TY - JOUR A1 - Dormann, Carsten F. A1 - Elith, Jane A1 - Bacher, Sven A1 - Buchmann, Carsten M. A1 - Carl, Gudrun A1 - Carre, Gabriel A1 - Garcia Marquez, Jaime R. A1 - Gruber, Bernd A1 - Lafourcade, Bruno A1 - Leitao, Pedro J. A1 - Münkemüller, Tamara A1 - McClean, Colin A1 - Osborne, Patrick E. A1 - Reineking, Bjoern A1 - Schröder-Esselbach, Boris A1 - Skidmore, Andrew K. A1 - Zurell, Damaris A1 - Lautenbach, Sven T1 - Collinearity a review of methods to deal with it and a simulation study evaluating their performance JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them. Y1 - 2013 U6 - https://doi.org/10.1111/j.1600-0587.2012.07348.x SN - 0906-7590 SN - 1600-0587 VL - 36 IS - 1 SP - 27 EP - 46 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Häring, Tim A1 - Reger, Birgit A1 - Ewald, Jörg A1 - Hothorn, Torsten A1 - Schröder-Esselbach, Boris T1 - Predicting Ellenberg's soil moisture indicator value in the Bavarian Alps using additive georegression JF - Applied vegetation science : official organ of the International Association for Vegetation Science N2 - Questions Can forest site characteristics be used to predict Ellenberg indicator values for soil moisture? Which is the best averaged mean value for modelling? Does the distribution of soil moisture depend on spatial information? Location Bavarian Alps, Germany. Methods We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALPecobase. All predictor variables were taken from area-wide geodata layers so that the model can be applied to some 250 000 ha of forest in the target region. We adopted methods developed in species distribution modelling to regionalize Ellenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of Ellenberg values with the R-library mboost, which is a feasible way to consider environmental effects, spatial autocorrelation, predictor interactions and non-stationarity simultaneously in our data. The framework is much more flexible than established statistical and machine-learning models in species distribution modelling. We estimated five different mboost models reflecting different model structures on 50 bootstrap samples in each case. Results Median R2 values calculated on independent test samples ranged from 0.28 to 0.45. Our results show a significant influence of interactions and non-stationarity in addition to environmental covariates. Unweighted mean indicator values can be modelled better than abundance-weighted values, and the consideration of bryophytes did not improve model performance. Partial response curves indicate meaningful dependencies between moisture indicator values and environmental covariates. However, mean indicator values <4.5 and >6.0 could not be modelled correctly, since they were poorly represented in our calibration sample. The final map represents high-resolution information of site hydrological conditions. Conclusions Indicator values offer an effect-oriented alternative to physically-based hydrological models to predict water-related site conditions, even at landscape scale. The presented approach is applicable to all kinds of Ellenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation. KW - Boosting KW - Mboost KW - Non-stationarity KW - Predictive vegetation mapping KW - Site ecology KW - Species distribution modelling Y1 - 2013 U6 - https://doi.org/10.1111/j.1654-109X.2012.01210.x SN - 1402-2001 VL - 16 IS - 1 SP - 110 EP - 121 PB - Wiley-Blackwell CY - Hoboken ER - TY - GEN A1 - Jeltsch, Florian A1 - Bonte, Dries A1 - Pe'er, Guy A1 - Reineking, Björn A1 - Leimgruber, Peter A1 - Balkenhol, Niko A1 - Schröder-Esselbach, Boris A1 - Buchmann, Carsten M. A1 - Müller, Thomas A1 - Blaum, Niels A1 - Zurell, Damaris A1 - Böhning-Gaese, Katrin A1 - Wiegand, Thorsten A1 - Eccard, Jana A1 - Hofer, Heribert A1 - Reeg, Jette A1 - Eggers, Ute A1 - Bauer, Silke T1 - Integrating movement ecology with biodiversity research BT - exploring new avenues to address spatiotemporal biodiversity dynamics N2 - Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 401 KW - mobile links KW - species coexistence KW - community dynamics KW - biodiversity conservation KW - long distance movement KW - landscape genetics KW - individual based modeling Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-401177 ER - TY - JOUR A1 - Jeltsch, Florian A1 - Bonte, Dries A1 - Peer, Guy A1 - Reineking, Björn A1 - Leimgruber, Peter A1 - Balkenhol, Niko A1 - Schröder-Esselbach, Boris A1 - Buchmann, Carsten M. A1 - Müller, Thomas A1 - Blaum, Niels A1 - Zurell, Damaris A1 - Böhning-Gaese, Katrin A1 - Wiegand, Thorsten A1 - Eccard, Jana A1 - Hofer, Heribert A1 - Reeg, Jette A1 - Eggers, Ute A1 - Bauer, Silke T1 - Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics Y1 - 2013 UR - http://download.springer.com/static/pdf/827/art%253A10.1186%252F2051-3933-1- 6.pdf?auth66=1394891271_f1a4cb74d6be42ee3f8872ef2ca22c24&ext=.pdf U6 - https://doi.org/10.1186/2051-3933-1-6 ER - TY - CHAP A1 - Jeltsch, Florian A1 - Schröder-Esselbach, Boris A1 - Blaum, Niels A1 - Badeck, Franz-Werner T1 - Einsatz der Fernerkundung in der Ökologie BT - Beispiele, Synergien und mögliche Verknüpfungen N2 - Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006 Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-7075 ER - TY - JOUR A1 - Pagel, Jörn A1 - Fritzsch, Katrin A1 - Biedermann, Robert A1 - Schröder-Esselbach, Boris T1 - Annual plants under cyclic disturbance regime : better understanding through model aggregation N2 - In their application for conservation ecology, 'classical' analytical models and individual-based simulation models (IBMs) both entail their specific strengths and weaknesses, either in providing a detailed and realistic representation of processes or in regard to a comprehensive model analysis. This well-known dilemma may be resolved by the combination of both approaches when tackling certain problems of conservation ecology. Following this idea, we present the complementary use of both an IBM and a matrix population model in a case study on grassland conservation management. First, we develop a spatially explicit IBM to simulate the long-term response of the annual plant Thlaspi perfoliatum (Brassicaceae), claspleaf pennycress, to different management schemes (annual mowing vs. infrequent rototilling) based on field experiments. In order to complement the simulation results by further analyses, we aggregate the IBM to a spatially nonexplicit deterministic matrix population model. Within the periodic environment created by management regimes, population dynamics are described by periodic products of annual transition matrices. Such periodic matrix products provide a very conclusive framework to study the responses of species to different management return intervals. Thus, using tools of matrix model analysis (e.g., loop analysis), we can both identify dormancy within the age-structured seed bank as the pivotal strategy for persistence under cyclic disturbance regimes and reveal crucial thresholds in some less certain parameters. Results of matrix model analyses are therefore successfully tested by comparing their results to the respective IBM simulations. Their implications for an enhanced scientific basis for management decisions are discussed as well as some general benefits and limitations of the use of aggregating modeling approaches in conservation. Y1 - 2008 UR - 1960 = DOI: 10.1890/07-1305.1 SN - 1051-0761 ER - TY - JOUR A1 - Reineking, Björn A1 - Schröder-Esselbach, Boris T1 - Constrain to perform : regularization of habitat models N2 - Predictive habitat models are an important tool for ecological research and conservation. A major cause of unreliable models is excessive model complexity, and regularization methods aim to improve the predictive performance by adequately constraining model complexity. We compare three regularization methods for logistic regression: variable selection, lasso, and ridge. They differ in the way model complexity is measured: variable selection uses the number of estimated parameters, the lasso uses the sum of the absolute values of the parameter estimates, and the ridge uses the sum of the squared values of the parameter estimates. We performed a simulation study with environmental data of a real landscape and artificial species occupancy data. We investigated the effect of three factors on relative model performance: (1) the number of parameters (16, 10, 6, 2) in the 'true' model that determined the distribution of the artificial species, (2) the prevalence, i.e. the proportion of sites occupied by the species, and (3) the sample size (measured in events per variable, EPV). Regularization improved model discrimination and calibration. However, no regularization method performed best under all circumstances: the ridge generally performed best in the 16-parameter scenario. The lasso generally performed best in the 10-parameter scenario. Variable selection with AIC was best at large sample sizes (EPV >= 10) when less than half of the variables influenced the species distribution. However, at low sample sizes (EPV < 10), ridge and lasso always performed best, regardless of the parameter scenario or prevalence. Overall, calibration was best in ridge models. Other methods showed overconfidence, particularly at low sample sizes. The percentage of correctly identified models was low for both lasso and variable selection. Variable selection should be used with caution. Although it can produce the best performing models under certain conditions, these situations are difficult to infer from the data. Ridge and lasso are risk-averse model strategies that can be expected to perform well under a wide range of underlying species-habitat relationships, particularly at small sample sizes. Y1 - 2006 UR - http://www.sciencedirect.com/science/journal/03043800 U6 - https://doi.org/10.1016/j.ecolmodel.2005.10.003 SN - 0304-3800 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 - 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 -