TY  - JOUR
A1  - Esther, Alexandra
A1  - Groeneveld, Juergen
A1  - Enright, Neal J.
A1  - Miller, Ben P.
A1  - Lamont, Byron B.
A1  - Perry, George L. W.
A1  - Blank, F. Benjamin
A1  - Jeltsch, Florian
T1  - Sensitivity of plant functional types to climate change : classification tree analysis of a simulation model
N2  - Question: The majority of studies investigating the impact of climate change on local plant communities ignores changes in regional processes, such as immigration from the regional seed pool. Here we explore: (i) the potential impact of climate change on composition of the regional seed pool, (ii) the influence of changes in climate and in the regional seed pool on local community structure, and (iii) the combinations of life history traits, i.e. plant functional types (PFTs), that are most affected by environmental changes. Location: Fire-prone, Mediterranean-type shrublands in southwestern Australia. Methods: Spatially explicit simulation experiments were conducted at the population level under different rainfall and fire regime scenarios to determine the effect of environmental change on the regional seed pool for 38 PFTs. The effects of environmental and seed immigration changes on local community dynamics were then derived from community-level experiments. Classification tree analyses were used to investigate PFT- specific vulnerabilities to climate change. Results: The classification tree analyses revealed that responses of PFTs to climate change are determined by specific trait characteristics. PFT-specific seed production and community patterns responded in a complex manner to climate change. For example, an increase in annual rainfall caused an increase in numbers of dispersed seeds for some PFTs, but decreased PFT diversity in the community. Conversely, a simulated decrease in rainfall reduced the number of dispersed seeds and diversity of PFTs. Conclusions: PFT interactions and regional processes must be considered when assessing how local community structure will be affected by environmental change.
Y1  - 2010
UR  - http://www3.interscience.wiley.com/journal/121642345/home
U6  - https://doi.org/10.1111/j.1654-1103.2009.01155.x
SN  - 1100-9233
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  - JOUR
A1  - Ayllon, Daniel
A1  - Railsback, Steven Floyd
A1  - Vincenzi, Simone
A1  - Groeneveld, Juergen
A1  - Almodoevar, Ana
A1  - Grimm, Volker
T1  - InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change
JF  - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog
N2  - Current rates of environmental change are exceeding the capacity of many populations to adapt to new conditions and thus avoid demographic collapse and ultimate extinction. In particular, cold-water freshwater fish species are predicted to experience strong selective pressure from climate change and a wide range of interacting anthropogenic stressors in the near future. To implement effective management and conservation measures, it is crucial to quantify the maximum rate of change that cold-water freshwater fish populations can withstand. Here, we present a spatially explicit eco-genetic individual-based model, inSTREAM-Gen, to predict the eco-evolutionary dynamics of stream-dwelling trout under anthropogenic environmental change. The model builds on a well-tested demographic model, which includes submodels of river dynamics, bioenergetics, and adaptive habitat selection, with a new genetic module that allows exploration of genetic and life-history adaptations to new environments. The genetic module models the transmission of two key traits, size at emergence and maturity size threshold. We parameterized the model for a brown trout (Salmo trutta L.) population at the warmest edge of its range to validate it and analyze its sensitivity to parameters under contrasting thermal profiles. To illustrate potential applications of the model, we analyzed the population's demographic and evolutionary dynamics under scenarios of (1) climate change-induced warming, and (2) warming plus flow reduction resulting from climate and land use change, compared to (3) a baseline of no environmental change. The model predicted severe declines in density and biomass under climate warming. These declines were lower than expected at range margins because of evolution towards smaller size at both emergence and maturation compared to the natural evolution under the baseline conditions. Despite stronger evolutionary responses, declining rates were substantially larger under the combined warming and flow reduction scenario, leading to a high probability of population extinction over contemporary time frames. Therefore, adaptive responses could not prevent extinction under high rates of environmental change. Our model demonstrates critical elements of next generation ecological modelling aiming at predictions in a changing world as it accounts for spatial and temporal resource heterogeneity, while merging individual behaviour and bioenergetics with microevolutionary adaptations.
KW  - Individual-based model
KW  - Eco-genetic modelling
KW  - Eco-evolution
KW  - Climate change
KW  - Brown trout
KW  - Next-generation modelling
Y1  - 2016
U6  - https://doi.org/10.1016/j.ecolmodel.2015.07.026
SN  - 0304-3800
SN  - 1872-7026
VL  - 326
SP  - 36
EP  - 53
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Radchuk, Viktoriia
A1  - Oppel, Steffen
A1  - Groeneveld, Juergen
A1  - Grimm, Volker
A1  - Schtickzelle, Nicolas
T1  - Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses
JF  - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog
N2  - Population viability analysis (PVA) models are used to estimate population extinction risk under different scenarios. Both simple and complex PVA models are developed and have their specific pros and cons; the question therefore arises whether we always use the most appropriate model type. Generally, the specific purpose of a model and the availability of data are listed as determining the choice of model type, but this has not been formally tested yet. We quantified the relative importance of model purpose and nine metrics of data availability and resolution for the choice of a PVA model type, while controlling for effects of the different life histories of the modelled species. We evaluated 37 model pairs: each consisting of a generally simpler, population-based model (PBM) and a more complex, individual-based model (IBM) developed for the same species. The choice of model type was primarily affected by the availability and resolution of demographic, dispersal and spatial data. Low-resolution data resulted in the development of less complex models. Model purpose did not affect the choice of the model type. We confirm the general assumption that poor data availability is the main reason for the wide use of simpler models, which may have limited predictive power for population responses to changing environmental conditions. Conservation biology is a crisis discipline where researchers learned to work with the data at hand. However, for threatened and poorly-known species, there is no short-cut when developing either a PBM or an IBM: investments to collect appropriately detailed data are required to ensure PVA models can assess extinction risk under complex environmental conditions. (C) 2015 Elsevier B.V. All rights reserved.
KW  - Model complexity
KW  - Individual-based model
KW  - Population-based model
KW  - Matrix model
KW  - Structured population model
KW  - Stage-based model
Y1  - 2016
U6  - https://doi.org/10.1016/j.ecolmodel.2015.11.022
SN  - 0304-3800
SN  - 1872-7026
VL  - 323
SP  - 87
EP  - 95
PB  - Elsevier
CY  - Amsterdam
ER  -