{"refrec":{"BRefID":338772,"RR":"<b>O’Mahony, J.; de la Torre Cerro, R.; Holloway, P.</b> (2021). Modelling the distribution of the red macroalgae <i>Asparagopsis</i> to support sustainable aquaculture development. <i>AgriEngineering 3(2)</i>: 251-265. <a href=\"https://hdl.handle.net/10.3390/agriengineering3020017\" target=\"_blank\">https://hdl.handle.net/10.3390/agriengineering3020017</a>","BEntID":335407,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":". <i>AgriEngineering 3(2)</i>: 251-265. <a href=\"https://hdl.handle.net/10.3390/agriengineering3020017\" target=\"_blank\">https://hdl.handle.net/10.3390/agriengineering3020017</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"O’Mahony, J.; de la Torre Cerro, R.; Holloway, P.","OrigTitleTranslFlag":0,"Authorstringtrunc":"O’Mahony, J.; de la Torre Cerro, R.; Holloway, P.","Englishabstract":"Fermentative digestion by ruminant livestock is one of the main ways enteric methane enters the atmosphere, although recent studies have identified that including red macroalgae as a feed ingredient can drastically reduce methane produced by cattle. Here, we utilize ecological modelling to identify suitable sites for establishing aquaculture development to support sustainable agriculture and Sustainable Development Goals 1 and 2. We used species distributions models (SDMs) parameterized using an ensemble of multiple statistical and machine learning methods, accounting for novel methodological and ecological artefacts that arise from using such approaches on non-native and cultivated species. We predicted the current distribution of two Asparagopsis species to high accuracy around the coast of Ireland. The environmental drivers of each species differed depending on where the response data was sourced from (i.e., native vs. non-native), suggesting that the length of time A. armata has been present in Ireland may mean it has undergone a niche shift. Subsequently, researchers looking to adopt SDMs to support aquaculture development need to acknowledge emerging conceptual issues, and here we provide the code needed to implement such research, which should support efforts to effectively choose suitable sites for aquaculture development that account for the unique methodological steps identified in this research","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Modelling the distribution of the red macroalgae <i>Asparagopsis</i> to support sustainable aquaculture development","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2024-12-10 01:33:17.368041","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"machine learning; methane; mitigation; ruminant livestock; species distribution modelling","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2021-09-21","DateCreate":"2021-06-07","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000667231700001","VABBcode":null,"OpenAcc":1,"Handle":"10.3390/agriengineering3020017"},"refs":null,"anarec":{"AnaID":338772,"PubliDate":2021,"Pagination":"251-265","XtraPublOfAnaID":null,"ISBN":null,"Volume":"3","Issue":"2","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":338771,"SerRR":"AgriEngineering. 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