arXiv:2605.16113v1 Announce Type: new
Abstract: Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on knowledge encapsulated from training corpora, they may produce hallucinations, stereotypes, and socially biased content. In particular, LLMs are prone to prejudiced responses involving race, gender, and age, which are collectively referred to as social biases. Prior studies have used fine-tuning and prompt engineering to mitigate such biases in LLMs, but these methods require additional training resources or domain knowledge to design the framework. Moreover, they may degrade the original capabilities of LLMs and often overlook the need for dynamic debiasing contexts for fairer inference. In this paper, we propose DebiasRAG, a novel tuning-free and dynamic query-specific debiasing framework based on retrieval-augmented generation (RAG). DebiasRAG improves fairness while preserving the intrinsic properties of LLMs, such as representation ability. DebiasRAG consists of three stages: (1) query-specific debiasing candidate generation; (2) context candidate pool construction; and (3) gradient-updated debiasing-guided context piece reranking. First, DebiasRAG leverages self-diagnosed bias contexts relevant to the query through regular retrieval, where the bias contexts are prepared offline by the DebiasRAG provider. Given the query-specific bias contexts, DebiasRAG reversely produces debiasing contexts, which are provided as additional fairness constraints for LLM outputs. Second, a regular RAG retrieval process produces query-related contexts from the regular RAG document database, such as a chunked Wikipedia dataset.
Science Journals
arXiv:2605.16114v1 Announce Type: new
Abstract: We propose a scalable neuromorphic architecture based on spiking dynamics emerging from the autonomous time-continuous evolution of clockless (asynchronous) digital circuits. Implemented on commercially available field-programmable gate arrays (FPGAs), our system implements networks of interacting Boolean spiking neurons with configurable excitatory and inhibitory synaptic weights. A complete processing pipeline enables efficient handling of spike-encoded data for solving machine-learning tasks. We demonstrate competitive performance for an audio classification task with spike-based encoding and high-speed processing. Power consumption is significantly lower than traditional digital implementations; this makes our approach an efficient alternative that bridges the gap to dedicated analog neuromorphic systems without the need for specialized hardware design. More generally, our approach establishes clockless digital hardware as a viable platform for neuromorphic computing. It paves the way for reconfigurable chips to be turned into energy-efficient quasi-analog neuromorphic processors.
arXiv:2605.16115v1 Announce Type: new
Abstract: As mobile service robots increasingly coexist with pedestrians, ensuring passively safe behaviour during confined emergency evacuations is critical. Existing multi-robot yielding strategies often focus solely on collision avoidance and macroscopic flow optimisation, overlooking environmental affordances and human spatial expectations. To bridge the gap between macroscopic theory and micro-level perception, we conducted a game-based virtual evacuation experiment (N=56). We investigated individual psychological responses to four multi-robot yielding strategies (Hide, LineEscape, Freeze, ShortestPath) across confined corridors with and without refuge niches. Our results establish a robust preference hierarchy (Hide > LineEscape > Freeze > ShortestPath), demonstrating that proactive space-yielding significantly outperforms freezing and efficiency-first approaches. Crucially, we found that environmental affordances heavily shape cognitive expectations. Actively utilising available niches amplifies the psychological comfort of proactive yielding (Hide). Conversely, failing to use an obvious niche (e.g., executing LineEscape) may trigger Expectation Violation. This is reflected in a drastically increased perceived cognitive delay, despite objectively unimpeded trajectories. Furthermore, prior robot interaction experience helps users decode complex social intents. Ultimately, this research demonstrates that safe human-robot interaction during emergencies must evolve from pure trajectory optimisation to semantically aware navigation. Future work will extend this framework to investigate complex interactions between robot swarms and pedestrian crowds.
arXiv:2605.15788v1 Announce Type: new
Abstract: Proactive autoscaling for containerized workloads depends on knowing the provisioning delay, i.e., the time between a scaling decision and the moment new capacity is ready to serve traffic. In practice, this cold-start duration can vary substantially across environments and even across consecutive scale-out events. We present ADAPT (Adaptive Duration Approximation for Predictive Timing), an online EWMA estimator that tracks coldstart duration at runtime. ADAPT feeds a dynamic planning horizon, FH-OPT, into a Model Predictive Controller (MPC) that optimizes replica counts over a rolling window. Together, these components form a closed-loop proactive autoscaling design that adapts its lookahead based on measured provisioning delay. Evaluated across three policies (MPC+LSTM, MPC+Prophet, HPA) and six workload archetypes with five random seeds, MPC+LSTM achieves below 5% SLA violation on all workloads, compared with 7-19% for reactive HPA and up to 28.7% for MPC+Prophet on bimodal traffic.
