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Office of the County Governor of Finnmark, Damsveien 1, N-9800 Vadsø, Norway and. §Department of Mathematical Sciences/Statistics, University of Oulu, ...
Journal of Fish Biology (1999) 55, 506–516 Article No. jfbi.1999.1011, available online at http://www.idealibrary.com on

Return migration of Atlantic salmon in the River Tana: the role of environmental factors J. E*0, F. Ø†, K. M‡, E. N¨ *  M. R§ *River Tenojoki Fisheries Research Station, Finnish Game and Fisheries Research Institute, FIN-99980 Utsjoki, Finland; †Norwegian Institute for Nature Research, Tungasletta 2, N-7005 Trondheim, Norway; ‡Department of Environmental Affairs, Office of the County Governor of Finnmark, Damsveien 1, N-9800 Vadsø, Norway and §Department of Mathematical Sciences/Statistics, University of Oulu, FIN-90570 Oulu, Finland (Received 14 December 1998, Accepted 7 May 1999) Multi-sea-winter Atlantic salmon (75–115 cm fork length LF, 2–4-winter fish) were radio-tagged in the Tanafjord (70 N), Norway, in 1992–1993, and 130 fish entered the large subarctic River Tana (Teno). They entered the fresh water at any time of the tidal cycle but more so during the high and ebbing tides. No diel rhythm was detected in river entry under polar day conditions. There were no differences in the change of flow between days when salmon moved and when they did not, but during active migration increasing discharge was associated with increased swimming activity of salmon, especially later in the summer. Increasing air temperature was also associated with enhanced migration activity. Low river flow was associated with increasing delay in salmon passing the first riffle area of the river, 35 km from the sea.  1999 The Fisheries Society of the British Isles

Key words: Salmo salar; flow; tide; temperature; mixed linear model.

INTRODUCTION Several studies have attempted to identify relationships between environmental factors and river entry or upstream migration of anadromous salmonids (Banks, 1969; Jonsson, 1991). There are two main methods of gathering time series information on fish migration: point-checking the numbers of fish (counting fences, fishways: Alabaster, 1970, 1990; Arnekleiv & Kraabøl, 1996; Quinn & Adams, 1996; Tre´panier et al., 1996) and following individual fish during their migration (telemetry: Potter, 1988; Laughton & Smith, 1992; Heggberget et al., 1996; Smith & Smith, 1997). It has been noticed that different factors influence the behaviour of ascending salmonids, such as tide (Potter, 1988; Smith & Smith, 1997), river flow (Potter, 1988; Arnekleiv & Kraabøl, 1996; Tre´panier et al., 1996; Quinn et al., 1997), temperature (Jensen et al., 1989; Alabaster, 1990; Quinn et al., 1997) and light (Potter, 1988). However, the factors may also be interrelated (Jonsson, 1991; Smith & Smith, 1997). In a recent study, Tre´panier et al. (1996) discussed the problems associated with detecting relationships between environmental variables and the upstream migration of fish. They highlighted the temporal resolution of the observations 0Author to whom correspondence should be addressed at present address: Oulu Game and Fisheries Research, Finnish Game and Fisheries Research Institute, Tutkijantie 2, FIN-90570 Oulu, Finland. Tel.: +358 205 751 871; fax: +358 205 751 879; email: [email protected] 506 0022–1112/99/090506+11 $30.00/0

 1999 The Fisheries Society of the British Isles

     

