TY  - JOUR
A1  - Thuiller, Wilfried
A1  - Albert, Cécile H.
A1  - Araújo, Miguel B.
A1  - Berry, Pam M.
A1  - Cabeza, Mar
A1  - Guisan, Antoine
A1  - Hickler, Thomas
A1  - Midgley, Guy F.
A1  - Paterson, James
A1  - Schurr, Frank Martin
A1  - Sykes, Martin T.
A1  - Zimmermann, Niklaus E.
T1  - Predicting global change impacts on plant species' distributions : future challenges
Y1  - 2008
U6  - https://doi.org/10.1016/j.ppees.2007.09.004
SN  - 1433-8319
ER  - 
TY  - JOUR
A1  - Schurr, Frank Martin
A1  - Pagel, Jörn
A1  - Sarmento, Juliano Sarmento
A1  - Groeneveld, Juergen
A1  - Bykova, Olga
A1  - O'Hara, Robert B.
A1  - Hartig, Florian
A1  - Kissling, W. Daniel
A1  - Linder, H. Peter
A1  - Midgley, Guy F.
A1  - Schröder-Esselbach, Boris
A1  - Singer, Alexander
A1  - Zimmermann, Niklaus E.
T1  - How to understand species' niches and range dynamics: a demographic research agenda for biogeography
JF  - Journal of biogeography
N2  - 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.
KW  - Biodiversity monitoring
KW  - climate change
KW  - ecological forecasts
KW  - ecological niche modelling
KW  - ecological theory
KW  - geographical range shifts
KW  - global environmental change
KW  - mechanistic models
KW  - migration
KW  - process-based statistics
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-2699.2012.02737.x
SN  - 0305-0270
VL  - 39
IS  - 12
SP  - 2146
EP  - 2162
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Kissling, W. D.
A1  - Dormann, Carsten F.
A1  - Groeneveld, Juergen
A1  - Hickler, Thomas
A1  - Kühn, Ingolf
A1  - McInerny, Greg J.
A1  - Montoya, Jose M.
A1  - Römermann, Christine
A1  - Schiffers, Katja
A1  - Schurr, Frank Martin
A1  - Singer, Alexander
A1  - Svenning, Jens-Christian
A1  - Zimmermann, Niklaus E.
A1  - O'Hara, Robert B.
T1  - Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents
JF  - Journal of biogeography
N2  - Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.
KW  - Community ecology
KW  - ecological networks
KW  - global change
KW  - guild assembly
KW  - multidimensional complexity
KW  - niche theory
KW  - prediction
KW  - species distribution model
KW  - species interactions
KW  - trait-based community modules
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-2699.2011.02663.x
SN  - 0305-0270
VL  - 39
IS  - 12
SP  - 2163
EP  - 2178
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - INPR
A1  - Wellstein, Camilla
A1  - Schröder-Esselbach, Boris
A1  - Reineking, Bjoern
A1  - Zimmermann, Niklaus E.
T1  - Understanding species and community response to environmental change - A functional trait perspective
T2  - Agriculture, ecosystems & environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere
KW  - Functional traits
KW  - Functional diversity
KW  - Database
KW  - Land use
KW  - Management
KW  - Climate change
KW  - Landscape
KW  - Ecosystem function
KW  - Clonal plants
KW  - Dispersal
KW  - Plant growth
KW  - Orthoptera
Y1  - 2011
U6  - https://doi.org/10.1016/j.agee.2011.06.024
SN  - 0167-8809
VL  - 145
IS  - 1
SP  - 1
EP  - 4
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Thuiller, Wilfried
A1  - Muenkemueller, Tamara
A1  - Schiffers, Katja H.
A1  - Georges, Damien
A1  - Dullinger, Stefan
A1  - Eckhart, Vincent M.
A1  - Edwards, Thomas C.
A1  - Gravel, Dominique
A1  - Kunstler, Georges
A1  - Merow, Cory
A1  - Moore, Kara
A1  - Piedallu, Christian
A1  - Vissault, Steve
A1  - Zimmermann, Niklaus E.
A1  - Zurell, Damaris
A1  - Schurr, Frank Martin
T1  - Does probability of occurrence relate to population dynamics?
JF  - Ecography : pattern and diversity in ecology ; research papers forum
N2  - Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with P-occ, while N, and for most regions K, was generally positively correlated with P-occ. Thus, in temperate forest trees the regions of highest occurrence probability are those with high densities but slow intrinsic population growth rates. The uncertain relationships between demography and occurrence probability suggests caution when linking species distribution and demographic models.
Y1  - 2014
U6  - https://doi.org/10.1111/ecog.00836
SN  - 0906-7590
SN  - 1600-0587
VL  - 37
IS  - 12
SP  - 1155
EP  - 1166
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  -