Recovery of Three Bering Sea Type Fish Populations from Catastrophic Larval Mortality

RECOVERY OF THREE BERING

SEA TYPE FISH POPULATIONS

FROM CATASTROPHIC LARVAL

MORTALITY - A SIMULATION APPROACH

Taina Honkalehto Compass Systems, Inc.

Final Report Outer Continental Shelf Environmental Assessment Program Research Unit 643

April 1985

711 This report is from a series of processed reports and program documentation produced by the Northwest and Alaska Fisheries Center, National Marine Fisheries Service, NOAA, in Seattle, Washington, and is individually available as Processed Report 85-13 from that source.

This study was funded by Minerals Management Service through an interagency agreement with NOAA.

712 TABLE OF CONTENTS

Page

List of Figures and Tables 715

Abstract -717

Introduction 719

Description of the Model 720

Characteristics of Selected Species 723

Atka mackerel 723

Walleye pollock 723

Pacific ocean perch 726

Computations 728

Results 731

Individual year class effects 731

Total exploitable biomass effects 731

Discussion and Conclusions 737

Acknowledgements 747

References Cited 749

NWAFC PROCESSED REPORT 85-13 This report does not constitute a publication and is for information only. All data herein are to be considered provisional. 713 LIST OF FIGURES AND TABLES

Table 1. Model Inputs: Life history parameters o+ Bering Sea Paci+ic ocean perch, Atka mackerel and walleye pollDck.

Table 2. Model Inputs: Growth coefficients (G), stable age structure in 100 biomass units (B), and mortality coe++icients (M) for Paci+ic ocean perch, Atka mackerel and walleye pollock. (Niggol, 1982).

Figure 1. Presumed range o+ Pleurogrammus m?nopterygfus and P. azonus in the North Paci+ic and Bering Sea. Both species are found +urther inshore than the map indicates (+rom Macy et.al. , 197S).

Figure 2. Distribution o+ walleye pollock, 7Aeregra cha[cograzma (Smith, 1981) modi+ied.

Figure 3. Distribution of Pacific ocean perch, Sebs!stes iaZutus (Major and Shippen, 19701, modi+ied.

Figure 4. E++ect o+ 100% mortality of Age 1 (O-1 year old) Atka mackerel in one year on the equilibrium biomass of selected year classes over time.

Figure 5. E++ect o+ 100% mortality o+ Age 1 (0-1 year old) walleye pollock during one year on the equilibrium biomass o+ selected year classes.

Figure 6. E++ect of 100% mortality of Age 1 (0-1 year old) Paci+ic ocean perch in one year on the equilibrium biomass of selected year classes.

Figure 7. Population responses o+ Atka mackerel to catastrophic oil induced losses o+ Age 1 and Age 2 (l-2 year old) +ish in a single year.

Figure 8. Total exploitable biomass responses of perch, pollock and mackerel to 100% loss o+ Age 1’s in year 13.

Figure 9. Pacific ocean perch-- total exploitable biomass responses~ including interannual recruitment variability~ to losses due to oil in year 15. Case I (see text?: linear relationship between spawning stock and recruits..

Figure 10. Walleye pollock --total exploitable biomass responses, including interannual recruitment variability,- to ail induced loss o+ Age 1’s in year 15. Case I (see text): Linear relationship between spawning stock and recruits.

715 .

. Figure 11. Atka macker’el-- total exploitable biomass response=, including interannual recruitment variability, to oil induced loss of Age 1’s in y=ar 1S. Case I (S6W? text): Linear relationship between spawning stock and recruits.

Figure 12. Pacific ocean perch --total exploitable biomass responses, with interannual recruitment variability, to oil induced loss o+ Age 1’s in year 15. Case II (see text): “environmental window” e+fect.

Figure 13. Walleye pollock-- total exploitable biomass responses, with interannual recruitment variability, to oil induced loss o+ Age 1’s in year 1S. Case II (see text]: “environmental window” effect.

