Identifying rice fields at risk from damage by the greater flamingo
1 Since the early 1980s, greater flamingos Phoenicopterus ruber roseus have been reported to cause damage to the rice fields of the Camargue, south‐eastern France. 2 We tested whether some rice fields had landscape features that were more attractive to flamingos than others, using data from the peri...
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description | 1 Since the early 1980s, greater flamingos Phoenicopterus ruber roseus have been reported to cause damage to the rice fields of the Camargue, south‐eastern France. 2 We tested whether some rice fields had landscape features that were more attractive to flamingos than others, using data from the period 1993–97 and from 1978 different paddies. Discriminant function analysis (DFA), logistic regression (LR) and artificial neural networks (ANN) were used to identify the environmental variables best explaining flamingo incursions. 3 The most accurate models (LR) gave 75% prediction success and used as predictors the surface area of rice fields, the presence of contiguous damaged fields, the presence of wooded margins and the distance to natural marshes. 4 Our study suggests that it is possible to identify accurately fields at risk from damage in order to concentrate scaring methods. We also suggest that planting hedges should be promoted, and wood cutting discouraged, in high‐risk areas. Following our study a programme of planting of hedges by the Natural Regional Park of the Camargue started at the beginning of 2000. 5 Our work reveals how presence–absence modelling can have clear applications in managing important species that sometimes cause negative impacts locally. |
doi_str_mv | 10.1046/j.1365-2664.2001.00581.x |
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Discriminant function analysis (DFA), logistic regression (LR) and artificial neural networks (ANN) were used to identify the environmental variables best explaining flamingo incursions. 3 The most accurate models (LR) gave 75% prediction success and used as predictors the surface area of rice fields, the presence of contiguous damaged fields, the presence of wooded margins and the distance to natural marshes. 4 Our study suggests that it is possible to identify accurately fields at risk from damage in order to concentrate scaring methods. We also suggest that planting hedges should be promoted, and wood cutting discouraged, in high‐risk areas. Following our study a programme of planting of hedges by the Natural Regional Park of the Camargue started at the beginning of 2000. 5 Our work reveals how presence–absence modelling can have clear applications in managing important species that sometimes cause negative impacts locally.</description><identifier>ISSN: 0021-8901</identifier><identifier>EISSN: 1365-2664</identifier><identifier>DOI: 10.1046/j.1365-2664.2001.00581.x</identifier><identifier>CODEN: JAPEAI</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Science Ltd</publisher><subject>Agroecology ; Applied ecology ; Artificial neural networks ; Biological and medical sciences ; bird pest ; Crops ; cultivated fields ; cutting ; discriminant analysis ; Ecological modeling ; environmental factors ; environmental variables ; France ; Freshwater ; Fundamental and applied biological sciences. Psychology ; Human ecology ; landscapes ; Life Sciences ; marshes ; Modeling ; neural networks ; Oryza sativa ; paddies ; Phoenicopterus roseus ; Phoenicopterus ruber ; Phoenicopterus ruber roseus ; Phytopathology. Animal pests. Plant and forest protection ; planting ; prediction ; predictive model ; regression analysis ; Rice ; risk ; surface area ; Sustainable agriculture ; Vertebrates ; Waterfowl ; wood</subject><ispartof>The Journal of applied ecology, 2001-02, Vol.38 (1), p.170-179</ispartof><rights>Copyright 2001 British Ecological Society</rights><rights>2001 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5301-9f5bb88bc0c6b1b12baa00a5327f8537cdc9a5a39a2ec4118ca1aa3fa7f273c93</citedby><cites>FETCH-LOGICAL-c5301-9f5bb88bc0c6b1b12baa00a5327f8537cdc9a5a39a2ec4118ca1aa3fa7f273c93</cites><orcidid>0000-0003-1716-9438 ; 0000-0001-5387-0243</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2655742$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2655742$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,776,780,799,881,1411,1427,27901,27902,45550,45551,46384,46808,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1056726$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-02674544$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Tourenq, Christophe</creatorcontrib><creatorcontrib>Aulagnier, Stéphane</creatorcontrib><creatorcontrib>Durieux, Laurent</creatorcontrib><creatorcontrib>Lek, Sovan</creatorcontrib><creatorcontrib>Mesléard, François</creatorcontrib><creatorcontrib>Johnson, Alan</creatorcontrib><creatorcontrib>Martin, Jean‐Louis</creatorcontrib><title>Identifying rice fields at risk from damage by the greater flamingo</title><title>The Journal of applied ecology</title><description>1 Since the early 1980s, greater flamingos Phoenicopterus ruber roseus have been reported to cause damage to the rice fields of the Camargue, south‐eastern France. 