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Southeast Alaska Pink Salmon Preseason Forecast

Forecasting Team:

Assessment links:

The current assessment follows Miller et al. 2022.

Advisory Announcements

Reports

Background 1

Pink salmon Oncorhynchus gorbuscha runs in Southeast Alaska support a valuable commercial fishery (Clark et al. 2006), although annual abundance varies tremendously. An average of 33 million pink salmon per year were harvested in Southeast Alaska in the last 10 years (2012–2021), with a range of 8 million (2018 and 2020) to 95 million (2013) fish. Although the inseason management of these stocks is focused on monitoring daily harvest and fishing effort, and using aerial survey counts to assess whether adequate numbers of pink salmon are present to meet escapement goals (Piston and Heinl 2020), the fishing industry benefits from meaningful preseason forecasts in order to plan appropriately for the harvest, processing, transportation, and marketing of these fish. Pink salmon runs are notoriously difficult to forecast (Adkison 2002; Haeseker et al. 2005; Shevlyakov and Koval 2012; Radchenko 2020) due to the species’ propensity to respond dramatically to changes in the marine environment (Farley et al. 2020), the odd- and even-year cycles of abundance (Heard 1991; Ruggerone et al. 2003; Krkošek et al. 2011), and the fact that there is only one cohort (i.e., only one age class) in the fishery each year (Heard 1991). With only a 2-year life cycle, information about cohort strength is not available from jacks or siblings as with other species of salmon (Adkison and Peterman 2000; Haeseker et al. 2007); sibling recruit modeling is not possible with pink salmon. Beginning in the late 1960s, the Alaska Department of Fish and Game (ADF&G) implemented programs to improve pink salmon stock assessment but met with limited success in forecasting pink salmon runs. Forecast accuracy (i.e., absolute percent error) from 1981 through 2006 was 57%. Past forecasts relied primarily on measures of pink salmon spawning abundance or success (Jones and Hofmeister 1985; Hofmeister and Jones 1989; Hofmeister and Blick 1993), both of which are poorly known and explain little of the variation in annual recruitment, which is largely determined in the early marine environment (Parker 1968; Mortensen et al. 2000; Willette et al. 2001).

NOAA Alaska Fisheries Science Center, Auke Bay Laboratories (National Oceanic and Atmospheric Administration [NOAA]) initiated the Southeast Alaska Coastal Monitoring (SECM) project in 1997 (Orsi et al. 1997; Murphy et al. 1999) to identify relationships between the year-class strength of juvenile salmon and the biophysical parameters influencing their growth and survival, their prey and predator interactions, their habitat utilization, and their stock interactions in marine waters (Orsi et al. 2000, 2005, 2009). Through this project, standardized monthly sampling and trawl surveys have been conducted annually from May to August (the August survey was dropped in 2020) to collect ecosystem (oceanographic data such as temperature and salinity profiles of the water column, surface water samples, and zooplankton samples) and catch per unit effort (CPUE) data associated with juvenile salmon at 8 stations in Icy Strait, a major migration corridor in northern Southeast Alaska (Orsi et al. 2001, 2006, 2012). The environmental and oceanographic data provided through the SECM project has become one of the longest continuous time series of its kind for the North Pacific. A major finding of the SECM survey is that relative abundance of juvenile pink salmon in June and July was highly correlated to harvest of adults in the subsequent year. As a result, NOAA used peak juvenile pink salmon CPUE and environmental information collected during SECM surveys to forecast the Southeast Alaska pink salmon harvest starting in 2004 (Murphy et al. 1999; Wertheimer et al. 2009a, 2010a, 2011–2015, 2017, 2018; Murphy et al. 2019b).

