Environmental pressures shape survival and life-history dynamics partly by triggering the release of glucocorticoids, whose short-term benefits but long-term costs make it essential to understand what drives their levels. Here, we used an experimental manipulation of two environmental stressors-food availability and parasite burden-to directly test how they influence fecal corticosterone metabolites (FCMs) in wild wood mice (Apodemus sylvaticus). To do so, we experimentally altered nutrition via high-quality food supplementation and reduced gastrointestinal nematode infection by anthelmintic treatment, using Heligmosomoides polygyrus as an indicator of treatment efficacy. FCM levels were not impacted by either treatment. However, our results may be mediated by variation in resource availability or masked by other factors that affect corticosterone. For example, FCMs declined seasonally, alongside a decline in the number of reproducing individuals. While we expected higher food availability and lower worm burdens to decrease stress, putatively higher rates of reproduction in food-supplemented areas may offset potential declines in corticosterone. Thus, alleviating some environmental stressors in the wild may have unintended consequences on host fitness.

Standard survival analyses often assume proportional hazards (PH) or impose parametric survival functions that poorly represent real populations. Ecological applications are further limited by missing age data. Since standard survival methods estimate probabilities of survival at birth, this introduces an important survivorship bias. To circumvent these modelling constraints, we developed a method that combines the Kaplan-Meier estimator with conditional probability theory to compute age-specific probabilities of survival up to some target age of choice τ . Marginalising this probability over the age distribution of the population yields O τ , the probability that a randomly sampled individual of unknown age will outlive the target age τ . Notably, τ is set for each group independently, which allows accounting for differences in pace of life across populations. We tested its application using a simulation study and two real-world datasets, and compared its performance against that of Cox PH and parametric survival models. The PH assumption was violated in the three examples, rendering the Cox PH models inappropriate. Parametric models offered a better alternative, but the best parametric fit missed at least some key survival patterns in all examples. The TAUS method provided a valid description of survival patterns in all cases. Its output also allowed finer analysis of survival differences between populations. The TAUS method is also available as an R package (https://github.com/casasgomezuribarri/TAUS). This new method, free of the PH and parametric assumptions, allows the comparison of survival probabilities between populations with different age structures and rates of pace of life. This makes it suitable for a wide range of ecological applications, including in population viability analysis, epidemiology, or life-history theory.

Feline infectious peritonitis (FIP) is a major disease of cats which, unless promptly diagnosed and treated, is invariably fatal. Although it has long been recognised that the condition is the result of an aberrant immune response to infection with feline coronavirus, there remain significant gaps in our understanding of its pathogenesis. Consequently, diagnosis is complex and relies on the combined interpretation of numerous clinical signs and laboratory biomarkers, many of which are non-specific. In the case of effusive FIP, a commonly encountered acute form of the disease where body cavity effusions develop; the interpretation of fluid analysis results is key to diagnosing the condition. We hypothesised that machine learning could be applied to fluid analysis test data in order to help diagnose effusive FIP. Thus, historical test records from a veterinary laboratory dataset of 718 suspected cases of effusive disease were identified, representing 336 cases of FIP and 382 cases that were determined not to be FIP. This dataset was used to train an ensemble model to predict disease status based on clinical observations and laboratory features. Our model predicts the correct disease state with an accuracy of 96.51%, an area under the receiver operator curve of 96.48%, a sensitivity of 98.85% and a specificity of 94.12%. This study demonstrates that machine learning can be successfully applied to the interpretation of fluid analysis results to accurately detect cases of effusive FIP. Thus, this method has the potential to be utilised in a veterinary diagnostic laboratory setting to standardise and improve service provision.

