"","created_at":"2017-05-09T04:26:41.484-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":53055347,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055347/thumbnails/1.jpg","file_name":"Climatic_effects_filtered_through_the_fo20170509-20547-1e2luwj.pdf","download_url":"https://www.academia.edu/attachments/53055347/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Climatic_effects_filtered_through_the_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055347/Climatic_effects_filtered_through_the_fo20170509-20547-1e2luwj-libre.pdf?1494329528=\u0026response-content-disposition=attachment%3B+filename%3DClimatic_effects_filtered_through_the_fo.pdf\u0026Expires=1734214762\u0026Signature=OSvkc80ouoz7MInkn2NlMWgXHmxw7cj8G179NyrCcSQmgfY1zrSFVXUP5IGitsetZjt-2wYmw4TJFvZOejBZkJVR8gaeehVO5DnYTmBZU2TjlAZMTZFu7FTlYjI4Mva3dw08Iz72V2DGVU88BcRhTSXLit9N2e08raNBWKs0tHXewiS-R5J6121YdT4OO6CKcRluiTtBD1IOCI1LLv~SNvKhloSJjdl7onVxQ0NmnOxdxTsUeXsXDI8vvqxPP3WWomzZR2pYbDLwvmtk5a7pPYclCnlLcXi1SH7fDvMsLYC6wtNY13VY0PyIqa4w-AKFpPvHwKAMEv2RtJicY~54ZQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Climatic_effects_filtered_through_the_food_web_affect_the_dynamics_of_Arcto_Norwegian_cod","translated_slug":"","page_count":6,"language":"en","content_type":"Work","summary":"The world\u0026#39;s largest cod stock, the Arcto-Norwegian cod (a.k.a. North-East Arctic cod), is heavily influenced by temperature in two ways: First, cod recruitment tends to be high when Barents Sea temperature in the spawning year is high. Secondly, there is a more indirect effect of climate via herring and capelin: Warm conditions increase the chance of high recruitment of Norwegian Spring-spawning herring; 1-2 year old herring eat 0-year old capelin; and cod cannibalism increases when the biomass of 1-4 year old capelin is low. While these relationships have been shown separately and for the later years, we develop and parameterize models for the effects of herring (via capelin) and temperature on cod recruitment at age 3, using data from 1973 until present. 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This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920602"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920602"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920602; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920602]").text(description); $(".js-view-count[data-work-id=32920602]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920602; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920602']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920602, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920602]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920602,"title":"Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)","translated_title":"","metadata":{"abstract":"Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920598"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920598/Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_"><img alt="Research paper thumbnail of Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920598/Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_">Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)</a></div><div class="wp-workCard_item"><span>Progress in Oceanography</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to man...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920598"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920598"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920598; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920598]").text(description); $(".js-view-count[data-work-id=32920598]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920598; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920598']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920598, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920598]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920598,"title":"Combined statistical and mechanistic modelling suggests food and temperature effects on survival of early life stages of Northeast Arctic cod (Gadus morhua)","translated_title":"","metadata":{"abstract":"ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Progress in Oceanography"},"translated_abstract":"ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.","internal_url":"https://www.academia.edu/32920598/Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_","translated_internal_url":"","created_at":"2017-05-09T04:26:40.823-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Combined_statistical_and_mechanistic_modelling_suggests_food_and_temperature_effects_on_survival_of_early_life_stages_of_Northeast_Arctic_cod_Gadus_morhua_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Understanding the causes of the large interannual fluctuations in the recruitment to many marine fishes is a key challenge in fisheries ecology. We here propose that the combination of mechanistic and statistical modelling of the pelagic early life stages (ELS) prior to recruitment can be a powerful approach for improving our understanding of local-scale and population-scale dynamics. Specifically, this approach allows separating effects of ocean transport and survival, and thereby enhances the knowledge of the processes that regulate recruitment. We analyse data on the pelagic eggs, larvae and post-larvae of Northeast Arctic cod and on