SEARCH

SEARCH BY CITATION

Keywords:

  • energy budget;
  • food consumption;
  • growth;
  • life history;
  • metabolic rate

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

1. We explored the mechanisms determining age and size at juvenile migration in brown trout Salmo trutta L. A 133Cs tracer methodology was used to estimate food consumption of juvenile brown trout in a Norwegian stream, and the energy budgets of early migrants and stream residents were compared.

2. Fast-growing brown trout migrated to the lake earlier and at a smaller body size than slower-growing individuals. The 2+ migrants were significantly larger than those that remained 1 or more years longer in the stream. The 3+ migrants were significantly larger than the 2+ migrants. Some fast-growing males matured in the stream, whereas all females left the stream before maturing sexually.

3. The food consumption and the energy budgets for 2+ migrants were more than four times higher than those of the resident 2+ fish. Total energy allocated to growth was also higher among migrants, and the total metabolic costs were five times higher among migrants than among resident fish.

4. The proportional energy allocation to growth among the 2+ migrants was much lower (about half) than that of those remaining longer in the stream. The reduction in the proportion of energy available for growth from age 1+ to 2+ was larger among migrants (88%) than among resident fish (68%). Reduction in the proportion of energy available for growth is a probable explanation for why migrations are initiated at age 2.

5. Our study supports the hypothesis that fast-growing individuals shift their niche earlier and at a smaller body size than slower-growing individuals because they maintain higher metabolic rates and are energetically constrained at a younger age by limited food resources than slow growers.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Many animals perform extensive niche shifts because their ability to utilize resources and avoid predation changes through their ontogeny (Werner & Gilliam 1984; L’Abée-Lund et al. 1993). While the ultimate causes of niche shifts are obvious, the maximization of fitness, the proximate mechanisms controlling the shifts are under debate. The timing of niche shifts has been related to body size (e.g. Werner 1979; Grossman 1980; Persson 1983; Werner & Gilliam 1984; Sandlund, Nrsje & Jonsson 1992; Bohlin, Dellefors & Faremo 1996), growth rate (e.g. Werner & Gilliam 1984; Thorpe, Metcalfe & Huntingford 1992; Økland et al. 1993; Forseth, Ugedal & Jonsson 1994), the ratio between growth rate and mortality (Werner & Gilliam 1984) and metabolic rate (e.g. Metcalfe, Wright & Thorpe 1992; Forseth et al. 1994; Metcalfe, Taylor & Thorpe 1995). These studies show that although body size is an important aspect of niche shifts, the timing of the shifts is probably related both to developmental rates and to the physiological state of the animals. Environmental cues inducing the niche shifts may be changes in food availability (e.g. Langeland et al. 1991; Nrsje et al. 1991; Sandlund et al. 1992) or other environmental factors like a change in water temperature (e.g. Forsythe 1968; Jonsson & Ruud-Hansen 1985; Hvidsten et al. 1995).

Among fishes, ontogenetic changes in resource use are nearly universal (Werner & Gilliam 1984). One classic example is the salmonids that migrate as juveniles from nursery streams to lakes or the ocean where they feed until they mature and return to spawn in their stream of origin. Gross (1987) developed a migration model for understanding the evolution of such strategies. According to this model, fishes migrate if the growth and survivorship advantages of utilizing a second habitat, plus the cost of moving between the habitats, exceed the advantages of staying in only one habitat for the same period of time. However, all individuals in a population do not always migrate (Jonsson & Jonsson 1993), and migrations may occur at different age and size (Jonsson, Jonsson & Hansen 1990), probably because growth and survivorship in different habitats may vary with body size (e.g. size-dependent feeding opportunities, growth rates and mortality risk). During the last decade, field and experimental studies have provided important knowledge of the mechanisms determining age and size at migration, particularly for anadromous salmonids. One general pattern is that fast-growing individuals smolt (i.e. the physiological, morphological and behavioural transformation of juvenile salmonids in preparation for life at sea) younger (Jonsson 1985; Thorpe 1987a,b; Metcalfe et al. 1989) and at a smaller body size (Jonsson 1985; Økland et al. 1993) than more slow-growing individuals. Smolt size is phenotypically plastic and related to individual growth rates as a norm of reaction (sensuStearns 1992). Økland et al. (1993) hypothesized that fast-growing individuals smolt earlier because they maintain higher metabolic rates and are energetically constrained earlier than slow growers by limited food resources. A recent experimental study may support this hypothesis. Metcalfe et al. (1995) demonstrated a strong relationship between social status and standard metabolic rate in juvenile Atlantic salmon (the higher the standard metabolic rate, the more dominant the fish), and they suggested an indirect link between intraspecific variation in metabolic rates and life-history strategies. Moreover, from experimental dominance studies, Huntingford et al. (1990) suggested that the larger size of dominant fish, reported for a number of salmonids, might be a consequence and not a cause of high status. A possible conclusion from these studies is that some individuals are born with a higher standard metabolic rate, or attain a higher rate shortly after hatching, than other individuals from the same population. This may influence their future growth and life history, including niche shifts. This also accords with the predictions from a new model, developed by Thorpe et al. (1998), that life-history events in salmonids are triggered by a combination of physiological state (e.g. lipid content or body mass) and the rate of change of state.

