## Correlation and process in species distribution models: bridging a dichotomy > [!Abstract]- > 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 correlative–process 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 process–correlation 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. > [!Cite]- > Dormann, Carsten F., Stanislaus J. Schymanski, Juliano Cabral, Isabelle Chuine, Catherine Graham, Florian Hartig, Michael Kearney, et al. “Correlation and Process in Species Distribution Models: Bridging a Dichotomy.” _Journal of Biogeography_ 39, no. 12 (December 2012): 2119–31. [https://doi.org/10.1111/j.1365-2699.2011.02659.x](https://doi.org/10.1111/j.1365-2699.2011.02659.x). ## Notes %% begin notes %% %% end notes %% %% begin annotations %% ## Annotations | evernote 2016.04.25 The most commonly used approaches to describe distributions of species and biodiversity are known as correlative (syn. phenomenological) species distribution models (Elith & Leathwick, 2009). Correlative models aim to describe the patterns, not the mechanisms, in the association between species occurrences and environmental data. Process-based distribution models (here used synonymously with mechanistic models) can address these deficits by explicitly including processes omitted from the correlative approach (Kearney & Porter, 2009) Here we will show that current approaches to modelling species distributions represent a continuum with respect to the explicit inclusion of processes. Correlative species distribution models statistically relate environmental variables directly to species occurrence or abundance. In contrast, process-based models formulate the ecology of a species as mathematical functions in a reductionist sense, defining causality; the species’ occurrence or abundance is an indirect, emergent consequence. Some process-based models are developed entirely ‘forward’, i.e. without any calibration of the model (Kleidon & Mooney, 2000; Morin et al., 2007).  However, correlative models employ explanatory variables that are expected to represent causal mechanisms (Austin, 2002). Furthermore, many process- based models also use distributional data to evaluate model structure or to calibrate and fine-tune some unmeasurable parameters. In correlative models, parameters have no a priori defined ecological meaning and processes are implicit. In contrast, process-based models are built around explicitly stated mechanisms and parameters have a clear ecological interpretation that is defined a priori. An obvious but sometimes forgotten point is that the usefulness of a model must be assessed with respect to its purpose. Process-based models can also be used to falsify hypotheses by formulating a hypothesis as a model and comparing it formally with data (e.g. Morin et al., 2007). Correlative models often provide a single, static prediction of a species distribution in these human-driven environmental scenarios. In contrast, process-based (and hybrid) models are often used to predict dynamic features of species distributions, such as invasion rate (Kearney et al., 2009a), succession and the influence of disturbance, land use and management measures on species persistence (Schumacher & Bugmann, 2006; Jeltsch et al., 2011). Both purely correlative models and fitted process-based models have the same statistical analysis assumptions: error structure assumptions (such as independence of data, homogeneity and stationarity of variance), homogeneity of sampling effort and constant observation error. %% end annotations %% %% Import Date: 2024-08-15T06:53:40.953-06:00 %%