arXiv:2605.15787v1 Announce Type: new
Abstract: Why does a Transformer that has memorized its training set wait thousands of steps before it generalizes? Existing accounts locate this delay in norm minimization, feature emergence, or the late discovery of sparse subnetworks. These explanations capture important parts of the transition, but ignore a constraint unique to attention-based models: if attention discards an informative token, no bounded downstream computation can recover it. We formalize attention as an implicit Bayesian posterior over the task dependency graph and prove that generalization requires two separable conditions: a familiar Goldilocks bound on MLP capacity, coinciding with norm-based theories of grokking, and a novel Bayesian structural condition requiring attention to place sufficient mass on every informative token. This decoupling explains delayed generalization as delayed structural inference. Early in training, the MLP memorizes through unaligned features, drives the cross-entropy loss near zero, and thereby starves attention of structural gradient. Weight decay must then erode memorization before the missing graph becomes learnable, yielding the known inverse-weight-decay delay, which we derive as a structural waiting time. We then prove that this explaining-away delay can be bypassed by a KL-based structural intervention, yielding an inverse-intervention-strength scaling law for the grokking time. Experiments on algorithmic sequence tasks isolate structure from capacity and show that this Bayesian ticket matches or outperforms lottery-ticket transfer.
arXiv:2603.18221v2 Announce Type: replace
Abstract: Students in our AI/ML course submitted polished, well-argued project analyses. Then, in class discussion, we asked them to walk through a single choice from their own work. Many could not. The writing looked great. The understanding often wasn't. Oral examinations retain an evidentiary link where written work no longer does: a student who can reason aloud, defend a decision under follow-up, and adapt when pushed demonstrates something no submitted document can certify. The obstacle has always been cost. A 25-minute oral reviewed by two graders takes roughly 30 combined instructor and TA hours for 36 students; at 100 the format is untenable. Voice AI and automated grading change the arithmetic. We built Viva, a system that conducts a personalized oral exam, then grades the transcript with a panel of three LLMs that score independently, read each other's assessments, and revise. Across two undergraduate cohorts at NYU Stern (36 students in Fall 2025, 37 in Spring 2026), grading-LLM cost stayed under one dollar per exam within the ElevenLabs subscription covering our voice minutes; for deployments exceeding an equivalent credit pool, budget about a dollar per ten minutes of graded exam time, practical for weekly assignments, not just finals. The system also broke instructively: the agent asked several questions at once, failed to randomize topics across the cohort, and a voice cloned from the professor's came across as harsh, replaced in Spring 2026 with a calm preset. These failures, with an earlier finding that a monolithic agent handling both examination and grading proved unreliable, point to five candidate transferable patterns: decompose into single-purpose modules, constrain behavior with code rather than prompts, keep randomization out of the LLM, grade with a multi-model panel whose members disagree, and choose voice characteristics with the same care as question design.
arXiv:2605.16052v1 Announce Type: new
Abstract: Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance reflects genuine legal reasoning ability or artifacts of data contamination. We present a comprehensive empirical study of tax law reasoning approaches and implement a contamination detection protocol to rigorously assess LLM reliability. We show that performance can be inflated by contamination. Building on this analysis, we conduct a systematic evaluation, comparing monolithic LLMs with hybrid systems that translate statutory text into formal representations and delegate inference to symbolic solvers. We build a novel test suite designed to probe generalization to unseen documents via case and rule variations. Our findings indicate that legal reasoning is inherently compositional and that neuro-symbolic frameworks offer a more reliable and robust foundation for legal AI, as well as improved generalization to unobserved situations.
