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Open-population spatial capture–recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free‐roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals. However, the use of HMMs to explore the population‐level consequences of movement is often limited by model complexity and insufficient sample sizes. Here, we introduce an alternative approach to current practices and provide evidence of how the inclusion of prior information in model structure can simplify the application of HMMs to multiple animal movement paths with two clear benefits: (a) consistent state allocation and (b) increases in effective sample size. To demonstrate the utility of our approach, we apply HMMs and adapted HMMs to over 100 multivariate movement paths consisting of conditionally dependent daily horizontal and vertical movements in two species of demersal fish: Atlantic cod (Gadus morhua; n = 46) and European plaice (Pleuronectes platessa; n = 61). We identify latent states corresponding to two main underlying behaviors: resident and migrating. As our analysis considers a relatively large sample size and states are allocated consistently, we use collective model output to investigate state‐dependent spatiotemporal trends at the individual and population levels. In particular, we show how both species shift their movement behaviors on a seasonal basis and demonstrate population space use patterns that are consistent with previous individual‐level studies. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and monitoring of marine fish populations. Our approach provides a promising way of adding value to tagging studies because inferences about movement behavior can be gained from a larger proportion of datasets, making tagging studies more relevant to management and more cost‐effective. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and the monitoring of marine fish populations. One criticism of tagging studies is that they often lack in sample size, limiting a researcher's ability to ask population‐ and management‐level questions of their data. By introducing a novel adaptation to the behavioural classification of movement observations, we demonstrate how researchers can use a combination of data‐rich and data‐poor movement paths to infer population‐level space use patterns, ultimately making tagging studies more cost‐effective and more relevant to management objectives.

The bigmouth buffalo Ictiobus cyprinellus is a long‐lived, migratory freshwater fish native to North America whose numbers are declining amidst increasing conservation concerns. Recent studies have uncovered long lifespans, delayed maturation, and episodic recruitment of bigmouth buffalo. Building from previous work in the Qu'Appelle watershed of Saskatchewan, here we quantify otolith‐derived population demographics of bigmouth buffalo and invasive common carp Cyprinus carpio across multiple sites in the drainage. The common carp ( n = 125) and bigmouth buffalo ( n = 173) collected from 2018 to 2024 reveal that common carp reach asymptotic size two times faster, live three times shorter lives, and invest significantly more into reproduction while also exhibiting recruitment stability during the water control era (post‐1958). Indeed, invasive common carp now outnumber native bigmouth buffalo in this watershed by at least an order of magnitude. In contrast, only a single year class (1997) was evident for bigmouth buffalo after 1949. Therefore, only one recruitment year was evident for this species since common carp were first detected in the system in 1955. Remarkably, we find that as of 2024 more than 90% of bigmouth buffalo in this system are greater than 75 years old with a known maximum age of 148 years. We now know that the bigmouth buffalo is the 11th longest‐lived vertebrate out of more than 66,000 species, and across diverse systems can have recruitment gaps longer than any other animal. Bigmouth buffalo require immediate conservation reassessment amidst ongoing population declines.

Objectives Optimal dementia risk reduction requires a combination of individual‐ and population‐level approaches. Societal stakeholders play a crucial role by raising awareness, supporting individual lifestyle change, and/or influencing certain risk factors through policy changes. This study aimed to identify relevant societal stakeholders for promoting dementia risk reduction, and explore perspectives regarding their role. Methods We used a qualitative approach with participatory research elements (i.e., collaborating with stakeholders in the research). An advisory panel of citizens (n = 14) was installed to provide input on various study aspects (e.g., study design and interpretation of findings). Thereafter, data collection involved two phases: 1) identification of potentially relevant societal stakeholders (based on advisory panel discussions, a conference workshop, and online searches); and 2) exploration of perspectives of participants from selected stakeholder domains, through 18 interviews and one focus group (total N = 32). We analysed data using thematic analysis. Results Phase 2 revealed that participants, such as religious leaders, labour service employees and board members of student associations, had limited knowledge and experienced little responsibility to act as a societal stakeholder in the context of dementia risk reduction. Rather, they called for policy and regulations to make dementia risk reduction efforts obligatory and a public priority. Participants recommended incorporating information on dementia and dementia risk in general health campaigns, rather than organising dementia‐specific campaigns, and stressed the need to stimulate dementia risk reduction early in life. Conclusions Effective dementia risk reduction could benefit from increased stakeholder involvement, as well as imposed policy‐level risk reduction measures. Our findings also highlight the importance of including dementia in education and healthy lifestyle programmes from an early age. Future studies are needed to validate our findings on a larger scale, and among different stakeholders. Patient or Public Contribution Citizens were involved in study conceptualisation and design, and in the interpretation, reporting and dissemination of findings.

