caa a22 4500 467928045 CHVBK 20180406152935.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s10109-006-0035-8 doi (NATIONALLICENCE)springer-10.1007/s10109-006-0035-8 Explicitly incorporating spatial dependence in predictive vegetation models in the form of explanatory variables: a Mojave Desert case study [Elektronische Daten] [Jennifer Miller, Janet Franklin] Predictive vegetation modeling is defined as predicting the distribution of vegetation across a landscape based upon its relationship with environmental factors. These models generally ignore or attempt to remove spatial dependence in the data. When explicitly included in the model, spatial dependence can increase model accuracy. We develop presence/absence models for 11 vegetation alliances in the Mojave Desert with classification trees and generalized linear models, and use geostatistical interpolation to calculate spatial dependence terms used in the models. Results were mixed across models and methods, but in general, the spatial dependence terms more consistently increased model accuracy for widespread alliances. GLMs had higher accuracy in general. Springer-Verlag, 2006 Spatial dependence nationallicence Generalized linear model nationallicence Classification tree nationallicence Predictive vegetation models nationallicence Miller Jennifer Department of Geology and Geography, West Virginia University, 26506, Morgantown, WV, USA aut Franklin Janet Departments of Biology and Geography, San Diego State University, 92182, San Diego, CA, USA aut Journal of Geographical Systems Springer-Verlag 8/4(2006-10-01), 411-435 1435-5930 8:4<411 2006 8 10109 https://doi.org/10.1007/s10109-006-0035-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10109-006-0035-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Miller Jennifer Department of Geology and Geography, West Virginia University, 26506, Morgantown, WV, USA aut NATIONALLICENCE 700 1- Franklin Janet Departments of Biology and Geography, San Diego State University, 92182, San Diego, CA, USA aut NATIONALLICENCE 773 0- Journal of Geographical Systems Springer-Verlag 8/4(2006-10-01), 411-435 1435-5930 8:4<411 2006 8 10109 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer