Philosophical Transactions of the Royal Society B

Rapid evolution allows populations to persist in environments where they would otherwise go extinct. This phenomenon, known as evolutionary rescue, is typically studied in the framework of biological evolution, yet adaptive traits can also arise and spread through cultural evolution. The present study developed a stochastic eco-evolutionary model to compare rescue probabilities through biological and cultural evolution. Transmission bias governed the rescue probability under cultural evolution by setting how readily a rare adaptive trait was copied. Conformity bias suppressed population persistence because a rare trait was the least likely to be copied. Content bias toward the adaptive trait enabled evolutionary rescue when social learning was rapid, but it typically yielded a lower rescue probability than biological evolution. Only anticonformity bias, together with a high social learning rate, exceeded the rescue probability of biological evolution by enabling the adaptive trait to be established more rapidly. These results demonstrate that transmission bias alters the demographic consequences of cultural evolution and highlight the importance of transmission processes in evolutionary rescue theory. Understanding how adaptive behaviours are socially transmitted may also improve predictions of animal population persistence and inform conservation efforts in rapidly changing environments.

Large language models perform well on medical examinations, but users routinely challenge their answers and invoke professional roles, and it is unclear what a system does when a medical credential and a stated task-specific accuracy point in opposite directions. In a factorial experiment on 480 items from four Polish specialty examination sets and three consumer large language model systems (ChatGPT, Claude, Gemini), each item and system received eleven independent conversations. Conditions crossed attributed source role (medical student, experienced specialist), stated prior accuracy on similar questions (2/10, 8/10) and suggestion correctness. The primary outcome was adoption of a prespecified incorrect option when the baseline answer matched the official key, comparing a specialist described as 2/10 with a student described as 8/10. Baseline agreement with the key was 87.2% across 15,683 analyzable conversations. The incorrect option was adopted more often from the specialist described as 2/10 than from the student described as 8/10 (10.2% vs. 7.6%; adjusted risk difference +2.82 percentage points, 95% CI +0.65 to +4.99). Estimates varied across the three systems and only one system-specific interval excluded zero. In a prespecified exploratory analysis with a shared eligibility rule, correct suggestions were adopted far more often than incorrect ones (risk difference +35.7 percentage points, 95% CI +30.8 to +40.7), indicating selective rather than indiscriminate compliance. An incorrect suggestion from a specialist with low stated accuracy was therefore slightly more influential than the same suggestion from a student with high stated accuracy, although the difference was modest and varied across systems. Agreement reached only after a user has disclosed a preferred answer should not automatically be treated as an independent second opinion, and medical large language model systems should be evaluated on how they revise answers after such disclosure, not solely on initial accuracy.

BACKGROUND: An individual's adaptation to threatening environments under uncertainty is reflected in stress responses. Predictability (the ability to anticipate events) and controllability (the ability to control outcomes) are central to how one adapts, yet their joint influence on aversive learning remains unclear. METHODS: Thirty healthy adults completed a probabilistic aversive learning task in which cue-outcome contingencies varied across levels of predictability and controllability, i.e. whether shock intensity depended on prediction accuracy. Prediction accuracy, reaction time, subjective stress ratings, and skin conductance responses were recorded throughout. Trial-wise learning dynamics were estimated using the Volatile Kalman Filter. RESULTS: Prediction accuracy reduced as environments became less predictable and negatively associated with higher learning rates across predictability levels, with the strongest relationship observed in highly predictable blocks. Skin conductance responses showed that moderately predictable environments elicited responses like those in highly predictable environments when accurate predictions reduced shock intensity, but resembled responses in unpredictable environments when shock intensity was uncontrollable. Model comparison revealed a double dissociation between subjective stress ratings and skin conductance responses. Subjective ratings were best explained by model-derived volatility when prediction accuracy determined shock intensity and by belief uncertainty when it was independent of prediction accuracy, whereas skin conductance responses showed the reverse pattern. Reaction times were best explained by belief uncertainty when predictions influenced shock intensity. Higher anxiety was associated with elevated learning rates in highly and moderately predictable blocks when predictions did not control shock intensity.