In the present study, we explored the hypothesis that fast-growing individuals shift their niche earlier and at a smaller body size than slower-growing individuals because they maintain higher metabolic rates and are energetically constrained earlier than slow growers by limited food resources. Energy budgets of juvenile brown trout (Salmo trutta L.) that migrated from a small study stream to a lake were compared with individuals that remained in the stream. Our prediction was that at the year of migration, the relative proportion of the available energy for growth should be lower for migratory than for resident individuals, as the migrants have higher metabolic rates. The life history of brown trout in this watercourse is similar to the life history of many anadromous salmonids.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Study area and sampling

Juvenile brown trout were sampled in Litjåa, a small spawning and nursery stream of large piscivorous brown trout in the Lake Femund (62 °N 12 °E, 662 m a.s.l.), the second largest (204 km2) natural lake in Norway. The average summer water discharge in Litjåa is 0·5 m3 s–1 (lower parts) and average maximum discharge during spring flood is ≈ 5 m3 s–1. Brown trout is the only fish species that spawn in the stream, but during summer minnows (Phoxinus phoxinus L.) occasionally enter the river to feed for shorter periods. In addition, the Lake Femund supports populations of whitefish (Coregonus lavaretus L.), pike (Esox lucius L.), perch (Perca fluviatilis L.), grayling (Thymallus thymallus L.) and burbot (Lota lota L.). Pike, perch, burbot and adult brown trout are predators on juvenile brown trout.

In Litjåa a fish-trap 50 m upstream from the outlet into the lake caught all ascending and descending fish (including 0+). A second trap, ≈ 2·5 km upstream, caught only descending fish and ensured no immigration into the study area. The traps were operational from early June, shortly after the spring flood, until late September and were monitored twice a day. All descending fish were sampled for further analysis, whereas ascending fish were measured (total length in cm), marked (by fin clipping) and released above the trap. Migrations may also occur during the spring flood before the trap was operational. Migration during spring flood is common among salmonids, but is not obligatory (review in Jonsson 1991). In this work only summer migration is addressed. About one-third of the study section is slow-flowing and meandering, whereas the remaining two-thirds are more rapid-flowing. Temperatures were recorded every 4 h by temperature loggers in the lower parts of the stream. Water discharge was recorded twice a day in the same area.

Brown trout were sampled by electrofishing in four periods (23 June, 12 July, 11 August and 5 September 1995). The whole area was sampled on 23 June, whereas only the rapid-flowing parts and smaller subsections of the slow-flowing meandering parts were fished at the other samplings. Very few fishes, and mainly older and larger adults, were caught in the slow-flowing meandering parts. At each sampling, the aim was to collect a minimum of 20 individuals from each of five year classes (0+ , 1+ , 2+ , 3+ and 4+). This was accomplished on most occasions, with the main exception of 4+ which occurred in low densities in the stream. In addition, some older fish were caught (5 and 6+).

Each fish was weighed (nearest 0·01 g), total length (mm) measured, and aged by use of sacculus otoliths. The stomachs were removed and deep frozen. To compare growth of individuals caught by electrofishing and the descending migrants caught in the lower trap, length at earlier ages was back-calculated by use of otoliths (Jonsson & Stenseth 1977). Back-calculation was done by direct proportion, i.e. Dahl–Lea method (Francis 1990). This procedure may overestimate the lengths at age–1, but the relationship between the groups should not be altered.

Food consumption estimates

Food consumption was estimated by a tracer method (Forseth et al. 1992), using stable caesium (133Cs). Caesium is naturally occurring in very low concentrations, not feasible for ordinary quantitative analyses. Although radioactive caesium has been globally dispersed through fallout from nuclear bomb tests and accidents (Chernobyl), the concentration is often too low for quantification. We thus added a total of 6 kg of stable caesium chloride to the stream. Five kg of caesium salt were dissolved in 400 L of stream water and slowly released 50 m above the upper trap during a 50-h period from 23 to 25 June 1995. Another kg of caesium, dissolved in 100 L water, was released in the lower one-third of the stream (just below the meandering section) during the following day.

The estimation of food consumption is based upon estimating the intake of caesium from an observed change in caesium body burden with time. The rates of assimilation and elimination are known (Forseth et al. 1992; Ugedal et al. 1992), and the food consumption is obtained by dividing the caesium intake by the caesium concentration in the food. Daily food rations (Di mg dry mass (dw)) were estimated by the tracer method according to Forseth et al. (1992) with a two-component caesium elimination:

  • image(eqn 1)

and

  • image(eqn 2)

where Ii is daily caesium intake (mg), Qi and Qi+1 are caesium body burdens (mg kg–1) in fish at times i and i + 1, respectively, given by Q = CfiWi where Cfi is caesium concentration (mg kg–1) and Wi is fish mass (g), k1i and k2i are the rate constants for slow and fast elimination (day–1), respectively, bij is the assimilation of caesium from the jth prey item, Ci is the caesium concentration in the prey items (mg kg–1) and fij is the proportion of the jth prey item in the diet of trout. Fish that receive an acute oral caesium dose generally show two-component elimination curves (Kevern 1966; Gallegos & Whicker 1971). According to experimental data on brown trout (Ugedal et al. 1992), ≈ 20% of the caesium body burden is eliminated fast and 80% slowly when the fish is given a single dose of caesium. Under conditions of a continuous intake of caesium, the importance of the short-lived component gradually decreases (Kolehmainen 1974; Forseth et al. 1992). The food consumption estimates in the present study were done from 3 to 11 weeks after caesium was added to the stream. We thus predicted (Ugedal et al. 1992) that 95% of the caesium body burden was eliminated slowly and 5% fast, and used elimination rates estimated by the equations for brown trout given by Ugedal et al. (1992). Calculations were made step-wise, with separate variable values for each successive day, because eqn 1 is valid only if elimination (k) and intake (I) are constant during the period under consideration. Elimination is a function of both fish mass and ambient temperature (Ugedal et al. 1992), and intake varies seasonally. However, both k and I were assumed to be constant within a day. At present, it is not possible to calculate the statistical precision of food consumption estimates by the Cs tracer method when the estimates are based on average input-values from groups of fish rather than individuals.