Background/aims:The WHO aims to eliminate schistosomiasis as a public health problem (EPHP) across 78 endemic countries, with interruption of transmission (IoT) as the goal in selected African countries by 2030. However, for low-prevalence settings that reach EPHP, guidance on managing transmission to maintain EPHP or move towards IoT is limited, partly due to insufficient knowledge of the drivers of persistence and/or re-emergence. In Uganda, some communities inland from Lake Victoria have achieved EPHP for Schistosoma mansoni but not progressed to IoT. This study aimed to assess whether routine, short-range travel to the highly endemic lake could sustain transmission in these settings. Methods:We conducted a cross-sectional study in five Ugandan villages ~5 km from Lake Victoria. Parasitological data were collected using Kato-Katz and Point-of-Care Circulating Cathodic Antigen tests alongside questionnaires on lake travel from 585 individuals aged 1-91 years. A structural causal model estimated the total and direct effects of travel frequency, activity type, water contact duration and drug treatment history on infection. Bayesian regression models and counterfactual simulations predicted infection under hypothetical interventions. Results:Reaching IoT in low-risk villages may be undermined by habitual short-range travel to high-risk sites driven by the nature and duration of lake contact. Daily lake-related travel caused a 1.7-fold increase in the odds of infection, while occupational activities caused a 3.4-fold increase compared with no lake activity. Counterfactual analysis showed that removing the lake contact duration variable most reduced infection risk among moderate-frequency travellers, while daily travellers showed smaller changes, and some transmission persisted among individuals with little or no lake contact. Simulations demonstrated that modifying lake contact behaviours could reduce individual infection risk but had limited population-level impact. Conclusion:These findings indicate that targeting only high-risk villages or individual behaviours is unlikely to achieve sustained, widespread IoT, underscoring the need for integrated control strategies that account for context-specific behaviours and local transmission ecology.

Vaccination is the most effective way to prevent infectious diseases and safeguard public health. Yet, most new vaccines fail in late clinical trials, and even established ones often underperform in populations apart from those in which they were initially tested. This can lead to reduced vaccine responsiveness, breakthrough infections, and prevent or delay herd immunity. While the causes of vaccine hyporesponsiveness remain difficult to identify, quantify, and therefore address, numerous reports indicate a predominant role of environmental factors. This has notably been demonstrated by a reduction in the immunogenicity and efficacy of various vaccines when transitioning from urban to rural human populations. Here, we tested whether and, if so, how the environment can cause vaccine hyporesponsiveness. We hypothesised that if the leading causes of vaccine hyporesponsiveness were environmental, then environmentally driven hyporesponsiveness would be exacerbated when individuals are under nutritional stress; specifically predicting that high-quality diet supplementation would increase vaccine responsiveness. Finally, we predicted that parasitic helminth infections, which are more common in rural populations, would degrade vaccine responsiveness, e.g., due to their ability to modulate host immunity, and that anthelmintic treatment could rescue vaccine responsiveness in infected individuals. To test these hypotheses, we coupled lab and field experiments with structural causal modelling, and quantified diphtheria toxoid-specific IgG1 optical density (OD) in paired conspecific cohorts of laboratory-reared and wild wood mice ( Apodemus sylvaticus ) given a single or two doses of diphtheria toxoid vaccine formulated with alum, with and without diet supplementation. We found that anti-toxoid IgG1 OD was ∼ 47 % lower in the wild wood mice compared to the laboratory-reared population. We also demonstrated that, across both habitats (wild and lab), substantial variation in vaccine responsiveness was caused by diet. However, contrary to our predictions, this high-quality dietary supplementation resulted in lower vaccine responsiveness. Further, once the effects of habitat, diet, and sex were adjusted for, increasing helminth infection burdens negatively affected anti-toxoid IgG1 OD. Counterfactual predictions from our structural causal model suggested that targeting anthelmintic treatment at heavily infected individuals could have improved their anti-toxoid IgG1 OD responses by approximately 2 to 4-fold. Our results indicate that the wild environment and access to a high-quality diet play a dramatic role in shaping the immune system’s response to immunisation. Further, we showed that laboratory settings, even when using a genetically diverse, non-traditional model, systematically yielded higher IgG1 OD than was observed in free-living conspecifics on the same protocol. We provide a causally explicit modelling approach to quantify how habitat, diet, and parasites jointly shape anti-diphtheria toxoid IgG1 levels in a focal population, and to prioritise adjunct interventions such as anthelminitc treatment where model assumptions hold.

