Biology subjects

Cheung, W. W. L.

Publications and source records attributed to Cheung, W. W. L..

4 recordsLinked to original sources

bioRxiv2026

Quantifying Exposure of Pacific Salmon and Steelhead to Climate Change in the Fraser River basin

Climate change can affect salmon and steelhead (Oncorhynchus spp.) throughout their anadromous life cycles, yet there have been no assessments of which Canadian populations face the greatest exposure. We developed a framework to quantify relative climate change exposure of salmon and steelhead populations based on the spatial and temporal distribution of different life stages. Exposure was calculated from climate model projections for freshwater and marine climate variables considering unique impact thresholds for each population and life stage. We applied this framework to 60 Conservation Units of Pacific salmon and steelhead in the Fraser River basin, British Columbia. Lake-type sockeye had the highest exposure, driven by elevated stream temperatures during adult freshwater migration and spawning stages and relatively low thermal tolerance of marine stages. Chinook salmon were the next most exposed, while coho, pink, and chum salmon had relatively low exposure. Uniquely, steelhead exposure was driven by high stream temperatures during incubation. Our framework is broadly applicable, and our findings provide critical input for climate change vulnerability assessments and forward-looking resilience planning for Pacific salmon.

ecology↗

bioRxiv2025

AquaX: An enhanced and revised AquaMaps framework to model marine species distributions and biodiversity

Marine biodiversity underpins ecosystem health and is critical for the provision of essential ecological services. Global efforts to mitigate biodiversity loss are underway but require comprehensive knowledge on the biogeography of species to be effective. However, key challenges limit comprehensive mapping of species distributions, including the complexity of ocean ecosystems and the difficulty of sampling the marine realm. Global initiatives such as AquaMaps pioneered large-scale marine species mapping using species distribution models or Ecological Niche models and provided the knowledge base for effective marine conservation and management. Recently, methodological and data advances have enabled a more modern and robust approach that enables higher resolution outputs more suited to conservation applications at all scales. Building on AquaMaps, we develop a next-generation marine species habitat suitability modelling platform called AquaX, providing a suite of advances that include an ensemble of ten machine learning algorithms, enabling spatial uncertainty assessments, validation indices, and ecological niche representation at a ten-fold improved spatial resolution of 0.05{degrees}. Furthermore, AquaX integrates (i) accepted taxonomy from the World Register of Marine Species, (ii) species-specific ecological, physiological, and biogeographical information (D3-Ocean system), (iii) updated occurrence records validated through expert input, and (iv) refined species range maps using expert knowledge and biogeographical divisions. AquaX also projects species habitat suitability for both present and future conditions based on two time periods and three climate scenarios. This work provides species range maps for numerous species compared to previously available datasets and improves the accurate use of observational data. The approaches described here improve predictive accuracy at scales more relevant to marine biodiversity conservation and offer an openly accessible tool to support marine biodiversity research and conservation planning under accelerating environmental change. AquaX represents an important step forward in species distribution modeling, enabling researchers and policymakers to better understand marine biodiversity patterns and develop more effective conservation strategies.

ecology↗

bioRxiv2021

Timing and magnitude of climate driven range shifts in transboundary fish stocks challenge their management

Climate change is shifting the distribution of shared fish stocks between neighboring countries Exclusive Economic Zones (EEZ) and the high seas. The timescale of these transboundary shifts determines how climate change will affect international fisheries governance. Coupling a large ensemble simulation of an Earth system model to a species distribution model, we show that by 2030, 23% of transboundary stocks would have shifted and 78% of the world EEZs will experience at least one shifting stock under a high emission climate change scenario. By the end of this century, 81% of EEZ waters will see at least one shifting stock with a total of 45% of stocks shifting globally, under a high emissions scenario. Importantly, many countries that are highly dependent on fisheries for income, food and nutrition security, as well as livelihoods emerge as hotspots for transboundary shifts showing early, and sometimes past shifts. Existing fisheries agreements need to be assessed for their capacity in addressing transboundary shifts, and strengthened where necessary to limit conflict over these fish stocks while new agreements are urged to considere this problematic in order to be resilient to global change.

ecology↗

bioRxiv2021

A quantitative review of abundance-based species distribution models

The contributions of species to ecosystem functions or services depend not only on their presence in a given community, but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence, rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance-based species distribution models fitted to 800,000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found high heterogeneity in performance of abundance-based models. While many models performed poorly, a subset of models consistently reconstructed range-wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species, and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but extrapolated predictions of abundance, e.g. under climate change, have a much greater uncertainty. Our synthesis provides a roadmap for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation.

ecology↗