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Agent-based models (ABM) offer a promising way to simulate these decisions, yet many fishery models simplify behavior to economic motivation. This article introduces FISHCODE, a spatio-temporal ABM for German fisheries in the southern North Sea, combining habitual behavior, profit maximization, competition, conformism, and planning insecurity. Validation against observed data shows realistic output ranges. The model is used to assess offshore wind farm expansion and rising fuel prices.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ecological-modelling-501-2025-simulating-fishery-dynamics-by-combining-empirical-data-and-behavioral-theory/272859/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ecological-modelling-501-2025-simulating-fishery-dynamics-by-combining-empirical-data-and-behavioral-theory/272859.png","ImageObject",300,407,{"name":92,"@type":93},"Aladdin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are agent-based models important for fisheries socio-ecological systems?","Question",{"text":113,"@type":114},"Agent-based models are increasingly valued for simulating human decision-making in socio-ecological systems. They can leverage growing empirical data and support scenario analysis as “virtual laboratories.”","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does FISHCODE add to existing fishery decision modeling?",{"text":118,"@type":114},"FISHCODE is a spatio-temporal ABM for German fisheries that combines multiple behavioral motivations. These include habitual behavior, profit maximization, competition, conformism, and planning insecurity rather than relying on a single economic driver.",{"name":120,"@type":111,"acceptedAnswer":121},"How was FISHCODE validated and what scenarios were tested?",{"text":122,"@type":114},"Model validation compared outputs with observed data and showed realistic ranges. The study applied FISHCODE to assess challenges such as offshore wind farm expansions and increasing fuel prices in the North Sea.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},272859,1789468101,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},2336478503145,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Ecological Modelling 501 (2025) 111036  \nContents lists available at ScienceDirect  \nEcological Modelling  \njournal [homepage:](homepage: www.elsevier.com/locate/ecolmodel)[ www.elsevier.com/locate/ecolmodel](homepage: www.elsevier.com/locate/ecolmodel)  \n| Simulating fishery dynamics by combining empirical data and behavioral theory\u003Cbr>Jonas Letschert a,* , Birgit Müller b,c,d, Gunnar Dresslerb, Christian M¨ollmanne, Vanessa Stelzenmüllera\u003Cbr>a Thuenen Institute of Sea Fisheries, Bremerhaven, Germany b Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany c Brandenburg University of Cottbus-Senftenberg, Cottbus, Germany\u003Cbr>d iDiv German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Leipzig, Germany e Institute for Marine Ecosystem and Fisheries Science, University of Hamburg, Hamburg, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Agent-based modeling Fisheries\u003Cbr>Human behavior Marine spatial planning Socio-ecological system |  | Understanding human decision-making in the context of complex fisheries socio-ecological systems remains oneof the key challenges for ecosystem-based management. Agent-based models (ABM) are increasingly seen as oneof the most promising methods to simulate human decision-making. In many fishery models, human behavior is highly simplified and reduced to an economic motivation, although scientific literature suggests that it is more multi‑facetted. Here, we present FISHCODE a spatio-temporal ABM for German fisheries in the southern North Sea. Our decision‑making submodel combines different behavioral motivations, i.e. habitual behavior, profitmaximization, competition, conformism, and planning insecurity. Using highly resolved information on fishing trips, we parameterized model parameters either straight from data or through pattern‑oriented modelling. Model validation showed that model outputs were in realistic ranges when compared to observed data. We applied FISHCODE to assess scenarios of two growing challenges to fisheries in the North Sea: expansions of offshore wind farms and increasing fuel prices. |\n\n1. Introduction  \nAgent-based modelling (ABM) is an increasingly valued method to simulate human decision-making in socio-ecological systems (An, 2012; Rounsevell et al., 2012; Schwarz et al., 2020). Growing amounts of available empirical data offer the chance for more complex models and increase their structural realism (An et al., 2023; Elsawah et al., 2020). With a higher degree of realism, ABMs can be applied for simulation and scenario analysis and may represent virtual laboratories, in which the manipulation of model parameters allows estimating effects on real-world counterpart (Bruch and Atwell, 2015).  \nOne facet where ABMs often come short is the representation of realistic human behavior. In a review of land-use ABMs, Groeneveldet al. (2017) found that most models lack a theoretical foundation for human behavior and among those that used theories, economics was the dominant discipline. The same holds true for models of fisher behavior with rational choice being the most applied theory (Andrews et al., 2020; Haase et al., 2023; Van Putten et al., 2012), although a growing  \namount of scientific literature suggests that human behavior is more multifaceted (Barz et al., 2020; Schlüter et al., 2017; Wijermans et al., 2020). Examples are fishers’ work rhythms, as some fishers prefer to be home on the weekend or by the end of the day (Letschert et al., 2023; Schadeberg et al., 2021) and social networks in which fishers share information and influence each other’s actions (Barnes et al., 2017; Wilson, 1990).  \nFishers often have a strong attachment to their occupation and despite fishing being hard work, very dangerous, and uncertain with regard to profits, fishers still continue their business, because to them it is more than a way of generating income (Pollnac and Poggie, 2008; Stelzenmüller et al., 2024a). F","cbCaibzL4pi3YHYP","https://ap.wps.com/l/cbCaibzL4pi3YHYP","pdf",6947618,14,"English","# Introduction","[{\"question\":\"Why are agent-based models important for fisheries socio-ecological systems?\",\"answer\":\"Agent-based models are increasingly valued for simulating human decision-making in socio-ecological systems. They can leverage growing empirical data and support scenario analysis as “virtual laboratories.”\"},{\"question\":\"What does FISHCODE add to existing fishery decision modeling?\",\"answer\":\"FISHCODE is a spatio-temporal ABM for German fisheries that combines multiple behavioral motivations. These include habitual behavior, profit maximization, competition, conformism, and planning insecurity rather than relying on a single economic driver.\"},{\"question\":\"How was FISHCODE validated and what scenarios were tested?\",\"answer\":\"Model validation compared outputs with observed data and showed realistic ranges. The study applied FISHCODE to assess challenges such as offshore wind farm expansions and increasing fuel prices in the North Sea.\"}]","Ecological Modelling 501 (2025) - Simulating fishery dynamics by combining empirical data and behavioral theory | PDF",35]