Biology subjects

Raatz, M.

Publications and source records attributed to Raatz, M..

3 recordsLinked to original sources

bioRxiv2018

Estimating parameters from multiple time series of population dynamics using Bayesian inference

O_LIEmpirical 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.\nC_LIO_LIIn 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.\nC_LIO_LIOur 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.\nC_LIO_LIBy 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.\nC_LI

ecology↗

bioRxiv2018

Resource co-limited growth in fluctuating environments

Variability in the supply of limiting resources determines consumer-resource interactions. Yet, how consumers are affected by variability when multiple resources co-limit growth remains unknown. We use a two-resource DEB model to predict how consumer somatic growth rate responds to the temporal structure (i.e. fluctuation frequency, phase and covariance) of single and co-limiting resources supply. Subsequently, we experimentally test the model predictions using Daphnia magna (co-)limited by dietary phosphorus and cholesterol supply. Both model and experiments indicate that for certain fluctuation frequencies, resource fluctuation phase and (co)variance can heavily affect somatic growth. The model suggests that dynamic resource storage and assimilation efficiency adjustment are key for predicting the frequencies at which the growth rate is mostly affected by (co)variance and phase. In a context of ongoing anthropogenic landscape homogenization, our results offer novel insights on how co-occurring perturbations to the temporal structure of resource supply can affect consumer performance.

ecology↗

bioRxiv2018

One man’s trash is another man’s treasure - the effect of bacteria on phytoplankton-zooplankton interactions in chemostat systems

Chemostat experiments are employed to study predator-prey and other trophic interactions, frequently using phytoplankton-zooplankton systems. These experiments often use population dynamics as fingerprints of ecological and evolutionary processes, assuming that the contributions of all major actors to these dynamics are known. However, bacteria are often neglected although they are frequently present. We argue that even without external carbon sources bacteria may affect the experimental outcomes depending on experimental conditions and the physiological traits of bacteria, phytoplankton and zooplankton. Using a static carbon flux model and a dynamic simulation model we predict the minimum and maximum impact of bacteria on phytoplankton-zooplankton population dynamics. Under bacteria-suppressing conditions, we find that the effect of bacteria is indeed negligible and their omission justified. Under bacteria-favouring conditions, however, bacteria may strongly affect average biomasses. Furthermore, the population dynamics may become highly complex resulting in wrong conclusions if bacteria are not considered. Our model results provide suggestions to reduce the bacterial impact experimentally. Next to optimizing experimental conditions (e.g. the dilution rate) the appropriate choice of the zooplankton predator is decisive. Counterintuitively, bacteria have a larger impact if they are not ingested by the predator as high bacterial biomasses and complex population dynamics arise via competition for nutrients with the phytoplankton. Only if the predator is at least partly bacterivorous the impact of bacteria is minimized. Our results help to improve both the design of chemostat experiments and their interpretation and thus advance the study of ecological and evolutionary processes in aquatic food webs.

ecology↗