Summary Helminths are widespread parasites that can modulate host immunity, potentially increasing susceptibility to viral infections. However, evidence for these effects varies across systems and environments, and links between laboratory and wild populations remain unclear. We developed a tractable system using wood mice, Heligmosomoides spp. nematodes, and wood mouse herpes virus (WMHV) to bridge this gap. Combining laboratory and field experiments with population modelling, we examined how helminth infection, anthelmintic treatment and diet affect viral dynamics. Across lab and field data, helminth infection consistently increased WMHV risk, with stronger effects at higher worm burdens. Field results showed that anthelmintic treatment reduced viral infection, and laboratory experiments showed that improved nutrition mitigates helminth-induced increases in viral susceptibility. Our population-level modelling suggested that helminth burden-dependent facilitation can generate nonlinear effects on viral spread, dependent on helminth virulence. Our findings highlight the potential importance of helminths as facilitators of viral infections, and suggest that anthelmintic treatment may provide indirect benefits for viral control. We also show the value of integrating lab and field approaches on the same (or closely related) species, in particular the potential offered by the wood mouse – Heligmosomoides – WMHV system, to understand the drivers and consequences of host-helminth-viral interactions.

Knowledge of viral infection in marine mammals, a group severely threatened by human activity, is largely limited to the pathology and epidemiology of few endemic viruses. The recent emergence in marine mammals of high-consequence viruses, such as H5N1 avian influenza and rabies, underscores the importance of understanding the ecology of viral transmission in these species. Metatranscriptomic approaches now enable relatively unbiased characterisation of full viral communities that can reveal ecological and evolutionary drivers of infection. We sequenced RNA from 15 marine mammal species (42 pools, 237 tissues, 128 animals) sampled in Scotland through the Scottish Marine Animal Strandings Scheme. Viral sequences were detected in 41 of 42 pools, representing more than 120 distinct viral taxonomic units (vOTUs). Virus host network analysis showed that viral communities were partly structured by host taxonomy, with clear differences between seals and cetaceans. However, vOTUs were frequently shared between species, mirroring reported ecological interactions, including cross-order sharing between seals and cetaceans. Generalised linear models showed no effect of host taxonomy on viral richness. Instead, age was the strongest predictor: juvenile pools contained roughly twice as many viral taxa as adults and more than neonates, indicating that changing population demography may impact viral transmission in marine mammals. These results provide a basis for understanding how anthropogenic stressors may exacerbate viral transmission in marine mammals and demonstrate the increasing practicality of using genomics to understand ecological and evolutionary drivers of virus infection in natural populations.