Across different modalities, signals play a core role in attracting mates and influencing mating success. In several non-human primate species, females produce calls during mating that are thought to promote male competition over receptive females. The extent to which social system characteristics modulate the function of copulation calls remains less clear. We studied copulation calls in wild Guinea baboons (Papio papio), who live in a multilevel society structured around units in which females associate and mate almost exclusively with a single male. We hypothesised that females use copulation calls as an indirect form of mate competition, with competition increasing in larger units. In addition, we hypothesised that females are more likely to mate again after calling. We analysed 6116 copulations between 2014 and 2025, involving 99 reproductively active females and 78 subadult and adult males. Females produced copulation calls in 72.7% of copulations, with large inter-individual variation. Neither unit size nor its interaction with the female's swelling size or the presence of simultaneously receptive females affected the probability of calling. A survival analysis with a subset of the data (2353 copulations) revealed no effect of calling on the latency to the next mating. Our results render the hypothesis that female Guinea baboons use calls in indirect mate competition unlikely. Yet, the probability of calling varied with sexual swelling size, suggesting that calls signal female fertility. Possibly, Guinea baboon copulation calls represent an evolutionary remnant, no longer under selective pressure, and can be considered index signals of female fertility.

Squid abundance has been reported to increase globally between 1970 and 2010. This increase has been hypothesized to result from two primary factors: the loss of top predators due to overfishing and rising ocean temperatures. The decline in apex predators may lead to the expansion of squid populations either through reduced predation pressure or diminished competition with juvenile predators. Concurrently, increased temperatures could enhance the somatic growth rates of squid, thereby accelerating their population growth. However, empirically disentangling the impacts of predator loss and temperature on squid biomass remains challenging, especially in a food-web context. In this study, we used a size- and trait-based model of upper trophic levels that resolves the ecosystem structure -- biomass and trophic interactions of fish and squid -- for varying depth, temperature, and secondary production, to investigate two hypotheses of the historical expansion of squid, i.e., the effects of predator depletion from fishing and rising temperatures on squid biomass. Our model reveals that intensified fishing of squid predators -- specifically large demersal fish in shelf systems and large pelagic fish in open oceans -- leads to a slight increase in squid biomass. Conversely, elevated temperatures are associated with a decline in squid biomass. This temperature-driven reduction in biomass is attributed to an increased metabolism of squids beyond the available food supply. If historic overfishing on large marine predators continues to be curtailed, we expect a corresponding reduction in global squid biomass and fisheries potential, which could be further exacerbated by rising temperatures.

Background: Consumer use of AI chatbots for health advice is rising, yet triage safety relative to established services remains unclear. Australia's Healthdirect, a government-backed symptom checker with 2.4 million uses in FY2024-25, remains unevaluated against frontier large language models (LLMs), and whether premium subscriptions improve triage safety remains unexplored. This study compared the triage accuracy and safety of Healthdirect against six LLM configurations across ChatGPT, Claude, and Gemini, assessed whether paid subscriptions improve triage safety, and characterised each system's error patterns. Methods: Forty-five clinical vignettes from the Semigran et al. benchmark spanning emergency, non-emergent, and self-care categories (15 each) were evaluated across seven systems. Healthdirect was tested following a seven-rule interaction protocol. LLMs were evaluated using first-person patient-language prompts under free-tier and paid-tier conditions. Outcomes were triage accuracy, emergency sensitivity, under-triage, and critical misses, analysed using Cochran's Q, Bonferroni-corrected McNemar tests, Cohen's kappa, and Wilson intervals. Findings: Triage accuracy differed significantly (Cochran's Q = 36.79, p < 0.001). Healthdirect achieved 48.9% accuracy (95% CI 35.0% to 63.0%; kappa = 0.233) versus 73.3% to 86.7% for LLMs (kappa = 0.600 to 0.800). Healthdirect operated under conservative interactive defaults while LLMs received complete information in a single prompt, which may have disadvantaged Healthdirect. Emergency sensitivity was 46.7% versus 80.0% to 86.7% for LLMs. Healthdirect produced two critical misses; no LLM produced any across 270 evaluations (95% CI 0% to 1.4%). When LLMs undertriaged, they recommended GP care rather than self-care. No tier differences were significant (all p > 0.05), and most systems over-triaged self-care cases. Interpretation: Frontier LLMs demonstrated higher triage accuracy and safer error profiles than Healthdirect. All LLMs avoided critical misses; Healthdirect did not. Premium subscriptions did not significantly improve triage safety. These findings support clinical governance decisions about whether LLMs warrant formal evaluation alongside government-backed symptom checkers.

