Discharge-dependent covariation patterns in the population dynamics ...

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Discharge-dependent covariation patterns in the population dynamics of brown trout (Salmo trutta) within a Cantabrian river drainage Javier Lobón-Cerviá

Abstract: Patterns of spatial covariation in the population dynamics of brown trout (Salmo trutta) across Rio Esva (northwestern Spain) were explored by using the residuals from stock–recruitment relationships as indices of survival rates of spawner-to-recruit (STR), spawner-to-cohort size (STC), and spawner-to-spawner (STS). Positive correlations in pairwise comparisons among survival rates together with highly significant spatiotemporal variation in STC (74.3%) and STS (51.5%) explained by variation in STR provided evidence for persistent spatial covariation across the river drainage during the whole lifetime. Split-line regressions fitted to the survival rates versus river discharge in March (when trout emerge) highlighted the importance of discharge during, or just after, trout emergence as a major determinant of recruitment whose effects are reflected in the population over the lifetime and emphasized the synchrony between environmental processes and brown trout dynamics. Synchrony in recruitment is caused by hydrological synchrony that, in turn, is determined by climatic synchrony (rainfall) operating at the regional scale. The importance of discharge for recruitment is consistent with studies on native and introduced populations, suggesting its broad effect on the dynamics of stream brown trout across geographical regions. Résumé : L’analyse des résidus des relations stock–recrutement comme indices de la survie des reproducteurs aux recrues (STR), de la survie des reproducteurs à la taille de la cohorte (STC) et de la survie des reproducteurs aux reproducteurs (STS) a permis d’explorer la structure de la covariation spatiale de la dynamique de population de la truite brune (Salmo trutta) dans le Rio Esva du nord-ouest de l’Espagne. Les corrélations positives dans les comparaisons appariées des taux de survie, ainsi que la variation spatio-temporelle très significative de STC (74,3 %) et de STS (51,5 %) qui s’explique par la variation de STR, sont des indices d’une covariation spatiale persistante au sein du bassin versant durant le cycle entier. Des régressions linéaires fragmentées ajustées aux taux de survie en fonction du débit de la rivière en mars (lors de l’émergence des truites) montrent l’importance du débit durant l’émergence des truites ou juste après l’émergence comme un facteur déterminant majeur du recrutement, dont les effets se répercutent durant la vie entière et mettent en lumière la synchronie entre les processus environnementaux et la dynamique de la truite brune. La synchronie du recrutement est causée par une synchronie hydrologique qui, à son tour, est déterminée par une synchronie climatique (précipitations) qui agit à l’échelle régionale. L’importance du débit pour le recrutement est confirmée par des études sur des populations indigènes et introduites, ce qui laisse croire que le débit a un effet étendu sur la dynamique de la truite brune des cours d’eau dans plusieurs régions géographiques. [Traduit par la Rédaction]

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Introduction Population studies on stream-living salmonids have shown that variability in numerical abundance across spatiotemporal scales is the rule (Heggenes et al. 1999; Gibson 2002; Klemetsen et al. 2003) and that brown trout (Salmo trutta) populations are not an exception. Temporal within-site variations in abundance (Crisp 1993; Elliott 1994; Waters 1999) of magnitudes similar to or even greater than variations among nearby sites within a stream or among closely related Received 16 October 2003. Accepted 14 April 2004. Published on the NRC Research Press Web site at http://cjfas.nrc.ca on 17 December 2004. J17792 J. Lobón-Cerviá. Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas, C/José Gutiérrez Abascal, 2, Madrid, 28006 Spain (e-mail: [email protected]).

Can. J. Fish. Aquat. Sci. 61: 1929–1939 (2004)

streams have been described for a variety of stream-living brown trout populations within the natural European distribution (Mann et al. 1989; Milner et al. 1993; Kelly-Quinn et al. 1996) and for populations introduced into lotic environments across distant geographical regions such as North America (Newman and Waters 1989) and New Zealand (Allen 1951; Hayes 1995). The abundance of spawners and recruits (the juveniles that incorporate into the population) and hydrological factors are thought to play a major role in determining population size (Elliott 1994; Knapp et al. 1998) upon which suites of density-dependent and density-independent factors are likely to operate at different spatiotemporal scales. However, while stock–recruitment relationships for single populations have been shown to explain little variation in the survival rates (Cattanéo et al. 2002; Lobón-Cerviá and Rincón 2004), an increasing appreciation of the importance of recruitment to population size suggested that adult abundance is recruitment dependent (Victor 1983; Freeman et al. 1988; Knapp et

