Molecular Ecology

Background Coccidioidomycosis is an emerging fungal disease across the arid Americas and a frequent cause of community-acquired pneumonia. Understanding where Coccidioides populations originate, how they move across space, and whether they are expanding is important for interpreting changing patterns of Valley fever and anticipating future infection risk. Methods We prospectively collected and whole-genome sequenced 186 Coccidioides-positive clinical isolates submitted to a national diagnostic laboratory, and included 126 previously sequenced genomes. We applied genomic clustering, time-calibrated phylogenetic reconstruction, ancestral area reconstruction, mating-type assignment, and demographic inference to identify major populations, infer dispersal patterns, assess evidence for recombination and clonality, and reconstruct historical population dynamics. Findings We analyzed 312 genomes (139 C. immitis; 173 C. posadasii) and identified three major genetic populations within each species. C. immitis included two California-centered populations and one Pacific Northwest population, whereas C. posadasii included two Arizona-centered populations and one Texas-centered population. The most recent common ancestor was estimated at approximately 127,000 years for C. immitis and 234,000 years for C. posadasii. Most populations were not fully monophyletic, consistent with retained ancestral variation and/or ongoing gene flow. Inferred dispersal was largely asymmetric, with most movement originating from California in C. immitis and from Arizona and Texas in C. posadasii. Most populations contained both mating types, but one C. immitis population and a Brazilian subgroup of C. posadasii were clonal. All populations showed recent demographic expansion. Interpretation The evolutionary history of Coccidioides is characterized by strong geographic structure, ongoing gene flow, and recent demographic expansion. These processes are likely to influence future patterns of Valley fever endemicity and supports the use of genomic surveillance to detect shifts in disease risk as environmental conditions change.

Does the distribution of fitness effects of new mutations vary across the genome? Under the classical Fisher Geometric Model (FGM) we might not expect it to. In FGM, phenotypic traits are envisioned as dimensions of a landscape, with fitness determined by position in the landscape, i.e., the particular combination of traits of an individual. New mutations are represented by vectors that move from an ancestral to a new phenotype. In classical FGM these vectors affect all trait dimensions simultaneously (universal pleiotropy). However, introducing partial and modular pleiotropy into an FGM framework leads to an expectation that parameters of the DFE will vary with mutational pleiotropy-the number of traits affected by individual mutations. Here we address this prediction by investigating whether traits related to mutational pleiotropy, expression level and network connectivity, affect the parameters of the DFE using whole genome data from A. thaliana and C. grandiflora, two closely related Brassica species that vary significantly in their demography and mating system, and therefore, in effective population size and the effects of linked selection. Results were similar across both species. We found that expression level and network connectivity were predictive of the parameters of the deleterious DFE, even once co-correlations among genome biology traits were accounted for. Our results suggest that, across the genome, molecular evolutio(high mutational pleiotropy). nary patterns agree with the predictions of FGM, albeit relaxing the assumption of universal pleiotropy, and that variation in mutational pleiotropy among genes is sufficient to have detectible effects on the DFE. Significance statementHow do the effects of new mutations vary across the genome? If mutations in some genes affect many traits (high mutational pleiotropy), we hypothesise they will be more strongly deleterious, with lower variance in their selective effects. We test this by investigating the distribution of effects of new mutations across genes that vary in features that are related to mutational pleiotropy: expression level, gene network connectivity, and number of associated GO terms. The mean strength and coefficient of variation of selection of new mutations varied across genes with different features in the manner expected by our hypothesis. This demonstrates that important parameters of molecular evolution can vary across the genome with genome architecture.