Figure 14. Atka mack@rel-- total explainable biomass responses with interannual recruitment variability, to oil induced loss o+ Age 1’s in year 15. Case 11 (see text): “environmental window” e+fect.

716 AESTRRCT

One approach toward= elucidating +ish stock and recruitment relationships is to simulate how changes in early stage mortality a.+-feet the exploitable stock biomass. Predatian~ starvation and pollution are known contributors to early larval mortality. This study examines the ef+ects o+ losses due to oil contamination on recruitment to exploitable biomass. Simulation methods are used to project larval mortalities caused by possible accidental release of oil through time +or three commercial Bering Sea fish stocks, Atka mackerel (PZeuPo9Paz?mus ?mnupterygfus), walleye pollock t7heragra chaZCo9mvnma) and Pacific ocean perch tSebastes

azueus) ● Two hypothesized relationships between adult and new recruit biomass are used. Case I models annual recruit biomass

(Age 1) as a proportion o+ the previous year’s reproducing adult biomass. Assuming no density dependence, a catastrophic mortality o+ all Age 1 +ish permanently lowers exploitable biomass +or all three species. Perch biomass declines the least and mackerel the most, although losses to the latter species are obscured by its high interannual recruitment variation. In Case

11, with no spawning stock and recruitment relationship, recruit biomass is a proportion of the long term mean biomass. Under these conditions, populations respond to loss o+ all Age 1’s by

+irst declining, then returning to near pre-oil spill biomass a+ter the year class cycles through. Results o+ early mortality on each species are discussed in light o+ life history di++erences between species. Ideas +ar further use of the 717 o 3 INTR~IIUCTION u . . . it seems to me that even though there be governing causes o+ mortality that may result in a true law 0+ mortality, any group o+ lives studied is so heterogeneous due to di++erences in. ..climates race, physical characteri=tics~ etc. that any +ormula must in practice be considered merely to be a generalization of what is actually happening. ” (Elstan,1923 p.681

Current +isheries research continues to tackle the problem o+ the relationship between spawning stock and subsequent recruits a= an important key to e+fective stock management. Increased understanding o+ stock and recruit relationships will arise +rom ongoing studies o+ larval stage mortality and growth but progress is slow due to high spatial and temporal variability. Meanwhile, model simulation o+ larval mortality and resulting e+fects on recruitment can aid in delineating the expected range o+ response to environmental perturbation.

Early mortality in marine fish has been attributed to consumption by predators (LebourS 1923? Theilacker and Lasker,

1974; Hunter, 1976; Alvarino, 1980! McGowan and Miller, 1980;

Frank and Leggett, 1983; and additional references in Hunter

1981;1983), starvation {Hunter and Kimbrell, 1980; Beyer and

Laurence, 1980) as well as to marine pollution (Nelson-Smith,

1972; Kuhnhold, 1972; Rosenthal and Alderdice, 1976; Kuhnhold et.al., 1978; IMCO/FAO/UNESCO/WMO/WHO/IAEA/UN, 1977; additional references in 13aX, 1985). The purpose of this study was to simulate the impact o+ catastrophic first year mortality due to oil contamination in marine +ishr and to project biomass losses to the exploitable stock through time. A stock as used in this

719 paper refers to a group o+ +ish spawning in the same place and

time; no al Iowance has been made +or discrete spawning units.

Thus the catastrophic loss applies to all potential recruits to

that stock.

DESCRIPTION DF THE MODEL

A biomass-based, single species simulation model was programmed to run on a Columbia PC to study the impacts o+ losses a+ +ish eggs and larvae on subsequent year class strengths. Three commercially important Bering Sea +ish species with dissimilar

life history patterns (Table 1) were selected to demonstrate potential stock biomass responses to catastrophic first year morts.lity +ollowing an (hypothetical) oil spill. Stable population age structures for Atka mackerel, walleye pollock and

Pacific ocean perch corresponding to long-term mean data from

N i ggo 1 ($9S2) and Bakkala and Low (1983) were used (Table 2) . Far conven ience, each species was initially ascribed 100 units o+ biomass. Oil loss e++ects @n exploitable biomass were analyzed by first deriving a general simulation, then running separate simulations with data -from each species. Each set o+ simulations contrasted two hypothesized relationships between recruit and adult biomass. The +irst case model led recruit biomass as a proportion o+ the previous years’ spawner biomass? the second assumed no spawner and recruit relationship. Interannual recruitment variability was determined empirically for each

720 Table 1. Floclel Inputs: Life history pa~am=terk o+ 13erinq Sea Pacific ocean perch, Atka macker=l and walle:ye pol lock.