Assessment

In the past, ADF&G and NOAA produced separate Southeast Alaska preseason pink salmon forecasts. In 2007, ADF&G began adjusting their simple trend forecasts with juvenile pink salmon abundance data from the SECM survey (Heinl et al. 2007; Piston and Heinl 2014, 2017; Appendix A). Forecast accuracy (i.e., absolute percent error) improved from 57% (1981 to 2006 preseason forecasts) to 31% (2007 through 2017 preseason forecasts; the 2018 preseason forecast was based on the average of 5 recent even-year harvests and did not use juvenile abundance indices from the SECM survey; Appendix B). The largest absolute percent error between the forecast and the actual harvest occurred in the 1987, 1988, 2006, and 2018 forecasts. In 2018, ADF&G and NOAA scientists collaborated to create a joint preseason forecast for 2019 (Piston et al. 2021a). The SECM project and Southeast Alaska pink salmon harvest forecasts are now conducted cooperatively by NOAA and ADF&G using the ADF&G research vessel (R/V) Medeia (Piston et al. 2021a, 2022). The current method (2019 to 2022 preseason forecasts) is to forecast the adult pink salmon harvest in a multiple linear regression model with peak monthly (June or July) juvenile pink salmon CPUE and a temperature index (Piston et al. 2019, 2020, 2021a, 2022). The temperature index is based on the overall average 20 m integrated water column temperature recorded during May–July or May–August at 8 stations in Icy Strait as part of the annual SECM survey (Icy Strait Temperature Index [ISTI]; Murphy et al. 2019a). Together, both agencies continue to examine alternative variables and statistical methods to improve annual forecasts.

Assessment timeline:

  • ~end of August: Andy Piston or Teresa Fish will send a data sheet with the Icy Strait CPUE data, the ISTI temperature data (May, June, July average), and harvest. It is optimal to rerun the last ten years of harvest data to update prior harvests in the data set. Andy (or Teresa) will coordinate with NOAA for the CPUE and ISTI data as this data is housed and assimiliated there.

  • Early October: Review preliminary model runs with NOAA and ADF&G Southeast Alaska Pink Salmon Forecasting Team.

  • ~end of October: Final write-up due with re-run of final model with updated harvest data for the current year.

  • mid-November: Advisory Announcement due. This is the primary responsibility of Teresa Fish and/or Andy Piston. The biometrician reviews the announcement mainly for accuracy.

Assessment instructions (effective since 2022):

Setting up the Folder Structure

  1. Copy over last year's 'yyyy_forecast' folder structure for the current assessment.

  2. Rename the folder to the current forecast year.

  3. Delete the files in the data folder except the [varyyyy_final.csv] file and the [sst_data_map.csv] file. The [varyyyy_final.csv] file will serve as the template for the new year. Update this file name to the data year (not the forecast year). To prepare for the coming year, add another year of mock data by adding the same data as last year into a new row. Keep a folder labeled raw_data within the data folder for any of the original data files that are used in the analysis. The raw data should not be manipulated in any way, but instead copied to the [varyyyy_final.csv] file for use in the model runs. The file [sst_data_map.csv] contains the latitude and longitude of the different regions for the satellite SST data. The details of these regions are found in Miller et al. 2022. This file does not need to be updated unless these regions change.

  4. Within the results folder, delete the MAPE folder, the model_figs folder, the figs folder, the explore_variables, the retro folder, the temperature_data folder, and all the csv files within the main results folder. Within the results/summary_tables folder, rename the [model_summary_table--template.xlsx] to the current year.

  5. Delete any word and pdf files in the code folder.

  6. Rename the [yyyy_forecast.Rmd] file and the [satellite_SST_process_yyyy.Rmd] in the folder to the forecast year. These files are important for documenting the process for the current forecast year (e.g., changes in models considered, changes in variables). The [satellite_SST_process_yyyy.Rmd] file mainly needs the title, data years for figures and tables, and forecast folder year updated, unless the process changes. Run this file and then save it as a pdf and the current date for future reference.

