Royal Society Open Science

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts is essential, as intracranial neuroscience guides the application of artificial intelligence for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

We present the first radiographic ageing framework for common dolphins (Delphinus delphis), based on ossification and epiphyseal fusion patterns in the pectoral flipper, demonstrating higher reliability for chronological age estimation than currently available epigenetic approaches for this species. Using individuals of known dental age, we calibrated two modelling approaches to predict dental age from radiographic bone scores: 1) a univariate polynomial regression using a total bone score (sum of 16 scores across all assessed flipper bones), and 2) a multivariate canonical analysis of principal coordinates (CAP) incorporating 16 individual bone-score variables. Both approaches successfully predicted dental age from skeletal ossification patterns. For an age range of 0 to 24 years, polynomial regression demonstrated high predictive accuracy with median absolute errors (MAEs) of 1.25 years in females (Spearmans {rho} = 0.93, R{superscript 2} = 0.90) and 1.08 years in males ({rho} = 0.95, R{superscript 2} = 0.86). The CAP model yielded MAEs of 1.35 years in females ({rho} = 0.90, R{superscript 2} = 0.85) and 1.80 years in males ({rho} = 0.94, R{superscript 2} = 0.84). Notably, both radiographic bone ageing models achieved equal or lower median absolute errors and higher coefficients of determination than a recently developed epigenetic clock for common dolphins derived from the same population (MAE = 1.80, Pearsons correlation (r) = 0.91, R{superscript 2} = 0.82). When applying the bone ageing models to individuals of unknown dental age, both models produced age estimates consistent with expected life-history stages (foetus, neonate, juvenile, subadult, adult), although accuracy declined in dolphins above 20 years, likely as a consequence of subtle age-related variation in skeletal changes in this species. Radiographic ageing provides an accurate non-invasive tool for demographic assessment to support conservation management of common dolphins.

Dental morphology and wear patterns provide insight into the dietary adaptations and ecological niches of living and extinct herbivores. Traditional classification statistics such as Linear Discriminant Analysis (LDA) are limited by assumptions of linearity, normality, and homoscedasticity. This study quantifies mesowear, the shape of molar cusps resulting from occlusal wear, and evaluates the performance of non-linear machine learning models in predicting herbivore diets based on geometric morphometric (GMM) data from adult mandibular second molars (M2) in bovids. We applied Generalized Procrustes Analysis and Principal Component Analysis (PCA) to digitized occlusal shape coordinates from 132 M2 specimens across 64 species. Using the resulting principal component scores, we compared the classification accuracy of LDA with three non-linear models: Random Forest, K-Nearest Neighbors, and Gradient Boosting. While LDA achieved a cross-validated accuracy of just 31%, all non-linear models achieved 99% cross-validation accuracy and 90% test accuracy, demonstrating substantially improved performance. Misclassification analyses revealed that non-linear models more effectively captured complex shape differences, particularly among species with overlapping wear patterns. Our findings support the integration of machine learning with geometric morphometrics to quantify mesowear and improve dietary classification, providing a framework for robust paleoecological inference.

Background: Peak height velocity (PHV) is a critical indicator of pubertal growth timing and is widely used in orthodontics to determine optimal timing for growth modification interventions. Secular trends toward earlier maturation have been reported, but a quantitative synthesis of PHV age reduction across generations is lacking. Objective: To systematically review and quantitatively synthesize evidence for secular trends in age at PHV and to estimate the pooled mean difference in PHV age between historical and contemporary cohorts. Methods: A systematic search was conducted in PubMed and Google Scholar from January 1990 to December 2021. The Directory of Open Access Journals (DOAJ) was also searched but yielded no eligible studies due to the specificity of the search string. Studies were included if they reported age at PHV in two or more birth cohorts separated by at least 20 years, used objective methods to determine PHV (longitudinal growth data with curve fitting), and reported means with standard deviations or standard errors. Risk of bias was assessed using the Newcastle-Ottawa Scale. A random-effects quantitative synthesis (meta-analytic approach) was performed to calculate the pooled mean difference in PHV age between historical and contemporary cohorts. Between-study variance (tau-squared) was estimated using the restricted maximum likelihood (REML) method. Heterogeneity was assessed using I-squared statistics. Given the limited number of eligible studies, findings should be interpreted as preliminary. Results: Two high-quality longitudinal studies met inclusion criteria, comprising 171 participants from historical cohorts (1969-1973) and 71 participants from contemporary cohorts (1996-2000). The pooled mean difference in PHV age was -0.48 years (95% CI: -0.72 to -0.24, P < 0.001), indicating that contemporary children reach PHV approximately 0.5 years earlier than their historical counterparts. PHV velocity showed a pooled increase of 0.71 cm/year (95% CI: 0.48 to 0.94, P < 0.001). Heterogeneity was low (I-squared = 0% for both analyses). Both studies were rated as low risk of bias. These findings are based on a limited number of studies and should be interpreted as preliminary. Conclusions: This preliminary quantitative synthesis provides evidence of a secular decline in age at peak height velocity of approximately 0.5 years in contemporary children compared to historical cohorts, accompanied by an increase in growth velocity. These findings suggest that orthodontic growth modification strategies may need to be initiated earlier than traditionally recommended. However, given the limited evidence base, results should be interpreted with caution and require confirmation in large-scale longitudinal studies.

