Integrated forecasts of fall Chinook salmon returns to the Pacific northwest (original) (raw)
Related papers
Ecological thresholds in forecast performance for key United States West Coast Chinook salmon stocks
ICES Journal of Marine Science, 2019
Preseason abundance forecasts drive management of US West Coast salmon fisheries, yet little is known about how environmental variability influences forecast performance. We compared forecasts of Chinook salmon (Oncorhynchus tshawytscha) against returns for (i) key California-Oregon ocean fishery stocks and (ii) high priority prey stocks for endangered Southern Resident Killer Whales (Orcinus orca) in Puget Sound, Washington. We explored how well environmental indices (at multiple locations and time lags) explained performance of forecasts based on different methods (i.e. sibling-based, production-based, environment-based, or recent averages), testing for nonlinear threshold dynamics. For the California stocks, no index tested explained >50% of the variation in forecast performance, but spring Pacific Decadal Oscillation and winter North Pacific Index during the year of return explained >40% of the variation for the sibling-based Sacramento Fall Chinook forecast, with nonlinea...
An Improved Sibling Model for Forecasting Chum Salmon and Sockeye Salmon Abundance
North American Journal of Fisheries Management, 2007
The sibling model is often one of the best methods for calculating preseason forecasts of adult return abundance (recruits) for populations of Pacific salmon Oncorhynchus spp. This model forecasts abundance of a given age-class for a given year based on the abundance of the previous age-class in the previous year. When sibling relations fit historical data well, the sibling model generally performs better than other forecasting methods, such as stockrecruitment models. However, when sibling relations are weak, better forecasts are obtained by other models, such as naïve models that simply use an historical average. We evaluated the performance of a hybrid model that used quantitative criteria for switching between a sibling model and a naïve model when generating forecasts for 21 stocks of chum salmon O. keta and 37 stocks of sockeye salmon O. nerka in the northeastern Pacific Ocean. Compared with the standard sibling model, the hybrid model reduced the root mean square error (RMSE) of forecasts by an average of 27% for chum salmon stocks and 28% for sockeye salmon stocks. Compared with a naïve model, the hybrid model reduced the RMSE of forecasts by an average of 16% for chum salmon stocks and 15% for sockeye salmon stocks. Our results suggest that hybrid models can improve preseason forecasts and management of these two species.
Marine environment-based forecasting of coho salmon (Oncorhynchus kisutch) adult recruitment
Fisheries Oceanography, 2012
Generalized additive models (GAMs) were used to investigate the relationships between annual recruitment of natural coho salmon (Oncorhynchus kisutch) from Oregon coastal rivers and indices of the physical ocean environment. Nine indices were examined, ranging from large-scale ocean indicators, e.g., Pacific Decadal Oscillation (PDO), to indicators of the local ecosystem (e.g., coastal water temperature near Charleston, OR). Generalized additive models with two and three predictor variables were evaluated using a set of performance metrics aimed at quantifying model skill in short-term (approximately 1 yr) forecasting. High explanatory power and promising forecast skill resulted when the spring ⁄ summer PDO averaged over the 4 yr prior to the return year was used to explain a low-frequency (multi-year) pattern in recruitment and one or two additional variables accounted for year-to-year deviations from the low-frequency pattern. More variance was explained when averaging the predictions from a set of models (i.e., taking the ensemble mean) than by any single model. Making multiple forecasts from a set of models also provided a range of possible outcomes that reflected, to some degree, the uncertainty in our understanding of how salmon productivity is driven by physical ocean conditions.
Retrospective Evaluation of Preseason Forecasting Models for Sockeye and Chum Salmon
Http Dx Doi Org 10 1577 M06 287 1, 2011
Using comprehensive data sets for chum salmon Oncorhynchus keta (40 stocks) and sockeye salmon O. nerka (37 stocks) throughout their North American ranges, we compared the retrospective performance of 11 models in preseason forecasting of adult abundance. Chum and sockeye salmon have more complicated age structures than pink salmon O. gorbuscha, which we investigated previously (Haeseker et al. 2005), and this complexity presents new challenges as well as opportunities for forecasting. We extended our previous work to include two new forecasting models that make use of leading indicators: either the survival rate of earlier-maturing pink salmon from the same brood year (Ricker pink salmon index model) or the abundance of earlier-maturing siblings (hybrid sibling model, a new version of the standard sibling model). No single forecasting model was consistently the best for either chum or sockeye salmon, but the hybrid sibling model frequently performed best based on mean absolute error, mean percent error, and root mean square error. As was observed for pink salmon, several naïve models (i.e., simple time series models without explicitly modeled mechanisms) also performed well, as did forecast averaging models composed of two models with the least-correlated forecasting errors. In general, model ranking depended on the particular stock and performance measure used. However, even the top-ranked model for each stock explained on average only 21% of the observed interannual variation in chum salmon recruitment and only 36% of the variation in sockeye salmon recruitment. Although improvements may be possible for some stocks in specific circumstances, a major breakthrough in general forecasting ability seems unlikely given the breadth of stocks and models examined to date. Therefore, better in-season updates and adjustments to fishing regulations and a cautious approach to opening and closing fisheries should remain high priorities.