The proportion of each of the prey categories (surface insects, Eurycercus lamellatus, chironomid zoobenthos and other zoobenthos) was determined through analysis of stomach contents. Assimilation data from laboratory brown trout were available (Forseth et al. 1992). The caesium concentration was measured in individual fish and pooled samples of stomach content from each age class and sampling date. This was done by instrumental neutron activation analysis (INAA), i.e. by irridation of the samples, activation of 133Cs to radioactive 134Cs and gamma spectrometry.

Daily values for fish mass and caesium concentrations in fish and stomach contents were calculated from linear interpolations between the geometric mean values determined for subsequent sampling dates. Daily temperatures were calculated from a Gaussian curve fitted to observations (Forseth et al. 1992). The curve fit smooths the day-to-day variation in temperature.

Food consumption was estimated for the age classes 0+ to 4+ from 12 July to 5 September 1995. In addition, consumption was estimated for descending 2+ brown trout caught in the lower trap between 29 August and 14 September, during a peak in the 2+ migration. Because we were not able to differentiate between migrating brown trout and those that remained in the stream before the fish actually started to move downstream, body mass and caesium concentration in July were not available for these fish. Thus, body mass at the start of the growth season (set at 1 June when temperatures exceeded 4 °C) was back-calculated from otoliths, and mass on 12 July was estimated from forward linear extrapolation. Two methods were used to estimate the caesium level for 2+ migrants on 12 July. First, we used the average of nine fish with the highest caesium concentration (upper one-third) of 28 fish sampled during electrofishing on 12 July. Second, we used a linear regression of the caesium concentration in 2+ brown trout caught in the trap and the day number on which they were caught. This analysis also included 2+ fish that migrated before the peak migration in September. The latter method gave concentrations twice as high as the first, and we compared the estimated food consumption using these two values to evaluate the sensitivity of the estimates to variation in caesium concentration at the start date.

Energy budgets

The balanced energy budget of fishes is given by:

  • image(eqn 3)

where C is the total energy in the food consumed, P is the energy in production (somatic and gonadal growth), R is the total energy of metabolism, F is the energy of the faeces and U is the energy of the excretory products. The total metabolism (R) is usually divided into three components: the metabolic cost of (i) maintaining the physiological state (maintenance metabolism, Rs), (ii) swimming (activity metabolism, Ra), and (iii) digesting and assimilating food (specific dynamic action, Rf).

The energy in the food consumed (C) was calculated as the product of the mass of food consumed from 12 July to 5 September and the energy in the prey animals (stomach content). Similarly, the energy of production (P) was calculated as the product of the mass gain during the period and the energy of fish body mass. The energy in the stomach content and fish body mass was estimated by determining the proportions of dry matter, ash, fat and protein, and multiplying the fat and protein proportions by energy conversion factors (Brafield & Llewellyn 1982; Jobling 1983). Individual fishes within each age class and sampling date were pooled in these analyses. The stomach contents were analysed for each age class but stomachs from different samplings were pooled because the variation was larger among age classes than among samplings. Ascending fish caught in the trap were analysed separately.

The proportion of energy intake lost in faeces (F) and excretory products (U) was estimated from the equations given for brown trout by Elliott (1976) with data on fish mass, ambient temperature and food consumption for brown trout from Litjåa. Finally, metabolic costs (R) were estimated from R = C – (FU) –P.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

A diverse pattern of downward movements of juvenile brown trout was observed (Fig. 1). Shortly after emergence from the gravel some 0+ brown trout were caught in the lower trap. These were among the smallest individuals from that cohort (trap 19 mm, stream 25 mm), presumably displaced by high water discharge or unable to establish territories in the stream. A few 1+ brown trout were also caught in the trap during the season. These individuals were smaller, albeit not significantly (t = – 1·18, P > 0·05), than those that remained in the stream (3·2 and 3·9 g, respectively). The 2- and 3-year-old trout dominated in catches in the lower trap. Descending 2+ brown trout caught in the trap between 29 August and 14 September, during a peak in the migration, were significantly larger (t = 3·12, P < 0·01) than those caught in the stream during electofishing on 5 September (10·9 and 8·0 g, respectively). Back calculations of lengths showed that descending 2+ trout had always been larger than those that remained in the stream, and that the difference was significant from the beginning of their second summer (F = 4·19, P < 0·05). Among 3-year-olds no such size difference was found (t = 0·29, P > 0·05). Descending 3+ individuals were significantly (t = – 2·96, P < 0·01) larger than descending 2+ (17·2 and 10·9 g, respectively).

image

Figure 1. Juvenile migration pattern in Litjåa: A flow diagram indicating the proportion of individuals at different ages (1–5+) that migrate from the stream to Lake Femund. Small and Large indicate that migrating individuals are smaller or larger than those that remain in the stream. Shading denotes sexually mature males.

Download figure to PowerPoint

We also compared the size of different aged brown trout with those in the Lake Femund (T.F. Nrsje, unpublished data). Asymptotic lengths (± SE), estimated by curve fitting to a von Bertalanffy (1938) growth model, were 151·8 (13·4) and 556·6 (31·3) mm, respectively. Thus, the average maximum size in the lake is 3·7 times that in the stream. Some of the fish caught in the Lake Femund may belong to other populations as there are at least two other tributaries near Litjåa where brown trout spawn. The majority of the adults that returned for spawning in Litjåa during 1995 (caught in the trap) were between 260 and 400 mm long, with a maximum at 480 mm.

