The influence of hydrological and biotic processes on brown trout ( Salmo trutta ) population dynamics

The influence of hydrological and biotic processeson brown trout (Salmo trutta) population dynamics

Franck Cattanéo, Nicolas Lamouroux, Pascal Breil, and Hervé Capra

Abstract: Hydrological and biotic forces constrain brown trout (Salmo trutta) population dynamics, but tests of theirrole across numerous streams are uncommon. In 30 French stream reaches, using 5–8 samples (1 per year) each, weinvestigated whether the year-to-year seasonal hydrology influenced annual trout densities within reaches, and whetherthe relationships were shared by all reaches. We also searched for intraspecific interactions between and within cohorts.Trout data were age class (0+, 1+, and adults) densities. For each year, hydrology was described using 13 variables,each computed for a reproduction, emergence, and growth period related to the biological cycle of trout. We used anal-yses of covariance (ANCOVA) to test how trout densities at year n – 1 and hydrology at year n influenced trout densi-ties at year n. High flows during emergence significantly reduced the 0+ densities, consistently across the 30 reaches.Then, 1+ and adult densities were linked, respectively, to 0+ and 1+ densities from the previous year. Analyses also re-vealed density-dependent survival mechanisms for the 0+ cohort, suggesting intracohort competition. Therefore, hydrol-ogy constrains trout dynamics only during the critical emergence period, after which intracohort interactions regulatethe 0+ density. Such mechanisms, validated across 30 environmentally different reaches, seem to be fundamental totrout population dynamics.

Résumé : Les forces hydrologiques et biotiques imposent des contraintes à la dynamique de population des Truitesbrunes; cependant, on a rarement évalué leur rôle simultanément dans un grand nombre de cours d’eau. À partir de5–8 échantillons (1 par année) récoltés dans chacun de 30 tronçons de cours d’eau français, nous avons examiné siles variations de l’hydrologie d’année en année influencent la densité annuelle des truites dans un tronçon et si lesrelations obtenues s’appliquent à tous les tronçons. Nous avons aussi cherché à trouver des interactions intra-spécifiques au sein des cohortes et d’une cohorte à l’autre. Nos données consistaient en des densités des différentesclasses d’âges (0+, 1+ et adulte). Nous avons mesuré, chaque année, 13 variables hydrologiques, regroupées selonles périodes d’émergence, de reproduction et de croissance du cycle biologique des truites. Des analyses de cova-riance (ANCOVA) nous ont permis de vérifier comment la densité des truites de l’année n – 1 et l’hydrologie del’année n pouvaient influencer la densité des truites de l’année n. Des débits élevés durant l’émergence réduisaientsignificativement les densités des truites d’âge 0+, de façon comparable dans tous les 30 tronçons. De plus, lesdensités des truites d’âges 1+ et adulte étaient reliées respectivement aux densités des poissons d’âges 0+ et 1+ del’année précédente. Les analyses ont aussi mis en évidence des mécanismes de survie reliés à la densité chez lacohorte d’âge 0+, ce qui semble indiquer l’existence de compétition au sein de la cohorte. L’hydrologie impose doncdes contraintes à la dynamique de population des truites seulement durant la période critique de l’émergence, aprèsquoi ce sont les interactions au sein de la cohorte qui contrôlent la densité des poissons d’âge 0+. De telsmécanismes, observés dans 30 tronçons qui diffèrent par leurs conditions environnementales, semblent être descaractéristiques fondamentales de la dynamique de population des truites.

[Traduit par la Rédaction] Cattanéo et al. 22

Introduction

Salmonid populations exhibit large variations in popula-tion size, in both space and time. The spatial variability ismainly affected by large- to local-scale environmental fac-

tors (e.g., climate, topography, local habitat features) that actas filters on fish occurrence, abundance, or production (Poff1997). Through time, salmonid population dynamics aredriven by a combination of both density-dependent anddensity-independent processes, whose respective influence

Can. J. Fish. Aquat. Sci. 59: 12–22 (2002) DOI: 10.1139/F01-186 © 2002 NRC Canada

12

Received 29 June 2001. Accepted 30 October 2001. Published on the NRC Research Press Web site at http://cjfas.nrc.ca on11 January 2002.J16430

F. Cattanéo.1 Cemagref, U.R. Biologie des Ecosystèmes Aquatiques, Laboratoire d’Hydroécologie Quantitative, 3 bis quaiChauveau, CP220, 69336 Lyon CEDEX 09, France, and Cemagref, U.R. Hydrologie-Hydraulique, 3 bis quai Chauveau, CP220,69336 Lyon CEDEX 09, France.N. Lamouroux and H. Capra. Cemagref, U.R. Biologie des Ecosystèmes Aquatiques, Laboratoire d’Hydroécologie Quantitative,3 bis quai Chauveau, CP220, 69336 Lyon CEDEX 09, France.P. Breil. Cemagref, U.R. Hydrologie-Hydraulique, 3 bis quai Chauveau, CP220, 69336 Lyon CEDEX 09, France.

1Corresponding author (e-mail: [email protected]).

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depends mainly on the life stage of the fish and the environ-mental conditions (Elliott 1989). Amongst these factors, hy-drological variability, food availability, and biologicalinteractions between individuals have been demonstrated tobe of major importance (Elliott 1989; Nehring and Anderson1993; Jowett 1995).

Studies that have previously attempted to assess the influ-ence of hydrological patterns on fish population dynamicshave often involved one or a few sites, within a singlestream or a few geographically close streams, although long-term investigations have sometimes compensated for thislimitation (Nelson 1986; Elliott 1989; Cattanéo et al. 2001).In a review of models derived to predict fish standing crop,Fausch et al. (1988) indicated that most models transferredpoorly to other streams, and therefore questioned their eco-logical relevance. A means of strengthening the ecologicalsignificance of a relationship is to test for its consistencyacross several distinct sites and years. The more a relation-ship is consistent across a wide diversity of sites, the higherthe chance for this relationship to be ecologically relevant.At present, substantial validations of the influence of hydrol-ogy on fish population dynamics across multiple sites andyears are scarce (e.g., Nehring and Anderson 1993).

