Random forests as cumulative effects models: A case study of lakes and rivers in Muskoka, Canada.
Cumulative effects assessment (CEA) ― a type of environmental appraisal ― lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regressio...
| Publicado en: | Journal of Environmental Management Vol. 201; pp. 407 - 425 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Oct2017
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=124302779&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 124302779 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Oct2017 vid: 201 pid: 735 pub: Academic Press Inc. artinfo: ui: 124302779 10.1016/j.jenvman.2017.06.011 ppf: 407 ppct: 18 formats: tig: atl: Random forests as cumulative effects models: A case study of lakes and rivers in Muskoka, Canada. aug: au: Jones, F. Chris Plewes, Rachel Murison, Lorna MacDougall, Mark J. Sinclair, Sarah Davies, Christie Bailey, John L. Richardson, Murray Gunn, John affil: Ontario Ministry of Environment and Climate Change, Dorset Environmental Science Centre, 1026 Bellwood Acres Road, Dorset, P0A1E0, Canada Carleton University, Department of Geography and Environmental Studies, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada Credit Valley Conservation, 1255 Old Derry Road, Mississauga, L5N 6R4, Canada River Labs, River Institute, 2 St Lawrence Drive, Cornwall, K6H 4Z1, Canada Conservation Ontario, Dorset Environmental Science Centre, 1026 Bellwood Acres Road, Dorset, P0A1E0, Canada Ontario Ministry of Environment and Climate Change, Dorset Environmental Science Centre, 1026 Bellwood Acres Road, Dorset, Canada Ontario Ministry of Environment & Climate Change, Cooperative Freshwater Ecology Unit, Laurentian University, 935 Ramsey Lake Road, Sudbury, P3E 2C6, Canada Carleton University, Department of Geography and Environmental Studies, B349 Loeb Building, Ottawa, ON, K1S 5B6, Canada Cooperative Freshwater Ecology Unit, Living With Lakes Centre, Laurentian University, 935 Ramsey Lake Road, Sudbury, P3E 2C6, Canada su: Lake ecology Cumulative effects assessment (Environmental assessment) Random forest algorithms Ecological models Environmental management sug: subj: Lake ecology Cumulative effects assessment (Environmental assessment) Random forest algorithms Ecological models Environmental management keyword: Cumulative effects Indicators Lakes Rivers Cumulative effects Indicators Lakes Rivers ab: Cumulative effects assessment (CEA) ― a type of environmental appraisal ― lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regression trees, which model response variables by recursive partitioning. Random forests were used to model a series of candidate ecological indicators that described lakes and rivers from a case study watershed (The Muskoka River Watershed, Canada). Suitability of the candidate indicators for use in cumulative effects assessment and watershed monitoring was assessed according to how well they could be predicted from natural habitat features and how sensitive they were to human land-use. The best models explained 75% of the variation in a multivariate descriptor of lake benthic-macroinvertebrate community structure, and 76% of the variation in the conductivity of river water. Similar results were obtained by cross-validation. Several candidate indicators detected a simulated doubling of urban land-use in their catchments, and a few were able to detect a simulated doubling of agricultural land-use. The paper demonstrates that random forests can be used to describe the combined and singular effects of multiple stressors and natural environmental factors, and furthermore, that random forests can be used to evaluate the performance of monitoring indicators. The numerical methods presented are applicable to any ecosystem and indicator type, and therefore represent a step forward for CEA. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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