arXiv:2605.16118v1 Announce Type: new
Abstract: The source distribution in conditional flow matching is a design parameter that can be calibrated to data, not a default isotropic prior. We exploit this in Multi-Fidelity Flow Matching (MFFM), a cascade refinement framework for parametric PDE solutions: the source is calibrated to the empirical low-to-high-fidelity residual scale with local Gaussian-blur correlation, and the velocity network is conditioned on the low-fidelity solution. Conditioning makes the residual refinement problem substantially easier than unconditional field generation, while residual-calibrated source noise improves the flow-matching training geometry. A multi-resolution cascade applies the same construction independently between adjacent fidelities. After level-wise flow-matching pretraining, we fine-tune the composed cascade end-to-end with a deterministic one-step rollout, which makes one velocity evaluation per cascade level the optimized operating point at inference. The result is a learned analog of multigrid refinement that reaches the finest grid in $L$ deterministic network evaluations per query. We validate MFFM on eight benchmarks: two super-resolution problems and six spatiotemporal forecasting tasks from PDEBench, The Well, and the FNO Navier--Stokes dataset.
arXiv:2605.16048v1 Announce Type: new
Abstract: State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layers, as recently applied in looped transformers - has not been explored in this model family. We show that a looped SSM with $k$ parameters iterated $L$ times consistently closely matches or outperforms a standard SSM with $k \cdot L$ independent parameters across four architectures (LRU, S5, LinOSS, LrcSSM) and six time series classification benchmarks, despite operating within a strictly smaller hypothesis space, as we formally establish. Since the larger model contains the looped model as a special case, this dominance cannot be explained by expressivity and instead points to parameter sharing across depth as a beneficial inductive bias that simplifies optimization. These results demonstrate that depth-recurrence is orthogonal to sequence-recurrence and independently beneficial. We further show that input reshaping is an equally neglected design axis: concatenating timesteps for low-dimensional inputs, or flattening and rechunking the joint feature-time dimension for high-dimensional ones, yields accuracy gains of 1-6% across all models, confirmed over 5 random seeds. Both techniques provide standalone improvements that compound when combined, suggesting that depth and input reshaping are two independent and underexplored design axes for SSMs on time series.
arXiv:2605.16120v1 Announce Type: new
Abstract: The growth of online video platforms drives the need for effective, semantically grounded event retrieval. We present MERVIN, a unified multimodal framework for Vietnamese news videos that integrates keyframes, transcripts, and video summaries. Transcript quality is enhanced via Gemini 1.5 Flash, reducing noise from accents, background sounds, and recognition errors. Visual features are extracted with Perception Encoder, while a Vietnamese language model produces textual embeddings; both are indexed in Milvus for efficient similarity-based retrieval. In addition, a React-based interface enables iterative query refinement across modalities, improving semantic alignment. Experimental results on Vietnamese news videos demonstrate the effectiveness of the proposed system, with MERVIN achieving 79 out of 88 points in AI Challenge HCMC 2025 qualification phase and successfully retrieved all results for every query in the final round.
Abstract As part of preparedness activities supporting pathogens classified under the UK High Consequence Infectious Diseases (HCID) framework, we previously evaluated both a whole-genome tiling amplicon sequencing scheme and a pan-viral hybridisation capture approach (TWIST-CVRP) for sequencing Andes virus (ANDV). In light of the recent outbreak, we make available viral sequencing datasets generated using a historical ANDV isolate (Chile, 1997). In addition, we provide an evaluation of tiling amplicon scheme performance and present recommended primer updates informed by in silico comparison with the recently released outbreak genome. These datasets are intended to support benchmarking, validation, and optimisation of bioinformatic pipelines across the community.
Holcosus orcesi, the Orces Blue Whiptail, is a Critically Endangered lizard endemic to the upper Jubones River basin in southern Ecuador. Restricted to a narrow elevational range within semi-arid Andean shrublands, it represents one of the few montane members of a predominantly lowland lineage. Here we present the first high-quality reference genome for H. orcesi, generated using Oxford Nanopore Technologies long-read sequencing. The assembly spans 1.68 Gb across only 91 contigs, with an N50 of 76.2 Mb and a BUSCO completeness of 96.8%, making it among the most contiguous and complete squamate genomes to date. Structural annotation predicted 25,682 genes, of which 85% showed homology to known proteins and 45% were assigned Gene Ontology terms. Repetitive elements accounted for 46.3% of the genome, with LINEs representing the predominant class. This genome provides a foundational resource for future evolutionary, comparative and conservation-genomic research of H. orcesi and other mountain reptiles, enabling studies of population genomics, local adaptation, and genomic erosion in isolated populations. By expanding the genomic representation of tropical montane reptiles, this work helps address longstanding phylogenetic and geographic gaps in global biodiversity genomics and provides a foundation for evidence-based conservation of H. orcesi and related taxa.