Green algae must continuously balance resource availability to maintain photosynthetic performance. The O2:CO2 ratio is a key determinant of their metabolic mode. Under hyperoxia or low CO2, many algae induce a carbon concentrating mechanism (CCM). In the model green alga Chlamydomonas reinhardtii, the CCM relies on a pyrenoid, a specialized microcompartment that elevates CO2 around rubisco. While ambient CO2 acclimation is well-studied, responses to hyperoxia remain poorly understood, despite its frequent occurrence in nature under high light. Using controlled bioreactors, we exposed two diverse Chlamydomonas ecotypes, CC1009 and CC2343, to 95% oxygen to analyze time-dependent, genome-wide transcriptomic and phenotypic changes. Both ecotypes induced CCM genes, but they exhibited distinct molecular and physiological phenotypes. The tolerant ecotype (CC1009) successfully adapted, developing a functional CCM with a structured starch sheath. Conversely, the sensitive ecotype (CC2343) suffered growth arrest and formed malformed pyrenoids. Transcriptomics revealed that CC1009 initiated a rapid initial response, upregulating chloroplast proteostasis and downregulating nucleotide metabolism. CC2343 showed a massive, delayed transcriptional response, downregulating genes coding for photosystems and tetrapyrrole biosynthesis. This unbiased transcriptomic approach identifies key candidate genes driving algal acclimation to hyperoxic stress in natural, high-light environments.

Symbiotic interactions between plants and nitrogen-fixing microorganisms are essential for sustainable agriculture, yet the molecular mechanisms underlying plant-cyanobacterium symbiosis remain poorly understood. In particular, the nature of the signalling mechanisms mediating partner recognition in associations involving Nostoc species is largely unknown. Recent proteomic analyses have identified proteins homologous to rhizobial Nod factors biosynthetic enzymes in Nostoc punctiforme, suggesting the existence of a Nod-like signalling system. However, the functional role of these components has not been experimentally validated. Here, we investigate the contribution of nod-like biosynthetic and regulatory genes to symbiosis by analysing mutants of N. punctiforme affected in genes with homology to nodB and nodD. Phenotypic characterization revealed that disruption of nodB-like genes does not impair free-living growth but affects early stages of plant association and colonization. Specifically, the nodB1 mutant is impaired in plant association and shows a mild defect in colonization, whereas the nodB3 mutant exhibits a severe defect in colonization. In contrast, nodD-like mutants exhibited altered symbiotic phenotypes, with specific regulators differentially affecting interaction and colonization efficiency in rice (Oryza sativa). In particular, mutation of nodD2 and nodD3 reduced plant association and severely compromised colonization in Oryza sativa, with a more pronounced phenotype in nodD3 mutant. Altogether, our results provide genetic evidence supporting the involvement of Nod-like components in cyanobacterial symbiosis and suggest the existence of a regulatory and biosynthetic module contributing to plant colonization. These findings shed new light on the evolution and diversity of symbiotic signalling mechanisms across plant-microbe interactions.

Objectives: To characterise the national epidemiology of tick-induced mammalian meat allergy (MMA) in Australia, including temporal trends in Gal specific IgE testing and case detection, geographic distribution, demographic risk factors, and longitudinal antibody dynamics. Design: Retrospective analysis of Gal sIgE ImmunoCAP test results from 1 January 2014 to 31 December 2024. Setting, Participants: Deidentified laboratory records from 14,075 individuals tested across Australia, with residential postcodes mapped to Statistical Areas Level 3 (SA3). A subset of 1,515 individuals with repeat testing contributed to longitudinal analyses. Main outcome measures: Test volumes, suspected MMA case counts, positivity rates, spatial clustering metrics, demographic risk ratios, and longitudinal Gal sIgE trajectories. Results: Overall, 35.7% (5,025) of individuals tested positive. Testing volume increased 331% over the study period, with case detection accelerating sharply from 2020. Decomposition analysis attributed 59-81% of case growth to expanded testing, with the remainder unexplained by surveillance expansion alone. Cases were concentrated along the eastern seaboard within the range of Ixodes holocyclus, with extreme spatial clustering: three SA3 regions accounted for over one-quarter of national cases. Females comprised the majority of positive cases, and MMA risk increased with age, peaking at 45-74 years. Among 1,515 individuals with serial testing, Gal sIgE levels antibodies declined predictably in 93% of people. Conclusions: This first national assessment of MMA in Australia reveals a substantial, geographically concentrated, and growing burden. The extreme spatial concentration of cases suggests that targeted public health interventions could efficiently address a large proportion of national disease burden. While testing expansion is the dominant driver of rising case numbers, it does not fully account for observed trends, and prospective studies are needed to disentangle surveillance effects from genuine disease emergence. Declining antibody levels support the utility of serial testing for clinical monitoring, though persistent sensitisation underscores the need for sustained risk mitigation and continued surveillance.