No sexually mature females were caught during electrofishing in the stream. Thus, all females appeared to leave the stream to feed in the lake. A few 3 , 40% of the 4+ and all 5+ males caught in the stream were sexually mature. The two mature males caught during electrofishing in September were the largest individuals at that sampling. Back-calculations showed that mature 3+ males (four individuals) were significantly larger (M–W: Z = – 1·95, P = 0·05; t = – 2·81 P < 0·01) than immatures (n = 26) at the start of the growth season. No such size difference was found among 4- and 5-year-old males.

The estimated daily food rations varied between periods and among age classes of brown trout (Table 1). The daily rations were generally higher during the first period from 12 July to 11 August than the second from 11 August to 5 September. Mass specific daily rations were highest for 0+. The daily rations for 1+ were less than one-third of the rations for 0+. Thereafter, the estimated daily rations in the first period declined gradually with age. During the second period daily rations first increased from age 1+ to 2+ and then declined from age 3+ to 4+ . As expected from the weight increment the absolute rations increased by age. The estimated daily food rations were generally similar to maximum food rations for brown trout predicted from the average fish mass and ambient temperatures by the model of Elliott (1975a,b), with an average ratio (all age classes and both periods) between observed and maximum rations of 1·12. However, for 0+ brown trout the estimated daily rations were much higher than the predicted maximum rations, particularly during the second period (ratio 2·16).

Table 1.  Mean body mass (M g,± 95% C.L.), ambient temperature (T°C), specific growth rate (Gw percentage, ± 95% C.L.), mass specific (D mg dw g fw) and absolute daily rations (Dabs mg dw) estimated for different age classes of brown trout from Litjåa during two periods in 1994. The ratios between observed and maximum growth rate (± 95% C.L., estimated under the assumption that the maximum growth are estimated without error), and between estimated and maximum weight specific daily rations are also tabulated. Maximum growth of brown trout was calculated from the equations in Elliott et al. (1995) and maximum daily rations from the equations in Elliott (1975a,b)
AgeTMGwDDabsGw/Gw maxD/Dmax
12 Jul–11 Aug
0 + 12·8 0·22 4·95 60·7 12·4 1·03 1·40
 (0·17–0·27) (4·12–5·77)   (0·86–1·20) 
1 +  2·16 1·93 19·2 38·3 0·89 0·81
 (1·87–2·45) (1·44–2·43)   (0·66–1·11) 
2 +  6·35 1·01 18·3 115·2 0·66 0·95
 (5·88–6·82) (0·65–1·37)   (0·42–0·89) 
3 +  12·92 1·30 16·3 196·3 1·01 0·99
 (11·43–14·41) (0·72–1·88)   (0·56–1·46) 
4 +  23·11 0·43 15·5 358·1 0·42 1·10
 (19·83–26·39) (– 0·45–1·32)   (– 0·44–1·28) 
11 Aug–5 Sep
0 + 10·2 0·48 2·00 49·9 23·2 0·63 2·16
 (0·43–0·54) (1·13–2·88)   (0·35–0·91) 
1 +  3·34 1·19 9·7 32·7 0·68 0·67
 (3·03–3·65) (0·57–1·80)   (0·33–1·04) 
2 +  7·74 0·33 10·9 83·8 0·24 0·92
 (7·18–8·30) (– 0·23–0·88)   (– 0·18–0·67) 
3 +  15·94 0·39 12·2 192·9 0·37 1·23
(14·34–17·54) (– 0·35–1·13)   (– 0·32–1·06) 
4 +  26·1 0·40 8·3 213·9 0·43 0·94
(22·87–29·33) (– 0·59–1·39)   (– 0·64–1·51) 

The growth rates were, in accordance with the daily rations, generally higher during the first than the second period (Table 1). The growth rate declined with age and size. The estimated growth rates were similar or slightly lower than the estimated maximum growth rate for brown trout predicted from the growth model of Elliott, Hurley & Fryer (1995), with an average ratio (all age classes and both periods) between observed and maximum growth at 0·64. On three occasions only, the upper 95% C.L. of this ratio were lower than 1, indicating observed growth rates significantly lower than predictions.

Comparisons between daily food rations of resident and migratory 2+ brown trout revealed large differences (Table 2). The estimated absolute daily ration (mg dw) for 2+ brown trout caught in the trap during downward migration between 29 August and 14 September (migrants) was more than four times higher than that of fish caught during electrofishing on 5 September (resident). The difference was somewhat smaller (ratio 3·6) when comparisons were made on the basis of weight specific food rations (mg dw g fw–1). The estimated daily rations for migrants do not appear to be sensitive to variation in initial caesium concentration as a 100% increase in concentration caused only a 4% reduction in the estimated food ration (Table 2). Migratory 2+ brown trout had only slightly higher growth rates than resident 2+ (Table 2), and the difference was not significant (overlapping 95% C.L.). The resident individuals grew at a maximum rate, whereas the migratory individuals had growth rates below the estimated maximum (Table 2).

Table 2.  Mean body mass (M g, ±95% C.L), ambient temperature (T°C), mass increase (ΔM), specific growth rate (Gw percentage, ±95% C.L), mass specific (D mg dv · g fw) and absolute daily rations (Dabs mg dv) estimated for 2 + brown trout caught during electrofishing in Litjåa 5 September 1994 (resident) and brown trout caught in a trap during downwards migration between 29 August and 14 September. All values are given for the period between 12 July and 5 September. Two estimates for relative daily rations are presented for migrants. The latter value is based on initial caesium concentration in fish twice as high as the first (confer method section for details). The ratios between observed and maximum growth rate (±95% C.L, estimated under the assumption that the maximum growth are estimated without error), and between estimated and maximum weight specific daily rations are also tabulated. Maximum growth of brown trout was calculated from the equations in Elliott et al. (1995) and maximum daily rations from the equations in Elliott (1975a,b)
MTΔMGwDDabsGw/GwmaxD/Dmax
Migratory9·07 11·644·41 0·89 54·2/52·0443·0 0·75
(7·78–10·35)   (0·84–0·94)   (0·70–0·79)
Resident6·66 11·64 2·96 0·82 14·9 100·90·98
(5·96–7·38)   (0·79–0·85)   (0·94–1·01)

Food consumption was not estimated for migratory 3+ brown trout and comparisons between resident and migratory individuals could not be made for this age class. However, major differences in consumption are unlikely as they had similar body size and caesium concentrations.