doi: 10.1139/F04-118

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al. 1998). Thus, annual recruitment magnitudes would be reflected in the age structure of the population (Fogarty et al. 1991; Doherty and Fowler 1994; Caley et al. 1996). Moreover, functional links between recruitment and hydrological factors have been described for a variety of stream-living populations of brown trout (Solomon and Paterson 1980; Jensen and Johnsen 1999; Spina 2001) and other salmonids (Clark 1992; Nehring and Anderson 1993; Latterell et al. 1998). The spatiotemporal scales under which suites of factors predominate have yet to be identified. An approach in recent studies has been to explore patterns of spatial covariation or levels of synchrony in the survival rates. These patterns should provide information on the processes involved at the corresponding spatial scale and could be explored for the operation of synchronous causative factors. Thus, positive spatial covariation in the survival rates of Pacific salmon species revealed that the environmental factors determining recruitment operate over similar spatial scales (Coronado and Hilborn 1998; Peterman et al. 1998; Pyper et al. 2002). If this were the case in stream-living brown trout, patterns of covariation in recruitment across spatial scales would be reflected in the population structure at the corresponding spatial scale (Milner et al. 1993; Cattanéo et al. 2002) and the factors driving recruitment would be reflected, to some extent, in successive life stages (Cattanéo et al. 2003). In this study, I attempted to (i) elucidate patterns of spatial covariation in the population dynamics of brown trout at the spatial scale of a river drainage, namely of Rio Esva, a typical drainage of the Cantabrian corridor of northwestern Spain, and (ii) identify the factors responsible for spatial covariation. Earlier studies in one of these streams revealed substantial differences in density among nearby sites but concomitant, discharge-dependent recruitment rates and linear relationships between annual recruitment magnitudes and cohort size (Lobón-Cerviá 2003; Lobón-Cerviá and Rincón 2004). This study is an extension to a substantially broader spatial scale expected to capture the habitat variability that typifies Cantabrian river drainages. The data set encompassed census data of 11–15 successive cohorts at 12 sites of four Rio Esva subbasins. I explored patterns of spatial covariation in the temporal fluctuations (synchrony) in the survival rates of three successive life stages and attempted to identify the relative importance of hydrological factors versus recruitment processes as causative factors of the observed patterns.

Materials and methods Rio Esva and the study sites Rio Esva was selected for this long-term, large-scale study because it is safe from major anthropogenic impacts. Stream morphology and hydrology remain natural and no humaninduced alterations prevent fish movements or migratory pathways throughout the river network. Rio Esva is a medium-sized (~500 km2) coastal river drainage whose waters flow north across the Cantabrian corridor of northwestern Spain (Fig. 1) and is essentially fed by rainfall and runoff with no snow effects owing to the low altitude of the uppermost springs (i.e., 700 m above sea level).

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Within relatively high rainy conditions (1500 mm annual rainfall), discharge remains low and stable during the driest summer months (July–September), but increased rainfall in spring, autumn, and early winter may lead to strong fluctuations. For any month between October and June, year-toyear variation in total monthly discharge at the main stem of Rio Esva (gauging station, Fig. 1) varied by >10-fold. Overall, monthly discharge ranged from 6 to 160 hm3 during the study years (1986–2003). All running waters across the mountainous and rugged Rio Esva landscape flow over quartzite bedrock (Aramburu and Bastida 1995) resulting in a variety of shallow, fastflowing, and well-oxygenated streams of various size and physiography. These streams whose substratum is essentially composed of pebble, gravel, and boulder are dominated by runs and riffles with a few scattered pools. The latter become deeper in bends where the streams crash against rocky slopes and shallower on runs just behind riffles on stream banks where vegetation grows or where erosive processes predominate. These streams flow through a mosaic of riparian forest alternating with meadowlands. In stream sections dominated by riparian forest, water surface is fully covered by leafy overhanging vegetation (i.e., canopy), preventing isolation, whereas in meadowlands, stream reaches are in full sunlit, with many sections holding intermediate characteristics. A variety of channel structures combine with substratum composition, current velocity, and canopy at the scale of stream sections (of a size ranging from several hundred to a few thousand metres) to conform the stream habitat heterogeneity available for brown trout. Surveys previous to this study (J. Lobón-Cerviá, unpublished data) revealed a full occupation of Rio Esva by brown trout, reflecting a continuum of individuals from the estuary in the Cantabrian Sea upstream to detectable water reaches. Juveniles and adult individuals showed substantial spatial variation in size and abundance (Lobón-Cerviá 2000, 2003), but all-age individuals thrive at all streams with not even 11 cm spawn successfully every year with just a few females surviving to spawn twice (Lobón-Cerviá et al. 1997). Data collection Daily discharge (m3·s–1) in the main stem of Rio Esva was © 2004 NRC Canada

