Visualizing Dynamic Distributions
One of the core applications of FaCeT is to provide stakeholders with an open-access online platform for map-based visualization and analysis of fishing fleets and fishery-relevant species distributions. Powered by dynamic predictive habitat models, stakeholders will be able to interactively access spatially predicted model output layers allowing users to explore the spatial dynamics of fisheries and species distributions and perform other relevant applications (e.g. habitat change, fishery-species interactions, etc.). FaCeT’s map-based dashboard will also provide a wide range of hindcasted and forecasted spatial layers that will allow users to capture baselines distribution patterns and assess how fishery and species distributions may shift in response to climate variability and projected climate change. Alongside mapped modeled projections, FaCeT will also host fishery-relevant satellite and data-assimilated environmental layers to help users visualize side-by-side the environmental changes affecting fishery and species distributions. In addition to long-term climatic changes, one of the key oceanographic changes that is already impacting species and vessel distributions is Marine Heat Waves (MHW). The FaCeT team is developing products that will help fisheries stakeholders identify the anomalous shifts in vessel distributions under MHW conditions and the impacts it can have on fisheries and coastal communities (e.g. travel distance and costs).
Tracking Velocity and Magnitude of
Important fishery resources are shifting their spatiotemporal distributions in response to climate-driven changes. However, species have not responded uniformly and observed distribution shifts have shown a wide range of directions and rates. Since fish populations and fishing fleets may also respond to climate change in divergent ways, accurately tracking species and fisheries spatial distribution shifts is necessary for stakeholders to understand how the changing climate is impacting or likely to impact productive fishing regions. As a result, in addition to map-based visualizations of model projections and environmental data to investigate past, current, and projected future ocean and fishery conditions, FaCeT will provide users dynamic time series graphs and spatial representations of distribution change metrics (i.e. habitat centroids and velocity / magnitude of change, habitat gain and loss). These data views will help stakeholders capture and track spatial distribution changes over timescales allowing users to see how current and future spatial patterns compare to historical spatiotemporal distributions.
Changing Fishing Portfolios
Divergent distribution shifts between fishing fleets and targeted species in response to changing climate conditions can lead to economic disruptions if the portfolio of fishers catch becomes less diverse from targeted fish populations moving out of their fishing range. In an effort to better understand how fishing portfolios may change in the future, FaCeT users will be able to explore the spatiotemporal footprint of fishing activity in relation to target species distributions overtime. Forecasted and hincasted data layers from dynamic vessel distribution models (dVDMs) and species distribution models (dSDMs) can be overlaid, which will allow users to evaluate regions of fishery-species interactions and exploitation hotspots, and how it will change as fleet and target species shift their distributions in the future. Assessing such variations in fishery-species spatial interactions is a key requirement in developing management strategies that will support climate-readiness and resilience as these analyses can be used to estimate the potential fishery costs/benefits of climate change as well as provide guidance to how fisheries may have to adapt as climate impacts increase.
Addressing and Communicating Uncertainty
Traditional decision-making processes for fisheries have relied on historical observations as good indicators of future states. These backward-looking approaches, however, are becoming increasingly problematic, supporting the need for forward-looking decision-making approaches which require models that can accurately predict species distributions under changing environmental conditions. Yet, our understanding of the predictive skill of predictive habitat models at near-term time scales (seasonal, annual, and multi-annual), which are more commonly aligned with many decision-making needs, remains limited. To help fill this knowledge gap, research conducted by the FaCeT team will be evaluating the near-term forecast skill of distribution models using a combination of simulated and real data from large marine ecosystems with unique responses to recent climate change. The project is also exploring whether dynamic distribution models built with “big data”, i.e., low resolution data from conventional tagging which is prevalent across ocean areas, helps account for or reduces the impact of uncertainty on model predictions. The results from this research will be made into a heuristic, visualization tools designed to improve how stakeholders and FaCeT users understand and interpret uncertainty in order to best support decision-making.