copepod nauplii, the main prey of the cod larvae. The data originate from two surveys, one in spring and one in summer, for 30 years. A coupled physical-biological model is used to simulate the transport, ambient temperature and development of cod ELS from spawning through spring and summer. The predictions from this model are used as input in a statistical analysis of the summer data, to investigate effects of covariates thought to be linked to growth and survival. We find significant associations between the local-scale ambient copepod nauplii concentration and temperature in spring and the local-scale occurrence of cod (post)larvae in summer, consistent with effects on survival. Moreover, years with low copepod nauplii concentrations and low temperature in spring are significantly associated with lower mean length of the cod (post)larvae in summer, likely caused in part by higher mortality leading to increased dominance of young and hence small individuals. Finally, we find that the recruitment at age 3 is strongly associated with the mean body length of the cod ELS, highlighting the biological significance of the findings.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920597"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920597/Climate_forcing_food_web_structure_and_community_dynamics_in_pelagic_marine_ecosystems"><img alt="Research paper thumbnail of Climate forcing, food web structure, and community dynamics in pelagic marine ecosystems" class="work-thumbnail" src="https://attachments.academia-assets.com/53055357/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920597/Climate_forcing_food_web_structure_and_community_dynamics_in_pelagic_marine_ecosystems">Climate forcing, food web structure, and community dynamics in pelagic marine ecosystems</a></div><div class="wp-workCard_item"><span>Aquatic Food Webs</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="354d4f75da4df623ac33231440c34f7d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055357,"asset_id":32920597,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055357/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920597"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920597"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920597; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920596"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_"><img alt="Research paper thumbnail of Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_">Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)</a></div><div class="wp-workCard_item"><span>Progress in Oceanography</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920596"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920596"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920596; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920596]").text(description); $(".js-view-count[data-work-id=32920596]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920596; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920596']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920596, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=32920596]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920596,"title":"Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)","translated_title":"","metadata":{"abstract":"ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. 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We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.","internal_url":"https://www.academia.edu/32920596/Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_","translated_internal_url":"","created_at":"2017-05-09T04:26:40.557-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Spatial_variations_in_mortality_in_pelagic_early_life_stages_of_a_marine_fish_Gadus_morhua_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920595"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920595/The_Responses_of_Fish_Populations_to_Ocean_Climate_Fluctuations"><img alt="Research paper thumbnail of The Responses of Fish Populations to Ocean Climate Fluctuations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920595/The_Responses_of_Fish_Populations_to_Ocean_Climate_Fluctuations">The Responses of Fish Populations to Ocean Climate Fluctuations</a></div><div class="wp-workCard_item"><span>Marine Ecosystems and Climate Variation</span><span>, 2005</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920595"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920595"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920595; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32920594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32920594/Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey"><img alt="Research paper thumbnail of Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey" class="work-thumbnail" src="https://attachments.academia-assets.com/53055352/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32920594/Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey">Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two factors determine whether pollution is likely to affect a population indirectly through loss ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. We here apply a statistical recruitment model for Northeast Arctic cod to evaluate the sensitivity of cod cohorts to loss of zooplankton prey, for example