TY - JOUR A1 - Zurell, Damaris A1 - Jeltsch, Florian A1 - Dormann, Carsten F. A1 - Schröder-Esselbach, Boris T1 - Static species distribution models in dynamically changing systems : how good can predictions really be? N2 - SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far- dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short- dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change. Y1 - 2009 UR - http://www3.interscience.wiley.com/journal/117966123/home?CRETRY=1&SRETRY=0 U6 - https://doi.org/10.1111/j.1600-0587.2009.05810.x SN - 0906-7590 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 - TY - GEN A1 - Zurell, Damaris A1 - Elith, Jane A1 - Schröder-Esselbach, Boris T1 - Predicting to new environments tools for visualizing model behaviour and impacts on mapped distributions T2 - Diversity & distributions : a journal of biological invasions and biodiversity N2 - 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. KW - Environmental niche KW - extrapolation KW - inflated response curves KW - novel environment KW - sampling space KW - species distribution models Y1 - 2012 U6 - https://doi.org/10.1111/j.1472-4642.2012.00887.x SN - 1366-9516 VL - 18 IS - 6 SP - 628 EP - 634 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Wintle, Brendan A. A1 - Bekessy, Sarah A. A1 - Keith, David A. A1 - van Wilgen, Brian W. A1 - Cabeza, Mar A1 - Schröder-Esselbach, Boris A1 - Carvalho, Silvia B. A1 - Falcucci, Alessandra A1 - Maiorano, Luigi A1 - Regan, Tracey J. A1 - Rondinini, Carlo A1 - Boitani, Luigi A1 - Possingham, Hugh P. T1 - Ecological-economic optimization of biodiversity conservation under climate change JF - Nature climate change N2 - Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report(1) cites 28,586 studies demonstrating significant biological changes in terrestrial systems(2). Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change(3-6). Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints(7). Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world's most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments(4). Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions. Y1 - 2011 U6 - https://doi.org/10.1038/NCLIMATE1227 SN - 1758-678X VL - 1 IS - 7 SP - 355 EP - 359 PB - Nature Publ. Group CY - London 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 - Vorpahl, Peter A1 - Elsenbeer, Helmut A1 - Märker, Michael A1 - Schröder-Esselbach, Boris T1 - How can statistical models help to determine driving factors of landslides? JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion. Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the 'black box' of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency. With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes. KW - Landslides KW - Tropical montane forests KW - Statistical modeling KW - Model comparison KW - Artificial neuronal network KW - Classification trees KW - Random forests KW - Boosted regression trees KW - Generalized linear models KW - Multivariate adaptive regression splines KW - Maximum entropy method KW - Weighted model ensembles Y1 - 2012 U6 - https://doi.org/10.1016/j.ecolmodel.2011.12.007 SN - 0304-3800 SN - 1872-7026 VL - 239 IS - 7 SP - 27 EP - 39 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Vorpahl, Peter A1 - Dislich, Claudia A1 - Elsenbeer, Helmut A1 - Märker, Michael A1 - Schröder-Esselbach, Boris T1 - Biotic controls on shallow translational landslides JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - In undisturbed tropical montane rainforests massive organic layers accommodate the majority of roots and only a small fraction of roots penetrate the mineral soil. We investigated the contribution of vegetation to slope stability in such environments by modifying a standard model for slope stability to include an organic layer with distinct mechanical properties. The importance of individual model parameters was evaluated using detailed measurements of soil and vegetation properties to reproduce the observed depth of 11 shallow landslides in the Andes of southern Ecuador. By distinguishing mineral soil, organic layer and above-ground biomass, it is shown that in this environment vegetation provides a destabilizing effect mainly due to its contribution to the mass of the organic layer (up to 973 t ha-1 under wet conditions). Sensitivity analysis shows that the destabilizing effect of the mass of soil and vegetation can only be effective on slopes steeper than 37.9 degrees. This situation applies to 36% of the study area. Thus, on the steep slopes of this megadiverse ecosystem, the mass of the growing forest promotes landsliding, which in turn promotes a new cycle of succession. This feedback mechanism is worth