Predicting distributions of seven bitterling fishes in northern Kyushu, Japan
The distributions of seven bitterling species and subspecies—Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius—in northern Kyushu were predicted using generalized linear models (GLMs) in order to...
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description | The distributions of seven bitterling species and subspecies—Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius—in northern Kyushu were predicted using generalized linear models (GLMs) in order to provide information helpful for conserving native bitterlings and preventing the expansion of alien bitterling species. Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4–6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. These predictive models can be used for evaluating potential native bitterling richness and the potential distribution expansion of an alien subspecies. |
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Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4–6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. These predictive models can be used for evaluating potential native bitterling richness and the potential distribution expansion of an alien subspecies.</description><identifier>ISSN: 1341-8998</identifier><identifier>EISSN: 1616-3915</identifier><identifier>DOI: 10.1007/s10228-011-0260-0</identifier><language>eng</language><publisher>Japan: Springer-Verlag</publisher><subject>Acheilognathus ; Animal Systematics/Taxonomy/Biogeography ; Biomedical and Life Sciences ; computer software ; Dispersal ; Ecology ; Environmental factors ; Fish ; Freshwater & Marine Ecology ; geographic information systems ; geographical distribution ; indigenous species ; introduced species ; Life Sciences ; linear models ; Marine ecology ; model validation ; Pisces ; prediction ; Prediction models ; Rhodeus ocellatus kurumeus ; Zoology</subject><ispartof>Ichthyological research, 2012-04, Vol.59 (2), p.124-133</ispartof><rights>The Ichthyological Society of Japan 2011</rights><rights>The Ichthyological Society of Japan 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-ac080dc87753eeddf643c59bfdac17f56e1c67a66a1d707ab14d376940959b333</citedby><cites>FETCH-LOGICAL-c466t-ac080dc87753eeddf643c59bfdac17f56e1c67a66a1d707ab14d376940959b333</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10228-011-0260-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10228-011-0260-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Onikura, Norio</creatorcontrib><creatorcontrib>Nakajima, Jun</creatorcontrib><creatorcontrib>Miyake, Takuya</creatorcontrib><creatorcontrib>Kawamura, Kouichi</creatorcontrib><creatorcontrib>Fukuda, Shinji</creatorcontrib><title>Predicting distributions of seven bitterling fishes in northern Kyushu, Japan</title><title>Ichthyological research</title><addtitle>Ichthyol Res</addtitle><description>The distributions of seven bitterling species and subspecies—Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius—in northern Kyushu were predicted using generalized linear models (GLMs) in order to provide information helpful for conserving native bitterlings and preventing the expansion of alien bitterling species. Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4–6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. 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Nakajima, Jun ; Miyake, Takuya ; Kawamura, Kouichi ; Fukuda, Shinji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-ac080dc87753eeddf643c59bfdac17f56e1c67a66a1d707ab14d376940959b333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Acheilognathus</topic><topic>Animal Systematics/Taxonomy/Biogeography</topic><topic>Biomedical and Life Sciences</topic><topic>computer software</topic><topic>Dispersal</topic><topic>Ecology</topic><topic>Environmental factors</topic><topic>Fish</topic><topic>Freshwater & Marine Ecology</topic><topic>geographic information systems</topic><topic>geographical distribution</topic><topic>indigenous species</topic><topic>introduced species</topic><topic>Life Sciences</topic><topic>linear models</topic><topic>Marine ecology</topic><topic>model validation</topic><topic>Pisces</topic><topic>prediction</topic><topic>Prediction models</topic><topic>Rhodeus ocellatus kurumeus</topic><topic>Zoology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Onikura, Norio</creatorcontrib><creatorcontrib>Nakajima, Jun</creatorcontrib><creatorcontrib>Miyake, Takuya</creatorcontrib><creatorcontrib>Kawamura, Kouichi</creatorcontrib><creatorcontrib>Fukuda, Shinji</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oceanic Abstracts</collection><collection>Biology Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</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 Central UK/Ireland</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>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</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>Biotechnology and BioEngineering Abstracts</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>Genetics Abstracts</collection><jtitle>Ichthyological research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Onikura, Norio</au><au>Nakajima, Jun</au><au>Miyake, Takuya</au><au>Kawamura, Kouichi</au><au>Fukuda, Shinji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting distributions of seven bitterling fishes in northern Kyushu, Japan</atitle><jtitle>Ichthyological research</jtitle><stitle>Ichthyol Res</stitle><date>2012-04-01</date><risdate>2012</risdate><volume>59</volume><issue>2</issue><spage>124</spage><epage>133</epage><pages>124-133</pages><issn>1341-8998</issn><eissn>1616-3915</eissn><abstract>The distributions of seven bitterling species and subspecies—Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius—in northern Kyushu were predicted using generalized linear models (GLMs) in order to provide information helpful for conserving native bitterlings and preventing the expansion of alien bitterling species. Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4–6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. These predictive models can be used for evaluating potential native bitterling richness and the potential distribution expansion of an alien subspecies.</abstract><cop>Japan</cop><pub>Springer-Verlag</pub><doi>10.1007/s10228-011-0260-0</doi><tpages>10</tpages></addata></record> |
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subjects | Acheilognathus Animal Systematics/Taxonomy/Biogeography Biomedical and Life Sciences computer software Dispersal Ecology Environmental factors Fish Freshwater & Marine Ecology geographic information systems geographical distribution indigenous species introduced species Life Sciences linear models Marine ecology model validation Pisces prediction Prediction models Rhodeus ocellatus kurumeus Zoology |
title | Predicting distributions of seven bitterling fishes in northern Kyushu, Japan |
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