This study develops a methodological framework that combines conventional antimicrobial susceptibility testing with Particle Swarm Optimisation (PSO) to enhance toothpaste formulations, employing Escherichia coli isolated from the oral cavity as a model organism. We used the agar well diffusion method to see if two fluoride toothpastes (Oral B and My-my) could kill oral E. coli isolates at 6.25%, 12.5%, 25%, 50%, and 100% concentrations. A surrogate Random Forest model was created using these experimental data to link formulation parameters to antimicrobial activity. Then, PSO was used to find the best formulation traits. Multi-objective optimisation that looks at the trade-offs between antimicrobial effectiveness and cytotoxicity was shown as a conceptual framework. Both toothpastes showed antimicrobial activity that depended on the concentration, with Oral B being more effective (23.0 mm at 100% concentration) than My-my (20.0 mm). The PSO framework, utilised as a methodological illustration while explicitly recognising data constraints, determined hypothetical formulation parameters (sodium fluoride 1100 ppm, hydrated silica abrasive, 2.5% SLS) with an anticipated zone of inhibition of 26.3 mm. These predictions are mathematically optimal for a surrogate model that was trained on very little data (n=10 formulation points). They need a lot of experimental testing before any claims about the formulation can be made. This work is presented as a proof-of-concept methodological framework, not as validated formulation guidance.

Movement is costly, and animals are under strong selective pressure to move efficiently, yet, in patchy, dynamic landscapes, decision-making is inherently uncertain. We quantify the energetic savings achieved by using up-to-date information presented within social cues for reducing movement costs. We use an agent-based model, founded on realistic aeronautical rules and parametrised on the Andean condor (Vultur gryphus), to study movement in patchy landscapes. By explicitly considering altitude, flight results in a sequence of soaring and gliding in the 3D space. We investigate how the cost of movement to an overall goal varies when birds use social information from others that are either fixed in space or moving collectively to the common goal, and under different risk-taking speed strategies, from slow and cautious to fast and risky. The value of social information is operationalised as energetic savings in units of basal metabolic rate. Under low predictability, agents with intermediate risk and high social-information use exhibit lowest movement costs, with up to 41% energy savings over asocial movement. By extending classical aeronautical theory to social and variable environments we demonstrate the adaptive value of social information for efficient movement in patchy, unpredictable landscapes.

The basic reproduction number of an infectious disease is known to depend on the structure of contacts between individuals in a population. This relationship has been explored mathematically through two well-known models: one which depends on a matrix of contact rates between different demographic groups, and another which depends on the variability of contact rates over the population. Here we introduce a model that combines and generalises these two approaches. We derive a formula for the basic reproduction number and validate it through comparisons to simulated outbreaks. Applying this method to contact survey data collected in Belgium between 2020 and 2022, we find that our model produces higher estimates of the basic reproduction number and larger relative changes over periods when social contact behaviour was changing during the COVID-19 pandemic. Our analysis suggests some practical considerations when using contact data in models of infectious disease transmission.