Some authors have investigated simultaneously the influ-ence of flow variability and biotic interactions on salmonidpopulation dynamics. In six Colorado streams, Latterell etal. (1998) found that 1+ trout densities were negatively af-fected by the 30-day maximum flow and adult stock the pre-vious year. Their model was relatively precise (R2 = 73%),but neither the variability explained by flow variables northat explained by adult trout densities was described. It isnow widely recognized that the impact of hydrologicalevents does not depend only on their magnitude, but also ontheir timing relative to the fish developmental stage affected.In Black Brows Beck, Elliott et al. (1997) estimated parame-ters of a Ricker stock–recruitment model for different lifestages of a sea trout (Salmo trutta) population and demon-strated that outliers of this model generally correspondedwith years that experienced a summer drought. The 0+ and1+ stages were the more sensitive and were negatively af-fected by summer droughts, possibly by reduction of avail-able suitable habitat. More generally, most investigationsconcluded that the early 0+ stage was particularly sensitiveto hydrological variability, owing to the inability of 0+ fishto cope with large environmental fluctuations (i.e., inabilityto find shelters or to face high current velocities). This canlead to bottlenecks in the population dynamics. For example,Nehring and Anderson (1993) reported a detrimental effectof high spring flows on emerging trout fry in 11 Coloradostreams, as did Jensen and Johnsen (1999) in two Norwegianrivers. In the Chena River (Alaska), Clark (1992) attemptedto relate year class strengths of arctic grayling (Thymallusarcticus) with spawner abundances and the flow regime dur-ing the spawning, emergence, and the larval stage. He failedto demonstrate any stock–recruitment relationship, butshowed that high discharges from the spawning to larvalstage had a negative impact on recruitment three years after.Therefore, long-term fish population dynamics can markedlydisplay the influence of hydrological events. An extremecase was reported by Beaudou et al. (1995) on a Corsicanriver that experienced a 40-year flood. The trout population

almost entirely disappeared and only few individuals foundshelter in less affected upstream tributaries. However, twoyears were sufficient to recover a normal size-structuredpopulation.

Relationships between cohorts at any given time can alsoaffect age class densities or survival. Although streamsalmonids have distinct habitat requirements depending ontheir size (i.e., age class), habitat use overlap can occur. Forexample, biotic interactions can result from ontogenic shiftsin habitat used. Symons and Héland (1978) reported interac-tions between 1+ (>10 cm) and 0+ (<6 cm) Atlantic salmon(Salmo salar). Once the 0+ fish reached 6.5 cm, their prefer-ence for deeper habitats increased and social interactionswith 1+ fish were displayed by way of territorial defence.Intercohort interactions between the 0+ and 1+ fish havealso been demonstrated for brown trout (Bohlin 1977).

In this paper, using fish and hydrological data collectedover 5–8 years in 30 scattered French reaches, we analyzedsimultaneously how trout populations, described by ageclass densities at year n, depended on both the year-to-yearseasonal hydrology and age class trout densities at year n –1. We did not focus on variations in age class densities be-tween reaches, which could be affected by factors other thanhydrology (e.g., nutrient availability, temperature, habitatcomplexity). Instead, our major goal was to test whetherwithin-reach changes in trout densities could be explainedby a biological or hydrological factor, and whether the dif-ferent reaches statistically shared such relationships. Finally,we discuss the relative role of hydrology and biotic pro-cesses for trout population dynamics.

Material and methods

Study sitesThis study involved 30 stream reaches (179 reaches ×

years combinations) for which both fish and hydrologicaldata were available (Fig. 1). Fish data came from a nationaldatabase held by the Conseil Supérieur de la Pêche, group-ing about 800 stream reaches sampled annually acrossFrance. Hydrological data (daily discharges, Qd) were ex-tracted from the Hydro national database, held by the FrenchDepartment of the Environment, and included approximately3500 gauged sites. The 30 study reaches were selected fromthe fish database using the following criteria: (i) the fishsampling reaches had to be close to a gauging station locatedon the same river (in our case, the distance between the fishand hydrological sites ranged from 0.3 to 16.6 km, with amedian distance of 6.6 km); (ii) the average proportion ofbrown trout vs. other species in a reach had to be greaterthan 0.25; (iii) reaches had to be sampled for at least fivecontinuous years; (iv) the daily discharge series had to in-clude the fish sampling period; and (v) each year, reacheshad to be sampled during the low flow period, in summer orearly autumn (to ensure a suitable catchability of the 0+fish). For a given reach, the sampling was carried out at acomparable date in the year, and at least three months afteremergence, to minimize bias because of the sampling period.The selected reaches were widespread across France, andencompassed a large diversity of environmental conditions(mountain to lowland sites and oceanic-influenced to Medi-terranean-influenced reaches). Reaches were between 1.2 m

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Cattanéo et al. 13



and 20.5 m wide; they were located from 1 to 75 km fromthe source, at altitudes ranging from 15 to 1320 m, with areach stream slope between 0.1% and 6%, a drainage areabetween 1 km2 and 450 km2, and an interannual median dis-charge from 0.02 to 34.8 m3·s–1 (Table 1). Among these 30reaches, five were subject to artificial hydrological regimes:four had water extraction resulting in minimum flow re-gimes and one was subject to hydropeaking.

Annual fish dataFish were sampled by two-pass removal electrofishing.

The two-pass method gave reliable estimates of fish abun-dance: about 80% (standard deviation, SD = 15.3%) of fishwere caught during the first pass, thus demonstrating theglobal efficiency of this method. The sampling procedure in-volved 1 to 4 fishing teams (each of 3 to 4 persons), depend-ing on the stream width, wading in an upstream direction.The fishing equipment was composed of a towed Héron-Dream Electronic 180–1000 V (1–4 A) direct current appa-ratus (assembled in Pessac, France) supplied by a Honda5 kW (11 hp) generator. Prospected areas ranged from 135to 3200 m2, depending on the site, and included severalpool–riffle sequences.

All fish were measured (total length) to the nearest milli-metre. We defined three age classes after examination oflength–frequency histograms. The 0+ fish (from emergencein year n to their first birthday in year n + 1) had a totallength below 100 mm. The 1+ fish (from their first birthdayin year n to their second birthday in year n + 1) had a maxi-mum length of 179 mm. All fish with length >179 mm weregrouped into a single “adult” category and were consideredthe potential reproductive pool.

Trout abundances were not always determined per unitpass, so we could not estimate the true density of fish. We

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14 Can. J. Fish. Aquat. Sci. Vol. 59, 2002

Fig. 1. Location of the 30 study reaches in France.