As one of the earliest-diverging multicellular eukaryotic lineages, the bladed Bangiales (Rhodophyta) possess a deep evolutionary history with a central role in the multi-billion-dollar global seaweed aquaculture industry. Although North Atlantic representatives are emerging candidates for regional mariculture, the scarcity of high-quality genomic resources for these taxa hinders both fundamental research and commercial optimization. To address this, we present the first chromosome-level genome assemblies for two native European species: Porphyra dioica (150.44 Mbp) and Porphyra linearis (95.22 Mbp). By integrating Oxford Nanopore Technologies (ONT) long-read sequencing with Hi-C proximity ligation, we generated highly contiguous nuclear genomes resolved into five chromosomes. Structural gene models were predicted through the BRAKER3 pipeline, identifying 12,548 and 10,382 protein-coding genes for P. dioica and P. linearis, respectively. Subsequent homology-based functional annotation characterized 57.4% and 59.8% of these predicted proteins. Supplemented by circularized organellar genomes, these reference genomes provide a critical framework for future research, enabling comparative studies of Atlantic-Pacific divergence and facilitating the development of selective breeding programs for sustainable European aquaculture.
Cliffs are environmentally extreme yet biodiversity-rich ecosystems that harbour specialist plants, many endemic and threatened. Plant persistence in these nutrient-poor substrates may depend on tightly linked soil- and root-associated microbial communities, which remain poorly understood. These interactions may become increasingly important with the global expansion of recreational climbing. While physical climbing impacts on vegetation are documented, potential chemical effects, from the use of climbing chalk (magnesium carbonate), on soil properties and plant-associated microbiota remain unknown. We sampled soils and roots beneath cliff-specialist and generalist plants, and unvegetated soils, across climbed and unclimbed routes in northern, central, and southern Spain. Soil physicochemical properties were quantified, fungal communities were characterized using ITS-metabarcoding, and structural equation modelling was used to disentangle direct and indirect effects. Climbing increased soil pH and altered soil chemical properties, driving shifts in fungal diversity and functional composition in soil and roots. The relative read abundance of root-associated symbiotrophic fungi declined, whereas arbuscular mycorrhizal fungi and pathogens increased in climbed cliffs. Overall effects were consistent, with cliff-specialist plants mediating nutrient and fungal shifts. Our findings show that climbing can reshape cliff soil chemistry and fungal communities, with potential cascading consequences for plant functional performance, nutrient dynamics, and ecosystem resilience.
Wildlife vaccination could become a powerful strategy to mitigate disease-induced biodiversity losses, yet many vaccines for wildlife diseases provide only limited protection. Notably, tools to control the fungal pathogen Batrachochytrium dendrobatidis (Bd) are urgently needed for amphibian conservation. Laboratory experiments have demonstrated that prophylactic exposure to Bd metabolites increases host resistance, significantly reducing infection intensity in amphibians subsequently challenged with live Bd. Because Bd metabolites are non-infectious and applied topically, this treatment has potential to be administered to waterbodies to vaccinate and protect amphibians. We developed an agent-based model that indicated imperfect vaccination could reduce or amplify Bd infections at the population level, depending on degree of enhanced resistance or tolerance. Utilizing a Before-After-Control-Impact design with ten years of data, we conducted an ecosystem-level trial where we applied low levels of Bd metabolites or a sham control treatment to ponds in California and subsequently quantified Bd prevalence and infection intensity in metamorphosing Pacific chorus frogs (Pseudacris regilla). Unexpectedly, infection intensity was significantly greater in treated ponds relative to control ponds following metabolite addition. Additional model simulations indicated that this could occur via two mechanisms: (1) if treatment greatly increased tolerance alone or in combination with smaller increases in resistance, or (2) if a deleterious environmental interaction caused the treatment to increase susceptibility, rather than promote resistance. Future research is needed to determine whether tolerance or environmental factors drove heightened Bd infection intensities in this field trial to identify contexts in which this treatment can be used as a conservation tool.