Drumming--rhythmic, percussive sound production using body parts or external objects--is rare among non-human animals, with confirmed tool-assisted cases previously limited to primates and Palm Cockatoos. Here, we report the first documented instance of spontaneous, tool-assisted drumming in a Galah (Eolophus roseicapilla). A captive, male Galah produced rhythmic tapping by striking a coconut shell against a metal bowl. Across 14 recorded sessions, the bird displayed consistent temporal structure characterised by two stable tapping rates (approximately 0.8 s and 0.2 s inter-onset intervals) arranged into recurring phrases. This pattern indicates a simple hierarchical rhythmic organisation with a 4:1 ratio between metrical levels. The birds behaviour emerged without training, apparent reinforcement, or known exposure to conspecific or human drumming models, suggesting an intrinsic capacity for rhythmic tool use. Although the function of the behaviour remains unclear--play, nutrient extraction, or communicative signalling--these observations extend known rhythmic and tool-using abilities within cockatoos and raise new evolutionary questions. Our findings highlight the potential for rhythmically structured, instrumental behaviour to arise in a broader range of avian taxa than previously recognised, motivating further comparative and experimental work on the cognitive and biomechanical foundations of drumming in parrots.

Infectious disease dynamics are strongly shaped by human mobility, social structure, and heterogeneous contact patterns, yet many epidemic models do not jointly capture these features. This study develops a spatial metapopulation epidemic model incorporating recurrent group-switch interactions to represent real-world transmission processes. Building on the Movement-Interaction-Return framework, the model integrates household structure, age-stratified contacts, and mobility between locations within a single SEIR framework. Using UK demographic, mobility, and social contact data, the model quantifies how within- and between-group interactions, mobility rates, and location connectivity influence epidemic spread. Both deterministic and stochastic simulations are implemented to analyse outbreak dynamics, variability, and fade-out probabilities for COVID-19-like and Ebola-like infections. Results shows that highly connected locations drive faster transmission, earlier epidemic peaks, and greater difficulty in containment, whereas larger but less connected locations tend to produce slower, more localised outbreaks despite their population size. Comparative analysis reveals that COVID-19-like infections spread rapidly and remain difficult to control even under interventions, while Ebola-like infections exhibit slower dynamics and are more effectively contained, particularly under targeted measures. Non-pharmaceutical interventions, particularly widespread closures, substantially reduce infections, hospitalisations, and deaths, although effectiveness depends on timing and pathogen characteristics. These findings highlight the importance of integrating mobility, clustering, and demographic heterogeneity to inform targeted and effective epidemic control strategies.

Objectives: Accurate assessment of orthognathic surgical accuracy is essential in the evaluation of operative techniques. Surgical accuracy is often reported as rotational and translational deviations from planned positions. This results in 6 separate values, translation in three planes, anterior-posterior (AP), superior-inferior (SI) and medial-lateral (ML) and rotations about three axes, pitch, roll and yaw. However, rotations will influence 3-dimensional positions and translational discrepancies. Methods: We have derived a mathematical formula using Euclidean geometry and quadratic functions that quantifies the impact of rotations on translational discrepancies. This allows for the calculation of a total discrepancy value that incorporates the three translations and rotations. Furthermore, we developed an interactive web-based application using the open-source shiny R package. Results: We have successfully reduced equations from Euclidean geometry into a quadratic form. The equation is as follows, [4(sin{theta}/2)2-2]x2 + [8d(sin{theta}/2)2-2d]x + 4d2(sin{theta}/2)2 = 0, where {theta} represents the rotational discrepancy in radians and d represents the translation discrepancy. This allows us to solve for the correction needed to be made to translational discrepancies to account for the influence of rotational discrepancies. We successfully developed a web application with a user-friendly graphical user interface. Clinicians upload their own data in the excel (.xlsx) file format and the application automatically performs the necessary calculations over many patients, returning a downloadable table of results. Conclusion: We present a mathematical formula incorporated into a web-application to combine translational and rotational discrepancies for deeper insight when evaluating orthognathic surgical accuracy. Clinical Relevance: This allows surgeons to account for rotational influence on 3-dimensional translational discrepancies.

We developed an agent-based model (ABM) to assess how area-based controls on fishing methods can reduce fishing mortality and population declines. The model incorporates the behavior and distributions of dolphins and fishing vessels, and realistic displacement of fishing effort when protection is extended. Our case study is New Zealand dolphin - Hectors and Maui dolphins. The model was designed and calibrated using pattern-oriented modeling. Our results show that mortality due to entanglement in fishing gears has been reduced thanks to a gradual increase in dolphin protection. However, current protection is not as effective as previously thought, and scarce populations are negatively affected by Allee effects. Neither national nor international goals for reducing bycatch are met by current dolphin protection. The IUCN has recommended banning gillnet and trawl fisheries in New Zealand waters < 100m deep. For most New Zealand dolphin populations, this would be effective in achieving national and international goals for reducing bycatch. Only two populations would require additional protection. This modelling approach is also suitable for assessing impacts of bycatch and ship strikes for other marine species, making it suitable for informing management decisions in many regions.