Running the Assessment

  1. Data

The data needed to run the code are updated in the file [varyyyy_final.csv]. The CPUE, harvest, and ISTI variables are collated by the ADF&G Ketchikan staff (Andy Piston and/or Teresa Fish). The satellite sea surface temperature variables are created by running the code [satellite_data_monthly.R]. The process for the temperature variables are then written up in the file [satellite_SST_process_yyyy.Rmd]. Therefore, run the code [satellite_data_monthly.R] and then add these temperature variables to the [varyyyy_final.csv] file. The satellite sea surface temperature data will be output here: [results/temperature_data/sst_regions_oisst_97_*yy*_monthly_data_summary.csv]. JYear is the juvenile year. The index variable stays the same unless the pink salmon forecasting group decides to change the process of the CPUE calculation for pink salmon. See the document [calibration_coefficient_discussion_Nov_2020.pdf] in the folder 2021_forecast. The weight_values variable was originally used to calculate a weighted MAPE and aimed to weight the current years greater than the former. This is not used and the 5-year and 10-year MAPE are used to compare the various models.

Note that the satellite SST data, ISTI20_MJJ, and CPUEcal variables should follow the JYear from 1997 on. The SEAK catch should follow the Year variable from 1998 on.

  1. Code

a. Satellite Temperature Code

First, run the [satellite_data_monthly.R] code in the code folder. This code script will create the environmental variables needed to fill in the [varyyyy_final.csv] sheet. The monthly data (referenced in the code) needs to be manually downloaded from the site https://coastwatch.pfeg.noaa.gov/erddap/griddap/NOAA_DHW_monthly.html. Once at the site, the time is set to 1997-04-16T00:00:00Z and yyyy-07-16T00:00:00Z (where yyyy is the data year), the latitude is set to 54 and 60, and the longitude is set to -137.2 and -130. Under the file type, choose '.nc-Download a NetCDF-3 binary file with COARDS/CF/ACDD metadata'and then 'submit'. Place the file in the data folder for the current forecast year [/YYYY_forecast/data/], and change the file name to 'NOAA_DHW_monthly_97_yy.nc' where yy is the final data year. The final extension should remain .nc.

The top of the script needs to be updated each year.

# create a folder for temperature_data
out.path <- paste0("2024_forecast/results/temperature_data/") # update year
if(!exists(out.path)){dir.create(out.path)}

# set up directories----
year.forecast <- "2024_forecast" # update year
data.directory <- file.path(year.forecast, 'data', '/')
results.directory <- file.path(year.forecast,  'results/temperature_data', '/')

Update all occurrences of the variable year (.csv files, x axes in the figures) in the [satellite_data_monthly.R] code script. Example: NOAA_DHW_monthly_97_yy.nc where yy needs to be the current year; [sst_oisst_97_yy_monthly_data.csv] where yy needs to be the final data year. This includes updating the figures to be 1997:yyyy where yyyy is the data year, and the file [varyyyy_final.csv] needs to contain the yyyy variables for the forecast year. There should be 27 occurrences.

read.csv(paste0(data.directory, 'var2023_final.csv')) %>% # update the year to the data year

In the script, the places to update are noted with

# update file name or

# update final year

The satellite SST variables will be output into the file [results/temperature_data/sst_regions_oisst_97_*yy*_monthly_data_summary.csv]. Then, these variables need to be copied and pasted into the [varyyyy_final.csv] sheet (the variables are: Chatham_SST_MJJ, Chatham_SST_May, Chatham_SST_AMJJ, Chatham_SST_AMJ, Icy_Strait_SST_MJJ, Icy_Strait_SST_May, Icy_Strait_SST_AMJJ, Icy_Strait_SST_AMJ, NSEAK_SST_MJJ, NSEAK_SST_May, NSEAK_SST_AMJJ, NSEAK_SST_AMJ, SEAK_SST_MJJ, SEAK_SST_May, SEAK_SST_AMJJ, SEAK_SST_AMJ). This seems a little backwards since this file is used in the satellite_data_monthly code, but it is only because an ISTI figure is created. The ISTI variable is not a satellite SST variable and is a SECM temperature variable. The file [satellite_SST_process.Rmd] does not need much updating if the same process as the prior year was used (e.g., the same latitude and longitude coordinates are used for the region of the satellite SST variables). It is helpful to run this file [satellite_SST_process.Rmd] every year so there is a record of the process. Save the output pdf file with a date so it does not get rewritten.

