Introduction

This R markdown document was used to clean and document a salmon age, sex and length dataset collected by the Alaska Department of Fish and Game.

Create indices that will be used to filter/flag data

Below are the indices created/used to correct data

#create species indices
index.sock <- all.dat$Species_Code == 420
index.chinook <- all.dat$Species_Code == 410
index.chum <- all.dat$Species_Code == 450
index.coho <- all.dat$Species_Code == 430
index.pink <- all.dat$Species_Code == 440

#indices to flag suspecious combinations of project & gear
#This index flags commercial harvest that is not some type of gillnet or 'mixed' gear
index.com.harvest.not.gillnet <-  (all.dat$PROJECT_ID == 1 & 
                                  (all.dat$Gear_Code %in% c(0,1,2,5:89,99)))

#This index flags commercial harvest in pt moller
index.com.harvest.at.ptmoller <- (all.dat$District_Name == "Port Moller" & 
                                  all.dat$Project_ID == 1)
#bad sockeye lengths
index.bad.length <- (all.dat$FISH_LENGTH < 200 | all.dat$FISH_LENGTH > 1200)

#create indices used for correcting sockeye weights
index.pre.ohfour <- all.dat$YEAR < 2004
index.post.ohfour <- all.dat$YEAR >= 2004
index.wt.lrg <- all.dat$FISH_WEIGHT >= 600 
index.wt.sm <- all.dat$FISH_WEIGHT < 100
index.wt.med <- (all.dat$FISH_WEIGHT >= 100 & all.dat$FISH_WEIGHT < 600)
index.wt.sm2 <- all.dat$FISH_WEIGHT > 10
index.lbs.yr <- all.dat$YEAR == 1978
index.nk83 <- (all.dat$YEAR == 1983 & all.dat$District_Id == 22)
index.nush81 <- (all.dat$YEAR == 1981 & all.dat$District_Id == 23)

#create index to remove Age-0.0 
index.bad.age <- (all.dat$FW_AGE == 0 & all.dat$SW_AGE == 0)

#commercial harvest at Port Moller
index.com.harvest.at.ptmoller <- (all.dat$District_Name == "Port Moller" & 
                                  all.dat$Project_ID == 1)

#commercial harvest where gear is anything other than 'Drift Gillnet', 'Mixed' or 'Set Gillnet' 
index.com.harvest.not.gillnet <-  (all.dat$PROJECT_ID == 1 & 
                                  (all.dat$Gear_Code %in% c(0,1,2,5:89,99)))

apply corrections

Corrections are applied to the dataset

#cleaning up age variables
all.dat$FW_AGE[(index.bad.age)] <- ""
all.dat$SW_AGE[(index.bad.age)] <- ""

#creating a total age variable combining FW and SW age
all.dat$tot.age <- paste(all.dat$FW_AGE,all.dat$SW_AGE,sep=".")

#pre-2004 weight correction
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.pre.ohfour)] <- .01
all.dat$FISH_WEIGHT_temp = all.dat$FISH_WEIGHT * all.dat$FISH_WEIGHT_CORRECTION

#sockeye length correction
all.dat$FISH_LENGTH_CORRECTION[(index.bad.length & index.sock)] <- 0

# default correction
all.dat$FISH_WEIGHT_CORRECTION <- 1

#sockeye weight corrections
#weights that are too large
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.wt.lrg)] <- 0
#correction for weights too small, years > 2004
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.wt.sm2 & index.post.ohfour)] <- 
#pre 2004, small weight
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.pre.ohfour & index.wt.sm)] <- .1
#pre 2004 intermediate weight
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.pre.ohfour & index.wt.med)] <- .01
# weights determined to be in pounds
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.lbs.yr )] <- .0453592
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.nk83)] <- .0453592
all.dat$FISH_WEIGHT_CORRECTION[(index.sock & index.nush81)] <- .0453592

# chinook weight correction
all.dat$FISH_WEIGHT_CORRECTION[(index.chinook & index.pre.ohfour)] <- .01

# chum weight correction
all.dat$FISH_WEIGHT_CORRECTION[(index.chum & index.pre.ohfour)] <- .01

# re-calculate weight and length
all.dat$FISH_WEIGHT_CORRECTED <- all.dat$FISH_WEIGHT_temp * all.dat$FISH_WEIGHT_CORRECTION
all.dat$FISH_LENGTH_CORRECTED <- all.dat$FISH_LENGTH * all.dat$FISH_LENGTH_CORRECTION

# change final weights less than one to NULL
all.dat[all.dat$FISH_WEIGHT_CORRECTED < 1 ,c("FISH_WEIGHT_CORRECTED")] <- ""

# change final lengths < 200 to NULL
all.dat[all.dat$FISH_LENGTH_CORRECTED < 200, c("FISH_LENGTH_CORRECTED")] <- ""

# change FW and SW_AGE to numeric 
all.dat <- all.dat %>%
  mutate(FISH_LENGTH_CORRECTED = as.numeric(FISH_LENGTH_CORRECTED)) %>%
  mutate(FISH_WEIGHT_CORRECTED = as.numeric(FISH_WEIGHT_CORRECTED))

Calculate fish condition factor

#calculate sockeye condition factor
all.dat <- all.dat %>%
  mutate(FISH_CONDITION = ((FISH_WEIGHT_CORRECTED * 1000)/(FISH_LENGTH_CORRECTED^3)) * 100000)

Source Description

This dataset contains 1,970,119 records of salmon age, sex, and length (ASL) data collected by the Division of Commercial Fisheries within the Bristol Bay management area between 1957 and 2018 (Table 1). This document covers the 1,702,623 sockeye records (Table 2) and the 110,260 Chinook records (Table 3), the 124,677 Chum data (Table 4), the 17,537 Coho data (table 5), and the 15,022 Pink salmon data (table 6).`

Salmon were measured from mid-eye to tail fork(+ 1 mm; METF). Prior to 2005 fish lengths were taken using a rigid measuring board or a caliper. From 2005 on fish lengths have been taken electronically using digital calipers or measuring boards. Round weights, when taken, were measured in kilograms (kg), generally to the nearest 10th of a kg, using either a portable spring scale or electronic kitchen scale.