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Can. J. Fish. Aquat. Sci. Vol. 61, 2004 Table 1. Major characteristics of the 12 sites of Rios Chaballos, La Viella, Castañedo, and Choudral (Rio Esva drainage) examined in this study. Also refer to Fig. 1. Site Chaballos S1 S2 S3 S4 La Viella S5 S6 S7 Castañedo S8 S9 S10 Choudral S11 S12

Length (m)

Width (m)

Depth (cm)

Area (m2)

Volume (m3)

Velocity (m·s–1)

Canopy (%)

75 80 70 75

2.5 3.3 3.8 3.9

20 26 22 27

240 250 220 370

43 86 42 92

0.25 0.36 0.31 0.18

0 15 20 80

70 66 70

3.4 2.7 3.2

18 22 23

260 160 190

46 44 74

0.20 0.23 0.30

85 60 30

65 64 75

2.9 2.9 3.9

23 26 20

150 180 320

54 73 79

0.13 0.16 0.21

40 40 70

63 70

2.3 2.5

15 16

150 200

18 26

0.16 0.15

92 95

Note: Data are mean values for each site averaged over the study years.

supplied by the Trevias gauging station located at middistance of the streams studied (Fig. 1). Assumedly, dayto-day variation in discharge in the main stem reflects dayto-day variation in the four streams studied. Brown trout density was tracked from 1986 (Rio Chaballos), 1989 (Rio Choudral), and 1990 (all other sites). Census data were obtained under base flow conditions in January, May, and September. Sampling was conducted by blocking the sites with nets to apply the three-pass removal method (Seber 1982) with electrofishing techniques (LobónCerviá 1991). These techniques have been shown to be size selective with reduced capture efficiency for smaller individuals (Lobón-Cerviá 1991). To prevent bias in the estimates of the youngest juveniles (i.e., recruits), I conducted sampling by mid-May when 2-month-old individuals have attained a size (4–6 cm) sufficient to be efficiently captured. All fish captured were measured (fork length) and assigned to age-classes easily detectable from length–frequency distributions. Doubtful assignments in larger (older) individuals were confirmed by scale readings. Bathymetric maps were drawn for every site and date just after sampling. Data analysis The number of individuals (N) for each site and date was calculated separately for age-0, age-1, and older fish. The values of N were estimated from N = Ct/1 – qs where qs was obtained by solving the equation 3

sq s /(1 − q s ) − q / p + ∑ (i − 1) C i / Ct = 0 i =1

All estimates were tested for the failure condition (Carle and Strub 1978; Otis et al. 1978) 3

∑ (s − 1) C i ≤ [(Ct − 1)(s − 1)/ 2] − 1 i =1

where s = 3 occasions and Ct is the sum of the captures (Ci) obtained on each occasion i. Once the failure condition was assessed, I examined the goodness of fit (i.e., whether the probability of capture remains constant across capture efforts). This was tested with the statistic T1 (Otis et al. 1978; Seber 1982) s

T1 =

∑ (C i − Ei)2 / Ei i =1

where Ei is the number of individuals expected to be captured on each occasion i, which is Np/qi–1 and where N is the estimated number of individuals, p is the probability of capture, and q = p – 1. The statistic T1 is distributed as a χ 2 with s – 2 degrees of freedom. This analysis revealed a high capture efficiency for all life stages, seasons, and sites, with 94% of the estimates being significant and an average capturability of 90% for age-1 and older individuals and 75% for age-0 individuals (LobónCerviá 2003). Densities were then referred to the numbers estimated for the area of each site and date (Lobón-Cerviá 2003). On average, the abundance of each cohort and site was quantified 10 times from emergence to its final disappearance. For the purpose of this study, I selected three cohort traits to summarize the dynamics of each cohort and site as follows. (i) Recruitment (R) (individuals per square metre) was measured as the abundance of 2-month-old juveniles. (ii) I used the mean cohort size (individuals per square metre) as a measure of the year-class strength. Mean cohort size was calculated as the abundance of a cohort averaged from the second sample (i.e., 6-month-old individuals) to the maximum age observed. (iii) Spawner abundance (SA) (individuals per square metre) was measured as the abundance of females about to spawn in September, 19 months after emergence. The residuals of the nonlinear stock (S) – recruitment (R) model (Ricker 1954) © 2004 NRC Canada

Lobón-Cerviá

(1)

