Individual Variability in the Movement Behaviour of Juvenile Atlantic Salmon

1Individual variability in the movement behaviour of juvenile Atlantic salmon

2Mathieu L. Roy1, André G. Roy1, James W. A. Grant2, Normand Bergeron3

31Département de géographie, Université de Montréal, 520 Côte Ste-Catherine, Montréal,

4QC, H2B 2V8, Canada.

52Department of Biology, Concordia University, 7141 Sherbrooke St. West, Montréal, QC,

6H4B 1R6, Canada.

73 INRS-Eau, Terre et Environnement, 490 rue de la Couronne, Québec, QC, G1K 9A9,

8Canada

1 1 2 24Abstract

25Stream-dwelling salmonid populations are generally thought to be composed of both

26relatively mobile and sedentary individuals, but this conclusion is primarily based on

27results obtained from recapture methods with low temporal resolution. In this study, the

28mobility of 50 juvenile Atlantic salmon was monitored using a large array of passive

29integrated transponder antennas buried in the bed of a natural stream. Fish locations were

30recorded at a high frequency for a period of three months in a 65 m reach. Four types of

31daily behaviour were identified: stationary (detected primarily at one location), sedentary

32(limited movement between a few locations), floater (frequent movements in a restricted

33home range) and wanderer (movements across the reach). Most individuals exhibited low

34mobility on most days, but also showed occasional bouts of high mobility. Between-

35individual variability accounted for only 12-17% of the variability in the mobility data.

36High mobility was more frequent at low flow, but no difference was observed between

37the summer (12-18oC) and the autumn (4-12oC). Individual variation on a daily basis

38suggested that movement behaviour is a response to changing environmental conditions

39rather than an individual behavioural trait.

41Keywords: Juvenile salmon, Mobility, Individual variability, Behaviour, Habitat.

3 2 4 47Introduction

48 Early studies depicted juvenile salmonids as sedentary, territorial animals

49exhibiting high site fidelity (Kalleberg 1958; Keenleyside 1962). The development of

50better tagging technology, which allowed for the tracking of individuals at a higher

51temporal resolution, revealed that the territorial mosaic of salmon parr was more flexible

52and dynamic than previously thought (Armstrong et al. 1999; Okland et al. 2004). In

53particular, Atlantic salmon parr have large, overlapping home ranges, with some

54individuals moving out of their home ranges to relocate either upstream or downstream

55(Okland et al. 2004; Ovidio et al. 2007), suggesting little fidelity to a particular

56microhabitat, sometimes undertaking habitat switches of several kilometres to lakes

57(Hutchings, 1986; Erkinaro et al. 1998) or estuaries (Cunjak et al. 1986).

58 It now seems broadly accepted that both sedentary and mobile individuals occur

59within a given population (Gowan et al. 1994; Rodriguez 2002; Morrissey and Ferguson

602011). While the size of the two fractions varies considerably between sites, species, and

61life stages, the sedentary fraction tends to be larger than the mobile fraction (e.g.

62Hesthagen 1988; Steingrimsson and Grant 2003). Some individuals have been

63characterized as “movers” (i.e. cruise foragers) whereas others as “stayers” (sit-and-wait

64foragers), based on the proportion of time spent moving (Grant and Noakes 1987;

65McLaughlin et al. 1994). Although spatial behaviour might be a heritable trait (Ferguson

66and Noakes 1983), Gowan et al. (1994) suggested that individuals may switch tactics in

67response to changing environmental conditions. Although some juvenile salmonids can

68defend the same territory for extended periods (Martel 1996), a fraction can switch

69between sedentary and mobile behaviour between two subsequent years (Harcup et al.

5 3 6 701984). However, it remains unclear how common this behaviour is, and at what temporal

71frequency the switching occurs.

72 Fish movements have been linked to changes in biotic and abiotic conditions

73(Gowan et al. 1994), likely due to variation in flow stage, temperature and daily light

74cycles. However, the effects of these variables on the behaviour of fish seem to be

75complex, as several studies have provided contrasting results. For instance, salmonids

76have been reported to decrease their mobility and territory size at high flows (Kemp et al.