following an oil spill. The calculations show that cod cohorts are highly sensitive to possible zooplankton biomass reductions in the distribution area of the cod larvae, and point to a need for more knowledge about oileffects on zooplankton. Our study illustrates how knowledge about population dynamics may guide which indirect effects to consider in environmental impact studies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f2b1126de7c64c8304a83b17802fb49" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":53055352,"asset_id":32920594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/53055352/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32920594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32920594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32920594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32920594]").text(description); $(".js-view-count[data-work-id=32920594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32920594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='32920594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 32920594, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0f2b1126de7c64c8304a83b17802fb49" } } $('.js-work-strip[data-work-id=32920594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":32920594,"title":"Environmental toxicology: Population modeling of cod larvae shows high sensitivity to loss of zooplankton prey","translated_title":"","metadata":{"grobid_abstract":"Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. We here apply a statistical recruitment model for Northeast Arctic cod to evaluate the sensitivity of cod cohorts to loss of zooplankton prey, for example following an oil spill. The calculations show that cod cohorts are highly sensitive to possible zooplankton biomass reductions in the distribution area of the cod larvae, and point to a need for more knowledge about oileffects on zooplankton. Our study illustrates how knowledge about population dynamics may guide which indirect effects to consider in environmental impact studies.","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"grobid_abstract_attachment_id":53055352},"translated_abstract":null,"internal_url":"https://www.academia.edu/32920594/Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey","translated_internal_url":"","created_at":"2017-05-09T04:26:40.302-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":4500242,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":53055352,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055352/thumbnails/1.jpg","file_name":"j.marpolbul.2010.11.03420170509-20541-1u1kelw.pdf","download_url":"https://www.academia.edu/attachments/53055352/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Environmental_toxicology_Population_mode.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055352/j.marpolbul.2010.11.03420170509-20541-1u1kelw-libre.pdf?1494329523=\u0026response-content-disposition=attachment%3B+filename%3DEnvironmental_toxicology_Population_mode.pdf\u0026Expires=1734214763\u0026Signature=cU7yv8MUXBOp~0ZbejKhgiGRQmmOPQLjj~QVsGG-NfTa-9gNpLOH77g040UgYPuwINF6gnvTOGOFGJWx1rrwCkgZjDVlPEcQDm6ITNB~xFJvHvS~hzzbeEIryTEKxLXoEZSv6Fs9LfO0J7iPhqvByUVRluCZC2UEkDzPGIDnajfIL19B5oJhmkTeRbwEpKavMOrxFewd1V9xLcE8bnkfpz-IZXMaZI7PcPkvRqy4wx9tXEE9Qo8s3G2P2UQEIX~Lke5Zv-oAPzA~X-SqEfCuQj3B0khn2K71ONyKwYk6wqFM9NzPWREtEvZSnMyNQrZOjdvLrsBKqJfn3IdKzjw~FA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Environmental_toxicology_Population_modeling_of_cod_larvae_shows_high_sensitivity_to_loss_of_zooplankton_prey","translated_slug":"","page_count":4,"language":"en","content_type":"Work","summary":"Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. We here apply a statistical recruitment model for Northeast Arctic cod to evaluate the sensitivity of cod cohorts to loss of zooplankton prey, for example following an oil spill. The calculations show that cod cohorts are highly sensitive to possible zooplankton biomass reductions in the distribution area of the cod larvae, and point to a need for more knowledge about oileffects on zooplankton. Our study illustrates how knowledge about population dynamics may guide which indirect effects to consider in environmental impact studies.","owner":{"id":4500242,"first_name":"Geir","middle_initials":"","last_name":"Ottersen","page_name":"GeirOttersen","domain_name":"havforskningsinstituttet","created_at":"2013-06-10T20:32:18.054-07:00","display_name":"Geir Ottersen","url":"https://havforskningsinstituttet.academia.edu/GeirOttersen"},"attachments":[{"id":53055352,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/53055352/thumbnails/1.jpg","file_name":"j.marpolbul.2010.11.03420170509-20541-1u1kelw.pdf","download_url":"https://www.academia.edu/attachments/53055352/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Environmental_toxicology_Population_mode.