SPECIES TYPICAL EXPLOITABLE REPRODUCTIVE SPAWHIN6 SPAW41f4G COEFFICIENT OF HABITAT FECUNDITY SOURCES LIFE$PAN AGES A6ES KODE SEASON VARIATION (adults) [eggs] ( years) [Fecrults) . ------.------.------PACIFIC OCEAN 20 11-20 6-20 ovoviviparous Mar, -Hay 0.23 demersal 27,000 - Ni9gol 1982 PERCH 180,000 Bakkala & Low 1983

UALLEYE 12 3-12 3-12 oviparous Mar. -June 0,47 seni-de~ersal 186,000 - Ni~gol 1982 POLLOCK 600,000 Bakkala k Lou 1983

ATKA 7 2-6 3-7 oviparous Jufie -Aug. 0.95 pelagic 5000- Ni9gol 1982 MACKEREL 43,000 Hacy et. al. 1978 Bakkala k Low 1983 Table Z. Model Inputs: Growth cne++icents (G) , stable age struc~ure in lGC) biomass units (B) , and mortality Coe++icients (M) +or Pacific ocean perch, Atka mackerel and walleye pollock. (Niggol, 19s2)...... ------SPECIES INPUT 86E CLIWSES

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

PRCIFIC OCEAN B 3.2 6.8 9.7 9.4 9.1 8.5 7,8 ?,4 b.? 5.? 4.9 4 3.4 2,8 2.3 2.3 1.6 1.1 0.7 0.4 PERCH 6 1,38 0,950.512 0,370.3060.2580,2540.174 0,1530.1550.1030.084 0.0760.061 0.0540.035 0.0i9 0.004 -0.01 N 1.12 0.6 0.52 0.42 0.38 0034 0.32 0,28 0,28 0,3 0.28 0.25 0,26 0,27 0,28 0.31 0.36 0.44 0,59 -1 Iv N UALLEYE B 6*6 12 13.6 14,2 13 11.5 9.7 7.6 5.3 3.4 2.1 1 POLLOCK 6 1.398016860.554 0.3850.2640.1440.112 0.0870,0770,0590,047 N 0,8 0.560.513 0.470.3850.3220.3540.451 0.5130.5170.752

AlK/! Ii 29.7 26.5 i9 12 7.2 3.8 1.8 NACKEREL 6 0.5690,3120.1620.107 0.067 0.5 1! 0.71 0.65 0.62 0,6 0.7 0.78 species and entered into the model.

CHARACTERISTICS OF SELECTED SPECIES

Atka mack~~~l

fltka mackerel are distributed across the North Paci+ic east o-f

165 W and north of 44 N (Figure 1). Though primarily pelagicz adult macke el aged three or -four begin moving inshore to spawn during May. Spawning peaks in summer in the straits between the

Aleutian Is ands~ as females deposit sticky egg masses on kelp fronds or an stones. Each +emale produces three or four batches o+ eggs at 5-7 da”y intervals at preferred water temperatures o+ around 5-8 C. After a 40-45 day incubation period during which they would be especially susceptible to smothering or contamination from oil, newly hatched, planktotrophic larvae are dispersed with currents in the open ocean. They display some vertical migration; more larvae reside in the upper layers o+ the water column at night than during the day (Macy et.al. , 1978).

