@misc{SchurrPagelSarmentoetal.2012,
  author    = {Schurr, Frank Martin and Pagel, J{\"o}rn and Sarmento, Juliano Sarmento and Groeneveld, Juergen and Bykova, Olga and O'Hara, Robert B. and Hartig, Florian and Kissling, W. Daniel and Linder, H. Peter and Midgley, Guy F. and Schr{\"o}der-Esselbach, Boris and Singer, Alexander and Zimmermann, Niklaus E.},
  title     = {How to understand species' niches and range dynamics: a demographic research agenda for biogeography},
  series = {Journal of biogeography},
  volume    = {39},
  journal   = {Journal of biogeography},
  number    = {12},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2012.02737.x},
  pages     = {2146 -- 2162},
  year      = {2012},
  abstract  = {Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.},
  language  = {en}
}
@unpublished{WellsteinSchroederEsselbachReinekingetal.2011,
  author    = {Wellstein, Camilla and Schr{\"o}der-Esselbach, Boris and Reineking, Bjoern and Zimmermann, Niklaus E.},
  title     = {Understanding species and community response to environmental change - A functional trait perspective},
  series = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere},
  volume    = {145},
  journal   = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere},
  number    = {1},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-8809},
  doi       = {10.1016/j.agee.2011.06.024},
  pages     = {1 -- 4},
  year      = {2011},
  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}
}