scholarly journals Effect of mutation supply on population dynamics and trait evolution in experimental microbial community

2021 ◽  
Author(s):  
Johannes Cairns ◽  
Alexandre Jousset ◽  
Lutz Becks ◽  
Teppo Hiltunen
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Mutation Rate ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Serial Passage ◽  
Trait Evolution ◽  
Adaptive Mutations ◽  
High Resource

Mutation supply can influence eco-evolutionary dynamics in important ways which have received little attention. Mutation supply determines key features of population genetics, such as the pool of adaptive mutations, evolutionary pathways available, and importance of processes such as clonal interference. The resultant trait evolutionary dynamics, in turn, can alter population size and species interactions. However, controlled experiments testing for the importance of mutation supply on rapid adaptation and thereby population and community dynamics are lacking. To close this knowledge gap, we performed a serial passage experiment with wild-type Pseudomonas fluorescens and an isogenic xerD mutant with reduced mutation rate. Bacteria were grown at two resource levels in combination with the presence of a ciliate predator. We found that a higher mutation supply enabled faster adaptation to the low-resource environment and anti-predatory defense. This was associated with higher population size at the ecological level and better access to high-recurrence mutational targets at the genomic level for the strain with higher mutation supply. In contrast, mutation rate did not affect growth under high-resource level, possibly because of more permissive conditions or high population size saturated in mutations. Our results demonstrate that intrinsic mutation rate influences population dynamics and trait evolution particularly when population size is constrained by extrinsic conditions.

scholarly journals The importance of species interactions in eco-evolutionary community dynamics under climate change

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna Åkesson ◽  
Alva Curtsdotter ◽  
Anna Eklöf ◽  
Bo Ebenman ◽  
Jon Norberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Negative Impact ◽  
Trophic Levels ◽  
Temperature Dependent ◽  
Modeling Framework ◽  
Impact Of Climate Change ◽  
The Impact

AbstractEco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which features more detailed species interactions, integrating evolution and dispersal. We include species interactions within and between trophic levels, and additionally, we incorporate the feature that species’ interspecific competition might change due to increasing temperatures and affect the impact of climate change on ecological communities. Our modeling framework captures previously reported ecological responses to climate change, and also reveals two key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, our trait-based perspective reveals a strong positive relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Temperature-dependent competition consistently results both in higher trait variation and more responsive communities to altered climatic conditions. Our study demonstrates the importance of species interactions in an eco-evolutionary setting, further expanding our knowledge of the interplay between ecological and evolutionary processes.

scholarly journals Reversion is most likely under high mutation supply, when compensatory mutations don’t fully restore fitness costs

2020 ◽  
Author(s):  
Pleuni S. Pennings ◽  
C. Brandon Ogbunugafor ◽  
Ruth Hershberg
Keyword(s):  
Mutation Rate ◽  
Experimental Evolution ◽  
Evolutionary Dynamics ◽  
Microbial Evolution ◽  
Compensatory Mutation ◽  
Adaptive Mutations ◽  
Fitness Effects ◽  
Theoretical Approaches ◽  
Evolution Study ◽  
Compensatory Mutations

AbstractAdaptive mutations are often associated with a fitness cost. These costs can be compensated for through the acquisition of additional mutations, or the adaptations can be lost through reversion, in settings where they are no longer favored. While the dynamics of adaptation, reversion and compensation have been central features in several studies of microbial evolution, few studies have attempted to resolve the population genetics underlying how and when either compensation or reversion occur. Specifically, questions remain regarding how certain actors—the evolution of mutators and whether compensatory mutations alleviate costs fully or partially—may influence evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from an experimental evolution study by utilizing computational and theoretical approaches towards a more refined understanding of how mutation rate and the fitness effects of compensatory mutation influence evolutionary dynamics. We find that high mutation rates increase the probability of reversion of deleterious adaptations when compensation is only partial. The existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion. Experimental results suggest that, in some contexts, compensatory mutations are not able to fully alleviate costs associated with adaption. Our findings emphasize the role of both mutation rate and the fitness effects of compensatory mutation in crafting evolutionary dynamics, and highlight the importance of population genetic theory for explaining findings from experimental evolution.

scholarly journals Stochasticity in the Parasite-Driven Trait Evolution of Competing Species Masks the Distinctive Consequences of Distance Metrics

2017 ◽  
Author(s):  
Christian Alvin H. Buhat ◽  
Dylan Antonio S.J. Talabis ◽  
Anthony L. Cueno ◽  
Maica Krizna A. Gavina ◽  
Ariel L. Babierra ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Carrying Capacity ◽  
Evolutionary Dynamics ◽  
Evolutionary Optimization ◽  
Quantitative Modeling ◽  
Distance Metrics ◽  
Competing Species ◽  
Trait Evolution

Various distance metrics and their induced norms are employed in the quantitative modeling of evolutionary dynamics. Minimization of these distance metrics when applied to evolutionary optimization are hypothesized to result in different outcomes. Here, we apply the different distance metrics to the evolutionary trait dynamics brought about by the interaction between two competing species infected by parasites (exploiters). We present deterministic cases showing the distinctive selection outcomes under the Manhattan, Euclidean and Chebyshev norms. Specifically, we show how they differ in the time of convergence to the desired optima (e.g., no disease), and in the egalitarian sharing of carrying capacity between the competing species. However, when randomness is introduced to the population dynamics of parasites and to the trait dynamics of the competing species, the distinctive characteristics of the outcomes under the three norms become indistinguishable. Our results provide theoretical cases when evolutionary dynamics using different distance metrics exhibit similar outcomes.