Walleye pollock

Walleye pollock are one o+ the most abundant north Paci+ic fish. They are semi-demersal and inhabit deep waters o+ the north Paci+ic and Bering Sea to o++ central California (Figure

2). Walleye pollack pre+er slightly colder temperatures than Atka

723 Figure i. Presumed range o+ Pleurogrammus monopterygius and P. azunus in the North Paci+ic and Bering Sea. Both species are +ound further inshore than the map indicates (from P’lacY eteal., 197=). m● . .- 1 ; I I ,I .,/ As.:,.q / J.; — .. —.. — . . . . . !::! ---- “. ~w l-”< —.— < g! . . x~ -...... 3..;: ~E # . ..>:. >.%-. . . *$7 ~3 ...... : J ...... - ...... ;0 .. . .:.:.:.~. . . . .:.:.:.:.:...... u I . :. >$~~y,:.:.:.:.:.~...... : ● $ >.+...... a . ..%% ...... G ‘Pb x ..-:+ ...... /’ ..:.:.:-:.:.:-: ...... -:.:.:.:.. . . . :+37. . . , ...... %%%. )...... %...... ,...%% . ...-.%%% ...... ,. .%..%.,...... %..%..%...... /...... %...... k...... %...... /...... ~jj;:...... W5!, t:j~${~:..{ ...... z %::::::::::yj.+x+:.:.. . !>y ~ ,.. “*::::).,...... — < ~~$@~,, ~- w+:.;+>...... %::::%:::: J ~::=j~:.’$iiwi f’ ...... ● ...... :...... ~... o.... )...... p ? ?:.:.:.:.:.:.:.:.:.:.:.:.:.;.;.:.:.:.;.:.:.:.: ,::::::’ }...%%...... %%%%...... ++:...... F$...... l$f- ...... 1... i ...... "...""...... " ...... """."...... *~...... \...... i . “...... ~::::::yjf f:::::.. .:.x.:.:.:...... — ...... > ...... 5 t ..’:$y$:,,. . - : ...... m - . . . %::::::::...... ::::. . . . .,4 1’%4 ...... :.:.. u. +~ +. . . ..:.:... “ .:$:? ~ b\I : -- z .-”’.”” ‘ f~<. I z“ m* e v o wb @l

725 mackerels with optima between 2?-5 C. Most of the population winters offshore, then migrates to spawning grounds on the southeastern Bering Sea continental slope and Gul+ o+ Alaska shel+ west and northwest a+ Unimak Island between Februar-y and

May. During the spawning season which peaks in late April, three to +~ur year (+) -F=mal~s release eggs that concentrate in the surface water-s and hatch in about twelve days (at 6-7 C) , Newly hatched larvae have been observed drifting o-f+shore with local current systems which may promote larval survival. By age lJ walleye pollock achieve their broad oceanic distribution

(tCasahara, 1961; Serobaba, 197S; Smith, 1981; hlorc~oss and Shaw,

19841.

Pacific ocean perch were once a dominant ichthyo+aunal companent in the north Paci+ic (Major and Shippen~ 3970;

GundersonP 1976) . However, heavy fishing during the past two decades has reduced their numbers. Their trans-Faci+ic range

(Figupe 3) includes open ocean habitat as well as rocky bottomed gullies~ caves and submarine depressions along the outer continental Shel+ and upper 510pe between 180 to 460 m. Bering

Sea stocks o+ Pacific ocean perch mature at 6-7 years o+ age.

They mate during January and February in Bristol Bay? southwest o+ the Pribilo+ Islands and in the Gulf o+ Alaska. Between March and May, females migrate” to deep water (around 400 m) and r-elease

726 I 140° 160” 180” 160@ 140° 120” I

-..! uIv

Figure 3. Distribution o+ Paci-fic ocean perch, Sebastes aZuitus (Major and Shippen$ 1970) , modi+ied. pela~ic larva= in spawning episodes lasting three or four hours.

Emergent larvae, 6-8 mm in length, remain pelagic +or up to five

years and feed on copepods and other crustaceans (Laevastu,

pet-s. carom. ) . A+ter two years$ they develop demersal habits.

