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Category: Environmental Microbiology
Evolutionary Ecology of Microorganisms: From the Tamed to the Wild, Page 1 of 2
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An overarching goal of biology is to understand how evolutionary and ecological processes generate and maintain biodiversity. While evolutionary biologists interested in biodiversity tend to focus on the mechanisms controlling rates of evolution and how this influences the phylogenetic relationship among species, ecologists attempt to explain the distribution and abundance of taxa based upon interactions among species and their environment. Recently, a more concerted effort has been made to integrate some of the theoretical and empirical approaches from the fields of ecology and evolutionary biology. This integration has been motivated in part by the growing evidence that evolution can happen on “rapid” or contemporary time scales, suggesting that eco-evolutionary feedbacks can alter system dynamics in ways that cannot be predicted based on ecological principles alone. As such, it may be inappropriate to ignore evolutionary processes when attempting to understand ecological phenomena in natural and managed ecosystems. In this chapter, we highlight why it is particularly important to consider eco-evolutionary feedbacks for microbial populations. We emphasize some of the major processes that are thought to influence the strength of eco-evolutionary dynamics, provide an overview of methods used to quantify the relative importance of ecology and evolution, and showcase the importance of considering evolution in a community context and how this may influence the dynamics and stability of microbial systems under novel environmental conditions.
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Conceptual diagram depicting feedbacks between ecological and evolutionary processes. Within the domain of ecological processes, there are interacting hierarchical levels of organization (individuals, populations, communities, and ecosystems), which can affect microevolutionary processes (i.e., anagenesis) and macroevolutionary processes (cladogenesis). Reciprocally, evolutionary processes can affect ecological processes. The strength of these feedbacks is influenced by the time scale at which ecological and evolutionary processes take place and by factors such as mutation rates, genetic drift, gene flow/disperal, and the diversity of a biological community. Adapted from ( 8 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f1
Conceptual diagram depicting feedbacks between ecological and evolutionary processes. Within the domain of ecological processes, there are interacting hierarchical levels of organization (individuals, populations, communities, and ecosystems), which can affect microevolutionary processes (i.e., anagenesis) and macroevolutionary processes (cladogenesis). Reciprocally, evolutionary processes can affect ecological processes. The strength of these feedbacks is influenced by the time scale at which ecological and evolutionary processes take place and by factors such as mutation rates, genetic drift, gene flow/disperal, and the diversity of a biological community. Adapted from ( 8 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f1
Relationship between phenotypic and genotypic change over time. Data originate from competing and evaluating fitness differences between ancestral and evolved E. coli lineages. While fitness increases saturate over time, fixed genetic changes continue to increase linearly over time. This pattern highlights some of the difficulties when trying to translate genotypic traits to phenotypic traits. Adapted from ( 48 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f2
Relationship between phenotypic and genotypic change over time. Data originate from competing and evaluating fitness differences between ancestral and evolved E. coli lineages. While fitness increases saturate over time, fixed genetic changes continue to increase linearly over time. This pattern highlights some of the difficulties when trying to translate genotypic traits to phenotypic traits. Adapted from ( 48 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f2
Determining rates of evolutionary in the wild. (a) Samples were collected from one location in the AMD system (C75) and de novo sequence assembly of sequencing reads led to the reconstruction of a genome for the dominant Leptospirillum group II at the site (type III). (b) Read recruitment of all 13 sequence data sets generated from C75 samples over 5 years to the type III reference genome allowed for the identification of additional fixed mutations and estimation of the nucleotide substitution rate. Lower frequency mutations could be observed in each of the data sets as well, but only fixed variants are included for rate calculations. Adapted from ( 61 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f3
Determining rates of evolutionary in the wild. (a) Samples were collected from one location in the AMD system (C75) and de novo sequence assembly of sequencing reads led to the reconstruction of a genome for the dominant Leptospirillum group II at the site (type III). (b) Read recruitment of all 13 sequence data sets generated from C75 samples over 5 years to the type III reference genome allowed for the identification of additional fixed mutations and estimation of the nucleotide substitution rate. Lower frequency mutations could be observed in each of the data sets as well, but only fixed variants are included for rate calculations. Adapted from ( 61 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f3
Pairwise sequence divergence of Sulfolobus populations isolated from a global survey of hot springs ecosystems scales positively with geographic distance providing evidence against the view of panmicitic microbial distributions. Adapted from ( 81 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f4
Pairwise sequence divergence of Sulfolobus populations isolated from a global survey of hot springs ecosystems scales positively with geographic distance providing evidence against the view of panmicitic microbial distributions. Adapted from ( 81 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f4
Evidence for local adaptation demonstrating the distance decay for the relative fitness of soil bacteria grown on resources from different geographic locations. Adapted from ( 84 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f5
Evidence for local adaptation demonstrating the distance decay for the relative fitness of soil bacteria grown on resources from different geographic locations. Adapted from ( 84 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f5
Some bacteria can rapidly evolve in response to starvation. The upper panel shows a typical growth curve of E. coli. When populations deplete resources, they enter stationary phase followed by a death phase. Subsequently, E. coli (and other types of bacteria) can enter growth advantage in stationary phase (GASP), where novel starvation-resistant mutants evolve and invade a system as depicted by the colored curves in the top panel (adapted from ( 88 ), with permission) and the conceptual model in the lower panel. doi: 10.1128/9781555818821.ch4.1.2.f6
Some bacteria can rapidly evolve in response to starvation. The upper panel shows a typical growth curve of E. coli. When populations deplete resources, they enter stationary phase followed by a death phase. Subsequently, E. coli (and other types of bacteria) can enter growth advantage in stationary phase (GASP), where novel starvation-resistant mutants evolve and invade a system as depicted by the colored curves in the top panel (adapted from ( 88 ), with permission) and the conceptual model in the lower panel. doi: 10.1128/9781555818821.ch4.1.2.f6
When challenged with conditions that are suboptimal for growth and reproduction, some microorganisms enter a reversible state of reduced metabolic activity or dormancy. The size of the active population is determined by the net reproductive rates, losses due to mortality, and losses due to dormancy. The size of the dormant population is determined by the rate at which active individuals transition into dormancy, the mortality rate during dormancy, and resuscitation from dormancy. This bet-hedging strategy is important for the maintenance of microbial biodiversity. Adapted from ( 94 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f7
When challenged with conditions that are suboptimal for growth and reproduction, some microorganisms enter a reversible state of reduced metabolic activity or dormancy. The size of the active population is determined by the net reproductive rates, losses due to mortality, and losses due to dormancy. The size of the dormant population is determined by the rate at which active individuals transition into dormancy, the mortality rate during dormancy, and resuscitation from dormancy. This bet-hedging strategy is important for the maintenance of microbial biodiversity. Adapted from ( 94 ), with permission. doi: 10.1128/9781555818821.ch4.1.2.f7