Murray M. Humphries’s research while affiliated with McGill University and other places

This story map accompanies the recent article "Hills Thought to Be Mountains: A Geobiocultural Characterization of Island Highlands in Canada’s Continental Plains". In this StoryMap we explain why we conducted this research, how we approached it, and our conclusions. https://arcgis.mcgill.ca/portal/apps/storymaps/stories/c250205b7f2a4093b5127db6cdd36efa

Environmental knowledge networks (EKNs) link research collaborators in a common purpose to produce data and knowledge to better understand social-ecological phenomena and address environmental challenges. Over recent years, as scientists have grappled with how to produce data and actionable knowledge for conservation and sustainability, more EKNs have been established. Although each network is founded for its own purposes and maintains its own goals and ways of operating, these networks are generally managed by scientists to produce knowledge to advance science and decision making. In this Insight article, we articulate key qualities and benefits of EKNs and shows how EKNs can address grand challenges that cannot be answered by a single team or institution, create a diverse, vibrant culture of science and community of practice, and provide innovative solutions and knowledge to society. We also discuss challenges of EKN governance, and how challenges may vary with a network's development. Finally, based on a synthesis of structured discussions about key issues in EKN management, we share recommendations and best practices, emphasizing management practices that are inclusive, reflexive, adaptive, and flexible, so that others may benefit from our experience leading EKNs.

Indigenous-driven and community-partnered research projects seeking to develop salient, legitimate, and credible knowledge bases for environmental decision-making require a multiple knowledge systems approach. When involving partners in addition to communities, diverging perspectives and priorities may arise, making the pathways to engaging in principled research while generating actionable knowledge unclear to disciplinarily-trained natural science researchers. Here, we share insights from the Eeyou Coastal Habitat Comprehensive Research Project (CHCRP), an interdisciplinary, Cree-driven community-academic partnership. This project brought together Cree community members, regional organizations, industry (Hydro-Québec), and academics from seven universities across Canada to address the unprecedented loss of seagrass Zostera marina (eelgrass), the concurrent decline in migratory Canada geese and its impact on fall goose harvest activities in Eeyou Istchee. After describing the history and context of the project, we discuss the challenges, complexities, and benefits of the collaborative approach balancing saliency, legitimacy, and credibility of the knowledge produced. We suggest the paper may be of use to researchers and partners seeking to engage in principled and actionable research related to environmental change including impacts of past development.

Current reductionist approaches to environmental governance cannot resolve social-ecological crises. Siloed institutions fail to address linked social and ecological processes, thereby neglecting issues of equity, justice, and cumulative effects. Global insights can be gained from Indigenous-led initiatives that support the resilience of relationships within and among places.

Natural resource governance challenges are often highly complex, particularly in Indigenous contexts. These challenges involve numerous landscape-level interactions, spanning jurisdictional, disciplinary, social, and ecological boundaries. In Eeyou Istchee, the James Bay Cree Territory of northern Quebec, Canada, traditional livelihoods depend on wild food species like moose. However, these species are increasingly being impacted by forestry and other resource development projects. The complex relationships between moose, resource development, and Cree livelihoods can limit shared understandings and the ability of diverse actors to respond to these pressures. Contributing to this complexity are the different knowledge systems held by governance actors who, while not always aligned, have broadly shared species conservation and sustainable development goals. This paper presents fuzzy cognitive mapping (FCM) as a methodological approach used to help elicit and interpret the knowledge of land-users concerning the impacts of forest management on moose habitat in Eeyou Istchee. We explore the difficulties of weaving this knowledge together with the results of moose GPS collar analysis and the knowledges of scientists and government agencies. The ways in which participatory, relational mapping approaches can be applied in practice, and what they offer to pluralistic natural resource governance research more widely, are then addressed.

The ice phenology of freshwater lakes throughout the Northern Hemisphere has undergone important climate-induced shifts over the past century. In Canada's North, where freshwater lakes and wetlands cover 15 to 40% of the landscape, monitoring ice phenology is vital to understand its impacts on climate, socio-economic, ecological, and hydrological systems. The rapid and dynamic nature of ice phenology events has restricted monitoring efforts to the use of satellite sensors with frequent revisit times (e.g., MODIS, AVHRR), but their low resolution (e.g., > 500 m) limits observations to larger water bodies. However, the increased abundance of high-resolution open-access satellite imagery combined with the rise of cloud-computing technologies has provided opportunities to reduce the trade-off between temporal (i.e., revisit time) and spatial (i.e., pixel size) resolution allowing for lake ice monitoring over broad scales. In this study, we present the Open Pixel-based Earth eNgine Ice (OPEN-ICE) algorithm implemented in Google Earth Engine (GEE), which classifies imagery from multiple open-access optical sensors, then combines them to construct dense annual time series of ice-water observations and estimate pixel spring breakup dates at a 30-m resolution. Using Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI scenes over lakes spanning northern latitudes, we build reference datasets to train decision trees that discriminate between ice, water, and clouds. We combine ice-water classifications from each sensor into annual time series and remove misclassifications with a temporal filter applied using a pixel-wise logistic regression. We then detect the sequence of transition from ice to water in each pixel's time series to estimate the occurrence of breakup each year. We deploy the OPEN-ICE algorithm over all freshwater pixels of Canada for the period of 2013 to 2021. Spring ice phenology events estimated by OPEN-ICE show high accuracy when compared to whole-lake breakup dates measured by the Canadian Ice Service in 105 lakes across 9 years, with mean bias errors of −1.10 and − 0.69 days for breakup start and end, respectively. We apply the OPEN-ICE algorithm to 4000 lakes across Canada and evaluate differences in breakup dates across ecozones and lake sizes. Our new OPEN-ICE tool provides accurate estimates of annual spring breakup events applicable across all boreal and arctic regions to monitor the rapid changes taking place in these vulnerable ecosystems.

Wildlife conservation is informed by detailed understanding of species demographics, habitat use, and interactions with environmental drivers. Challenges to collecting this information, particularly in remote places and on widely ranging species, can contribute to data deficiencies that detract from conservation status assessment and the effectiveness of management actions. Polar bears in James Bay face rapidly changing environmental conditions at the southern edge of their global range, but studying their ecology has been limited by community concerns about the methods typically used in polar bear research. Using a community-led and non-invasive approach, we deployed hair snare and camera trap sampling stations across 400 km of the Eeyou Marine Region in eastern James Bay. Stations collected >100 hair samples and thousands of photographs in one eight-week period that allowed for a novel investigation of this population’s distribution and body condition during the ice-free season. Polar bears were in average to above average body condition, and model selection of detections at stations revealed distance to mainland as a significant predictor of polar bear presence. Given its high potential, we suggest community-based monitoring using this method become a standard protocol to expand the scope and local leadership of polar bear research across the North.