COMPUTATION=

Yearly biomass change= in the stocks were simulated as +O11OWS:

Gi(Bio/Bit)-Mi B = e Y 9 (1) i+l ,t+l ‘i $ t where

i- year class; i = [1~20) +or Pacific ocean perch (1,7) far Atka mackerel (1,12) for Walleye pollock t- time (year); t = O at start a-f simulation B- biomass units; initially 100 units in population G- empirical growth coe++icient M- mortality coe++icient (natural + +ishing mortality) e- natural constant (2.71828. ..)

Since the mortality schedule relating recruits to prior adult

biomass is poorly known +or most species (CushingJ 1971; Hunter,

1976; Gunderson, 1976), recruitment was simulated +or both

maximum e++ect and minimum e++ect cases under the assumption that

actual population responses would lie somewhere between the two.

IrI Case I, the maximum ef+ect of oil losses on subsequent years’

exploitable stock biomass was simulated assuming direct linear proportionality between stock and recruits (ages O-1) biomass.

Thus in years +ollowing the oil spill, the a++ected year class contributed zero biomass towards the exploitable stock. Case 11 assumed that recruited biomass was independent o+ parental

728 biomass, representing the control of recruit biomass via an environmental “window” that allowed only a prescribed number o+

larvae to successfully recruit no matter how many were spawned in a given year (Hempel$ 1965) . Recruit biomass was determined as the proportion of the equilibrium spawner biomass necessary to sustain that equilibrium biomass. Recruit biomass for year (t+l) was computed in year (t) +or each case as follows:

Case I: Maximum effect o+ oil losses possible

;: R = (2) t+l ‘.t p Case II: Minimum e+fect (environmental window)

R ~+, = S,. ‘$ p (3) where

t5 t+l - time (years)~ and O denotes equilibrium year

. - denotes summation over mature age classes unique to each species: Pacific ocean perch = 11- 20 Atka mackerel = 3- 7 Walleye pollock = 3- 12

R - biomass recruited, based on a starting population biomass o+ 100 units

s - reproductive stock (no. o+ units) in a given age class, during a given year, (t)

P - species specific, empirically determined proportionality constant relating equilibrium population biomass to recruit biomass

Using empirical growths mortality and biomass distribution data

(Table 1), equations (1) and either (2) or (3) were computed +or each species in one hundred year time series. Growth and mortality coe++icients were adjusted slightly, i+ necessary,

729 until each population maintained a stable biomass over successive years. .Justi+ication +or using a stable biomass model was presented in Laevastu and Larkins (19819 p.98) . In year 1 o+ the study, each +ish population had an age and biomass structure that totaled 100 units. Later age and biomass structures do not necessarily sum to 1O(I.

CInce the equilibrium population structure was obtained, early mortality due to an oil spill was simulated by setting first year fish biomass in year +ifteen (R(15,1)) equal to zero. Population responses to oil contamination losses were graphed both f-or individual year classes within species and ● or total exploitable biomass between species.

Annual recruitment variability due to unexplained fluctuations in the environment predator and prey populations? adult

+ertility and other changing +actors was included in the second set of simulations using a random number generator. For each species, a normally distributed interannual coe++icient of variation o+ recruits was matched to that obtained from available data (13akkala and Low, 1983; Chikuni, 1975). Total exploitable biomass responses to 100% recruitment failure in year +i+teen were then graphed +or each species using Case I and Case II recruitment regimes. Density-dependent growth and mortality were omitted -from the simulations +or simplicity and because few relevant empirical data exist to support their inclusion

(Gunderson3 1976).

730 RESULTS

Individual Year Class E++ects

Responses o+ representative year classes to catastrophic loss o+ recruits under Case I and prior to inclusion o+ interannual

recruitment variability are illustrated in Figures 4-7. The

+irst simulation (Figures 4-6) shows di++erent between species

responses: Atka mackerel declined the m~st, and Pacific ocean

perch the least. An example o+ individual year class responses

to 100’% mortality o+ recruits and Age 2’5 (Figure 7) was included

+or comparison with Figures 4-6. For Atka mackerels the effect o+

losing all o+ the two youngest year classes in one year was much

greater than losing just one year class.

