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Gewählte Publikation:

Ng, WT; Silva, ACD; Rima, P; Atzberger, C; Immitzer, M.
(2018): Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya
ECOL EVOL. 2018; 8(23): 11921-11931. FullText FullText_BOKU

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 invading 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 knowledge-based variables through variable correlation, collinearity, and bias tests. The variables 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 continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.
Autor*innen der BOKU Wien:
Atzberger Clement
Immitzer Markus
Ng Wai Tim

Find related publications in this database (Keywords)
ensemble modeling
expert knowledge
invasive alien species
Prosopis
species distribution modeling


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