Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics
Kerr, L. A., Cadrin, S. X., and Secor, D. H. 2010. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. – ICES Journal of Marine Science, 67: 1631–1639. An understanding of the mechanisms underlying population...
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description | Kerr, L. A., Cadrin, S. X., and Secor, D. H. 2010. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. – ICES Journal of Marine Science, 67: 1631–1639. An understanding of the mechanisms underlying population persistence makes fisheries management more effective. A model framework is described, which can test hypotheses about spatial structure and connectivity within and between populations and their influence on the productivity (spawning-stock biomass, SSB), stability (variation in SSB), resilience (time to rebuild SSB after environmental disturbance), and sustainability (maximum sustainable fishing mortality and yield) of systems. The general model consists of linked age-structured submodels that incorporate the unique demographics and dynamics of population components, along with the degree and type of connectivity between them. The flexibility of this framework is illustrated with three case studies examining (i) spatial structure within a population of white perch, (ii) different types and degrees of connectivity between populations of Atlantic herring, and (iii) spatial heterogeneity and connectivity within a stock of Atlantic cod. System variance is reduced by abundant, stable population components, and the asynchronous responses of those components. Components with high productivity contributed disproportionately to the resilience of systems. Increased synchrony of component responses to environmental forcing decreased the stability of the overall system. Simulation modelling is a useful approach to evaluate the consequences of spatial structure and connectivity, and can be used to understand better the productivity and dynamics of local and regional populations. |
doi_str_mv | 10.1093/icesjms/fsq053 |
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A., Cadrin, S. X., and Secor, D. H. 2010. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. – ICES Journal of Marine Science, 67: 1631–1639. An understanding of the mechanisms underlying population persistence makes fisheries management more effective. A model framework is described, which can test hypotheses about spatial structure and connectivity within and between populations and their influence on the productivity (spawning-stock biomass, SSB), stability (variation in SSB), resilience (time to rebuild SSB after environmental disturbance), and sustainability (maximum sustainable fishing mortality and yield) of systems. The general model consists of linked age-structured submodels that incorporate the unique demographics and dynamics of population components, along with the degree and type of connectivity between them. The flexibility of this framework is illustrated with three case studies examining (i) spatial structure within a population of white perch, (ii) different types and degrees of connectivity between populations of Atlantic herring, and (iii) spatial heterogeneity and connectivity within a stock of Atlantic cod. System variance is reduced by abundant, stable population components, and the asynchronous responses of those components. Components with high productivity contributed disproportionately to the resilience of systems. Increased synchrony of component responses to environmental forcing decreased the stability of the overall system. 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A., Cadrin, S. X., and Secor, D. H. 2010. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. – ICES Journal of Marine Science, 67: 1631–1639. An understanding of the mechanisms underlying population persistence makes fisheries management more effective. A model framework is described, which can test hypotheses about spatial structure and connectivity within and between populations and their influence on the productivity (spawning-stock biomass, SSB), stability (variation in SSB), resilience (time to rebuild SSB after environmental disturbance), and sustainability (maximum sustainable fishing mortality and yield) of systems. The general model consists of linked age-structured submodels that incorporate the unique demographics and dynamics of population components, along with the degree and type of connectivity between them. The flexibility of this framework is illustrated with three case studies examining (i) spatial structure within a population of white perch, (ii) different types and degrees of connectivity between populations of Atlantic herring, and (iii) spatial heterogeneity and connectivity within a stock of Atlantic cod. System variance is reduced by abundant, stable population components, and the asynchronous responses of those components. Components with high productivity contributed disproportionately to the resilience of systems. Increased synchrony of component responses to environmental forcing decreased the stability of the overall system. Simulation modelling is a useful approach to evaluate the consequences of spatial structure and connectivity, and can be used to understand better the productivity and dynamics of local and regional populations.