The Dungeness crab (Metacarcinus magister) fishery is one of the highest value fisheries in the US Pacific Northwest, but its catch size fluctuates widely across years. Although the underlying causes of this variability are not well understood, the abundance of M. magister megalopae has been linked to recruitment into the adult fishery four years later. These pelagic megalopae are exposed to a range of ocean conditions during their dispersal period, which may drive their occurrence patterns. Environmental exposure history has been found to be important for some pelagic organisms, so we hypothesized that inclusion of environmental exposure history would improve our ability to predict M. magister megalopae occurrence patterns compared to using ‘in situ’ conditions alone. We combined local observations of M. magister megalopae and regional simulations of ocean conditions to model megalopae occurrence using a generalized linear model (GLM) framework. The modeled ocean conditions were extracted from J-SCOPE, a high-resolution coupled physical-biogeochemical model. The analysis included variables from J-SCOPE identified in the literature as important for larval crab occurrence: temperature, salinity, dissolved oxygen concentration, nitrate concentration, phytoplankton concentration, aragonite and calcite saturation state, and pH. GLMs were developed with either in situ ocean conditions or environmental exposure histories generated using particle tracking experiments. We found that inclusion of exposure history improved the ability of the GLMs to predict megalopae occurrence. Of the five swimming behaviors used to simulate megalopae dispersal, several behaviors generated GLMs with superior fits to the observations, so a biological ensemble of these models was constructed. Our results highlight the importance of including exposure history in larval occurrence modeling and help provide a method for predicting pelagic megalopae occurrence. This work is a step towards developing a forecast product to support management of the fishery.

In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and, top-down processes, directly affecting species at all trophic levels. We applied analytical approaches to better understand future climate change effects on temperature and salinity in Puget Sound. We used empirical downscaling techniques to derive high resolution time series of future sea surface temperature and salinity, based on scenario outputs of two coarse resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, which were created as part of the CMIP6 - Coupled Model Intercomparison Project Phase 6. We calculated long-term averages for historical simulations, calculated anomalies for future years, and applied a delta-downscaling approach to a Regional Ocean Modeling System (ROMS) time series, yielding short (2020–2050) and long-term (2070–2100) forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity: including higher temperatures in the South Basin and higher salinity in the North Basin. Caveats to our methodology include the assumption that variable relationships are static and cannot represent interactions between large scale and local change, but this study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. The climate projections for Puget Sound oceanography will be used to drive the Atlantis ecosystem model for Puget Sound, an end-to-end ecosystem modeling approach that represents all trophic levels and evaluates the species-level impacts of climate change. This project is part of a Washington State Sea Grant funded project, “Evaluating the effects of Southern Resident orcas recovery actions and external threats in the marine ecosystem of Puget Sound.”