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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)

   Abstract 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. Beyond simple adaptation: Incorporating other evolutionary processes and concepts into eco‐evolutionary dynamics

   https://doi.org/10.1111/ele.14197  

   Yamamichi, Masato; Ellner, Stephen P.; Hairston, Nelson G. (September 2023, Ecology Letters)

   Studies of eco‐evolutionary dynamics have integrated evolution with ecological processes at multiple scales (populations, communities and ecosystems) and with multiple interspecific interactions (antagonistic, mutualistic and competitive). However, evolution has often been conceptualised as a simple process: short‐term directional adaptation that increases population growth. Here we argue that diverse other evolutionary processes, well studied in population genetics and evolutionary ecology, should also be considered to explore the full spectrum of feedback between ecological and evolutionary processes. Relevant but underappreciated processes include (1) drift and mutation, (2) disruptive selection causing lineage diversification or speciation reversal and (3) evolution driven by relative fitness differences that may decrease population growth. Because eco‐evolutionary dynamics have often been studied by population and community ecologists, it will be important to incorporate a variety of concepts in population genetics and evolutionary ecology to better understand and predict eco‐evolutionary dynamics in nature. 

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5. Multiple merger coalescent inference of effective population size

   https://doi.org/10.1098/rstb.2023.0306  

   Zhang, Julie; Palacios, Julia A (February 2025, Philosophical Transactions of the Royal Society B: Biological Sciences)

   NA (Ed.)

   Variation in a sample of molecular sequence data informs about the past evolutionary history of the sample’s population. Traditionally, Bayesian modelling coupled with the standard coalescent is used to infer the sample’s bifurcating genealogy and demographic and evolutionary parameters such as effective population size and mutation rates. However, there are many situations where binary coalescent models do not accurately reflect the true underlying ancestral processes. Here, we propose a Bayesian non-parametric method for inferring effective population size trajectories from a multifurcating genealogy under the Lambda-coalescent. In particular, we jointly estimate the effective population size and the model parameter for the Beta-coalescent model, a special type of Lambda-coalescent. Finally, we test our methods on simulations and apply them to study various viral dynamics as well as Japanese sardine population size changes over time. The code and vignettes can be found in the phylodyn package. This article is part of the theme issue ‘“A mathematical theory of evolution”: phylogenetic models dating back 100 years’. 

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