R = aS–bS

were used as indices of survival rates. For each single site, fits of this model to the abundance of recruits, mean cohort size, and spawner abundance over the parental population (i.e., the abundance of female spawners the winter previous to recruitment) were obtained for the cohorts of the years 1987–2001 (sites S1, S3, and S4, Rio Chaballos), 1989– 2001 (site S2, Rio Chaballos), 1990–2001 (Rio Choudral), and 1991–2001 (Rios Castañedo and La Viella). Thus, independent sets of survival rates were obtained for spawner-torecruit (STR), spawner-to-cohort size (STC), and spawnerto-spawner (STS). Covariation patterns were assessed through Pearson’s product moment correlations among pairwise comparisons of the indices of survival rate of STR, STC, and STS. To examine whether the distance among sites influences levels of spatial covariation, Myers et al. (1997) proposed an estimate of the spatial scale as the distance over which the pairwise correlations are reduced by a factor e–1. This can be readily estimated by fitting the covariance function (2)

Rd = R0e–d/v

where R0 is the correlation between sites at zero separation, v is the e-folding scale, and d is the distance among sites. The R0 was constrained to have a value of ≤1. Two-phase or split-line regressions (Perry 1982; Nickerson et al. 1989) were used to fit the trajectories described by the indices of survival rate over hydrological factors. A two-phase regression is defined as (3)

Y = a1 + b1 X (for any X ≤ k) + a2 – b2 X (for X > k)

with the restriction for continuity a1 + b1 k = a2 – b2 k, at the break point (k), where the positive direction of the trajectory described by the dependent variable (Y) switches towards a negative direction. Least squares, iterative quasi-Newton algorithms (Dennis and Schnabel 1983), as offered by the statistical package Statistica 6.0TM, were used to fit all linear and nonlinear regressions.

Results Stock–recruitment relationships Year-to-year variation in the abundance of parental stocks showed weak potential as a predictor of the abundance of recruits, cohort size, and the subsequent abundance of the parental stock. Fits of the nonlinear Ricker model (eq. 1) revealed poor relationships between the abundance of recruits versus the abundance of female spawners. The only exceptions were site S1 where 63.5% of the variations in recruitment were nonsignificatively (p = 0.47) explained by variations in the abundance of female spawners and sites S2, S5, and S6 where the fits were significant (p < 0.05) and explained 47.2%, 30.0%, and 20.5% of the recruitment variations, respectively. At all other sites, the fits were nonsignificant and the variance explained ranged from 0.1% (site S9) to 13.5% (site S12). Weaker predictive potentials were yet detected when fitting the nonlinear model to cohort size and to the subsequent parental stock versus the abundance of female spawners. These fits showed an overall lack of significance

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with the only exceptions being cohort size for site S5 (21.0% of the variance explained, p = 0.04), site S6 (7.5% of the variance explained, p = 0.004), and site S7 (15.9% of the variance explained, p = 0.009). Patterns of covariation in the indices of survival rates Pairwise correlations were computed for the indices of survival rates of STR, STC, and STS. All correlation coefficients (66 for each data set) were positive (Table 2). For STR, there were 48 significant correlations (p < 0.05), the significance of 10 correlation coefficients was in the range 0.05 < p < 0.10, and in eight instances, the significance was at p > 0.10 (Table 2). For STC, there were 36 significant correlations, 13 correlations were in the range 0.05 < p < 0.10, and 17 correlations were at p > 0.10 (Table 2). For STS, there were 28 significant correlations, eight correlations in the range 0.05 < p < 0.10, and 30 correlations were at p > 0.10 (Table 2). The amount and magnitude of the correlation coefficients decreased slightly for the older life stages. Yet, numerous sites located in different streams showed consistent significance versus all other sites and revealed no trend to greater magnitudes among nearby sites. Moreover, plots of these coefficients versus stream distance among sites (kilometres) showed no tendency to decreased values with increased distance (Fig. 2), and fits of eq. 2 to the STR, STC, and STS indices of survival rates versus stream distance among sites showed extremely weak relationships with levels of significance at p = 0.54 (STR) (Fig. 2a), p = 0.73 (STC) (Fig. 2b), and p = 0.65 (STS) (Fig. 2c). Relationships among the indices of survival rates of STR, STC, and STS An inspection of spatiotemporal variations of the indices of survival rates of STC and STS showed strongly consistent, positive trends versus STR, and linear regressions explained 74.3% and 51.5% of the spatiotemporal variations in STC and STS due to variations in STR indices of survival rates (Figs. 3a and 3b). Moreover, a linear regression explained 76.5% of the spatiotemporal variations in the STS due to variations in STC (Fig. 3c). The high magnitudes and significance levels of the correlation coefficients observed among sites in combination with these highly significant linear trends offered strong evidence of covariation in the indices of survival rates of recruitment, cohort size, and spawner abundance over the river drainage. Discharge variations and the patterns of survival Visual inspections of the trajectories described by the indices of survival rate of STR over total discharge in March (cubic hectometres) when brown trout emerge revealed strongly consistent patterns (Fig. 4a). At the 12 sites, these indices increased from the lowest discharge levels recorded during the dry springs of the years 1990, 1997, and 1998 (6.3, 13.6, and 14.8 hm3, respectively) to attain maximum values in years of average discharge (30–40 hm3, years 1987, 1988, and 1996) beyond which they reversed to decreased values with increased discharge. Thus, at the 12 sites, these indices of survival rates attained the lowest values at the two extremes of discharge, during the severely drought spring of 1990 (6.3 hm3) and during the most rainy © 2004 NRC Canada