Sex was determined by examining external secondary sexual characteristics (Groot and Margolis 1991). Scales were plucked using forceps from the preferred area of each salmon: an area 2-3 rows above the lateral line, posterior to the dorsal fin and anterior to the anal fin (INPFC 1963). Scales were cleaned and mounted ridged side up on numbered gum card (40 or 48 scales per card) and then heat pressed onto acetate cards for reading and archival. Images of scale impressions were magnified 35x and projected on a microfiche reader so the number of fresh and saltwater annuli per scale could be counted to determine age. Age data is stored using the European age designation system (Koo 1962) wherein the first digit refers to the number of freshwater annuli, the second digit refers to the number of marine annuli, and the total age is the sum of the two digits plus one. For example, an age 1.2 salmon is a 4-year old salmon that spent 2 years in freshwater (in addition to an initial winter spent in the gravel as an alevin) and 2 years at sea before returning to their natal stream to spawn. Large portions of the historical paper dataset had ages originally recorded in the Gilbert-Rich format, were converted to the European age designation system over time as data were put into electronic forms.

The metadata (geographic stat code, gear, species, date, etc) for each scale card is also summarized directly on the back of the gum cards on which scales are collected. Gum cards and acetates are archived together and archived by year, stat code, and species in filing cabinets located in Anchorage, Dillingham, or King Salmon. Operational plans for research projects describe ASL sampling around Bristol Bay. Buck and Brazil (2016) describe ASL sampling at the Nushagak sonar project. Brazil and Salomone (2016) describe ASL sampling at tower escapement enumeration projects. West and Brazil (2013) describe ASL sampling conducted by Inriver test fishing crews in Bristol Bay. West and Buck (In Prep.) describe ASL sampling of the commercial salmon harvest.

Although methods for measuring fish and collecting and aging scales have remained fairly consistent throughout time, the data format changed has changed over the years, generally in response to evolving technology (e.g., computer hardware and software). Following are descriptions of the original data formats before they were standardized and assembled into this database.

1957-1978: ASL data during this period were generally recorded using a variety of data sheets and then hand entered to create digital records. To conserve space, a series of numerical codes representing standardized values were used in various data fields (e.g., species, gear type, length type, weight type, age type, age error codes, etc.). Header and fish records were combined so each fish also included the header information.

Large amounts of ASL data from this period was stored on magnetic tapes which have been lost. Only small amounts of data from this time period exist electronically (Table 2). It appears, however, that significant amounts of this data may exist as paper records. We have recently begun efforts to enter this data although none of the fruits of this recent data entry effort is included in the dataset documented here. Preliminary examination of these paper records reveal numerous data sheet formats with a range of english and metric length/weight measurements as well as a mixture of age data in a mixture of Gilbert-Rich and European formats. Preliminary data entry efforts indicate that approximately 350/fish records per man-hour is feasible entering data directly ‘as-is’. Post-entry standardization of data will be accomplished using R.

1979-2012: ASL data during this period were generally collected on ADF&G Standard AWL scantron field forms that were specifically designed for ASL (a.k.a. AWL for age, weight, length) data collection using scantron bubble sheets. Digital records were stored in MS Access DB. Minor changes to the FMB software resulted in there being two different ascii text formats over the years (1986-1990; 1991-2001). Over time, data from this era were slowly standardized to a common MS Access DB format compatible with a FORTRAN program developed by University of Washington researchers to collate and organize individual ascii files generated from the ASL scantron forms.

2005-Present: Currently all ASL data collected in Bristol Bay is done using a software ecosystem that incorporates a server-side system used to collect, collate, and manage ASL data and a separate Windows based mobile application used to collect ASL data at the point of sampling-the Fisheries Data Management System (FDMS; Sechrist et. al. In Prep.). Digital collection of ASL data at the point of sampling began in 2005 with early versions of the software currently used for commercial harvest sampling. Over time the software has been further developed and it’s use has spread throughout all ASL sampling projects such as escapement and test fishing projects. Currently digital ASL data collection in Bristol Bay is standard. Data entered through the mobile FDMS application were uploaded to the server on a daily basis where QA/QC was conducted. The mobile version of the app has standardized QA/QC built into the platform which will not allow outliers to be entered. Initially FDMS was only utilized inseason and data were archived in individual MS Access DB databases each year but more recently FDMS has become both an inseason tool and archive for digital ASL.

Processing Step Description

The Source Description above provides some of the processing steps that were applied to the data at the time they were collected and initially summarized. Following are the processing steps that were applied to archived data when they were combined and assimilated before being imported into the Central Region ASL database:

Edited stat codes to conform to current definitions of locations. Over the course of this dataset there were 205 unique stat code locations used. In our current data collection protocol we use about 115. Old stat codes that are no longer in use were updated to match the current version of the code. When changes were made the historical stat code was archived in a separate field within the database so there would not be a discrepancy between paper data sheets and the database. A matrix of old and current stat codes is included in the metadata for this project.
Edited to conform to a common format with standardized field names and values. The main changes revolved around species, gear, and project codes that were used differently throughout the history of the dataset. (Note: when original values were edited, a comment documenting the change was recorded in a new field.
Datasets were then merged into a single dataset in .csv format
Additional QA/QC of the dataset were conducted on the merged dataset. That effort is described in the Data Consistency section of this report.

Attribute Accuracy Report

The biological data in the Bristol Bay ASL data are assessed with respect to accuracy using the procedures laid out in the Operational Plan associated with the project where the ASL was collected. Scale age accuracy is sufficient to estimate the proportion of each of the major age classes in the salmon catch and escapement in selected major river systems to within 5% of the true proportion 90% of the time (West, et al. 2012).

There were several stat codes that were used from 1957-2000 that are not part of our current stat code regime and corresponding descriptions of areas were difficult to find. In cases where paper data sheets could not be used to confirm historical locations an “unknown” (999) field was used. The location was used to the most detailed level that could be confirmed (i.e. District, Subdistrict) and then 999 was used for what could not be verified (i.e. Stream, Location). It is believed that most of these fish were sampled as part of subsistence or sport fish sampling event and were largely non-sockeye fish and not part of any routine ASL sampling associated with the research activities of the Commercial Fisheries Division.

