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Funded Research

Utilizing ecosystem information to improve the decision support system for central California salmon

Chavez, Francisco: MBARI (Project Lead)
Bograd, Steven: NOAA NMFS Pacific Fisheries Environmental Laboratory (Institution Lead)
Wells, Brian: NOAA Southwest Fisheries Science Center, Santa Cruz (Institution Lead)

Project Funding: 2009 - 2013

NRA: 2008 NASA: Decision Support through Earth Science Research Results   

Funded by NASA

Abstract:
Salmon, the quintessential species representing healthy Pacific ecosystems, fluctuate on interannual to multi-decadal time scales, but understanding their population fluctuations is complicated by the fact that salmon spawn in rivers, yet spend much of their life in the open ocean. Salmon management traditionally has focused on fresh water (watershed) aspects of their life cycle, but recently the emphasis has shifted to the ocean and pelagic food webs, which have been implicated in the closure of salmon fisheries in California and Oregon for 2008 (and probably 2009). Recognizing that variation in the oceanic environment plays a critical role in fish abundance, management of marine fishes, including salmon, in the U.S. has been moving toward an ecosystem-based approach, in which climatic and oceanographic information is integrated in ecologically-based stock assessments and harvest control rules (i.e., quotas). At the same time the quantity and quality of (i) in situ climatic and oceanographic data, (ii) synoptic, large scale measurements of the biosphere from space-based real time sensors, (iii) high performance computing capabilities, (iv) high-resolution physical models, and (v) theories of marine ecosystem dynamics have increased dramatically. These advances now make possible a novel approach to produce timely and accurate forecasts of the environmental conditions which drive fish populations that would allow fisheries managers to adapt and modify management strategies. The coupling of operational oceanography and management science is the overarching goal of this project: we can no longer afford to wait several years for information to be digested by scientists before it is made available to managers. Herein we propose to combine two highly-qualified scientific teams, one specializing in physical and numerical modeling of marine ecosystem dynamics using NASA remote- sensing products and models and the other specializing in empirically-based food web and fisheries oceanography, to improve the existing decision support system for California salmon management. Using an integrative physical-ecological approach, including NASA climatic and oceanographic products, we will design and implement ecosystem-level nowcasts and forecasts of oceanographic conditions and their impact on ocean salmon survival. Using combined numerical and empirical-statistical modeling of ocean survival of individual salmon cohorts while at sea, we will improve forecasts of salmon abundance (and fisheries) to facilitate better decisions by management. We will emphasize the translation of our scientific findings by conducting targeted outreach (presentations) to end-users (managers) as the project progresses, including the Salmon Technical Team of the Pacific Fisheries Management Council and NOAA-NMFS managers charged with assessing the viability of threatened and endangered salmon stocks under the Endangered Species Act (ESA). More specifically, we will develop biophysical indices that can be incorporated directly into salmon stock assessment models, harvest control rules, and ESA biological opinions and rulings. In the course of this applied research, we will explore ecological interactions between salmon and the environment, ecosystem productivity, and predator-prey dynamics within the California- Oregon coastal ecosystem. Importantly, the biophysical indices that we will develop will represent a novel approach to integrate climate, regional oceanography, estimates of primary productivity and mid-trophic level prey (i.e., krill, juvenile rockfish).


More details may be found in the following project profile(s):