@article{costaFuzzySetTheory2015,
  title = {Fuzzy Set Theory for Predicting the Potential Distribution and Cost-Effective Monitoring of Invasive Species},
  author = {Costa, Hugo and Ponte, Nuno B. and Azevedo, Eduardo B. and Gil, Artur},
  year = {2015},
  volume = {316},
  pages = {122--132},
  doi = {10.1016/j.ecolmodel.2015.07.034},
  abstract = {The presence of invasive species has been predicted using species distribution models (SDMs) and presence-only data to assist environmental management. However, SDMs include substantial uncertainty and the lack of absence data hampers the use of probabilistic predictions. A non-statistical theoretical basis able to deal with uncertainty on which to model invasive species distributions with presence-only data is thus needed. Fuzzy set theory satisfies these two requirements but has been little used. This paper proposes a fuzzy modelling approach for predicting invasive species potential distributions using presence-only data and SDMs to support the design of cost-effective monitoring schemes. The invasion of Gunnera tinctoria (Molina) Mirbel (Giant rhubarb) in the island of S\~ao Miguel (the Azores, Portugal) is used as case study. The latter involved the prediction of the potential distribution of the invader using MaxEnt and the selection of priority areas for monitoring the spread of the invader in case of scarcity of resources. In addition, MaxEnt was used within a traditional (non-fuzzy) approach, unable to quantity and report on uncertainty. The results of the fuzzy and non-fuzzy approaches are compared and their differences discussed, thus highlighting the potential benefits of using fuzzy set theory for species distribution modelling and management.},
  journal = {Ecological Modelling},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14599035,fuzzy,management,modelling-uncertainty,monitoring,species-distribution,species-invasions},
  lccn = {INRMM-MiD:c-14599035}
}

{"_id":"p3mbAnAFgYexGEJ2u","bibbaseid":"costa-ponte-azevedo-gil-fuzzysettheoryforpredictingthepotentialdistributionandcosteffectivemonitoringofinvasivespecies-2015","authorIDs":[],"author_short":["Costa, H.","Ponte, N. B.","Azevedo, E. B.","Gil, A."],"bibdata":{"bibtype":"article","type":"article","title":"Fuzzy Set Theory for Predicting the Potential Distribution and Cost-Effective Monitoring of Invasive Species","author":[{"propositions":[],"lastnames":["Costa"],"firstnames":["Hugo"],"suffixes":[]},{"propositions":[],"lastnames":["Ponte"],"firstnames":["Nuno","B."],"suffixes":[]},{"propositions":[],"lastnames":["Azevedo"],"firstnames":["Eduardo","B."],"suffixes":[]},{"propositions":[],"lastnames":["Gil"],"firstnames":["Artur"],"suffixes":[]}],"year":"2015","volume":"316","pages":"122–132","doi":"10.1016/j.ecolmodel.2015.07.034","abstract":"The presence of invasive species has been predicted using species distribution models (SDMs) and presence-only data to assist environmental management. However, SDMs include substantial uncertainty and the lack of absence data hampers the use of probabilistic predictions. A non-statistical theoretical basis able to deal with uncertainty on which to model invasive species distributions with presence-only data is thus needed. Fuzzy set theory satisfies these two requirements but has been little used. This paper proposes a fuzzy modelling approach for predicting invasive species potential distributions using presence-only data and SDMs to support the design of cost-effective monitoring schemes. The invasion of Gunnera tinctoria (Molina) Mirbel (Giant rhubarb) in the island of São Miguel (the Azores, Portugal) is used as case study. The latter involved the prediction of the potential distribution of the invader using MaxEnt and the selection of priority areas for monitoring the spread of the invader in case of scarcity of resources. In addition, MaxEnt was used within a traditional (non-fuzzy) approach, unable to quantity and report on uncertainty. The results of the fuzzy and non-fuzzy approaches are compared and their differences discussed, thus highlighting the potential benefits of using fuzzy set theory for species distribution modelling and management.","journal":"Ecological Modelling","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14599035,fuzzy,management,modelling-uncertainty,monitoring,species-distribution,species-invasions","lccn":"INRMM-MiD:c-14599035","bibtex":"@article{costaFuzzySetTheory2015,\n title = {Fuzzy Set Theory for Predicting the Potential Distribution and Cost-Effective Monitoring of Invasive Species},\n author = {Costa, Hugo and Ponte, Nuno B. and Azevedo, Eduardo B. and Gil, Artur},\n year = {2015},\n volume = {316},\n pages = {122--132},\n doi = {10.1016/j.ecolmodel.2015.07.034},\n abstract = {The presence of invasive species has been predicted using species distribution models (SDMs) and presence-only data to assist environmental management. However, SDMs include substantial uncertainty and the lack of absence data hampers the use of probabilistic predictions. A non-statistical theoretical basis able to deal with uncertainty on which to model invasive species distributions with presence-only data is thus needed. Fuzzy set theory satisfies these two requirements but has been little used. This paper proposes a fuzzy modelling approach for predicting invasive species potential distributions using presence-only data and SDMs to support the design of cost-effective monitoring schemes. The invasion of Gunnera tinctoria (Molina) Mirbel (Giant rhubarb) in the island of S\\~ao Miguel (the Azores, Portugal) is used as case study. The latter involved the prediction of the potential distribution of the invader using MaxEnt and the selection of priority areas for monitoring the spread of the invader in case of scarcity of resources. In addition, MaxEnt was used within a traditional (non-fuzzy) approach, unable to quantity and report on uncertainty. The results of the fuzzy and non-fuzzy approaches are compared and their differences discussed, thus highlighting the potential benefits of using fuzzy set theory for species distribution modelling and management.},\n journal = {Ecological Modelling},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14599035,fuzzy,management,modelling-uncertainty,monitoring,species-distribution,species-invasions},\n lccn = {INRMM-MiD:c-14599035}\n}\n\n","author_short":["Costa, H.","Ponte, N. B.","Azevedo, E. B.","Gil, A."],"key":"costaFuzzySetTheory2015","id":"costaFuzzySetTheory2015","bibbaseid":"costa-ponte-azevedo-gil-fuzzysettheoryforpredictingthepotentialdistributionandcosteffectivemonitoringofinvasivespecies-2015","role":"author","urls":{},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14599035","fuzzy","management","modelling-uncertainty","monitoring","species-distribution","species-invasions"],"downloads":0},"bibtype":"article","biburl":"https://sharefast.me/php/download.php?id=zOUKvA&token=29","creationDate":"2020-07-03T22:46:17.950Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14599035","fuzzy","management","modelling-uncertainty","monitoring","species-distribution","species-invasions"],"search_terms":["fuzzy","set","theory","predicting","potential","distribution","cost","effective","monitoring","invasive","species","costa","ponte","azevedo","gil"],"title":"Fuzzy Set Theory for Predicting the Potential Distribution and Cost-Effective Monitoring of Invasive Species","year":2015,"dataSources":["5S2zj2hKW8TWTkuMq"]}