Physical characteristics N Min. Mean Max.

Width (m) 30 1.2 7.5 20.5Reach slope (%) 28 0.1 1.2 6Elevation (m) 30 15 552 1320Distance from the source (km) 30 1 18.1 75Basin area (km2) 30 1 103.1 450Median discharge (m3·s–1) 30 0.02 3.9 34.8% of run 19 25 61.6 100% of riffle 19 0 28.7 65% of pool 19 0 9.7 40

Note: N, number of reaches with data. Values: Min., minimum; Mean,mean; Max., maximum. Except for reach slope, elevation, distance fromthe source, and basin area, the given characteristics are average valuescomputed on all samples in a reach.

Table 1. Physical characteristics of the study reaches.



therefore described trout populations at year n using cohortdensities at year n, calculated as catch-per-unit-effort (i.e.,the total number of trout caught after two passes standard-ized by the prospected area, expressed in number of fish af-ter two passes·100 m–2).

For each sample, age class densities were ln(x + 1) trans-formed to normalize their distribution. Because we lookedfor relationships between fish at year n and fish at year n –1, we built a biological matrix with fish data in year n andyear n – 1 on the same rows. We therefore “lost” one yearfor each reach. The final fish data matrix contained 149 sam-ples in 30 reaches, with a minimum of four samples perreach. The biological characteristics of the 149 reaches ×year combinations are recorded in Table 2.

Hydrological variablesFrom the national database, we extracted a 12-year series

(from 1988 to 2000) of daily discharge data for the selectedreaches. For each stream reach, we calculated theinterannual median daily discharge (Q50i) computed overthe entire data series.

The year-to-year hydrology was described for three sea-sons in the year, matching the main stages of brown troutannual biological cycle: (i) the “reproduction” period, start-ing on 1 October, continuing until 31 December (year n – 1);(ii) the “emergence” period, from 1 January until 31 May(year n); and (iii) the “growth” period, from 1 June until 31August.

Season limits were the same in all sites, and were definedaccording to the literature. The spawning season starts in au-tumn and we can reasonably consider that most of thespawning activity occurs before January (Beard and Carline1991; Crisp 1992). The emergence period included the emer-gence phase stricto sensu, the intragravel phase, and the lar-val and possibly the fry stages. Limits were chosen to fitwith the dates reported in Crisp (1992) for three rivers withdifferent temperature regimes. The growth period includedthe summer months. The end of the growth period was seton 31 August for all sites, which represented an “average”sampling date.

We defined 13 variables to describe the hydrological con-ditions at each reach × year × season (Table 3). Daily dis-charge series could contain some missing values, but lessthan 20% of values were missing for each reach × year ×season combination. Variables reflected average conditions,low and high flow conditions, “infrequent” flow conditions,rate of change in daily discharge, and overall variability.Most variables were rescaled by the interannual mediandaily discharge Q50i to reflect the magnitude of flow char-acteristics consistently in all reaches.

The average conditions of seasonal discharge were as-sessed using the median daily discharge divided by Q50i(denoted Q50). We also computed the mean seasonal dis-charge divided by Q50i (denoted QM), which could differfrom the median discharge for regimes exhibiting skewness.

Low seasonal discharges were described by the dischargeexceeded 90% of the time during the season, divided byQ50i (denoted Q90). The minimum daily discharge value di-vided by Q50i, (denoted MIN) was also used.

High discharge values were described by three variables.The 10th percentile of the daily discharges distribution di-

vided by Q50i (denoted Q10) represented the dischargevalue exceeded 10% of the time over the season. The peakseasonal flow was assessed using the maximum daily dis-charge divided by Q50i (denoted MAX). Because not onlythe magnitude but also the duration of a high flow eventscan be ecologically important, we computed the maximumdischarge over a 7-day continuous period (Richter et al.1996), divided by Q50i (denoted MAX7).

We defined an infrequent high discharge threshold relativeto each season. For each season, we estimated the statistical2-year return period discharge, using the entire data series(1988–2000), by fitting an exponential law to discharge vs.empirical return period among high discharge values ex-tracted from the data (“peaks over threshold” method, Langet al. 1999). The choice of a 2-year return period dischargethreshold enabled the hydrological data set to contain infre-quent flows relative to the duration of the fish sample re-cords for each site, which would have been scarce if we hadchosen a higher threshold (5-year, for example). We thencomputed NQ2 as the number of times the seasonal dis-charge exceeded this threshold (i.e., if the threshold was ex-ceeded several days on, only one event was counted) andDQ2 as the duration (in percentage of time during the sea-son) above this threshold (see Cattanéo et al. 2001). Al-though some zero values appeared for NQ2 and DQ2 in thedata matrix, this was not a restriction for further analyses.Similar variables were not computed in relation to a lowflow threshold, since low flows are generally time-extendedperiods: such variables would have been redundant to Q90.

The rate of change in discharge can rapidly influence hab-itat availability and suitability and therefore limit fish abun-dances within a site. Thus, variations in discharge betweentwo consecutive days were described. We first derived theseries of daily discharge variations (Qd – Qd–1) over the sea-son, and separated positive (i.e., increases) from negative(decreases) fluctuations. We then calculated the mean valuefor increases and decreases (Richter et al. 1996), both di-vided by Q50i (denoted INC and DEC, respectively).

The overall seasonal variability was assessed using twodescriptors. We first computed VAR, an index of variabilitybased on the daily discharge fluctuations (Cattanéo et al.2001). It was defined as the difference between the 10th and90th percentile of the distribution of classified daily fluctua-tions, divided by Q50i. Finally, we calculated the coefficientof variation in daily discharges over the season (CV), de-fined as the ratio of the standard deviation of the daily dis-charge distribution divided by the mean.

For each season, flow variables were arranged in a reach ×year × flow variables matrix (30 reaches in 149 rows × 13

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Cattanéo et al. 15

Biological characteristics Min. Mean Max.

Total trout relative density (%) 25.6 72.1 100Total trout density (fish·100 m–2) 6.9 32.9 100.30+ trout density (fish·100 m–2) 0.8 13.8 55.01+ trout density (fish·100 m–2) 1.8 13.4 48.2Adult trout density (fish·100 m–2) 0.6 5.7 19.2

Note: Values are statistics based on average values by reach. For eachcharacteristic, Min., minimum; Mean, mean; and Max., maximum averagevalues of the 30 reaches.

