Thomas LaBar

Restoration of plant–pollinator interaction networks via species translocation

The recent decline in pollinator biodiversity, notably in the case of wild bee populations, puts both wild and agricultural ecosystems at risk of ecological community collapse. This has triggered calls for further study of these mutualistic communities in order to more effectively inform restoration of disturbed plant–pollinator communities. Here, we use a dynamic network model to test a variety of translocation strategies for restoring a community after it experiences the loss of some of its species. We consider the reintroduction of extirpated species, both immediately after the original loss and after the community has reequilibrated, as well as the introduction of other native species that were originally absent from the community. We find that reintroducing multiple highly interacting generalist species best restores species richness for lightly disturbed communities. However, for communities that experience significant losses in biodiversity, introducing generalist species that are not originally present in the community may most effectively restore species richness, although in these cases the resultant community often shares few species with the original community. We also demonstrate that the translocation of a single species has a minimal impact on both species richness and the frequency of community collapse. These results have important implications for restoration practices in the face of varying degrees of community perturbations, the refinement of which is crucial for community management.

Global versus local extinction in a network model of plant–pollinator communities

The loss of a species from an ecological community can trigger a cascade of additional extinctions; the complex interactions that comprise ecological communities make the dynamics and impacts of such a cascade challenging to predict. Previous studies have typically considered global extinctions, where a species cannot re-enter a community once it is lost. However, in some cases a species only becomes locally extinct, and may be able to reinvade from surrounding communities. Here, we use a dynamic, Boolean network model of plant–pollinator community assembly to analyze the differences between global and local extinction events in mutualistic communities. As expected, we find that compared to global extinctions, communities respond to local extinctions with lower biodiversity loss, and less variation in topological network properties. We demonstrate that in the face of global extinctions, larger communities suffer greater biodiversity loss than smaller communities when similar proportions of species are lost. Conversely, smaller communities suffer greater loss in the face of local extinctions. We show that targeting species with the most interacting partners causes more biodiversity loss than random extinctions in the case of global, but not local, extinctions. These results extend our understanding of how mutualistic communities respond to species loss, with implications for community management and conservation efforts.

Evolution of Genome Size in Asexual Digital Organisms

Genome sizes have evolved to vary widely, from 250 bases in viroids to 670 billion bases in some amoebas. This remarkable variation in genome size is the outcome of complex interactions between various evolutionary factors such as mutation rate and population size. While comparative genomics has uncovered how some of these evolutionary factors influence genome size, we still do not understand what drives genome size evolution. Specifically, it is not clear how the primordial mutational processes of base substitutions, insertions, and deletions influence genome size evolution in asexual organisms. Here, we use digital evolution to investigate genome size evolution by tracking genome edits and their fitness effects in real time. In agreement with empirical data, we find that mutation rate is inversely correlated with genome size in asexual populations. We show that at low point mutation rate, insertions are significantly more beneficial than deletions, driving genome expansion and the acquisition of phenotypic complexity. Conversely, the high mutational load experienced at high mutation rates inhibits genome growth, forcing the genomes to compress their genetic information. Our analyses suggest that the inverse relationship between mutation rate and genome size is a result of the tradeoff between evolving phenotypic innovation and limiting the mutational load.

Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms

A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.

Does self-replication imply evolvability?

The most prominent property of life on Earth is its ability to evolve. It is often taken for granted that self-replication--the characteristic that makes life possible--implies evolvability, but many examples such as the lack of evolvability in computer viruses seem to challenge this view. Is evolvability itself a property that needs to evolve, or is it automatically present within any chemistry that supports sequences that can evolve in principle? Here, we study evolvability in the digital life system Avida, where self-replicating sequences written by hand are used to seed evolutionary experiments. We use 170 self-replicators that we found in a search through 3 billion randomly generated sequences (at three different sequence lengths) to study the evolvability of generic rather than hand-designed self-replicators. We find that most can evolve but some are evolutionarily sterile. From this limited data set we are led to conclude that evolvability is a likely--but not a guaranteed-- property of random replicators in a digital chemistry.