Social living often confers substantial fitness benefits; however, close spatial association among individuals can also elevate opportunities for pathogen transmission, especially where the populations are dense. Despite this, the extent to which avoidance behaviours are expressed by wild reptiles facing fungal disease remains unclear. We examined Eastern Water Dragons (EWDs; Intellagama lesueurii) in Roma Street Parklands, Brisbane, Australia, where a population is affected by the emerging fungal pathogen Nannizziopsis barbatae (Nb). Using a five-year dataset (2018-2023) spanning 146 individuals, we quantified social distance, as the minimum distance to the nearest neighbour, in relation to the number of diseased conspecifics that overlapped each individuals seasonal core home area. Social distance decreased as diseased conspecifics became more numerous, indicating a strong crowding effect; however, this reduction was weaker for diseased individuals, which maintained larger distances than healthy individuals even under high disease pressure. Together, these patterns support partial social avoidance consistent with behavioural changes in infected individuals, suggesting that infection risk constrains density-driven proximity. Our findings provide new insights into how disease pressure shapes social spacing in reptiles and contribute to a broader understanding of behavioural responses to emerging infectious fungal diseases.

Venom is an important functional trait that helps predatory animals capture prey. Centipede predatory venoms are complex cocktails of multiple proteins, such as neurotoxins (scoloptoxins), cytotoxins, {beta}-pore-forming toxins, and enzymes. We examined venom phenotypes in two closely related and co-occurring centipede species, Scolopendra morsitans (n=28) and S. hardwickei (n=11), in peninsular India to determine whether their venoms are similar or dissimilar. An integrated proteo-transcriptomic approach was used to characterise the venom phenotypes of the two species across multiple individuals in peninsular India. We used species occurrence records and species distribution models to assess the distributional overlap among these species within the peninsular Indian region. The species showed significant overlap in their current and projected geographical ranges, corresponding with their co-occurrence. We characterised the venom profiles of both species and found that the venoms were cocktails of enzymes, {beta}-pore-forming toxins, and neurotoxins comprising 110 and 84 proteins in S. morsitans and S. hardwickei, respectively. However, the venom composition of both species differed significantly in toxin abundance and species-specific protein repertoires. This indicates trait divergence in venom phenotypes, suggesting that distinct venom compositions may facilitate coexistence among ecologically similar predatory centipedes. The observed variation in venom phenotypes among co-distributed species opens up important avenues for future research into their ecological roles and functional significance. In this study, we provided a detailed account of venom composition across multiple individuals from the species geographic range and highlighted the importance of investigating the role of venom as a trait that could influence species interactions and shape communities in these diverse tropical forests.

Diatoms exhibit remarkable diversity in valve morphology, with the raphe system being a fundamental feature in classification of raphid pennate diatoms. The repeated loss of one of the two raphes during evolution has led to multiple independent origins of monoraphid diatoms. The phylogenetic affinities of the monoraphid genus Crenotia A. Z. Wojtal, erected from Achnanthidium thermale Rabenhorst, have not yet been clarified with molecular data. In this study, natural populations of Crenotia and monoclonal strains derived from them were examined using morphological observations and multilocus phylogenetic analyses based on nuclear and plastidial molecular markers. Three species of the genus Crenotia form a well-supported clade placed within a subgroup of monoraphid genera, which are closely related to Cymbellales D.G. Mann and other biraphid diatoms. This study establishes the first molecular framework for representatives of the genus Crenotia, demonstrating their monophyly and congruent interspecific relationships recovered with multiple molecular markers. The low intraspecific sequence variability and substantial interspecific divergence, together with clear morphological and ecological differentiation, support the recognition of the three investigated species.

Traditionally, primates have been considered primarily visual animals. However, studies across a variety of taxa suggest that, in the context of food evaluation, the reliance on this sense might be more nuanced that previously thought, with dietary specialization and food item properties leading to differences in sensory prioritization. We performed a field-based study assessing the use of sensory cues during food evaluation as well as food-related behaviours such as muzzle contact in two mixed-sex groups of wild vervet monkeys including three age classes over a period of five months (nmonkeys = 44). Using a total of 18868 food evaluation observations collected over 44 hours of focal follows, we found that vervets mainly relied on their sense of vision when evaluating food (96.8% of all instances). Sensory usage varied according to food category and sex differences were only observed in the use of smell for a subset of these. Juveniles initiated muzzle contact and used tactile inspection more often than adults whereas females received muzzle contact more often than males. In addition, the low rejection rates suggest that most food items were familiar to the vervets regardless of age and sex. These findings are in line with optimal foraging theory according to which the food evaluation process should be adapted to the familiarity of food items and allows individuals to maximize their intake of energy and critical nutrients, while minimizing the time and effort in food evaluation.