Poisoning from lead fishing tackle has been identified as the leading cause of mortality in adult common loons (Gavia immer). As a K-selected species, adult survival is a critical component in the population demography of loons, but the population-level effects of mortality from ingested lead tackle on loons have not been quantified. We used a long-term dataset (1989–2012) on common loon mortality in New Hampshire, USA, to describe the types of lead tackle ingested by loons, investigate methods of ingestion of lead tackle, document the number and rate of adult mortalities resulting from lead tackle, and test for a population-level effect of lead tackle on the loon population in New Hampshire. Nearly half (48.6%) of collected adult mortalities resulted from lead toxicosis from ingested lead fishing tackle, representing an adjusted annual mortality rate of 1.7 ± 0.6% (SD) of the statewide population. Jigs accounted for 52.6% and sinkers for 38.8% of the archived lead tackle objects removed from loons, a higher proportion of jigs than has been reported in previous studies. The timing of lead tackle mortalities and a high incidence of accompanying non-lead associated fishing gear (hooks, fishing line, leaders, swivels, wire), which peaked in July and August, suggest that loons obtain the majority of lead tackle from current fishing activity rather than from a reservoir of lead tackle on lake bottoms. To project the statewide loon population in the absence of lead fishing tackle as a stressor, we constructed a retrospective population model, which re-inserted loons that died from lead tackle into the population, and used linear regression to test for a population-level effect. We defined a population-level effect as a difference in the population growth rate (λ). We estimated that lead tackle mortality reduced the population growth rate (λ) by 1.4% and the statewide population by 43% during the years of the study. This study suggests that replacing lead fishing sinkers and jigs weighing ≤28.4 g with non-toxic alternatives would result in an immediate benefit to the loon population in New Hampshire.

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used three approaches to investigate the potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine‐caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or from a matrix model parameterized from literature‐derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management objectives. Different results among the methods reveal substantial gaps in knowledge and uncertainty in both demographic parameters and species‐specific estimates of fatalities from wind turbines. Our results suggest that, of the 14 species studied, those with relatively higher potential of population‐level impacts from wind turbine collisions included barn owl, ferruginous hawk, golden eagle, American kestrel, and red‐tailed hawk. Burrowing owl, Cooper’s hawk, great horned owl, northern harrier, turkey vulture, and osprey had a relatively lower potential for population impacts, and results were not easily interpretable for merlin, prairie falcon, and Swainson’s hawk. Projections of current levels of fatalities to future wind energy scenarios at 241 GW of installed capacity suggest some species could experience population declines because of turbine collisions. Populations of those species may benefit from research to identify tools to prevent or reduce raptor collisions with wind turbines.

Background Colorectal cancer (CRC) is one of the most common cancers worldwide. Comprehensive data on the economic burden of CRC at a population‐level is critical in informing policymaking, but such data are currently limited in China. Methods From a societal perspective, the economic burden of CRC in 2019 was estimated, including direct medical and nonmedical expenditure, disability, and premature‐death‐related indirect expenditure. Data on disease burden was taken from the GBD 2019 and analyzed using a prevalence‐based approach. The per‐person direct expenditure and work loss days were from a multicenter study; the premature‐death‐related expenditure was estimated using a human capital approach. Projections were conducted in different simulated scenarios. All expenditure data were in Chinese Yuan (CNY) and discounted to 2019. Results In 2019, the estimated overall economic burden of CRC in China was CNY170.5 billion (0.189% of the local GDP). The direct expenditure was CNY106.4 billion (62.4% of the total economic burden), 91.4% of which was a direct medical expenditure. The indirect expenditure was CNY64.1 billion, of which 63.7% was related to premature death. The predicted burden would reach CNY560.0 billion in 2030 given constant trends for disease burden; however, it would be alternatively reduced to

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