The shape of density-dependence governs species persistence, and ecosystem stability. Yet, whether per-capita growth declines sublinearily, or superlinearily with density remains hotly debated. Growth rates across the tree of life have been shown to decline sublinearly with density, whereas theory founded on resource competition predicts the opposite. Here, we resolve this discrepancy and show that sublinearity can readily emerge from geometric constraints on consumer interactions. By linking inter individual spacing, movement and interference rates, we derive two limiting-interference regimes, one of which the well-mixed limit recovers the form of classic Beddington DeAngelis interference response. We then developed an individual-based model from first principles which reproduces the derived sublinearity response, and further use empirical data from published consumer-resource experiments that also bears the signature of sublinear density-dependence. Further, embedding the interference mechanisms underlying the emergence of sublinear density-dependence in coexistence theory opens a new regime for species coexistence where classical theory fails to predict. Our framework indicates that non-consumptive interactions are not merely a correction to resource competition but might be a distinct axis along which diverse communities may potentially coexist.

Most evidence of the consequences of climate change in natural systems has focussed on shifts in mean temperature (1,2), but the effects of extreme climatic events (ECEs) remain far less understood. This is particularly true for very severe ECEs that may occur only once every few decades. Understanding the consequences of these severe events for natural populations is nonetheless critical, since their frequency is predicted to rise under current climate change (3). Here we combine a unique long-term dataset spanning almost five decades of breeding (>20,000 events) and morphological data (>120,000 observations) in adult and nestling great tits (Parus major) and blue tits (Cyanistes caeruleus) with fine-scale temperature records to examine the effects of an unprecedented heatwave in May 2026 on breeding success and morphology. Average temperature during the heatwave (22-29 May 2026) was 7.85 C above the historical record, reaching +10.5 C (+4.32 SD) at its peak (25-26 May). These record-breaking temperatures significantly reduced adult breeding success and nestling bmass relative to expectation in the absence of a heat-wave. Given the heatwave was widespread (Fig. 1A), our findings from a single, exceptionally well-studied population are likely to generalise to other species exposed to the same event, providing key evidence that severe ECEs can substantially harm wild populations.

Animal mitochondrial rRNAs are commonly viewed as structurally reduced, yet sponge mt SSU rRNAs range from compact to highly expanded structures. Using nine conserved structural anchors, we compared 216 taxonomically resolved records from four classes and 22 orders, including 16 freshwater Spongillida and 200 marine sponges. Twelve homologous hypervariable substructures were coded as structural types, and their ordered combinations as composite types. We identified 38 structural types and 62 composite types across molecules ranging from 853 to 2,019 nt. Hexactinellida and freshwater Spongillida were each uniform for a distinct composite type but differed markedly in overall structure: hexactinellid mt SSU rRNAs were compact, whereas those of Spongillida were long and contained four to five candidate insertion regions. These results show that a conserved scaffold can accommodate extensive lineage-associated structural variation and provide a practical framework for comparing highly divergent mitochondrial rRNAs.