</description><subject>Computer simulation</subject><subject>connectivity</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Gadus morhua</subject><subject>Marine</subject><subject>Mathematical models</subject><subject>metapopulation</subject><subject>Modelling</subject><subject>persistence</subject><subject>population</subject><subject>Productivity</subject><subject>Regional</subject><subject>spatial structure</subject><issn>1054-3139</issn><issn>1095-9289</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kUtPxSAQhRujic-ta3a66RVKXyyN8ZkbNblqjBtCKSjaQmWo8f4I_7M0V126AoZv5uTMSZJ9gmcEM3pkpILXHo40vOOCriVbsVqkLKvZ-nQv8pQSyjaTbYBXjHGVl3gr-VqYfuxEMM6i3rWq64x9RgKQQMG5DmnnkfoUvbFTPbwoJJ0F9T4qG-WQ0wiG2C06BMGPMoxeIWHbibJKBvNhwhLF2Z2TkZl-vHqOYvExuOFXuV3aKCFhN9nQogO193PuJPdnp3cnF-n85vzy5HieSlpnIXqSbaHrHMtMtJjWDcl10ZQVzcuswEKWjBCSadGIVtRt3AJrGoUJI2VOmaaC7iQHq7mDd9EKBN4bkNG8sMqNwFmGK5YXVR7Jw39JUlU1JhmjLKKzFSq9A_BK88GbXvglJ5hPCfGfhPgqodiQrhoMBPX5Rwv_xqOXquAXj0_8luCrp-vFGX-g32WEmZU</recordid><startdate>201011</startdate><enddate>201011</enddate><creator>Kerr, Lisa A.</creator><creator>Cadrin, Steven X.</creator><creator>Secor, Dave H.</creator><general>Oxford University Press</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>7ST</scope><scope>7TN</scope><scope>7U6</scope><scope>C1K</scope><scope>F1W</scope><scope>H95</scope><scope>H96</scope><scope>L.G</scope><scope>SOI</scope></search><sort><creationdate>201011</creationdate><title>Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics</title><author>Kerr, Lisa A. ; Cadrin, Steven X. ; Secor, Dave H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-92cd5f840c2ad038b14f5b67346250ac691112fabada8d0959bbe01916439f3a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Computer simulation</topic><topic>connectivity</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>Gadus morhua</topic><topic>Marine</topic><topic>Mathematical models</topic><topic>metapopulation</topic><topic>Modelling</topic><topic>persistence</topic><topic>population</topic><topic>Productivity</topic><topic>Regional</topic><topic>spatial structure</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kerr, Lisa A.</creatorcontrib><creatorcontrib>Cadrin, Steven X.</creatorcontrib><creatorcontrib>Secor, Dave H.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>ICES journal of marine science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kerr, Lisa A.</au><au>Cadrin, Steven X.</au><au>Secor, Dave H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics</atitle><jtitle>ICES journal of marine science</jtitle><date>2010-11</date><risdate>2010</risdate><volume>67</volume><issue>8</issue><spage>1631</spage><epage>1639</epage><pages>1631-1639</pages><issn>1054-3139</issn><eissn>1095-9289</eissn><abstract>Kerr, L. A., Cadrin, S. X., and Secor, D. H. 2010. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. – ICES Journal of Marine Science, 67: 1631–1639. An understanding of the mechanisms underlying population persistence makes fisheries management more effective. A model framework is described, which can test hypotheses about spatial structure and connectivity within and between populations and their influence on the productivity (spawning-stock biomass, SSB), stability (variation in SSB), resilience (time to rebuild SSB after environmental disturbance), and sustainability (maximum sustainable fishing mortality and yield) of systems. The general model consists of linked age-structured submodels that incorporate the unique demographics and dynamics of population components, along with the degree and type of connectivity between them. The flexibility of this framework is illustrated with three case studies examining (i) spatial structure within a population of white perch, (ii) different types and degrees of connectivity between populations of Atlantic herring, and (iii) spatial heterogeneity and connectivity within a stock of Atlantic cod. System variance is reduced by abundant, stable population components, and the asynchronous responses of those components. Components with high productivity contributed disproportionately to the resilience of systems. Increased synchrony of component responses to environmental forcing decreased the stability of the overall system. Simulation modelling is a useful approach to evaluate the consequences of spatial structure and connectivity, and can be used to understand better the productivity and dynamics of local and regional populations.</abstract><pub>Oxford University Press</pub><doi>10.1093/icesjms/fsq053</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Computer simulation connectivity Dynamical systems Dynamics Gadus morhua Marine Mathematical models metapopulation Modelling persistence population Productivity Regional spatial structure |
title | Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics |
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