Hem Nalini Morzaria-Luna

While individual polycyclic aromatic hydrocarbons (PAHs) from oil spills have been documented to be toxic to marine species, the combined effect across a marine ecosystem of multiple PAH toxicities is difficult to measure. Detailed assessments of ecosystem impacts in coastal waters require species-specific responses that can account for varying exposure durations, species tissue concentrations, and resulting biological impacts over time. Modelling studies that account for such factors are data-intensive, with high numbers of parameters and uncertainty, but are helpful to disentangle the effects of PAH toxicity that can be masked by other ecosystem changes. Using an Atlantis whole-of-ecosystem model, we provide an environmental baseline for comparison, and attempt to understand the long-term impacts of combined PAHs from specific oil spills in marine waters of the Salish Sea. Combined PAH toxicity from different types of oils, such as Bunker C, diesel, and diluted bitumen, led to long-term ecosystem impacts that continued to increase through to 20 years. Impacts from PAH toxicity had ripple effects through the food web, decreasing the relative biomass of major groups, including pelagic fish, pinnipeds, and whales. PAH toxicity (without oil smothering effects), impacted predators and released predation pressure on some benthic groups, increasing their relative biomass compared to the ecosystem baseline (control). Overall, modelled results highlighted potential hidden long-term effects on wildlife from combined hydrocarbon contamination in marine waters, especially from heavy fuel oils. Despite the data-intensive nature of accounting for multiple PAH toxicities, ecosystem risk assessments for oil spills of heavy fuel oils (such as Bunker C or diluted bitumen) could benefit from similar modelling exercises that examine the long-term ecosystem impacts from various hydrocarbon groups.

Small-scale, multispecific fisheries in the Gulf of California face significant challenges including limited species-specific catch data, uncertainty about climate change impacts, and insufficient biological information needed for traditional deterministic models. These knowledge gaps hamper efforts to forecast future conditions and develop appropriate management strategies accurately. The complexity of these multi-species fisheries, combined with data scarcity for many target species, creates substantial barriers to quantifying and addressing climate vulnerability. Deep learning approaches offer a promising alternative by leveraging available data to identify patterns and project trends despite these limitations, providing valuable insights for fisheries management in data-poor contexts. Here, we apply a Mixture of Expert, a deep learning forecasting models for small-scale, multi-specific fisheries in the Gulf of California under future climate change scenarios. Results show varied responses across marine habitats, with reef and benthic fish projected to experience substantial declines (-12.46% and -9.37%) during the 2050s-2060s, followed by recovery in the 2070s-2080s. Economic implications are significant, with reef fish facing projected losses of $1.2 million by the 2050s before recovering by the 2080s. Shapley Additive Explanations (SHAP) analysis was applied to evaluate the importance of features for each predictive model, the analysis revealed the effects of the temperature in different depths for each fishery, and the sensitive analysis pointed to the magnitude of the effect. Our findings suggest that climate impacts will not be uniform across the Gulf, necessitating region-specific management approaches and highlighting the value of maintaining diverse fishing portfolios to enhance resilience against climate-driven changes.

As the urgency to evaluate the impacts of climate change on marine ecosystems increases, there is a need to develop robust projections and improve the uptake of ecosystem model outputs in policy and planning. Standardizing input and output data is a crucial step in evaluating and communicating results, but can be challenging when using models with diverse structures, assumptions, and outputs that address region‐specific issues. We developed an implementation framework and workflow to standardize the climate and fishing forcings used by regional models contributing to the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) and to facilitate comparative analyses across models and a wide range of regions, in line with the FishMIP 3a protocol. We applied our workflow to three case study areas‐models: the Baltic Sea Mizer, Hawai'i‐based Longline fisheries therMizer, and the southern Benguela ecosystem Atlantis marine ecosystem models. We then selected the most challenging steps of the workflow and illustrated their implementation in different model types and regions. Our workflow is adaptable across a wide range of regional models, from non‐spatially explicit to spatially explicit and fully‐depth resolved models and models that include one or several fishing fleets. This workflow will facilitate the development of regional marine ecosystem model ensembles and enhance future research on marine ecosystem model development and applications, model evaluation and benchmarking, and global‐to‐regional model comparisons.

Research has the potential to simultaneously generate new knowledge and contribute meaningful social–ecological benefits; however, research processes and outcomes can also perpetuate extractive patterns that have manifested the climate, biodiversity, and social justice crises. One approach to enhance the societal value of research processes is to strengthen relationships with places of study and the peoples of those places. Deepening relational engagement with the social–ecological context and history of a place can lead to more accurate results and improved public trust in the scientific process and is particularly important for natural scientists who work at the interface of nature and society. We provide three actionable pathways that range from individual to systemic change to enhance place-based relationships within research systems: 1) deepen reflection and communication about relationships with places and peoples; 2) strengthen collaboration among research teams and partners; and 3) transform systems of knowledge creation to foster place-based roots. Action on any of these proposed pathways, but especially action taken across all three, can build empathy and connections to place and people, strengthening the meaningful impact of research both locally and globally.

As the urgency to evaluate the impacts of climate change on marine ecosystems increases, there is a need to develop robust projections and improve the uptake of ecosystem model outputs in policy and planning. Standardising input and output data is a crucial step in evaluating and communicating results, but can be challenging when using models with diverse structures, assumptions, and outputs that address region-specific issues. We developed an implementation framework and workflow to standardise the climate and fishing forcings used by regional models contributing to the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) and to facilitate comparative analyses across models and a wide range of regions, in line with the FishMIP 3a protocol. We applied our workflow to three case study areas-models: the Baltic Sea Mizer, Hawai’i-based Longline fisheries therMizer, and the southern Benguela ecosystem Atlantis marine ecosystem models. We then selected the most challenging steps of the workflow and illustrated their implementation in different model types and regions. Our workflow is adaptable across a wide range of regional models, from non-spatially explicit to spatially explicit and fully-depth resolved models and models that include one or several fishing fleets. This workflow will facilitate the development of regional marine ecosystem model ensembles and enhance future research on marine ecosystem model development and applications, model evaluation and benchmarking, and global-to-regional model comparisons.