CC BY-NC-ND
0Cent. Eur J. Biol. • 8(3) • 2013 • 263-271 D0l:10.2478/s11535-013-0133-1
VERSITA
Central European Journal of Biology
Distribution and growth of brown trout in pristine headwaters of Central Europe
Research Article
Libor Závorka1*, Pavel Horky2, Ondrej Slavík2
Institute for Environmental Studies, Faculty of Science, Charles University in Prague, 12843 Prague 2, Czech Republic
department of Zoology and Fisheries,
Faculty of Agrobiology, Food and Natural Resources,
Czech University of Life Sciences Prague,
165 21 Prague 6, Czech Republic
Received 08 June 2012; Accepted 05 December 2012
Abstract: The majority of stream-dwelling salmonid populations in Europe are affected by artificial stocking and the fragmentation of riverine ecosystems. The present study was performed in the unique pristine headwaters of the Otava River in the Elbe catchment area of the Czech Republic. The aim was to investigate the spatial distribution and individual growth pattern of brown trout, Salmo trutta, populations. Twenty sites in two main streams and their tributaries were sampled twice a year (spring and autumn) during the period 2005-2011. The sampling sites were grouped into fourteen so-called synchronised population units within the boundaries of three populations, according to analyses of synchrony in population abundance. The individual growth of juveniles (age-0, age-1) varied between all three spatial units (sampling sites, synchronised population units and populations), while the individual growth of adults (age-2 and older) did not. The distinctiveness regarding individual growth and demographic independence among the synchronised population units and populations indicates their suitability for use as population units for management purposes.
Keywords: Demographic synchrony • Salmonid fish • Body length increment • Ontogeny • Metapopulation © Versita Sp. z o.o.
1. Introduction
In poikilothermic organisms, body size, as a direct consequence of individual growth, is strongly correlated with many physiological traits [1]. The key role of individual growth as a driver of population dynamics in stream-dwelling salmonids has been widely reported [2-6]. Individual growth influences brown trout, Salmo trutta, populations beginning in the early ontogenetic phases, as demonstrated by the positive correlation between juvenile survival and body size [7]. However, this relationship is highly variable across seasons and populations [8]. In the later phases of the life cycle, individual growth affects the fertility of individuals, as the number and weight of eggs increase with female body size [2], and individuals that grow faster achieve
earlier sexual maturation than their conspecifics [9]. Throughout their life span, the growth of trout has an influence on their competitive ability, which increases with body size [10], and migration behaviour, as larger and faster growing individuals show higher mobility [11,12]. Generally, growth has an essential influence on the fitness of individuals [13], and variations in growth trajectories can have a substantial effect on brown trout population dynamics [5].
Stream-dwelling salmonids often exhibit a high level of population differentiation [14,15]. Partially isolated brown trout populations have been observed at local geographical scale in streams fragmented by migration barriers [16,17] as well as in streams with free migration corridors, where the populations has been isolated by distance [18,19]. Isolation among populations or among
E-mail: libor_zavorka@vuv.cz
Springer
smaller population units can lead to differentiation in life traits, such as individual growth [11,20].
The aim of the present study was to investigate spatial distribution and the individual growth patterns of brown trout populations in the headwaters of the Otava River. To our knowledge, this is the first intensive study addressing individual growth and the spatial population distribution in brown trout in headwaters within central Europe.
2. Experimental Procedures
2.1 Study area
The headwaters of the Otava River are located in Sumava National Park in the Czech Republic (49°1'N, 13°29'E; Figure 1). The relief of the landscape in the Sumava National Park region is mountainous, and the most widespread vegetation type is spruce forest, which alternates with meadows and peat bogs.
The studied headwater streams consist of two main tributaries, the Vydra and Kremelnâ Rivers, which spring at 1,100 m a.s.l. and achieve confluence after ca. 30 km, creating the Otava River. The overall area of the Vydra and Kremelnâ basins is approximately 224 km2. The study streams are oligotrophic and pristine conditions prevail. Twenty sampling sites were chosen along the longitudinal gradients of the study streams and their tributaries (Table 1 ) according to National Park access permission. Nine of the sampling sites had a riparian canopy, while nine flowed through meadows and peat bogs. The average flow at the sampling sites ranged from 0.01 to 2 m3 s-1. The substratum of stream beds was heterogeneous and contained sand, gravel, pebbles and boulders (Table 1). No obstacles prevent migration; the only exception was found in Svelsky Stream, where there is a natural, impassable 2.5 m-high steep boulder located approximately 80 m from confluence with the Vydra River. The boulder barrier prevented upstream migration to this tributary.