b. Model Code

To create the 18 models, the code is run in the following order;

  1. 1_summarize_models.R;

  2. 2_diagnostics.R;

  3. 2a_diagnostics.R;

  4. 3_sensitivity.R; and

  5. 4_retro_analysis.R

1_summarize_models.R script

This script needs to be modified based on the variables in the multiple linear regression for the particualr year. The script creates the [model_summary_table1.csv], [model_summary_table2.csv], [model_summary_table3.csv], [model_summary_table4.csv], [seak_model_summary.csv], [data_used_a.csv], [data_used_b.csv], and a separate results_mxx.csv file for each model run. The columns 'model1_sim' and 'sigma' in the results_mxx.csv files need to be copied to the excel workbook [model_summary_table_mm_yyyy.xlsx] (into each model) in the summary tables folder so that the one-step-ahead MAPE for 5 and 10 years is calculated correctly. The [forecasts.csv] file in the data folder is created from the results in the [model_summary_table_mm_yyyy.xlsx] file. The [model_summary_table5.csv] file is also created from the excel workbook [model_summary_table_mm_yyyy.xlsx] (although the adjusted R squared values are from the [model_summary_table2.csv] file). The forecast_models.png figure is also produced from this script.

The top of the script needs to be updated each year.

year.forecast <- "2023_forecast" # forecast year 
year.data <- 2022 # last year of data
year.data.one <- year.data - 1
sample_size <-  (year.data-1998)+1 # number of data points in model (this is used for Cook's distance)
forecast2022 <- 15.6 # input last year's forecast for the forecast plot
data.directory <- file.path(year.forecast, 'data', '/')
results.directory <- file.path(year.forecast,'results', '/')
results.directory.MAPE <- file.path(year.forecast,  'results/MAPE', '/')
results.directory.retro <- file.path(year.forecast,  'results/retro', '/')
source('2023_forecast/code/functions.r')

In order to correctly calculate the one-step-ahead MAPE for each of the 18 models, the bias-corrected forecast needs to be calculated for each forecast of the MAPE. This is one step I have thought about deleting and just going with the non-bias corrected MAPE for the 18 models (for simplicity). So there are two choices. The first choice is not entirely correct, but it is simpler.

The two options:

(1) use the function f_model_one_step_ahead_multiple which outputs the 5-year and 10-year MAPE to the csv file [seak_model_summary_one_step_ahead5.csv] and [seak_model_summary_one_step_ahead10.csv]; or

(2) run the function f_model_one_step_ahead for each of the 18 models (which produces a results_mx.csv file each each model in the results folder), and take the forecast and sigma from this file (for each model) and paste it in the excel file model_summary_Table_month_year.xlsx. Then the bias-corrected MAPE for the 18 models is calculated in the spreadsheet.The only reason the calculation is done in excel is that I haven't figured out how to do to bias-corrected one-step-ahead MAPE in R.

Option #1 is saved in the file model_summary_table2 and option #2 is saved in the file model_summary_table3 (in the [results/summary] folder).

2_diagnostics.R

This script is used to explore the best model (based on the lowest one-step-ahead MAPE and group discussions). The outputs include model_summary_table4_best_model.csv. This csv files includes the residuals, hat values, cook's distance values, standardized residuals, and fitted values that are used to create the diagnostic figures catch_plot_pred_mxx.png, fitted_mxx.png, general_diagnostics_mxx.png, and influential_mxx.png. In addition, the top of the script outputs the lack of fit test (Bonferroni p-values), and the lack of fit curvature test.