Tuberculosis (TB) remains a leading cause of death globally, with early mortality often driven by severe malnutrition and human immuno-deficiency virus (HIV) co-infection. Traditional survival analyses identify risk factors but remain associative, failing to capture the dynamic physiological collapse preceding death. In a novel interdisciplinary adaptation, we applied the Merton jump-diffusion structural framework from quantitative finance to model survival as a state of biological solvency, in which mortality occurs when a stochastic health trajectory crosses a critical failure threshold. We analysed a retrospective cohort of 15,182 TB patients in Cameroon over two decades. Adjusted body mass index (BMI) was conceptualized as a proxy for health capital and modeled using a stochastic process accounting for individual recovery trends, physiological instability, and acute clinical shocks. The study included predominantly young adult males (median age: 33 years) with a median BMI of 20.7 kg/m2. HIV co-infection was present in 35% of patients. The overall mortality rate during the 240 days follow-up period was 7.0%, with 55.1% of deaths occurring within the first 30 days. The model identified a critical failure threshold at BMI 17.329 kg/m2. HIV co-infection emerged as a key driver of metabolic instability, significantly increasing physiological volatility. Statistical validation confirmed that sudden clinical shocks were necessary to explain observed mortality patterns. The resulting Distance-to-Death (DtD) metric slightly outperformed standard associative extended Cox models in predicting survival, achieving a higher discriminative ability in testing set (Harrells C-index: 0.781 vs. 0.772; p = 0.038). Patients stratified into the highest-risk category showed a mortality rate of 16.7%, compared with 1.6% in the most stable group.This study bridges financial engineering and clinical epidemiology, offering a mechanistic understanding of how physiological reserves and metabolic instability determine survival. To support clinical application, we developed an interactive digital triage tool enabling identification of high-risk patients in resource-limited settings. Author summaryTuberculosis remains a major cause of death worldwide, particularly in people with poor nutrition or co-infection with HIV. In this study, we explored a new way to understand why some patients survive while others do not. We adapted a method originally used in finance to track the "health reserves" of patients over time, using body weight and related measures to estimate how close someone is to a critical health threshold. Our approach captures both gradual health decline and sudden medical complications, such as severe infections or rapid deterioration. By applying this method to a large group of patients in Cameroon, we found that a very low body weight is a strong warning sign for impending death and that HIV infection makes health outcomes less predictable. We also created a simple scoring tool that can help doctors identify patients at greatest risk, so that life-saving interventions and closer monitoring can be prioritized. This work bridges mathematical modeling and clinical care, offering a new way to assess patient vulnerability and improve outcomes in resource-limited settings.

Network-based epidemic models have been instrumental in understanding how contact structure shapes infectious disease dynamics, yet widely used frameworks such as Erd[o]s-Renyi, configuration-model, and stochastic block networks do not explicitly capture the combination of fully accessible (saturated) within-group interactions and constrained between-group connectivity characteristic of many real-world settings. Here, we introduce the Multi-Clique (MC) network model, a generative framework in which individuals are organised into fully connected cliques representing stable contact groups (e.g., households, classrooms, or workplaces), with a limited number of external connections governing inter-group transmission. Using stochastic susceptible-infectious-recovered (SIR) simulations on degree-matched networks, we compare epidemic dynamics on MC networks with those on classical random graph models. Despite having an identical mean degree, MC networks exhibit systematically distinct behaviour, including slower epidemic growth, reduced peak prevalence, increased fade-out probability, and delayed time to peak. These effects arise from rapid within but constrained between clique transmission, creating structural bottlenecks that standard models do not capture. The MC framework provides an interpretable, data-driven representation of recurrent contact structure, with parameters that map directly to observable quantities such as household and classroom sizes. By isolating the role of intergroup connectivity, the model offers a basis for evaluating targeted intervention strategies that reduce between-group mixing while preserving within-group interactions. Our results highlight the importance of explicitly representing the real-life clique-based network structure in epidemic models and suggest that classical degree-matched networks may systematically overestimate epidemic speed and intensity in structured populations.

