24

Rapp. P.-v. Réun. Cons. int. Explor. Mer, 178: 24-27. 1981.

ENVIRONMENTAL CONSTRAINTS ON LARVAL FISH SURVIVAL IN THE BERING SEA

J o h n J . W a l s h , C r e i g h t o n D . W i r i c k , D w i g h t A . D i e t e r l e , and A r t h u r G . T i n g l e

Oceanographic Sciences Division,Brookhaven National Laboratory,

Upton, New York 11973, USA

The critical period in the early life history of fish is a phase during which the strength of a year-class may be determined (Hjort, 1914; Marr, 1956; May, 1974). The developmental stages and the sources of mortality acting upon fish in this period are thus highly important in assessing and managing a fishery. In many marine fishes a high rate of mortality during the pelagic larval stages suggests that the critical phase occurs during early development. However, larval mortality is a difficult process to understand and estimate because it results from the combination and interaction of many biological and physical processes. Despite this intrinsic difficulty, certain aspects of mortality can be studied in the laboratory and the field when particular care is taken to isolate variables. This approach has been used successfully in the laboratory to study larval growth (Lasker et al., 1970; Laurence, 1977), larval susceptibility to starvation (Blaxter and Staines, 1971), the effect of prey density on larval searching and feeding (Hunter, 1972), and the suitability and availability of prey items found in nature (Lasker, 1975).

The critical period hypothesis assumes that many larvae die either directly from starvation orfrom other associated ills, such as increased vulnerability to predation. Since overall larval survivorship is so low it is apparent that the survivors are select individuals that have travelled exceptionally favorable paths through the resources and hazards of their environment. Extrapolating this hypothesis to real populations is difficult because larval metabolism, as well as distribution of resources and hazards and the exact manner in which larvae encountered them, must all be described. Simulation models appear to be the most appropriate way to make this extrapolation to field studies.

As an example of this approach, a numerical experiment is presented to illustrate the effect of advection and sub-grid scale diffusion on the distribution of eggs and larvae of Alaska pollock ( Theragra chalcogramma). These fish spawn from February through June on or near the bottom, laying positively buoyant eggs (Serobaba, 1974). Incubation time from fertilization to hatching is ~25 days at 2°C (Hamai et al., 1971) and the larvae begin feeding a short time before complete yolk sac absorption. The diet of these small (4.8-5.7 mm) larvae consists mainly of copepod nauplii (Kamba, 1977; Clarke, 1978). Egg and larval surveys, conducted aboard the R/V Acona

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Figure 1. Flow diagram for interactions of physical and biological submodels for prediction of larval fish drift.

Environmental Constraints on Larval Fish 25

and Thompson during spring of 1976, 1977, and 1978, suggest that a major spawning occurs in May at a site north of Unimak Island (55°N, 166° W). Although the major motion in this area is tidal (order 50 cm s ), the long term mean flow is ~5 cm s 1 towards the northwest (Coachman and Charnell, 1979). Larvae are usually confined to the upper 30 m of the water column which suggests that the wind driven circulation may be responsible for much of their drift.

Based upon this scenario, we developed a model to simulate: (1) the drift of larvae spawned at the proposed site; (2) the growth of these larvae; (3) the mortality processes acting upon them; and (4) their distribution, length frequency, etc. which were to be observed in field investigations. Correspondingly, the simulation model consists of; (1) a circulation submodel; (2) a submodel to simulate the distribution

of larvae within the flow field; and (3) a biological processes model for individual pollock larvae.

The currents on the Bering Sea continental shelf were computed using a linearized, single vertical layer, barotropic, free surface model on a two-dimensional horizontal grid (Tingle et al., 1979). This model simulates a time-dependent, spatially homogeneous ocean, driven by the wind stress, the bottom friction and topography, the Coriolis force, and the geostrophic pressure gradient. The current due to tides has been omitted from this initial work.

The larval distributions are assumed to be described by the state equation for a non-conservative substance in the sea. The numerical solution to this equation is performed using the “Particle-in-Cell” method Sklarew et al., 1971) in which the larval fish are represented by Lagrangian marker particles in the

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LONGITUDEFigure 2. Distribution of 10 dav old larvae on 1 May. Large symbols denote the four spawning locations. Each larva is represented by a small

symbol. The shape of the small symbol indicates spawning location.'Zooplankton field (wet wt gm n r) taken from Motoda and Minoda

(1974).

26 John J. Walsh, Creighton D. Wirick, Dwight A. Dieterle, and A rthur G. Tingle

Eulerian grid and are moved at each time step by the currents and Fickian diffusion. Each particle, perhaps as many as ~50,000, has a vector assigned to it which contains its present position and its biological properties.

In the biological model, the larva experiences a feeding, a growth, and perhaps a mortality event each day. The results of the feeding and mortality events are stochastic. The local prey concentration about a larva is a random deviate drawn from a distribution with a spatially varying mean and variance. Ingestion is proportional to the local prey concentration and the larva's weight. Growth in weight is a fraction of the ingestion. The probability that a larva falls prey is inversely proportional to its physiological condition and proportional to the local predator density. The local predator density is stochastic but the mean density does not vary spatially. In short, the outcome of an event is governed by probabilistic relationships (which remain to be verified by observation) between the larva's prey, its predators, and its biological properties, (i.e., age, length, and weight). The model is summarized in Figure I.

