Patterns of population variability in marine fish stocks
Exploited marine fish and invertebrate stocks fluctuate in a myriad of complex patterns, exhibiting variability on interannual, decadal, and longer time scales. To characterize various patterns of variation, time series of catch, catch per unit effort, or biomass from 30 stocks were examined with a...
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Veröffentlicht in: | Fisheries oceanography 1997-10, Vol.6 (3), p.188-204 |
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description | Exploited marine fish and invertebrate stocks fluctuate in a myriad of complex patterns, exhibiting variability on interannual, decadal, and longer time scales. To characterize various patterns of variation, time series of catch, catch per unit effort, or biomass from 30 stocks were examined with a variety of statistical methods including autocorrelation analysis and Lowess smoothing. A hierarchical cluster analysis classified the stocks into six identifiable groups: steady‐state; low‐variation, low‐frequency; cyclic; irregular; high‐variation, high‐frequency; and spasmodic. The observed patterns are consistent with life history traits; for example, stocks with high variability are generally small, pelagic species whereas low‐variability stocks are generally slow‐growing, demersal fish. Each of the six general patterns of variability can be produced from a simple multiple‐equilibrium population model by varying the intrinsic rate of population growth, and the time scale and amplitude of environmental variability. Suitable management policies depend on the type of variation observed, and the vast majority of stocks examined do not correspond to the steady‐state assumptions of classical fisheries models. For example, management of spasmodic stocks may alternate between periods of active exploitation and periods of rebuilding, a process enhanced by the existence of alternative fisheries. |
doi_str_mv | 10.1046/j.1365-2419.1997.00039.x |
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To characterize various patterns of variation, time series of catch, catch per unit effort, or biomass from 30 stocks were examined with a variety of statistical methods including autocorrelation analysis and Lowess smoothing. A hierarchical cluster analysis classified the stocks into six identifiable groups: steady‐state; low‐variation, low‐frequency; cyclic; irregular; high‐variation, high‐frequency; and spasmodic. The observed patterns are consistent with life history traits; for example, stocks with high variability are generally small, pelagic species whereas low‐variability stocks are generally slow‐growing, demersal fish. Each of the six general patterns of variability can be produced from a simple multiple‐equilibrium population model by varying the intrinsic rate of population growth, and the time scale and amplitude of environmental variability. Suitable management policies depend on the type of variation observed, and the vast majority of stocks examined do not correspond to the steady‐state assumptions of classical fisheries models. 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Suitable management policies depend on the type of variation observed, and the vast majority of stocks examined do not correspond to the steady‐state assumptions of classical fisheries models. For example, management of spasmodic stocks may alternate between periods of active exploitation and periods of rebuilding, a process enhanced by the existence of alternative fisheries.</description><subject>cluster analysis</subject><subject>fisheries management</subject><subject>Marine</subject><subject>Pisces</subject><subject>regime shifts</subject><subject>variability patterns</subject><issn>1054-6006</issn><issn>1365-2419</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNqNkFFLwzAQx4soOKffIU--tV6SNmnAFxmuCtNNUQRfQpYmmK1ra9Pp9u1trezZp_sfd7_j-AUBwhBhiNnVKsKUJSGJsYiwEDwCACqi3VEwOgyOuwxJHDIAdhqceb8CwJxyPgrShWpb05QeVRbVVb0tVOuqEn2pxqmlK1y7R65Em64tDbLOfyDfVnrtz4MTqwpvLv7qOHid3r5M7sLZPLuf3MxCHfNYhJhoa7QxS8pFknObqMSkGHIAK3RCc6CUYCviJcsxN0RpBkpboDZNwWJC6Di4HO7WTfW5Nb6VG-e1KQpVmmrrJWZEpIRAt5gOi7qpvG-MlXXjur_3EoPsVcmV7I3I3ojsVclfVXLXodcD-u0Ks_83J6fzrAsdHg64863ZHXDVrCXrLCfy7TGTkwV7f06fHmRGfwDS536p</recordid><startdate>199710</startdate><enddate>199710</enddate><creator>SPENCER, PAUL D.</creator><creator>COLLIE, JEREMY S.</creator><general>Blackwell Science Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7TN</scope><scope>C1K</scope><scope>F1W</scope><scope>H95</scope><scope>L.G</scope></search><sort><creationdate>199710</creationdate><title>Patterns of population variability in marine fish stocks</title><author>SPENCER, PAUL D. ; COLLIE, JEREMY S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4749-12cfeceeb3795d7f5a5e810d00f9c53d03321f94b6d17e2ac60acf03f880f1223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>cluster analysis</topic><topic>fisheries management</topic><topic>Marine</topic><topic>Pisces</topic><topic>regime shifts</topic><topic>variability patterns</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>SPENCER, PAUL D.</creatorcontrib><creatorcontrib>COLLIE, JEREMY S.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Oceanic 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) Professional</collection><jtitle>Fisheries oceanography</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>SPENCER, PAUL D.</au><au>COLLIE, JEREMY S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Patterns of population variability in marine fish stocks</atitle><jtitle>Fisheries oceanography</jtitle><date>1997-10</date><risdate>1997</risdate><volume>6</volume><issue>3</issue><spage>188</spage><epage>204</epage><pages>188-204</pages><issn>1054-6006</issn><eissn>1365-2419</eissn><abstract>Exploited marine fish and invertebrate stocks fluctuate in a myriad of complex patterns, exhibiting variability on interannual, decadal, and longer time scales. To characterize various patterns of variation, time series of catch, catch per unit effort, or biomass from 30 stocks were examined with a variety of statistical methods including autocorrelation analysis and Lowess smoothing. A hierarchical cluster analysis classified the stocks into six identifiable groups: steady‐state; low‐variation, low‐frequency; cyclic; irregular; high‐variation, high‐frequency; and spasmodic. The observed patterns are consistent with life history traits; for example, stocks with high variability are generally small, pelagic species whereas low‐variability stocks are generally slow‐growing, demersal fish. Each of the six general patterns of variability can be produced from a simple multiple‐equilibrium population model by varying the intrinsic rate of population growth, and the time scale and amplitude of environmental variability. Suitable management policies depend on the type of variation observed, and the vast majority of stocks examined do not correspond to the steady‐state assumptions of classical fisheries models. For example, management of spasmodic stocks may alternate between periods of active exploitation and periods of rebuilding, a process enhanced by the existence of alternative fisheries.</abstract><cop>Oxford, UK</cop><pub>Blackwell Science Ltd</pub><doi>10.1046/j.1365-2419.1997.00039.x</doi><tpages>17</tpages></addata></record> |
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subjects | cluster analysis fisheries management Marine Pisces regime shifts variability patterns |
title | Patterns of population variability in marine fish stocks |
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