TY - JOUR A1 - Gutt, Julian A1 - Zurell, Damaris A1 - Bracegridle, Thomas J. A1 - Cheung, William A1 - Clark, Melody S. A1 - Convey, Peter A1 - Danis, Bruno A1 - David, Bruno A1 - De Broyer, Claude A1 - di Prisco, Guido A1 - Griffiths, Huw A1 - Laffont, Remi A1 - Peck, Lloyd S. A1 - Pierrat, Benjamin A1 - Riddle, Martin J. A1 - Saucede, Thomas A1 - Turner, John A1 - Verde, Cinzia A1 - Wang, Zhaomin A1 - Grimm, Volker T1 - Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic a cross-disciplinary concept JF - Polar research : a Norwegian journal of Polar research N2 - Developments of future scenarios of Antarctic ecosystems are still in their infancy, whilst predictions of the physical environment are recognized as being of global relevance and corresponding models are under continuous development. However, in the context of environmental change simulations of the future of the Antarctic biosphere are increasingly demanded by decision makers and the public, and are of fundamental scientific interest. This paper briefly reviews existing predictive models applied to Antarctic ecosystems before providing a conceptual framework for the further development of spatially and temporally explicit ecosystem models. The concept suggests how to improve approaches to relating species' habitat description to the physical environment, for which a case study on sea urchins is presented. In addition, the concept integrates existing and new ideas to consider dynamic components, particularly information on the natural history of key species, from physiological experiments and biomolecular analyses. Thereby, we identify and critically discuss gaps in knowledge and methodological limitations. These refer to process understanding of biological complexity, the need for high spatial resolution oceanographic data from the entire water column, and the use of data from biomolecular analyses in support of such ecological approaches. Our goal is to motivate the research community to contribute data and knowledge to a holistic, Antarctic-specific, macroecological framework. Such a framework will facilitate the integration of theoretical and empirical work in Antarctica, improving our mechanistic understanding of this globally influential ecoregion, and supporting actions to secure this biodiversity hotspot and its ecosystem services. KW - Environmental change KW - integrative modelling framework KW - spatially and temporally explicit modelling macroecology KW - biodiversity KW - habitat suitability models Y1 - 2012 U6 - https://doi.org/10.3402/polar.v31i0.11091 SN - 0800-0395 VL - 31 IS - 6 PB - Co-Action Publ. CY - Jarfalla ER - TY - GEN A1 - Zurell, Damaris A1 - Elith, Jane A1 - Schröder-Esselbach, Boris T1 - Predicting to new environments tools for visualizing model behaviour and impacts on mapped distributions T2 - Diversity & distributions : a journal of biological invasions and biodiversity N2 - Data limitations can lead to unrealistic fits of predictive species distribution models (SDMs) and spurious extrapolation to novel environments. Here, we want to draw attention to novel combinations of environmental predictors that are within the sampled range of individual predictors but are nevertheless outside the sample space. These tend to be overlooked when visualizing model behaviour. They may be a cause of differing model transferability and environmental change predictions between methods, a problem described in some studies but generally not well understood. We here use a simple simulated data example to illustrate the problem and provide new and complementary visualization techniques to explore model behaviour and predictions to novel environments. We then apply these in a more complex real-world example. Our results underscore the necessity of scrutinizing model fits, ecological theory and environmental novelty. KW - Environmental niche KW - extrapolation KW - inflated response curves KW - novel environment KW - sampling space KW - species distribution models Y1 - 2012 U6 - https://doi.org/10.1111/j.1472-4642.2012.00887.x SN - 1366-9516 VL - 18 IS - 6 SP - 628 EP - 634 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Zurell, Damaris A1 - Grimm, Volker A1 - Rossmanith, Eva A1 - Zbinden, Niklaus A1 - Zimmermann, Niklaus E. A1 - Schröder-Esselbach, Boris T1 - Uncertainty in predictions of range dynamics black grouse climbing the Swiss Alps JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change. Y1 - 2012 U6 - https://doi.org/10.1111/j.1600-0587.2011.07200.x SN - 0906-7590 VL - 35 IS - 7 SP - 590 EP - 603 PB - Wiley-Blackwell CY - Hoboken ER -