The top of the script needs to be updated each year.

fit_value_model<-18.841 #best model outputs (bias-corrected); value of forecast (from model_summary_table3)
lwr_pi_80<-12.273 # 80% PI from model_summary_table2
upr_pi_80<-28.922 # 80% PI from model_summary_table2
best_model<-m11
model<-'m11'
year.forecast <- "2023_forecast" # forecast year
year.data <- 2022 #last year of data
year.data.one <- year.data - 1

# source code and functions
source('2023_forecast/code/1_summarize_models.r') # current forecast year folder
source('2023_forecast/code/functions.r') # current forecast year folder

# best model based on performance metrics
lm(SEAKCatch_log ~ CPUE + NSEAK_SST_May, data = log_data_subset) -> m11

2a_diagnostics.R

This script is used to explore an alternative best model. The script is very similar to the [2_diagnostics.R ] script.

3_sensitivity.R

This code is used to filter out certain influential years (to see the effect on the model results) but was not used in the 2023 forecast process.

4_retro_analysis.R

This script creates model hindcasts for the best models and combines them with the [forecasts.csv] file. The result is a data frame with hindcasts (for each model) using data up to a certain year, and then the forecast for that reduced data set. The data frame is then used to create multiple figures (e.g., year_minus_5.png) that help show how the MAPE is calculated. For example, the figure year_minus_5.png shows the hindcasts for models m1, m2, and m11 using data from 1998 to 2017 only, and the 2018 forecast based on these three models (and only using data from 1998-2017). The figures are output into the folder results/retro/figs. This script also produces the MAPE_forecasts.png figure for the best (or chosen) models which are the one-step-ahead MAPE forecasts for the chosen models.

The [forecasts.csv] files in the data folder needs to be created manually from the spreadsheet model_summary_Table_month_year.xlsx in the [results/summary_tables] folder.

This script is very long, but is basically just repeating the process for the three models (CPUE-only model and two best models).

References

Adkison, M. D. 2002. Preseason forecasts of pink salmon harvests in Southeast Alaska using Bayesian model averaging. Alaska Fishery Research Bulletin 9(1):1–8.

Adkison, M. D., and R. M. Peterman. 2000. Predictability of Bristol Bay, Alaska sockeye salmon Oncorhynchus nerka returns 1 to 4 years in the future. North American Journal of Fisheries Management 20(1):69–80.

Clark, J. H., A. McGregor, R. D. Mecum, P. Krasnowski, and A. M. Carroll. 2006. The commercial salmon fishery in Alaska. Alaska Fishery Research Bulletin 12(1):1–146.

Farley Jr., E. V., J. M. Murphy, K. Cieciel, E. M. Yasumiishi, K. Dunmall, T. Sformo, and P. Rand. 2020. Response of pink salmon to climate warming in the northern Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography 177:104830.

Haeseker, S. L., R. M. Peterman, Z. Su, and C. C. Wood. 2005. Retrospective evaluation of preseason forecasting models for pink salmon. North American Journal of Fisheries Management 25(3):897–918.

Haeseker, S. L., B. Dorner, R. M. Peterman, and Z. Su. 2007. An improved sibling model for forecasting chum salmon and sockeye salmon abundance. North American Journal of Fisheries Management 27(2):634–642.

Heard, W.R. 1991. Life history of pink salmon (Oncorhynchus gorbuscha). Pages 119–230 [In] C. Groot and L. Margolis, editors. Pacific salmon life histories. University of British Columbia Press, Vancouver, B.C.

Heinl, S., X. Zhang, and H. Geiger. 2007. Pages 48–51 [In] D. M. Eggers. Run forecasts and harvest projections for 2007 Alaska salmon fisheries and review of the 2006 season. Alaska Department of Fish and Game, Special Publication No. 07-01, Anchorage.

Hofmeister, K., and J. Blick. 1993. Pages 43–45 [In] H. J. Geiger, and H. Savikko, editors. Preliminary forecasts and projections for 1993 Alaska salmon fisheries and review of the 1992 season. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Information Report No. 5J93-04, Juneau.

Hofmeister, K. T., and D. Jones. 1989. Pages 33–35 [In] H. J. Geiger, and H. Savikko, editors. Preliminary forecasts and projections for 1989 Alaska salmon fisheries. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Information Report No. 5J89-01, Juneau.