Objective To measure the accuracy of clinical coding, clinician review, and a locally deployed large language model (LLM) in identifying alcohol, drug, and self-harm involvement in emergency department (ED) attendances, and quantify prevalence. Design Two-phase diagnostic accuracy study. In a validation week, the identification strategies were assessed against a conflict-adjudicated reference standard (n=2,256); the LLM was then applied to n=105,096 annual attendances at the same site. Setting UK Type 1 Emergency Department treating patients [&ge;]16yrs. Main outcome measures Prevalence quantification compared with the reference standard; sensitivity, specificity, and balanced accuracy of each strategy; monthly identification rates and adjusted annual prevalence. Results The reference standard identified 12.1% of attendances as involving alcohol, drugs, or self-harm (coding 6.0%; clinician 10.0%, LLM 15.6%). LLM balanced accuracy matched or outperformed clinician review in all three domains (alcohol 0.942 v 0.930, p=0.635; drug 0.959 v 0.791, p<0.001; self-harm 0.982 v 0.908, p=0.004). Coding recorded 1.07 domains per identified patient against 1.32 in the reference standard. Adjusted annual prevalence corresponded to 12,890 domain involvements per year not identifiable in coded data. Subdomain classification found at least 81.6% of self-harm attendances required medical assessment for injury or overdose before psychiatric review. Conclusions Clinical coding identified fewer than half of presentations involving alcohol, drugs, and self-harm and rarely captured co-occurring domains; under-recording was present across a full year. A locally deployed LLM generated more complete structured data from existing clinical text within NHS infrastructure, at a scale which is not feasible for manual review.

Background. Routine service databases are attractive sources of training labels for clinical prediction models, but the processes that write those labels are rarely audited before the labels are used. In a deployed community cognitive-screening programme, we audited the routine cognitive-status label, built a matrix of twenty-four model arms over the same patients under a specialist reference standard, and measured what each supervision choice bought or cost. Methods. The study cohort is the 672 individuals whose cognitive status was recorded by a titled (attending-or-above) physician, that record being the reference standard; after holding out one institution entirely, a development panel of 642 individuals at 38 institutions. The routine cognitive-status label these individuals also carry was first audited at the operator level: for each data-entry account we counted diagnoses entered and the proportion recording any impairment, and tested a competing bulk-timestamp explanation. Twenty-four arms span the supervision choices such a programme faces: an incumbent 21-variable logistic regression; local language models (Qwen2.5-1.5B/3B, Qwen3-4B/8B) zero-shot, with chain-of-thought, fine-tuned on physician labels, on routine labels with and without decontamination, or on a proxy scale-band task; preference-optimised (DPO) and reinforcement-trained (GRPO) variants; a proprietary frontier model queried zero-shot; and knowledge distillation of that frontier model into the regression and into the local 4B, using 943 teacher-labelled records from the programme's unlabelled pool. All arms are scored out-of-fold under one five-fold split grouped on registry-resolved institution clusters (no cluster spans a fold); paired contrasts use a 2,000-draw cluster bootstrap. Results. 181 operator accounts (each entering at least 100 diagnoses with zero recorded impairments) account for 45,315 rows - 40.5% of the outcome column; recorded impairment falls monotonically with account volume (15.7% for 1-9 rows to 0.7% for 500-999); a bulk-timestamp explanation was tested and refuted, identifying the write-time column as a migration artefact. Under the specialist standard, no locally fine-tuned arm beat the incumbent regression (AUROC 0.926): physician-label SFT reached 0.924 (4B), DPO 0.881, and GRPO 0.789; the pre-registered two-stage proxy-then-RL recipe was worse than its single-stage contaminated baseline (-0.030, 95% CI -0.077 to -0.004). Chain-of-thought reduced discrimination at every size (-0.072, -0.080, -0.041 at 1.5B/3B/4B; -0.012, n.s., at 8B). The frontier model scored 0.932 (vs. regression +0.007, n.s.). The distilled 4B reached 0.940 - above the incumbent (+0.014, 0.004 to 0.031) and above its own teacher (+0.008, 0.001 to 0.017) - with near-teacher calibration; it reached the teacher's level by 50 teacher labels and changed little beyond 200. Conclusions. The audit and the arm matrix support one deployment recipe: audit the routine label at the operator level before training on it; do not expect fine-tuning, preference optimisation, or reinforcement learning on a few hundred specialist cases to beat a well-calibrated regression; and if a frontier model is available but undeployable, spend a bounded number of queries on it as a labelling instrument and distil. A companion paper uses these frozen predictions to quantify how evaluation design choices compare with model choice.