consideration in further investigations of the impact of landslides on plant diversity in similar environments. KW - shallow translational landslides KW - tropical montane forest KW - biomass KW - organic layer Y1 - 2013 U6 - https://doi.org/10.1002/esp.3320 SN - 0197-9337 VL - 38 IS - 2 SP - 198 EP - 212 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Tanneberger, Franziska A1 - Flade, Martin A1 - Preiksa, Zydrunas A1 - Schröder-Esselbach, Boris T1 - Habitat selection of the globally threatened Aquatic Warbler Acrocephalus paludicola at the western margin of its breeding range and implications for management N2 - The globally threatened Aquatic Warbler Acrocephalus paludicola is an umbrella species for fen mires and is at risk of extinction in its westernmost breeding population due to severe habitat loss. We used boosted regression trees to model Aquatic Warbler habitat selection in order to make recommendations for effective management of the last remnant habitats. Habitat data were collected in the years 2004-2006 in all remaining breeding sites in Pomerania (eastern Germany and western Poland) as well as in recently abandoned sites. Models were validated using data from similar Aquatic Warbler habitats in Lithuania. The probability of occurrence of Aquatic Warblers in late May/early June was positively associated with low isolation from other occupied sites, less eutrophic conditions, a high proportion of area mown early in the preceding year, high availability of vegetation 60-70 cm high, high prey abundance and high habitat heterogeneity. Early summer land management is needed in the more productive sites to prevent habitat deterioration by succession to higher and denser vegetation. As this also poses a serious threat to broods, management that creates a mosaic of early and late used patches is recommended to preserve and restore productive Aquatic Warbler sites. In less productive sites, winter mowing can maintain suitable habitat conditions. Aquatic Warbler-friendly land use supports a variety of other threatened plant and animal species typical of fens and sedge meadows and can meet the economic interests of local land users. Y1 - 2010 UR - http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1474-919X U6 - https://doi.org/10.1111/j.1474-919X.2010.01016.x SN - 0019-1019 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 - Schulz, Jennifer J. A1 - Cayuela, Luis A1 - Rey-Benayas, Jose M. A1 - Schröder-Esselbach, Boris T1 - Factors influencing vegetation cover change in Mediterranean Central Chile (1975-2008) JF - Applied vegetation science : official organ of the International Association for Vegetation Science N2 - Questions: Which are the factors that influence forest and shrubland loss and regeneration and their underlying drivers? Location: Central Chile, a world biodiversity hotspot. Methods: Using land-cover data from the years 1975, 1985, 1999 and 2008, we fitted classification trees and multiple logistic regression models to account for the relationship between different trajectories of vegetation change and a range of biophysical and socio-economic factors. Results: The variables that most consistently showed significant effects on vegetation change across all time-intervals were slope and distance to primary roads. We found that forest and shrubland loss on one side and regeneration on the other often displayed opposite patterns in relation to the different explanatory variables. Deforestation was positively related to distance to primary roads and to distance within forest edges and was favoured by a low insolation and a low slope. In turn, forest regeneration was negatively related to the distance to primary roads and positively to the distance to the nearest forest patch, insolation and slope. Shrubland loss was positively influenced by slope and distance to cities and primary roads and negatively influenced by distance to rivers. Conversely, shrubland regeneration was negatively related to slope, distance to cities and distance to primary roads and positively related to distance from existing forest patches and distance to rivers. Conclusions: This article reveals how biophysical and socioeconomic factors influence vegetation cover change and the underlying social, political and economical drivers. This assessment provides a basis for management decisions, considering the crucial role of perennial vegetation cover for sustaining biodiversity and ecosystem services. KW - Deforestation KW - Driving forces KW - Forest regeneration KW - Land-cover change KW - Shrubland regeneration Y1 - 2011 U6 - https://doi.org/10.1111/j.1654-109X.2011.01135.x SN - 1402-2001 VL - 14 IS - 4 SP - 571 EP - 582 PB - Wiley-Blackwell CY - Hoboken ER -