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@Article{NgSilRimAtzImm:2018:TuKe,
               author = "Ng, Wai Tim and Silva, Alexsandro C{\^a}ndido de Oliveira and 
                         Rima, Purity and Atzberger, Clement and Immitzer, Markus",
          affiliation = "{University of Natural Resources and Life Sciences (BOKU)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Kenya 
                         Forestry Research Institute (KEFRI)} and {University of Natural 
                         Resources and Life Sciences (BOKU)} and {University of Natural 
                         Resources and Life Sciences (BOKU)}",
                title = "Ensemble approach for potential habitat mapping of invasive 
                         Prosopis spp. in Turkana, Kenya",
              journal = "Ecology and Evolution",
                 year = "2018",
               volume = "8",
               number = "23",
             keywords = "ensemble modeling, expert knowledge, invasive alien species, 
                         Prosopis, species distribution modeling.",
             abstract = "Aim: Prosopis spp. are an invasive alien plant species native to 
                         the Americas and well adapted to thrive in arid environments. In 
                         Kenya, several remote\‐sensing studies conclude that the 
                         genus is well established throughout the country and is rapidly in 
                         \‐vading new areas. This research aims to model the 
                         potential habitat of Prosopis spp.by using an ensemble model 
                         consisting of four species distribution models. Furthermore, 
                         environmental and expert knowledge\‐based variables are 
                         assessed.Location: Turkana County, Kenya.Methods: We collected and 
                         assessed a large number of environmental and expert 
                         knowl\‐edge\‐based variables through variable 
                         correlation, collinearity, and bias tests. The varia\‐bles 
                         were used for an ensemble model consisting of four species 
                         distribution models: (a) logistic regression, (b) maximum entropy, 
                         (c) random forest, and (d) Bayesian networks. The models were 
                         evaluated through a block cross\‐validation providing 
                         statistical measures.Results: The best predictors for Prosopis 
                         spp. habitat are distance from water and built\‐up areas, 
                         soil type, elevation, lithology, and temperature seasonality. All 
                         species distribution models achieved high accuracies while the 
                         ensemble model achieved the highest scores. Highly and moderately 
                         suitable Prosopis spp. habitat covers 6% and 9% of the study area, 
                         respectively.Main conclusions: Both ensemble and individual models 
                         predict a high risk of continuedinvasion, confirming local 
                         observations and conceptions. Findings are valuable to 
                         stake\‐holders for managing invaded area, protecting areas 
                         at risk, and to raise awareness.",
                  doi = "10.1002/ece3.4649",
                  url = "http://dx.doi.org/10.1002/ece3.4649",
                 issn = "2045-7758",
             language = "en",
           targetfile = "ng_ensemble.pdf",
        urlaccessdate = "26 nov. 2020"
}


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