Data Consistency Report

A variety of quality control measures were implemented at multiple steps throughout the data collection, data entry, and error checking processes. The first firewall against errors is crew training. Senior crew train new crew and manuals are available for all software, hardware as well as how to sex and speciate Pacific Salmon species. To avoid transcription errors in the field, the person recording data manually or electronically (e.g., Juniper Systems Allegro) repeated the values called out by the person measuring the fish to confirm that they heard them correctly. The FDMS mobile software has several features to assist with the QA/QC of the data such as warning messages when unexpected or outsized values are entered. Upon uploading, the uploader must ‘verify’ the data before it is available for data reporting and summarizing. This forces the uploader to review the metadata for each sampling session before it is added to the dataset.

Post-season quality control includes careful examination of the data for outliers before switching to handheld devices, a FORTRAN program program was used to compile scantron data sheets. The program also identified outliers of length and weight. All records with missing length, sex, and or age were removed from the summary and not included in the dataset. When staff moved towards excel workbooks, outliers were established by simple visual checks by sorting and filtering data. Any obvious outliers (lengths vs age, missing digits, etc) were removed or noted and not included in analysis.

Sex code

A frequency table of ASL data by sex and species reveals nothing more of note than a fairly skewed M:F sex ratio for chum salmon (table 7).

Freshwater and Ocean Ages

Next we examine Freshwater and Ocean ages by species. The first item of note is that there is one record where SW_AGE equals a character (’*‘). That record is changed to ’NA’. Also There are 489 records with age = ‘0.0’. These records appear to have age error codes that indicate indicating no age should be associated with these fish, therefore records where both ‘FW_AGE’ and ‘SW_AGE’ equaled zero, both variables were set to ‘NA’. Once these corrections are applied the remaining age data appear correct (Table 8).

Gear and Project codes

Frequency tables are generated for ASL data by project and district for each species (Tables 9-13). Next, frequency tables by gear and project (Tables 14-18) we discover two code combinations that appear contridictory and we flag them for further investigation. Specifically we flag two combinations:

commercial harvest project code at Port Moller
commercial harvest project code where gear is anything other than ‘Drift Gillnet’, ‘Mixed’ or ‘Set Gillnet’

Sockeye Length and Weight Data

For length and weight data corrections I introduce two new variables to the dataframe: ‘LENGTH_CORRECTION’ and ‘WEIGHT_CORRECTION’ with default values set at one. These will serve as correction factors such that when multiplied with the original weight and length data to get corrected length and weights. Note: the original length/weight data remains part of the dataset. Any weights or lengths that are corrected to zero are further corrected ‘NA’. Records where Lengths that are corrected are also flaged ‘L’ where lengths are corrected

Length Data

Considering the entire dataset across all species and ages we deem lengths < 200 mm or greater than 1,200 mm to be invalid and removed from the dataset (Figure 1).

Weight Data

It appears that we began measuring weights in kilograms in 2004 and that if we apply a correction of 0.01 to weights pre-2004 and examine the mean weight by year and district it appears that we have the majority of the data in kilograms (Table 19). Next we take a detailed look at weight data by graphing histograms by year and district (for example, Egegik 1989; Figure 2). District/year combinations that appear to have been measured are corrected to kilograms. A very casual look at the hard datasheets tells us that there are times and locations where both pounds and kilograms were being collected, however with resources available to us, we were unable to do any QA/QC on any finer scale beyond visual examination of weight histograms by year and species. In summary the weight corrections that were applied to the data are:

for pre-2004 fish with weights < 100: FISH_WEIGHT_CORRECTION = 0.1
for pre-2004 fish with weights > 600: FISH_WEIGHT_CORRECTION = 0
for pre-2004 fish with weights between 100 and 600: FISH_WEIGHT_CORRECTION = 0.01
for years >= 2004 fish with weights > 10: FISH_WEIGHT_CORRECTION = 0
for weights that appear to be in pounds: correction = 0.0453592

corrected fish weight is calculated as:

FISH_WEIGHT_CORRECTED = FISH_WEIGHT * FISH_WEIGHT_CORRECTION

A summary of the sockeye corrections (Table 20).

Plotting the corrected weights and lengths for ocean age-2 (Figure 3) and ocean age-3 (Figure 4) it appears that we have successfully corrected the weight and length data.

Condition Factor

A condition factor for each fish is calculated and added to the dataset (Bolger and Connolly 1989). Condition factor (k) is calculated as

\[\ K = (W/L^3) * 1000\]

where:

W = Fish weight (corrected)

L = Fish length (corrected)

The mean condition factor for sockeye, chum and chinook is 1.7 (Table 21).

Chinook and chum Length and Weight

The .01 correction appears to be necessary for all pre-2004 chinook (Tables 22) and chum weights (Tables 23) in order to put the dataset into Kilograms.

Completeness Report

There are large chunks of missing data from this dataset. Unfortunately, large sets of the Bristol Bay dataset are not in any digital format. A rough estimate puts this at ~12,000 cards of sockeye salmon samples, and ~650 cards of Chinook salmon samples. Each of these sockeye cards could represent anywhere from 1 to 40 records which puts our rough estimate of missing sockeye samples at about 12,000-480,000 (most likely on the higher end of this range). Chinook cards hold anywhere from 1 to 10 samples which puts our rough estimate of missing Chinook samples at about 650 - 6,500 (most likely on the higher end of this range). The largest set of missing data is represented by the years 1957-1977 for the east side of Bristol Bay (Egegik, Ugashik, and Naknek-Kvichak districts). However, there are also sporadic missing samples from throughout the bay from about 1957 thorough the early 2000’s. An inventory of samples that are not included in the digital dataset is included in the metadata. This inventory was developed around 2004 by ADF&G staff. It was intended to be a full inventory of all ASL sampling events that took place throughout the history of Bristol Bay. In this inventory staff took special care to go through all data types (Paper, Acetate, Scale Card, Electronic, and Database) and denote what was on hand at ADF&G for each sampling event. This completed spreadsheet is considered the best inventory of what sampling was done, where data is located, and what format it is available in. The condensed version of this spreadsheet only includes sampling events that are not currently represented in this database, as well as what format it is in, and where it is stored. All sampling events after this inventory was created in 2004 are in database form and included in this dataset.

REFERENCES CITED

Bolger, T. and Connolly, P. L. 1989. The Selection of suitable indices for the measurement and analysis of fish condition. Journal of Fish Biologogy 34(2) 171-182.