Figure 1. Map showing locations of study area with highlighted sampling sites, synchronized population units and populations.
Sampling site Sampling site ID Synchnize population unit Population Temperature (°C) pH Slope (%) Dominant substrate (%) Substratum size (mm)
Kremelna_1 K1 1 Kremelna 7.4 (4.6 - 14.0) 6.3 (5.5 - 7.7) 0.9 gravel (64) 72 (1 - 350)
Kremelna_2 K2 2 Kremelna 9.6 (6.2 - 17.0) 6.7 (5.6 8.1) 1.24 gravel (45) 110 (1 - 650)
Kremelna_3 K3 3 Kremelna 10.1 (7.0 13.7) 6.5 (4.5 - 7.7) 1.6 gravel (48) 145 (1 - 825)
Svelsky S 4 Svelsky 7.1 (3.5 9.0) 5.8 (4.2 6.7) 35 boulders (51) 314 (1 - 1580)
Hamersky_2 H2 5 Vydra 8.5 (4.4 - 12.6) 6.3 (4.2 - 7.7) 9.56 pebbles (38) 260 (1 - 1030)
Vydra V 5 Vydra 7.1 (4.5 - 14.1) 5.8 (4.5 - 7.1) 2.45 pebbles (53) 278 (2 - 1370)
Hamersky_1 H1 6 Vydra 7.8 (4.1 - 10.3) 5.6 (4.2 - 7.8) 2.34 gravel (57) 72 (1 - 410)
Filipohuisky F 7 Vydra 7.4 (3.1 9.9) 5.3. (3.8 - 7.8) 3.56 pebbles (48) 111 (1 - 610)
Modravsky_1 M1 8 Vydra 9.1 (4.2 - 14.5) 6.4 (5.4 - 7.3) 2.78 pebbles (57) 263 (2 - 860)
Modravsky_2 M2 8 Vydra 9.1 (7.1 - 14.5) 6.2 (5.3 - 7.3) 2.43 boulders (42) 294 (2 - 1200)
Breznicky B 9 Vydra 8.4 (3.4 - 12.4) 5.6 (3.8 - 7.1) 6.13 pebbles (59) 147 (1 - 880)
Luzensky_1 L1 9 Vydra 8.2 (4.7 - 11.2) 5.5 (4.2 7.7) 1.56 gravel (75) 103 (2 - 650)
Luzensky_2 L2 9 Vydra 8.8 (6.4 - 12.2) 5 (3.4 - 7.3) 0.89 gravel(51) 122 (1 - 850)
Roklansky_1 R1 10 Vydra 8.0 (6.1 - 10.0) 5.5 (4.0 - 7.2) 2.27 gravel (83) 61 (1 - 350)
Rokytka R 11 Vydra 6.2 (2.3 9.7) 5.6 (3.5 7.4) 0.68 gravel (83) 61 (1 - 350)
Javori_1 J1 12 Vydra 6.5 (2.9 - 9.4) 5.8 (4.1 - 8.0) 1.72 pebbles (64) 138 (5 - 450)
Tmavy T 13 Vydra 6.6 (3.1 - 10.5) 5.6 (3.6 7.3) 3.78 pebbles (57) 101 (10 - 285)
Roklansky_3 R3 14 Vydra 8.8 (4.0 - 14.0) 6.0 (4.6 7.3) 1.6 pebbles (52) 181 (1 - 980)
Javori_2 J2 14 Vydra 7.7 (3.5 - 11.4) 6.3 (4.1 8.8) 1.87 pebbles (52) 181 (1 - 980)
Roklansky_2 R2 14 Vydra 8.8 (3.5 - 13.4) 6.2 (4.6 7.4) 1.87 pebbles (52) 181 (1 - 980)
Table 1. Spatial structure of brown trout population within studied sites and variability of selected abiotic factors (means, range in brackets provided).
Fishing is banned and no stocking occurs in study streams. Therefore, the local ichthyofauna includes populations of autochthonous species. Only brown trout occurs in study streams and is accompanied by bullhead Cottus gobio in Kremelna River.