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/53055352/j.marpolbul.2010.11.03420170509-20541-1u1kelw-libre.pdf?1494329523=\u0026response-content-disposition=attachment%3B+filename%3DEnvironmental_toxicology_Population_mode.pdf\u0026Expires=1734214763\u0026Signature=cU7yv8MUXBOp~0ZbejKhgiGRQmmOPQLjj~QVsGG-NfTa-9gNpLOH77g040UgYPuwINF6gnvTOGOFGJWx1rrwCkgZjDVlPEcQDm6ITNB~xFJvHvS~hzzbeEIryTEKxLXoEZSv6Fs9LfO0J7iPhqvByUVRluCZC2UEkDzPGIDnajfIL19B5oJhmkTeRbwEpKavMOrxFewd1V9xLcE8bnkfpz-IZXMaZI7PcPkvRqy4wx9tXEE9Qo8s3G2P2UQEIX~Lke5Zv-oAPzA~X-SqEfCuQj3B0khn2K71ONyKwYk6wqFM9NzPWREtEvZSnMyNQrZOjdvLrsBKqJfn3IdKzjw~FA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":5285,"name":"Petroleum","url":"https://www.academia.edu/Documents/in/Petroleum"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling"},{"id":9513,"name":"MARINE POLLUTION","url":"https://www.academia.edu/Documents/in/MARINE_POLLUTION"},{"id":11417,"name":"Population Dynamics","url":"https://www.academia.edu/Documents/in/Population_Dynamics"},{"id":16664,"name":"Risk assessment","url":"https://www.academia.edu/Documents/in/Risk_assessment"},{"id":21724,"name":"Water Pollution","url":"https://www.academia.edu/Documents/in/Water_Pollution"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":35123,"name":"Oil Spill","url":"https://www.academia.edu/Documents/in/Oil_Spill"},{"id":112289,"name":"Gadus morhua","url":"https://www.academia.edu/Documents/in/Gadus_morhua"},{"id":187216,"name":"Zooplankton","url":"https://www.academia.edu/Documents/in/Zooplankton"},{"id":251651,"name":"Environmental Impact","url":"https://www.academia.edu/Documents/in/Environmental_Impact"},{"id":363776,"name":"Population Model","url":"https://www.academia.edu/Documents/in/Population_Model"},{"id":373754,"name":"Ecosystem","url":"https://www.academia.edu/Documents/in/Ecosystem"},{"id":442493,"name":"Larva","url":"https://www.academia.edu/Documents/in/Larva"},{"id":588785,"name":"Prey","url":"https://www.academia.edu/Documents/in/Prey"},{"id":622589,"name":"Risk Assessment","url":"https://www.academia.edu/Documents/in/Risk_Assessment-2"},{"id":962638,"name":"High Sensitivity","url":"https://www.academia.edu/Documents/in/High_Sensitivity"},{"id":1013028,"name":"Food Chain","url":"https://www.academia.edu/Documents/in/Food_Chain"},{"id":1237825,"name":"Population dynamic","url":"https://www.academia.edu/Documents/in/Population_dynamic"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="15646226"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/15646226/Direct_and_indirect_climate_forcing_in_a_multi_species_marine_system"><img alt="Research paper thumbnail of Direct and indirect climate forcing in a multi-species marine system" class="work-thumbnail" src="https://attachments.academia-assets.com/43001706/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/15646226/Direct_and_indirect_climate_forcing_in_a_multi_species_marine_system">Direct and indirect climate forcing in a multi-species marine system</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://havforskningsinstituttet.academia.edu/GeirOttersen">Geir Ottersen</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://spbu.academia.edu/DmitryLajus">Dmitry Lajus</a></span></div><div class="wp-workCard_item"><span>Proceedings. Biological sciences / The Royal Society</span><span>, Jan 22, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Interactions within and between species complicate quantification of climate effects, by causing ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Interactions within and between species complicate quantification of climate effects, by causing indirect, often delayed, effects of climate fluctuations and compensation of mortality. Here we identify direct and indirect climate effects by analysing unique Russian time-series data from the Norwegian Sea-Barents Sea ecosystem on the first life stages of cod, capelin, herring and haddock, their predators, competitors and zooplanktonic prey. By analysing growth and survival from one life stage to the next (eggs-larvae-juveniles-recruits), we find evidence for both bottom-up, direct and top-down effects of climate. Ambient zooplankton biomass predicts survival of all species, whereas ambient temperature mainly affects survival through effects on growth. In warm years, all species experienced improved growth and feeding conditions. Cohorts born following a warm year will, however, experience increased predation and competition because of increased densities of subadult cod and herring, ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="96f87194ee0486d3e4e5a965a1d98f44" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":43001706,"asset_id":15646226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/43001706/download_file?st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&st=MTczNDIxMTE2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="15646226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="15646226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15646226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=15646226]").text(description); $(".js-view-count[data-work-id=15646226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 15646226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='15646226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 15646226, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "96f87194ee0486d3e4e5a965a1d98f44" } } $('.js-work-strip[data-work-id=15646226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":15646226,"title":"Direct and indirect climate forcing in a multi-species marine system","translated_title":"","metadata":{"abstract":"Interactions within and between species complicate quantification of climate effects, by causing indirect, often delayed, effects of climate fluctuations and compensation of mortality. 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