Brazil, C. E., and P. Salomone. 2016. Operational plan: Sockeye salmon counting towers, Bristol Bay, 2016. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Operational Plan ROP.CF.2A.2016.03, Anchorage.

Buck, G. B. and C. E. Brazil. 2016. Operational plan: Enumeration of Pacific Salmon Escapement into the Nushagak River. Alaska Department of Fish and Game, Regional Operational Plan ROP.CF.2A.2016.01, Anchorage.

Groot, C., and L. Margolis, editors. 1991. Pacific salmon life histories. University of British Columbia Press, Vancouver, Canada. INPFC (International North Pacific Fisheries Commission). 1963. Annual report 1961. Vancouver, British Columbia.

Koo, T. S. Y. 1962. Age designation in salmon. Pages 37-48 [in]: T. S. Y. Koo, editor. Studies of Alaska red salmon. University of Washington Publications in Fisheries, New Series, Volume I, Seattle.

Sechrist, K., Buck, G. B., Vega, S., Boutin, I., and Kimball, H. In Prep. A Software System for the Collection of Age, Sex and Length, (ASL) Data on Salmon: The Fisheries Data Management System (FDMS). Alaska Department of Fish and Game, Regional Information Report, RIR-XXXX, Anchorage.

West, F. and G. Buck. In Prep. Age, Sex, Size and Genetic Sampling of the Commercial Salmon Harvest, Bristol Bay, Alaska. Alaska Department of Fish and Game, Regional Operational Plan ROP.CF.2A.XXXX.XX, Anchorage.

West, F., L. Fair, T. Baker, S. Morstad, K. Weiland, T. Sands, and C. Westing. 2012. Abundance, age, sex, and size statistics for Pacific salmon in Bristol Bay, 2005. Alaska Department of Fish and Game, Fishery Data Series No. 12-02, Anchorage.

TABLES

Table 1. ASL data by species.

	Chinook	Chum	Coho	Pink	Sockeye
1957	14	0	0	0	0
1959	92	0	0	0	0
1960	0	184	0	0	0
1961	68	440	0	0	0
1962	34	19	0	360	0
1963	0	276	0	0	0
1964	354	28	40	2,314	0
1965	588	1,092	0	0	19
1966	1,199	1,811	404	2,223	65
1967	2,983	3,208	1,125	0	4
1968	2,699	2,753	144	0	20
1969	500	6	450	0	1
1970	2,972	2,438	0	411	9
1971	2,283	3,912	4	0	42
1972	1,879	3,830	0	782	120
1973	1,223	3,711	0	0	76
1974	918	1,347	0	1,418	16
1975	569	1,532	2	0	37
1976	1,957	3,079	0	1,335	149
1977	1,851	2,768	4	0	50
1978	1	90	1	1	21,774
1979	1,723	2,178	907	1	32,526
1980	833	2,763	182	2,113	29,155
1981	2,762	2,701	0	1	27,681
1982	3,681	2,158	0	1,677	23,961
1983	4,588	2,286	162	0	26,986
1984	3,536	3,516	2,525	2,242	26,824
1985	2,575	1,762	227	0	34,614
1986	2,206	2,433	440	0	36,122
1987	3,130	3,149	121	0	36,481
1988	2,114	3,298	513	0	51,754
1989	1,923	1,652	632	0	57,380
1990	0	2,902	4,094	0	72,926
1991	2,462	4,114	556	0	72,788
1992	4,455	4,686	0	0	59,434
1993	3,721	3,592	213	0	45,392
1994	2,657	4,003	955	144	62,095
1995	2,989	3,319	1,269	0	69,327
1996	2,072	3,732	1,034	0	48,105
1997	1,954	2,781	212	0	29,633
1998	3,134	1,749	555	0	28,224
1999	1,768	0	0	0	32,074
2000	612	0	0	0	33,077
2001	1,781	2,829	301	0	38,336
2002	1,687	1,637	164	0	32,064
2003	1,383	1,074	0	0	44,161
2004	2,339	1,244	301	0	41,844
2005	2,242	2,343	0	0	36,578
2006	2,339	2,104	0	0	44,786
2007	3,552	2,353	0	0	42,267
2008	2,407	704	0	0	46,028
2009	2,509	1,363	0	0	44,666
2010	1,453	2,609	0	0	43,226
2011	1,449	1,699	0	0	43,009
2012	2,191	3,080	0	0	35,754
2013	1,425	1,547	0	0	35,722
2014	863	2,076	0	0	40,024
2015	1,463	1,683	0	0	40,087
2016	1,731	2,438	0	0	41,812
2017	1,240	1,676	0	0	42,557
2018	1,127	920	0	0	50,761
total	110,260	124,677	17,537	15,022	1,702,623

Table 2. Sockeye ASL data by year and district.