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Can. J. Fish. Aquat. Sci. Vol. 61, 2004

Table 2. Pairwise correlation coefficients for the spawner-to-recruit (STR), spawner-to-cohort size (STC), and spawner-to-spawner (STS) indices of survival rates among the 12 sites (S1–S12) examined in Rios Chaballos, La Viella, Castañedo, and Choudral. S1 (15)

S2 (13)

S3 (15)

S4 (15)

S5 (11)

S6 (11)

S7 (11)

S8 (11)

S9 (11)

S10 (12)

S11 (12)

0.57 0.64 0.51 0.72 0.59 0.56 0.71 0.90 0.76 0.83 0.68

0.40 0.68 0.58 0.70 0.71 0.79 0.69 0.49 0.70 0.64

0.72 0.49 0.72 0.70 0.69 0.77 0.60 0.64 0.67

0.24 0.73 0.55 0.84 0.69 0.06 0.31 0.49

0.68 0.65 0.34 0.71 0.82 0.85 0.68

0.67 0.57 0.65 0.53 0.59 0.60

0.56 0.66 0.62 0.83 0.67

0.81 0.26 0.50 0.52

0.47 0.75 0.61

0.85 0.85

0.60 0.59 0.59 0.68 0.81 0.59 0.82 0.85 0.76 0.72 0.70

0.68 0.69 0.23 0.57 0.64 0.73 0.70 0.40 0.29 0.55

0.93 0.46 0.59 0.71 0.84 0.78 0.63 0.53 0.68

0.26 0.48 0.57 0.80 0.74 0.41 0.29 0.51

0.76 0.48 0.41 0.44 0.83 0.69 0.60

0.36 0.57 0.74 0.63 0.47 0.55

0.71 0.54 0.56 0.46 0.57

0.91 0.60 0.59 0.70

0.61 0.54 0.58

0.92 0.92

Spawner-to-spawner S2 0.56 S3 0.46 0.57 S4 0.45 0.50 S5 0.04 0.31 S6 0.29 0.87 S7 0.09 0.75 S8 0.35 0.74 S9 0.48 0.66 S10 0.37 0.87 S11 0.14 0.75 S12 0.06 0.51 Mean 0.30 0.65

0.56 0.46 0.74 0.34 0.65 0.53 0.38 0.42 0.02 0.46

0.74 0.77 0.33 0.71 0.67 0.71 0.43 0.46 0.60

0.59 0.11 0.44 0.75 0.50 0.23 0.26 0.41

0.68 0.73 0.64 0.83 0.65 0.44 0.66

0.29 0.31 0.76 0.83 0.42 0.52

0.56 0.57 0.61 0.54 0.57

0.69 0.39 0.32 0.47

0.70 0.46 0.58

0.74 0.74

Spawner-to-recruit S2 0.72 S3 0.55 S4 0.20 S5 0.74 S6 0.26 S7 0.67 S8 0.69 S9 0.78 S10 0.60 S11 0.49 S12 0.67 Mean 0.58 Spawner-to-cohort S2 0.66 S3 0.58 S4 0.48 S5 0.77 S6 0.14 S7 0.53 S8 0.62 S9 0.76 S10 0.75 S11 0.57 S12 0.43 Mean 0.57

size

Note: The number of data pairs (i.e., cohorts) for each site is provided in parentheses. Levels of significance: p < 0.05 (bold); 0.05 ≤ p < 0.10 (underlined); p > 0.10 (others).

springs of the years 1995 and 2001 (70.3 and 65.5 hm3, respectively). Consistent with the highly significant linear relationships previously detected among STC and STS as a function of STR, the indices of survival rates of STC and STS described the very same trajectories over river discharge at the time of brown trout emergence (Figs. 4b and 4c), and significant fits of two-phase regressions (eq. 3) were obtained between the indices of survival of STC and STS versus discharge for either single site. As well, fits for pooled sites were highly significant for the three life stages. In these fits, river discharge explained 35%, 44%, and 40% of the overall year-to-year variation in the indices of survival rates of STR, STC, and

STS, respectively. Constants for the two-phase regressions for single and for pooled sites are shown in Table 3. These results revealed that despite substantial among-site differences in density and survival rates across life stages, river discharge during brown trout emergence consistently predicted the survival rates of the recruitment of the current year, of the cohort size, and of the abundance of females about to spawn 2 years later (Fig. 4).