Elucidating how habitat degradation facilitates extinction is critical for effective conservation efforts. Here, we propose integrating physiologically-structured population models into stochastic population viability analyses to assess how differing consequences of habitat degradation interact to drive extinction dynamics in a focal population. Using the isolated spectacled caiman Caiman crocodilus population/ecomorph from the Apaporis River as a case study, we find that threatening the resource base, which individuals increasingly rely upon, to outgrow vulnerable size ranges and mature accelerates extinction. We also found that when habitat degradation impacts both the primary adult and juvenile resource bases, this can have marked synergistic effects on threatening population viability. By contrast, destroying nesting sites has only a small effect on accelerating the impact of deteriorating prey availability. Through integrating community-level feedback between habitat degradation/change and population dynamics/structure, our approach provides a comparative framework for assessing the relative importance of distinct mechanisms through which habitat degradation ultimately drives extinction risk.
Although resources are typically distributed continuously in space, species distributions often organize into discrete clusters. In his seminal paper, Turing demonstrated that such clusters can spontaneously arise in population densities, even when populations evolve in environments with continuously varying conditions. This phenomenon is known as Turing instability. In this work, we focus on two models grounded in population dynamics: a one-dimensional model based on the nonlocal Fisher-KPP equation, and a two-dimensional model involving an environmental gradient. We show that phenotypic clusters (sometimes referred to as "species") emerge in these models. We prove that they do not emerge because of Turing instability, but because of stochasticity, and that they disappear when stochasticity is reduced. First, for both models, we start our simulations with initial populations uniformly distributed in the state space. We show that phenotypic clusters quickly emerge and that the distances between them depend on the population size, that is, on the degree of stochasticity. Next, we start from already clearly defined phenotypic clusters. We identify three regimes in the connection between population size, the initial distances between clusters, and the distances between clusters at equilibrium. Last, on the two-dimensional model, we relax the hypothesis of complete clonality by varying the effective recombination rate, explore its effect on phenotypic clustering, and show that phenotypic clustering decays drastically with slight recombination.
Biodiversity is commonly summarized by macroecological mean patterns, most prominently the species-area relationship (SAR) linking habitat area to expected species richness. Yet conservation, policy, and economic decisions increasingly require risk metrics: probabilities of rare but consequential biodiversity shortfalls, including local collapse. Such tail risks are central in finance and insurance but remain difficult to quantify in ecology because the data needed to estimate full richness distributions are rarely available at decision scales. Here we provide a mechanistic route from species-area relationships to biodiversity risk metrics. We show that when regional species abundances are well approximated by Fisher's log-series, a minimal immigration-extinction mechanism yields a closed-form stationary distribution for local richness whose structure tightly couples the mean SAR to richness variability and lower-tail probabilities. This coupling implies exact fluctuation-response identities and an explicit integral transform that reconstructs collapse probabilities and other tail risk measures directly from the mean SAR. These results define ecological analogues of financial risk metrics---such as collapse probability and lower-tail quantiles---without requiring direct estimation of the full richness distribution. Using high-resolution ForestGEO tree censuses spanning tropical, subtropical, and temperate forests, we find empirical support for these predictions across spatial scales. Together, our results show how widely measurable species-area relationships can be elevated from descriptive averages to operational tools for biodiversity risk assessment and reliability-based conservation planning.
The evolution of reproductive isolation is central to speciation, yet the earliest stages of this process remain poorly understood. In particular, it is unclear how rapidly barriers to mating arise during adaptation, whether they accumulate predictably, and how they depend on ecological context. Here, we investigate the evolution of mating efficiency during prolonged asexual adaptation in diploid Saccharomyces cerevisiae. Twelve replicate populations were evolved for 1200 generations in two distinct carbon environments, glucose and galactose, under strictly asexual conditions. At regular intervals, we induced sporulation and quantified mating efficiency using three complementary assays: within-population crosses, crosses between populations evolved in different environments, and crosses between evolved populations and the ancestral strain. We find that mating efficiency evolves during asexual adaptation, with outcomes that depend strongly on the environment. While glucose-evolved populations remain largely stable, galactose-evolved populations exhibit a reversible decline. Overall, changes in mating efficiency are dynamic, heterogeneous, and often transient, with evidence for both intrinsic reductions in mating competence and context-dependent incompatibilities between populations. Together, these results show that asexual adaptation can generate rapid but non-monotonic changes in mating compatibility. Early reductions in mating efficiency are heterogeneous, environment-dependent, and often reversible, and do not accumulate into stable reproductive isolation over the timescale examined. Our findings suggest that the initial stages of divergence are characterized by dynamic and contingent perturbations of reproductive traits, rather than a steady progression toward speciation.