Table 2. Biological characteristics of the 30 study reaches.



columns). Flow variables were ln(x + 1) transformed to nor-malize their distribution.

Data analysis

Within-reach variations in seasonal hydrologyBecause our study focused on the within-reach variability

in trout dynamics related to hydrological changes, we firstanalysed the within-reach hydrological variability (betweenyears) for each season. For this purpose, we performed awithin-reach principal components analysis (within-reachPCA, Dolédec and Chessel 1989) for each season. Thewithin-reach PCA reduces the number of explanatory vari-ables by defining independent synthetic variables (linearcombinations of hydrological characteristics) that best re-flect within-reach variations in hydrological patterns.

Which factors could explain changes in trout densitiesbetween years?

We first searched for interrelationships between cohorts atyear n. Then, we related the annual age class densities atyear n (0+, 1+, and adults) to age class densities at year n –1 (0+, 1+, and adults) and hydrological axes during the bio-logical year. All tests were made using covariance analyses(ANCOVA). The ANCOVA tests if there is a common effectwithin reaches of each explanatory variable on trout densityat year n. In each ANCOVA performed, the reach was usedas a group variable and each explanatory variable (biologicalor hydrological) used as a covariate. Models withoutcovariates were used to test for between-reaches differences.ANCOVA is useful for comparing regression lines amonggroups (i.e., reaches), even if the dependent variable and thecovariate differ in their mean values or ranges among groups(Sokal and Rohlf 1998). It is based on the model (model 1)

y = areach + breach x + e

where y was the biological dependent variable (trout densi-ties at year n), areach an intercept depending on the reach,breach the slope parameter depending on the reach, x a hydro-logical or biological covariate (used as explanatory variable),and e a random deviation. If the interaction between thereach and the covariate was not significant (i.e., values for bdid not differ across reaches), a common value for b was fit-ted for all reaches and we tested a simpler model (model 2):

y = areach + bx + e

The null hypotheses tested in model 2 were (i) the covariatex has no effect on the dependent variable, i.e., b was not sta-tistically different from 0; and (ii) the dependent variable didnot significantly differ between reaches, i.e., a was not sta-tistically different between reaches.

The significant threshold for probabilities was correctedaccording to Bonferroni’s method to provide protection formultiple tests (i.e., 30 tests). Therefore, probabilities wereconsidered significant below 0.0017.

In the ANCOVA analyses, we used the synthetic hydro-logical variables (PCA axes) to avoid redundancy and multi-plicity of tests. However, if there was a significant effect of ahydrological axis on a biological variable, we also tested acovariance model after replacing the PCA axis with a sim-pler hydrological variable representing this axis. This pro-vided a better quantification of the effect of year-to-yearhydrology on trout densities.

Was the 0+ fish survival density dependent?If there was a significant relationship shared by all

reaches between the 1+ fish and the 0+ fish the previousyear, we attempted to investigate whether the survival func-tion involved density-dependent mechanisms. For this pur-pose, we used the “unit-slope test” based on natural log (ln)transformed densities (Lebreton 1989). We included a con-

© 2002 NRC Canada


Flow characteristics Variable Definition

Average conditions Q50 Median daily discharge divided by the interannual median dischargeQM Mean daily discharge divided by the interannual median discharge

Low flows MIN Minimum daily discharge divided by the interannual median dischargeQ90 Daily discharge exceeded 90% of time during the season divided by the interannual median

dischargeHigh flows MAX Maximum daily discharge divided by the interannual median discharge

MAX7 Maximum daily discharge over a 7-day period divided by the interannual median dischargeQ10 Daily discharge exceeded 10% of time during the season divided by the interannual median

dischargeInfrequent flows NQ2 Number of times the daily discharge exceeds the 2-year high seasonal discharge (in number of

events)DQ2 Percentage of time over the season where flow exceeds the 2-year high seasonal discharge

Rate of change INC Mean value for discharge increases between two consecutive days divided by the interannualmedian discharge

DEC Mean value for discharge decreases between two consecutive days divided by the interannualmedian discharge

Overall variability VAR Variability index, i.e., difference between the 10th and 90th percentiles of the distribution of dis-charge variations for two consecutive days divided by the interannual median discharge

CV Coefficient of variation in daily discharge, i.e., ratio of the standard deviation of the meanmultiplied by 100

Note: All variables were computed by season. Most variables are dimensionless.

Table 3. Hydrological variables used and their definitions.



stant to account for migration mechanisms; therefore, den-sity dependence was assessed according to apparent survivalrates. This test assumed that, under the hypothesis of nodensity dependence, the slope b of the covariance model re-lating the ln(densities) of a cohort at year n to the ln(densi-ties) of the same cohort at year n + 1 is 1. Densitydependence results in a slope significantly smaller than 1(Lebreton 1989). However, this test suffers from inflatedtype I error in the presence of measurement uncertainties(i.e., concluding there is density dependence when there isnone, Lebreton 1989). Therefore, we simulated virtual mea-surement errors on the 0+ and 1+ fish to check if they couldinfluence our results.

Was 0+ fish survival affected by the hydrologicalconditions?

If there was a significant relationship shared by allreaches between the 1+ fish and the 0+ fish in the previousyear, we checked whether the survival function was influ-enced by the hydrological conditions. To do this, we used anANCOVA model as described above, with the 0+ fish den-sity as first covariate, and successively added the hydrologi-cal variables (PCA axes) as second covariate. All possibleinteractions were tested and removed from the model in thecase of nonsignificance. If the gain in explained variabilityowing to the second covariate was significant afterBonferroni’s correction, we concluded that hydrology had aninfluence on the 0+ fish survival.

Results

Within-reach variations in seasonal hydrologyFor each season, most of the within-reach variability

could be projected onto the F1 × F2 factorial plane, whichaccounted for 71–84% of the total variance of hydrologicalvariables within reaches (Fig. 2a). For each season, the firstaxis (53–65%) reflected the overall discharge level duringthe season (all variables were positively correlated with F1,except CV during the emergence period). The second axis(16–19%) reflected the shape of the hydrological signal, in-dependently of the discharge level. The three variables CV,NQ2, and DQ2 had negative coordinates on F2, and wereopposed to MIN, Q90, and Q50 with positive coordinates.Therefore, for each season, F2 discriminated years with“flat” hydrographs, characterized by stable flows, from yearswith variable flows and peaks above the infrequent dischargethreshold. An illustration of characteristic discharge patternsfor each part of the factorial plane is given in Fig. 2b.

