Parks Canada

The growing prevalence of dementia highlights the need for a risk assessment tool that is accessible, facilitates the identification of at-risk individuals, and provides evidence-based guidance on how to reduce dementia risk. We developed, deployed, and evaluated the MoCA-CHAI, a self-administered, online dementia risk assessment for the general public. We provide a brief overview of its development, the self-enrolled population that has completed it, and a preliminary evaluation of its predictive performance. Drawing on the 2024 Lancet report, we developed the MoCA-CHAI translating the 14 identified risk factors into a questionnaire. We used the MoCA XpressO as a measure of cognitive impairment and a proxy measure for the probability of having or developing dementia. The MoCA-CHAI was completed by 3886 people. Based on their XpressO score, 11.3% showed a high probability of cognitive impairment. Using a logistic regression analysis, we found that each 1-point increase in MoCA-CHAI score decreases the odds of having cognitive impairment by 1%. Physical inactivity and exposure to air pollution are the most prevalent risk factors across the lifespan. Depression is more prevalent in young adults, while high cholesterol, hypertension, diabetes, and excessive alcohol consumption are more prevalent in middle-aged and older adults. These findings demonstrate that the MoCA-CHAI provides insight into modifiable lifestyle factors and dementia risk. Differences in prevalences of risk factors indicate that prevention strategies need to be tailored to age-specific risk profiles. The MoCA-CHAI may help identify at-risk individuals who could benefit from targeted prevention and monitoring.

Abstract The number and distribution of animals in space form the basis of many wildlife studies. Yet, reliable estimation of population abundance remains challenging. Generalized spatial mark‐resight (gSMR) models are widely applicable abundance estimators that do not require all individuals be uniquely identifiable. Despite their flexibility, gSMR models assume independence in distribution and detections of individuals throughout space. Group‐living species that aggregate (i.e. share activity centres) and move cohesively (i.e. share detections) violate these assumptions, limiting the applicability of gSMR models to solitary species with limited home range overlap. We developed a gSMR model that accounts for group associations to estimate spatial abundance. We treated groups as the measurable ‘units’ and included a submodel that uses Poisson processes to estimate group size. We conducted a simulation study to compare our novel group‐based gSMR to a gSMR that ignores group associations. We generated data across varying levels of aggregation and cohesion parameters. We then quantified and compared the precision and bias of parameter estimates between the two models. The group‐based gSMR model outperformed the individual‐based gSMR under all aggregation and cohesion scenarios. Group‐based 95% Bayesian Credible Intervals (BCI) overlapped the true value of abundance in 91% of simulations, compared to only 15% of simulations in the individual‐based gSMR. Both the group‐based and individual‐based models became less precise and more biased as aggregation and group size increased. However, the group‐based gSMR model was less biased in estimating all parameters when compared to the individual‐based gSMR model. We applied our group‐based gSMR model to grey wolf ( Canis lupus ) monitoring data within a Canadian national park complex (Banff, Yoho and Kootenay National Parks, and Ya Ha Tinda). We used 12 years (2012–2023) of remote camera data, along with information from GPS radio‐collared wolves ( N = 16) to estimate spatiotemporal variation in abundance. Over a 12‐year period, wolf abundance averaged 73 (95% BCI = 20–207) individuals, and a density of 6.69 (95% BCI = 1.83–19.0) wolves per 1000 km 2 within the park complex. Our novel group‐based gSMR model extends the applicability of spatial mark‐resight to species with group associations.

A passive air monitoring network was established in the Canadian Arctic and Great Lakes Basin (GLB) in 2015 as a pilot study to assess the spatial distribution of contaminants. At each site, a XAD-based passive air sampler (PAS) and a polyurethane form-based PAS were deployed. Results from 2015 to 2022 showed that hexachlorobutadiene (HCBD), a highly volatile organic chemical (HVOC), is the most abundant and widespread chemical in air. Other frequently detected chemicals were hexachlorobenzene (HCB), di- and tri-bromoanisoles (BAs), and 1,2,4,5-tetrachloro-3,6-dimethoxybenzene (DAME). Air concentrations of HCB, BAs, DAME, pentachlorobenzene, and α-hexachlorocyclohexane in the Arctic and GLB air were similar. These chemicals are highly volatile and thus easily disperse in air. On the contrary, semi-VOCs such as polychlorinated biphenyls, chlordanes, and dichlorodiphenyldichloroethylene showed higher atmospheric concentrations in the Great Lakes Basin than the Arctic. Urban air showed elevated levels in organophosphate esters and neutral per- and polyfluorinated substances in comparison with Arctic air. These chemicals are mostly related to industrial activities and consumer products. Their atmospheric levels are positively related to urbanization. At all the sites, there were no consistent temporal trends among the chemicals. Our pilot study has established the protocols for sampling and analytical methods for a long-term monitoring network at difficult-to-access remote environments providing spatially resolved contaminant levels and temporal trends.

