TY - JOUR A1 - Pennekamp, Frank A1 - Iles, Alison C. A1 - Garland, Joshua A1 - Brennan, Georgina A1 - Brose, Ulrich A1 - Gaedke, Ursula A1 - Jacob, Ute A1 - Kratina, Pavel A1 - Matthews, Blake A1 - Munch, Stephan A1 - Novak, Mark A1 - Palamara, Gian Marco A1 - Rall, Bjorn C. A1 - Rosenbaum, Benjamin A1 - Tabi, Andrea A1 - Ward, Colette A1 - Williams, Richard A1 - Ye, Hao A1 - Petchey, Owen L. T1 - The intrinsic predictability of ecological time series and its potential to guide forecasting JF - Ecological monographs : a publication of the Ecological Society of America. KW - empirical dynamic modelling KW - forecasting KW - information theory KW - permutation entropy KW - population dynamics KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.1002/ecm.1359 SN - 0012-9615 SN - 1557-7015 VL - 89 IS - 2 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Malchow, Anne-Kathleen A1 - Bocedi, Greta A1 - Palmer, Stephen C. F. A1 - Travis, Justin M. J. A1 - Zurell, Damaris T1 - RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and speciesu0027 responses to environmental changes JF - Ecography N2 - 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. KW - connectivity KW - conservation KW - dispersal KW - evolution KW - population dynamics KW - range dynamics Y1 - 2021 SN - 1600-0587 VL - 44 IS - 10 PB - John Wiley & Sons, Inc. CY - New Jersey ER - TY - JOUR A1 - Malchow, Anne-Kathleen A1 - Bocedi, Greta A1 - Palmer, Stephen C. F. A1 - Travis, Justin M. J. A1 - Zurell, Damaris T1 - RangeShiftR BT - an R package for individual-based simulation of spatial changes JF - Ecography : pattern and diversity in ecology / Nordic Ecologic Society Oikos N2 - 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. KW - connectivity KW - conservation KW - dispersal KW - evolution KW - population dynamics KW - range dynamics Y1 - 2021 U6 - https://doi.org/10.1111/ecog.05689 SN - 1600-0587 VL - 44 IS - 10 SP - 1443 EP - 1452 PB - Wiley-Blackwell CY - Oxford [u.a.] ER - TY - JOUR A1 - Reil, Daniela A1 - Binder, Florian A1 - Freise, Jona A1 - Imholt, Christian A1 - Beyrers, Konrad A1 - Jacob, Jens A1 - Krüger, Detlev H. A1 - Hofmann, Jörg A1 - Dreesman, Johannes A1 - Ulrich, Rainer Günter T1 - Hantaviren in Deutschland BT - Aktuelle Erkenntnisse zu Erreger, Reservoir, Verbreitung und Prognosemodellen JF - Berliner und Münchener tierärztliche Wochenschrift N2 - 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. N2 - Hantaviren sind Kleinsäuger-assoziierte Krankheitserreger, die vor allem in Nagetieren, aber auch in Spitzmäusen, Maulwürfen und Fledermäusen vorkommen. Ziel dieser Arbeit ist es, einen aktuellen Überblick zur Epidemiologie und Ökologie der Hantaviren in Deutschland zu geben und Modelle zur Vorhersage von Virusausbrüchen zu diskutieren. In Deutschland werden die meisten humanen Erkrankungsfälle beim Menschen durch das von der Rötelmaus (Myodes glareolus) übertragene Puumalavirus (PUUV) verursacht. PUUV ist mit der westlichen evolutionären Linie der Rötelmaus assoziiert und fehlt im östlichen und nördlichen Teil Deutschlands. Ein zweites humanpathogenes Hantavirus ist das Dobrava-Belgrad-Virus (DOBV), Genotyp Kurkino, dessen Reservoir die vor allem im östlichen Teil Deutschlands vorkommende Brandmaus (Apodemus agrarius) ist. Ein PUUV-verwandtes Hantavirus ist das selten humanpathogene Tulavirus (TULV), das mit der Feldmaus (Microtus arvalis) assoziiert ist. Darüber hinaus wurden mit dem Seewis-, Asikkala- und Brugesvirus Spitzmaus- und Maulwurf-assoziierte Hantaviren mit noch unklarer Humanpathogenität gefunden. Die humanen Erkrankungen sind jeweils mit den verschiedenen Hantaviren in deren regionaler Verteilung assoziiert und können mild bis schwer, aber auch subklinisch verlaufen. Das Auftreten von Häufungen humaner, durch PUUV verursachter Erkrankungen in den Jahren 2007, 2010, 2012, 2015 und 2017 korreliert mit dem Auftreten einer starken Fruktifikation der Buche („Buchenmast“) im jeweiligen Vorjahr. Auf der Basis von Wetterparametern sind Modelle zur Vorhersage von PUUV-Erkrankungshäufungen entwickelt worden, die zukünftig validiert und optimiert werden müssen. Neben dem Ausmaß des Virusvorkommens im Reservoir wird das Risiko humaner Infektionen durch das Expositionsverhalten des Menschen beeinflusst. Durch die Anwendung von Prognosemodellen soll der öffentliche Gesundheitsdienst in die Lage versetzt werden, räumlich und zeitlich gezielte und sachgerechte Präventionsempfehlungen für die Bevölkerung abzugeben. T2 - Hantaviruses in Germany: current knowledge on pathogens, reservoirs, distribution and forecast models KW - early warning system KW - hantavirus KW - hantavirus disease KW - rodents KW - population dynamics KW - Frühwarn-System KW - Hantavirus KW - Hantavirus-Erkrankung KW - Nagetiere KW - Populationsdynamik Y1 - 2018 U6 - https://doi.org/10.2376/0005-9366-18003 SN - 0005-9366 SN - 1439-0299 VL - 131 IS - 11-12 SP - 453 EP - 464 PB - Schlütersche Verlagsgesellschaft mbH & Co. KG. CY - Hannover ER - TY - JOUR A1 - Rosenbaum, Benjamin A1 - Raatz, Michael A1 - Weithoff, Guntram A1 - Fussmann, Gregor F. A1 - Gaedke, Ursula T1 - Estimating parameters from multiple time series of population dynamics using bayesian inference JF - Frontiers in ecology and evolution N2 - 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. KW - Bayesian inference KW - chemostat experiments KW - ordinary differential equation KW - parameter estimation KW - population dynamics KW - predator prey KW - time series analysis KW - trait variability Y1 - 2019 U6 - https://doi.org/10.3389/fevo.2018.00234 SN - 2296-701X VL - 6 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Raatz, Michael A1 - van Velzen, Ellen A1 - Gaedke, Ursula T1 - Co‐adaptation impacts the robustness of predator–prey dynamics against perturbations JF - Ecology and Evolution N2 - 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. KW - disturbance KW - evolutionary rescue KW - population dynamics KW - stability KW - trait adaptation Y1 - 2019 U6 - https://doi.org/10.1002/ece3.5006 SN - 2045-7758 VL - 9 IS - 7 SP - 3823 EP - 3836 PB - John Wiley & Sons CY - Hoboken, NJ ER -