The hockey-stick (HS) stock recruitment relationship (SRR) has been widely used as an empirical alternative to conventional SRRs such as the Beverton-Holt (BH) and Ricker (RI) models. However, the management performance and risks associated with estimating maximum-sustainable-yield (MSY) reference points (RPs) based on HS remain insufficiently understood. This study first defines deterministic and stochastic MSY RPs under the HS model and provides an overview of their properties. We then conduct simulation experiments to investigate the bias and management consequences that arise when MSY RPs are estimated from the HS model (HS-derived MSY RPs) rather than from the true SRR (e.g., BH) across a range of biological and stochastic parameters, with particular focus on scenarios with insufficient data contrast. Our results show that HS-derived MSY RPs tend to exhibit higher bias but lower variance than MSY RPs derived from the true SRR. Management strategy evaluation simulations further reveal that management procedures combining HS-derived MSY RPs with adaptive model learning and some precautionary measures gradually reduce this bias and achieve average spawning biomass and yield that are comparable to those obtained under management based on the true BH SRR. We also show that the management effectiveness of the precautionary measures depends on life-history traits and recruitment variability. These findings indicate that although HS-derived MSY RPs may be biased and require cautious use, combining them with appropriate precautionary measures allows management to remain robust while limiting variability and yield losses. This broadens the range of management options that are available for supporting sustainable fisheries management.

Most cancers are initiated by mutations that inactivate tumour-suppressor genes or activate oncogenes. Fitting a multistage model of carcinogenesis to the increase in cancer mortality with breed-specific size and lifespan in dogs has predicted that four somatic driver mutations are typically required to initiate cancer. This result is reconsidered here because it depends on the relationship between the number of at-risk cells and breed weight. Using a power function for this relationship results in higher quality models that support single driver mutations activating oncogenes. In addition, parameter estimates suggest that somatic mutation rates increase with weight, likely because of reduced investment in somatic maintenance. Regression of cancer mortality on body weight and lifespan shows that 56% of cancers in dogs are the result of mutations arising from somatic cell division, compared to 66% in humans. A further 7% of cancers may be due to inherited recessive mutations deactivating tumour suppressor genes, as indicated by the relationship between cancer mortality and breed germline homozygosity. Some of the remaining unexplained variation in cancer mortality may be explained by germline mutations underlying breed predispositions to specific cancers. Contrasting results with humans provide novel insights into the dynamics of carcinogenesis.

BackgroundIn hibernating mammals, the timing of den entry and exit reflects complex interactions among environment, physiology, and energetic constraints, with consequences for fitness. Consequently, shifts in denning phenology can affect population dynamics, particularly under climate change. Reliable estimation of denning timing is therefore critical, yet current methods often rely on GPS-derived movement data, limited by coarse sampling intervals, detection issues, and the inability to distinguish true inactivity from active presence at the den site. In this study, we developed and apply a method to estimate denning phenology in a brown bear population in south-central Sweden using accelerometer-derived activity data. Our approach employs adaptive, individual-specific thresholds to account for variation in baseline activity across bears, focusing on day-to-day changes to identify the start and end of inactivity periods. This method allows flexible and reproducible detection of den entry and exit dates, overcoming limitations associated with fixed thresholds and small sample sizes. ResultsWe compared activity-based estimates with GPS-derived den occupancy and examined variation in denning behavior across demographic groups. Analyzing 388 bear-winters, the method successfully identified inactivity periods in 360 cases. The method failed to identify clear start and end dates of hibernation for 28 (7%) bear-winters, which were characterized by unusually high or low daily activity levels at the boundaries of the inactivity period. Den site occupancy ranged from September 5 to June 2, with durations of 112-260 days, whereas inactivity periods detected from activity data extended from September 6 to May 13, lasting 83-217 days. Our comparison of activity-based and GPS-based methods indicates that bears may arrive at the den site several weeks before the onset of inactivity, with timing varying among demographic groups. ConclusionWe show that activity-based analysis provides a robust framework for estimating denning phenology, distinguishing actual inactivity from site presence, and improving understanding of the timing and variability of bear denning behavior. Applying an individual-level activity-based method improves accuracy in assessing ecological mechanisms underlying hibernation in bears and other hibernators, while also enhancing interpretation of environmental drivers and providing a reliable tool to monitor phenological shifts in response to climate change.