@phdthesis{Massie2011, author = {Massie, Thomas Michael}, title = {Dynamic behavior of phytoplankton populations far from steady state : chemostat experiments and mathematical modeling}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-58102}, school = {Universit{\"a}t Potsdam}, year = {2011}, abstract = {Nature changes continuously and is only seemingly at equilibrium. Environmental parameters like temperature, humidity or insolation may strongly fluctuate on scales ranging from seconds to millions of years. Being part of an ecosystem, species have to cope with these environmental changes. For ecologists, it is of special interest how individual responses to environmental changes affect the dynamics of an entire population - and, if this behavior is predictable. In this context, the demographic structure of a population plays a decisive role since it originates from processes of growth and mortality. These processes are fundamentally influenced by the environment. But, how exactly does the environment influence the behavior of populations? And what does the transient behavior look like? As a result from environmental influences on demography, so called cohorts form. They are age or size classes that are disproportionally represented in the demographic distribution of a population. For instance, if most old and young individuals die due to a cold spell, the population finally consists of mainly middle-aged individuals. Hence, the population got synchronized. Such a population tends to show regular fluctuations in numbers (denoted as oscillations) since the alternating phases of individual growth and population growth (due to reproduction) are now performed synchronously by the majority of the population.That is, one time the population growths, and the other time it declines due to mortality. Synchronous behavior is one of the most pervasive phenomena in nature. Gravitational synchrony in the solar system; fireflies flashing in unison; coordinate firing of pacemaker cells in the heart; electrons in a superconductor marching in lockstep. Whatever scale one looks at, in animate as well as inanimate systems, one is likely to encounter synchrony. In experiments with phytoplankton populations, I could show that this principle of synchrony (as used by physicists) could well-explain the oscillations observed in the experiments, too. The size of the fluctuations depended on the strength by which environmental parameters changed as well as on the demographic state of a population prior to this change. That is, two population living in different habitats can be equally influenced by an environmental change, however, the resulting population dynamics may be significantly different when both populations differed in their demographic state before. Moreover, specific mechanisms relevant for the dynamic behavior of populations, appear only when the environmental conditions change. In my experiments, the population density declined by 50\% after ressource supply was doubled. This counter-intuitive behavior can be explained by increasing ressource consumption. The phytoplankton cells grew larger and enhanced their individual constitution. But at the same time, reproduction was delayed and the population density declined due to the losses by mortality. Environmental influences can also synchronize two or more populations over large distances, which is denoted as Moran effect. Assume two populations living on two distant islands. Although there is no exchange of individuals between them, both populations show a high similarity when comparing their time series. This is because the globally acting climate synchronizes the regionally acting weather on both island. Since the weather fluctuations influence the population dynamics, the Moran effect states that the synchrony between the environment equals the one between the populations. My experiments support this theory and also explain deviations arising when accounting for differences in the populations and the habitats they are living in. Moreover, model simulations and experiments astonishingly show that the synchrony between the populations can be higher than between the environment, when accounting for differences in the environmental fluctuations ("noise color").}, language = {de} } @article{MassieRyabovBlasiusetal.2013, author = {Massie, Thomas Michael and Ryabov, Alexei and Blasius, Bernd and Weithoff, Guntram and Gaedke, Ursula}, title = {Complex transient dynamics of stage-structured populations in response to environmental changes}, series = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences}, volume = {182}, journal = {The American naturalist : a bi-monthly journal devoted to the advancement and correlation of the biological sciences}, number = {1}, publisher = {Univ. of Chicago Press}, address = {Chicago}, issn = {0003-0147}, doi = {10.1086/670590}, pages = {103 -- 119}, year = {2013}, abstract = {Stage structures of populations can have a profound influence on their dynamics. However, not much is known about the transient dynamics that follow a disturbance in such systems. Here we combined chemostat experiments with dynamical modeling to study the response of the phytoplankton species Chlorella vulgaris to press perturbations. From an initially stable steady state, we altered either the concentration or dilution rate of a growth-limiting resource. This disturbance induced a complex transient response-characterized by the possible onset of oscillations-before population numbers relaxed to a new steady state. Thus, cell numbers could initially change in the opposite direction of the long-term change. We present quantitative indexes to characterize the transients and to show that the dynamic response is dependent on the degree of synchronization among life stages, which itself depends on the state of the population before perturbation. That is, we show how identical future steady states can be approached via different transients depending on the initial population structure. Our experimental results are supported by a size-structured model that accounts for interplay between cell-cycle and population-level processes and that includes resource-dependent variability in cell size. Our results should be relevant to other populations with a stage structure including organisms of higher order.}, language = {en} } @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} }