Discussion The results of this study offered strong evidence for positive spatial covariation in the survival rates of successive life © 2004 NRC Canada

Lobón-Cerviá Fig. 2. Magnitude of pairwise correlations among the indices of survival rates of (a) spawner-to-recruit (STR), (b) spawner-tocohort size (STC), and (c) spawner-to-spawner (STS) as a function of stream distance among sites. Horizontal lines represent mean correlations.

stages of brown trout within an environmentally complex Cantabrian drainage. Similar levels of spatial covariation in recruitment and in the adult components of the population (i.e., STC and STS) and highly significant correlations between the indices of survival rates of STC and STS versus STR and also between STC versus STS revealed persistent synchrony during the whole lifetime. Consistent spatial covariation suggested that a shared environmental factor of temporal variation operates across sites of different streams located over >30 km (maximum separation among sites), a distance slightly smaller than the hypothesized 50-km spatial scale expected to result in recruitment correlations for freshwater fishes (Myers et al. 1997). Covariation across spatial scales may be caused by several factors. Dispersal or fish movements have been postulated to be a major synchronizing factor (Paradis et al. 1999). If this

1935 Fig. 3. Positive linear trends of the indices of survival rates of (a) spawner-to-cohort size (STC) and (b) spawner-to-spawner (STS) as a function of spawner-to-recruit (STR). Regression equations: STC = 0.003 + 0.361STR (r2 = 0.744, p < 0.0001) and STS = –0.0001 + 0.092STR (r2 = 0.503, p < 0.0001). (c) Positive linear trend of the indices of survival rates of STS as a function of STC. Regression equation: STS = –0.0017 + 0.272STC (r2 = 0.750, p < 0.0001).

were the case, levels of covariation would have been stream specific, or grouped for a variety of nearby sites or related to the distance among sites. That is, the trajectories described by the correlation coefficients among survival rates versus distance among sites would have shown consistent trends to increased levels of covariation across fixed spatial scales. No evidence of such trends among the study sites is consistent with the restricted movements and high levels of site fidelity revealed by several-year mark–recapture tagging procedures assessed in Rios Chaballos and La Viella brown trout (Lobón-Cerviá 2000, 2003), and it makes dispersal to be a most unlikely factor determining covariation patterns. A continuum of biotic–abiotic interactions has been suggested to regulate fish populations along stream gradients (Zalewski and Naiman 1985). Thus, the location of the site © 2004 NRC Canada

1936 Fig. 4. Two-phase trajectories described by the indices of survival rates of (a) spawner-to-recruit (STR), (b) spawner-to-cohort size (STC), and (c) spawner-to-spawner (STS) over total monthly discharge in March of the year of recruitment. For each life stage, an overall two-phase regression for pooled sites is depicted with indication of recruitment year. Coefficients of two-phase regressions for single and for pooled sites are given in Table 3.

within the stream would determine the predominance and intensity of a particular suite of factors. Reduced hydrological effects and increased biotic interactions would be expected to operate at downstream sites (Zalewski et al. 1985) due to an overall increase in habitat complexity (Horwitz 1978). Owing to the geomorphologic and landscape structures of Rio Esva, streams resemble a mosaic of habitats of heterogeneous quality rather than upstream–downstream gradients of habitat complexity. Therefore, hydrological factors appear to operate in a similar manner at all sites with no evidence of reduced intensity downstream. Linear relationships among the indices of survival rates of STR, STC, and STS across spatiotemporal scales further support this claim. Discharge appears as a major factor responsible for the high levels of spatial covariation in the population dynamics of brown trout across the Rio Esva drainage. Year-to-year variation in river discharge at the time of emergence (i.e., March) consistently predicted recruitment rates and, at similar levels of significance, the survival rates of mean cohort size and the abundance of females about to spawn. Dis-