Maintaining a prescribed composition in engineered microbial consortia is difficult because small fitness differences can drive competitive exclusion. We study a two-strain consortium in continuous culture and develop a feedback architecture that regulates composition by selectively slowing the fast strain as a function of the population ratio. At the population level, we derive an idealized ratio-feedback law with a tunable positive coexistence equilibrium. We then propose a biomolecular realization using orthogonal quorum sensing, an sRNA-based ratiometric controller, and a ppGpp-mediated growth actuator. Exploiting the separation between slow population growth and faster intracellular controller dynamics, we use singular perturbation theory to show that, for sufficiently fast controller dynamics, the full implementation model inherits the coexistence equilibrium and its local stability properties from the reduced model. Numerical simulations validate the reduction and show how weaker timescale separation or loss of the assumed molecular regime degrades performance.

Vaccine effectiveness (VE) estimates can vary widely between years and populations, even for the same vaccine. Estimated VE is known to be sensitive to susceptible depletion and differences in pre-vaccination infection risk between vaccinated and unvaccinated populations. However, how variation in pre-vaccination risk within and between the two groups affects VE estimates over time remains unclear. This uncertainty is especially important given negative VE estimates. We investigated the difference between estimated VE and true vaccine protection considering continuous distributions of pre-vaccination infection risk under three scenarios. When the vaccinated and unvaccinated populations differ in their mean risk, estimated VE can be higher or lower than true vaccine protection. Similar patterns arise when both populations share identical means but different risk distributions. Finally, if infection-derived immunity lasts longer than vaccine protection, annual VE estimates can vary by tens of percentage points between years despite constant true vaccine protection. These theoretical results underscore that VE studies estimate contrasting risk between vaccinated and unvaccinated individuals in a particular time and place, and VE estimates can vary counterintuitively between years and populations even with constant vaccine-induced protection. Explaining variability in estimated VE thus requires a more complete understanding of populations' distributions of infection risk.

Communities exposed to flooding can experience markedly different mental health outcomes, yet conventional resilience indicators capture only part of the social and contextual conditions that may explain this variation. This study develops a multilevel and predictive framework for examining community resilience and depressive symptoms following flood exposure in Indonesia. Data were drawn from 20,303 respondents aged 15 years and older nested within 312 communities in the Indonesia Family Life Survey (IFLS-5). Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10), with Rasch Partial Credit Model calibration used to examine measurement properties. Bayesian multilevel models quantified between-community heterogeneity and assessed how far observable structural resources accounted for this variation. Community resilience was represented through two complementary constructs: structural resilience, based on observable socioeconomic and social-capital resources, and Latent Community Protective Capacity (LCPC), a model-derived proxy for residual contextual variation in depressive-symptom risk. Approximately 6 percent of variation was attributable to between-community differences, while observable structural resources explained only part of this heterogeneity. Structural resilience and LCPC were weakly correlated (r = 0.155). Moderation analyses provided no clear evidence that structural resilience altered the flood-depression association, while LCPC showed a directionally consistent but uncertain buffering pattern. Predictive models incorporating community-level information improved discrimination, with the best-performing model reaching an ROC-AUC of approximately 0.71. The findings suggest that observable resource-based indices provide an incomplete account of community-level mental health vulnerability and that residual contextual measures may provide complementary information, while requiring cautious interpretation and independent validation.