Total Exploitable Biopass E++ects

Total exploitable biomasses, the percent o+ each species

utilized by commercial -fisheries, were computed and their

responses to 100% mortality of Age O-l’s were compared (Figure 8)

prior to inclusion o-F interannual recruitment variability in the

simulations. Atka mackerel biomass fell the most within a year

o+ the oil kills yet the population increased slightly before

stabilizing. Paci-fic ocean perch declined the least, and showed

no change until nearly a decade a+ter the catastrophic event.

Walleye pollock biomass +ell nearly as rapidly as mackerel and

731 26 --—------— -- --—— ——-— —--— -——— ---— ------— -—..-———————. --—------—— -,

24- I 22

1 4-

0 10 3cd 35

l!k AGE 7’

Figure 4. Effect a+ 100% mortality of Age 1 (0-1 year old) Atka mackerel in one year on the equi librium biomass o+ selected year c lasses over time. 26 —------—————______

24- ‘r

;+-:G’1 16 .-c: .-J, 1 4-

1 c)

0 la 15 2C3 25 30 35

l-i rr71e y= a ~p[, ;~ •1 AGE 1 +- }~I>E3 ii A G E: 7’

Figure 5. Effect o+ 100% mortality of Age 1 (O-1 year old) walleye pol lock during one year on the equilibrium biomass of selected year classes. ,...... ______,______.______

-i .- 1

Figure 6. Effect o+ 100% mortality o+ Age 1 (0-1 year old) pacific ocean perch in one year on the equilibrium biomass o+ selected year classes. 26 ------.——- —————— -..——————— ------— --- -— ——— ——_—_ —______,______—.

24- ‘r -1 22

2 c)

14- w w Ci 12 E: , Q ccl 1 cl El

❑

Figure 7. Population responses o+ Atka mackerel to catastrophic oi 1 induced losses o+ Age 1 and Age 2 (l-2 year old) fish in a single year. IC)13 ------,. - ...... —.- -— ---- - ...... -— . . . ______-— —.- ______. ___

!3(3 -

w, ,.+-, -. c: .-1,.- _,Q_(.*: .*.6 ““ 7

2(3 -

lc)-

(2- 1 c1 “1 !!5 3 !!3 .3 .s ‘4- c1

l-i rn I= ‘flea rs) Cl la [; F:f: H + F’01.l (;I(>}$ ‘u. hA,A <::K [; l=? F L.

Figure 8. Total exploitable biomass responses o+ perch? pol lock and mackerel to 100% loss of Age 1’s in year 15. did not rebound at all during successive years.

The two +inal sets o+ simulations included a normally distributed interannual random component o+ recruitment computed from recent +isheries data +or each species. Results +or Case I with 1 inear dependence o+ recruitment on parent stock size are shown in Figures 9-11. Seed random numbers were used two times-- with and without a simulated oil spill--+or each species. Figure

9 shows that losses due to oil in year 13 do not a+fect the exploitable perch biomass until ten years later (year 25) , and that the total percent biomass a++ected is low. Pollock re’apond more quickly to oil losses and display a periodic biomass curve.

Mackerel show a reduced biomass +rom oil losses that is somewhat masked by the amplitude a+ its normally high interannual recruitment fluctuations (Figure 111.

Results -From Case II simulations with recruitment based on the equilibrium stock showed that e+fects o+ oil losses appeared with the first year class exploited by the fishery and diminished as a function n+ the longevity o+ the species involved (Figures

12-14) .

