@article{ReilBinderFreiseetal.2018, author = {Reil, Daniela and Binder, Florian and Freise, Jona and Imholt, Christian and Beyrers, Konrad and Jacob, Jens and Kr{\"u}ger, Detlev H. and Hofmann, J{\"o}rg and Dreesman, Johannes and Ulrich, Rainer G{\"u}nter}, title = {Hantaviren in Deutschland}, series = {Berliner und M{\"u}nchener tier{\"a}rztliche Wochenschrift}, volume = {131}, journal = {Berliner und M{\"u}nchener tier{\"a}rztliche Wochenschrift}, number = {11-12}, publisher = {Schl{\"u}tersche Verlagsgesellschaft mbH \& Co. KG.}, address = {Hannover}, issn = {0005-9366}, doi = {10.2376/0005-9366-18003}, pages = {453 -- 464}, year = {2018}, abstract = {Hantaviruses are small mammal-associated pathogens that are found in rodents but also in shrews, moles and bats. Aim of this manuscript is to give a current overview of the epidemiology and ecology of hantaviruses in Germany and to discuss respective models for the prediction of virus outbreaks. In Germany the majority of human disease cases are caused by the Puumala virus (PUUV), transmitted by the bank vole (Myodes glareolus). PUUV is associated with the Western evolutionary lineage of the bank vole and is not present in the eastern and northern parts of Germany. A second human pathogenic hantavirus is the Dobrava-Belgrade virus (DOBV), genotype Kurkino; its reservoir host, the striped field mouse (Apodemus agrarius), is mostly occurring in the eastern part of Germany. A PUUV-related hantavirus is the rarely pathogenic Tula virus (TULV), that is associated with the common vole (Microtus arvalis). In addition, Seewis virus, Asikkala virus, and Bruges virus are shrew- and mole-associated hantaviruses with still unknown pathogenicity in humans. Human disease cases are associated with the different hantaviruses according to their regional distribution. The viruses can cause mild to severe but also subclinical courses of the respective disease. The number of human PUUV disease cases in 2007, 2010, 2012, 2015 and 2017 correlates with the occurrence of high levels of seed production of beech trees ("beech mast") in the preceding year. Models based on weather parameters for the prediction of PUUV disease clusters as developed in recent years need further validation and optimisation. in addition to the abundance of infected reservoir rodents, the exposure behaviour of humans affects the risk of human infection. The application of robust forecast models can assist the public health service to develop and communicate spatially and temporally targeted information. Thus, further recommendations to mitigate infection risk for the public may be provided.}, language = {de} } @article{RosenbaumRaatzWeithoffetal.2019, author = {Rosenbaum, Benjamin and Raatz, Michael and Weithoff, Guntram and Fussmann, Gregor F. and Gaedke, Ursula}, title = {Estimating parameters from multiple time series of population dynamics using bayesian inference}, series = {Frontiers in ecology and evolution}, volume = {6}, journal = {Frontiers in ecology and evolution}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {2296-701X}, doi = {10.3389/fevo.2018.00234}, pages = {14}, year = {2019}, abstract = {Empirical time series of interacting entities, e.g., species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the respective parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise parameter estimates. We detected significant variability among parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.}, language = {en} } @article{PennekampIlesGarlandetal.2019, author = {Pennekamp, Frank and Iles, Alison C. and Garland, Joshua and Brennan, Georgina and Brose, Ulrich and Gaedke, Ursula and Jacob, Ute and Kratina, Pavel and Matthews, Blake and Munch, Stephan and Novak, Mark and Palamara, Gian Marco and Rall, Bjorn C. and Rosenbaum, Benjamin and Tabi, Andrea and Ward, Colette and Williams, Richard and Ye, Hao and Petchey, Owen L.}, title = {The intrinsic predictability of ecological time series and its potential to guide forecasting}, series = {Ecological monographs : a publication of the Ecological Society of America.}, volume = {89}, journal = {Ecological monographs : a publication of the Ecological Society of America.}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {0012-9615}, doi = {10.1002/ecm.1359}, pages = {17}, year = {2019}, language = {en} } @article{RaatzvanVelzenGaedke2019, author = {Raatz, Michael and van Velzen, Ellen and Gaedke, Ursula}, title = {Co-adaptation impacts the robustness of predator-prey dynamics against perturbations}, series = {Ecology and Evolution}, volume = {9}, journal = {Ecology and Evolution}, number = {7}, publisher = {John Wiley \& Sons}, address = {Hoboken, NJ}, issn = {2045-7758}, doi = {10.1002/ece3.5006}, pages = {3823 -- 3836}, year = {2019}, abstract = {Global change threatens the maintenance of ecosystem functions that are shaped by the persistence and dynamics of populations. It has been shown that the persistence of species increases if they possess larger trait adaptability. Here, we investigate whether trait adaptability also affects the robustness of population dynamics of interacting species and thereby shapes the reliability of ecosystem functions that are driven by these dynamics. We model co-adaptation in a predator-prey system as changes to predator offense and prey defense due to evolution or phenotypic plasticity. We investigate how trait adaptation affects the robustness of population dynamics against press perturbations to environmental parameters and against pulse perturbations targeting species abundances and their trait values. Robustness of population dynamics is characterized by resilience, elasticity, and resistance. In addition to employing established measures for resilience and elasticity against pulse perturbations (extinction probability and return time), we propose the warping distance as a new measure for resistance against press perturbations, which compares the shapes and amplitudes of pre- and post-perturbation population dynamics. As expected, we find that the robustness of population dynamics depends on the speed of adaptation, but in nontrivial ways. Elasticity increases with speed of adaptation as the system returns more rapidly to the pre-perturbation state. Resilience, in turn, is enhanced by intermediate speeds of adaptation, as here trait adaptation dampens biomass oscillations. The resistance of population dynamics strongly depends on the target of the press perturbation, preventing a simple relationship with the adaptation speed. In general, we find that low robustness often coincides with high amplitudes of population dynamics. Hence, amplitudes may indicate the robustness against perturbations also in other natural systems with similar dynamics. Our findings show that besides counteracting extinctions, trait adaptation indeed strongly affects the robustness of population dynamics against press and pulse perturbations.}, language = {en} } @article{MalchowBocediPalmeretal.2021, author = {Malchow, Anne-Kathleen and Bocedi, Greta and Palmer, Stephen C. F. and Travis, Justin M. J. and Zurell, Damaris}, title = {RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and speciesu0027 responses to environmental changes}, series = {Ecography}, volume = {44}, journal = {Ecography}, number = {10}, publisher = {John Wiley \& Sons, Inc.}, address = {New Jersey}, issn = {1600-0587}, pages = {10}, year = {2021}, abstract = {Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.}, language = {en} } @article{MalchowBocediPalmeretal.2021, author = {Malchow, Anne-Kathleen and Bocedi, Greta and Palmer, Stephen C. F. and Travis, Justin M. J. and Zurell, Damaris}, title = {RangeShiftR}, series = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos}, volume = {44}, journal = {Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos}, number = {10}, publisher = {Wiley-Blackwell}, address = {Oxford [u.a.]}, issn = {1600-0587}, doi = {10.1111/ecog.05689}, pages = {1443 -- 1452}, year = {2021}, abstract = {Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.}, language = {en} }