Symbiosis, the living together of unlike organisms as symbionts, is ubiquitous in the natural world. Symbioses occur within and across all scales of life, from microbial to macro-faunal systems. Further, the interactions between symbionts are multimodal in both strength and type, can span from parasitic to mutualistic within one partnership, and persist over generations. Studying the ecological and evolutionary dynamics of symbiosis in natural or laboratory systems poses a wide range of challenges, including the long time scales at which symbioses evolve de novo , the limited capacity to experimentally control symbiotic interactions, the weak resolution at which we can quantify interactions, and the idiosyncrasies of current model systems. These issues are especially challenging when seeking to understand the ecological effects and evolutionary pressures on and of a symbiosis, such as how a symbiosis may shift between parasitic and mutualistic modes and how that shift impacts the dynamics of the partner population. In digital evolution, populations of computational organisms compete, mutate, and evolve in a virtual environment. Digital evolution features perfect data tracking and allows for experimental manipulations that are impractical or impossible in natural systems. Furthermore, modern computational power allows experimenters to observe thousands of generations of evolution in minutes (as opposed to several months or years), which greatly expands the range of possible studies. As such, digital evolution is poised to become a keystone technique in our methodological repertoire for studying the ecological and evolutionary dynamics of symbioses. Here, we review how digital evolution has been used to study symbiosis, and we propose a series of open questions that digital evolution is well-positioned to answer.
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Digital Evolution for Ecology Research: A Review
In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.
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- Award ID(s):
- 1655715
- NSF-PAR ID:
- 10308552
- Date Published:
- Journal Name:
- Frontiers in Ecology and Evolution
- Volume:
- 9
- ISSN:
- 2296-701X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Heritable trait variation is a central and necessary ingredient of evolution. Trait variation also directly affects ecological processes, generating a clear link between evolutionary and ecological dynamics. Despite the changes in variation that occur through selection, drift, mutation, and recombination, current eco‐evolutionary models usually fail to track how variation changes through time. Moreover, eco‐evolutionary models assume fitness functions for each trait and each ecological context, which often do not have empirical validation. We introduce a new type of model, Gillespie eco‐evolutionary models (
GEM s), that resolves these concerns by tracking distributions of traits through time as eco‐evolutionary dynamics progress. This is done by allowing change to be driven by the direct fitness consequences of model parameters within the context of the underlying ecological model, without having to assume a particular fitness function.GEM s work by adding a trait distribution component to the standard Gillespie algorithm – an approach that models stochastic systems in nature that are typically approximated through ordinary differential equations. We illustrateGEM s with the Rosenzweig–MacArthur consumer–resource model. We show not only how heritable trait variation fuels trait evolution and influences eco‐evolutionary dynamics, but also how the erosion of variation through time may hinder eco‐evolutionary dynamics in the long run.GEM s can be developed for any parameter in any ordinary differential equation model and, furthermore, can enable modeling of multiple interacting traits at the same time. We expectGEM s will open the door to a new direction in eco‐evolutionary and evolutionary modeling by removing long‐standing modeling barriers, simplifying the link between traits, fitness, and dynamics, and expanding eco‐evolutionary treatment of a greater diversity of ecological interactions. These factors makeGEM s much more than a modeling advance, but an important conceptual advance that bridges ecology and evolution through the central concept of heritable trait variation. -
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Abstract The field of eco‐evolutionary dynamics is developing rapidly, with a growing number of well‐designed experiments quantifying the impact of evolution on ecological processes and patterns, ranging from population demography to community composition and ecosystem functioning. The key challenge remains to transfer the insights of these proof‐of‐principle experiments to natural settings, where multiple species interact and the dynamics are far more complex than those studied in most experiments.
Here, we discuss potential pitfalls of building a framework on eco‐evolutionary dynamics that is based on data on single species studied in isolation from interspecific interactions, which can lead to both under‐ and overestimation of the impact of evolution on ecological processes. Underestimation of evolution‐driven ecological changes could occur in a single‐species approach when the focal species is involved in co‐evolutionary dynamics, whereas overestimation might occur due to increased rates of evolution following ecological release of the focal species.
In order to develop a multi‐species perspective on eco‐evolutionary dynamics, we discuss the need for a broad‐sense definition of “eco‐evolutionary feedbacks” that includes any reciprocal interaction between ecological and evolutionary processes, next to a narrow‐sense definition that refers to interactions that directly feed back on the interactor that evolves.
We discuss the challenges and opportunities of using more natural settings in eco‐evolutionary studies by gradually adding complexity: (a) multiple interacting species within a guild, (b) food web interactions and (c) evolving metacommunities in multiple habitat patches in a landscape. A literature survey indicated that only a few studies on microbial systems so far developed a truly multi‐species approach in their analysis of eco‐evolutionary dynamics, and mostly so in artificially constructed communities.
Finally, we provide a road map of methods to study eco‐evolutionary dynamics in more natural settings. Eco‐evolutionary studies involving multiple species are necessarily demanding and might require intensive collaboration among research teams, but are highly needed.
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plain language summary is available for this article. -
null (Ed.)We develop a method to identify how ecological, evolutionary, and eco-evolutionary feedbacks influence system stability. We apply our method to nine empirically parametrized eco-evolutionary models of exploiter–victim systems from the literature and identify which particular feedbacks cause some systems to converge to a steady state or to exhibit sustained oscillations. We find that ecological feedbacks involving the interactions between all species and evolutionary and eco-evolutionary feedbacks involving only the interactions between exploiter species (predators or pathogens) are typically stabilizing. In contrast, evolutionary and eco-evolutionary feedbacks involving the interactions between victim species (prey or hosts) are destabilizing more often than not. We also find that while eco-evolutionary feedbacks rarely altered system stability from what would be predicted from just ecological and evolutionary feedbacks, eco-evolutionary feedbacks have the potential to alter system stability at faster or slower speeds of evolution. As the number of empirical studies demonstrating eco-evolutionary feedbacks increases, we can continue to apply these methods to determine whether the patterns we observe are common in other empirical communities.more » « less
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Abstract Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. ‘Oligomorphic dynamics’ can be used to analyse eco‐evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non‐normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency‐ and density‐dependent selection, on the dynamics of multi‐modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco‐evolutionary theory and empirical data.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fevo.2021.750779