Most malaria vector control strategies target female Anopheles mosquitoes that transmit parasites, but emerging approaches such as gene drive, sterile insect technique, or Wolbachia require the release of males. Successful deployment depends on reliable knowledge of the species composition and age structure of male populations to overcome potential barriers to mating. Although approaches including near-infrared spectroscopy have been explored for male species identification and age grading, these methods remain constrained by limited validation, scalability, and accuracy under operational field conditions. Here we tested mid-infrared spectroscopy (MIRS) coupled with machine learning (ML) as a rapid and cost-effective approach for classifying species and age of male mosquitoes within the Anopheles gambiae complex. We used male mosquitoes from two laboratory-reared colonies in Burkina Faso (Anopheles coluzzii and An. gambiae) to develop a MIRS-ML model for species and age group (1–4, 5–10, 11–17 days) prediction under controlled conditions. This model was tested against a genetic and environmentally variable dataset consisting of male offspring obtained from gravid or blood fed Anopheles coluzzii and An. gambiae females collected from houses from two villages, Vallée du Kou and Soumousso and reared to adulthood in a semi-field system. The MIRS spectra from 2,120 males, representing both species, all age groups and both laboratory and semi-field backgrounds, were analysed using an extreme gradient boosting (XGBoost) algorithm to assess the ability to correctly predict age group and species. The XGBoost model classified mosquito species (86%) and age groups (85%) accurately in laboratory data, but performance declined under semi-field conditions (64% for species, 50% for age), reflecting environmental variability. Incorporating semi-field samples through transfer learning improved accuracy to 73% for species and 70% for age, underscoring both the limits of laboratory-only models and the value of transfer learning for enhancing generalisability in field settings. Our results demonstrate that mid-infrared spectroscopy with supervised machine learning (MIRS-ML) holds potential as a rapid tool for identifying the species and age group of cryptic male malaria vectors and represent one of the first applications of this approach to male Anopheles gambiae s.l. evaluated under semi-field conditions. However, before the approach can be used, larger datasets are needed to improve the classification algorithms and validate them for prediction in field populations.

ABSTRACT Standard survival analysis methods often rely on the assumption of proportional hazards (PH) or parameterisations of the survival function that might not be appropriate for wild populations. To enable survival analysis without these modelling constraints, we developed an approach that combines the Kaplan-Meier estimator with conditional probability theory to compute age-specific probabilities of survival up to some target age of choice τ . Marginalising this probability over the age distribution of the population yields O τ , the probability that a randomly sampled individual of unknown age will outlive the target age τ . Notably, the value for τ is set by the analyst for each group independently, which allows accounting for differences in pace of life across populations. We tested its application using a simulation study and two real-world datasets, and compared its performance against that of Cox PH and parametric survival models. The PH assumption was violated in the three examples, rendering the Cox PH models inappropriate. Parametric models offered a better alternative, but the best parametric fit missed at least some key survival patterns in all examples. The TAUS model provided a valid description of survival patterns in all cases. Its richer output also allowed finer analysis of survival differences between populations. The TAUS model is also available as an R package ( https://github.com/casasgomezuribarri/TAUS ). This new approach to survival analysis without PH or parametric assumptions allows the comparison of survival probabilities across populations with different age structures and rates of pace of life. This makes it suitable for a wide range of ecological applications, including in population viability analysis, epidemiology, or life-history theory

Temperature is a key environmental factor influencing the development, survival, and transmission potential of malaria vectors. While most laboratory studies use constant temperature (CT) regimes, mosquitoes in natural habitats experience fluctuating temperatures (FTs), which may affect their life-history traits. We investigated the effects of CT (27 °C) and FT (27 ± 3 °C) on larval and adult traits of 2 major malaria vectors, Anopheles gambiae and An. coluzzii, under laboratory conditions. We measured larval survival, development time, adult body size, and adult survival, using survival and mixed-effects models. Species-specific and stage-specific responses to temperature regimes were observed. An. gambiae larvae exhibited higher survival under FT, while An. coluzzii larvae survived better under CTs. However, this pattern reversed in adulthood: An. coluzzii adults showed increased survival and larger body size under FT, whereas An. gambiae adults performed better under CT. Development time was slightly longer under FT for both species, with An. coluzzii pupating faster overall. These opposing patterns suggest that differential larval survival under FTs may influence adult fitness in a species-specific manner. The contrasting and reversed responses of An. gambiae and An. coluzzii across life stages might reflect their ecological adaptations: An. gambiae, found in small and thermally variable habitats, performed better under FT during larval stages, while An. coluzzii, associated with larger, more thermally stable habitats, showed improved adult performance under FT. These findings underscore the importance of incorporating species-specific, stage-dependent thermal responses into models of vector dynamics and control strategies under climate change.