Cancer progression is increasingly understood as an evolutionary process shaped not only by competition but also by cooperative interactions including those mediated through diffusible ``public goods'' (PGs). Classical evolutionary game theory predicts that PG-producing (altruistic) subclones cannot invade well-mixed populations of non-producers, creating a paradox given their observed emergence in tumors. Here, we resolve this contradiction by combining stochastic spatial simulations with an analytically tractable Moran model to study the invasion dynamics of PG-producing cells in structured populations. Starting from a single producer cell, we explicitly model stochastic PG secretion, diffusion, binding/unbinding, and cell proliferation across biologically relevant parameter ranges. We demonstrate that spatial structure fundamentally alters invasion dynamics, enabling PG producers to invade and establish even when production incurs a fitness cost. Both numerical and analytical approaches converge on a key unifying parameter, a characteristic length scale {delta}, that captures the combined effects of diffusivity, binding kinetics, and degradation. This length scale determines the spatial extent of PG availability and thus the selective advantage of producers. We identify distinct regimes: when PGs are localized (small {delta}), producers preferentially benefit and invasion is likely; when PGs are widely dispersed (large {delta}), benefits are shared and invasion approaches neutrality or is suppressed by costs. Our results highlight that invasion of cooperative traits is governed by spatially mediated resource localization rather than intrinsic fitness alone. This framework provides a mechanistic basis for understanding the emergence of cooperative subclones in tumors and suggests that modulating biophysical transport properties of signaling molecules could influence tumor evolution, metastasis, and therapeutic resistance.
Strong population contractions can leave a persistent genomic legacy that can influence populations long after their demographic recovery. While bottlenecks facilitate the removal of strongly deleterious mutations, the effectiveness of purging may be limited in historically small populations. The Kirtlands warbler (Setophaga kirtlandii) is a rare North American songbird with an ancestrally small population. After narrowly evading extinction, they are one of few species that have been delisted from federal protections in the USA. Despite their recovery, a previous study showed evidence for recent inbreeding and a high burden of deleterious mutations that may have not been purged despite strong bottlenecks. Historical DNA offers a unique opportunity to understand the consequences of recent demographic declines on genetic diversity. Here, we use DNA from over 100-year-old museum specimens to estimate changes in genetic load in the Kirtlands warblers pre- and post-bottleneck. We validate our results with forward-in-time genetic simulations and explore how sample size and missing data can affect estimates. Both empirical data and simulations suggest a reduced ability to purge deleterious mutations in this historically small population. Our simulations also highlight that limited sampling design and data quality can constrain the ability to detect changes.
Environmental heterogeneity across freshwater systems often promotes phenotypic variation, yet disentangling environmentally induced variation from heritable differentiation remains a central goal in evolutionary ecology. We investigated the geographic distribution and morphological differentiation, and heritability of shell traits among populations of the freshwater lymnaeid snail Pectinidens diaphanus in Patagonia. Extensive field surveys across 196 freshwater sites revealed that the species occupies a broad range of lentic and lotic habitats and constitutes the only lymnaeid inhabiting southern Patagonia. While reproductive anatomical structures were conserved across populations, shell shape differed markedly among populations from contrasting habitat types, with population identity explaining nearly 50% of total shape variation. Populations from hydrologically unstable habitats (ponds and streams) exhibited more elongated shells and relatively smaller apertures, a pattern consistent with functional responses to hydroperiod variability and desiccation risk. To assess the heritability of this differentiation, we conducted a common-garden experiment across two generations. Shell shape differences between permanent- (lagoon) and temporary- (pond) habitat-derived populations persisted into the G2 generation reared under standardized laboratory conditions, indicating that the observed variation is not solely a response to local environmental conditions but includes a heritable component. Together, our findings demonstrate that P. diaphanus constitutes the sole lymnaeid across southern Patagonia, occupying a broader range than previously documented, and that populations show heritable shell differentiation potentially associated with contrasting freshwater habitats. By integrating large-scale biogeographic surveys with morphometric and experimental approaches, this study provides new insight into how habitat variation may contribute to ecological and evolutionary di…
