@article{GuttZurellBracegridleetal.2012, author = {Gutt, Julian and Zurell, Damaris and Bracegridle, Thomas J. and Cheung, William and Clark, Melody S. and Convey, Peter and Danis, Bruno and David, Bruno and De Broyer, Claude and di Prisco, Guido and Griffiths, Huw and Laffont, Remi and Peck, Lloyd S. and Pierrat, Benjamin and Riddle, Martin J. and Saucede, Thomas and Turner, John and Verde, Cinzia and Wang, Zhaomin and Grimm, Volker}, title = {Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic a cross-disciplinary concept}, series = {Polar research : a Norwegian journal of Polar research}, volume = {31}, journal = {Polar research : a Norwegian journal of Polar research}, number = {6}, publisher = {Co-Action Publ.}, address = {Jarfalla}, issn = {0800-0395}, doi = {10.3402/polar.v31i0.11091}, pages = {23}, year = {2012}, abstract = {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.}, language = {en} } @misc{ZurellElithSchroederEsselbach2012, author = {Zurell, Damaris and Elith, Jane and Schr{\"o}der-Esselbach, Boris}, title = {Predicting to new environments tools for visualizing model behaviour and impacts on mapped distributions}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {18}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {6}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/j.1472-4642.2012.00887.x}, pages = {628 -- 634}, year = {2012}, abstract = {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.}, language = {en} } @article{ZurellGrimmRossmanithetal.2012, author = {Zurell, Damaris and Grimm, Volker and Rossmanith, Eva and Zbinden, Niklaus and Zimmermann, Niklaus E. and Schr{\"o}der-Esselbach, Boris}, title = {Uncertainty in predictions of range dynamics black grouse climbing the Swiss Alps}, series = {Ecography : pattern and diversity in ecology ; research papers forum}, volume = {35}, journal = {Ecography : pattern and diversity in ecology ; research papers forum}, number = {7}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/j.1600-0587.2011.07200.x}, pages = {590 -- 603}, year = {2012}, abstract = {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.}, language = {en} }