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1. Interpreting the Tape of Life: Ancestry-Based Analyses Provide Insights and Intuition about Evolutionary Dynamics

   https://doi.org/10.1162/artl_a_00313  

   Dolson, Emily ; Lalejini, Alexander ; Jorgensen, Steven ; Ofria, Charles ( April 2020 , Artificial Life)

   Fine-scale evolutionary dynamics can be challenging to tease out when focused on the broad brush strokes of whole populations over long time spans. We propose a suite of diagnostic analysis techniques that operate on lineages and phylogenies in digital evolution experiments, with the aim of improving our capacity to quantitatively explore the nuances of evolutionary histories in digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four phylogeny measurements from biology: phylogenetic richness, phylogenetic divergence, phylogenetic regularity, and depth of the most-recent common ancestor. In addition to quantitative metrics, we also discuss several existing data visualizations that are useful for understanding lineages and phylogenies: state sequence visualizations, fitness landscape overlays, phylogenetic trees, and Muller plots. We examine the behavior of these metrics (with the aid of data visualizations) in two well-studied computational contexts: (1) a set of two-dimensional, real-valued optimization problems under a range of mutation rates and selection strengths, and (2) a set of qualitatively different environments in the Avida digital evolution platform. These results confirm our intuition about how these metrics respond to various evolutionary conditions and indicate their broad value. 

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2. Hereditary Stratigraphy: Genome Annotations to Enable Phylogenetic Inference over Distributed Populations

   https://doi.org/10.1162/isal_a_00550  

   Moreno, Matthew Andres ; Dolson, Emily ; Ofria, Charles ( July 2022 , The 2022 Conference on Artificial Life (ALIFE 2022))

   Phylogenies provide direct accounts of the evolutionary trajectories behind evolved artifacts in genetic algorithm and artificial life systems. Phylogenetic analyses can also enable insight into evolutionary and ecological dynamics such as selection pressure and frequency-dependent selection. Traditionally, digital evolution systems have recorded data for phylogenetic analyses through perfect tracking where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where evolutionary individuals may migrate between a large number of disjoint processing elements. To provide for phylogenetic analyses in these environments, we propose an approach to enable phylogenies to be inferred via heritable genetic annotations rather than directly tracked. We introduce a “hereditary stratigraphy” algorithm that enables efficient, accurate phylogenetic reconstruction with tunable, explicit trade-offs between annotation memory footprint and reconstruction accuracy. In particular, we demonstrate an approach that enables estimation of the most recent common ancestor (MRCA) between two individuals with fixed relative accuracy irrespective of lineage depth while only requiring logarithmic annotation space complexity with respect to lineage depth. This approach can estimate, for example, MRCA generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We also simulate inference over known lineages, recovering up to 85.70% of the information contained in the original tree using 64-bit annotations. 

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3. Multiple paths toward repeated phenotypic evolution in the spiny‐leg adaptive radiation ( Tetragnatha ; Hawai'i)

   https://doi.org/10.1111/mec.17082  

   Cerca, José ; Cotoras, Darko D. ; Santander, Cindy G. ; Bieker, Vanessa C. ; Hutchins, Leke ; Morin‐Lagos, Jaime ; Prada, Carlos F. ; Kennedy, Susan ; Krehenwinkel, Henrik ; Rominger, Andrew J. ; et al ( July 2023 , Molecular Ecology)

   The repeated evolution of phenotypes provides clear evidence for the role of natural selection in driving evolutionary change. However, the evolutionary origin of repeated phenotypes can be difficult to disentangle as it can arise from a combination of factors such as gene flow, shared ancestral polymorphisms or mutation. Here, we investigate the presence of these evolutionary processes in the Hawaiian spiny‐legTetragnathaadaptive radiation, which includes four microhabitat‐specialists or ecomorphs, with different body pigmentation and size (Green, Large Brown, Maroon, and Small Brown). We investigated the evolutionary history of this radiation using 76 newly generated low‐coverage, whole‐genome resequenced samples, along with phylogenetic and population genomic tools. Considering the Green ecomorph as the ancestral state, our results suggest that the Green ecomorph likely re‐evolved once, the Large Brown and Maroon ecomorphs evolved twice and the Small Brown evolved three times. We found that the evolution of the Maroon and Small Brown ecomorphs likely involved ancestral hybridization events, while the Green and Large Brown ecomorphs likely evolved through novel mutations, despite a high rate of incomplete lineage sorting in the dataset. Our findings demonstrate that the repeated evolution of ecomorphs in the Hawaiian spiny‐legTetragnathais influenced by multiple evolutionary processes.

    

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4. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

   https://doi.org/10.1371/journal.pbio.3001633  

   Avecilla, Grace ; Chuong, Julie N. ; Li, Fangfei ; Sherlock, Gavin ; Gresham, David ; Ram, Yoav ( May 2022 , PLOS Biology)

   de Visser, J. Arjan (Ed.)

   The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10 −4.7 to 10 −4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution. 

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5. Ancient and recent introgression shape the evolutionary history of pollinator adaptation and speciation in a model monkeyflower radiation (Mimulus section Erythranthe)

   https://doi.org/10.1371/journal.pgen.1009095  

   Nelson, Thomas C. ; Stathos, Angela M. ; Vanderpool, Daniel D. ; Finseth, Findley R. ; Yuan, Yao-wu ; Fishman, Lila ( February 2021 , PLOS Genetics)

   Buerkle, Alex (Ed.)

   Inferences about past processes of adaptation and speciation require a gene-scale and genome-wide understanding of the evolutionary history of diverging taxa. In this study, we use genome-wide capture of nuclear gene sequences, plus skimming of organellar sequences, to investigate the phylogenomics of monkeyflowers in Mimulus section Erythranthe (27 accessions from seven species ) . Taxa within Erythranthe , particularly the parapatric and putatively sister species M . lewisii (bee-pollinated) and M . cardinalis (hummingbird-pollinated), have been a model system for investigating the ecological genetics of speciation and adaptation for over five decades. Across >8000 nuclear loci, multiple methods resolve a predominant species tree in which M . cardinalis groups with other hummingbird-pollinated taxa (37% of gene trees), rather than being sister to M . lewisii (32% of gene trees). We independently corroborate a single evolution of hummingbird pollination syndrome in Erythranthe by demonstrating functional redundancy in genetic complementation tests of floral traits in hybrids; together, these analyses overturn a textbook case of pollination-syndrome convergence. Strong asymmetries in allele sharing (Patterson’s D-statistic and related tests) indicate that gene tree discordance reflects ancient and recent introgression rather than incomplete lineage sorting. Consistent with abundant introgression blurring the history of divergence, low-recombination and adaptation-associated regions support the new species tree, while high-recombination regions generate phylogenetic evidence for sister status for M . lewisii and M . cardinalis . Population-level sampling of core taxa also revealed two instances of chloroplast capture, with Sierran M . lewisii and Southern Californian M . parishii each carrying organelle genomes nested within respective sympatric M . cardinalis clades. A recent organellar transfer from M . cardinalis , an outcrosser where selfish cytonuclear dynamics are more likely, may account for the unexpected cytoplasmic male sterility effects of selfer M . parishii organelles in hybrids with M . lewisii . Overall, our phylogenomic results reveal extensive reticulation throughout the evolutionary history of a classic monkeyflower radiation, suggesting that natural selection (re-)assembles and maintains species-diagnostic traits and barriers in the face of gene flow. Our findings further underline the challenges, even in reproductively isolated species, in distinguishing re-use of adaptive alleles from true convergence and emphasize the value of a phylogenomic framework for reconstructing the evolutionary genetics of adaptation and speciation. 

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