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v. that of the environmental factors, for instance whether the observation frequency at a fishway or a counting fence matches the occurrence of short-term variation of flow. Moreover, human activities affecting the environment where migration is studied may bias the responses of salmon, for example, water abstraction, weirs, and the observation device itself, counting fence or fish passage may be obstructions (Banks, 1969). Finally, there may be serious problems in interpreting the data if serial correlation, typical to time series data, is not accounted for in statistical analysis (Ostrom, 1990; Tre´panier et al., 1996). In this study, radio-telemetric methods were used in assessing the effects of environmental conditions: tide, river flow and temperature, on river entry and upstream migration of multi-sea-winter (MSW) Atlantic salmon Salmo salar L. in the large subarctic River Tana (Teno) in the far north of Scandinavia. The Tana is one of the few remaining large salmon rivers which has an abundant and virtually pristine distribution area for Atlantic salmon with no manmade obstructions (other than fishing gear; Erkinaro et al., 1999) on the riverine migration route. Thus, the telemetric observations on salmon responses on environmental stimuli represent as unbiased information from a natural environment as possible. MATERIALS AND METHODS STUDY AREA The subarctic River Tana (Teno in Finnish, Deatnu in Sa´mi) forms the border between northern Norway and Finland (70 N, 28 E; Fig. 1), being one of the largest (catchment area 16 386 km2) and the most productive salmon rivers of both countries, with annual river catches between 100 and 200 t of Atlantic salmon (Niemela¨ et al., 1996). About 1100 km of different stretches of the system are accessible to ascending salmon. The longest distance salmon may migrate from the sea is c. 300 km along all three major headwater branches, Ana´rjohka, Ka´ra´sˇjohka and Iesˇjohka (Fig. 1). Most of the salmon enter the river in early June–early August, and the main spawning period in the main stem and in the largest tributaries is between late September and early October (Erkinaro et al., 1999; unpubl. data). The mean annual discharge of the main stem is c. 160 m3 s 1 but seasonal variation is wide, ranging from a minimum of 15–20 m3 s 1 during the winter to a maximum of 2500–3500 m3 s 1 during the spring flood in May–June (Siirala & Huru, 1992; unpubl. data of Lapland Regional Environment Centre). The river discharges of the study period were measured by the Norwegian Water Resources and Energy Administration (NVE) and they represent daily discharges in Polmak (Fig. 1), 56 km from the outlet of the river. As water temperature data was not available for the entire tracking periods in both years, air temperature was used in the analyses. Air temperature was recorded continuously with an automatic temperature logger at a meteorological station in Kevo, close to Utsjoki (Fig. 1). CATCHING, TAGGING AND TRACKING THE SALMON Salmon were caught with bag nets in the Tanafjord, Arctic Ocean, 5–18 km from the outlet of the River Tana (Fig. 1). Eighty-one and 93 MSW (2–4 sea-winters; 75–115 cm fork length, LF) Atlantic salmon were radio-tagged in June–July in 1992 and 1993, respectively, and released immediately at the site of capture. The catching and handling procedures are presented in detail in Erkinaro et al. (1999). The radio-tags used were Model 16 M (ATS, Advanced Telemetry Systems Inc., U.S.A.) flat transmitters with dimensions of 5·5 cm (length)2·5 cm (width)1·0 cm (height) and a weight of 5 g in water. Each transmitter had an individual combination of

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Bag nets Rødbergnes Storfossen Alaköngäs Tana bru Ailestrykene Yläköngäs

Polmak

a

Norway

an rT

Utsjoki

ve

Iesjohka ˘

a

hk

jo ás˘

ár

K

Aná rjoh ka

Ri

Finland

0 10 20 30 40 km Border

F. 1. The location of the River Tana catchment area, salmon tagging sites (bag nets, ) in the Tanafjord and the automatic logger stations in the river valley (). One of the three loggers was in Utsjoki (Karnjarga) in 1992, but in Tana bru in 1993.

transmitting frequency (142·010–142·500 MHz, 10 kHz apart) and pulse rate (50 or 80 per min). An often recommended maximum transmitter weight to fish weight ratio of 2% (Winter, 1983) was not even close in this study, as the ratio ranged between c. 0·03% and 0·1%. Thus, the tag size used should not have had any major effect on the salmon behaviour (Mellas & Haynes, 1985; Be´gout Anras et al., 1998). The tags were attached externally below the dorsal fin of the fish. The tagging procedure is presented in more detail in Erkinaro et al. (1999). The radio-tagged fish were detected first close to the river mouth (Rødbergnes, 10·5 km upstream, above the tidal zone, Fig. 1) with an automatic data logger (ATS Model D5041) connected to a receiver (ATS Model R2100) that recorded the time of entering the river for each fish. Two other data loggers were used in three different localities in the lower part of the river (Fig. 1). Also, the fish were tracked manually and located in the river to the nearest 500 m employing a nine-element Yagi antenna installed on the roof of a car, and a receiver. In most cases the radio signals were detectable at a minimum distance of 1 km, but occasionally the signals were received at distances up to 5 km. The fish were located at intervals of 3 days in 1992 and daily in 1993 during the main migration period of 6 June–9 August. From 10 August until the end of the