Differences in physical features and disease prevalence between men and women are examples of sexual dimorphisms. However, sex differences can manifest not only in trait means but also in how strongly risk factors are linked to diseases (e. g. BMI to cardiovascular disease), a question heavily under-researched. To fill this gap, we set out to identify sex differences in phenotype correlations (rP) and decompose them into genetic (rG) and environmental (rE) contributions. Our analysis revealed 250 trait pairs with significant sex-different phenotypic correlations in the UK Biobank. Overall, we observed a predominance of environmental contributions to sex-different effects: 182 trait pairs (73%) exhibited exclusively sex-different rE, while 68 (27%) showed sex differences in both rE and rG, and no trait pair was affected solely by sex-specific rG. For example, we detected sex-different environmental correlation between C-reactive protein and BMI (rE(men) = 0.07 vs rE(women) = 0.25), but no sex-difference in genetic correlation. On the contrary, glycated haemoglobin and LDL cholesterol showed genetic correlation only in women (rG(women) = 0.17; 95% CI = [0.1, 0.23]), but environmental correlation only in men (rE(men) = -0.18; 95% CI = [-0.19, -0.16]). Some of the observed sex differences - including those involving testosterone, SHBG, urate, waist-hip ratio, and triglycerides - may reflect underlying sex-specific genetic architectures, as evidenced by low between-sex genetic correlations. In conclusion, environmental factors are the predominant contributors to sex differences in phenotypic correlations between complex traits, with modest detectable contributions from sex-specific genetic architectures. Recognising these patterns can inform the development of more effective, sex-informed interventions.

Species distribution models (SDMs) increasingly inform conservation and biosecurity decisions in freshwater systems, where the reliability of its uncertainty estimates matters as much as its point predictions. Ensemble SDMs derive prediction intervals from across-replicate variance, but this variance captures systematic error only when replicates disagree about it, an assumption that fails when training data are contaminated with low-accuracy records, the norm in citizen-science datasets. Whether this failure is spatially uniform or concentrates in identifiable parts of a range is unknown. Using a panel of European freshwater crayfish spanning native headwater-associated species and invasive lowland colonizers, we show that contamination-induced calibration failure is strongly spatially structured: it concentrates at stream-network headwaters, the topological tops of the network, where upstream-aggregated predictors are structurally undefined, and scales with contamination severity, replicated across four species and both dominant ensemble protocols (replicate and consensus). The failure is driven by upward prediction bias, not by intervals failing to widen: contaminated ensembles overpredict suitability in headwaters, and because the bias is shared across ensemble members, the intervals do not flag it. This is a conservation-relevant blind spot, because headwaters are both refugia for threatened native crayfish and front lines for invasion; an SDM that silently overpredicts suitability there misdirects survey and management effort toward the segments where its predictions are least trustworthy. Standard leave-one-basin-out conformal calibration, the recommended panel-wide remedy, repairs marginal coverage but leaves headwaters undercovered, because a single calibration threshold is dominated by the abundant non-headwater segments. A group-conditional (Mondrian) variant, calibrating the two populations separately, restores reliable coverage in both at no extra cost and reallocates width where it is needed. We recommend network-position-stratified calibration as a default for ensemble SDMs in dendritic freshwater systems.

