Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances

River ecosystem function depends on flow regimes that are increasingly modified by changes in climate, land use, water extraction, and flow regulation. Given the wide range of variation in flow regime modifications and autotrophic communities in rivers, it has been challenging to predict which rivers will be more resilient to flow disturbances. To better understand how river productivity is disturbed by and recovers from high-flow disturbance events, we used a continental-scale dataset of daily gross primary production time series from 143 rivers to estimate growth of autotrophic biomass and ecologically relevant flow disturbance thresholds using a modified population model. We compared biomass recovery rates across hydroclimatic gradients and catchment characteristics to evaluate macroscale controls on ecosystem recovery. Estimated biomass accrual (i.e., recovery) was fastest in wider rivers with less regulated flow regimes and more frequent instances of biomass removal during high flows. Although disturbance flow thresholds routinely fell below the estimated bankfull flood (i.e., the 2-y flood), a direct comparison of disturbance flows estimated by our biomass model and a geomorphic model revealed that biomass disturbance thresholds were usually greater than bed disturbance thresholds. We suggest that primary producers in rivers vary widely in their capacity to recover following flow disturbances, and multiple, interacting macroscale factors control productivity recovery rates, although river width had the strongest overall effect. Biomass disturbance flow thresholds varied as a function of geomorphology, highlighting the need for data such as bed slope and grain size to predict how river ecosystems will respond to changing flow regimes.

Travelling with a parasite: the evolution of resistance and dispersal syndromes during experimental range expansion

Rapid evolutionary change during range expansions can lead to diverging range core and front populations, with the emergence of dispersal syndromes (coupled responses in dispersal and life-history traits). Besides intraspecific effects, range expansions may be impacted by interspecific interactions such as parasitism. Yet, despite the potentially large impact of parasites imposing additional selective pressures on the host, their role on range expansions remains largely unexplored. Using microcosm populations of the ciliate Paramecium caudatum and its bacterial parasite Holospora undulata, we studied experimental range expansions under parasite presence or absence. We found that the interaction of range expansion and parasite treatments affected the evolution of host dispersal syndromes. Namely, front populations showed different associations of population growth parameters and swimming behaviours than core populations, indicating divergent evolution. Parasitism reshaped trait associations, with hosts evolved in the presence of the parasite exhibiting overall increased resistance and reduced dispersal. Nonetheless, when comparing infected range core and front populations, we found a positive association, suggesting joint evolution of resistance and dispersal at the front. We conclude that host-parasite interactions during range expansions can change evolutionary trajectories; this in turn may feedback on the ecological dynamics of the range expansion and parasite epidemics.

Exploring cocoa bean fermentation mechanisms by kinetic modelling

Compared with other fermentation processes in food industry, cocoa bean fermentation is uncontrolled and not standardized. A detailed mechanistic understanding can therefore be relevant for cocoa bean quality control. Starting from an existing mathematical model of cocoa bean fermentation we analyse five additional biochemical mechanisms derived from the literature. These mechanisms, when added to the baseline model either in isolation or in combination, were evaluated in terms of their capacity to describe experimental data. In total, we evaluated 32 model variants on 23 fermentation datasets. We interpret the results from two perspectives: (1) success of the potential mechanism, (2) discrimination of fermentation protocols based on estimated parameters. The former provides insight in the fermentation process itself. The latter opens an avenue towards reverse-engineering empirical conditions from model parameters. We find support for two mechanisms debated in the literature: consumption of fructose by lactic acid bacteria and production of acetic acid by yeast. Furthermore, we provide evidence that model parameters are sensitive to differences in the cultivar, temperature control and usage of steel tanks compared with wooden boxes. Our results show that mathematical modelling can provide an alternative to standard chemical fingerprinting in the interpretation of fermentation data.

The Stochastic Nature of Functional Responses

Functional responses are non-linear functions commonly used to describe the variation in the rate of consumption of resources by a consumer. They have been widely used in both theoretical and empirical studies, but a comprehensive understanding of their parameters at different levels of description remains elusive. Here, by depicting consumers and resources as stochastic systems of interacting particles, we present a minimal set of reactions for consumer resource dynamics. We rigorously derived the corresponding system of ODEs, from which we obtained via asymptotic expansions classical 2D consumer-resource dynamics, characterized by different functional responses. We also derived functional responses by focusing on the subset of reactions describing only the feeding process. This involves fixing the total number of consumers and resources, which we call chemostatic conditions. By comparing these two ways of deriving functional responses, we showed that classical functional response parameters in effective 2D consumer-resource dynamics differ from the same parameters obtained by measuring (or deriving) functional responses for typical feeding experiments under chemostatic conditions, which points to potential errors in interpreting empirical data. We finally discuss possible generalizations of our models to systems with multiple consumers and more complex population structures, including spatial dynamics. Our stochastic approach builds on fundamental ecological processes and has natural connections to basic ecological theory.