DISCUSSION AND CONCLUSIONS

Predation, starvation, natural environmental and man-made factors leading to early mortality in marine fish populations

5till require extensive investigation. As mentioned, literature

737 n+ G-i rl+ n u -4-

S+lufl. . Ssuwr.llf+.-

738 15cl - —, —-- -——— --—------— -,. ------—— -- ---— -——- —--- —------— --—, 1 4CI - 13C) - 12cl - 11 cl- Ioc) - ;*_lx c 90 - .1 .d [Y 8C) ; [3 In c1 70 - -t- .+ -+ ~ i- 60 - -+ + 50 - 40 - 30 - 20 - lcl- 0 - 10 15 20 25 31.3 35 4-0 4.!5 5(3 55 60 “I-i rrnle (~ea rs) WITH OIL. :; 1=1 L-l-

Figure 10. Walleye pol lock-- total exploitable biomass responses, including interannual recruitment variability, to oil induced loss a+ Age 1’s in year 15. Case I (see text) : Linear relationship between spawning stock and recruits. 15 Cl -r------– -– ------–--––-----–-––--–--–---–- 1 40 - CI 1 ..313- 12(3 - ❑ c1 llCl- U [3 c1 cl t. c1 [3 ❑ •1 c1 [3 80 “7 c) 60 50 4 c1 3(3 20 10 c) 1 c1 15 20 25 3 cl 3!5 4!5 :55

l-i rm,e (y,ea rs] [3 N 0 01 L ‘3 f= lLJ- i V/l T. I-{ OIL. ?; 1=1 L.l-

Figure 11. Atka mackQrel -- total exploitable biomass responses, including interannual recruitment variability, to oil induced loss of Age 1’s in year 15. Case I (se@ text) : Linear relationship between spawning stock and recruits. 40 ------.- —--— -— ------—— ---—— ---- -—-—. — ---— ---- —— -- —------— -- -— -- —---

3!5 -

3(3 -

[3 m ❑ 93 =1 Es a3 E9 ES E ~1 1= E] E9 m El Ill El m c 2? . ‘= El 03 Cl ~1 [3 [~ m El ,-+. 1% El c] [3 c’ I-J n !qJ ‘a [3 E[ EH ‘1 ,-c ER + .+ ~1 ❑ ‘3 13 d ❑ + 20 ‘ + +- .+ + +- 15

1 c) 1 El 2“ o 25 30 3:3 4-0 4.!3 50 55 60 l-im~e (ytears) C2 N C) (> I L. !S F’ I L-L. + W T.t-i OIL. S 1=1 L-l-

Figure 12. Pacific ocean perch --total explainable biomass responses, with interannual recruitment variability, to oil induced loss o-f Age i’s in year 15. Case II (see text): “environmental window” e+fect. 1 5[;) ------`--'--" 14CI - 13C) - 120 - 11 cl- [

+. .+ 5 c) 4.C) 3 c) 2 c) 1 0 c1

l-in-i~e (y3ars) [3 NC) OIL. SfalL.L. ‘WIT”H IO IL. Sl=l L.l-

Figure 13. Walleye pollock-- total exploitable biomass responses, with interannual recruitment variability, to oil induced lass o+ Age 1’s in year 15. Case II (see text): “environmental window” effect. 150 - .——__— -——- ,. ——— ——— — ------_— ______,______,______1 4CI -

13C) -[ lie 12C) -I

T- EH 9 c) - “ ffl .--1 [3 .A ❑ 8 [) - EEl 13 ❑ El cl E6 ❑ “7 c) - i- 03 EI Eg m 133 6 c1 - c1 E3 ~= El El E6 5 c) - El w 4.CI - a + 13 .+ 3C) - EEI 03

2 c1 -’ + 1 c) -

1 c1 15 20 25 30 35 4-C> 4.5 5CI 55 60 I-im,e (yrears] u NO OIL. Sf=l L.L- -t- WIT” I-{ OIL. Sl=l L.lm

Figure 14. Atka mackerel --total exploitable biomass responses, with interannual recruitment variability, to oil induced loss of Age 1’s in year 15. Case II (see text): “environmental window” ef+ect. currently available quantifying relative importance o+ these mortality factars is sparse. Rather than attempting to mDdel first year mortality processes per se, this study assumes a mechanism +or early mortality (oil contamination), computes recruit biomass that is sensitive (Case 1) and non-sensitive

(Case 11) to previous-year adult biomass? and tracks the impact o+ low-biomass year classes through time. It is instructive in estimating di++erent species’ responses to catastrophic population phenomena other than fishing mortality.