772006), whereas others report the opposite trend (Scruton et al. 2003; Riley et al. 2009), or

78no trend at all (Berland et al. 2004; Heggenes et al. 2007). Similarly, while water

79temperature affects fish metabolism (Jonsson et al. 2001), its effect on fish activity is less

80certain (Fraser et al. 1993; Breau et al. 2007). As temperature drops in the autumn,

81Atlantic salmon parr suppress their daytime activity, presumably as a result of a tradeoff

82between growth and predation risk (Fraser et al. 1995; Johnston et al. 2004).

83Nevertheless, other proximate factors must also influence fish activity on a seasonal basis

84(Bremset 2000), as other studies report either no decrease or an increase in fish mobility

85(Nykanen et al. 2004; Riley et al. 2006).

86 The spatial arrangement of microhabitats might also influence mobility because

87habitat heterogeneity decreases territory size (Venter et al. 2008) and mobility (Heggenes

88et al. 2007). In less heterogeneous habitats, individuals might have to move farther to

89encounter complementary microhabitats that provide foraging opportunities and shelter

90(Venter et al. 2008). However, information on the relationship between habitat structure

91and fish mobility remains fragmentary. Furthermore, juvenile salmonids are often

92captured using methods that might be better suited for catching sedentary than mobile

7 4 8 93fish (Gowan and Fausch 1996). Therefore, if mobility affects habitat use, the estimation

94of habitat preference might be biased towards sedentary fish.

95 The results from movement studies depend on how frequently fish have been

96located and for what duration (Lucas and Baras 2000). For instance, fish mobility

97estimates from radio-telemetry studies are generally greater than those obtained from

98mark-recapture studies. However, radio-telemetry suffers from the inability to sample

99small fish and from relatively large sampling units of habitat, making difficult the

100quantification of small-scale movements. Recent developments in flat-bed passive

101integrated transponder (PIT) antenna grid provide fish tracking data at both high temporal

102and spatial resolutions over extended periods of time (Greenberg and Giller 2000; Riley

103et al. 2003). In this study, we used a large PIT antenna grid to monitor daily movement of

104a group of individually marked Atlantic salmon parr 1+ in a natural stream. Positions of

105tagged fish were recorded continually during three months of the summer and autumn.

106While previous studies have reported a high between-individual variation in parr mobility

107(Okland et al. 2004; Ovidio et al. 2007), within-individual mobility variation has received

108little attention. Hence, our primary objective was to document the magnitude of

109individual variation of parr daily mobility to test the competing predictions that

110individuals: (1) adopt consistent mobile or sedentary tactics over long periods of time; or

111(2) modify their mobility based on changing biotic and abiotic conditions. Second, if the

112data support the second prediction, we tested whether changes in behaviour could be

113predicted by environmental fluctuations. In particular, we tested the predictions that parr

114will be more sedentary: (3) when flow stage increases, as both the availability of drifting

115prey and swimming energy costs increase; (4) in the autumn than in the summer; and, (5)

9 5 10 116in heterogeneous habitats, which likely provide complementary feeding and sheltering

117habitats in closer proximity.

119Material and methods

120Study site

121This study was carried out on the Xavier Brook, a tributary of the Ste-Marguerite River in

122Saguenay, Québec, Canada (48°2591799 N; 69°5394899 W). The study reach, located

123425 m from the main river confluence, was approximately 65 x 10 m (length x width),

124composed of two pools separated by a steep riffle, providing high physical habitat

125diversity. In the thalweg at low stage (0.4 m3·s-1), depth ranged from approximately 0.1 m

126in the riffle to 1.65 m in the upstream pool. Median substrate size (B-axis, i.e. particle

127width) varied from gravel-cobble in the riffle to gravel-cobble in the deep portion of the

128pools and gravel-sand in the pool recirculation zones (substrate classification according to

129Wolman 1954).

131Fish tracking system

132To monitor fish movements, we used a large flatbed antenna grid covering the entire

133study reach. The system was used to monitor tagged fish locations in the reach during 97

134days (24 July to 1 November 2008). The tracking system consisted of an array of 149

135circular antennas (50 cm in diameter), which were buried within the river bed and

136designed to detect the presence of 23mm PIT tags (Texas Instruments (TIRIS) model RI-

137TRP-RRHP, 134 2 kHz) and other tags complying with the ISO 11784/11785

138international standards. Each antenna was interrogated for fish presence every 34 s (i.e.

11 6 12 1390.03 Hz). Antennas were distributed along cross-channel transects each composed of five

140antennas. Overall, the detection field of the antenna grid covered 19% of the wetted area

141of the site at a discharge of 0.4 m3· s-1.