Fitting stochastic predator-prey models using both population density and kill rate data

Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.

Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities

Predator-prey interactions heavily influence the dynamics of many ecosystems. An increasing body of evidence suggests that rapid evolution and coevolution can alter these interactions, with important ecological implications, by acting on traits determining fitness, including reproduction, anti-predatory defence and foraging efficiency. However, most studies to date have focused only on evolution in the prey species, and the predator traits in (co)evolving systems remain poorly understood. Here, we investigated changes in predator traits after approximately 600 generations in a predator-prey (ciliate-bacteria) evolutionary experiment. Predators independently evolved on seven different prey species, allowing generalization of the predator's evolutionary response. We used highly resolved automated image analysis to quantify changes in predator life history, morphology and behaviour. Consistent with previous studies, we found that prey evolution impaired growth of the predator, although the effect depended on the prey species. By contrast, predator evolution did not cause a clear increase in predator growth when feeding on ancestral prey. However, predator evolution affected morphology and behaviour, increasing size, speed and directionality of movement, which have all been linked to higher prey search efficiency. These results show that in (co)evolving systems, predator adaptation can occur in traits relevant to foraging efficiency without translating into an increased ability of the predator to grow on the ancestral prey type.

Gene swamping alters evolution during range expansions in the protist Tetrahymena thermophila

At species' range edges, individuals often face novel environmental conditions that may limit range expansion until populations adapt. The potential to adapt depends on genetic variation upon which selection can act. However, populations at species' range edges are often genetically depauperate. One mechanism increasing genetic variation is reshuffling existing variation through sex. Sex, however, can potentially limit adaptation by breaking up existing beneficial allele combinations (recombination load). The gene swamping hypothesis predicts this is specifically the case when populations expand along an abiotic gradient and asymmetric dispersal leads to numerous maladapted dispersers from the range core swamping the range edge. We used the ciliate Tetrahymena thermophila as a model for testing the gene swamping hypothesis. We performed replicated range expansions in landscapes with or without a pH-gradient, while simultaneously manipulating the occurrence of gene flow and sexual versus asexual reproduction. We show that sex accelerated evolution of local adaptation in the absence of gene flow, but hindered it in the presence of gene flow. However, sex affected adaptation independently of the pH-gradient, indicating that both abiotic gradients and the biotic gradient in population density lead to gene swamping. Overall, our results show that gene swamping alters adaptation in life-history strategies.

Hierarchical Bayesian myocardial perfusion quantification

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila

Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH-stress under high population densities. Using the protist species Tetrahymena thermophila, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (r₀ ). Overall, evolution at low pH led to the convergence of r₀ and intraspecific competitive ability (α) across the four genotypes. Given the strong correlation between r₀ and α, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in r₀ , or decrease of α, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.

Clothing the Emperor: Dynamic Root-Shoot Allocation Trajectories in Relation to Whole-Plant Growth Rate and in Response to Temperature

We quantified how root-shoot biomass allocation and whole-plant growth rate co-varied ontogenetically in contrasting species in response to cooling. Seven grass and four forb species were grown for 56 days in hydroponics. Growth was measured repeatedly before and after day/night temperatures were reduced at 28 days from 20 °C/15 °C to 10 °C/5 °C; controls remained unchanged. Sigmoid trajectories of root and shoot growth were reconstructed from the experimental data to derive continuous whole-plant relative growth rates (RGRs) and root mass fractions (RMFs). Root mass fractions in cooled plants generally increased, but this originated from unexpected and previously uncharacterised differences in response among species. Root mass fraction and RGR co-trajectories were idiosyncratic in controls and cooled plants. The RGR-RMF co-trajectories responded to cooling in grasses, but not forbs. The RMF responses of stress-tolerant grasses were predictably weak but projected to eventually out-respond faster-growing species. Sigmoid growth constrains biomass allocation. Only when neither root nor shoot biomass is near-maximal can biomass allocation respond to environmental drivers. Near maximum size, plants cannot adjust RMF, which then reflects net above- and belowground productivities. Ontogenetic biomass allocations are not equivalent to those based on interspecific surveys, especially in mature vegetation. This reinforces the importance of measuring temporal growth dynamics, and not relying on "snapshot" comparisons to infer the functional significance of root-shoot allocation.