	Egegik	General District	Naknek-Kvichak	Nushagak	Port Moller	Togiak	Ugashik
1965	0	0	0	4	0	15	0
1966	0	0	0	65	0	0	0
1967	0	0	0	4	0	0	0
1968	0	0	0	13	0	7	0
1969	0	0	0	0	0	1	0
1970	0	0	0	6	0	3	0
1971	0	0	0	39	0	3	0
1972	0	0	0	108	0	12	0
1973	0	0	0	65	0	11	0
1974	0	0	0	14	0	2	0
1975	0	0	0	31	0	6	0
1976	0	0	0	97	0	52	0
1977	0	0	0	43	0	7	0
1978	4,021	0	6,701	6,636	0	3,298	1,118
1979	6,179	0	7,366	8,283	0	5,432	5,266
1980	3,825	0	8,615	8,006	0	4,305	4,404
1981	5,053	0	6,259	8,106	0	4,881	3,382
1982	4,041	0	5,514	7,580	0	4,811	2,015
1983	1,571	0	7,664	7,895	0	3,780	6,076
1984	0	0	8,632	10,854	0	3,641	3,697
1985	4,602	0	10,959	10,381	0	3,278	5,394
1986	6,478	0	10,557	9,877	0	4,404	4,806
1987	7,868	0	9,264	8,434	0	4,056	6,859
1988	12,866	0	13,573	11,572	0	4,781	8,962
1989	13,545	0	18,938	10,798	0	3,250	10,849
1990	29,653	0	21,636	9,822	0	4,757	7,058
1991	26,189	0	17,749	12,863	0	7,419	8,568
1992	11,811	39	21,594	13,651	0	5,348	6,991
1993	8,633	0	13,172	13,145	0	4,076	6,366
1994	12,891	12,328	12,819	11,258	0	4,825	7,974
1995	15,252	5,992	17,216	12,852	0	4,432	13,583
1996	10,761	0	11,124	14,040	0	3,695	8,485
1997	7,484	0	5,994	9,321	0	2,787	4,047
1998	6,125	0	7,330	9,341	0	2,865	2,563
1999	5,686	0	9,730	8,888	0	3,298	4,472
2000	7,875	0	9,152	8,242	0	3,249	4,559
2001	9,679	0	10,715	10,070	0	3,134	4,738
2002	7,017	0	8,161	9,990	0	2,907	3,989
2003	8,040	0	12,071	11,482	0	3,640	8,928
2004	8,559	3,056	10,618	11,086	0	2,611	5,914
2005	6,603	0	9,510	9,927	3,998	2,828	3,712
2006	6,028	0	11,019	12,960	6,184	3,536	5,059
2007	6,236	0	9,181	11,729	3,704	4,980	6,437
2008	6,609	0	11,452	10,733	6,577	4,697	5,960
2009	7,385	0	11,028	9,618	6,235	4,556	5,844
2010	5,871	0	11,465	11,317	4,211	4,186	6,176
2011	6,051	0	10,836	11,645	4,031	4,070	6,376
2012	5,743	0	9,781	8,575	2,715	3,674	5,266
2013	6,430	0	8,175	9,362	3,005	3,918	4,832
2014	6,741	0	11,295	10,232	3,739	3,695	4,322
2015	6,895	0	8,610	10,965	4,002	3,760	5,855
2016	7,546	0	9,574	9,746	3,194	5,125	6,627
2017	7,392	0	10,750	12,997	2,388	4,626	4,404
2018	6,865	0	14,096	18,073	3,368	4,148	4,211
total	338,099	21,415	449,895	432,841	57,351	166,878	236,144

Table 3. Chinook ASL data by year and district.

	Egegik	General District	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1957	0	0	0	14	0	0
1959	0	0	0	92	0	0
1961	0	0	0	68	0	0
1962	0	0	0	34	0	0
1964	0	0	0	307	47	0
1965	0	0	0	302	286	0
1966	0	0	0	858	341	0
1967	0	0	0	2,540	443	0
1968	0	0	0	2,057	642	0
1969	0	0	0	0	500	0
1970	0	0	0	2,445	527	0
1971	0	0	0	1,773	510	0
1972	0	0	0	1,252	627	0
1973	0	0	0	636	587	0
1974	0	0	0	498	420	0
1975	0	0	0	500	69	0
1976	0	0	0	1,259	698	0
1977	0	0	0	1,151	700	0
1978	0	0	0	1	0	0
1979	0	0	0	1,133	590	0
1980	0	0	0	789	44	0
1981	0	0	0	2,159	603	0
1982	1	0	0	2,947	733	0
1983	0	0	599	3,323	609	57
1984	0	0	0	2,630	707	199
1985	0	0	22	1,786	625	142
1986	8	0	0	1,599	599	0
1987	0	0	0	2,530	600	0
1988	0	0	0	1,494	620	0
1989	0	0	75	1,004	724	120
1991	78	0	0	1,491	636	257
1992	0	0	1,254	2,643	308	250
1993	0	0	0	3,021	700	0
1994	0	0	0	2,123	534	0
1995	0	0	0	2,216	773	0
1996	0	0	0	1,451	621	0
1997	0	0	0	1,405	549	0
1998	0	0	0	2,534	600	0
1999	0	0	0	1,768	0	0
2000	0	0	0	612	0	0
2001	0	0	0	1,391	390	0
2002	0	0	0	1,504	183	0
2003	0	0	0	1,383	0	0
2004	0	187	0	2,152	0	0
2005	85	0	94	1,798	265	0
2006	0	0	0	2,139	200	0
2007	0	0	0	2,637	915	0
2008	0	0	0	1,922	485	0
2009	0	0	0	2,027	482	0
2010	0	0	0	1,117	336	0
2011	0	0	0	1,242	207	0
2012	0	0	0	1,489	702	0
2013	0	0	0	1,315	110	0
2014	0	0	0	779	84	0
2015	0	0	0	1,459	4	0
2016	0	0	0	1,581	150	0
2017	0	0	0	962	278	0
2018	0	0	0	773	354	0
total	172	187	2,044	84,115	22,717	1,025

Table 4. Chum ASL data by year and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1960	0	0	184	0	0
1961	0	0	240	200	0
1962	0	0	19	0	0
1963	0	0	36	240	0
1964	0	0	12	16	0
1965	0	0	74	1,018	0
1966	0	0	651	1,160	0
1967	0	0	1,872	1,336	0
1968	0	0	1,977	776	0
1969	0	0	4	2	0
1970	0	0	1,619	819	0
1971	0	0	2,435	1,477	0
1972	0	0	1,730	2,100	0
1973	0	0	1,503	2,208	0
1974	0	0	559	788	0
1975	0	0	726	806	0
1976	0	0	1,924	1,155	0
1977	0	0	1,575	1,193	0
1978	34	29	10	16	1
1979	33	7	913	1,210	15
1980	49	12	1,667	1,030	5
1981	24	104	1,450	1,118	5
1982	71	59	1,156	870	2
1983	1	0	937	1,348	0
1984	9	13	1,660	1,812	22
1985	0	33	875	794	60
1986	0	0	1,173	1,260	0
1987	0	0	1,229	1,920	0
1988	20	4	1,755	1,519	0
1989	0	0	847	725	80
1990	0	0	2,187	715	0
1991	0	0	2,083	2,031	0
1992	0	84	3,399	1,203	0
1993	0	0	1,978	1,614	0
1994	0	0	2,444	1,559	0
1995	0	0	2,002	1,317	0
1996	0	0	1,931	1,801	0
1997	0	0	1,674	1,107	0
1998	0	0	1,359	390	0
2001	0	0	1,884	945	0
2002	0	0	1,099	538	0
2003	0	0	1,074	0	0
2004	0	0	1,244	0	0
2005	0	1	1,757	585	0
2006	0	0	1,438	625	41
2007	0	0	2,353	0	0
2008	0	0	656	48	0
2009	0	0	1,363	0	0
2010	0	0	1,657	952	0
2011	0	0	1,219	480	0
2012	0	0	1,730	1,350	0
2013	0	0	1,227	320	0
2014	0	0	1,108	968	0
2015	0	0	915	768	0
2016	0	0	1,702	736	0
2017	0	0	605	1,071	0
2018	0	0	584	336	0
total	241	346	73,484	50,375	231