Disease Ecology

Ecology ◽  
2015 ◽  
Author(s):  
Richard S. Ostfeld
Keyword(s):  
Infectious Disease ◽  
Population Dynamics ◽  
Species Interactions ◽  
Disease Ecology ◽  
Molecular Mechanisms ◽  
Community Dynamics ◽  
Interspecific Interactions ◽  
Biological Organization ◽  
Late 20Th Century ◽  
Individual Organism

Disease ecology is a rapidly developing subdiscipline of ecology concerned with how species interactions and abiotic components of the environment affect patterns and processes of disease. To date, disease ecology has focused largely on infectious disease. The scientific study of infectious disease has a long history dominated by specialists on the taxa of infectious agents (e.g., bacteriologists, virologists), mechanisms of host defense (e.g., immunologists), effects of infection on individual hosts (e.g., pathologists), effects on host populations (epidemiologists), and treatment (e.g., practicing physicians and veterinarians). Disease ecology arose as scientists increasingly recognized that the interactions between pathogen and host could be conceptually united with other interspecific interactions, such as those between predator and prey, competitors, or mutualists. At its simplest, an infectious disease consists of an interaction between one species of pathogen and one species of host. The evolution of disease ecology since the late 20th century has incorporated additional layers of complexity, including recognition that most pathogens infect multiple species of host, that hosts are infected with multiple pathogens, and that abiotic conditions (e.g., temperature, moisture) interact with biotic conditions to affect transmission and disease. As a consequence, a framework broader than the simplest host-pathogen system is often required to understand disease dynamics. Disease ecologists are interested both in the ecological causes of disease patterns (for instance, how the population density of a host influences transmission rates), and the ecological consequences of disease (for instance, how the population dynamics of a host species change as an epidemic progresses). Consequently, disease ecology today often integrates across several levels of biological organization, from molecular mechanisms of pathology and immunity; to individual-organism changes in health, survival, and reproduction; to population dynamics of hosts and pathogens; to community dynamics of hosts and pathogens; to impacts of disease on ecosystem processes; to ecosystem-level effects of climate change and landscape change on disease.

scholarly journals Inferring the Effect of Species Interactions on Trait Evolution

2020 ◽  
Author(s):  
Liang Xu ◽  
Sander Van Doorn ◽  
Hanno Hildenbrandt ◽  
Rampal S Etienne
Keyword(s):  
Population Dynamics ◽  
Species Interactions ◽  
Approximate Bayesian Computation ◽  
Fitness Function ◽  
Species Interaction ◽  
Competitive Interactions ◽  
Summary Statistics ◽  
Summary Statistic ◽  
Trait Evolution ◽  
Approximate Bayesian

Abstract Models of trait evolution form an important part of macroevolutionary biology. The Brownian motion model and Ornstein–Uhlenbeck models have become classic (null) models of character evolution, in which species evolve independently. Recently, models incorporating species interactions have been developed, particularly involving competition where abiotic factors pull species toward an optimal trait value and competitive interactions drive the trait values apart. However, these models assume a fitness function rather than derive it from population dynamics and they do not consider dynamics of the trait variance. Here, we develop a general coherent trait evolution framework where the fitness function is based on a model of population dynamics, and therefore it can, in principle, accommodate any type of species interaction. We illustrate our framework with a model of abundance-dependent competitive interactions against a macroevolutionary background encoded in a phylogenetic tree. We develop an inference tool based on Approximate Bayesian Computation and test it on simulated data (of traits at the tips). We find that inference performs well when the diversity predicted by the parameters equals the number of species in the phylogeny. We then fit the model to empirical data of baleen whale body lengths, using three different summary statistics, and compare it to a model without population dynamics and a model where competition depends on the total metabolic rate of the competitors. We show that the unweighted model performs best for the least informative summary statistic, while the model with competition weighted by the total metabolic rate fits the data slightly better than the other two models for the two more informative summary statistics. Regardless of the summary statistic used, the three models substantially differ in their predictions of the abundance distribution. Therefore, data on abundance distributions will allow us to better distinguish the models from one another, and infer the nature of species interactions. Thus, our framework provides a conceptual approach to reveal species interactions underlying trait evolution and identifies the data needed to do so in practice. [Approximate Bayesian computation; competition; phylogeny; population dynamics; simulations; species interaction; trait evolution.]

scholarly journals The effective population size and mutation rate of influenza A virus in acutely infected individuals

2020 ◽  
Author(s):  
John T. McCrone ◽  
Robert J. Woods ◽  
Arnold S. Monto ◽  
Emily T. Martin ◽  
Adam S. Lauring
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Influenza A ◽  
Evolutionary Dynamics ◽  
Sequence Data ◽  
Influenza Viruses ◽  
Influenza A Viruses ◽  
Effective Population

AbstractThe global evolutionary dynamics of influenza viruses ultimately derive from processes that take place within and between infected individuals. Recent work suggests that within-host populations are dynamic, but an in vivo estimate of mutation rate and population size in naturally infected individuals remains elusive. Here we model the within-host dynamics of influenza A viruses using high depth of coverage sequence data from 200 acute infections in an outpatient, community setting. Using a Wright-Fisher model, we estimate a within-host effective population size of 32-72 and an in vivo mutation rate of 3.4×10−6 per nucleotide per generation.