Jones, J. D., and K. T. Hofmeister. 1985. Pages 29–31 [In] D. M. Eggers, editor. Preliminary forecasts and projections for 1985 Alaska salmon fisheries. Alaska Department of Fish and Game, Division of Commercial Fisheries, Informational Leaflet No. 244, Juneau.

Krkošek, M., R. Hilborn, R. M. Peterman, and T. P. Quinn. 2011. Cycles, stochasticity and density dependence in pink salmon population dynamics. Proceedings of the Royal Society B: Biological Sciences 278(1714):2060–2068.

Miller, S. E., J. M. Murphy, S. C. Heinl, A. W. Piston, E. A. Fergusson, R. E. Brenner, W. W. Strasburger, and J. H. Moss. 2022. Southeast Alaska pink salmon forecasting models. Alaska Department of Fish and Game, Fishery Manuscript No. 22-03, Anchorage.

Mortensen, D., A. Wertheimer, C. M. Taylor, and J. Landingham. 2000. The relation between early marine growth of pink salmon, Oncorhynchus gorbuscha, and marine water temperature, secondary production, and survival to adulthood. Fishery Bulletin 98(2):319–335.

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Murphy, J. M., E. A. Fergusson, A. Piston, S. Heinl, A. Gray, and E. Farley. 2019a. Southeast Alaska pink salmon growth and forecast models. NPAFC Technical Report 15:75–81. (Available at http://www.npafc.org)

Murphy, J. M., A. C. Wertheimer, E. Fergusson, A. Piston, S. Heinl, C. Waters, J. Watson, and A. Gray. 2019b. 2018 pink salmon harvest forecast models from Southeast Alaska coastal monitoring surveys. NPAFC Doc. 1848. 19 pp. National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Alaska Fisheries Science Center, Auke Bay Laboratories, and Alaska Department of Fish and Game (Available at http://www.npafc.org)

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Orsi, J. A., M. V. Sturdevant, J. M. Murphy, D. G. Mortensen, and B. L. Wing. 2000. Seasonal habitat use and early marine ecology of juvenile Pacific salmon in southeastern Alaska. North Pacific Anadromous Fish Commission Bulletin 2: 111–122. (Available at www.npafc.org)

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Orsi, J. A., E. A. Fergusson, M. V. Sturdevant, B. L. Wing, A. C. Wertheimer, and W. R. Heard. 2006. Annual survey of juvenile salmon and ecologically-related species in the marine waters of southeastern Alaska, May–August 2005. (NPAFC Doc. 955) Auke Bay Lab., Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 11305 Glacier Highway, Juneau, AK 99801-8626, USA. (Available at www.npafc.org)

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Orsi, J. A., E. A. Fergusson, M. V. Sturdevant, W. R. Heard, and E. V. Farley, Jr. 2012. Annual survey of juvenile salmon, ecologically-related species, and biophysical factors in the marine waters of southeastern Alaska, May–August 2011. (NPAFC Doc.1428, Rev. 1) Auke Bay Lab., Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, NMFS, 17109 Point Lena Loop Road, Juneau, 99801, USA. (Available at http://www.npafc.org)

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Piston, A., S. Heinl, S. Miller, R. Brenner, J. Murphy, J. Watson, A. Gray, and E. Fergusson. 2020. Pages 42–45 [In] R. E. Brenner, S. J. Larsen, A. R. Munro, and A. M. Carroll, editors. 2020. Run forecasts and harvest projections for 2020 Alaska salmon fisheries and review of the 2019 season. Alaska Department of Fish and Game, Special Publication No. 20-06, Anchorage.

Piston, A., S. Heinl, S. Miller, R. Brenner, J. Murphy, J. Moss, W. Strasburger, E. Fergusson, and A. Gray. 2021a. Pages 42–45 [In] R. E. Brenner, S. J. Larsen, A. R. Munro, and A. M. Carroll, editors. Run forecasts and harvest projections for 2021 Alaska salmon fisheries and review of the 2020 season. Alaska Department of Fish and Game, Special Publication No. 21-07, Anchorage.