Neurological conditions are the leading cause of illness and disability in the world today, yet their impacts are experienced differently by different individuals and communities across different geographic spaces. Often treated as an add-on or “footnote” in brain health solutions, this chapter explores the potential of community-based interventions not only to improve the lives of those affected by brain health challenges but also to address inequities in brain health. Without a full understanding of the role of community in brain health care, it is difficult to develop a theory of change for ‘integrated’ brain health solutions that can address the dynamic needs of diverse clients. Drawing on both the literature and our own experiences evaluating community interventions focused on brain health, this chapter explores some key elements of what community-level care can supply. We find that community approaches have the potential to be particularly good at providing care that is more holistic and person-centered and also meets the needs of individuals over time. Community tends to be uniquely positioned to help meet diverse and multiple needs due to its proximity and sensitivity to context, including values, knowledge, preferences, cultures, and lived experiences of individuals. Given the book’s focus on integration of care, we conclude the chapter by reflecting on the complexities of understanding the community’s causal role in a comprehensive framework of solutions for brain health care. We discuss the implications of these findings for brain health and the evaluation of brain health interventions, noting especially the value of supporting more integrated solutions that incorporate both neuroscience and community.

Abstract Stemming biodiversity loss requires greater investment in conservation and more efficient use of available resources. Prioritizing conservation actions that yield the most biodiversity benefit for the least cost can help maximize return on investment. Actions that have co‐benefits for other objectives, such as climate change mitigation, can also help mobilize additional funds for conservation. We used Priority Threat Management to identify actions to secure the greatest number of species groups of conservation concern for the least cost in the Lake Simcoe‐Rideau ecoregion, Ontario—one of Canada's biodiversity crisis ecoregions. We also estimated the carbon sequestration benefits of actions related to land protection and restoration. We found that without additional investment in conservation, 13 of 16 species groups were expected to have <50% probability of persistence in this ecoregion by 2050. Implementing all proposed strategies would yield the greatest biodiversity benefits and secure 12 of the 16 species groups with ≥60% probability of persistence, at a cost of CA$113 million per year over 27 years. In comparison, investing CA$97 million per year in landowner stewardship, habitat protection and restoration and regeneration strategies could secure 10 species groups and improve the probability of persistence of one additional group from 39% to 55%. The habitat protection and restoration strategies also deliver direct carbon benefits of around 11.2 Mt in total avoided CO 2 emissions and 137.6 Mt CO 2 in total potential sequestration, respectively, over the long‐term, thus supporting alignment with climate change mitigation targets and delivering co‐benefits that may further justify investment. Practical implication. By estimating the costs and demonstrating the expected benefits and potential carbon co‐benefits of conservation actions, Priority Threat Management can help maximize return on investment and identify actions that address multiple environmental crises.

Abstract With increased human populations and tourism in coastal areas, there is greater potential for disturbance of marine wildlife. Because of their high metabolic rates, sea otters (Enhydra lutris) are at particular risk of increased energetic costs due to human disturbance. We used scan surveys to monitor southern sea otter (E. l. nereis) activity and potential disturbance stimuli over 5 years (2015–2020) at 3 California, USA, study sites: Monterey, Moss Landing, and Morro Bay. We developed a process‐based, hierarchical model of sea otter behavior, which we fit to survey data to examine how activity varies in response to the occurrence of and proximity to disturbance stimuli, while controlling for location, group size, pup‐to‐adult ratio, and presence‐absence of kelp or eelgrass canopy. We combined model results with published estimates of activity‐specific metabolic rates, translating estimated activity change into corresponding energetic costs. We found that effects of disturbance stimuli on sea otter behavior were location specific and varied non‐linearly with distance from disturbance stimuli. Our model results suggest that, on average, the likelihood of a group of sea otters being disturbed is <10% when stimuli are >29 m away, although this threshold varies by location, group size, and several other covariates. Based on the observed frequency and magnitude of disturbance at Cannery row in Monterey, we estimated that energetic costs were increased by 7.2%, 5.4%, and 5.4% for adult males, females, and females with large pups, respectively. We observed similar cost increases at the wildlife platform in Moss Landing (5.8%, 4.4%, and 4.3%) and T‐pier in Morro Bay (5.2%, 4.0%, and 3.9%). Our analyses represent a novel approach for estimating behavioral responses and energetic costs of human disturbance, furthering understanding of how human activities affect sea otters and providing a sound scientific basis for management.

Sharon H. M. MacDonald. Disruptive Women: The Untold Story of Nova Scotia’s Pioneers of Peace and Suffrage. Halifax: Nimbus Publishing, 2025. 264 pp.; Paperback: ISBN 978-1774714331; $23.95. Un article de la revue Journal of the Royal Nova Scotia Historical Society (Volume 28, 2025, p. 1-134) diffusée par la plateforme Érudit.