Can. J. Fish. Aquat. Sci. Vol. 61, 2004

charge-dependent survival rates during the lifetime are consistent with the linear relationships shown by the survival rates of recruits, cohort size, and spawner abundance and further imply that the importance of river discharge is limited to the time of emergence but its strength is reflected in the population structure over the years. As a consequence, population size at the scale of the river drainage mainly depends on discharge through its profound effects on the youngest juveniles (Lobón-Cerviá and Rincón 2004) in a manner that the operation of density-dependent or density-independent postrecruitment processes, including natural disturbance such as severe floods and drought (which were recorded during the study period; Lobón-Cerviá 1996, 2003), might not be sufficiently strong to obscure the overwhelming importance of discharge on recruitment. That is, spatial covariation in the adult components of the population depends on the previous synchrony on the youngest juveniles rather than on the operation of synchronizing factors acting on postrecruitment stages. In a previous study, Lobón-Cerviá and Rincón (2004) highlighted the mechanism operating on recruitment in Rio Chaballos, namely the synchronized discharge-dependent availability of microhabitats suitable for the youngest juveniles soon after emergence. Year-to-year variation in the availability of microhabitats suitable for recruits matches the split-line trajectory described by discharge. In turn, discharge and stream channel structure interact at a site scale to result in site-specific areas suitable for the youngest juveniles and therefore in site-specific recruitment magnitudes. The large-scale spatial patterns in the survival rates of recruits revealed in this study emphasize the importance of discharge and support the operation of synchronized environmental mechanisms on recruitment throughout the river drainage. The annual rates of recruitment are determined by the availability of microhabitats imposed by discharge that, in turn, is induced by rainfall, a climatic factor operating at a broader, regional scale. Thus, year-to-year variation in March rainfall induces variations in discharge to reset the survival rates of recruits over the river drainage. Linear relationships among the survival rates of STR, STC, and STS or among the abundance of successive life stages appear to be common in stream salmonids (Beard and Carline 1991; Kennedy and Crozier 1993; Whalen et al. 2000), although more complex and even negative relationships have been described (Elliott 1996; Knapp et al. 1998; Cattanéo et al. 2002). Continuous population growth predicted by linear processes appears to be especially relevant in discharge-driven, short-living populations like the Rio Esva brown trout. Year-class strength is to be maintained from recruitment to spawning, regardless of whether mortality is density dependent, and even under most unfavorable discharge conditions, minor levels of recruitment may yet guarantee survivors to spawn. Few studies have reported covariation patterns in streamliving brown trout at a drainage scale (Milner et al. 1993; Cattanéo et al. 2003), and still fewer have identified the operation of a common causative factor (Cattanéo et al. 2003). However, temporal variations in discharge have long been recognized to influence recruitment. Discharge-limited survival rates of the youngest juveniles have been described for © 2004 NRC Canada

Lobón-Cerviá

1937 Table 3. Coefficients of the two-phase regressions for the indices of survival rates of spawner-torecruit (STR), spawner-to-cohort size (STC), and spawner-to-spawner (STS) versus total monthly discharge (hm3) in March for each single site (S1–S12) and for pooled sites over the years. Site

a1

b1

k

a2

b2

r2

Spawner-to-recruit S1 (15) S2 (13) S3 (15) S4 (15) S5 (11) S6 (11) S7 (11) S8 (11) S9 (11) S10 (11) S11 (12) S12 (12) Pooled (148)

–0.366 –0.297 –0.683 –0.082 –0.193 –0.734 –0.347 –0.241 –0.318 –0.117 –0.078 –0.133 –0.277

0.017 0.015 0.034 0.005 0.011 0.033 0.018 0.012 0.017 0.005 0.003 0.006 0.013

33.4 34.5 34.5 33.3 25.5 40.3 32.9 37.3 27.1 37.3 39.6 40.5 35.8

0.556 0.490 1.390 0.251 0.167 1.683 0.604 0.579 0.278 0.226 0.104 0.373 0.539

–0.011 –0.007 –0.026 –0.005 –0.003 –0.027 –0.011 –0.103 –0.005 –0.004 –0.001 –0.006 –0.009

0.43 0.47 0.65 0.70 0.81 0.71 0.71 0.61 0.59 0.50 0.46 0.62 0.35

Spawner-to-cohort S1 (15) S2 (13) S3 (15) S4 (15) S5 (11) S6 (11) S7 (11) S8 (11) S9 (11) S10 (11) S11 (12) S12 (12) Pooled (148)

size –0.146 –0.128 –0.226 –0.057 –0.120 –0.154 –0.098 –0.178 –0.225 –0.227 –0.047 –0.060 –0.121

0.007 0.007 0.011 0.003 0.007 0.006 0.005 0.008 0.011 0.013 0.002 0.003 0.006

31.9 35.6 33.9 37.3 27.1 51.5 35.1 37.3 34.3 25.1 43.9 45.6 36.8

0.179 0.370 0.469 0.212 0.135 1.030 0.169 0.293 0.348 0.175 0.199 0.278 0.281

–0.003 –0.007 –0.009 –0.004 –0.003 –0.017 –0.003 –0.005 –0.006 –0.003 –0.004 –0.005 –0.005