Piston, A., S. Heinl, S. Miller, R. Brenner, J. Murphy, J. Moss, W. Strasburger, E. Fergusson, and A. Gray. 2022. Pages 45–48 [In] R. E. Brenner, S. J. Donnellan, and A. R. Munro, editors. Run forecasts and harvest projections for 2022 Alaska salmon fisheries and review of the 2021 season. Alaska Department of Fish and Game, Special Publication No. 22-11, Anchorage.

Radchenko, V. 2020. Lost in the Ocean: Where have pink salmon been during our quest for salmon in the Gulf of Alaska? Pages 4–11 [In] North Pacific Anadromous Fish Commission Newsletter No. 47, January 2020.

Ruggerone, G. T., M. Zimmermann, K. W. Myers, J. L. Nielsen, and D. E. Rogers. 2003. Competition between Asian pink salmon (Oncorhynchus gorbuscha) and Alaskan sockeye salmon (O. nerka) in the North Pacific Ocean. Fisheries Oceanography 12(3):209–219.

Shevlyakov, E. A., and M. V. Koval. 2012. Forecast and production dynamics of the pink salmon of Kamchatka. North Pacific Anadromous Fish Commission Technical Report 8:121–125. (Available at www.npafc.org)

Wertheimer, A.C., J.A. Orsi, E.A. Fergusson, and M. V. Sturdevant. 2009a. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated environmental parameters: 2008 returns and 2009 forecast. NPAFC Document 1202. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and M. V. Sturdevant. 2010a. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated environmental parameters: 2009 Harvest and 2010 Forecast. NPAFC Doc. 1278. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and M. V. Sturdevant. 2011. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated environmental parameters: 2010 Returns and 2011 Forecast. NPAFC Doc. 1343. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and M. V. Sturdevant. 2012. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated biophysical parameters: 2011 returns and 2012 forecast. NPAFC Doc. 1414, Rev. 1. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and M. V. Sturdevant. 2013. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated environmental parameters: 2012 Returns and 2013 Forecast. NPAFC Doc. 1486 24 pp. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and M. V. Sturdevant. 2014. Forecasting pink salmon harvest in southeast Alaska from juvenile salmon abundance and associated biophysical parameters: 2013 returns and 2014 forecast. NPAFC Doc. 1555. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, and E. A. Fergusson. 2015. Forecasting pink salmon harvest in southeast Alaska from juvenile salmon abundance and associated biophysical parameters: 2014 returns and 2015 forecast. NPAFC Doc. 1618. (Available at http://www.npafc.org)

Wertheimer, A.C., J.A. Orsi, and E.A. Fergusson. 2017. Forecasting pink salmon harvest in southeast Alaska from juvenile salmon abundance and associated biophysical parameters: 2015 returns and 2016 forecast. NPAFC Doc. 1740. (Available at http://www.npafc.org)

Wertheimer, A. C., J. A. Orsi, E. A. Fergusson, and J. M. Murphy. 2018. Forecasting pink salmon harvest in Southeast Alaska from juvenile salmon abundance and associated biophysical parameters: 2016 returns and 2017 forecast. NPAFC Doc. 1772. (Available at http://www.npafc.org)

Willette, T. M., R. T. Cooney, V. Patrick, D. M. Mason, G. I. Thomas, and D. Scheel. 2001. Ecological processes influencing mortality of juvenile pink salmon (Oncorhynchus gorbuscha) in Prince William Sound, Alaska. Fisheries Oceanography 10(1):14–41.

Footnotes

  1. The background section was taken directly from Miller, S. E., J. M. Murphy, S. C. Heinl, A. W. Piston, E. A. Fergusson, R. E. Brenner, W. W. Strasburger, and J. H. Moss. 2022. Southeast Alaska pink salmon forecasting models. Alaska Department of Fish and Game, Fishery Manuscript No. 22-03, Anchorage. ↩

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