Blood banks represent highly controlled healthcare environments where microbiological surveillance has traditionally focused on blood products rather than environmental microbial reservoirs. Despite their critical role in transfusion safety, the ecology of surface-associated microorganisms and the persistence traits that enable their long-term survival remain poorly understood. Here, we combined scanning electron microscopy, culture-based microbiology, phenotypic characterization, MALDI-TOF mass spectrometry, and whole-genome sequencing to investigate whether surfaces within a public blood bank facility constitute reservoirs of environmentally derived bacteria with enhanced persistence potential. Samples collected from a public blood bank in Tucuman, Argentina yielded 37 culturable bacterial isolates, predominantly Gram-positive environmental taxa together with a limited number of opportunistic Gram-negative species. More than 30% of the isolates exhibited multidrug resistance, while several strains displayed strong biofilm formation, amyloid-like fiber production, motility, and hemolytic activity, indicating multiple phenotypic strategies associated with long-term surface persistence. Whole-genome sequencing of six representative isolates confirmed species identity, identified genes related to antimicrobial resistance, adhesion, biofilm formation, stress adaptation, and cytotoxicity, and revealed frequent genotype-phenotype discordance, highlighting the importance of integrating genomic and phenotypic analyses. Notably, one isolate exhibited less than 92% average nucleotide identity with publicly available genomes, suggesting the presence of a previously undescribed environmental species. Thus, blood bank surfaces function as selective ecological niches favoring bacteria with persistence-associated traits rather than simply reflecting contamination from blood products. These microorganisms may constitute latent biosafety hazards if environmental barriers fail, particularly in facilities handling biological materials intended for vulnerable patients. Our results support the incorporation of integrated bioimaging, phenotypic characterization, and genome-resolved environmental surveillance into infection prevention strategies and transfusion biosafety programs within a One Health framework.

Antimicrobial resistance (AMR) is an ever-increasing threat to population health. Industrial, environmental and societal factors are increasingly recognized as important contributors to AMR within communities. Here, we investigated the spatial distribution of AMR genes (ARGs) across Alberta, Canada and their association with socio-economic, immigration-related, and agro-industrial characteristics using municipal wastewater-based surveillance. We analyzed monthly wastewater metagenomes collected between March 2022 and March 2023 across eleven municipalities, representing 39% of Alberta's population. Integration with census data enabled multivariate analysis, revealing that municipal resistome profiles were strongly structured along income and immigration-related population gradients. ARGs spanning 14 resistance classes exhibited distinct distributional patterns across income and immigration gradients, including contrasting associations among beta-lactam, aminoglycoside, and macrolide-lincosamide-streptogramin ARGs, consistent with heterogeneous selection pressures across sub-populations. These findings demonstrate the capacity of longitudinal wastewater surveillance to identify persistent population-level resistome patterns and highlight the importance of incorporating sociodemographic context into AMR surveillance and mitigation strategies.

Objectives: We tested how multi-level socioeconomic disadvantage relates to biological aging and systemic inflammation in women and men from the population-based Canadian Longitudinal Study on Aging (CLSA). Methods: We examined cross-sectional data from 8,516 CLSA participants with baseline measures on systemic inflammatory biomarkers (C-reactive protein, interleukin-6, and tumoral necrosis factor-) and biological aging (metabolomic and six DNA methylation [DNAm] age estimates). Plasma samples underwent metabolomic profiling by Metabolon, Inc. Metabolomic age was estimated separately in males and females using sex-stratified models based on age-correlated metabolite levels. DNAm data generated using the Illumina Infinium MethylationEPIC v1.0 array were used to estimate DNAm age across six established models, including Horvath, Hannum, PhenoAge, GrimAge, GrimAge2, and DunedinPACE. We used log-transformed metabolite levels to calculate metabolomic age by sex. We linked education, income, material and social deprivation to biomarkers of systemic inflammation and biological aging stratified by sex using generalized linear models. Multivariable models were adjusted by age, major behavioral risk factors, and chronic conditions. Results: Participants were aged on average of 62.6 years of age, and approximately 50% were females. In multivariable linear adjusted models, we found that in comparison to those earning [&ge;]$100K a year, women earning less <$20K were on average 1.14 (95%CI: 0.46, 1.82) year older with respect to metabolomic age; those earning [&ge;]$20K & <$50K were on average 0.90 (95%CI: 0.26, 1.53) years older; and those earning [&ge;]$50K & <$100K were on average 0.70 (95%CI: 0.05, 1.34) years older. We did not observe this dose response among men. A similar dose-response association was observed for interleukin-6 in both men and women. Discussion: These findings suggest that socioeconomic adversity influences not only inflammatory pathways but also distinct biological aging processes, including metabolomic aging.