On 1, 6, and 11 May, 501 larvae were placed into the model at each of the four drop locations marked in Figure 2. A total of 2004 larvae in each experiment were spawned in this manner, and each larva was assumed to begin feeding upon copepod nauplii, three days after entering the model. To obtain a spatial distribution for the nauplii, it was assumed that nauplii were proportional to the total zooplankton where the zooplankton field was taken from Motoda and Minoda (1974).

The larvae placed in the model on 1 May were dispersed to the southwest (Fig. 2), while their 6 and 11 May counterparts (not shown) travelled more to the northeast. Copepod nauplii were assumed to be more abundant to the northeast than to the southwest of Unimak Island and the survivorship of these two groups (1 May vs 6 and 11 May) of larvae was thus different. The mortality estimate for larvae spawned on 1 May was two times greater than that of the larvae spawned on 11 May. Figure 3 is a frequency distribution of the surviving larvae in terms of their individual biomass (X 102 ng dw) after each experiment. It was initially assumed that each first feeding larvae of 5-6 mm length had a body weight of 40-100 jugdw. The pollock larvae spawned later in the model were larger for their age and in better physiological condition (Fig. 3). Of the three spawning dates considered, 11 May was the best for larval survival and for a given date the most northeast spawning location resulted in the highest survival.

30

20

MAY

z 20UJz>o 0 - 6 MAYLiJ

Ll

0.030

20

II MAY

0.0 1.5 3.0 4.5 6.0 7.5 9.0 10.5 12.0 13.5INDIVIDUAL BIOMASS

Figure 3. Frequency distribution of surviving individual larval biomass (X 102 ng dw) of 20 day old pollock spawned on 1,6, and 11 May in the eastern Bering Sea.

This preliminary work suggests that the timing of events, both physical and biological, is a key to understanding and predicting the success of larval pollock. Although both the model and the numerical experiment are greatly oversimplified, the results suggest that this approach, where each larva can be modeled as an individual complete with its own biological attributes, is a feasible one in assessing sources of larval mortality in the sea.

This research was sponsored by Grants from the Division of Polar Programs, NSF, as part of the Processes and Resources of the Bering Sea (PROBES) program.

REFERENCES

Blaxter, J. H. S., and Staines, M. E. 1971. Food searching potential in marine fish larvae. Proc. Fourth European Mar. Biol. Symp.: 467-485.

Clarke, M. E. 1978. Some aspects of the feeding biology of larval walleye pollock, Theragra chakogramma (Pallas) in the southeast Bering Sea. M. S. Thesis, University of Alaska, pp.1-46.

Coachman, L. K. and Charnell, R. L. 1979. On lateral water mass interaction — a case study, Bristol Bay, Alaska. J. Phys. Oceanogr., 9: 278-297.

Environmental Constraints on Larval Fish 27

Hamai, L, Kyushin, K., and Kionshita, T. 1971. Effects of temperature on the body form and mortality in the developmental and early larval stages of the Alaska pollock ( Theragra chakogramma Pallas). Hokkaido Univ. Fac. Fish. Bull., 22:11-29.

Hjort, J. 1914. Fluctuations in the great fisheries of Northern Europe viewed in the light of biological research. Rapp. P.-v. Reun. Cons. int. Explor. Mer, 20: 1-228.

Hunter, J. R. 1972. Swimming and feeding behavior of larval anchovy Engraulis mordax. Fish. Bull., U. S., 70: 821-838.

Kamba. M. 1977. Feeding habitsand vertical distribution of walleye pollock. Theragra chalcogramma (Pallas), in early life history stage in Uchiura Bay, Hokkaido. Rep. Inst. N. Pac. Fish., Hokkaido Univ. Spec. Vol. 123-273.

Lasker, R. 1975. Field criteria for survival of anchovy larvae: the relation between inshore chlorophyll maximum layers and successful first feeding. Fish. Bull., U. S., 73: 453-463.

Lasker, R., Feder, H. M., Theilacker, G. H . ,and May, R. C. 1970. Feeding, growth, and survival of Engraulis mordax larvae reared in the laboratory. Mar. Biol. 5: 345-353.

Laurence, G. C. 1977. A bioenergetic model for the analysis of feeding and survival potential of winter flounder, Pseudo-

pleuronectes americartus, larvae during the period from hatching to metamorphosis. Fish. Bull., U. S., 75: 529-546.

Marr, J. C. 1956. The “critical period” in the early life history of marine fishes. J. Cons. int. Explor. Mer, 21: 160-170.

May, R. C. 1974. Larval mortality in marine fishes and the critical period concept. In The early life history of fish, Ed. by J. H. S. Blaxter. Springer-Verlag, pp. 3-19.

Motoda, S. and Minoda, T. 1974. Plankton of the Bering Sea. In Oceanography of the Bering Sea. Ed. by D. W. Hood and E. D. Kelley, Int. Mar. Sei., Fairbanks, Alaska, pp. 207-243.

Serobaba, I. I. 1974. Spawning ecology of the walleye pollock (Theragra chakogramma Pallas) in the Bering Sea. J. Ichthyol. 14: 544-552.

Sklarew, R. C., Fabrich, A. J . ,and Pruger, S. E. 1971. A Particle-in- Cell method for numerical solution of the atmospheric diffusion equation and applications to air pollution problems. Division of Meteorology, National Environmental Research Center, Report

3SR-844.Tingle, A. G., Dieterle, D. A. and Walsh, J. J. 1979. Perturbation

analysis of the New York Bight. In Ecological processes in coastal and marine systems. Ed. by R. J. Livingston. Plenum Press, pp. 395-435.