Previaus work on population dynamics o-F marine +ish populations has emphasized individual year class fluctuations. Combining year classes from a particular stock into total exploitable biomass damps out individual responses, distributing ef+ects o+ perturbations through time iLaevastu and Lark ins, 1981). In this study, the e++ects o+ oil induced losses of recruit= to important commercial Bering Sea fish stocks were considered +rom the total exploitable biomass point o+ view with the following assumptions:

(1) death o+ Age O-l’s was modelled, as ail contact potential would be highest during the pelagic phases o+ perch and p~llack larvae and during oceanward transport o+ mackerel larvae

(Kasahara, 1961; Gunderson$ 1976) and (2) the worst case scenario a+ 100% mortality (catastrophic) was modelled~ as true oil-related mortality after contact is poorly known +or any species (Samuels and Ladino, 1984) . Actual mortality would be considerably less than 100%, and would more likely range +rom 1 to 10%3 even in a major oil spill (Laevastu~ pers. comm. ) .

744 Simulated pollockg mackerel and perch populations with twelvey seven and twenty year classes~ respectively, responded differently to catastrophic oil losses. These results are

attributable to di++erences in life history characteristics among

the three species.

Pacific ocean perch embody two inherently stabilizing trait=s

longevity and adult demersality (Nikol’skii, 1967?; Laevastu and

Lark ins, 1981). Fecundity and interannual recruitment variability

are low, and the number o+ recruits may be sensitive to stock

size (Gunderson, 1976) . Thus in natures this stock probably

behaves more like a Case I (see Figure 9, this report) simulation

than Case II. The absolute percent biomass loss to the population

would be damped by numerous year classes. However, some +orm of

compensatory growth (density-dependent) would be required to

elevate the population back to its pre-oil spill biomass.

Walleye pollock biomass, when perturbed by catastrophic oil

losses, +luctuated moderately. A cyclical pattern became evident

in runs with different seed random numbers (Figure 10) . This

corresponded well to Laevastu and Lark ins’ results (1981) which

they attributed to cannibalism among the older pollock year

classes. Not enough information existed to categorize pollack as

either Case I or Case II fish. In the +OPmt2P simulation,

recovery of the stock would require compensatory growth. 1+ they

behave as in Case II (Figure 13) recovery would occur in about

ten years.

745 The relatively short- Iived, pelagic Atka mackerel undergo large

interannual recruitment variability (Macy et.al.197S; Ronholt,

1983) . They most likely behave as in the Case II simulation

(Figure 14). Since interannual recruitment fluctuations are on

the same scale as +Iuctuations due to oil losses, the long-term

average mackerel population biomass would appear little changed

a+ter oil-caused deaths occurred. In the shnrt termz however,

because Age 1’s and 2’s make up such a large proportion o+ the

total biomass, lnsses would be swift and acute. Recovery under a

Case 11 scenario would take +ive to six years.

Some similar responses among the three populations were also

noted. In Case I simulations, all three species stabilized at

lower exploitable biomass levels that, without inclusion o+

compensatory density-dependence in the simulation, never returned

to original levels. When recruitment was made independent o+

parent stock size (Case II) exploitable biomass always returned

to original levels after a number o+ years equivalent to the

number o+ di++erent exploitable cohorts in the stock.

Finally, with the inclusion o+ density-dependent growth and/or mortality (Samuels and Ladino,1984), the simulations presented

here could be used to model other mortality factors af+ecting fish larvae in the ocean such as predation, starvation and anomalous environmental conditions once more data Qn larval fish biology and distribution become available.

746 ACKNOWLEDGEMENTS

I thank N. Bax and T. Laevastu +CIr their inspiration and for critical review o+ this manuscript. This study was funded by the

Northwest and Alaska Fisheries Center, NDAA grant No. 84-/$BC-CI98,

Dr. T. Laevastu, COTR.

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