142 Each group of five antennas was linked to a tuning capacitor, which was wired to

143a CYTEK multiplexer (JX/256 series, mercury wetted 256 single poles relay, www.cytec-

144ate.com). The multiplexer was connected to an Aquartis controller (custom made by

145Technologie Aquartis; www.aquartis.ca) composed of a TIRIS S-2000 reader, a

146datalogger and a custom-made controller unit. The system was powered by three solar

147panels connected to four 6V batteries plugged in series and two 12V batteries plugged in

148parallel. Each antenna was activated successively for the detection of PIT tag presence.

149When a PIT tagged fish was detected, the date (dd/mm/yy), time (hh/mm/ss), antenna ID

150(multiplexer card and port number) and fish ID (tag number) were recorded. Detection

151range varied from 300-400 mm above the bed surface and 600-800 mm in diameter.

152During the study period, all antennas detected at least one fish. For more technical details

153on the antenna grid, see Johnston et al. (2009).

155Fish capture and tagging

156 We captured 69 Atlantic salmon parr (1+) in the study reach on two occasions

157using a backpack electrofishing device: 44 fish were caught on 24 July 2008, and 25 on

15828 August 2008. During the second electrofishing session, fish were captured

159immediately upstream of the reach to avoid re-capturing tagged fish. Only parr of body

160length >80 mm were kept for the experiment to avoid the potential negative effects of PIT

161tagging on parr survival and growth (Sigourney et al. 2005); smaller juveniles as well as

13 7 14 162juvenile brook trout (Salvelinus fontinalis) and longnose dace (Rhinichthys cataractae)

163were released at individual capture locations. Fish were then anesthetised in a clove oil

164solution (3 ml/10 L) and implanted with 23-mm PIT tags (Texas Instruments) in the

165abdominal cavity secured with surgical tissue adhesive (Vetbond©). Tagged fish were

166allowed a recovery period of approximately 2 hours in a fish tank before being released

167on the study site. A total of three fish died during tagging, two during the first tagging

168session and one during the second. Average fork length (L ± SD) and average mass (M±

169SD) of tagged fish were: LA: 98 ± 7.4 mm; MA= 9.7 ± 1.7 g; LB: 109 ± 8.3 mm; MB: 10.7

170± 2.3 g. The fish captured in August were larger than those captured one month earlier

171(fork length t = -5.73, df =64, p < 0.001 (mass t = -1.92, df = 64, p=0.06).

173Habitat characterization

174 Flow stage and water temperature fluctuations were recorded every 15 min using

175a pressure transducer (Level logger) installed at the bottom of the upstream pool. Water

176stage was estimated by correcting the recorded pressure values for changes in

177atmospheric data obtained from the closest meteorological station, and then subtracting

178the minimum value observed during the study period. Therefore, stage was defined as the

179water level above the minimum summer low flow level. The study period was

180characterized by substantial discharge variability (Fig. 1). A high magnitude flow event

181occurred at the beginning of August, followed by a stage decrease in the following

182month. Then, a prolonged low flow lasted until the end of October when it was

183interrupted by several precipitation events. Base flow between these events was

184approximately 10 cm over the minimum flow, which corresponded roughly to the median

15 8 16 185flow recorded during the study period. Flow stage values were categorized as low (0-10

186cm, 35% of days), medium (10-15 cm, 35% of days), high (15-25 cm, 13% of days) and

187very high (25 cm and higher, 17% of days). Using a field based digital elevation model of

188the reach, bankful flow was estimated to occur at a stage of 60 cm.

189 During the same period, water temperature decreased from 19.0oC to 2.8oC (Fig.

1901). From 24 July to 1 September, daily average water temperature fluctuated around

19115oC. After 1 September, water temperature decreased linearly. Water temperature

192reached 12oC on 9 September, which corresponds to the upper boundary of the

193temperature range at which parr suppress their daytime activity (Valdimarsson et al.

1941997). The study period was therefore divided into two periods: summer (12-18 oC) and

195autumn (4-12 oC).

