@article{VorpahlDislichElsenbeeretal.2013, author = {Vorpahl, Peter and Dislich, Claudia and Elsenbeer, Helmut and M{\"a}rker, Michael and Schr{\"o}der-Esselbach, Boris}, title = {Biotic controls on shallow translational landslides}, series = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, volume = {38}, journal = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, number = {2}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0197-9337}, doi = {10.1002/esp.3320}, pages = {198 -- 212}, year = {2013}, abstract = {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.}, language = {en} } @article{RichterSchroederEsselbachHartmannetal.2013, author = {Richter, Dania and Schr{\"o}der-Esselbach, Boris and Hartmann, Niklas K. and Matuschka, Franz-Rainer}, title = {Spatial stratification of various Lyme disease spirochetes in a Central European site}, series = {FEMS microbiology ecology}, volume = {83}, journal = {FEMS microbiology ecology}, number = {3}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0168-6496}, doi = {10.1111/1574-6941.12029}, pages = {738 -- 744}, year = {2013}, abstract = {To determine whether the genospecies composition of Lyme disease spirochetes is spatially stratified, we collected questing Ixodes ricinus ticks in neighboring plots where rodents, birds, and lizards were present as reservoir host and compared the prevalence of various genospecies. The overall prevalence of spirochetes in questing ticks varied across the study site. Borrelia lusitaniae appeared to infect adult ticks in one plot at the same frequency as did Borrelia afzelii in the other plots. The relative density of questing nymphal and adult ticks varied profoundly. Where lizards were exceedingly abundant, these vertebrates seemed to constitute the dominant host for nymphal ticks, contributing the majority of infected adult ticks. Because lizards support solely B.lusitaniae and appear to exclude other genospecies, their narrow genospecies association results in predominance of B.lusitaniae in sites where lizards are abundant, while limiting its spread to the host's habitat range. To the extent that Central European B.lusitaniae strains are nonpathogenic, the presence of numerous lizards should locally decrease risk of infection for people. Evaluation of regional risk of infection by Lyme disease spirochetes should take the spatial effect of hosts into consideration, which stratify the distribution of specifically infected ticks on a small scale.}, language = {en} } @article{PalmvanSchaikSchroederEsselbach2013, author = {Palm, Juliane and van Schaik, N. Loes M. B. and Schr{\"o}der-Esselbach, Boris}, title = {Modelling distribution patterns of anecic, epigeic and endogeic earthworms at catchment-scale in agro-ecosystems}, series = {Pedobiologia : international journal of soil biology}, volume = {56}, journal = {Pedobiologia : international journal of soil biology}, number = {1}, publisher = {Elsevier}, address = {Jena}, issn = {0031-4056}, doi = {10.1016/j.pedobi.2012.08.007}, pages = {23 -- 31}, year = {2013}, abstract = {Species distribution models are useful for identifying driving environmental factors that determine earthworm distributions as well as for predicting earthworm distribution patterns and abundances at different scales. However, due to large efforts in data acquisition, studies on larger scales are rare and often focus on single species or earthworms in general. In this study, we use boosted regression tree models (BRTs) for predicting the distribution of the three functional earthworm types, i.e. anecics, endogeics and epigeics, in an agricultural area in Baden-Wurttemberg (Southwest Germany). First, we predicted presence and absence and later earthworm abundances, considering predictors depicting land management, topography, and soil conditions as well as biotic interaction by using the abundance of the other functional earthworm types. The final presence-absence models performed reasonably well, with explained deviances between 24 and 51\% after crossvalidation. Models for abundances of anecics and endogeics were less successful, since the high small-scale variability and patchiness in earthworm abundance influenced the representativeness of the field measurements. This resulted in a significant model uncertainty, which is practically very difficult to overcome with earthworm sampling campaigns at the catchment scale. Results showed that management practices (i.e. disturbances), topography, soil conditions, and biotic interactions with other earthworm groups are the most relevant predictors for spatial distribution (incidence) patterns of all three functional groups. The response curves and contributions of predictors differ for the three functional earthworm types. Epigeics are also controlled by topographic features, endogeics by soil parameters.}, language = {en} } @article{KramerSchadtNiedballaPilgrimetal.2013, author = {Kramer-Schadt, Stephanie and Niedballa, J{\"u}rgen and Pilgrim, John D. and Schr{\"o}der-Esselbach, Boris and Lindenborn, Jana and Reinfelder, Vanessa and Stillfried, Milena and Heckmann, Ilja and Scharf, Anne K. and Augeri, Dave M. and Cheyne, Susan M. and Hearn, Andrew J. and Ross, Joanna and Macdonald, David W. and Mathai, John and Eaton, James and Marshall, Andrew J. and Semiadi, Gono and Rustam, Rustam and Bernard, Henry and Alfred, Raymond and Samejima, Hiromitsu and Duckworth, J. W. and Breitenmoser-Wuersten, Christine and Belant, Jerrold L. and Hofer, Heribert and Wilting, Andreas}, title = {The importance of correcting for sampling bias in MaxEnt species distribution models}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {19}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {11}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/ddi.12096}, pages = {1366 -- 1379}, year = {2013}, abstract = {AimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. LocationBorneo, Southeast Asia. MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.}, language = {en} } @article{JeltschBontePeeretal.2013, author = {Jeltsch, Florian and Bonte, Dries and Peer, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke}, title = {Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics}, doi = {10.1186/2051-3933-1-6}, year = {2013}, language = {en} } @misc{JeltschBontePe'eretal.2013, author = {Jeltsch, Florian and Bonte, Dries and Pe'er, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke}, title = {Integrating movement ecology with biodiversity research}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401177}, pages = {13}, year = {2013}, abstract = {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.}, language = {en} } @article{HaeringRegerEwaldetal.2013, author = {H{\"a}ring, Tim and Reger, Birgit and Ewald, J{\"o}rg and Hothorn, Torsten and Schr{\"o}der-Esselbach, Boris}, title = {Predicting Ellenberg's soil moisture indicator value in the Bavarian Alps using additive georegression}, series = {Applied vegetation science : official organ of the International Association for Vegetation Science}, volume = {16}, journal = {Applied vegetation science : official organ of the International Association for Vegetation Science}, number = {1}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1402-2001}, doi = {10.1111/j.1654-109X.2012.01210.x}, pages = {110 -- 121}, year = {2013}, abstract = {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.}, language = {en} } @article{DormannElithBacheretal.2013, author = {Dormann, Carsten F. and Elith, Jane and Bacher, Sven and Buchmann, Carsten M. and Carl, Gudrun and Carre, Gabriel and Garcia Marquez, Jaime R. and Gruber, Bernd and Lafourcade, Bruno and Leitao, Pedro J. and M{\"u}nkem{\"u}ller, Tamara and McClean, Colin and Osborne, Patrick E. and Reineking, Bjoern and Schr{\"o}der-Esselbach, Boris and Skidmore, Andrew K. and Zurell, Damaris and Lautenbach, Sven}, title = {Collinearity a review of methods to deal with it and a simulation study evaluating their performance}, series = {Ecography : pattern and diversity in ecology ; research papers forum}, volume = {36}, journal = {Ecography : pattern and diversity in ecology ; research papers forum}, number = {1}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0906-7590}, doi = {10.1111/j.1600-0587.2012.07348.x}, pages = {27 -- 46}, year = {2013}, abstract = {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.}, language = {en} }