2.2 Data collection and analyses
Sampling of fish was performed at 20 sampling sites twice a year (in May and October) during seven consecutive years, from autumn 2005 to autumn 2011. Fish were captured using a backpack electro shocker (EFKO, Germany). A single pass electrofishing method was used, which is considered sufficient for the determination of brown trout abundance in mountain headwater streams [21,22]. The location and assessed area of sampling sites as well as the fishing effort were maintained constant throughout the study period. Every specimen was measured (standard length to the
nearest mm), weighed (to the nearest g) and individually tagged at the lower left jaw using VIA (visible implant alphanumeric) tags (Northwest Marine Technology, USA). Specimens that were of insufficient size for individual tagging (standard length smaller than 90 mm) were marked using VIE (visible implant elastomer) tags (Northwest Marine Technology, USA). The detection of tagged fish was noticed as recapture. Scale samples were obtained from 709 randomly selected individuals.
The morphological parameters of the sampling sites were measured once, at the beginning of the study. The river slope (%) was measured using a Pulse Total Station (Topcon GPT 2000, Itabashi, Tokyo, Japan) and was determined for the stretches delineated by fish sampling. The river slope was considered to correspond to the difference between water levels in two adjacent stream cross-sections [23]. The river substratum was quantified according to Wolman [24]. Water temperature
and pH (WTW, pH/Cond 340i SET) were measured before every sampling event (Table 1).
The individual growth and age of fish were estimated via scale readings performed along the anterior-posterior axis of scales [25]. For these readings, only fully developed scales were used, and regenerated or distorted scales were disregarded. Age was estimated by examining winter annuli, and growth was back-calculated using the Fraser-Lea formula [26]. Because of the well-known high level of estimation errors that occur when ageing salmonids after the third year of life [27,28], individuals older than three years were grouped in a single category for all analyses.
The data obtained from the mark-recapture program were used in analyses of recapture rate and movements. The recapture rate was calculated as the percentage of recaptured individuals among the total number of marked individuals. Site fidelity was measured as the percentage of recaptured individuals at their original tagging site among the total number of recaptured individuals. Dispersal distance was calculated as the average distance travelled by recaptured individuals among sampling sites.
The twenty sampling sites were linked together into demographically independent population units. This grouping was assessed via synchronisation of demographic dynamics among sampling sites, considering the existence of migration obstacles and individual migration behaviour. To examine the demographic synchrony among sampling sites, we determined the Pearson moment correlation of seasonal (spring, autumn) growth rates of individual's abundance between pairs of sampling sites [29]. The growth rate was expressed as the change in a number of individuals over yearly increments, expressed as the percentage of year-1 values (see Petranka et al. [30]). Sampling sites in which at least one zero abundance result occurred during the total period of observation were excluded from the analyses (there were four excluded sampling sites in spring and three in autumn). The spatial extent of synchrony among sampling sites was estimated as the x-intercept of the linear regression of the correlation coefficient of individual's abundance growth rate on sampling site distance [29,31]. The analyses were performed separately for each season. Sampling sites were grouped into populations based on the spatial synchrony of individual's abundance growth rate and the occurrence of impassable migration obstacles. Sampling sites within single populations were further sorted into synchronised population units. Sampling sites with a mean correlation coefficient of demographic synchrony that was higher than moderate (p=0.56; see Koizumi et al. [32]) and with a distance between them smaller
than the average dispersal distance of individuals in the focal area [30] were grouped into a synchronised population unit. Sampling sites that were not included in the analyses of demographic synchrony were grouped with the nearest sampling site if the distance between them was smaller than the average dispersal distance of individuals in the focal area. Otherwise, they were considered independent synchronised population units.