1. Sandy beaches along urbanized coasts are increasingly managed through beach nourishment and hard infrastructure, yet these interventions often constrain natural dune-building processes. Along the Belgian coast, where much of the beach-dune interface is bordered by dikes, promenades and intensive recreation, strandline vegetation may provide an overlooked mechanism for retaining sand and initiating embryo dune development. 2. We assessed the potential for four pioneer dune plant species (Cakile maritima, Calamagrostis arenaria, Elymus farctus and Salsola kali) to establish, develop vegetation cover and contribute to sand accumulation along the Belgian coast. Using field surveys from 2017-2023, LiDAR-derived beach elevation and annual sediment dynamics, we modelled species occurrence and abundance/cover in low-disturbance reference zones and projected these relationships across the wider coastline. 3. Occurrence models identified where abiotic conditions allow plants to establish and persist until the late growing season, whereas zero-inflated abundance/cover models estimated expected vegetation development across environmental gradients. Predicted occurrence was widespread for several species, suggesting that the abiotic gradients modelled here are not the primary constraints on potential establishment across large parts of the coast. In contrast, expected abundance/cover showed stronger species-specific responses, particularly to sand accretion, indicating that sediment dynamics mainly affect post-establishment vegetation development rather than occurrence alone. 4. Independent field measurements of embryo dunes showed positive relationships between vegetation cover and local sand accumulation for all four species. When scaled using spatial predictions of potential abundance/cover, pioneer vegetation could retain substantial volumes of sand, with Cakile maritima contributing the largest share, followed by Salsola kali, Elymus farctus and Calamagrostis arenaria. Estimated volumes depended on assumptions about whether vegetation occurs as dispersed units or aggregated patches. 5. Synthesis and applications. Our results show that, even along a heavily urbanized and nourished coastline, abiotic conditions can support strandline vegetation and embryo dune initiation where disturbance is reduced. Management actions such as limiting trampling, adapting beach cleaning and protecting strandline vegetation could enhance the retention of nourished sand and support nature-based coastal defense. Rather than replacing engineered interventions, strandline vegetation may increase the efficiency with which available sediment is retained within the beach-dune system.

Background and AimsThe development of unisexual flowers relies on the tight coordination of flower organ identity and sex determination. The genus Quercus is typically considered strictly monoecious, bearing fully segregated male and female flowers within the same individual tree. However, several reports of atypical flowering across the genus challenge this canonical view, suggesting that flowering in oaks may be more flexible than traditionally assumed. In this work, the dynamics of flower development in Quercus orocantabrica were examined to correlate contrasting floral morphologies with divergent molecular profiles. MethodsThe flowering phenology of Q. orocantabrica trees was closely monitored over several individuals and years, together with a detailed floral morphological analysis of male, female and atypical flowers. Key floral homeotic gene homologues were identified, and their expression assayed in the development of different flowers. Key ResultsRecurrent and widespread hermaphroditic flowering was detected in several Q. orocantabrica trees, frequently associated with unseasonal flowering events. Gene expression analysis of male, female and hermaphroditic flowers revealed a sex-biased expression of Q. orocantabrica B- and C-class genes, with the B-class gene QoPI in particular being tightly associated with the presence of fully-developed stamens. In addition, the expression of the C-class gene QoSHP contrasted with reports in other Fagaceae, highlighting a potential functional divergence of the C/D-class lineage within the family. ConclusionsThe results here depicted indicate that the dynamics of floral sex identity in oaks are more plastic than traditionally assumed, supporting a reinterpretation of oak reproductive biology based on a versatile and resilient framework responsive to different developmental contexts.

