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This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 3.1.2 and depends on R packages 'leaps', 'earth', 'fields', 'mgcv', 'stringr', 'gsubfn', 'randomForest' and 'nnet'. ","distributionFormats":[{"name":"R Script","version":"3.1.2"}],"distributorContacts":[{"organisationName":"NERC EDS Environmental Information Data Centre","organisationIdentifier":"https://ror.org/04xw4m193","role":"distributor","email":"info@eidc.ac.uk"}],"id":"94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","incomingCitationCount":3,"incomingCitations":[{"description":"Wood, C.M., Smart, S.M., & Bunce, R.G.H.(2015). Woodland Survey of Great Britain 1971-2001. Earth System Science Data, 7(2), 203-214.","url":"https://doi.org/10.5194/essd-7-203-2015","type":"academic"},{"description":"ADAS (2019). Habitat suitability modelling: scoping study. The Welsh Government.","url":"https://www.gov.wales/habitat-suitability-modelling-scoping-study","type":"policy"},{"description":"Wood, C.M., Metzger, M.J., & Bunce, R.G.H. (2025). Protecting the Bunce Legacy: Lessons Learned From Safeguarding Long-term Ecological Survey Datasets in Great Britain. In Environmental Management. Springer Science and Business Media LLC. ","url":"https://doi.org/10.1007/s00267-025-02175-5","type":"academic"}],"infoLinks":[{"url":"https://data-package.ceh.ac.uk/sd/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9.zip","name":"Supporting information","description":"Supporting information available to assist in re-use of this model","function":"information","type":"OTHER"}],"keywordsOther":[{"value":"Vegetation science"},{"value":"ecology","uri":"http://www.eionet.europa.eu/gemet/concept/2470"}],"keywordsPlace":[{"value":"Great Britain"}],"keywordsTheme":[{"value":"Modelling","uri":"http://onto.nerc.ac.uk/CEHMD/topic/13"}],"licences":[{"value":"This resource is made available under the terms of the Open Government Licence ","code":"license","uri":"https://eidc.ac.uk/licences/ogl/plain"}],"lineage":"Modelling methods used: Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990) Generalised Linear Models (GLMs) (McCullagh and Nelder, 1989) Random Forests (RF) (Liaw and Wiener, 2002) Multiple Adaptive Regression Splines (MARS) (Friedman, 1991). Neural Networks (NNet) (Venables and Ripley, 2002) Data sources used in the models: National Vegetation Classification (Rodwell 1991 et seq). Data collected at 4-250 m2 using targeted homogenous stands to census full range of plant community variation across Britain. Sampled from 1950-1980. The Key Habitats surveys of Britain (Hornung 1996). Data collected at 4m2 using random sampling within a random selection of 1-km squares stratified by three landscape GIS masks for upland, calcareous grassland and lowland heathland ecosystems. Sampled in 1992. Broadleaved woodland survey of Britain (Kirby et al. 2005; Corney et al. 2006). Data collected at 200 m2 using 16 plots located at random within each of 103 representative sites in Britain. Sampled in 1971. Countryside Survey of Great Britain (Smart et al. 2003). Data collected at 4-200 m2 using randomly located plots within a random selection of 569 1-km squares across Britain stratified by ITE Land Class. Data collected in 1998/1999. 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