Which factors can explain changes in trout densitiesbetween years?

In all the covariance models we tested, the interactionterm was never significant (P values between 0.21 and 0.70),indicating that all significant relationships were statisticallyshared by all reaches. For any given year, we found no rela-tionship between biological variables, suggesting nointercohort interactions (P values between 0.03 and 0.94).The significant models relating the 0+, 1+, and adult ageclasses to annual biological variables at year n – 1 and hy-drological variables are presented in Table 4.

The 0+ fish largely depended on the reach (R2 = 63%, P <0.0001). The remaining within-reach variability only ac-counted for 37% of the total 0+ variability. Amongst the hy-drological variables, only one significant relationship wasfound with the first PCA axis during the emergence period(F1emergence, P = 0.0002), which explained 11.2% of thewithin-reach variability. The negative slope indicated thatwhen F1emergence increased within reach, i.e., the waterlevel increased, the abundance of 0+ fish decreased. Themodel was slightly improved when we used a representativevariable of F1emergence instead of F1emergence itself. UsingQ10emergence (because it was highly covariant with F1 andhad a zero coordinate on F2), the global model explained upto 69% of the total 0+ variability, with 16.6% of the 0+within-reach variability (P < 0.0001) caused by the hydro-logical variable. Because our study sites were roughlygrouped in three regions that could possibly experience dif-ferent hydrological patterns, we checked that the clusteringin three regions did not change our findings: there was nodifference in 0+ density among regions, and the effect ofQ10emergence during the emergence period was similar acrossthe three regions (Pregion = 0.210, Pinteraction = 0.288). The 0+fish did not exhibit any relationships with either the 0+ fishone year before or the adult fish one year before. However,the 0+ fish were negatively influenced by the abundance of1+ fish the previous year (i.e., 11.7% of the within-reachvariability, P = 0.0001).

The variability in the 1+ fish abundances was largely dueto differences between reaches (R2 = 67%, P < 0.0001). Norelationships were found between the 1+ fish and the hydro-logical variables. Concerning the biological variables, onlythe 0+ fish abundances the previous year had a significantpositive effect on the 1+ fish (P < 0.0001). This meant that,within a reach, the 0+ fish abundance in year n – 1 ac-counted for 20.7% of the variability in 1+ fish abundance inyear n.

Abundances of adult brown trout strongly depended onthe reach (R2 = 72%, P < 0.0001); therefore, the interannualwithin-reach variability was low (28%). Adult fish were notrelated to any hydrological variables, but were highly relatedto the abundance of 1+ fish the previous year (P < 0.0001).Within a reach, the abundance of 1+ fish explained 22.4% ofthe variability in adult fish abundances.

Was the 0+ fish survival density dependent?Without simulating the sampling bias, the model relating

the 1+ fish density to the 0+ fish density the previous yearhad a slope b significantly smaller than 1 (b = 0.330, P <0.0001; Table 4), therefore suggesting density dependencewithin the 0+ cohort survival. To test for the potential influ-ence of measurement errors, we successively increased the0+ densities of 5, 10, 15, and 20 fish·m–2, and the 1+ densityof 0, 5, 10, and 15 fish·m–2. Because the sampling error issize dependent (Bohlin et al. 1989), we increased the 0+density more than the 1+ density in each simulation. Withsimulated bias, all models had a slope parameter b with a95% confidence upper limit significantly lower than 1, ex-cept for the model simulating the more extreme bias (i.e., nobias on the 1+ fish, increase of 20 0+ fish·100 m–2). What-ever the simulated bias, the effect of the 0+ fish density the

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previous year remained highly significant (P < 0.0001), andthe relationship was statistically similar between reaches.

Was the 0+ fish survival affected by the hydrologicalconditions?

The models relating the 1+ fish density to the 0+ fish den-sity the previous year were not significantly improved when

a hydrological covariate was added (probability for thecovariate was always P > 0.03).

Discussion

This study aimed to reveal general relationships (i.e., sta-tistically shared by various reaches) between brown trout

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Fig. 2. (a) Within-reach PCA (factorial plane F1 × F2) of hydrological variables performed for the spawning, emergence, and growthperiods. Definitions of variables are given in Table 3. For each season, insets represent the scale on the F1 and F2 axis. (b) Syntheticrepresentation of typical hydrographs after the within-reach PCA analysis. This example is given for a single-gauged station during theemergence period that exhibited different hydrological characteristics across years. On the hydrographs, the horizontal solid line repre-sents the mean discharge over the season for the considered year, the horizontal broken line defining the “infrequent” high flow thresh-old for this gauged station. The two hydrographs with negative F1 coordinates are characterized by low mean discharges, but the shapeof the signal can be “flat” (F2 > 0) or “variable” (F2 < 0). Conversely, the hydrographs with positive F1 coordinates exhibited a highmean discharge over the season, either with little variability (F2 > 0) or with variable and infrequent flows (F2 < 0).



age class densities at year n, year-to-year seasonal hydrol-ogy, and brown trout age class densities at year n – 1. Wefound only one reliable relationship between trout variablesand the seasonal hydrology. The 0+ fish density wasstrongly and negatively related to the discharge rate duringthe emergence period, i.e., the higher the discharge duringthis period, the lower the 0+ fish density. This relationshipwas statistically comparable across the 30 tested reaches,and was slightly improved when the discharge value ex-ceeded 10% of the time during the emergence period wasconsidered a representative characteristic of the dischargerate. This confirmed previous results obtained on fewersites. Using mean monthly discharges, Nehring and Ander-son (1993) reported similar effects in a study of 11 Coloradostreams, as did Nuhfer et al. (1994) in a Michigan stream.Clark (1992) concluded that the recruitment of Arcticgrayling was negatively affected by high discharges duringthe critical spawning, emergence, and larval periods. How-ever, the exact mechanism that leads high discharges to re-duce 0+ fish density is not well identified. A possible causeis the flushing of the 0+ fish as a consequence of their in-ability to maintain their stream position or to find shelterswhen the water velocities increase during high discharges.Heggenes and Traaen (1988) showed that brown trout larvaeentering the free-feeding stage are sensitive to water veloci-ties for a few weeks, and are not able to resist velocitiesabove 25 cm·s–1. Although their sensitivity declines as theygrow, they are only able to withstand velocities up to50 cm·s–1 on average after two months. Less extreme watervelocities, although not displacing fish downstream, maydrive young trout to starve in nonoptimal habitats, withhigher energy costs or lower food availability, therefore pos-sibly leading to mortality (Elwood and Waters 1969). Otherstudies have related the mechanistic destruction of eggs ornewly hatched fish by substrate movements under high dis-charges (DeVries 1997 and references therein). Nonetheless,given the average egg clutch burial depth for brown trout(typically between 10 cm and 15 cm, DeVries 1997), wethink that only the more extreme discharges could lead to asubstantial destruction of eggs. Lapointe et al. (2000) esti-