Associations between the variables related to the individual growth variation were tested using a linear mixed model (LMM). The data were transformed for normality prior to LMM analyses when necessary. To account for repeated measures, all analyses were performed using a mixed model with random factors (PROC MIXED; SAS, Version 9.1; SAS Institute Inc.; www.sas.com). Separate models were applied for the following dependent variables: brown trout abundance throughout season (LMM I; fixed factor: season; random factors: locality, year and their mutual interaction); individual growth throughout ontogenesis (hereafter assessed on the basis of scale readings; LMM II; fixed factor: age; random factors: locality, year, scale samples and their mutual interactions); individual growth of age-0 across the sampling site spatial units (LMM III; fixed factor: sampling site; random factors: year, scale samples and their interaction); individual growth of age-0 across the synchronised spatial population units (LMM IV; fixed factor: synchronised population unit; random factors: year, scale samples and their mutual interaction); individual growth of age-0 across the population spatial units (LMM V; fixed factor: population; random factors: year, scale samples and their mutual interaction); individual growth of age-1 across the sampling site spatial units (LMM VI; fixed factor: sampling site; random factors: year, scale samples and their mutual interaction); individual growth of age-1 across the synchronised population spatial units (LMM VII; fixed factor: synchronised population unit; random factors: year, scale samples and their mutual interaction); individual growth of age-1 across the population spatial units (LMM VIII; fixed factor: population; random factors: year, scale samples and their mutual interaction); individual growth of age-2 across the sampling site spatial units (LMM IX; fixed factor: sampling site; random factors: year, scale samples and their mutual interaction); individual growth of age-2 across the synchronised population spatial units (LMM X; fixed factor: synchronised population unit; random factors: year, scale samples and their mutual interaction); individual growth of age-2 across the population spatial units (LMM XI; fixed factor: population; random factors: year, scale samples and their mutual interaction); individual growth of age-3 and older across
the sampling site spatial units (LMM XII; fixed factor: sampling site; random factors: year, scale samples and their mutual interaction); individual growth of age-3 and older across the synchronised population spatial units (LMM XIII; fixed factor: synchronised population unit; random factors: year, scale samples and their mutual interaction); individual growth of age-3 and older across the population spatial units (LMM XIV; fixed factor: population; random factors: year, scale samples and their mutual interaction). The significance of each fixed effect in the mixed LMM models was assessed using an F-test. Least-squares means (LSM), henceforth referred to as adjusted means, were computed for each class, and differences between classes were tested using a t-test. For multiple comparisons, we used a Tukey-Kramer adjustment. The degrees of freedom were calculated using the Kenward-Roger method [33].
3. Results
3.1 Mark-recapture analyses
A total of 5195 individual brown trout were caught and tagged throughout the study period. Total abundance differed across seasons, being higher during autumn (LMM I; F1216=139.47, P<0.0001; Adj. P<0.0001). The overall recapture rate was 9%. The recaptured individuals were largely caught only once (89%), though some were caught twice (10%) or three times (1%). The majority of recaptured individuals (92%) displayed site fidelity. Those that were caught outside of their original tagging site were predominantly found in adjacent ones. The average distance of recaptured individuals travelling among sampling sites was 5828 m.
3.2 Demographic synchrony and population distribution
The linear regression analysis of demographic synchrony among sampling sites was significant only for autumn season. The spring season was therefore excluded from further analyses. The spatial extent of synchrony among sampling sites in autumn was estimated as the x-intercept of the linear regression of the correlation coefficient of individuals abundance growth rate on sampling site distance (r=-0.39, P>0.0001, n=120; y=-14.56x+17938.47). According to the extent of demographic synchrony and the occurrence of migration obstacles, three populations were defined (Figure 1). Two of them overlapped main river basins (Vydra and Kremelna Rivers), and the third was located in a small tributary Svelsky Stream, which was separated from the rest of the river system by an impassable migration barrier. Within populations, fourteen synchronised
population units were established. The mean cross-correlation coefficient between sampling sites within synchronised population units ranged from 0.75 to 0.96.
3.3 Individual growth and age
The estimated age of brown trout varied from age-0 to age-7, and individuals belonging to the age-1 and age-2 groups were the most numerous. The individual growth of all age groups were significantly different (LMM II; F31268=13.25, P<0.0001; Adj. P<0.0001) and decreased throughout ontogenesis. The variation of individual growth among the spatial units was strongly age dependent. For age-0 (Figure 2) and age-1 (Figure 3) this relationship was significant or corresponded approximately to the limit of significance (Table 2). In contrast, the differences in individual growth among spatial units for individuals older than two years were non-significant (Table 2).
