Biology subjects

Fussmann, G. F.

Publications and source records attributed to Fussmann, G. F..

7 recordsLinked to original sources

bioRxiv2024

Pre-exposure to stress reduces loss of community and genetic diversity following severe environmental disturbance

Environmental stress caused by anthropogenic impacts is increasing worldwide. Understanding the ecological and evolutionary consequences for biodiversity will be crucial for our ability to respond effectively. Historical exposure to environmental stress is expected to select for resistant species, shifting community composition towards more stress-tolerant taxa. Concurrent with this species sorting process, genotypes within resistant taxa that have the highest relative fitness under severe stress are expected to increase in frequency, leading to evolutionary adaptation. However, empirical demonstrations of these dual ecological and evolutionary processes in natural communities are lacking. Here, we provide the first evidence for simultaneous species sorting and evolutionary adaptation across multiple species within a natural freshwater bacterial community. Using a two-phase stressor experimental design (acidification pre-exposure followed by severe acidification) in aquatic mesocosms, we show that pre-exposed communities were more resistant than naive communities to taxonomic loss when faced with severe acid stress. However, after sustained severe acidification, taxonomic richness of both pre-exposed and naive communities eventually converged. All communities experiencing severe acidification became dominated by an acidophilic bacterium, Acidiphilium rubrum, but this species retained greater genetic diversity and followed distinct evolutionary trajectories in pre-exposed relative to naive communities. These patterns were shared across other acidophilic species, providing repeated evidence for the impact of pre-exposure on evolutionary outcomes despite the convergence of community profiles. Our results underscore the need to consider both ecological and evolutionary processes to accurately predict the responses of natural communities to environmental change.

evolutionary biology↗

bioRxiv2023

Ecosystem connectivity and configuration can mediate instability at a distance in metaecosystems

Ecosystems are connected by flows of nutrients and organisms. Changes to connectivity and nutrient enrichment may destabilise ecosystem dynamics far from the nutrient source. We used gradostats to examine the effects of trophic connectivity (movement of consumers and producers) versus nutrient-only connectivity in different metaecosystem configurations (linear vs dendritic) on dynamics of Daphnia pulex (consumers) and an algae (resources). We found that peak population size and instability (coefficient of variation; CV) of Daphnia populations increased as distance from the nutrient input increased, but were lower in metaecosystems connected by all trophic levels compared to nutrient-only connected systems and in dendritic systems compared to linear ones. We examined the effects of trophic connectivity (i.e. how many trophic levels are moving rather than one or the other) using a generic model to qualitatively assess patterns of ecosystem dynamics. Our model predicted increased population sizes and fluctuations in ecosystems with nutrient connectivity, with this pattern being more pronounced in linear rather than dendritic systems. These results confirm that connectivity may propagate and even amplify instability over a metaecosystem to communities far from the source disturbance, and suggest a pathway for future experiments, that recreate conditions closer to those found in natural systems.

ecology↗

bioRxiv2020

Resistance, resilience, and functional redundancy of freshwater microbial communities facing multiple agricultural stressors in a mesocosm experiment

Agricultural pollution with fertilizers and pesticides is a common disturbance to freshwater biodiversity. Bacterioplankton communities are at the base of aquatic food webs, but their responses to these potentially interacting stressors are rarely explored. To test the extent of resistance and resilience in bacterioplankton communities faced with agricultural stressors, we exposed freshwater mesocosms to single and combined gradients of two commonly used pesticides: the herbicide glyphosate (0-15 mg/L) and the neonicotinoid insecticide imidacloprid (0-60 g/L), in high or low nutrient backgrounds. Over the 43-day experiment, we tracked variation in bacterial density with flow cytometry, carbon substrate use with Biolog EcoPlates, and taxonomic diversity and composition with environmental 16S rRNA gene amplicon sequencing. We show that only glyphosate (at the highest dose, 15 mg/L), but not imidacloprid, nutrients, or their interactions measurably changed community structure, favoring members of the Proteobacteria including the genus Agrobacterium. However, no change in carbon substrate use was detected throughout, suggesting functional redundancy despite taxonomic changes. We further show that communities are resilient at broad, but not fine taxonomic levels: 24 days after glyphosate application the precise amplicon sequence variants do not return, and tend to be replaced by phylogenetically close taxa. We conclude that high doses of glyphosate - but still within commonly acceptable regulatory guidelines - alter freshwater bacterioplankton by favoring a subset of higher taxonomic units (i.e. genus to phylum) that transiently thrive in the presence of glyphosate. Longer-term impacts of glyphosate at finer taxonomic resolution merit further investigation.