mated the probability of salmonid egg clutch scour owing tofrequent flood (recurrence period <10 years) to be 5%. Thisprobability reached up to 20% for an event recurring lessfrequently than once every 100 years. Other high-discharge-related geomorphic processes, such as fine sediment fill overthe redds and subsequent decrease in oxygen content, canalso be detrimental for egg survival (Sear 1993).

Our analysis faced the difficulty of precisely determiningwhich discharge characteristic best explained variation in 0+densities, because most hydrological descriptors covaried to-gether. In this context, PCA analysis was useful for synthe-sizing the hydrological pattern into a few independentvariables. We found that the within-reaches hydrological pat-tern could be summarized by a discharge rate axis (F1) and avariability axis (F2), the latter representing the shape of thehydrological signal. Our results indicated that, withinreaches, it was the discharge rate during the emergence pe-riod, but not the shape of the hydrological signal, that influ-enced the 0+ cohort size. In other words, for a givendischarge level, a hydrograph or flow regime that was eitherstable or highly variable had the same influence on troutdensities (in the range of hydrological variability encoun-tered in our study reaches).

To better quantify this effect, the model relating the 0+fish density to the discharge value exceeded 10% of the timeduring the emergence period predicted that, on average be-tween reaches, there would be a loss of about 4 fish·100 m–2

(i.e., on average, 28% of the 0+ fish) for a rise in Q10 from1 to 2 times the interannual median discharge (Fig. 3). Thisloss would be equal to 8 fish·100 m–2 (52%) for a rise from1 to 4 times the interannual median discharge. Althoughlargely significant, the seasonal hydrology only explained asmall proportion of variability in 0+ fish density (16.6% ofthe within-reaches variability). However, fish sampling bi-ases and subsequent errors in the calculation of densities in-duced noise in the dependent variable, which is known toreduce the slope parameter estimation in statistical models(Snedecor and Cochran 1967). Uncertainties in the calcula-tion of hydrological variables caused by the distance be-tween the fish reach and the gauging station could also have

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Cattanéo et al. 19

Dependent Covariate Slope b (P value)Variance covariate(within reach) (%)

Total variancemodel (%)

0+ None –(<0.0001) — 63.3F1emergence –0.105 (0.0002) 11.2 67.4

Q10emergence* –0.732 (<0.0001) 16.6 69.4

1+n–1 –0.503 (0.0001) 11.7 67.61+ None –(<0.0001) — 66.6

0+n–1 0.330 (<0.0001) 20.7 73.5Adults None –(<0.0001) — 72.3

1+n–1 0.366 (<0.0001) 22.4 78.5

Note: For each age class, a model without covariate (i.e., equal to an ANOVA with the reach as categoricalvariable) is first tested to estimate the between-reach variability. The significant threshold for the influence of thecovariate is Bonferroni adjusted; therefore, probabilities were considered significant below 0.0017. Listed for eachmodel are the slope for the covariate effect b and its associated corrected probability, the within-reach variabilityaccounted for by the covariate, and total variance explained by the model. Only significant models are presented.

*Model built with a representative variable of the PCA axis (i.e., Q10emergence).

Table 4. ANCOVA models relating the 0+, 1+, and adult densities at year n to the hydrologicalvariables (PCA axes by season) and the 0+, 1+, and adult densities at year n – 1.



an effect. It therefore seems likely that the influence of theseasonal hydrology on the 0+ fish density within reaches isstronger than that reported herein.

The influence of the year-to-year seasonal hydrology hasalso been investigated in nonsalmonid fish. The early 0+stage represents a critical period of fish development, whereabiotic (e.g., hydrological) conditions are of primary impor-tance. In small streams, spring floods were reported togreatly reduce the 0+ cyprinid and centrarchid abundances(Schlosser and Angermeier 1990). In a larger stream,Cattanéo et al. (2001) found that the annual changes inabundance of 10 cyprinid species were related to the hydro-logical conditions during the spawning period, in agreementwith the biological traits of these species. Such studies were,however, descriptive, and did not really quantify the influ-ence of hydrology.

The other significant models only involved biologicalvariables. The 1+ density was strongly related to the 0+ den-sity the previous year, similarly across reaches, as was theadult density with the 1+ density the previous year. Theserelationships, widely found in population dynamics, re-flected the follow-up of the cohorts from one year to thenext. As for the 0+ densities, both the 1+ and adult densitieswere highly dependent on the reach (R2 = 67% and 72%, re-spectively). Within reaches, the knowledge of the 0+ or 1+densities at year n explained about 20% of the variability ofthese cohorts at year n + 1. This proportion is rather small.Nelson (1986) estimated that the size of a year class ex-plained between 63% and 93% of the variation in density of

the cohort the next year. However, these estimates werebased on older trout (>2+), whose year class strength variesless between years than younger fish (Jowett 1995).

