570 Biowissenschaften; Biologie
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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.
Estimating parameters from multiple time series of population dynamics using bayesian inference
(2019)
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.
Hantaviren in Deutschland
(2018)
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.
RangeShiftR
(2021)
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.
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.