Genetic variation is the raw material for evolution. One source of variation is chromosomal rearrangements, which can bring genes together and form genetic linkage. Rearrangements can also suppress recombination and gene flow, as in the case of sex chromosome evolution. We conducted the first population genomic study of the red harvester ant Pogonomyrmex barbatus to investigate genomic rearrangements that differentiate the lineages J1 and J2 in the "dependent-lineage system" (also known as "social hybridogenesis"). In this unusual reproductive system, males and females from different lineages mate to create hybrids, yet these hybrids develop into sterile offspring (workers), and so the two lineages remain reproductively isolated. We sequenced high-quality reference genomes for the two lineages to search for a potential explanation of the suppression of gene flow between them. Comparison of the two genome assemblies revealed multiple large-scale genomic rearrangements, all of which occurred in the J1 lineage. The rearrangements formed some of the largest J1 chromosomes, including the largest scaffold in the assembly that was formed by at least two translocation events and additional intra-chromosomal rearrangements. The translocations brought together 118 odorant receptor (OR) genes on this rearranged chromosome, 44 of which are 9-exon ORs, which are implicated in chemical communication in ants. We also identified an enrichment of transposable elements in a large synteny gap between the translocated segments. The discovery of multiple translocations that formed large rearranged chromosomes provides a potential explanation for the reproductive isolation between the pair of dependent lineages in this system, and opens the way for the study of the molecular genetic basis of an intriguing evolutionary phenomenon in these and in other ant lineages.
The deployment of clothianidin-based insecticide formulations in malaria vector control has highlighted the capacity of Anopheles funestus to displace more susceptible mosquito species in treated areas and to rapidly evolve resistance under selection pressure. Metabolic detoxification, together with structural and genetic changes in nicotinic acetylcholine receptors (nAChRs), the primary molecular targets of neonicotinoids, can reduce insecticide efficacy. Here, we characterized amino acid substitutions across all 11 nAChR subunits in An. funestus to assess standing variation that may facilitate adaptive responses to chemical exposure. Using whole-genome sequencing data from 656 mosquitoes sampled in 13 African countries, we found marked contrasts in the distribution of nonsynonymous variants among nAChR subunits. Most subunits are strongly constrained and carry no missense variants, whereas two loci (3 and 7) display three geographically widespread amino acid substitutions across the continent. In contrast, 9 and {beta}2 accumulate dozens of nonsynonymous mutations occurring at intermediate to high frequencies, including within domains involved in orthosteric ligand binding and channel gating. Genetic differentiation at nAChR loci among populations from different countries is low to moderate, although several nonsynonymous mutations display high FST values consistent with geographic structuring. These results highlight relaxed constraint on two subunits that may provide opportunities for evolutionary diversification within a conserved family of multimeric receptor assemblies. Such diversification has not been observed in vector species displaced by An. funestus in indoor residual spraying areas, and the potential implications for reduced sensitivity to neonicotinoids are discussed.
Birds and mammals are shrinking and shapeshifting as global temperatures rise. Ecogeographic rules predict that such changes should ease heat stress by increasing surface-area-to-volume ratios, and thus, the capacity for heat exchange. This has led to the hypothesis that body size reductions are driven by thermoregulatory selection or adaptive plasticity, although recent syntheses point to more complex, multifactorial causes. Crucially, recent theoretical models predict that thermoregulatory benefits of smaller body size only emerge at extreme deviations from average phenotypes. Here, we exploit agricultural selection in Japanese quail to directly test this hypothesis, using three breeds spanning extreme differences in body mass, surface area, and relative appendage lengths. Evaporative cooling capacity and the scope for evaporative water loss broadly followed allometric predictions when contrasting small and larger breeds. As expected, this allowed the smallest breed to tolerate higher air temperatures. However, differences in heat tolerance limits between breeds were consistently much smaller than predicted. Additionally, the breadth of thermoneutral zones overlapped in full, and upper critical temperatures were remarkably similar, between breeds. Together, these results show that heat tolerance is only weakly linked to surface-area-to-volume relationships and cannot be explained by size alone. Thus, although smaller bodies may modestly enhance heat dissipation when size variation in a population is substantial, our findings suggest that recent body size reductions and morphological shifts are unlikely to be driven primarily by thermoregulatory benefits.