Background. A primary constraint on the capacity of EMS programs to meet industry demand is psychomotor instruction and verification, requiring direct observation of each student by a qualified evaluator. Whether AI video analysis can relieve it is untested; none has been applied to EMS skill examination or compared with human examiners. Objective. To quantify human EMS evaluator inter-rater reliability and evaluate an AI video-analysis platform against it. Methods. In a prospective, fully crossed study, five certified EMS evaluators and an AI platform independently scored identical video-recorded EMT performances of cervical collar application (n=15), bag-valve-mask (BVM) ventilation (n=14), and medical assessment (n=15) on dichotomous checklists with critical-failure criteria. Agreement was assessed at item, score, and decision levels using Fleiss' kappa, Krippendorff's alpha, Gwet's AC1, and ICC(2,1)/ICC(2,k). Results. Human item agreement was moderate (kappa 0.409 to 0.467), as was single-rater reliability (ICC(2,1) 0.539 to 0.694), against good panel reliability (ICC(2,k) 0.854 to 0.919). Recorded pass/fail agreement was fair (kappa 0.297 to 0.388) and critical-failure agreement near zero for two skills (kappa 0.028, 0.119). AI alignment tracked rubric observability rather than task complexity: r = 0.857 (collar, exceeding every human), -0.173 (BVM), 0.664 (medical), and it was most lenient on two skills. Conclusions. Human evaluators are an imperfect standard, especially on critical failures. The AI was a legitimate additional rater where checklist items were discrete and visually verifiable, but not where credit required judging continuous quantities such as ventilation rate, volume, or suction duration. Defensible uses are formative and archival, not summative. These results reflect an early, non-specialist configuration: a baseline, not a limit.

Urbanization is reshaping freshwater ecosystems, with well-documented effects across gradients of land-use change, hydrologic alteration, and habitat degradation. However, how biodiversity is organized among neighboring urban aquatic habitats that differ in hydrologic connectivity, disturbance transmission, residence time, management history, and opportunities for species movement is often less clear. This creates a challenge for interpreting urban fish communities at local scales as species occurrence may reflect both contemporary habitat filtering and historical contingencies including native persistence, interbasin transfer, stocking, and nonindigenous introductions. Here we use eDNA detections, historical records, phylogenetic information, and species trait data to investigate the fish assemblages of the Charlotte metropolitan region. We detect a highly mixed fauna that also depicts a strong signature of structured biodiversity profiles across taxonomic, phylogenetic, functional, and life-history dimensions between habitat types. In particular, bounded habitats contained assemblages with larger-bodied species that are fecund and faster to reproduce relative to free-flowing habitats. Species-level occurrence models did not support a simple trait-by-habitat rule. Instead our results demonstrate that urban aquatic habitats can sort historically mixed regional species pools into predictable assemblage-level life-history profiles while simultaneously retaining signatures of evolutionary and historical biogeographic contingency.

Study Objective Little is known about where in hospital care for multiple long-term conditions (MLTC) is delivered. We aimed to describe pathways of care (ward transfers) and outcomes for people admitted to hospital for unscheduled care by MLTC status and other key sociodemographic characteristics. Design and setting Analysis of routinely-collected electronic health records from a large acute UK hospital. Participants Adult unscheduled care admissions from 1st July 2018 to 30th June 2019. The presence of two or more of 59 long-term conditions was ascertained using ICD-10 codes from previous hospital discharges. Main outcome measures Markov state transition probabilities were derived for ward moves and compared for MLTC vs no MLTC, age, sex, ethnicity and neighbourhood deprivation. Outcomes (length of stay, death, readmission, move from definitive ward) and time spent in emergency and assessment departments were compared between subgroups. Results A total of 33,252 adults, mean age 56.0 (SD 21.9) years were analysed; 14,834 (42.4%) had MLTC. People with MLTC were more likely to die in hospital (4.2 vs 1.9%, p<0.001), transfer to internal medicine wards or older peoples medicine wards, were less likely to transfer to surgical wards, had longer median length of stay (1.83 vs 0.69 days, p<0.001), stayed longer in acute medical units (15.5 vs 9.6 hours, p<0.001), and were more likely to move from their definitive ward (18.2 vs 16.4%, p=0.002). Conclusion Unscheduled hospital care pathways are complex and differ for people with MLTC, who have worse outcomes and may be less likely to receive optimal care.