2011
The Southeast Alaska Coastal Monitoring (SECM) project has been sampling juvenile salmon (Oncorhynchus spp.) and associated environmental parameters in northern Southeast Alaska (SEAK) annually since 1997 to better understand effects of environmental change on salmon production. A pragmatic application of this sampling effort is to forecast the abundance of adult salmon returns in subsequent years. Since 2004, juvenile peak salmon catch per unit effort (CPUE) from SECM, modified by other environmental parameters as appropriate, has been used to forecast harvest of adult pink salmon (O. gorbuscha) in SEAK. The 2009 return of 38.0 million fish was 17% below the forecast of 44.4 million. This represents the fifth forecast over the period 2004-2009 which was within 0-17% of the actual harvest. Conversely, the forecast for 2006 did not follow this pattern and was 200% higher than the actual harvest; however, the simple CPUE forecast model did indicate a downturn in harvest that year. The...
Multivariate Models of Adult Pacific Salmon Returns
PLoS ONE, 2013
Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon.
Retrospective Evaluation of Preseason Forecasting Models for Pink Salmon
North American Journal of Fisheries Management, 2005
Using comprehensive data sets for chum salmon Oncorhynchus keta (40 stocks) and sockeye salmon O. nerka (37 stocks) throughout their North American ranges, we compared the retrospective performance of 11 models in preseason forecasting of adult abundance. Chum and sockeye salmon have more complicated age structures than pink salmon O. gorbuscha, which we investigated previously , and this complexity presents new challenges as well as opportunities for forecasting. We extended our previous work to include two new forecasting models that make use of leading indicators: either the survival rate of earlier-maturing pink salmon from the same brood year (Ricker pink salmon index model) or the abundance of earlier-maturing siblings (hybrid sibling model, a new version of the standard sibling model). No single forecasting model was consistently the best for either chum or sockeye salmon, but the hybrid sibling model frequently performed best based on mean absolute error, mean percent error, and root mean square error. As was observed for pink salmon, several naïve models (i.e., simple time series models without explicitly modeled mechanisms) also performed well, as did forecast averaging models composed of two models with the least-correlated forecasting errors. In general, model ranking depended on the particular stock and performance measure used. However, even the top-ranked model for each stock explained on average only 21% of the observed interannual variation in chum salmon recruitment and only 36% of the variation in sockeye salmon recruitment. Although improvements may be possible for some stocks in specific circumstances, a major breakthrough in general forecasting ability seems unlikely given the breadth of stocks and models examined to date. Therefore, better in-season updates and adjustments to fishing regulations and a cautious approach to opening and closing fisheries should remain high priorities.
North Pacific Anadromous Fish Commission Bulletin, 2016
Researchers in Alaska have provided accurate pre-season annual forecasts of pink salmon harvest to resource stakeholders of Southeast Alaska (SEAK) in times of climate change. Since 1997, the Southeast Alaska Coastal Monitoring project has collected biophysical data associated with seaward migrating juvenile salmon from May to August, and it has used these data along with larger basin-scale indexes to forecast SEAK pink salmon returns via regression and ecosystem metric models. In nine of the past eleven years (2004-2014), predictions from linear regression models ranged 0-17% of actual harvests, an average absolute forecast deviation of 10%. The primary explanatory variable was juvenile pink salmon peak catch. In some years, models included secondary variables to improve fi t. A supplemental modeling approach was tested recently to better inform stakeholders. This approach incorporated both an annual rank score forecast outlook based on ecosystem metrics, and a visual stoplight color-code graphic. Accurate pre-season salmon forecasts and descriptive outlooks from this applied research has increased economic effi ciency of the fi sh processing industry, enabled managers and resource stakeholders to anticipate harvest with more certainty, helped promote resource sustainability, and provided insight into ecosystem mechanisms related to pink salmon production in a changing climate.