2 We tested whether some rice fields had landscape features that were more attractive to flamingos than others, using data from the period 1993–97 and from 1978 different paddies. Discriminant function analysis (DFA), logistic regression (LR) and artificial neural networks (ANN) were used to identify the environmental variables best explaining flamingo incursions. 3 The most accurate models (LR) gave 75% prediction success and used as predictors the surface area of rice fields, the presence of contiguous damaged fields, the presence of wooded margins and the distance to natural marshes. 4 Our study suggests that it is possible to identify accurately fields at risk from damage in order to concentrate scaring methods. We also suggest that planting hedges should be promoted, and wood cutting discouraged, in high‐risk areas. Following our study a programme of planting of hedges by the Natural Regional Park of the Camargue started at the beginning of 2000. 5 Our work reveals how presence–absence modelling can have clear applications in managing important species that sometimes cause negative impacts locally.</description><subject>Agroecology</subject><subject>Applied ecology</subject><subject>Artificial neural networks</subject><subject>Biological and medical sciences</subject><subject>bird pest</subject><subject>Crops</subject><subject>cultivated fields</subject><subject>cutting</subject><subject>discriminant analysis</subject><subject>Ecological modeling</subject><subject>environmental factors</subject><subject>environmental variables</subject><subject>France</subject><subject>Freshwater</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Human ecology</subject><subject>landscapes</subject><subject>Life Sciences</subject><subject>marshes</subject><subject>Modeling</subject><subject>neural networks</subject><subject>Oryza sativa</subject><subject>paddies</subject><subject>Phoenicopterus roseus</subject><subject>Phoenicopterus ruber</subject><subject>Phoenicopterus ruber roseus</subject><subject>Phytopathology. Animal pests. Plant and forest protection</subject><subject>planting</subject><subject>prediction</subject><subject>predictive model</subject><subject>regression analysis</subject><subject>Rice</subject><subject>risk</subject><subject>surface area</subject><subject>Sustainable agriculture</subject><subject>Vertebrates</subject><subject>Waterfowl</subject><subject>wood</subject><issn>0021-8901</issn><issn>1365-2664</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqNkk9v1DAQxS1EJZaWb4CEDwiJQ8KMHTuJxKVatbRopVaCnq2J195mSTbFTkv329dpqhU3OPnP_N4ba54Z4wg5QqG_bHOUWmVC6yIXAJgDqArzx1dscSi8ZgsAgVlVA75hb2PcAkCtpFyw5eXa7cbW79vdhofWOu5b160jpzEd4y_uw9DzNfW0cbzZ8_HW8U1wNLrAfUd9kg0n7MhTF927l_WY3Zyf_VxeZKurb5fL01VmlQTMaq-apqoaC1Y32KBoiABISVH6SsnSrm1NimRNwtkCsbKERNJT6UUpbS2P2efZ95Y6cxfansLeDNSai9OVme5A6LJQRfGAif00s3dh-H3v4mj6NlrXdbRzw300WFYaJNb_Bguly-SbwGoGbRhiDM4fnoBgpijM1kwTN9PEzRSFeY7CPCbpx5ceFC11PtDOtvEv_XOLhH2dsT9t5_b_bW--X5-lTZK_n-XbOA7hIBdaqbIQqfxhLnsaDG1SuObmhwBU6WsIibKUT97XrMk</recordid><startdate>200102</startdate><enddate>200102</enddate><creator>Tourenq, Christophe</creator><creator>Aulagnier, Stéphane</creator><creator>Durieux, Laurent</creator><creator>Lek, Sovan</creator><creator>Mesléard, François</creator><creator>Johnson, Alan</creator><creator>Martin, Jean‐Louis</creator><general>Blackwell Science Ltd</general><general>Blackwell Science</general><general>Wiley</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7SN</scope><scope>F1W</scope><scope>H95</scope><scope>L.G</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-1716-9438</orcidid><orcidid>https://orcid.org/0000-0001-5387-0243</orcidid></search><sort><creationdate>200102</creationdate><title>Identifying rice fields at risk from damage by the greater flamingo</title><author>Tourenq, Christophe ; Aulagnier, Stéphane ; Durieux, Laurent ; Lek, Sovan ; Mesléard, François ; Johnson, Alan ; Martin, Jean‐Louis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5301-9f5bb88bc0c6b1b12baa00a5327f8537cdc9a5a39a2ec4118ca1aa3fa7f273c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Agroecology</topic><topic>Applied