0.60 0.80 0.53 0.49 0.50 0.75 0.50 0.68 0.63 0.75 0.55 0.48 0.44

0.002 0.002 0.002 0.001 0.0005 0.002 0.0008 0.003 0.006 0.002 0.001 0.0007 0.0017

32.9 35.4 36.0 42.8 37.3 38.3 39.5 37.3 27.1 38.1 42.2 52.2 36.5

0.042 0.106 0.134 0.109 0.034 0.113 0.071 0.117 0.081 0.103 0.074 0.126 0.083

–0.001 –0.002 –0.003 –0.002 –0.0007 –0.002 –0.001 –0.002 –0.002 –0.002 –0.001 –0.002 –0.0016

0.50 0.85 0.40 0.45 0.17 0.87 0.49 0.69 0.66 0.81 0.52 0.60 0.40

Spawner-to-spawner S1 (15) –0.037 S2 (13) –0.039 S3 (15) –0.046 S4 (15) –0.019 S5 (11) –0.009 S6 (11) –0.035 S7 (11) –0.016 S8 (11) –0.069 S9 (11) –0.125 S10 (11) –0.049 S11 (12) –0.021 S12 (12) –0.019 Pooled (148) –0.032

Note: Number of data pairs used to fit each two-phase regression is given in parentheses. k is the break point upon which the trajectories switch the direction of the survival rates and r2 is the variance explained. All slopes and k values are significant at, at least, p < 0.05.

a variety of populations across the European range of brown trout including streams in the United Kingdom (Solomon and Paterson 1980; Milner et al. 1993; Elliott et al. 1997), in northern and boreal latitudes (Jensen and Johnsen 1999; Mäki-Petäys et al. 1999), and in southern Mediterranean latitudes (Cattanéo et al. 2002, 2003). Similarly, numerous introduced populations across geographical (climatic) North American regions appear to be constrained by discharge

variations, including Michigan (Nuhfer et al. 1994), Montana (Nelson 1986), Minnesota (Newman and Waters 1989), Colorado (Nehring and Anderson 1993; Latterell et al. 1998), and California (Strange et al. 1992). These studies provide evidence for the operation of stochastic processes related to rainfall- or snowmelt-induced discharge conditions on the temporal within-site variation of recruitment as a common phenomenon across geographical © 2004 NRC Canada

1938

regions and point out that, similar to Rio Esva, its operation is of major importance during or just after brown trout emergence. Nonetheless, relative to the more complex, two-phase patterns elucidated for Rio Esva, the relationships between recruitment and discharge described by those studies were essentially linear with negative effects of increased discharge. Differences in shape among discharge–recruitment relationships may reflect different ranges of discharge variability among geographical regions or different effects of discharge at a stream scale but also that the data sets examined by those studies might not have been long enough to capture the range of variability needed to detect two-phase patterns. The range of driest versus rainiest springs rarely occurs within short periods of years and the studies above were short-term assessments relative to a 15-year data set examined for Rio Esva. In conclusion, the results of this study are consistent with recent studies that indicate a predominant role of regional processes in determining survival rates of salmonids including Pacific (Coronado and Hilborn 1998; Peterman et al. 1998; Pyper et al. 2002) and Atlantic–Mediterranean (Cattanéo et al. 2002, 2003) species or populations and emphasize the importance of discharge as a major factor determining spatial covariation in recruitment whose consequences persist during the lifetime. In the light of these results, forecasting the population size of Rio Esva and probably of other nearby river drainages subject of the same climatic conditions (i.e., rainfall-induced discharge conditions) may be readily obtained from stream flow measurements, data that are currently recorded by numerous gauging stations along the Cantabrian range of brown trout. The operation of discharge as a determinant of recruitment apparently differs among riverine systems or among climatic regions. Research is needed to identify common spatial regions for the operation of discharge and levels of transferability of discharge–recruitment relationships among regions. Further explorations into the complex relationships between stream channel morphology and discharge conditions to arise habitat suitable for recruits at a site scale (Lobón-Cerviá and Rincón 2004) may contribute substantially to the development of models to quantify the impact of a variety of natural processes and anthropogenic activities at local and regional scales.

Acknowledgments This study was financially supported by three successive Comisión Inter-misisterial para la Ciencia y la Technología (CICYT) (formerly MEC-DGCYT) projects (reference No. PB84-0142, PB89-0048, and PB92-0045) and partially funded by the Municipality of Valdés (Asturias). Permission for fishing was by agreement between the Consejo Superior de Investigaciones Científicas and Principado de Asturias.

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