@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} } @misc{KisslingDormannGroeneveldetal.2012, author = {Kissling, W. D. and Dormann, Carsten F. and Groeneveld, Juergen and Hickler, Thomas and K{\"u}hn, Ingolf and McInerny, Greg J. and Montoya, Jose M. and R{\"o}mermann, Christine and Schiffers, Katja and Schurr, Frank Martin and Singer, Alexander and Svenning, Jens-Christian and Zimmermann, Niklaus E. and O'Hara, Robert B.}, title = {Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents}, series = {Journal of biogeography}, volume = {39}, journal = {Journal of biogeography}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2011.02663.x}, pages = {2163 -- 2178}, year = {2012}, abstract = {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.}, language = {en} } @article{MarionMcInernyPageletal.2012, author = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.}, title = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche}, series = {JOURNAL OF BIOGEOGRAPHY}, volume = {39}, journal = {JOURNAL OF BIOGEOGRAPHY}, number = {12}, publisher = {WILEY-BLACKWELL}, address = {HOBOKEN}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2012.02772.x}, pages = {2225 -- 2239}, year = {2012}, abstract = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.}, language = {en} }