ecology</topic><topic>Artificial neural networks</topic><topic>Biological and medical sciences</topic><topic>bird pest</topic><topic>Crops</topic><topic>cultivated fields</topic><topic>cutting</topic><topic>discriminant analysis</topic><topic>Ecological modeling</topic><topic>environmental factors</topic><topic>environmental variables</topic><topic>France</topic><topic>Freshwater</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Human ecology</topic><topic>landscapes</topic><topic>Life Sciences</topic><topic>marshes</topic><topic>Modeling</topic><topic>neural networks</topic><topic>Oryza sativa</topic><topic>paddies</topic><topic>Phoenicopterus roseus</topic><topic>Phoenicopterus ruber</topic><topic>Phoenicopterus ruber roseus</topic><topic>Phytopathology. Animal pests. Plant and forest protection</topic><topic>planting</topic><topic>prediction</topic><topic>predictive model</topic><topic>regression analysis</topic><topic>Rice</topic><topic>risk</topic><topic>surface area</topic><topic>Sustainable agriculture</topic><topic>Vertebrates</topic><topic>Waterfowl</topic><topic>wood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tourenq, Christophe</creatorcontrib><creatorcontrib>Aulagnier, Stéphane</creatorcontrib><creatorcontrib>Durieux, Laurent</creatorcontrib><creatorcontrib>Lek, Sovan</creatorcontrib><creatorcontrib>Mesléard, François</creatorcontrib><creatorcontrib>Johnson, Alan</creatorcontrib><creatorcontrib>Martin, Jean‐Louis</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Ecology 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) Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>The Journal of applied ecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tourenq, Christophe</au><au>Aulagnier, Stéphane</au><au>Durieux, Laurent</au><au>Lek, Sovan</au><au>Mesléard, François</au><au>Johnson, Alan</au><au>Martin, Jean‐Louis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying rice fields at risk from damage by the greater flamingo</atitle><jtitle>The Journal of applied ecology</jtitle><date>2001-02</date><risdate>2001</risdate><volume>38</volume><issue>1</issue><spage>170</spage><epage>179</epage><pages>170-179</pages><issn>0021-8901</issn><eissn>1365-2664</eissn><coden>JAPEAI</coden><abstract>1 Since the early 1980s, greater flamingos Phoenicopterus ruber roseus have been reported to cause damage to the rice fields of the Camargue, south‐eastern France. 2 We tested whether some rice fields had landscape features that were more attractive to flamingos than others, using data from the period 1993–97 and from 1978 different paddies. Discriminant function analysis (DFA), logistic regression (LR) and artificial neural networks (ANN) were used to identify the environmental variables best explaining flamingo incursions. 3 The most accurate models (LR) gave 75% prediction success and used as predictors the surface area of rice fields, the presence of contiguous damaged fields, the presence of wooded margins and the distance to natural marshes. 4 Our study suggests that it is possible to identify accurately fields at risk from damage in order to concentrate scaring methods. We also suggest that planting hedges should be promoted, and wood cutting discouraged, in high‐risk areas. Following our study a programme of planting of hedges by the Natural Regional Park of the Camargue started at the beginning of 2000. 5 Our work reveals how presence–absence modelling can have clear applications in managing important species that sometimes cause negative impacts locally.</abstract><cop>Oxford, UK</cop><pub>Blackwell Science Ltd</pub><doi>10.1046/j.1365-2664.2001.00581.x</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-1716-9438</orcidid><orcidid>https://orcid.org/0000-0001-5387-0243</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agroecology Applied ecology Artificial neural networks Biological and medical sciences bird pest Crops cultivated fields cutting discriminant analysis Ecological modeling environmental factors environmental variables France Freshwater Fundamental and applied biological sciences. Psychology Human ecology landscapes Life Sciences marshes Modeling neural networks Oryza sativa paddies Phoenicopterus roseus Phoenicopterus ruber Phoenicopterus ruber roseus Phytopathology. Animal pests. Plant and forest protection planting prediction predictive model regression analysis Rice risk surface area Sustainable agriculture Vertebrates Waterfowl wood |
title | Identifying rice fields at risk from damage by the greater flamingo |
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