Chinook salmon (Oncorhynchus tshawytscha) originating from rivers throughout the west coast of North America support mixed-stock ocean fisheries and provide important ecosystem services, for example as prey for marine mammals. Chinook salmon distributions in the ocean and how they vary through time are incompletely understood, but are key to successful fisheries management. We developed a Bayesian state space model jointly estimating the ocean distribution of all major fall-run Chinook salmon stocks from California to British Columbia, fit to 40 years of fishery-dependent sampling of tagged fish. This model tracks local abundance for each stock, accounting for initial cohort size, natural mortality, harvest, and distribution as a function of sea surface temperature (SST). Using each stock’s fitted SST-distribution relationship, we predicted how each stock would redistribute under SST predictions for 2030-2090 from Max Planck Institute Earth System Model RCP45 scenario, and examined ocean area-specific abundances summed across stocks to explore spatial shifts in aggregate salmon abundance. We demonstrate that different stocks have fundamentally different ocean distributions, distinct associations with SST, and contrasting distributional responses to historical ocean SST variation. This means that species-level models of ocean distributions and drivers that ignore among stock variation will likely yield errant predictions. We predict substantial ocean re-distribution in response to SST change, but not a simple poleward shift. Instead, we predict net movement into some ocean regions (British Columbia, central California) but net movement out of others (northern California, Washington), although unmodeled changes in stock productivity will also affect net changes in area-specific abundance.

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Nearshore submarine canyons are unique features that bring the deep sea close to shore, potentially functioning as highways connecting shallow and deep sea ecosystems. Canyons are keystone structures for local fisheries that provide ecosystem services and ae very diverse due to increased environmental variability and proximity to shore. Currently 27% of large canyons in California are protected by government agencies, but this does not include the smaller nearshore canyons, most of which are unprotected and understudied. These are more common in Southern California than Central and Northern California due to differing shelf characteristics along the coast. To evaluate protection of nearshore submarine canyons, we surveyed the bottom topography along a transect 5 km from shore, identifying features that are deeper than 200 m and incised at least 100 m into the slope. Applying this definition, we identified 32 nearshore submarine canyons in the Southern California Bight alone. To better understand the ecology of nearshore submarine canyons, we developed two low-cost, spatially flexible autonomous lander systems: the Nanolander DOV BEEBE and Picolander DOV JEAN. The Nanolander has three spherical housings containing a camera system, an acoustic communication system, as well as an SBE MicroCAT for measuring conductivity, temperature, pressure, and dissolved oxygen over several weeks. The smaller, two-sphere Picolander is equipped with a Zebra-Tech sensor, camera system, and timed release for 24-hour deployments. Both systems are positively buoyant and deployed by hand from a small boat. The landers will collect high-frequency environmental and seafloor community data at depths of 100-500 m in the La Jolla Canyon. Because small autonomous landers collect paired biological, physical, and biogeochemical data in hard to access areas, they can serve as powerful tools to study the unique ecology of nearshore submarine canyons and inform management decisions.

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While the loss of biodiversity is recognized as a major threat to our oceans and society, our understanding of how biological communities across habitats and ecosystems are changing is critically impaired by persistent data gaps over large portions of the ocean and the lack of sustained monitoring of the occurrence and abundance of life forms, from microbes to whales, especially in the tropics and polar regions. The community must work together to build capacity across the global ocean in the monitoring of marine life in a standardized and consistent fashion to enable intercomparison of biodiversity observations and make them fit for purpose. The Marine Biodiversity Observation Network Pole to Pole of the Americas (MBON Pole to Pole) aims to build a collaborative framework to accelerate and sustain the collection, curation and sharing of biodiversity data across the continent. The MBON Pole to Pole brings marine ecologists, resource managers and decision-makers together to build knowledge on the status and trends of marine biodiversity in the coastal zone by applying best practices and FAIR data principles. The program partners with Ocean Biodiversity Information System (OBIS), the Global Ocean Observing System (GOOS) and the Ocean Best Practices System (OBPS), all under the Intergovernmental Oceanographic Commission of UNESCO, to build capacity in 1) field data collection and sharing following agreed standards, 2) computing applications for data synthesis and visualization, 3) machine learning tools for monitoring benthic communities, and 4) accessing and using satellite observations to investigate environmental drivers of marine ecosystems. The MBON Pole to Pole has conducted several workshops to train participants of the network in these topics. These activities have enabled the implementation of common biodiversity survey protocols along the east and west coasts of the Americas and the sharing of over 20,000 new taxonomic records through OBIS.

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Spatial distributions of marine fauna are determined by complex interactions between environmental conditions and animal behaviors. As climate change leads to warmer, more acidic, and less oxygenated oceans, species are shifting away from their historical distribution ranges, and these trends are expected to continue into the future. Correlative Species Distribution Models (SDMs) can be used to project future habitat extent for marine species, with many different statistical methods available. However, it is vital to assess how different statistical methods behave under novel environmental conditions before using these models for management advice, and to consider whether future projections based on these techniques are biologically reasonable. In this study, we built SDMs for adults and larvae of two ecologically important pelagic fishes in the California Current System (CCS): Pacific sardine (Sardinops sagax) and northern anchovy (Engraulis mordax). We used five different SDM methods, ranging from simple [thermal niche model (TNM)] to complex (artificial neural networks). Our results show that some SDMs trained on data collected between 2003 and 2013 lost substantial predictive skill when applied to observations from more recent years, when ocean temperatures associated with a marine heatwave were outside the range of historical measurements. This decrease in skill was particularly apparent for adult sardine, which showed non-stationary relationships between catch locations and sea surface temperature (SST) through time. While sardine adults and larvae shifted their distributions markedly during the marine heatwave, anchovy largely maintained their historical spatiotemporal distributions. Our results suggest that correlative relationships between species and their environment can become unreliable during anomalous conditions. Understanding the underlying physiology of marine species is therefore essential for the construction of SDMs that are robust to rapidly changing environments. Developing distribution models that offer skillful predictions into the future for species such as sardine and anchovy, which are migratory and include separate sub-stocks, may be particularly challenging.

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