Table 5. Coho ASL data by year and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1964	0	0	40	0	0
1966	0	0	404	0	0
1967	0	0	1,065	60	0
1968	0	0	144	0	0
1969	0	0	197	253	0
1971	0	0	4	0	0
1975	0	0	1	1	0
1977	0	0	4	0	0
1978	0	0	0	1	0
1979	0	0	565	342	0
1980	0	0	71	111	0
1983	0	0	0	0	162
1984	0	0	827	1,698	0
1985	0	0	227	0	0
1986	0	0	200	240	0
1987	0	0	121	0	0
1988	0	0	513	0	0
1989	0	0	632	0	0
1990	1,070	804	575	773	872
1991	373	0	183	0	0
1993	0	0	213	0	0
1994	319	0	636	0	0
1995	500	0	310	459	0
1996	0	0	740	294	0
1997	0	0	212	0	0
1998	0	0	555	0	0
2001	0	0	301	0	0
2002	0	0	164	0	0
2004	0	0	301	0	0
total	2,262	804	9,205	4,232	1,034

Table 6. Pink ASL data by year and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1962	0	0	360	0	0
1964	0	0	2,311	3	0
1966	0	0	2,223	0	0
1970	0	0	411	0	0
1972	0	0	782	0	0
1974	0	0	1,418	0	0
1976	0	0	1,335	0	0
1978	0	0	1	0	0
1979	0	0	0	0	1
1980	0	1	2,112	0	0
1981	0	0	1	0	0
1982	0	0	1,675	1	1
1984	0	0	2,242	0	0
1994	144	0	0	0	0
total	144	1	14,871	4	2

Table 7. ASL data by sex and species.

	Female	Male	Missing	Not Taken	Unknown	F.M.ratio
Chinook Salmon	54,364	55,543	5	0	348	0.98
Chum Salmon	72,812	51,553	3	0	309	1.41
Coho Salmon	8,397	8,980	0	0	160	0.94
Pink Salmon	7,423	7,599	0	0	0	0.98
Sockeye Salmon	897,802	800,001	50	11	4,759	1.12

Table 8. ASL age by species.

	Chinook Salmon	Chum Salmon	Coho Salmon	Pink Salmon	Sockeye Salmon	Total
.	4	31	0	0	454	489
0.1	7	5	2	14,535	54	14,603
0.2	5	4,096	5	0	2,274	6,380
0.3	233	71,270	6	0	12,784	84,293
0.4	237	35,243	7	0	1,813	37,300
0.5	35	1,402	0	0	6	1,443
0.6	0	8	0	0	2	10
1.1	561	2	1,150	1	2,984	4,698
1.2	20,272	22	18	0	362,339	382,651
1.3	33,406	66	25	2	548,625	582,124
1.4	35,101	11	2	0	9,008	44,122
1.5	2,892	1	0	0	17	2,910
1.6	20	0	0	0	0	20
2.0	0	0	1	0	1	2
2.1	4	8	11,575	0	5,779	17,366
2.2	82	9	53	1	325,149	325,294
2.3	191	17	4	0	187,913	188,125
2.4	229	0	0	0	713	942
2.5	16	0	0	0	2	18
2.6	1	0	0	0	0	1
2.NA	0	0	0	0	1	1
3.0	0	0	4	0	2	6
3.1	4	2	386	0	73	465
3.2	0	0	141	0	2,512	2,653
3.3	0	0	0	0	920	920
3.4	0	0	0	0	4	4
7.1	0	4	0	0	0	4
8.1	0	1	0	0	0	1
8.2	10	0	0	0	0	10
8.3	3	0	0	0	0	3
NA.NA	16,947	12,479	4,158	483	239,194	273,261
Total	110,260	124,677	17,537	15,022	1,702,623	1,970,119

Table 9. Sockeye ASL data by project and district.

	Egegik	General District	Naknek-Kvichak	Nushagak	Port Moller	Togiak	Ugashik
Commercial Harvest	234875	21415	253382	219799	526	106838	141274
Escapement	88573	0	178784	207683	0	59998	81906
Escapement - Spawning Grounds	0	0	1648	0	0	0	0
Subsistence Harvest	8732	0	9104	4689	0	40	7643
Test Fishing	5793	0	6927	17	56825	0	5321
Unknown	126	0	50	653	0	2	0

Table 10. chinook ASL data by project and district.

	Egegik	General District	Naknek-Kvichak	Nushagak	Togiak	Ugashik
Commercial Harvest	94	187	2044	59625	22500	1025
Escapement	0	0	0	19079	217	0
Subsistence Harvest	0	0	0	1882	0	0
Unknown	78	0	0	3529	0	0

Table 11. chum ASL data by project and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
Commercial Harvest	186	220	53300	48941	222
Escapement	21	26	18821	868	1
Subsistence Harvest	0	10	160	0	0
Test Fishing	34	6	0	0	8
Unknown	0	84	1203	566	0

Table 12. coho ASL data by project and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
Commercial Harvest	1267	680	4104	3259	1033
Escapement	500	21	5101	973	1
Unknown	495	103	0	0	0

Table 13. pink ASL data by project and district.

	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
Commercial Harvest	0	0	6018	4	0
Escapement	0	1	8853	0	2
Unknown	144	0	0	0	0

Table 14. Sockeye ASL data by gear and project.

	Commercial Harvest	Escapement	Escapement - Spawning Grounds	Subsistence Harvest	Test Fishing	Unknown
Beach Seine	1340	572413	0	40	10	289
Drift Gillnet	761175	24237	0	27505	74261	10
Handpicked	0	0	1648	0	0	0
Mixed	82821	0	0	0	0	0
Pots	0	0	0	0	0	1
Purse Seine	80	117	0	0	0	0
Set Gillnet	101821	206	0	1087	20	479
Trap	16291	6131	0	0	505	0
Troll	73	40	0	0	0	0
Unknown/Other	14508	12994	0	1576	87	52
Weir	0	806	0	0	0	0

Table 15. Chinook ASL data by gear and project.