Evolutionary community ecology

2019 ◽  
pp. 307-333
Author(s):  
Gary G. Mittelbach ◽  
Brian J. McGill
Keyword(s):  
Population Dynamics ◽  
Community Ecology ◽  
Species Interactions ◽  
Evolutionary Dynamics ◽  
Ecological Interactions ◽  
Predator Prey ◽  
Evolutionary Rescue ◽  
Species Pools ◽  
Laboratory Populations ◽  
Long Time

Ecology and evolution go hand in hand. However, since evolution occurs over relatively long time scales, ecologists had long thought it unlikely that evolutionary events could affect population dynamics or species interactions in ecological time. This view is changing. Today, there are multiple areas of research examining how evolutionary processes feedback directly on ecology. For example, eco-evolutionary dynamics focus on the cyclical interaction between ecology and adaptive evolution, such that changes in ecological interactions drive selection on organismal traits that, in turn, alter the outcome of ecological interactions. Striking examples of eco-evolutionary feedbacks are found in predator–prey interactions of laboratory populations. However, less is known about eco-evolutionary feedbacks in nature. Evolutionary rescue describes a process whereby rapid adaptation may prevent extinction in a changing environment. Other topics covered in this chapter are community phylogenetics and the evolution of regional species pools.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

2019 ◽  
Vol 35 (20) ◽  
pp. 4053-4062 ◽  
Author(s):  
Louis Gauthier ◽  
Rémicia Di Franco ◽  
Adrian W R Serohijos
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Protein Evolution ◽  
Evolutionary Dynamics ◽  
Fitness Landscapes ◽  
Supplementary Information ◽  
Biological Organization ◽  
Physical Force ◽  
Protein Biochemistry ◽  
Asexual Populations

Abstract Motivation Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution. Results To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution. Availability and implementation Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The importance of species interactions in spatially explicit eco-evolutionary community dynamics under climate change

2020 ◽  
Author(s):  
Anna Åkesson ◽  
Alva Curtsdotter ◽  
Anna Eklöf ◽  
Bo Ebenman ◽  
Jon Norberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Negative Relationship ◽  
Trophic Levels ◽  
Spatially Explicit ◽  
Modeling Framework ◽  
Impact Of Climate Change ◽  
The Impact

AbstractEco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which integrates evolution, dispersal, and species interactions within and between trophic levels. This allows us to analyze how these processes interact to shape species- and community-level dynamics under climate change. Additionally, we incorporate the heretofore unexplored feature that species interactions themselves might change due to increasing temperatures and affect the impact of climate change on ecological communities. The new modeling framework captures previously reported ecological responses to climate change, and also reveals two new key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on global biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, using a trait-based perspective, we found a strong negative relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Communities resulting from different ecological interaction structures form distinct clusters along this relationship, but varying species’ abilities to disperse and adapt to new temperatures leave it unaffected.

scholarly journals Environmental fluctuations restrict eco-evolutionary dynamics in predator–prey system

2015 ◽  
Vol 282 (1808) ◽  
pp. 20150013 ◽  
Author(s):  
Teppo Hiltunen ◽  
Gökçe B. Ayan ◽  
Lutz Becks
Keyword(s):  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Temporal Dynamics ◽  
Rapid Evolution ◽  
Interactive Effects ◽  
Top Down ◽  
Bottom Up ◽  
Evolutionary Response ◽  
Environmental Fluctuations

Environmental fluctuations, species interactions and rapid evolution are all predicted to affect community structure and their temporal dynamics. Although the effects of the abiotic environment and prey evolution on ecological community dynamics have been studied separately, these factors can also have interactive effects. Here we used bacteria–ciliate microcosm experiments to test for eco-evolutionary dynamics in fluctuating environments. Specifically, we followed population dynamics and a prey defence trait over time when populations were exposed to regular changes of bottom-up or top-down stressors, or combinations of these. We found that the rate of evolution of a defence trait was significantly lower in fluctuating compared with stable environments, and that the defence trait evolved to lower levels when two environmental stressors changed recurrently. The latter suggests that top-down and bottom-up changes can have additive effects constraining evolutionary response within populations. The differences in evolutionary trajectories are explained by fluctuations in population sizes of the prey and the predator, which continuously alter the supply of mutations in the prey and strength of selection through predation. Thus, it may be necessary to adopt an eco-evolutionary perspective on studies concerning the evolution of traits mediating species interactions.

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