The energy budgets were higher during the first than the second period, and increased with age (Fig. 2a). The allocation pattern differed among age classes but was similar in both periods. The proportional allocation of energy to growth was approximately twice as high for 1+ than for 0+ brown trout, and 1+ brown trout allocated ≈ 40% of the available energy to growth (Fig. 2b). The allocation to growth was much lower for 2+ (16% during the first period) and for older fish.

image

Figure 2. The energy budgets from 12 July to 11 August (left panel) and 11 August to 5 September (right panel) 1995 for the age classes 0+ to 4+ of brown trout in Litjåa. Total energy allocation (a) and proportional allocation (b) of energy to growth (P) and metabolism (R) and energy lost through faeces and urine (FU).

Download figure to PowerPoint

Migratory 2+ brown trout had an energy budget 4·5 times higher than resident 2+ (Fig. 3a), and migrants had more energy available for growth. However, the proportional allocation to growth was low among migrants (4·6%) compared to resident brown trout (12%) (Fig. 3b). The proportion of energy lost through faeces and excretory products was similar (≈ 30%) for the two groups of fish, but migrants allocated a higher proportion of energy to metabolism (64%) than those that remained in the stream (57%). The total metabolic costs were five times higher among migrating than among resident 2+.

image

Figure 3. The energy budgets for migratory and stream resident 2+ brown trout in Litjåa from 12 July to 5 September 1995. Total energy allocation (a) and proportional allocation (b) of energy to growth (P) and metabolism (R) and energy lost through faeces and urine (FU) for 2+ brown trout caught in a trap during downward migration between 29 August and 14 September (migrants) and fish caught in the stream on 5 September (resident).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Evaluation of methods

The use of stable caesium (133Cs) as a tracer for estimating brown trout food consumption appeared to be very successful. With the exception of age-group 0+, the estimated daily food rations were similar to predictions from a laboratory-based model for maximum consumption (Elliott 1975a,b). For the smallest fish (0+), the food rations were much higher than the maximum rations. This may be expected as Elliott (1975a,b) never used fish this small and extrapolations from larger fish may be invalid. The estimated daily rations matched the general expectation for the effect of temperature, body size and season. Daily rations were higher during the first sampling period (July–August) than during the second period (August–September), when temperature was 2·7 °C lower and food abundance was probably lower as, e.g. large insect larvae have hatched. In accordance with expectations, daily mass specific rations declined by age and body size (e.g. Wootton 1990).

To our knowledge, stable caesium as a tracer element has been used to estimate food consumption in fish on one occasion only (Hakonson, Gallegos & Whicker 1975). Forseth et al. (1992) compared estimates for brown trout food consumption based on the turnover of radioactive caesium (137Cs) from the Chernobyl fallout, with estimates from a well established method based on the amount of food in the stomach and the rate of gastric evacuation (Elliott 1972; Eggers 1977). The estimates were very similar, and Forseth et al. (1992) concluded that the use of caesium as a tracer is a reliable method for estimating food consumption. In principle, there is no difference between using stable and radioactive caesium as a trace element.

The high food consumption among 2+ migrants and the large difference in consumption rate between migrants and the stream dwellers is essential to our conclusions. As we were unable to differentiate between migrating brown trout and those that remained in the stream before the fish actually started to move downstream, we had to estimate the initial caesium concentration (on 12 July) of the migratory individuals. However, the estimated daily ration was essentially insensitive to variation in initial caesium concentration. A 100% increase in concentration 1 month before the downstream migration started caused only a 4% reduction in the estimated food ration. The main reason for the much higher food consumption among migrants than those that remained in the stream is that migrants had 1·5 times higher caesium concentration when they were caught in the trap. The estimated food consumption for migrants was nearly four times higher than maximum consumption predicted from laboratory studies (Elliott 1975a,b). This is very high and indicates that fish leaving the river at an age of 2 years are feeding at an exceptionally high rate, making them energy-wise the best performing fish in the population.

Evaluation of hypothesis

In accordance with our hypothesis, fast-growing brown trout migrated earlier and at smaller body size than slower-growing individuals. 2+ migrants were significantly larger than those that remained in the stream, and 3+ migrants were significantly larger than 2+ migrants. Comparisons across cohort (i.e. 2 and 3+ migrants) may be questionable because cohorts may experience different growth rates (for example due to variable year-class strength and density-dependent growth), but judging from the magnitude of the size difference (3+ being nearly twice as heavy as 2+), it is obvious that the young migrants were smaller than those that migrated 1 year older. This result appears general among migratory salmonids as it accords with findings from studies on brown trout (Bohlin, Dellefors & Faremo 1993; Jonsson & Gravem 1985; Jonsson 1985; Bohlin et al. 1996), Atlantic salmon (Jonsson et al. 1990; Økland et al. 1993), sockeye salmon (Oncorhynchus nerka) (Burgner 1991) and Arctic charr (Salvelinus alpinus) (Forseth et al. 1994).