Our results also suggested density dependence in the 0+survival function, unless we assumed a sampling bias of20 0+ fish·100 m–2 and no bias on the 1+ densities. How-ever, this particular model was unlikely to reflect a reliablesampling bias for two main reasons: first, there was certainlyan error in estimating the 1+ fish density, and second, thisbias on the 0+ densities would mean that we only caughtabout 40% of the 0+ fish (their average density in sampleswas 13.8 fish·100 m–2), which was improbable given thatthey were readily catchable at the time of sampling. Densitydependence in the young-of-the-year survival is a somewhatcommon feature of salmonid populations. Intracohort density-dependent mortality has been shown to occur during the firstfew months of life (Le Cren 1973; Mortensen 1977), and hasbeen largely attributed to competition for limited resources,such as food and space (Le Cren 1973). Mortensen (1977)suggested that a physically complex habitat, which wouldprevent 0+ fish from seeing one another, would reduce mor-tality owing to territorial competition. Competition for foodis expected to play a role in the amount of energy stored bythe 0+ fish before the overwintering period, during whichthey survive primarily on their energy reserves (Cunjak1988). As a result, insufficient reserves associated with re-duced food intakes could partly explain the 0+ mortality dur-ing the winter (Cunjak 1988). The discharge pattern did notinfluence the survival of the 0+ cohort. This reinforces the

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Fig. 3. Relationships between the 0+ fish density and the hydrological variable Q10 during the emergence period, detailed for the 30reaches. Broken lines represent the regression line for the reach; solid lines represented the fit according to the analysis of covariance.The slope is common to all reaches and the intercept depends on the reach.



idea that the 0+ fish are sensitive to the hydrological patternduring a limited critical period, after which they becomeable to cope with flow fluctuations. Similarly, Hayes (1995)reported that a large flood six months after emergence hadno apparent effect on the 0+ trout (80–100 mm in length) ina New Zealand river. Mesick (1995) found a negative corre-lation between winter stream flows and the 1+ trout survivalindex, but no relationship with the 0+ survival.

Strikingly, we found a significant negative relationship be-tween the 0+ fish at year n and the 1+ fish the previous year,statistically shared by all reaches. To our knowledge, a simi-lar relationship has not been demonstrated elsewhere. Wewould rather have expected a negative relationship betweenthe 0+ fish at year n and the 1+ fish during the same year, asreported by Bohlin (1977). It is difficult to find a sound bio-logical reason to account for this relationship. We can onlyspeculate the following. (i) The microhabitat used by the 0+fish largely overlaps that used by 1+ fish (Baglinière andMaisse 1991), after which a change in microhabitat use ap-pears when the fish becomes 2+. At the time of sampling,the 1+ fish of the previous year just changed theirmicrohabitat use, but there could have been some interac-tions (competition or predation) with the 0+ fish thatsearched for key positions. (ii) High densities within a reachwere mainly due to the 0+ and 1+ fish, so it is possible thathigh densities of 1+ fish might disturb spawning, and there-fore lead to a low 0+ cohort the next year. This relationshipshould therefore be tested in further studies to confirmwhether it is reliable or not. We observed no stock–recruitment relationship between the reproductive pool size(adult density) and the 0+ density the following year, con-versely to Elliott (1989). This suggested that even a low den-sity of adults could produce a large 0+ cohort the next year,as already mentioned by Le Cren (1973).

Our results suffered from some limitations, inherent in theuse of large data sets. First, although the fishing procedurewas standardized, variability in the fishing efficiency couldnot be avoided. Second, using data collected by a large num-ber of people over many sites prevented any precise knowl-edge of each sampled reach or sampling campaign, whichmay lead to a loss of important information useful for the in-terpretation of the results. Overall, using large data sets re-sults in a coarse grain data quality collected in numeroussamples compared to the fine grain quality of smaller samplesizes.

In summary, we demonstrated that both the hydrologicaland biological forces constrain trout population dynamics,mainly during the first year of life. Owing to their complexlife histories, freshwater fish are sensitive to abiotic and bi-otic factors at different developmental stages. These factorscan operate simultaneously, but the predominance of eachcan be more marked at a particular life stage. We found thatthe 0+ densities were first regulated by the discharge patternduring the emergence period. After this period, we foundthat a density-dependent regulation occurred during the firstyear of life, during which the hydrological pattern did nothave any impact on fish survival. Thereafter, classicalfollow-up of cohorts was shown, but the hydrological patterndid not influence either the 1+ fish or the adults. In terms oftrout population management, these results suggested that at-

tention should be paid to limiting the flow during the emer-gence period in regulated streams, possibly by avoidinglarge flow releases downstream from reservoirs. Here, wefocused on the transferability of relationships between hy-drology and biotic interactions and trout dynamics observedwithin reaches. It is important to note, however, that troutpopulations mainly exhibited variability in space (between-reaches variability between 63% and 72%) rather than tem-poral fluctuations between years within reaches. A highlevel of between-reach variability has been observed else-where (Hayes 1995; Jowett 1995) and associated with vari-ous environmental and biotic features, such as the reachgradient, the width, the cover, the streambed sediment, theavailability of spawning habitat, or the benthic invertebratebiomass (see Fausch et al. 1988; Beard and Carline 1991;Jowett 1995). Our within-reaches study may be useful foridentifying the major hydrological variables that could be in-cluded in between-reaches studies; the hydrological vari-ables, within a particular season, that systematically affectpopulation dynamics may be good candidates for explainingbetween-reaches differences in population structure.

Acknowledgements

We thank the Conseil Supérieur de la Pêche (CSP) for thefish data and the French Department of the Environment forthe hydrological data. We are especially grateful to E.Baglinière, D. Beaudou, D. Pujo, P. Roche, P.Y. Genêt, T.Vigneron, and W. Sremski for their help in organizing thefish data and their valuable comments. We are also gratefulto Ton Snelder and the two anonymous referees for theirhelpful comments on an earlier draft of this manuscript.

References

Baglinière, J.L., and Maisse, G. 1991. La truite, biologie etécologie. INRA Editions, Paris.

Beard, T.D., and Carline, R.F. 1991. Influence of spawning andother stream habitat features on spatial variability of wild browntrout. Trans. Am. Fish. Soc. 120: 711–722.

Beaudou, D., Baril, D., Roche, B., Le Baron, M., Cattanéo-Berrebi,G., and Berrebi, P. 1995. Recolonisation d’un cours d’eau corsedévasté: contribution respective des truites sauvages etdomestiques. Bull. Fr. Piscic. 337/338/339: 259–266.

Bohlin, T. 1977. Habitat selection and intercohort competition ofjuvenile sea-trout Salmo trutta. Oïkos, 29: 112–117.

Bohlin, T., Hamrin, S., Heggberget, T.G., Rasmussen, G., andSalveit, S.J. 1989. Electrofishing—theory and practice with spe-cial emphasis on salmonids. Hydrobiologia, 173: 9–43.

