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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.
Theory predicts that resource variability hinders consumer performance. How this effect depends on the temporal structure of resource fluctuations encountered by individuals remains poorly understood. Combining modelling and growth experiments with Daphnia magna, we decompose the complexity of resource fluctuations and test the effect of resource variance, supply peak timing (i.e. phase) and co-limiting resource covariance along a gradient from high to low frequencies reflecting fine- to coarse-grained environments. Our results show that resource storage can buffer growth at high frequencies, but yields a sensitivity of growth to resource peak timing at lower ones. When two resources covary, negative covariance causes stronger growth depression at low frequencies. However, negative covariance might be beneficial at intermediate frequencies, an effect that can be explained by digestive acclimation. Our study provides a mechanistic basis for understanding how alterations of the environmental grain size affect consumers experiencing variable nutritional quality in nature.
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.
Predator-prey interactions provide central links in food webs. These interaction are directly or indirectly impacted by a number of factors. These factors range from physiological characteristics of individual organisms, over specifics of their interaction to impacts of the environment. They may generate the potential for the application of different strategies by predators and prey. Within this thesis, I modelled predator-prey interactions and investigated a broad range of different factors driving the application of certain strategies, that affect the individuals or their populations. In doing so, I focused on phytoplankton-zooplankton systems as established model systems of predator-prey interactions.
At the level of predator physiology I proposed, and partly confirmed, adaptations to fluctuating availability of co-limiting nutrients as beneficial strategies. These may allow to store ingested nutrients or to regulate the effort put into nutrient assimilation. We found that these two strategies are beneficial at different fluctuation frequencies of the nutrients, but may positively interact at intermediate frequencies. The corresponding experiments supported our model results. We found that the temporal structure of nutrient fluctuations indeed has strong effects on the juvenile somatic growth rate of {\itshape Daphnia}.
Predator colimitation by energy and essential biochemical nutrients gave rise to another physiological strategy. High-quality prey species may render themselves indispensable in a scenario of predator-mediated coexistence by being the only source of essential biochemical nutrients, such as cholesterol. Thereby, the high-quality prey may even compensate for a lacking defense and ensure its persistence in competition with other more defended prey species.
We found a similar effect in a model where algae and bacteria compete for nutrients. Now, being the only source of a compound that is required by the competitor (bacteria) prevented the competitive exclusion of the algae. In this case, the essential compounds were the organic carbon provided by the algae. Here again, being indispensable served as a prey strategy that ensured its coexistence.
The latter scenario also gave rise to the application of the two metabolic strategies of autotrophy and heterotrophy by algae and bacteria, respectively. We found that their coexistence allowed the recycling of resources in a microbial loop that would otherwise be lost. Instead, these resources were made available to higher trophic levels, increasing the trophic transfer efficiency in food webs.
The predation process comprises the next higher level of factors shaping the predator-prey interaction, besides these factors that originated from the functioning or composition of individuals. Here, I focused on defensive mechanisms and investigated multiple scenarios of static or adaptive combinations of prey defense and predator offense. I confirmed and extended earlier reports on the coexistence-promoting effects of partially lower palatability of the prey community. When bacteria and algae are coexisting, a higher palatability of bacteria may increase the average predator biomass, with the side effect of making the population dynamics more regular. This may facilitate experimental investigations and interpretations. If defense and offense are adaptive, this allows organisms to maximize their growth rate. Besides this fitness-enhancing effect, I found that co-adaptation may provide the predator-prey system with the flexibility to buffer external perturbations.
On top of these rather internal factors, environmental drivers also affect predator-prey interactions. I showed that environmental nutrient fluctuations may create a spatio-temporal resource heterogeneity that selects for different predator strategies. I hypothesized that this might favour either storage or acclimation specialists, depending on the frequency of the environmental fluctuations.
We found that many of these factors promote the coexistence of different strategies and may therefore support and sustain biodiversity. Thus, they might be relevant for the maintenance of crucial ecosystem functions that also affect us humans. Besides this, the richness of factors that impact predator-prey interactions might explain why so many species, especially in the planktonic regime, are able to coexist.