Atlantic bluefin tuna in the Gulf of Maine, I: estimation of seasonal abundance accounting for movement, school and school-aggregation behaviour
Direct assessment of the abundance of highly migratory pelagic species, such as tuna, is rarely available and most indices are based on catch information. We estimate the seasonal abundance of North Atlantic bluefin tuna, Thunnus thynnus, in the Gulf of Maine (GOM) from a 3-year aerial survey conduc...
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description | Direct assessment of the abundance of highly migratory pelagic species, such as tuna, is rarely available and most indices are based on catch information. We estimate the seasonal abundance of North Atlantic bluefin tuna, Thunnus thynnus, in the Gulf of Maine (GOM) from a 3-year aerial survey conducted with commercial spotter pilots, while also utilizing findings from analyses of tracking and tagging data. We apply statistical correction and calibration to seasonal abundance estimates accounting for measured changes in horizontal and vertical movement behaviour, size, shape and aggregation of bluefin tuna schools. Our approach relies on ecological knowledge of bluefin tuna to extrapolate survey observations across areas not sampled by correcting survey abundance estimates based on range of movement search pattern and depth preference. We demonstrate how separate findings obtained through the analysis of data collected across different spatial and temporal scales can be integrated to correct and calibrate estimates of population abundance. We obtain fitted estimates of seasonal abundance of bluefin tuna in the GOM during 1994-1996 in the range of 45,000-51,000 individuals. If tuna behaviour is not accounted for, we estimate that the base or residual survey precision would be 4-7% determined from analysis of recent spotter survey data in the study region. We estimate the precision in estimating seasonal abundance accounting for tuna behaviour to lie within a range of 1,301-3,302%. Under hypothetical future improvements in survey design that achieve a precision of 20% in transect length and placement, we calculate net-precision to lie within a range of 82-93%. This calculation assumes reducible uncertainty in school size estimation and irreducible uncertainty in movement and school-aggregation behaviour. We infer that survey precision could be further reduced by 43-32% to attain 10-50% in which a 3-8 years adaptive survey design may reliably detect a seasonal abundance trend.[PUBLICATION ABSTRACT] |
doi_str_mv | 10.1007/s10641-006-9069-5 |
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We estimate the seasonal abundance of North Atlantic bluefin tuna, Thunnus thynnus, in the Gulf of Maine (GOM) from a 3-year aerial survey conducted with commercial spotter pilots, while also utilizing findings from analyses of tracking and tagging data. We apply statistical correction and calibration to seasonal abundance estimates accounting for measured changes in horizontal and vertical movement behaviour, size, shape and aggregation of bluefin tuna schools. Our approach relies on ecological knowledge of bluefin tuna to extrapolate survey observations across areas not sampled by correcting survey abundance estimates based on range of movement search pattern and depth preference. We demonstrate how separate findings obtained through the analysis of data collected across different spatial and temporal scales can be integrated to correct and calibrate estimates of population abundance. We obtain fitted estimates of seasonal abundance of bluefin tuna in the GOM during 1994-1996 in the range of 45,000-51,000 individuals. If tuna behaviour is not accounted for, we estimate that the base or residual survey precision would be 4-7% determined from analysis of recent spotter survey data in the study region. We estimate the precision in estimating seasonal abundance accounting for tuna behaviour to lie within a range of 1,301-3,302%. Under hypothetical future improvements in survey design that achieve a precision of 20% in transect length and placement, we calculate net-precision to lie within a range of 82-93%. This calculation assumes reducible uncertainty in school size estimation and irreducible uncertainty in movement and school-aggregation behaviour. We infer that survey precision could be further reduced by 43-32% to attain 10-50% in which a 3-8 years adaptive survey design may reliably detect a seasonal abundance trend.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 0378-1909</identifier><identifier>EISSN: 1573-5133</identifier><identifier>DOI: 10.1007/s10641-006-9069-5</identifier><identifier>CODEN: EBFID3</identifier><language>eng</language><publisher>Dordrecht: Springer</publisher><subject>Aggregation behavior ; Agnatha. Pisces ; Animal and plant ecology ; Animal, plant and microbial ecology ; Animals ; Applied ecology ; Autoecology ; Biological and medical sciences ; Estimates ; Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.) ; Fish ; Fundamental and applied biological sciences. 