Light leaf spot (LLS), caused by subcuticular hemibiotrophic ascomycete fungus Pyrenopeziza brassicae, is a major constraint on oilseed rape (Brassica napus) production, yet the genetic and biochemical mechanisms of quantitative disease resistance (QDR) remain poorly defined. Here, disease phenotyping, pathogen quantification, microscopy, gene expression profiling and glucosinolate (GSL) analysis were integrated to dissect resistance mechanisms in B. napus. Disease assays of 19 diverse lines revealed clear contrasts between susceptible and resistant genotypes, with the commercial cultivar Ambassador showing a phenotype inconsistent with the UK Recommended List rating. Microscopy demonstrated that resistance within doubled haploid line Cubs Root does not inhibit spore germination or penetration but restricts hyphal branching and subcuticular colonisation from 4 to 8 days post-inoculation. Expression profiling of seven candidate gene expression markers (GEMs) and pathogenesis-related PR1 showed that cinnamate-4-hydroxylase, phospholipase C4, {beta}-adaptin, universal stress protein and the 40S ribosomal subunit protein S24 were strongly pathogen-induced in resistant lines, whereas a BAHD acyltransferase, a putative susceptibility factor, was induced only in susceptible cultivars. GSL profiling identified negative correlations between disease severity and total GSLs, particularly aliphatic and aromatic GSLs, with 2{square}phenylethyl and 7-methylsulfinyl heptyl GSLs showing the strongest associations with resistance. Together, these results highlight coordinated transcriptional and metabolic responses that limit pathogen proliferation and provide targets for breeding durable LLS resistance in B. napus.

Genome-wide association studies (GWAS) have identified >1,200 signals associated with type 2 diabetes (T2D), yet identifying functional variants remains challenging because the majority of them lie in noncoding regions of the genome and are in areas of high linkage disequilibrium (LD). While chromatin accessibility QTL (caQTL) and expression QTL (eQTL) analyses are useful for nominating regulatory mechanisms underlying GWAS signals, limitations still exist in pinpointing functional variants within regions of high LD. A complementary approach that has been less frequently applied is to focus on the allele-specific effect on chromatin accessibility at heterozygous single-nucleotide polymorphisms (SNPs), hereafter referred to as allelic imbalance. We analyzed the allelic imbalance of reads generated from an assay for transposase-accessible chromatin with sequencing (ATAC-seq) across genotyped samples from 490 donors in T2D-relevant tissues: skeletal muscle, liver, pancreatic islets, adipose tissue, and relevant cell types. We identified 119,949 allelically imbalanced SNPs (FDR<0.05) across the genome. The allelic imbalance was often most prominent in one tissue and showed an enrichment overlapping with tissue-specific transcription factor (TF) binding footprints. Focusing on the 8,581 SNPs in previously published 99% credible sets from 338 T2D GWAS signals, we identified 256 imbalanced SNPs across 123 (36.4% of) signals, each showing allelic imbalance in at least one tissue or cell type. Of these, 71 signals contained only a single imbalanced SNP, representing excellent candidate causative variants. As a proof-of-concept, we showed that 23 of the 256 imbalanced SNPs were supported by allelic assays from previous studies. Further, we experimentally validated two imbalanced SNPs as likely functional variants: rs34584161 among a seven-SNP T2D credible set at the RNF6 signal in islets and rs849134 among a 13-SNP credible set at the JAZF1 signal in liver. This study demonstrates the power of integrating ATAC-seq allelic imbalance (ASAI) with GWAS statistical fine-mapping to identify candidate functional regulatory variants from among tightly linked GWAS variants in disease-relevant tissues. While applied here in T2D, this approach represents a widely applicable high-throughput framework for refining the genetic architecture of complex traits.