Background/aims The World Health Organization (WHO) aims to eliminate schistosomiasis as a public health problem (EPHP) across 78 endemic countries by 2030. However, for low-prevalence settings that reach EPHP, guidance on managing transmission to maintain EPHP or move towards Interruption of Transmission (IoT) is limited, partly due to insufficient evidence on drivers of resurgence. In Uganda, some communities inland from Lake Victoria have achieved EPHP for Schistosoma mansoni but not progressed to IoT. This study explored whether routine, short-range travel to the highly endemic lake could sustain transmission in these settings. Methods We conducted a cross-sectional study in five Ugandan villages ~5 km from Lake Victoria. Parasitological data were collected using Kato-Katz and Point-of-Care Circulating Cathodic Antigen tests, alongside questionnaires on lake travel from 585 individuals aged 1-91 years. A structural causal model estimated the total and direct effects of travel frequency, activity type, water contact duration, and drug treatment history on infection. Bayesian regression models and counterfactual simulations predicted infection under hypothetical interventions. Results Reaching IoT in low-risk villages may be undermined by habitual, short-range travel to high-risk sites, driven by the nature and duration of lake contact. Daily lake travel caused a 1.7-fold increase in odds of infection, while occupational activities caused a 3.4-fold increase compared with no lake activity. Counterfactual analysis showed that removing lake contact duration most reduced infection risk among moderate-frequency travellers, while daily travellers showed smaller changes, and some transmission persisted among individuals with little or no lake contact. Simulations demonstrated that modifying lake contact behaviours could reduce individual infection risk but had limited population-level impact. Conclusion These findings indicate that targeting only high-risk villages or individual behaviours is unlikely to achieve sustained, wide-spread IoT, underscoring the need for integrated control strategies that account for mobility, behaviour, and local transmission ecology. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Wellcome [204820/Z/16/Z] awarded to JC, the European Research Council [SchistoPersist ERC starting grant 680088] awarded to PHLL and the Engineering and Physical Sciences Research Council [EP/T003618/1] awarded to PHLL. Rivka May Lim was supported by the Wellcome Trust (218492/Z/19/Z; supervised by ABP, JPW, and PHLL). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of University of Glasgow MVLS (200160068) and Ministry of Health Uganda, Vector Control Division research ethics committee (VCDREC/062) and Uganda National Council for Science & Technology (UNCST-HS 2193) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://github.com/iamjessclark/travelStudySchisto

Assessing long-term population health is an important feature of wildlife monitoring and is essential for understanding population resilience. Quantitative assessments of health indices are essential for effective monitoring yet they remain a challenge for cetaceans due to their highly mobile and largely cryptic nature. Quantified assessments of cetacean health typically rely on evaluating body condition, which reflects energetic reserves and nutritional status. However, this method may not capture sub-lethal impacts or comorbidities resulting from chronic or cumulative stressors. Necropsies of stranded individuals provide unique insight into the morbidity of an individual and offer clues about the pressures an individual has faced over its lifetime, making necropsy reports a valuable source for understanding health. However, the predominantly qualitative and narrative format of necropsy reports limits their utility for systematic statistical analysis, thereby constraining their application in generating robust, population-level inferences. Using 349 necropsy records of harbour porpoises (Phocoena phocoena ) stranded on the Scottish coastline as a case study, we developed a structured pathology database suitable for statistical analysis. The application of unsupervised and supervised machine learning techniques has revealed underlying patterns in harbour porpoise pathology. This has shown that necropsy data may provide a more comprehensive insight into health than traditional methods. We identified the respiratory and hepatic systems as explaining a high degree of variation in the data. This highlights these organ systems as priority areas for data collection and subsequent analysis. Patterns in the data revealed underlying compromised health, particularly in adult harbour porpoises, suggesting a vulnerability to additional stressors in this group. Our approach demonstrates how pathology data from necropsy reports can be used to derive population-level health insights, offering a tool for monitoring the impacts of environmental and anthropogenic pressures on wildlife populations.