The food consumption and energy budgets were much higher for migratory than stream resident trout. The absolute daily ration for 2+ migrants was more than four times higher and the energy budget (i.e. the energy of consumed food) 4·5 times higher than for resident 2+ fish. Despite this large difference in food consumption, the specific growth rate did not differ significantly between resident and migratory individuals. However, the total energy allocated to growth, and thus their mass increase, was higher among migrants. Moreover, the total metabolic costs were five times higher among migrants than among resident fish. In the present study, it was impossible to differentiate between the different components of metabolic costs. A large proportion of the estimated difference can probably be explained by the higher costs of digesting and assimilating (specific dynamic action, Rf in eqn 3) a much larger amount of food for migratory than resident fish. However, as the differences in metabolic costs between the two groups were larger than the differences in energy accumulated through food, the hypothesis of higher standard metabolic rates among early migrants of Atlantic salmon (Økland et al. 1993; Metcalfe et al. 1995) is given some support. An alternative explanation is that migrants have higher activity costs than those that remained in the stream. The distribution of metabolic cost among the components is, however, not essential to the conclusions in the present study.

Although the total energy allocated to growth and the mass increase was higher among migrants, their proportional allocation to growth was much lower than that of resident fish (about half). All 2+ brown trout had lower proportional allocation to growth than 1+ fish, but the reduction was larger among migrants (88%) than among resident fish (68%). A reduction in the proportional energy available for growth is a likely explanation for why migration is initiated at age 2. Moreover, it may explain why some individuals, those with higher metabolic rates, migrate earlier than others. They experience a larger drop in their proportional energy available for growth and seek alternative actions, such as migrating to an alternative feeding niche, to maintain their status as fast growers. Slower-growing individuals experience a smaller drop in proportional energy available for growth and remain in the stream for one more year. Most 3+ individuals migrate to the lake, but some of the largest males mature sexually and remain in the stream.

Migratory costs for juveniles in Litjåa can probably be neglected as the migrations are short and no change in salinity occurs. Thus, the optimal time for migration is the one that maximizes the ratio between the growth benefit of changing habitat and the costs of increased mortality after migration. Post-migration mortality of salmonids is often assumed to be negatively size-dependent because predation is size-dependent; small migrants are susceptible to a higher number of predatory species and a wider size range of predators than larger ones (Bohlin et al. 1993, 1996). As young migrants are smaller than older migrants, early migration is more risky than late migration.

Brown trout cannot, prior to migration, measure the growth they will attain in a new habitat. To optimize the time of migration, individuals thus have to respond to some change in conditions in their present habitat. The underlying mechanism which supports the ‘decision’ to migrate is assumed to be related to growth rate, or a physiological process like metabolic rate which is correlated with growth rate (Jonsson & Jonsson 1993). Brown trout from Litjåa experienced a relatively large drop in growth rate from age 1+ to 2+ , but migrants maintained as high growth rates in the summer of migration as resident fish. Thus, the growth rate per se cannot explain why some individuals migrate earlier than others. However, their relative allocation to growth declined significantly (from age 1+ to 2+), and young migrants experienced a larger drop than older ones. Thus, it appears that the fish are able to measure, by some physiological mechanism, changes in their amount of surplus energy available for growth, as postulated by Thorpe (1986).

Juvenile brown trout thus appear to migrate from one habitat to another as a phenotypically plastic response to declining growth performance as they reach an environmental threshold in their present habitat. This accords with the general assumption that migration is a biological response to adversity (Taylor & Taylor 1977). Individuals may reach this threshold at different ages and sizes depending on their metabolic status. Fast-growing individuals migrate earlier and at a smaller body size than slower-growing individuals, because their metabolic rates are higher, and consequently experience a larger drop in their allocation of energy to growth. By migrating, the fish are probably able to retain a higher growth rate than possible under the feeding opportunities in the original habitat.

For fast-growing individuals, an alternative to migration is to mature sexually in the stream. The size advantage attained in the stream, relative to slower-growing individuals, may then be converted into a fitness advantage by earlier reproduction and the possibly of participating in more spawning events during life. Among brown trout in Litjåa, this tactic was followed by a small proportion of the males only. These males were among the largest within their cohorts. Among females, the fitness gained by migrating to the lake and returning as large spawners with high fecundity appears to be more than balanced by the higher risk of mortality by postponing maturation. Among males, alternative mating strategies such as sneaking, may promote early maturation among fast-growing individuals, as fitness may be high for both small and large individuals (Jonsson 1985). For fast-growing males it thus appears to be alternative strategies of migration or early maturation. Most follow the first route, but some use the alternative.

All in all, the present study supports the hypothesis that fast-growing individuals shift niche earlier and at a smaller body size than slower-growing individuals, because they maintain higher metabolic rates and are energetically constrained younger than slow growers by limited food resources (Jonsson & Jonsson 1993). The sources of the variation in metabolic rates among individuals are unknown, but maternal and developmental effects and genetic diversity may all cause such variation in metabolic rates. Egg size, time of hatching and emergence from the gravel (Metcalfe & Thorpe 1992), early developmental effects and even random effects (e.g. spatial and temporal variation in the quality and availability of food items at first exogenous feeding) giving some individuals a head-start in life, may cause differentiated metabolic rates within one cohort. Genotypic differences in metabolic rates may also be maintained within a population due to variable selection pressures. A high metabolic rate is advantageous only if an additional energy intake can be attained through food consumption (Forseth et al. 1994). An individual's possibility to attain such an additional intake may depend upon cohort or population size (density dependency) and environmental factors influencing prey availability. It is important to understand the sources of variation in metabolic rates, because the consequences may be many. Recent studies indicate that in salmonids early metabolic rates represent an important premise for several of the life-history decisions the fish has to make later in life (Metcalfe et al. 1989; Metcalfe 1991; Titus & Mosegaard 1991; Metcalfe et al. 1992; Forseth et al. 1994; Metcalfe et al. 1995), and the present study shows that the metabolic status is also important for the timing of juvenile migrations in salmonids.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We thank Randi Saksgård, Karl Ove Søndmør, Sturla Brørs, Barbro Kløven and Jens Gisle Haukdal for assistance in the fieldwork. We also thank Engerdal Fjellstyre for allowing us to work undisturbed in Litjåa. Finally, we are grateful for the comments and corrections made by Malcolm Elliott and John Thorpe. Financial support was provided by the Directorate for Nature Management, Norway, by the Norwegian Institute for Nature Research and by the European Commission (FAIR Programme, contract CT95–0009).