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spawning period in October the fish were located once a week. Because of the different detection intervals, the data for the 2 years have been analysed separately and the data for 1992 have not been used in all analyses. STATISTICAL ANALYSIS The effect of tide on the river entry of the salmon was analysed by splitting the time period in two: the flooding tide (6 h before the high tide) and the ebbing tide (6 h after the high tide). The frequency distributions of the entering fish were compared between the two periods using a test of independence (Pearson ÷2; SYSTAT, 1996). Since the radio transmitters cannot be detected in salt water, river entry refers to the point where the freshwater zone of the river starts and where the entering salmon were detected first (Rødbergnes automatic logging station, 10·5 km from the river mouth, Fig. 1). The general effect of the environmental variables on fish migration speed was approached first by analysing the possible differences in environmental factors between the days when salmon were migrating actively (minimum 1·5 km day 1) or not migrating. The minimum migration speed was chosen to account for the minimum location accuracy of 1 km in some cases. Then, for the days of active migration, the relationships between the daily distances covered by the salmon and the corresponding daily environmental variables (discharge, air temperature) were analysed. As daily tracking information was available for the year 1993 only, the data for 1992 were not used in this analysis. Because of the longitudinal nature of the data and the problems of autocorrelation in time series data sets (Ostrom, 1990; Diggle et al., 1994; Tre´panier et al., 1996), the data were analysed in the framework of linear regression models with random effects (linear mixed models) as discussed in Diggle et al. (1994). It was evident from the very beginning that the migratory patterns of the salmon varied individually, but it soon turned out that the effects of air temperature on the movements also varied from fish to fish. In order to take this variation into account it was assumed that the individual mean levels of the movements and the individual coefficients of the air temperature would follow a multivariate normal distribution. Thus, a linear regression model with random coefficients was employed as explained in more detail in the Appendix. Because the number of fish was relatively large (n=68), a relatively parsimonious description of the data was achieved. No other coefficients showed any considerable individual variation and they were consequently assumed to be fixed constants. It also turned out that models with these random effects could describe adequately the correlations between the consecutive observations from each fish and no further autocorrelation structures for the error terms were needed. The effect of the season and the fish size were accounted for by distinguishing the fish marked in June and July and fish of age groups 2 SW (two sea-winter) and 3–4 SW by means of two dummy variables. Several models of this type were estimated tentatively and tested for all possible interactions between the factors and variables mentioned above. By excluding all clearly unnecessary interactions, the model was formulated finally as presented in the Appendix and in Tables I and II. The delaying effect of river discharge on the salmon migration was investigated in the two first major riffle areas of the River Tana, the Tana bru area (35–41 km from the sea) and the Alako¨nga¨s/Storfossen area (67–73 km from the sea; Fig. 1). This was done by relating the time spent (short time: 1 or 2 days; long time: more than 2 days) in the area and the prevailing mean river discharge over that time period. Salmon behaviour at the third major riffle section, the Yla¨ko¨nga¨s/Ailestrykene area (Fig. 1), was not analysed because of the small number of fish passing the area (Erkinaro et al., 1999). The discharge variable was classified in two categories: >300 m3 s 1 and 300 m3 s 1. The frequency distributions among the classified time and discharge variables were tested using a test of independence (log-likelihood ratio test; SYSTAT, 1996).

RESULTS ENTRY TO FRESH WATER V. TIDE AND TIME

The salmon entered the freshwater zone of the River Tana at all stages of the tidal cycle (Fig. 2), but more salmon entered during the ebb than during the

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T I. Estimates of the parameters in the full model Parameter

Model I

Model II (ã6 =0)

Estimate

..

t-ratio

Estimate

..

t-ratio

ã1 ã2 ã3 ã4 ã5 ã6 ã7 ã8 â0 â1 â2

0·00778 8·506 16·578 2·242 0·0763 0·0203 2·2058 0·1051 44·215 1·686 0·0743

0·00378 10·483 7·795 1·323 0·0487 0·0115 1·2861 0·0442 26·230 1·207 0·0423

2·06 0·81 2·13 1·70 1·57 1·77 1·72 2·38 1·69 1·40 1·76

0·00977 22·324 17·319 2·384 0·0805 — 3·319 0·138 62·152 1·945 0·0809

0·00356 6·841 7·996 1·358 0·0500 — 1·131 0·0405 24·390 1·239 0·0435

2·75 3·26 2·17 1·76 1·62 — 2·93 3·41 2·55 1·57 1·86

ô2 ó11 ó21 ó22 ó31 ó32 ó33

82·806 86·319 12·419 2·544 0·216 0·0766 0·00290

Log likelihood

83·220 117·63 18·276 3·512 0·435 0·110 0·00393 1448·36

1452·10

See Appendix for more detailed description.