ecology↗

bioRxiv2019

Adaptation and evolutionary rescue in a community context

Evolution might rescue populations from extinction in changing environments. Using experimental evolution with microalgae, we investigated if competition influences adaptation to an abiotic stressor, and vice versa, if adaptation to abiotic change influences competition. In a first set of experiments, we propagated monocultures of five species with and without increasing salt stress for ~180 generations. When assayed in monoculture, two of the five species showed signatures of adaptation, that is, lines with a history of salt stress had higher population growth rates at high salt than lines without prior exposure to salt. When assayed in mixtures of species, however, only one of these two species had increased population size at high salt, indicating that competition can alter how adaptation to abiotic change influences population dynamics. In a second experiment, we cultivated two species in monocultures and in pairs, with and without increasing salt. While we found no effect of competition on adaptation to salt, our experiment revealed that evolutionary responses to salt can influence competition. Specifically, one of the two species had reduced competitive ability in the no-salt environment after long-term exposure to salt stress. Collectively, our results highlight the complex interplay of adaptation to abiotic change and competitive interactions.

ecology↗

bioRxiv2018

Community rescue in experimental phytoplankton communities facing severe herbicide pollution.

Evolutionary rescue occurs when adaptation prevents local extinction in deteriorating environments. Laboratory experiments with microorganisms have shown that the likelihood of evolutionary rescue is greatest in large populations that have previously experienced sublethal doses of stress. To assess this result in natural communities, we conducted a mesocosm experiment with semi-natural phytoplankton communities exposed to glyphosate, a widely used herbicide. We tested whether community biomass and pre-exposure to sublethal stress would facilitate community rescue after severe contamination. Exposure to sublethal stress, but not community biomass, facilitated rescue significantly-even though it led to biodiversity loss. Furthermore, glyphosate had modest effects on community composition, suggesting that community resistance to glyphosate was primarily driven by changes in resistance within taxa, not by community turnover. Our results expand the scope of community evolutionary rescue theory to complex ecosystems and confirm that prior stress exposure is a key predictor of rescue.

ecology↗

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

The mechanics of predator-prey interactions: first principles of physics predict predator-prey size ratios

O_LIRobust predictions of predator-prey interactions are fundamental for the understanding of food webs, their structure, dynamics, resistance to species loss and invasions and role in ecosystem functioning. Most current food web models are empirically based. Thus, they are sensitive to the quality of the data, and ineffective in predicting non-described and disturbed food webs. There is a need for mechanistic models that predict the occurrence of a predator-prey interaction based on the traits of organisms and the properties of their environment.\nC_LIO_LIHere, we present such a model that focuses on the predation act itself. We built a Newtonian, mechanical model for the processes of searching, capture and handling of a prey item by a predator. Associated with general metabolic laws, we predict the net energy gain from predation for pairs of predator and prey species depending on their body sizes.\nC_LIO_LIPredicted interactions match well with data from the most extensive predator-prey database, and overall model accuracy is greater than the niche model.\nC_LIO_LIOur model shows that it is possible to accurately predict the structure of food webs using only a few ecomechanical traits. It underlines the importance of physical constraints in structuring food webs.\nC_LI

ecology↗