Anopheles mosquitoes are major malaria vectors, encompassing several species complexes with diverse life histories, transmission risks and insecticide resistance profiles that challenge malaria control efforts. This study investigated the genetic structure and insecticide resistance profiles of Anopheles gambiae complex mosquitoes in Tanzania. We analysed whole-genome sequence data of 300 mosquitoes collected between 2012 and 2015 across four regions in northern Tanzania and identified An. gambiae s.s., An. arabiensis and a distinct taxonomic group that was previously unknown. This distinct taxon has a unique profile of genetic diversity and appears restricted to the coastal region, and we refer to it as the Pwani molecular form. Analysis of insecticide resistance based on target-site mutations and copy number variations (CNV) showed that these markers were strikingly absent from the Pwani molecular form in contrast to other taxa. Our analysis also revealed a pattern of geographical isolation in the An. gambiae s.s. populations, with samples from the north-western site (Muleba) clustering separately from those collected in the north-eastern site (Muheza). These geographically isolated subpopulations also had differing resistance and selection profiles, with An. gambiae s.s. from the north-western site showing genomic evidence of higher resistance to pyrethroids compared with the north-eastern population. Conversely, An. arabiensis showed no geographical population structuring, with a similar insecticide resistance profile across all sampling locations, suggesting unrestricted gene flow. Our findings underscore the need to incorporate genetic data into malaria vector surveillance and control decisions and could inform the development and deployment of new interventions.

Theory predicts that high population density leads to more strongly connected spatial and social networks, but how local density drives individuals' positions within their networks is unclear. This gap reduces our ability to understand and predict density-dependent processes. Here we show that density drives greater network connectedness at the scale of individuals within wild animal populations. Across 36 datasets of spatial and social behaviour in >58,000 individual animals, spanning 30 species of fish, reptiles, birds, mammals and insects, 80% of systems exhibit strong positive relationships between local density and network centrality. However, >80% of relationships are nonlinear and 75% are shallower at higher values, indicating saturating trends that probably emerge as a result of demographic and behavioural processes that counteract density's effects. These are stronger and less saturating in spatial compared with social networks, as individuals become disproportionately spatially connected rather than socially connected at higher densities. Consequently, ecological processes that depend on spatial connections are probably more density dependent than those involving social interactions. These findings suggest fundamental scaling rules governing animal social dynamics, which could help to predict network structures in novel systems.

Resource availability is crucial in shaping exposure and resistance to parasites. Although effects are expected to vary across parasite species, most of our knowledge comes from single-host, single-parasite studies. In a wild wood mouse population ( Apodemus sylvaticus ), we supplemented dietary resources over 3 years and quantified effects on a diverse parasite community alongside host behaviour and physiology. Resource supplementation had diverse impacts on 12 parasite taxa including positive (3/12), negative (6/12), and null (3/12) results. Supplementation also influenced host biology, with supplemented mice moving less, experiencing higher conspecific densities, improved conditions, and increased reproduction. These findings experimentally demonstrate that resource availability can multifacetedly affect disease dynamics. The complexity likely arises due to combined effects on exposure and susceptibility processes. Our study advances understanding of disease drivers at the human-wild interface and underscores the importance of assessing whole parasite communities to predict responses to changing resource landscapes in the wild.