	Commercial Harvest	Escapement	Subsistence Harvest	Unknown
Beach Seine	1282	728	0	0
Drift Gillnet	55012	17048	0	80
Handpicked	60	0	0	902
Mixed	9101	0	0	0
Set Gillnet	3875	291	1882	0
Sport Hook and Line	74	0	0	0
Trap	908	96	0	0
Unknown/Other	15163	945	0	2625
Weir	0	188	0	0

Table 16. Chum ASL data by gear and project.

	Commercial Harvest	Escapement	Subsistence Harvest	Test Fishing	Unknown
Beach Seine	2159	8725	0	0	633
Drift Gillnet	57952	9309	10	48	0
Mixed	18181	0	0	0	0
Set Gillnet	1895	90	160	0	158
Trap	104	187	0	0	0
Unknown/Other	22578	1426	0	0	1062

Table 17. Coho ASL data by gear and project.

	Commercial Harvest	Escapement	Unknown
Beach Seine	564	1630	24
Drift Gillnet	3321	3833	0
Mixed	773	0	0
Set Gillnet	959	9	0
Sport Hook and Line	532	21	138
Trap	404	0	103
Unknown/Other	3790	130	333
Weir	0	973	0

Table 18. Pink ASL data by gear and project.

	Commercial Harvest	Escapement	Unknown
Beach Seine	0	5495	98
Drift Gillnet	2544	0	0
Set Gillnet	161	0	0
Sport Hook and Line	0	0	46
Unknown/Other	3317	3361	0

Table 19. Sockeye mean weight (kg) by District and year (pre-2004 correction applied).

YEAR	Egegik	General District	Naknek-Kvichak	Nushagak	Port Moller	Togiak	Ugashik
1957	NA	NA	NA	0.00	NA	NA	NA
1959	NA	NA	NA	0.00	NA	NA	NA
1960	NA	NA	NA	0.00	NA	NA	NA
1961	NA	NA	NA	0.00	NA	0.00	NA
1962	NA	NA	NA	0.00	NA	NA	NA
1963	NA	NA	NA	0.00	NA	0.00	NA
1964	NA	NA	NA	146.54	NA	408.20	NA
1965	NA	NA	NA	752.59	NA	316.75	NA
1966	NA	NA	NA	210.42	NA	191.21	NA
1967	NA	NA	NA	219.87	NA	62.58	NA
1968	NA	NA	NA	163.21	NA	242.09	NA
1969	NA	NA	NA	288.89	NA	158.11	NA
1970	NA	NA	NA	89.46	NA	153.06	NA
1971	NA	NA	NA	112.27	NA	132.65	NA
1972	NA	NA	NA	113.71	NA	176.48	NA
1973	NA	NA	NA	116.73	NA	130.97	NA
1974	NA	NA	NA	63.33	NA	159.22	NA
1975	NA	NA	NA	143.09	NA	79.80	NA
1976	NA	NA	NA	86.85	NA	120.83	NA
1977	NA	NA	NA	141.38	NA	151.15	NA
1978	0.95	NA	0.23	0.07	NA	0.12	0.31
1979	0.65	NA	0.28	27.36	NA	35.31	1.61
1980	1.16	NA	0.26	25.78	NA	16.35	0.31
1981	0.29	NA	1.29	36.59	NA	21.85	0.06
1982	1.49	NA	0.36	20.48	NA	39.41	0.09
1983	0.00	NA	2.69	50.95	NA	42.47	1.91
1984	84.44	NA	0.19	15.81	NA	41.69	0.04
1985	0.07	NA	0.09	23.66	NA	57.58	0.43
1986	0.05	NA	0.00	27.92	NA	33.53	0.05
1987	0.22	NA	0.24	31.01	NA	47.30	0.28
1988	0.34	NA	0.43	14.15	NA	39.04	0.36
1989	0.32	NA	0.24	25.83	NA	75.33	0.23
1990	1.25	NA	1.64	10.24	NA	23.87	9.79
1991	0.09	NA	0.17	41.03	NA	2.97	0.27
1992	0.25	0.00	0.10	28.81	NA	57.16	0.18
1993	0.26	NA	0.17	29.63	NA	33.29	0.06
1994	0.20	0.00	0.16	31.91	NA	34.45	0.02
1995	0.20	0.00	0.22	31.04	NA	34.03	0.11
1996	0.42	NA	0.31	21.27	NA	36.18	0.23
1997	0.35	NA	0.32	19.37	NA	51.30	0.30
1998	0.25	NA	0.29	24.19	NA	27.28	0.22
1999	0.23	NA	0.23	18.15	NA	0.39	0.27
2000	0.28	NA	0.41	0.54	NA	0.62	0.24
2001	0.54	NA	0.49	28.84	NA	56.13	0.40
2002	0.35	NA	0.36	14.01	NA	9.46	0.58
2003	0.46	NA	0.41	17.19	NA	0.24	0.48
2004	0.37	0.44	0.23	0.48	NA	0.09	0.44
2005	0.42	NA	0.43	0.20	0	0.03	0.37
2006	0.37	NA	0.20	0.28	0	0.00	0.25
2007	0.17	NA	0.12	0.27	0	0.33	0.17
2008	0.19	NA	0.16	0.12	0	0.29	0.21
2009	0.25	NA	0.19	0.24	0	0.31	0.28
2010	0.21	NA	0.20	0.22	0	0.36	0.24
2011	0.24	NA	0.20	0.25	0	0.30	0.25
2012	0.23	NA	0.22	0.25	0	0.40	0.22
2013	0.28	NA	0.23	0.21	0	0.30	0.26
2014	0.27	NA	0.25	0.21	0	0.31	0.21
2015	0.19	NA	0.16	0.22	0	0.16	0.20
2016	0.22	NA	0.19	0.23	0	0.26	0.23
2017	0.24	NA	0.18	0.19	0	0.25	0.19
2018	0.22	NA	0.18	0.17	0	0.25	0.23

Table 20. Sockeye weight corrections.