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • Bohlin, T., Dellefors, C., Faremo, U. 1993 Optimal time and size for smolt migration in wild sea trout (Salmo trutta). Canadian Journal of Fisheries and Aquatic Sciences, 50, 224 232.
  • Bohlin, T., Dellefors, C., Faremo, U. 1996 Date of smolt migration depends on body-size but not age in wild sea-run brown trout. Journal of Fish Biology, 49, 157 164.
  • Brafield, A.E. & Llewellyn, M.J. 1982. Animal Energetics, Glasgow, Blackie.
  • Burgner, R.L. 1991 Life history of sockeye salmon (Oncorhynchus nerka). Pacific Salmon: Life Histories (eds C.Groot & L.Margolis), pp. 3 117. UBC Press, Vancouver.
  • Eggers, D.M. 1977 Factors in interpreting data obtained by diel sampling of fish stomachs. Journal of Fisheries Research Board of Canada, 34, 290 294.
  • Elliott, J.M. 1972 Rates of gastric evacuation in brown trout, Salmo trutta L. Freshwater Biology, 2, 1 18.
  • Elliott, J.M. 1975a Weight of food and time required to satiate brown trout, Salmo trutta L. Freshwater Biology, 5, 51 64.
  • Elliott, J.M. 1975b Numbers of meals in a day, maximum weight of food consumed in a day and maximum rate of feeding for brown trout, Salmo trutta L. Freshwater Biology, 5, 287 303.
  • Elliott, J.M. 1976 The energetics of feeding, metabolism and growth of brown trout (Salmo trutta L.) in relation to body weight, water temperature and ration size. Journal of Animal Ecology, 45, 923 948.
  • Elliott, J.M., Hurley, M.A., Fryer, R.J. 1995 A new, improved growth model for brown trout, Salmo trutta. Functional Ecology, 9, 290 298.
  • Forseth, T., Jonsson, B.N, Fumann, R., Ugedal, O. 1992 Radioisotope method for estimating brown trout food consumption. Canadian Journal of Fisheries and Aquatic Sciences, 49, 1328 1335.
  • Forseth, T., Ugedal, O., Jonsson, B. 1994 The energy budget, niche shift, reproduction and growth in a population of Arctic charr, Salvelinus alpinus. Journal of Animal Ecology, 63, 116 126.
  • Forsythe, M.G. 1968 Analysis of the 1966 smolt run in the North-west Miramichi River, New Brunswick. Fisheries Research Board of Canada, Technical Report 91.Francis, R.I.C.C. (1990) Back-calculation of fish length: a critical review. Journal of Fish Biology, 36, 883 902.
  • Gallegos, A.F. & Whicker, F.W. 1971 Radiocesium retention by rainbow trout as affected by temperature and weight. Radionuclides in Ecosystems (ed. D.J.Nelson), USAEC Report CONF-710501-P1, pp. 361 371, Washington DC.
  • Gross, M.R. 1987 Evolution of diadromy in fishes. American Fisheries Society Symposium, 1, 14 25.
  • Grossman, G.D. 1980 Ecological aspects of ontogenetic shifts in prey size utilization in the bay goby (Pisces: Gobiidae). Oecologia, 47, 233 238.
  • Hakonson, T.E., Gallegos, A.F., Whicker, F.W. 1975 Cesium kinetics data for estimating food consumption rates of trout. Health Physics, 29, 301 306.
  • Huntingford, F.A., Metcalfe, N.B., Thorpe, J.E., Graham, W.D., Adams, C.E. 1990 Social dominance and body size in Atlantic salmon parr, Salmo salar L. Journal of Fish Biology, 36, 877 881.
  • Hvidsten, N.A., Jensen, A.J., Vivås, H., Bakke, O, Heggberget, T.G. 1995. Downstream migration of Atlantic salmon smolts in relation to water flow, water temperature, moon phase and social interaction. Nordic Journal of Freshwater Research, 70, 38 48.
  • Jobling, M. 1983 A short review and critique of methodology used in fish growth and nutrition studies. Journal of Fish Biology, 23, 685.
  • Jonsson, B. 1985 Life history patterns of freshwater resident and sea-run migrant brown trout in Norway. Transactions of the American Fisheries Society, 114, 182 194.
  • Jonsson, N. 1991 Influence of water flow, water temperature and light on fish migration in rivers. Nordic Journal of Freshwater Research, 66, 20 35.
  • Jonsson, B. & Gravem, F.R. 1985 Use of space and food by resident and migrant brown trout, Salmo trutta. Environmental Biology of Fishes, 14, 281 293.
  • Jonsson, B. & Jonsson, N. 1993 Partial migration: niche shift versus sexual maturation in fishes. Reviews in Fish Biology, 3, 348 365.
  • Jonsson, N., Jonsson, B., Hansen, L.P. 1990 Partial segregation in the timing of migration of Atlantic salmon of different ages. Animal Behaviour, 40, 313 321.
  • Jonsson, B. & Ruud-Hansen, J. 1985 Water temperature as the primary influence on timing of seaward migrations of Atlantic salmon (Salmo salar) smolts. Canadian Journal of Fisheries and Aquatic Sciences, 42, 593 595.
  • Jonsson, B. & Stenseth, N.C. 1977 A method for estimating fish length from otolith size. Report from the Institute of Freshwater Research, Drottningholm, 56, 81 86.
