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keywords = {Ecological niche modeling},  abstract = {Species distribution modeling (SDM: statistical and/or mechanistic approaches to the assessment of range determinants and prediction of species occurrence) offers new possibilities for estimating and studying past organism distributions. \{SDM\} complements fossil and genetic evidence by providing (i) quantitative and potentially high-resolution predictions of the past organism distributions, (ii) statistically formulated, testable ecological hypotheses regarding past distributions and communities, and (iii) statistical assessment of range determinants. In this article, we provide an overview of applications of \{SDM\} to paleobiology, outlining the methodology, reviewing SDM-based studies to paleobiology or at the interface of paleo- and neobiology, discussing assumptions and uncertainties as well as how to handle them, and providing a synthesis and outlook. Key methodological issues for \{SDM\} applications to paleobiology include predictor variables (types and properties; special emphasis is given to paleoclimate), model validation (particularly important given the emphasis on cross-temporal predictions in paleobiological applications), and the integration of \{SDM\} and genetics approaches. Over the last few years the number of studies using \{SDM\} to address paleobiology-related questions has increased considerably. While some of these studies only use \{SDM\} (23%), most combine them with genetically inferred patterns (49%), paleoecological records (22%), or both (6%). A large number of SDM-based studies have addressed the role of Pleistocene glacial refugia in biogeography and evolution, especially in Europe, but also in many other regions. SDM-based approaches are also beginning to contribute to a suite of other research questions, such as historical constraints on current distributions and diversity patterns, the end-Pleistocene megafaunal extinctions, past community assembly, human paleobiogeography, Holocene paleoecology, and even deep-time biogeography (notably, providing insights into biogeographic dynamics >400 million years ago). We discuss important assumptions and uncertainties that affect the \{SDM\} approach to paleobiology – the equilibrium postulate, niche stability, changing atmospheric \{CO2\} concentrations – as well as ways to address these (ensemble, functional SDM, and non-SDM ecoinformatics approaches). We conclude that the \{SDM\} approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and hereby also offers the potential for an enhanced contribution of paleobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration.},  }  @article{Varela2011451,  title = {{Using species distribution models in paleobiogeography: A matter of data, predictors and concepts}},  journal = {Palaeogeography, Palaeoclimatology, Palaeoecology},  volume = {310},  number = {3–4},  pages = {451 - 463},  year = {2011},  note = {},  issn = {0031-0182},  doi = {http://dx.doi.org/10.1016/j.palaeo.2011.07.021},  url = {http://www.sciencedirect.com/science/article/pii/S0031018211004044},  author = {Sara Varela and Jorge M. Lobo and Joaquín Hortal},  keywords = {Scenopoetic variables},  abstract = {The increasing interest in the effects of climate changes on species distributions has been followed by the development of Species Distribution Models (SDMs). Although these techniques are starting to be used to study the location and dynamics of past species distributions, a sound theoretical framework for their use in paleoecology is still lacking. In this paper we are reviewing the main challenges for constructing Paleo-Species Distribution Models to describe and project the past distribution of species, namely data limitations, selection of predictors and choice of a biologically-relevant modeling procedure. We also review and discuss the current state-of-the-art in Paleo-SDMs, providing a series of recommendations for their use, and proposing future research lines to improve the use of these techniques in paleobiogeography.},  }