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Estimating thermal response metrics for North American freshwater fish using Bayesian phylogenetic regression
Physiological performance in fish peaks within a well-defined range of temperatures, which is distinct for each species. Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on...
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Published in: | Canadian journal of fisheries and aquatic sciences 2018-11, Vol.75 (11), p.1878-1885 |
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creator | Hasnain, Sarah S Escobar, Michael D Shuter, Brian J |
description | Physiological performance in fish peaks within a well-defined range of temperatures, which is distinct for each species. Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on six specific metrics: optimum growth temperature and final temperature preferendum (growth), upper incipient lethal temperature and critical thermal maximum (survival), and optimum spawning temperature and optimum egg development temperature (reproduction). These values remain unknown for many North American freshwater fish species. In this paper, we present a new statistical method (Bayesian phylogenetic regression) that uses relationships between these metrics and phenetic relatedness to estimate unknown metric values. The reliability of these estimates was compared with those derived from models incorporating taxonomic family and models without any taxonomic information. Overall, incorporating taxonomic family relatedness improved estimation accuracy across all metrics. For Salmonidae and Cyprinidae, estimates derived from Bayesian phylogenetic regression typically had the highest expected reliability. We used our methods to generate 274 estimates of unknown metric values for over 100 North American freshwater fish species. |
doi_str_mv | 10.1139/cjfas-2017-0278 |
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Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on six specific metrics: optimum growth temperature and final temperature preferendum (growth), upper incipient lethal temperature and critical thermal maximum (survival), and optimum spawning temperature and optimum egg development temperature (reproduction). These values remain unknown for many North American freshwater fish species. In this paper, we present a new statistical method (Bayesian phylogenetic regression) that uses relationships between these metrics and phenetic relatedness to estimate unknown metric values. The reliability of these estimates was compared with those derived from models incorporating taxonomic family and models without any taxonomic information. Overall, incorporating taxonomic family relatedness improved estimation accuracy across all metrics. For Salmonidae and Cyprinidae, estimates derived from Bayesian phylogenetic regression typically had the highest expected reliability. 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Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on six specific metrics: optimum growth temperature and final temperature preferendum (growth), upper incipient lethal temperature and critical thermal maximum (survival), and optimum spawning temperature and optimum egg development temperature (reproduction). These values remain unknown for many North American freshwater fish species. In this paper, we present a new statistical method (Bayesian phylogenetic regression) that uses relationships between these metrics and phenetic relatedness to estimate unknown metric values. The reliability of these estimates was compared with those derived from models incorporating taxonomic family and models without any taxonomic information. Overall, incorporating taxonomic family relatedness improved estimation accuracy across all metrics. 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We used our methods to generate 274 estimates of unknown metric values for over 100 North American freshwater fish species.</description><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>Biogeny</subject><subject>Cyprinidae</subject><subject>Estimates</subject><subject>Fish</subject><subject>Fish eggs</subject><subject>Fish reproduction</subject><subject>Fishery research</subject><subject>Freshwater</subject><subject>Freshwater fish</subject><subject>Freshwater fishes</subject><subject>Growth</subject><subject>Heat tolerance (Biology)</subject><subject>Laboratory experimentation</subject><subject>Larvae</subject><subject>Larval stage</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Reliability</subject><subject>Reproduction</subject><subject>Reproduction (biology)</subject><subject>Salmonidae</subject><subject>Spawning</subject><subject>Speciation</subject><subject>Species</subject><subject>Species identification</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Survival</subject><subject>Taxonomy</subject><subject>Temperature</subject><subject>Temperature effects</subject><subject>Temperature preferences</subject><subject>Temperature tolerance</subject><subject>Thermal response</subject><subject>Water</subject><subject>Water temperature</subject><issn>0706-652X</issn><issn>1205-7533</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqVks1v1DAQxSMEEkvhzDWCE4e0HnudZI9L1S-pKhItEjfL8Y4Tr5I4tb2C_e87YTlQaQVCPlga_94bj_2y7D2wUwCxOjNbq2PBGVQF41X9IlsAZ7KopBAvswWrWFmUkn9_nb2JccsYcAlskQ0XMblBJze2eeowDLrPA8bJjxHzAVNwJubWh_zOh9Tl6wGposfcEtT90AlDbl3s8l2cHT7rPUZHx1O3732LIyZnyK8lOjo_vs1eWd1HfPd7P8m-XV48nF8Xt1-ubs7Xt4WRwFPB0fKmrJHVTCwrsJVZlaVuuBBmI-jeUDfGMAuWIYcGwaygXgoJVjaaZt6Ik-zjwXcK_nGHMamt34WRWirOl9WqLsn4rxQ9D2OSVxVRxYFqdY_KjdanoM08W9C9H9E6Kq8lOQJIAcR_OMKbyT2qP6HTIxCtDQ7OHHX99ExATMKfqdW7GNXN_df_YO-es2cH1gQfY0CrpkB5CHsFTM25Ur9ypeZcqTlXpOAHxRgMfSvqYLp_ip4ANa7O-w</recordid><startdate>20181101</startdate><enddate>20181101</enddate><creator>Hasnain, Sarah