Using unmanned aerial vehicles (UAVs) to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding the collective behavior of primates. Previous studies have demonstrated the feasibility of automating primate behavior classification from video data, but these studies have been carried out in captivity or from ground-based cameras. However, to understand group behavior and the self-organization of a collective, the whole troop needs to be seen at a scale where behavior can be seen in relation to the natural environment in which ecological decisions are made. To tackle this challenge, this study presents a novel dataset for baboon detection, tracking, and behavior recognition from drone videos where troops are observed on-the-move in their natural environment as they move to and from their sleeping sites. Videos were captured from drones at Mpala Research Centre, a research station located in Laikipia County, in central Kenya. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. A tiling method was subsequently applied to create a pyramid of images at various scales from the original 5.3K resolution images, resulting in approximately 30K images used for baboon detection. The baboon tracking dataset is derived from the baboon detection dataset, where bounding boxes are consistently assigned the same ID throughout the video. This process resulted in half an hour of dense tracking data. The baboon behavior recognition dataset was generated by converting tracks into mini-scenes, a video subregion centered on each animal. These mini-scenes were annotated with 12 distinct behavior types and one additional category for occlusion, resulting in over 20 hours of data. Benchmark results show mean average precision (mAP) of 92.62 https://baboonland.xyz .

Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.

Bumble bees ( Bombus impatiens ) undergo a radical transformation in social dynamics as a natural part of their colony life cycle. Early in the season, bumble bee societies are eusocial and contain a reproductive queen and functionally sterile workers. As the colony grows, the colony transitions to a competition phase in which reproductive division of labor breaks down and some workers activate their ovaries and lay unfertilized, male-destined eggs. This transition represents a key shift in colony organization, but its underlying social dynamics, including the relationship between individual- and colony-level behavior, are poorly understood. Using multi-animal pose tracking to quantify social interactivity and related movement dynamics, we compared queenright and queenless colonies that capture these distinct cooperative and competitive phases of the colony life cycle. Queenright colonies displayed highly centralized social networks in which the queen received, rather than initiated, the majority of social interactions. In contrast, queenless colonies displayed higher worker interaction rates, and a subset of workers exhibited queen-like social behaviors and higher reproductive potential relative to nestmates. The unmasking of these "influencers" in a queenless, competitive context transformed the colony's social network from a centralized to distributed social organization with higher overall network efficiency. Thus, the bumble bee queen masks a hidden relationship between worker influence and reproductive potential that underlies their latent social flexibility. In the absence of a queen, the colony social structure is transformed, revealing that significant modifications in information transfer can accompany shifts in colony social organization and highlighting a novel layer of resilience in this social system. ### Competing Interest Statement The authors have declared no competing interest.

Evolutionary adaptation to new environments likely results from a combination of selective sweeps and polygenic shifts, depending on the genetic architecture of traits under selection. While selective sweeps have been widely studied, polygenic responses are thought to be more prevalent but remain challenging to quantify. The infinitesimal model makes explicit the hypothesis about the dynamics of changes in allele frequencies under selection, where only allelic effect sizes, frequencies, linkage, and gametic disequilibrium matter. Departures from this, like long-range correlations of allele frequency changes, could be a signal of epistasis in polygenic response. We performed an Evolve & Resequence experiment in Drosophila melanogaster exposing flies to a high-sugar diet for over 100 generations. We tracked allele frequency changes in >3000 individually sequenced flies and population pools and searched for loci under selection by identifying sites with allele frequency trajectories that differentiated selection regimes consistently across replicates. We estimate that at least 4% of the genome was under positive selection, indicating a highly polygenic response. The response was dominated by small, consistent allele frequency changes, with few loci exhibiting large shifts. We then searched for signatures of selection on pairwise combinations of alleles in the new environment and found several strong signals of putative epistatic interactions across unlinked loci that were consistent across selected populations. Finally, we measured differentially expressed genes (DEGs) across treatments and show that DEGs are enriched for selected SNPs. Our results suggest that epistatic contributions to polygenic selective response are common and lead to detectable signatures.

Most genetic polymorphisms associated with complex traits are found in non-coding regions of the genome. Characterizing their effect presents a formidable challenge, and expression quantitative trait locus (eQTLs) mapping has been a key approach to do so. As comprehensive eQTL maps are available only for a few species, here we developed the Drosophila outbred synthetic population (Dros-OSP) and used it to characterize the landscape of transcriptional regulation in Drosophila melanogaster. We collected head and body transcriptomes and genomes from 1,286 outbred flies and mapped local and distant eQTLs for 98% of the genes. We characterized the network organization of the transcriptome across tissues and described the properties of local and distal eQTLs in terms of genetic diversity, heritability, connectivity, and pleiotropy. These results provide new insights into the genetic basis of transcriptional regulation in the fruit fly and offer a new mapping resource that will expand the possibilities currently available for the Drosophila community.

Ethological research increasingly benefits from the growing affordability and accessibility of drones, which enable the capture of high-resolution footage of animal movement at fine spatial and temporal scales. However, analyzing such footage presents the technical challenge of separating animal movement from drone motion. While non-trivial, computer vision techniques such as image registration and Structure-from-Motion (SfM) offer practical solutions. For conservationists, open-source tools that are user-friendly, require minimal setup, and deliver timely results are especially valuable for efficient data interpretation. This study evaluates three approaches: a bioimaging-based registration technique, an SfM pipeline, and a hybrid interpolation method. We apply these to a recorded escape event involving 44 plains zebras, captured in a single drone video. Using the best-performing method, we extract individual trajectories and identify key behavioral patterns: increased alignment (polarization) during escape, a brief widening of spacing just before stopping, and tighter coordination near the group's center. These insights highlight the method's effectiveness and its potential to scale to larger datasets, contributing to broader investigations of collective animal behavior.