4. Discussion
The individual growth of brown trout in the studied populations was generally lower than in other populations located in streams with comparable latitudes [34,35]. This is most likely a result of environmental conditions correlated with the altitude (e.g., climate, nutrients, physical stream characteristics; [36]) as well as endogenous (e.g., density) [4] and genetic factors [37]. The individual growth rate was age dependent, as it decreased along ontogeny, which is typical for brown trout [2,37,38]. The highest growth rate was found for the age age-0 class, most likely because small trout exhibit minimal foraging costs and a short satiation time [39]. In addition, juvenile fish also allocate a larger energy budget to structural growth [40], while older individuals display decreased growth, most likely as a result of reproduction costs [41,42] and increased lipid storage intensity [43].
In accordance with prior studies [20,44], differences in individual growth between populations divided by an impassable migration barrier were observed. This variations in individual growth might be due to differences in environmental conditions as well as in endogenous (e.g., density) and genetic factors between Svelsky Stream and the Vydra River [37]. Nevertheless, in this study, significant variation in the individual growth of brown trout in a continuously passable small river basin was documented, similar to the pattern Lobon-Cervia reported [45]. The differences in growth between spatial units were strongly age dependent. Individual growth varied significantly across all of the observed spatial units (sampling sites, synchronised population
Figure 2. Annual individual growth of age-0 individuals across a) sampling sites; b) synchronised population units; c) populations (empty columns - population Kremelna; light filled column - population Svelsky; dark filled columns - population Vydra). Values are adjusted means ± S.E.
Figure 3. Annual individual growth of age-1 individuals across a) sampling sites; b) synchronised population units; c) populations (empty columns - population Kremelna; light filled column - population Svelsky; dark filled columns - population Vydra). Values are adjusted means ± S.E.
units and populations), but only in age-1; for age-0, the differences were close to the limit of significance (the greatest difference was observed at the level of synchronised population units). For age-2 and age-3 and older, the variation in individual growth among spatial units was non-significant. As brown trout in the headwaters of rivers in the Czech Republic usually achieve maturity at age-2 [35], it can be concluded that the individual growth of the juvenile stock varied among the defined spatial units, while the individual growth
of adults did not. The fact that the individual growth of the adult stock did not reflect their affiliation with the defined population units could be caused by their lower sensitivity to the differences in environmental conditions
[46] or variations in intra- and interspecific competition in headwaters [2,38]. Similarly, the majority of the adult stock was found in the lower parts of the Otava River during most of the year, and these fish migrated to tributaries only for spawning (see Klementsen et al.
[47]). This suggests that the time spent in the home
Age group Spatial scale Model No. Result
age-0 sampling site LMM III F,9;663 = 1.48, p>°.°839
age-0 synchronised population units LMM IV Fu.663 = 1.81, P>0.0339
age-0 population LMM V F2;664=4.83, P>0.0100
age-1 sampling site LMM VI F,9;48O=3.96, P<0.0001
age-1 synchronised population units LMM VII F,3;48O=3.84, P<0.0001
age-1 population LMM VIII F2;483 = 16.65, P<0.0001
age-2 sampling site LMM IX F19;142=1.01, P>°.4536
age-2 synchronised population units LMM X F13.143=112, P>0.3436
age-2 population LMM XI F2148=2.47, P>0.0878
age-3 and older sampling site LMM XII F714=1.57, P>0.2222
age-3 and older synchronised population units LMM XIII F4;14=0.55, P>0.7004
age-3 and older population LMM XIV F1;14=0.33, P>0.5731
Table 2. Results of LMM of annual individual growth of all age groups across sampling sites, synchronised population units and populations.
stream may not be sufficiently long to induce spatial variations in growth rates.
5. Conclusions
Sumava National Park is part of the largest pristine natural area in central Europe and represents an important European centre of biodiversity, with many endangered and rare species and habitats, including the headwaters of the Otava River. Brown trout in the headwaters of the Otava River are found in demographically independent synchronised population units that differ in terms of individual growth. The distinctiveness regarding individual growth and demography indicate the suitability of these population units for use as independent management units [32]. Improved conservation management can also
be achieved via more rigorous analyses of the relationships among synchronised population units in the sense of metapopulation dynamics [48]. The observed populations in the main river basins (Vydra and Kremelná Rivers) are essentially dependent on spawning migrants from downstream areas of the Otava River. Therefore, it can be suggested that wise management of hatchery fish stocking and the fisheries themselves [49,50] and the restoration of longitudinal river continuity [51] in downstream river stretches are necessary to achieve viability of the brown trout populations in Sumava National Park.
Acknowledgements
The authors thank the technical staff for their valuable assistance during the experimental period.
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