Origin of life in a digital microcosm

While all organisms on Earth share a common descent, there is no consensus on whether the origin of the ancestral self-replicator was a one-off event or whether it only represented the final survivor of multiple origins. Here, we use the digital evolution system Avida to study the origin of self-replicating computer programs. By using a computational system, we avoid many of the uncertainties inherent in any biochemical system of self-replicators (while running the risk of ignoring a fundamental aspect of biochemistry). We generated the exhaustive set of minimal-genome self-replicators and analysed the network structure of this fitness landscape. We further examined the evolvability of these self-replicators and found that the evolvability of a self-replicator is dependent on its genomic architecture. We also studied the differential ability of replicators to take over the population when competed against each other, akin to a primordial-soup model of biogenesis, and found that the probability of a self-replicator outcompeting the others is not uniform. Instead, progenitor (most-recent common ancestor) genotypes are clustered in a small region of the replicator space. Our results demonstrate how computational systems can be used as test systems for hypotheses concerning the origin of life. This article is part of the themed issue ‘Reconceptualizing the origins of life’.

Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Background Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore adaptation through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to “flat” genotypes (with low fitness but high robustness) out-competing “fit” genotypes with high fitness but low robustness—a phenomenon known as “survival of the flattest”. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of “fit” and “flat” genotypes has been lacking, leading to uncertainty about the genetic basis of the survival of the flattest effect. Results Here, we repeated the survival of the flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Conclusions Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest effect emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

Genome size and the extinction of small populations

Species extinction is ubiquitous throughout the history of life. Understanding the factors that cause some species to go extinct while others survive will help us manage Earth’s present-day biodiversity. Most studies of extinction focus on inferring causal factors from past extinction events, but these studies are constrained by our inability to observe extinction events as they occur. Here, we use digital experimental evolution to avoid these constraints and study “extinction in action”. Previous digital evolution experiments have shown that strong genetic drift in small populations led to larger genomes, greater phenotypic complexity, and high extinction rates. Here we show that this elevated extinction rate is a consequence of genome expansions and a concurrent increase in the genomic mutation rate. High genomic mutation rates increase the lethal mutation rate, leading to an increase the likelihood of a mutational meltdown. Genome expansions contribute to this lethal mutational load because genome expansions increase the likelihood of lethal mutations. We further find that an increased phenotypic complexity does not contribute to an increased risk of extinction. These results have implications for the causes of extinction in small populations and suggest that complexity increases in small populations may be limited by high rates of extinction.

Evolution of drift robustness in small populations

Most mutations are deleterious and cause a reduction in population fitness known as the mutational load. In small populations, weakened selection against slightly-deleterious mutations results in an additional fitness reduction. Many studies have established that populations can evolve a reduced mutational load by evolving mutational robustness, but it is uncertain whether small populations can evolve a reduced susceptibility to drift-related fitness declines. Here, using mathematical modeling and digital experimental evolution, we show that small populations do evolve a reduced vulnerability to drift, or ‘drift robustness’. We find that, compared to genotypes from large populations, genotypes from small populations have a decreased likelihood of small-effect deleterious mutations, thus causing small-population genotypes to be drift-robust. We further show that drift robustness is not adaptive, but instead arises because small populations can only maintain fitness on drift-robust fitness peaks. These results have implications for genome evolution in organisms with small effective population sizes.Genetic drift can reduce fitness in small populations by counteracting selection against deleterious mutations. Here, LaBar and Adami demonstrate through a mathematical model and simulations that small populations tend to evolve to drift-robust fitness peaks, which have a low likelihood of slightly-deleterious mutations.

Evolution of Drift Robustness in Small Populations of Digital Organisms

Most mutations are deleterious and cause a reduction in population mean fitness known as the mutational load. In small populations, weakened selection against slightly-deleterious mutations results in an additional reduction in fitness: the drift load. Many studies have established that populations can evolve a reduced mutational load by evolving mutational robustness, but it is uncertain whether populations can evolve a reduced drift load. Here, using digital experimental evolution, we show that small populations do evolve reduced drift loads, that is, they evolve robustness to genetic drift, or “drift robustness”. We find that, compared to genotypes from large populations, genotypes from small populations have a decreased likelihood of small-effect deleterious mutations, thus causing small-population genotypes to be drift-robust. We further show that drift robustness is not under direct selection, but instead arises because small populations preferentially adapt to drift-robust fitness peaks. These results have implications for genome evolution in organisms with small effective population sizes.