The exponential growth in biological data generation has created an urgent need for efficient, reproducible computational analysis workflows. Here, we present pipemake, a computational platform designed to streamline the development and implementation of efficient and reproducible Snakemake workflows. pipemake creates modular pipelines that can be seamlessly integrated or removed from the platform without requiring reconfiguration of the core system, enabling flexible adaptation of workflows to different analytical needs across diverse fields. To demonstrate the platform's capabilities, we created and implemented pipelines to reanalyze two distinct biological datasets. First, we recreated a population genomics analysis of the socially flexible halictid bee, Lasioglossum albipes , using pipemake-generated workflows for de novo genome annotation, processing of variant data, dimensionality reduction, and a genome-wide association study (GWAS). We then used pipemake to analyze behavioral tracking data from the common eastern bumble bee, Bombus impatiens . In both cases, pipemake workflows produced results consistent with published findings while substantially reducing hands-on analysis time. Overall, pipemake's modular design allows researchers to easily modify existing pipelines or develop new ones without software development expertise. Beyond streamlining workflow creation, pipemake leverages the full Snakemake ecosystem to enable parallel processing, automated error recovery, and comprehensive analysis documentation. These features make pipemake an efficient and accessible solution for analyzing complex biological datasets. pipemake is freely available as a conda package or direct download at https://github.com/kocherlab/pipemake. ### Competing Interest Statement The authors have declared no competing interest.

Abstract The microbiome contributes to many different host traits, but its role in host adaptation remains enigmatic. The fitness benefits of the microbiome often depend on ecological conditions, but theory suggests that fluctuations in both the microbiome and environment modulate these fitness benefits. Moreover, vertically transmitted bacteria might constrain the ability of both the microbiome and host to respond to changing environments. Drosophila melanogaster provides an excellent system to investigate the impacts of interactions between the microbiome and the environment. To address this question, we created field mesocosms of D. melanogaster undergoing seasonal environmental change with and without the vertically transmitted bacteria, Wolbachia pipientis. Sampling temporal patterns in the microbiome revealed that Wolbachia constrained microbial diversity. Furthermore, Wolbachia and a dominant member of the microbiome, Commensalibacter, were associated with differences in two higher‐order fitness traits, starvation resistance and lifespan. Our work here suggests that the interplay between the abiotic context and microbe–microbe interactions may shape key host phenotypes that underlie adaptation to changing environments. We conclude by exploring the consequences of complex interactions between Wolbachia and the microbiome for our understanding of eco‐evolutionary processes that shape host‐microbiome interactions.

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease. Author summaryGene expression variance, or the variation in the level of gene expression within a population, can have significant impacts on physiology, disease, and evolutionary adaptations. While the average level of gene expression is typically the focus of research, the variation around this average level (i.e., gene expression variance) can also be important for understanding complex traits and disease. Here, we investigate the landscape of transcriptional variance across tissues, populations, and studies. Using large publicly available RNA-seq data sets, we were able to identify the general properties associated with high- and low-variance genes, as well as factors driving variation in variance across genes. Specifically, we uncovered gene expression variance was significantly associated with gene length, nucleotide diversity, the degree of connectivity and the presence of non-coding RNA. Our results suggest that the mechanisms responsible for maintaining optimal levels of variation in high- versus low-variance differ, and that this variability is the result of different patterns of selection.

Significant advances in computational ethology have allowed the quantification of behavior in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. To capture finely resolved behaviors while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioral variation within individuals in a group. Expanding the toolkit for capturing the constituent behaviors of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviors and can provide critical data for understanding how individual variation influences collective dynamics.

The microbiome contributes to many different host traits, but its role in host adaptation remains enigmatic. The fitness benefits of the microbiome often depend on ecological conditions, but fluctuations in both the microbiome and environment modulate these fitness benefits. Moreover, vertically transmitted bacteria might constrain the ability of both the microbiome and host to respond to changing environments. Drosophila melanogaster provides an excellent system to investigate the evolutionary effects of interactions between the microbiome and the environment. To address this question, we created field mesocosms of D. melanogaster undergoing seasonal adaptation with and without the vertically transmitted bacteria, Wolbachia pipientis. Sampling temporal patterns in the microbiome revealed that Wolbachia constrained microbial diversity. Furthermore, interactions between Wolbachia and the microbiome contributed to fitness-associated traits. Wolbachia often exerted negative fitness effects on hosts, and the microbiome modulated these effects. Our work supports recent theoretical advances suggesting that hosts in temporally fluctuating environments benefit from flexible microbial associations with low transmission fidelity—specifically when changes in the microbiome can better enable host phenotypes to match environment change. We conclude by exploring the consequences of complex interactions between Wolbachia and the microbiome for our understanding of eco-evolutionary processes and the utility of Wolbachia in combating vector-borne disease.

Next-generation sequencing provides a more efficient way to characterize the transcriptome; meanwhile, it introduces significant issues for the effective analysis of large-scale sequencing data. To counteract this growing problem, we developed a robust and modular pipeline for examining whole transcriptome sequencing data quickly. Utilizing a Docker for containerization and unanimous cross-platform support, we modularized the pipeline and created a practical, automated structure for pragmatic use. Furthermore, we have developed segregated implementations of the pipeline sections to allow end-users to craft custom pipelines easily. We applied our tool to various datasets and showed that our pipeline offered high-quality sequencing data analysis.