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We estimate the seasonal abundance of North Atlantic bluefin tuna, Thunnus thynnus, in the Gulf of Maine (GOM) from a 3-year aerial survey conducted with commercial spotter pilots, while also utilizing findings from analyses of tracking and tagging data. We apply statistical correction and calibration to seasonal abundance estimates accounting for measured changes in horizontal and vertical movement behaviour, size, shape and aggregation of bluefin tuna schools. Our approach relies on ecological knowledge of bluefin tuna to extrapolate survey observations across areas not sampled by correcting survey abundance estimates based on range of movement search pattern and depth preference. We demonstrate how separate findings obtained through the analysis of data collected across different spatial and temporal scales can be integrated to correct and calibrate estimates of population abundance. We obtain fitted estimates of seasonal abundance of bluefin tuna in the GOM during 1994-1996 in the range of 45,000-51,000 individuals. If tuna behaviour is not accounted for, we estimate that the base or residual survey precision would be 4-7% determined from analysis of recent spotter survey data in the study region. We estimate the precision in estimating seasonal abundance accounting for tuna behaviour to lie within a range of 1,301-3,302%. Under hypothetical future improvements in survey design that achieve a precision of 20% in transect length and placement, we calculate net-precision to lie within a range of 82-93%. This calculation assumes reducible uncertainty in school size estimation and irreducible uncertainty in movement and school-aggregation behaviour. We infer that survey precision could be further reduced by 43-32% to attain 10-50% in which a 3-8 years adaptive survey design may reliably detect a seasonal abundance trend.[PUBLICATION ABSTRACT]</description><subject>Aggregation behavior</subject><subject>Agnatha. Pisces</subject><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>Animals</subject><subject>Applied ecology</subject><subject>Autoecology</subject><subject>Biological and medical sciences</subject><subject>Estimates</subject><subject>Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.)</subject><subject>Fish</subject><subject>Fundamental and applied biological sciences. 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Psychology</topic><topic>Marine</topic><topic>Migratory species</topic><topic>Polls & surveys</topic><topic>Schools</topic><topic>Thunnus thynnus</topic><topic>Tuna</topic><topic>Vertebrata</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>NEWLANDS, Nathaniel K</creatorcontrib><creatorcontrib>LUTCAVAGE, Molly E</creatorcontrib><creatorcontrib>PITCHER, Tony J</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Aqualine</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Biology Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><jtitle>Environmental biology of fishes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>NEWLANDS, Nathaniel K</au><au>LUTCAVAGE, Molly E</au><au>PITCHER, Tony J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Atlantic bluefin tuna in the Gulf of Maine, I: estimation of seasonal abundance accounting for movement, school and school-aggregation behaviour</atitle><jtitle>Environmental biology of fishes</jtitle><date>2006-10-01</date><risdate>2006</risdate><volume>77</volume><issue>2</issue><spage>177</spage><epage>195</epage><pages>177-195</pages><issn>0378-1909</issn><eissn>1573-5133</eissn><coden>EBFID3</coden><abstract>Direct assessment of the abundance of highly migratory pelagic species, such as tuna, is rarely available and most indices are based on catch information. We estimate the seasonal abundance of North Atlantic bluefin tuna, Thunnus thynnus, in the Gulf of Maine (GOM) from a 3-year aerial survey conducted with commercial spotter pilots, while also utilizing findings from analyses of tracking and tagging data. We apply statistical correction and calibration to seasonal abundance estimates accounting for measured changes in horizontal and vertical movement behaviour, size, shape and aggregation of bluefin tuna schools. Our approach relies on ecological knowledge of bluefin tuna to extrapolate survey observations across areas not sampled by correcting survey abundance estimates based on range of movement search pattern and depth preference. We demonstrate how separate findings obtained through the analysis of data collected across different spatial and temporal scales can be integrated to correct and calibrate estimates of population abundance. We obtain fitted estimates of seasonal abundance of bluefin tuna in the GOM during 1994-1996 in the range of 45,000-51,000 individuals. If tuna behaviour is not accounted for, we estimate that the base or residual survey precision would be 4-7% determined from analysis of recent spotter survey data in the study region. We estimate the precision in estimating seasonal abundance accounting for tuna behaviour to lie within a range of 1,301-3,302%. Under hypothetical future improvements in survey design that achieve a precision of 20% in transect length and placement, we calculate net-precision to lie within a range of 82-93%. This calculation assumes reducible uncertainty in school size estimation and irreducible uncertainty in movement and school-aggregation behaviour. We infer that survey precision could be further reduced by 43-32% to attain 10-50% in which a 3-8 years adaptive survey design may reliably detect a seasonal abundance trend.[PUBLICATION ABSTRACT]</abstract><cop>Dordrecht</cop><pub>Springer</pub><doi>10.1007/s10641-006-9069-5</doi><tpages>19</tpages></addata></record> |
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subjects | Aggregation behavior Agnatha. Pisces Animal and plant ecology Animal, plant and microbial ecology Animals Applied ecology Autoecology Biological and medical sciences Estimates Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.) Fish Fundamental and applied biological sciences. Psychology Marine Migratory species Polls & surveys Schools Thunnus thynnus Tuna Vertebrata |
title | Atlantic bluefin tuna in the Gulf of Maine, I: estimation of seasonal abundance accounting for movement, school and school-aggregation behaviour |
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