  • Kevern, N.R. 1966 Feeding rate of carp estimated by a radioisotopic method. Transactions of the American Fisheries Society, 95, 363 371.
  • Kolehmainen, S.E. 1974 Daily feeding rates of bluegill (Lepomis macrochirus) determined by a refined radioisotope method. Journal of Fisheries Reseach Board of Canada, 31, 67 74.
  • L’Abée-Lund, J.H., Langeland, A.L., Jonsson, B. 1993 Spatial segregation by age and size in brown trout and Arctic charr: a trade-off between feeding possibilities and risk of predation. Journal of Animal Ecology, 62, 160 168.
  • Langeland, A.L., L’Abée-Lund, J.H., Jonsson, B., Jonsson, N. 1991 Resource partitioning and niche shift in Arctic charr, Salvelinus alpinus and brown trout, Salmo trutta. Journal of Animal Ecology, 60, 895 912.
  • Metcalfe, N.B. 1991 Competitive ability influences seaward migration age in Atlantic salmon. Canadian Journal of Zoology, 69, 815 817.
  • Metcalfe, N.B., Huntingford, F.A., Graham, W.D., Thorpe, J.E. 1989 Early social status and the development of life-history strategies in Atlantic salmon. Proceedings of the Royal Society of London B, 236, 7 19.
  • Metcalfe, N.B., Taylor, A.C., Thorpe, J.E. 1995 Metabolic rate, social status and life-history strategies in Atlantic salmon. Animal Behaviour, 49, 431 436.
  • Metcalfe, N.B. & Thorpe, J.E. 1992 Early predictors of life-history events: the links between first feeding date, dominance and seaward migration in Atlantic salmon, Salmo salar L. Journal of Fish Biology, 41 (Suppl. B), 93 99.
  • Metcalfe, N.B., Wright, P.J., Thorpe, J.E. 1992 Relationship between social status, otolith size at first feeding and subsequent growth in Atlantic salmon (Salmo salar L.). Journal of Animal Ecology, 61, 585 589.
  • Nrsje, T.F., Jonsson, B., Sandlund, O.T., Kjellberg, G. 1991 Habitat switch and niche overlap in coregonid fishes: effects of zooplankton abundance. Canadian Journal of Fisheries and Aquatic Sciences, 48, 2307 2315.
  • Økland, F., Jonsson, B., Jensen, A., Hansen, L.P. 1993 Is there a threshold size regulating smolt size in brown trout and Atlantic salmon ? Journal of Fish Biology, 42, 541 550.
  • Persson, L. 1983 Food consumption and competition between age classes in a perch (Perca fluviatilis) population in a shallow eutrophic lake. Oikos, 40, 197 207.
  • Sandlund, O.T., Nrsje, T.F., Jonsson, B. 1992 Ontogenetic changes in habitat use by whitefish, Coregonus lavaretus. Environmental Biology of Fishes, 33, 341 349.
  • Stearns, S.C. 1992 The Evolution of Life Histories. Oxford University Press, Oxford.
  • Taylor, L.R. & Taylor, R.A.J. 1977 Aggregation, migration and population mechanisms. Nature, 265, 415 421.
  • Thorpe, J.E. 1986 Age at first maturity in Atlantic salmon Salmo salar freshwater period influences and conflicts with smolting. Canadian Special Publications in Fisheries and Aquatic Science, 89, 7 14.
  • Thorpe, J.E. 1987a Smolting versus residency: developmental conflicts in salmonids. American Fisheries Society Symposium, 1, 244 252.
  • Thorpe, J.E. 1987b Environmental regulation of growth patterns in juvenile Atlantic salmon. Age and Growth of Fish (eds J.D.Summerfelt & G.E.Hall), pp. 463 474. Iowa State University Press, Ames.
  • Thorpe, J.E., Mangel, M., Metcalfe, N.B., Huntingford, F.A. 1998 Modeling the proximate basis of salmonid life history variation, with application to Atlantic salmon, Salmo salar L. Evolutionary Ecology, 5, 581 599.
  • Thorpe, J.E., Metcalfe, N.B., Huntingford, F.A. 1992 Behavioural influences on life-history variation in juvenile Atlantic salmon, Salmo salar. Environmental Biology of Fishes, 33, 331 340.
  • Titus, R.G. & Mosegaard, H. 1991 Selection for growth potential among migratory brown trout (Salmo trutta) fry competing for territories: evidence from otoliths. Canadian Journal of Fisheries and Aquatic Sciences, 48, 19 27.
  • Ugedal, O., Jonsson, B., NjDstad, O., Nfumann, R. 1992 Effects of temperature and body size on radiocaesium retention in brown trout (Salmo trutta L.). Freshwater Biology, 28, 165 171.
  • Von Bertalanffy, L. 1938 A quantitative theory of organic growth. Human Biology, 10, 181 213.
  • Werner, E.E. 1979 Niche partitioning by food size in fish communities. Predator–Prey Systems in Fisheries Management (eds R.H.Stroud & H.Clepper), pp. 311 322. Sport Fishing Institute, Washington D.C.
  • Werner, E.E. & Gilliam, J.F. 1984 The ontogenetic niche and species interaction in size-structured populations. Annual Review of Ecology and Systematics, 15, 393 425.
  • Wootton, R.J. 1990. Ecology of Teleost Fishes. Chapman & Hall, London.

Received 16 April 1998;revisionreceived 4 November 1998