Topic: To advance our understanding of the current (and potentially future) spatial and temporal footprints of multiple ocean stressors – ocean acidification (OA), hypoxia (low oxygen), and increasing temperatures – and how they affect coastal and marine living resources and the communities that rely on them. An important component of such efforts will be the development of analytical approaches and products that are useful to policy makers and managers.

Background: Climate change is associated with unprecedented shifts in oceanic and coastal environments that pose substantial challenges to coastal, fisheries, and marine scientists, resource managers, policy makers, and citizens. Ocean acidification and the decline in carbonate ion concentrations (Chan et al., 2012), increasing water temperature, including a greater frequency of “marine heatwaves” (Hobday et al., 2018; Oliver et al., 2018), and hypoxic conditions (Breitburg et al., 2018) create unique and potentially very stressful conditions for many marine organisms and can have population-level effects (Doney et al., 2012). The dynamics and trends of these stressors often spatially coincide to create stress-scapes — spatial extents where multiple stressors may interact (Bopp et al., 2013).  An ability to understand and, ideally, predict the effects of climate change on marine habitats and populations is integral for effective conservation and management. However, our understanding of the organismal- and population-level effects of these collective stressors is limited. Additionally, the development of policy frameworks, management strategies, and business decisions that incorporate climate predictions or scenarios in a meaningful manner remains a major challenge due, at least partly, to uncertainty in climate models and the lack of information at spatial and temporal scales appropriate for policy makers and managers (i.e., “downscaling”) (Asch et al. 2016; Eyring et al. 2016; Rheuban et al., 2017).

Quantifying the spatial and temporal footprint of modern stressors in the coastal and ocean environment is thus a key imperative. There is the potential to combine efforts occurring at multiple spatial scales.  For example, there are a growing number of in situ and satellite observing systems within the marine environment. Additionally, there is a growing body of information, often generated through laboratories or small-scale field studies, on how individuals respond to single and multiple stressors such as OA, elevated temperatures, and hypoxia. A current challenge is the need to develop our mechanistic understanding of how organisms respond to these stressors in situ while we also develop tools and resources that are accessible and useful to decision makers and stakeholders. Meeting this challenge will require interdisciplinary teams that can effectively incorporate physical, biological, and social information. Thus, we propose to develop a team that can take an interdisciplinary approach to these challenges in order to advance our understanding of the current and future “stress-scape” and quantify its potential impact to key marine species within the California Current and the Gulf of Alaska (Kavanaugh et al., 2017; Rheuban et al., 2017).

Potential Objectives/Questions
1) Collate remotely-sensed data on temperature, winds, and sea surface height (SSH) to generate habitat condition maps at regional scales, such as an upwelling system (California Current) and a downwelling system (Gulf of Alaska). In addition, derived products (e.g. carbonate system parameters such as pH, pCO2 and aragonite saturation state, Hales et al., 2012) will be compiled.  We will utilize available products from the Coupled Model Intercomparison Project – Phase 6 (CMIP6) and ocean reanalysis efforts, in particular the North American Regional Reanalysis products (NARR), which are used to inform national weather predictions, and HYCOM (Hybrid Coordinate Ocean Model). Satellite and reanalysis data can be used for downscaling of CMIP6 model output to include local variability in historical and forecast trend analysis.

2a) Compile available in situ data for model validation. Create seasonally resolved climatologies and multiple linear regression relationships (e.g. Alin et al., 2012) for downscaling of benthic fields.
2b) Validate the accuracy of these derived stress-scapes with in situ observations and evaluate predictive capacity of validated seascapes using multivariate analyses.

3) Collate and synthesize physiological information for key resource species in relation to temperature (and potentially other variables) to generate likely thresholds or establish ”stress risk” given current (and potentially predicated) patterns. Potentially adopt a bioenergetics approach.

4)  Evaluate potential risk (and benefits) to key species/regions and evaluate potential impacts to coastal communities (or industries) reliant on those species. We propose an approach similar to Mathis et al., 2015. For example, they collated fisheries and socio-economic data sets that were not divided into comparable geographic regions. Hence, they fit those data into standard federally-assigned census areas and boroughs for the state of Alaska in order to generate spatially-explicit estimates of adaptive capacity and overall risk.

5) Potential topics that can be addressed with this approach include: 1) Determine the potential effects, such as decline in growth or productivity, on key resource species given current (and potentially future) scenarios. 2) Compare effects across systems or contrast species’ responses within systems. 3) Evaluate how natural resources managers and other decision makers can evaluate the trade-off associated with relying on some available synthesis that has inherent error and uncertainty versus no synthesis due to lack of certainty.


Existing Data:
1) Satellite-derived SST, SSH, and chlorophyll concentrations (1997-present), output fields of carbonate system model (1997- present).
2) Buoy-derived and vessel data on water temperature, oxygen, carbonate system, wind, and currents (~1990–present).
3) Global model / reanalysis project outputs. Coupled Model Intercomparison Project – Phase 6 (CMIP 6) historical analyses typically span from 1850s to 2014, are 1 degree resolution, and provided on monthly time steps. The North American Regional Reanalysis (NARR) data set spans 1990s to present, are 1/3 degree resolution,  sub-daily time steps, includes surface fields of SSH, SST, and winds. The Hybrid Coordinate Ocean Model (HYCOM) also includes SSH, but has vertically resolved temperature and salinity at 1/12 degree resolution and sub-daily time steps.
4) Literature (field and lab-derived) temperature-growth relationships for key (or related) marine resource species. There is more limited information available on growth and condition response to OA but some recent work (i.e., on Pacific Cod and Alaskan Pollock) can inform the analysis.
5) State and federal catch statistics for key marine species, such as Dungeness Crab, Chinook salmon, Alaskan Pollock, Pacific Hake, Pacific Cod, and Tanner Crab (at least 1990 to present)
6) State and federal economic, labor, and demographic statistics.

Desired Areas of Expertise for Students which complete the three “legs” core concepts of the Risk and Uncertainty Quantification in Marine Science Program.

Fisheries, Organismal biology
Statistics, Remote sensing, Oceanography,
Data Science
Sociology, Economics, Human Dimensions, Natural Resource Management, Ocean Policy

Faculty Contacts:

Maria Kavanaugh, College of Earth, Ocean, and Atmospheric Sciences (CEOAS)
Sarah Emerson, Statistics
Jessica Miller, Fisheries & Wildlife, Coastal Oregon Marine Experiment Station, Hatfield Marine Science Center

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