High-phenolic plant genera present a major technical limitation in genomic research. Standard extraction approaches that perform reliably across diverse flora often perform poorly when applied to recalcitrant taxa, producing low DNA yield and integrity incompatible with sequencing requirements. The genus Anigozanthos (Kangaroo paws) from the family Haemodoraceae exemplifies this problem. We identified key physicochemical factors governing extraction failure in this genus and resolved them through targeted modifications to lysis chemistry and contaminant management. The resulting protocol achieved a near threefold improvement in DNA purity, substantially reducing contaminant carry over and consistently yielded high-integrity, long DNA fragments (DIN > 7) across a diverse sample set spanning cultivated and wild material across four diverse genera of Haemodoraceae. We also tested a straightforward purity assessment framework that can be implemented in any standard molecular laboratory, enabling rapid pre-submission quality assessment without the need for specialised equipment. Together these advances open a practical path to genomic characterisation of Anigozanthos that establishes a transferable model for genomic research across Australia ' s chemically complex native flora.

The ocean tides influence key Earth system processes at a range of spatial and temporal scales. It is known that the geometry of ocean basins is the leading controller of tidal energetics, so well-constrained palaeogeographic reconstructions and tidal properties for Earths past are imperative when investigating other Earth system processes. Here, we present a novel way to constrain both deep-time tidal model results and reconstructions, by combining palaeoecology with sedimentology. We compare new palaeo-tidal model simulations for the Cambrian period, significant for the early origin and radiation of major animal fauna, to tidal proxies. One of the most abundant soft-bodied organisms preserved during this time are cnidarian medusae (jellyfish). A total of 17 cnidarian medusae localities were obtained through the literature, which had an adequate global distribution and occurred at regular intervals throughout the period of study. In some locations there were also estimates of palaeo-tidal range. Our results show a good agreement between the simulations and proxy data. In the few locations where there is disagreement, it is proposed that the palaeogeographic reconstructions are missing details, e.g., island chains, and our results allow for the palaeogeographic reconstructions to be improved. The proxy method presented is promising and can be applied to other time-periods with different marine fossils, particularly at evolutionary and extinction periods where the marginal marine environment is of importance.

Context Pumping stations pose a threat to fish globally through land use change, habitat fragmentation and entrainment risk, with the catadromous and critically endangered European eel particularly impacted. Objectives/methods Establish, model, assess and understand the present-day distribution of European eel and resident fishes in 152 pumping station catchments in a once extensive wetland (The Fens) using eDNA metabarcoding (855 samples over two and half years), with specific focus on anthropogenic influences on hydrological connectivity and habitat quality. A removal survey design maximised confidence in negative results while minimising time and consumable costs. Results Eel occurrence upstream of pumping stations was low (occupancy = 28.3%) and positively associated with catchment area, fish species richness and natural hydrological connectivity (gravity drainage or flooding) and negatively associated with distance from the tidal limit. Fish species richness replaced catchment area and improved model performance, potentially acting as a biotic indicator of habitat quality and connectivity. Pumped catchments with manually operated upstream water transfers had reduced eel presence, potentially linked to the direction of water flow or the timing of operation. By contrast, fish species richness increased in these catchments during summer, suggesting displacement into unsuitable long-term habitats. Physical habitat maintenance had no detectable effect on eel occurrence or fish species richness. Conclusions This study provides the first landscape-scale assessment of European eel distribution and drivers of occurrence in pumped river catchments. The highly novel and comprehensive insights have implications for European eel conservation as well as infrastructure and catchment management, including compliance with legislation (EC Regulation No. 1100/2007).