The main olfactory epithelium (MOE) is the primary site of olfaction and consists of multiple cell types including olfactory sensory neurons (OSNs), sustentacular cells, and immune cells. Neuroimmune interactions in epithelial tissues are critical in maintaining tissue function, but how OSNs and immune cells interact in the MOE in healthy and diseased states is largely unknown. Cellular responses in the MOE determine how and whether OSNs maintain olfactory function and are repaired or replenished following inflammatory environmental exposures. We hypothesized that acute nasal aeroallergen exposure alters immune cell function in the MOE to elicit a neuroprotective response, thereby preserving OSN function. We developed an environmental aeroallergen exposure consisting of one week of daily intranasal house dust mite extract (HDM) instillations. Spectral flow cytometry indicated only subtle changes in resident immune cells proportions and phenotypes in the MOE. Immunohistochemical evaluation did not reveal extensive changes in immune cell distribution in the sensory epithelium or lamina propria, but instead we observed increases in axonal olfactory marker protein (OMP) expression in the lamina propria, where resident immune cells are most abundant. To evaluate the effects of HDM exposure on OSN function, we performed live ex vivo Ca2+ imaging of MOEs from HDM- and sham-exposed transgenic mice using objective-coupled planar illumination (OCPI) microscopy. OSN responses to multiple odorants revealed increased chemosensory sensitivity and decreased across-trial adaptation in HDM-treated epithelia. These results indicate that short-term nasal aeroallergen exposure minimally alters immune cell phenotypes, and instead induces functional changes in OSN physiology that preserve olfactory function.

Social relationships are an important social determinant of health. Loneliness, the perceived gap between one's actual and desired relationships, has emerged as an important mechanism through which social relationships impact health. Like other intrapersonal-level factors associated with health, loneliness is influenced by broader social and structural factors, including characteristics of one's neighborhood social environment. Although neighborhood-level protective and risk factors for loneliness and for mental health have been identified, prior studies have often focused solely on self-reported perceptions of the neighborhood environment. Further, few have considered aspects of the neighborhood social environments, such as neighborhood stability (i.e., stability of the community with long or short-term residents), independent of neighborhood socioeconomic conditions. In the current analysis, we explored longitudinal patterns of loneliness in conjunction with neighborhood stability among women with HIV (WWH) enrolled into the MACS/WIHS Combined Cohort Study (MWCCS) from 2014-2019 (N2019=1,394) to examine whether trajectories of loneliness and neighborhood stability were associated with depressive symptoms, non-prescription substance use, past-year cannabis use, number of alcoholic drinks per week, and several domains of quality of life. Loneliness at baseline (Betas = 0.24 - 0.54) and changes in loneliness over time (Betas = 0.11 - 0.26) were associated with each outcome, except for the association between changes in loneliness over time and drinks per week (Beta=0.13, p = 4.14x10-2), which did not persist after correcting for multiple comparisons. Neighborhood stability at baseline was associated with past year cannabis use (Beta=0.26, p = 1.00x10-2), depressive symptoms (Beta=-0.12, p = 1.54x10-3), and overall self-reported health (Beta=-0.08, p = 2.05x10-2). Changes in neighborhood stability across time were not associated with any outcome. Neighborhood stability moderated the association between changes in loneliness and general health perceptions. Our results demonstrate both overall loneliness and changes in loneliness over time have implications for current mental health in WWH, while changes in neighborhood stability did not.