S</creator><creator>Escobar, Michael D</creator><creator>Shuter, Brian J</creator><general>NRC Research Press</general><general>Canadian Science Publishing NRC Research Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7QG</scope><scope>7QH</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7U7</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>F1W</scope><scope>H95</scope><scope>H96</scope><scope>H97</scope><scope>H98</scope><scope>H99</scope><scope>L.F</scope><scope>L.G</scope></search><sort><creationdate>20181101</creationdate><title>Estimating thermal response metrics for North American freshwater fish using Bayesian phylogenetic regression</title><author>Hasnain, Sarah S ; Escobar, Michael D ; Shuter, Brian J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c512t-2ef2b68e0803471f7c966ab233cd325118bcc0f1f0e21be1c9184351f5ba205d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bayesian analysis</topic><topic>Bayesian theory</topic><topic>Biogeny</topic><topic>Cyprinidae</topic><topic>Estimates</topic><topic>Fish</topic><topic>Fish eggs</topic><topic>Fish reproduction</topic><topic>Fishery research</topic><topic>Freshwater</topic><topic>Freshwater fish</topic><topic>Freshwater fishes</topic><topic>Growth</topic><topic>Heat tolerance (Biology)</topic><topic>Laboratory experimentation</topic><topic>Larvae</topic><topic>Larval stage</topic><topic>Mathematical models</topic><topic>Modelling</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Reliability</topic><topic>Reproduction</topic><topic>Reproduction (biology)</topic><topic>Salmonidae</topic><topic>Spawning</topic><topic>Speciation</topic><topic>Species</topic><topic>Species identification</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Survival</topic><topic>Taxonomy</topic><topic>Temperature</topic><topic>Temperature effects</topic><topic>Temperature preferences</topic><topic>Temperature tolerance</topic><topic>Thermal response</topic><topic>Water</topic><topic>Water temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hasnain, Sarah S</creatorcontrib><creatorcontrib>Escobar, Michael D</creatorcontrib><creatorcontrib>Shuter, Brian J</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>Animal Behavior Abstracts</collection><collection>Aqualine</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Toxicology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</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) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Aquaculture Abstracts</collection><collection>ASFA: Marine Biotechnology Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Marine Biotechnology Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Canadian journal of fisheries and aquatic sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hasnain, Sarah S</au><au>Escobar, Michael D</au><au>Shuter, Brian J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating thermal response metrics for North American freshwater fish using Bayesian phylogenetic regression</atitle><jtitle>Canadian journal of fisheries and aquatic sciences</jtitle><date>2018-11-01</date><risdate>2018</risdate><volume>75</volume><issue>11</issue><spage>1878</spage><epage>1885</epage><pages>1878-1885</pages><issn>0706-652X</issn><eissn>1205-7533</eissn><abstract>Physiological performance in fish peaks within a well-defined range of temperatures, which is distinct for each species. Species-specific thermal responses for growth, survival, and reproduction are most commonly quantified directly through laboratory experiment or field observation, with a focus on six specific metrics: optimum growth temperature and final temperature preferendum (growth), upper incipient lethal temperature and critical thermal maximum (survival), and optimum spawning temperature and optimum egg development temperature (reproduction). These values remain unknown for many North American freshwater fish species. In this paper, we present a new statistical method (Bayesian phylogenetic regression) that uses relationships between these metrics and phenetic relatedness to estimate unknown metric values. The reliability of these estimates was compared with those derived from models incorporating taxonomic family and models without any taxonomic information. Overall, incorporating taxonomic family relatedness improved estimation accuracy across all metrics. For Salmonidae and Cyprinidae, estimates derived from Bayesian phylogenetic regression typically had the highest expected reliability. We used our methods to generate 274 estimates of unknown metric values for over 100 North American freshwater fish species.</abstract><cop>Ottawa</cop><pub>NRC Research Press</pub><doi>10.1139/cjfas-2017-0278</doi><tpages>8</tpages></addata></record> |
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subjects | Bayesian analysis Bayesian theory Biogeny Cyprinidae Estimates Fish Fish eggs Fish reproduction Fishery research Freshwater Freshwater fish Freshwater fishes Growth Heat tolerance (Biology) Laboratory experimentation Larvae Larval stage Mathematical models Modelling Phylogenetics Phylogeny Regression Regression analysis Reliability Reproduction Reproduction (biology) Salmonidae Spawning Speciation Species Species identification Statistical analysis Statistical methods Survival Taxonomy Temperature Temperature effects Temperature preferences Temperature tolerance Thermal response Water Water temperature |
title | Estimating thermal response metrics for North American freshwater fish using Bayesian phylogenetic regression |
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