T II. Likelihood ratio (LR) or Wald test statistics for the hypotheses Hypothesis H1 H2 H3 H4 H5 H6 H7

Restrictions

LR test statistic

Reference distribution

P

ã1 =ã6 =0 ã6 =0 â2 =0, ó31 =ó32 =ó33 =0 ó21 =ó22 =ó31 =ó32 =ó33 =0 ã7 =ã8 =0 ã3 =ã4 =ã5 =0 ã4 =ã5 =0

12·59 7·48 17·42 20·27 11·44* 20·39 3·01*

÷22 ÷12 ÷42 ÷52 ÷22 ÷32 ÷22

0·0018 0·0062 c0·0016 c0·0011 0·0033 0·00011 0·222

*Wald test. See Appendix for the definition of the variables.

flood tide (Pearson ÷2 =5·22 and 6·39, P=0·022 and 0·011 in 1992 and 1993, respectively). The largest numbers of salmon entered fresh water within 1 h prior to or after high tide (Fig. 2). Salmon were detected by the first automatic logging station evenly at different times of the day with no tendency to favour a certain time period (Pearson ÷2 =0·81 and 3·88, P=0·847 and 0·247 in 1992 and 1993, respectively, tested using 6-h periods; Fig. 3).

      –6

1992 n = 62

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1993 n = 68

–5 –4

High tide (h)

–3 –2 –1 +1 +2 +3 +4 +5 +6 0

2

4

6

8

10

12

14 0 2 Number of fish

4

6

8

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F. 2. Numbers of salmon entering the River Tana during various tidal phases.

GENERAL EFFECT OF RIVER DISCHARGE AND TEMPERATURE

The changes in river discharge and air temperature [a difference between the present day and 1 or 5 days before (ÄFt1 and ÄFt5 for flow; ÄTt1 and ÄTt5 for temperature)] were not different between the days when the salmon were migrating and days when they were not (Mann–Whitney U-test; ÄFt1: P=0·41, ÄFt5: P=0·06, ÄTt1: P=0·81, ÄTt5: P=0·06). However, during the days of active migration, an increase in the discharge seemed to increase the migration speed of the fish (linear mixed model: P]0·0018). This tendency was particularly strong among individuals that entered the river in July (P]0·00062). More detailed results concerning the hypotheses H1 and H2 and the parameter estimates are presented in Tables I and II. The migration speed of salmon tended to increase with decreasing air temperatures, but the relationship turned out to be parabolic rather than linear (hypothesis H3, Pc0·0016, Table II). The effects of temperature varied in dividually from fish to fish (H4, Pc0·0011), but low temperatures were associated with swimming speed especially in salmon tagged in July (H5, P]0·0033, Table II). In cold water, early in the season, 3–4 SW salmon were swimming on the average 3·5 km longer distances per day than were the 2 SW salmon (H6, P]0·0001), but both age groups (2 SW and 3–4 SW) tended to respond to decreasing temperature in the same manner (H7, P]0·222, Table II). No interaction could be detected between the river flow and age group either. Consequently, this interaction was deleted from Model I (Table I). The migration delay in the lowest riffle area in the river, at Tana bru, tended to vary with the river discharge: during high flows (>300 m3 s 1) salmon passed the area faster than during lower flows (c300 m3 s 1, log-likelihood ratio=16·63, P=0·004). The second riffle area, the Alako¨nga¨s/Storfossen, did

.    .