Collective animal behavior occurs in high-stakes contexts where failing to coordinate effectively with group-mates can spell disaster for individuals. Yet, identifying instances of coordination failure is challenging, meaning their evolutionary effects remain mysterious. Synchronous calls in alternating frog choruses (i.e., inadvertent signal collisions) are unambiguous failure events that impose steep attractiveness costs. We modeled tungara frog chorusing dynamics to reveal the sensorimotor and social mechanisms underpinning synchrony. Ultimately, inter-male variation in two key sensorimotor attributes, the periods of male calling rhythms and call latencies, generated divergent synchrony engagement patterns. Modeling female preferences revealed that these varied behavioral outcomes then yielded disparate attractiveness consequences. By mechanistically linking the causes and consequences of coordination failure, we demonstrate that non-random failure patterns in collectives generate selection gradients that refine sensorimotor tuning.

Dengue is a mosquito-borne, viral disease of increasing public health significance. Currently, most public health interventions target the vector, with efficacy dependent on timing within the season. Whilst seasonal profiles have been characterised in some endemic settings a global assessment is lacking. Here, we develop and apply a proportion-based measure of dengue seasonality to reported case time series from 1990 to 2024 across 106 countries and territories, the largest assessment of this phenomenon to date. We identify regional differences in seasonality such that every month of the year saw cases peak in at least one country or territory. Latitude was identified as influencing seasonality, with cases peaking between March and April in the southern hemisphere and July and October in the northern hemisphere. Equatorial locations displayed flat seasonality, and amplitude increased with distance from the equator. K-means clustering identified three seasonal profile types: two with pronounced seasonal outbreaks (with distinct peak timing and shape) and one with flatter, more endemic transmission. Peak month timing covaried among locations within the same seasonality cluster, with phase differences meaning that information on shifts in peak timing may be available several months in advance in some settings, of potential significance for prediction and intervention planning. Beyond aiding public health planning, identification of seasonal clusters suggests that information on dynamics in one location could be leveraged to improve forecasting power in others with similar seasonal dynamics.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.

AO_SCPLOWBSTRACTC_SCPLOWO_LICommunity assembly graphs (CAGs) summarize which species combinations can coexist and how single-species invasions drive transitions between them, encoding the pathways, alternative endpoints, and cycles that make up a communitys assembly history. Constructing CAGs from dynamical models requires methods that are both computationally tractable and faithful to the underlying ecological dynamics. However, existing methods rely on restrictive assumptions, such as global stability, that exclude alternative stable states and non-equilibrium dynamics known to occur in empirical systems. C_LIO_LIWe develop a computational pipeline that constructs CAGs from any generalized Lotka-Volterra model. Building on the invasion graph framework and its connection to permanence, the pipeline verifies that community dynamics are bounded, identifies which subsets of species coexist in the sense of permanence, determines which single-species invasions are dynamically realized, and assigns each community a topographic height equal to the length of the longest assembly path leading to it. We also provide a numerical algorithm to simulate the dynamics of community assembly. C_LIO_LIWe prove several general properties of the resulting graphs, including that a successful invader is never subsequently excluded and that, in the absence of assembly cycles, permanent communities can be reassembled by introducing their species one at a time in the right order. We prove that the CAG faithfully reproduces the compositional shifts seen in the numerically simulated dynamics of assembly. Applying the pipeline to three empirically based models (a New Zealand grassland, a European pasture, and a Puerto Rican ant community), we show how competition strength and mutualistic feedbacks reshape the assembly landscape and how intransitive competition generates assembly cycles. C_LIO_LIOur approach accommodates alternative stable states and non-equilibrium dynamics without requiring global stability, and it turns the long-standing landscape metaphor into a quantitative, mechanistically grounded object by resolving what "height" means. More broadly, it makes the topography of the assembly pathways measurable, providing a way to compare the historical contingency and predictability of the assembly in ecological systems. C_LI