Cattanéo, F., Carrel, G., Lamouroux, N., and Breil, P. 2001. Rela-tionship between hydrology and cyprinid reproductive successin the Lower Rhône at Montélimar, France. Arch. Hydrobiol.151: 427–450.

Clark, R.A. 1992. Influence of stream flows and stock size on re-cruitment of arctic grayling (Thymallus arcticus) in the ChenaRiver, Alaska. Can. J. Fish. Aquat. Sci. 49: 1027–1033.

Crisp, D.T. 1992. Measurement of stream water temperature andbiological applications to salmonid fishes, grayling and dace (in-cluding ready reckoners). Freshw. Biol. Assoc. Occas. Publ.No. 29.

© 2002 NRC Canada

Cattanéo et al. 21



© 2002 NRC Canada


Cunjak, R.A. 1988. Physiological consequences of overwinteringin streams: the cost of acclimatization? Can. J. Fish. Aquat. Sci.45: 443–452.

DeVries, P. 1997. Riverine salmonid egg burial depths: review ofpublished data and implications for scour studies. Can. J. Fish.Aquat. Sci. 54: 1685–1698.

Dolédec, S., and Chessel, D. 1989. Rythmes saisonniers etcomposantes stationnelles en milieu aquatique. II: Prise encompte et élimination d’effets dans un tableau faunistique. ActaOecol. Oecol. Gen. 10: 207–232.

Elliott, J.M. 1989. The natural regulation of numbers and growth incontrasting populations of brown trout, Salmo trutta, in towLake District streams. Freshw. Biol. 21: 7–19.

Elliott, J.M., Hurley, M.A., and Elliott, J.A. 1997. Variable effectsof droughts on the density of a sea-trout Salmo trutta populationover 30 years. J. Appl. Ecol. 34: 1229–1238.

Elwood, J.W., and Waters, T.F. 1969. Effects of floods on foodconsumption and production rates of a stream brook trout popu-lation. Trans. Am. Fish. Soc. 98: 253–262.

Fausch, K.D., Hawkes, C.L., and Parsons, M.G. 1988. Models thatpredict standing crop of stream fish from habitat variables:1950–85. Portland, U.S. Forest Service, General Technical Re-port PNW-GTR-213, Portland, Ore., U.S.A.

Hayes, J.W. 1995. Spatial and temporal variation in the relativedensity and size of juvenile brown trout in the Kakanui River,North Otago, New Zealand. N.Z. J. Mar. Freshw. Res. 29: 393–407.

Heggenes, J., and Traaen, T. 1988. Downstream migration and crit-ical water velocities in stream channels for fry of four salmonidspecies. J. Fish Biol. 32: 717–727.

Jensen, A.J., and Johnsen, B.O. 1999. The functional relationshipbetween peak spring floods and survival and growth of juvenileAtlantic salmon (Salmo salar) and brown trout (Salmo trutta).Funct. Ecol. 13: 778–785.

Jowett, I.G. 1995. Spatial and temporal variability of brown troutabundance: a test of regression models. Rivers, 5. 1–12.

Lang, M., Ouarda, T., and Bobée, B. 1999. Towards operationalguidelines for over-threshold modeling. J. Hydrol. (Amsterdam),225: 103–117.

Lapointe, M., Eaton, B., Driscoll, S., and Latulippe, C. 2000.Modelling the probability of salmonid egg pocket scour due tofloods. Can. J. Fish. Aquat. Sci. 57: 1120–1130.

Latterell, J.J., Fausch, K.D., Gowan, C., and Riley, S.C. 1998. Re-lationship of trout recruitment to snowmelt runoff flows and

adult trout abundance in six Colorado mountain streams. Rivers,6: 240–250.

Lebreton, J.D. 1989. Statistical methodology for the study of ani-mal populations. Bulletin of the International Statistical Institute(Voorburg, The Netherlands), 53: 267–282.

Le Cren, E.D. 1973. The population dynamics of young trout(Salmo trutta) in relation to density and territorial behaviour.Rapp. P.-V. Reun. Cons. Int. Explor. Mer, 164: 241–246.

Mesick, C.F. 1995. Response of brown trout to streamflow, temper-ature, and habitat restoration in a degraded stream. Rivers, 5:75–95.

Mortensen, E. 1977. Population, survival, growth and productionof trout Salmo trutta in a small Danish stream. Oïkos, 28: 9–15.

Nehring, R.B., and Anderson, M.A. 1993. Determination of popu-lation-limiting critical salmonid habitats in Colorado streams us-ing the physical habitat simulation system. Rivers, 4: 1–19.

Nelson, F.A. 1986. Effect of flow fluctuations on brown trout inthe Beaverhead River, Montana. N. Am. J. Fish. Manag. 6: 551–559.

Nuhfer, A.J., Clark, R.D., Jr., and Alexander, G.R. 1994. Recruit-ment of brown trout in the south branch of the Au Sable River,Michigan in relation to streamflow and winter severity. FisheriesResearch Report 2006, Michigan Department of Natural Re-sources Fisheries Division, Lansing, Mich., U.S.A.

Poff, N.L. 1997. Landscape filters and species traits: towardsmechanistic understanding and prediction in stream ecology. J.North Am. Benthol. Soc. 16: 391–409.

Richter, B.D., Baumgartnert, J.V., Powell, J., and Braun, D.P. 1996.A method for assessing hydrologic alteration within ecosystems.Conserv. Biol. 10: 1163–1174.

Schlosser, I.J., and Angermeier, P.J. 1990. The influence of envi-ronmental variability, resource abundance, and predation on ju-venile cyprinid and centrarchid fishes. Pol. Arch. Hydrobiol. 37:265–284.

Sear, D.A. 1993. Fine sediment infiltration into gravel spawningbeds within a regulated river experiencing floods: ecological im-plications for salmonids. Regul. Rivers, 8: 373–390.

Snedecor, G.W., and Cochran, W.G. 1967. Statistical methods. 6thed. Ames: Iowa State University Press, Ames, Iowa, U.S.A.

Sokal, R.R., and Rohlf, F.J. 1998. Biometry. 3rd ed. W.H. Freemanand Company, U.S.A.

Symons, P.E.K., and Héland, M. 1978. Stream habitats and behav-ioral interactions of underyearling and yearling Atlantic salmon(Salmo salar). J. Fish. Res. Board Can. 35: 175–183.



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The influence of hydrological and biotic processes on brown trout ( Salmo trutta ) population dynamics