512 8

1993 n = 68

6

Number of salmon

4 2

8 1992 n = 62 6 4 2

0

6

12 Time of day (h)

18

24

F. 3. Numbers of salmon entering the River Tana at different times of day.

not cause a different delay in the salmon migration under different flow conditions (log-likelihood ratio=2·06, P=0·152). For salmon passing the two riffle areas (some of the fish stayed in the areas until spawning; Erkinaro et al., 1999), the delay was shorter at the Tana bru (mean 2·2 days, .. 2·5, n=49) than in the Alako¨nga¨s/Storfossen area (mean 4·6 days, ..=5·6, n=34; Mann–Whitney U=527, P=0·002). DISCUSSION The River Tana salmon entered the river during all times of the day and at all times of the tidal cycle, although a tendency was detected showing that salmon were in favour of the high tide and the ebbing tide after that. The lack of any diel rhythm was rather as expected because of the 24-h daylight in high latitudes (70 N) in early summer, whereas specific diel rhythms in the river entry of salmon have been observed in more southern latitudes where diel changes in illumination are more pronounced (Dunkley & Shearer, 1982; Potter, 1988). Different responses of salmon to tidal rhythm have been observed, pointing towards associations with flooding or ebbing tide, or no associations have been found (Smith & Smith, 1997 and references therein). The possibilities of uncovering such relationships between tide and salmon ascent may be hampered depending on the place where movements have been detected in relation to where they begin (Smith & Smith, 1997). In the present study, a problem like this was evident, in the fact that the first observation point was 10 km upstream from the actual river mouth—a point above the tidal zone where radio signals can be

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detected. In some studies, this problem has been avoided by using combined acoustic and radio-tags (Potter, 1988; Smith & Smith, 1997). Thus, the river entry of the River Tana salmon represents rather their entry to a freshwater zone than an actual river entry, although an exact definition of the river mouth would be ambiguous in an estuary of a large river like the Tana where the lowermost part is almost 2 km wide. Using point-checking data from fishways or fish counters and relating those with environmental variables may include a problem of fish availability below the check point (Alabaster, 1970; Jensen et al., 1986; Tre´panier et al., 1996). If no fish are available downstream of the fish counter, there will be no count data to relate to changes in environmental factors and thus the effect of the environmental stimuli over the whole migration will be underestimated. Telemetric methods provide an advantage of following the reponses of individual fish and are not dependent on fish availability at a certain point of the river. However, tracking of the very same individuals in time also includes problems in statistical interpretation of the results, especially in terms of serial correlation or autocorrelation (Ostrom, 1990; Diggle et al., 1994). The models used account for individual variation in the migration activity and in the effects of air temperature on it. The effects of the season and the size of the fish, on the other hand, did not show any individual variation and were assumed to be fixed. There are considerable data on river flow affecting salmon migration (see reviews by Banks, 1969; Jonsson, 1991; Tre´panier et al., 1996), but no clear consensus exists on the effect and its mechanisms. Moreover, a question has arisen about the roles of absolute and relative flow causing the responses of migrating salmon (Smith et al., 1994). In the present study, increasing flows were associated with the increasing migration speed of salmon once they were moving, but no differences in flow change were detected between the days of migration and the days without migration. The relative and absolute flows in the Tana are different from those in Scotland, where wide ranges of discharges are available over several months of the year (Smith et al., 1994). In the River Tana, typically for large northern rivers, the flow decreases fairly steadily after the ice break-up and the subsequent spring flood in June, and considerable spates are rare later in the summer. Thus the relationships between salmon migration and flow are also interrelated with season (i.e. time). The tendency for early migration (i.e. during higher flows) of large salmon may be adaptive (Tre´panier et al., 1996). Large MSW salmon are known to spawn far up in the headwaters of the river system, 250–300 km from the sea (Rivers Ka´ra´sˇjohka and Iesˇjohka, Fig. 1; unpubl. data), and ascent to headwaters may require suitable flow conditions (Summers, 1996). Jonsson et al. (1991) concluded that below a certain threshold discharge level, increased discharge was related positively to size of ascending salmon, but above the threshold no such relationship was detected. Large salmon also suffer delays in migration caused by low flow more than do small salmon (Jonsson et al., 1990). Thus, the successful river ascent of MSW salmon may be dependent on a certain level of discharge, although in the main stems of large rivers like the Tana the minimum level may be reached rarely during the natural migration time in spring–summer. Heggberget et al. (1996) suggested that differences in water flow during the normal migration period in another large subarctic river, the River Alta,