Weight Correction	Frequency
0	100
0.01	277,537
0.0453592	37,544
0.1	937,100
1	717,838

Table 21. Mean condition factor (k)for Chinook, chum and sockeye.

Species_Name	FISH_CONDITION
Chinook Salmon	1.762637
Chum Salmon	1.659112
Sockeye Salmon	1.679253

Table 22. corrected mean Chinook weights (kg).

YEAR	Egegik	General District	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1964	NA	NA	NA	6.78	7.19	NA
1965	NA	NA	NA	9.09	9.87	NA
1966	NA	NA	NA	8.23	9.37	NA
1967	NA	NA	NA	9.52	9.64	NA
1968	NA	NA	NA	10.07	11.48	NA
1969	NA	NA	NA	NA	2.36	NA
1970	NA	NA	NA	10.00	8.71	NA
1971	NA	NA	NA	10.95	10.66	NA
1972	NA	NA	NA	9.28	12.36	NA
1973	NA	NA	NA	10.84	11.53	NA
1974	NA	NA	NA	11.79	12.29	NA
1975	NA	NA	NA	10.01	4.33	NA
1976	NA	NA	NA	10.18	7.05	NA
1977	NA	NA	NA	11.04	8.92	NA
1979	NA	NA	NA	8.41	10.23	NA
1980	NA	NA	NA	9.73	8.56	NA
1981	NA	NA	NA	8.24	5.26	NA
1982	3.18	NA	NA	9.11	8.48	NA
1983	NA	NA	9.88	8.36	9.14	1.32
1984	NA	NA	NA	8.36	9.82	NA
1985	NA	NA	NA	7.86	9.64	7.88
1986	NA	NA	NA	8.47	7.46	NA
1987	NA	NA	NA	8.91	9.37	NA
1988	NA	NA	NA	4.58	9.16	NA
1989	NA	NA	NA	8.10	9.28	NA
1991	NA	NA	NA	7.17	1.16	NA
1992	NA	NA	NA	7.62	6.42	NA
1993	NA	NA	NA	6.56	6.85	NA
1994	NA	NA	NA	9.94	9.42	NA
1995	NA	NA	NA	7.84	8.74	NA
1996	NA	NA	NA	8.09	8.30	NA
1997	NA	NA	NA	9.06	8.03	NA
1998	NA	NA	NA	8.37	6.92	NA
1999	NA	NA	NA	8.41	NA	NA
2001	NA	NA	NA	7.55	9.35	NA
2002	NA	NA	NA	8.48	NA	NA
2003	NA	NA	NA	8.12	NA	NA
2004	NA	4.62	NA	7.54	NA	NA
2005	6.38	NA	5.73	7.21	NA	NA
2006	NA	NA	NA	8.04	NA	NA
2007	NA	NA	NA	6.86	6.34	NA
2008	NA	NA	NA	8.04	7.33	NA
2009	NA	NA	NA	5.69	6.78	NA
2010	NA	NA	NA	6.03	9.67	NA
2011	NA	NA	NA	6.98	7.67	NA
2012	NA	NA	NA	7.54	7.15	NA
2013	NA	NA	NA	6.77	11.53	NA
2014	NA	NA	NA	8.20	5.52	NA
2015	NA	NA	NA	9.64	NA	NA
2016	NA	NA	NA	5.22	5.34	NA
2017	NA	NA	NA	5.08	5.06	NA
2018	NA	NA	NA	4.86	4.72	NA

Table 23. corrected mean chum weights (kg)).

YEAR	Egegik	Naknek-Kvichak	Nushagak	Togiak	Ugashik
1964	NA	NA	3.39	3.02	NA
1965	NA	NA	2.78	3.10	NA
1966	NA	NA	3.87	3.38	NA
1967	NA	NA	2.98	3.18	NA
1968	NA	NA	3.14	3.37	NA
1969	NA	NA	2.00	NA	NA
1970	NA	NA	2.99	2.97	NA
1971	NA	NA	3.04	3.27	NA
1972	NA	NA	3.07	3.35	NA
1973	NA	NA	3.17	3.31	NA
1974	NA	NA	3.04	3.42	NA
1975	NA	NA	2.77	2.95	NA
1976	NA	NA	2.98	3.44	NA
1977	NA	NA	3.27	3.65	NA
1978	1.09	NA	NA	NA	NA
1979	3.31	4.08	3.03	3.76	2.91
1980	2.65	2.39	2.98	3.17	3.00
1981	3.08	3.11	3.18	3.46	NA
1982	3.42	3.27	1.13	3.56	NA
1983	NA	NA	3.35	3.42	NA
1984	3.80	4.25	3.18	3.33	NA
1985	NA	NA	3.08	3.44	NA
1986	NA	NA	3.44	3.00	NA
1987	NA	NA	3.21	3.27	NA
1988	NA	3.30	3.08	3.33	NA
1989	NA	NA	2.88	3.44	NA
1990	NA	NA	2.95	3.43	NA
1991	NA	NA	2.83	NA	NA
1992	NA	NA	2.86	3.26	NA
1993	NA	NA	2.80	3.10	NA
1994	NA	NA	3.23	3.17	NA
1995	NA	NA	2.90	3.14	NA
1996	NA	NA	3.61	3.41	NA
1997	NA	NA	2.88	3.22	NA
1998	NA	NA	3.45	3.45	NA
2001	NA	NA	3.14	3.51	NA
2002	NA	NA	3.42	3.91	NA
2003	NA	NA	3.05	NA	NA
2004	NA	NA	3.53	NA	NA
2005	NA	3.65	3.30	NA	NA
2006	NA	NA	NA	NA	3.40
2007	NA	NA	2.76	NA	NA
2008	NA	NA	NA	3.00	NA
2010	NA	NA	3.15	3.56	NA
2011	NA	NA	2.73	3.41	NA
2012	NA	NA	2.88	3.08	NA
2013	NA	NA	2.77	5.32	NA
2014	NA	NA	2.85	3.43	NA
2015	NA	NA	2.74	2.87	NA
2016	NA	NA	2.74	3.00	NA
2017	NA	NA	3.10	3.08	NA
2018	NA	NA	NA	2.92	NA

Bristol Bay Age Sex and Length (ASL) Data

Greg Buck

2019-12-04 14:04:56