TY - JOUR A1 - Dormann, Carsten F. A1 - Schymanski, Stanislaus J. A1 - Cabral, Juliano Sarmento A1 - Chuine, Isabelle A1 - Graham, Catherine A1 - Hartig, Florian A1 - Kearney, Michael A1 - Morin, Xavier A1 - Römermann, Christine A1 - Schröder-Esselbach, Boris A1 - Singer, Alexander T1 - Correlation and process in species distribution models: bridging a dichotomy JF - Journal of biogeography N2 - Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions. KW - Hypothesis generation KW - mechanistic model KW - parameterization KW - process-based model KW - species distribution model KW - SDM KW - uncertainty KW - validation Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02659.x SN - 0305-0270 VL - 39 IS - 12 SP - 2119 EP - 2131 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Hothorn, Torsten A1 - Müller, Jörg A1 - Schroeder, Boris A1 - Kneib, Thomas A1 - Brandl, Roland T1 - Decomposing environmental, spatial, and spatiotemporal components of species distributions JF - Ecological monographs : a publication of the Ecological Society of America. N2 - Species distribution models are an important tool to predict the impact of global change on species distributional ranges and community assemblages. Although considerable progress has been made in the statistical modeling during the last decade, many approaches still ignore important features of species distributions, such as nonlinearity and interactions between predictors, spatial autocorrelation, and nonstationarity, or at most incorporate only some of these features. Ecologists, however, require a modeling framework that simultaneously addresses all these features flexibly and consistently. Here we describe such an approach that allows the estimation of the global effects of environmental variables in addition to local components dealing with spatiotemporal autocorrelation as well as nonstationary effects. The local components can be used to infer unknown spatiotemporal processes; the global component describes how the species is influenced by the environment and can be used for predictions, allowing the fitting of many well-known regression relationships, ranging from simple linear models to complex decision trees or from additive models to models inspired by machine learning procedures. The reliability of spatiotemporal predictions can be qualitatively predicted by separately evaluating the importance of local and global effects. We demonstrate the potential of the new approach by modeling the breeding distribution of the Red Kite (Milvus milvus), a bird of prey occurring predominantly in Western Europe, based on presence/absence data from two mapping campaigns using grids of 40 km 2 in Bavaria. The global component of the model selected seven environmental variables extracted from the CORINE and WorldClim databases to predict Red Kite breeding. The effect of altitude was found to be nonstationary in space, and in addition, the data were spatially autocorrelated, which suggests that a species distribution model that does not allow for spatially varying effects and spatial autocorrelation would have ignored important processes determining the distribution of Red Kite breeding across Bavaria. Thus, predictions from standard species distribution models that do not allow for real-world complexities may be considerably erroneous. Our analysis of Red Kite breeding exemplifies the potential of the innovative approach for species distribution models. The method is also applicable to modeling count data. KW - boosting KW - model selection KW - nonstationarity KW - spatial autocorrelation KW - species distribution model KW - structured additive model KW - variable selection Y1 - 2011 U6 - https://doi.org/10.1890/10-0602.1 SN - 0012-9615 VL - 81 IS - 2 SP - 329 EP - 347 PB - Wiley CY - Washington ER -