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northern Norway, may not explain the differences detected in migratory behaviour between wild (mostly MSW) and farmed salmon. The relationship between discharge and time delay at the Tana bru area in the lower part of the River Tana may have implications for fisheries management. As a large low-gradient river, the main stem of the Tana has only few bottleneck areas, stretches of rapids or cascades which hold salmon during their migration for some time, causing accumulations of fish within a restricted area and thus increasing fishing pressure (Erkinaro et al., 1999). Heavy fishing in the Tana bru area during low flow may cause excessive fishing mortality if fish tend to stay in the area for extended periods. The same tendency may be true also for the Alako¨nga¨s/Storfossen and the Yla¨ko¨nga¨s/Ailestrykene areas, although large individual variation (Alako¨nga¨s) or the small number of observations (Yla¨ko¨nga¨s) hindered the detection of significant relationships. Additional delay in these areas with extensive fishing activity may be caused by fixed fishing gear, especially weirs (Erkinaro et al., 1999) that are allowed 3 days per week and that block off part of the river. Several problems have been noted to arise from salmon crowding during low flows, for example, elapsed time delay at obstacles, higher mortality caused by fisheries and predators, and higher susceptibility to diseases (Mills, 1989; Smith et al., 1994 and references therein). The authors thank O. Møllenes, B. R. Mikkelsen and D. Karlsen for help with tagging operations and local fishermen for co-operation with their bag nets; K. Sundelin, H. Aagesen and T. Guttorm, for assistance in tracking salmon in the river valleys; and M. Julkunen for comments on an early draft of the manuscript.

References Alabaster, J. S. (1970). River flow and upstream movement and catch of migratory salmonids. Journal of Fish Biology 2, 1–13. Alabaster, J. S. (1990). The temperature requirements of adult Atlantic samon, Salmo salar L., during their upstream migration in the River Dee. Journal of Fish Biology 35, 659–661. Arnekleiv, J. V. & Kraabøl, M. (1996). Migratory behaviour of adult fast-growing brown trout (Salmo trutta, L.) in relation to water flow in a regulated Norwegian River. Regulated Rivers 12, 39–49. Banks, J. W. (1969). A review of literature on the upstream migration of adult salmonids. Journal of Fish Biology 1, 85–136. Be´gout Anras, M. L., Bodaly, R. A. & McNicol, R. (1998). Use of an acoustic beam actograph to assess the effects of external tagging procedure on lake whitefish swimming activity. Transactions of the American Fisheries Society 127, 329–335. Diggle, P. J., Liang, K.-Y. & Zeger, S. L. (1994). Analysis of Longitudinal Data. Oxford: Oxford University Press. Dunkley, D. A. & Shearer, W. M. (1982). An assessment of the performance of a resistivity fish counter. Journal of Fish Biology 20, 717–737. Erkinaro, J., Økland, F., Moen, K. & Niemela¨, E. (1999). Return migration of the Atlantic salmon in the Tana River: distribution and exploitation of radiotagged multi-sea-winter salmon. Boreal Environment Research, in press. Heggberget, T. G., Økland, F. & Ugedal, O. (1996). Prespawning migratory behaviour of wild and farmed Atlantic salmon, Salmo salar L., in a north Norwegian river. Aquaculture Research 27, 313–322. Jensen, A. J., Johnsen, B. O., Hansen, L. P. (1989). Effect of river flow and water temperature on the upstream migration of adult Atlantic salmon Salmo salar L. in

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.    . APPENDIX

The data were analysed within the framework of linear mixed models of the following type:

and where yit ↔the average daily distance swum by the fish i at time t Tt ↔air temperature at time t dt ↔discharge of the river at time t mi ↔time of marking of fish i (dichotomous; June=0, July=1) ai ↔age of fish i (dichotomous; 2 SW=0, 3–4 SW=1) åit ↔random error term âio, âi1, âi2 ↔coefficients varying from fish to fish; the distribution of these coefficients over the fish population is assumed to resemble a three-dimensional normal distribution. Individually varying random coefficients have been denoted by â and constant coefficients by ã. Framework (A) represents one of the most parsimonious models that still can fit the data well. For further information on the analysis of longitudinal data by means of mixed models, see for instance Diggle et al. (1994). The estimation results are displayed in Table I. In addition to the full model (A), the estimates have also been given corresponding to the hypothesis H2 (ã6 =0). Some test results are displayed in Table II.