337 papers
Evolutionary biology explores the dynamic history of life on Earth, tracing how species change, adapt, and diversify over time. This field investigates the mechanisms driving everything from the development of antibiotic resistance in bacteria to the complex social behaviors of primates, revealing the deep connections that bind all living organisms together.
On Gist.Science, we ensure these groundbreaking discoveries remain accessible by processing every new preprint uploaded to bioRxiv in this category. Our team generates both plain-language overviews and detailed technical summaries for each paper, bridging the gap between raw research and public understanding without requiring a specialized background.
Below are the latest studies in evolutionary biology, offering fresh insights into the ongoing story of life.
Through experimental evolution and multi-omics analyses, this study demonstrates that the diamondback moth rapidly adapts to contrasting thermal environments via coordinated genetic mutations, epigenetic regulation, and metabolic reprogramming, underscoring its significant resilience to climate change.
This study demonstrates that a conserved gene network involving *hedgehog*, *orthodenticle*, and *odd-paired* regulates anterior head segmentation in both spiders and insects, suggesting an ancient evolutionary origin for this patterning mechanism despite differences in segment composition.
This study demonstrates that antibiotic history reshapes bacterial community responses through coupled eco-evolutionary and environmental feedbacks, where resistance priming buffers against immediate disturbance via evolved resistance and environmental detoxification but ultimately reinforces competitive dominance, creating a trade-off that limits community diversity and recovery.
By combining a wild immune survey with a common garden experiment, this study reveals that both heritable constitutive and environmentally influenced inducible fibrosis drive population-specific immune variation in threespine stickleback, linking their defense against *Schistocephalus solidus* to ecological differences in lake eutrophication.
PhytClust is a new, threshold-free algorithm that provides an efficient and optimal way to partition rooted phylogenetic trees into monophyletic clusters by minimizing within-cluster dispersion, demonstrating superior speed, accuracy, and scalability across diverse biological datasets.
This study reveals that in *Alnus glutinosa* aggregates, genomic introgression between diploid and tetraploid lineages is concentrated in pericentromeric regions, suggesting that centromere drive may facilitate gene flow across ploidy barriers rather than solely causing reproductive isolation.
By sequencing 43 new transcriptomes to construct a comprehensive phylogenomic framework, this study reveals that the Zygnematophyceae, the closest algal relatives of land plants, possess a deep and ancient genetic structure characterized by a major split between Spirogyrales and Desmidiales, highlighting the necessity of diverse sampling to understand the evolutionary origins of land plants.
This paper argues that the eusocial reproduction strategy itself, through its mathematical impact on population growth dynamics and the concentration of reproduction in a single queen, inherently selects for longer lifespans by favoring a slower rate of age-related mortality increase over reduced baseline risk.
The paper introduces InPhyNet, a novel method that achieves linear scalability and high accuracy in inferring phylogenetic networks for large datasets by merging independently inferred sub-networks, thereby overcoming the computational limitations of existing model-based approaches while providing biologically meaningful insights into complex evolutionary histories.
This paper introduces SNaQ.jl, a new Julia package that significantly enhances the scalability and computational efficiency of phylogenetic network inference through parallelization, weighted random quartet selection, and probabilistic decision-making, achieving up to a 499% reduction in runtime without compromising accuracy.