Although short-lived, easily manipulated wild systems could be useful for studying ageing, developing epigenetic clocks for them is challenging because their chronological age is often unknown. Here, we present a multi-tissue epigenetic clock for the wood mouse ( Apodemus sylvaticus ) that was developed in a laboratory colony and then applied to wild individuals. We used the mammalian methylation array to profile CpG sites across highly conserved stretches of DNA in blood, ear, spleen, and liver of colony-reared mice. We trained an elastic net model with Leave-One-Out-Cross Validation (LOOCV), which identified 77 key age-related CpG sites as being highly predictive of chronological age (r = 0.99; MAE = 3.29 days). Upon validation in an independent dataset, the LOOCV clock predicted age with an MAE of 54.68 days. Epigenome-wide association study and ‘Genomic Regions Enrichment of Annotations Tool’ analysis of age-related CpGs primarily revealed hypermethylation of promoter regions linked to development and transcription factor activity, particularly via changes in methylation of PRC2 targets sites. Critically, our epigenetic clock was able to predict broad age categories in wild mice and increased over chronological time in 75% of individuals. This and similar clock developments in other short-lived wild systems, that can be bred in captivity, will enhance our ability to conduct experimental manipulations of ageing in ecology and evolution. ### Competing Interest Statement The Regents of the University of California are the sole owner of patents and patent applications directed at epigenetic biomarkers and the mammalian methylation array for which Steve Horvath is a named inventor; SH is a founder and paid consultant of the non-profit Epigenetic Clock Development Foundation that licenses these patents. SH is a Principal Investigator at the Altos Labs, Cambridge Institute of Science, a biomedical company that works on rejuvenation.

Growing anthropogenic pressures increasingly impact marine wildlife, with cetaceans being particularly vulnerable to cumulative effects of stressors due to their position as top predators. As sensors and sentinels of ocean health, cetaceans offer critical insight into known and emerging threats to marine ecosystems. Stranding schemes provide a cost-effective means to assess mortality rates and population demographics, offering insights that are often challenging to obtain through live monitoring. Using a 30-year dataset from the Scottish Marine Animal Stranding Scheme (SMASS) we demonstrate how opportunistically obtained stranding data can be used to monitor populations and guide conservation strategies. Species were clustered into broad ecological groups - baleen whales, short-beaked common dolphins, deep divers, harbour porpoises and pelagic dolphins - for spatiotemporal analysis of stranding patterns. All groups showed increases in annual stranding rates over the study period, with common dolphins and baleen whales exhibiting exponential increases, suggesting these species may be facing heightened pressures. Distinct seasonal and spatial trends were detected, with harbour porpoises predominantly stranding on the east coast and other groups clustering to Scotland’s west coast. Identifying these trends helps focus surveillance and mitigation efforts, underscoring the importance of this approach for monitoring vulnerable species.

It remains unclear how animals are affected by different environmental stressors, including climatic, anthropogenic and conspecific interactions. Assessing seasonal changes in hair cortisol concentration (HCC) in fallow deer from 10 populations across England, we found a negative correlation with precipitation in May-June and with average temperature in late winter (January-February). There was a positive correlation with precipitation in November-March and the number of days of airfrost, two measures which would also reflect increased environmental challenge over winter. While the same climatic factors appeared to influence HCC of both sexes, the primary social factor affecting HCC in females was the level of competition experienced for quality food resources. By contrast, the major factor affecting HCC in males was the proportion of males within the population overall, perhaps reflecting the degree of male-male competition.

Effective diagnosis of malaria, including the detection of infections at very low density, is vital for the successful elimination of this deadly yet treatable disease. Unfortunately, existing technologies are either inexpensive but poorly sensitive - Rapid Diagnostic Tests (RDTs) and microscopy - or sensitive but costly - Polymerase Chain Reactions (PCR). Here, we demonstrate an AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra acquired from dried blood spots to detect malaria infections with high accuracy under varying parasite densities and anaemic conditions. Our AI classifier initially trained on 4655 spectra from parasite-spiked blood samples from 70 adult volunteers, in controlled laboratory settings, attained 90% accuracy in detecting infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural Plasmodium falciparum infections in blood samples collected during a field survey in rural Tanzania. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. These findings suggest that the AI-driven mid-infrared spectroscopy approach has the potential to evolve into a cost-effective and highly sensitive malaria-screening tool adaptable to field conditions, including low-resource settings, thereby accelerating malaria elimination worldwide.