196 Depth and bed roughness were also characterized in detail throughout the reach.

197Topography was surveyed using a robotic total station (Trimble 5600DR) by combining a

198systematic transect sampling approximately 1 m apart with the characterization of

199individual roughness elements that protruded approximately 10 cm above the local mean

200bed elevation. This strategy was adopted to optimize sampling effort, as sampling point

201density increased proportionally with bed complexity. From the total of 6250 sampled

202points, a digital elevation model was created using a triangular irregular network

203interpolation with pixel size of 10 cm. Topography was detrended for mean thalweg slope

204and water surface at median flow was subtracted to obtain flow depth. Therefore,

205variability of flow depth mainly reflected height variation induced by the riffle-pool

206channel morphology. Depth was not temporally adjusted to flow stage to reflect the use

207of specific habitats rather than specific depth values. This way, across flow stages, high

17 9 18 208depth use could be interpreted as the use of habitat located in a pool rather than be

209confused with habitats located in the riffle at a higher flow stage.

210 Bed roughness, expressed as the spatial standard deviation of bed elevation values

211of the DEM pixels included in a moving window of 65 cm2, was characterized by

212computing an index based on the estimate of local bed elevation variability. The size of

213the window was determined in order to characterize the roughness of most of the largest

214particles present on the reach. We focused on protuberance from the bed that might be

215more important in creating flow refuges and cover than average particle size. For

216instance, it is common to observe large particles buried in the bed that do not protrude

217higher above the average bed height than smaller particles (Nikora et al. 1998). The

218downstream pool exhibited the largest coherent region of high bed roughness, whereas

219the remainder of the reach showed an apparently random spatial pattern of bed roughness.

220For every antenna, mean depth at median flow and bed roughness were estimated by

221averaging all pixel values located in a circle matching the antenna detection range. Fish

222daily habitat use was then estimated by averaging the mean values associated with all

223visited locations weighted by the number of detections per antenna.

225Data analysis

226 Fish behaviour was characterized on a daily basis using four variables. The

227number of movements and the distance travelled provided estimates of fish mobility,

228whereas the number of sites visited and the extent of the reach used by fish gave estimates

229of home range size. We defined a fish movement as a change of fish location (antenna):

230i.e. every time a fish was detected at two different locations successively, a movement

19 10 20 231was recorded. The number of movements was an indicator of activity that did not take

232into account the distance between locations. In contrast, the daily distance travelled was

233defined as the sum of the distance (m) between each antenna successively visited. The

234number of sites represented the number of different antenna locations where a fish was

235detected in a day. However, despite a high spatial coverage and a high temporal sampling

236frequency of the tracking system, fish could sometimes travel between two distant

237locations without being detected by antennas located in between. Hence, the variable

238extent fills this gap by representing a home range length, or the distance between the two

239most distant locations visited by a fish in a day.

240 We used a principal component analysis (PCA) on the daily mobility variables to

241describe the variability of every fish. Prior to the PCA, each variable was normalized

242(log10+1) and standardized. Then, based on the mobility variables, fish behaviour was

243classified using a k-means clustering algorithm. The correct number of behavioural types

244(clusters) was determined by comparing silhouette values between three and five

245behavioural types (Kaufman and Rousseeuw, 1990)

246 The frequency of occurrence of behavioural types per individual that spent six

247days or more in the study reach was examined. Then, the components of variance of the

248four mobility variables were estimated using an additive-variance component model,

249using individuals as a random factor, in which yij (mobility of fish I on day j) = µ+αi+εij where µ is

250the mean of the population, i, deviation from the mean of the ith fish (i=1 to 24) εij, is the

251residuals containing the intra-individual variation. To meet the model assumptions,

252transformed variables were used (log10+1). However, the descriptive statistics shown in

253the figures and tables are based on non transformed data.

21 11 22 254To examine the temporal variability of fish behaviour, the proportion of behavioural

255types adopted by each individual was plotted on a time series. Then, the frequency of

256occurrence of behavioural types was examined in relation to flow-stage categories and

257season. A generalized estimation equation (GEE) approach was used to describe the

258observed and expected occurrence of a behavioural type as a function of flow stage and

259season. GEEs are an extension of generalized linear models that accommodate repeated

260measurement of the same individuals and a categorical response variable (Diggle et al.

2612002). Therefore, the variable days was used as a repeated measure, fish as subjects, flow

262stage and temperature as fixed factors and behavioural types as a dependent categorical

263variable. GEEs were performed by SPSS 17 © (SPSS Inc. Chicago, Illinois) using a

264Poisson distribution with a log link and repeated measurement covariance structure set to

265first order autoregressive to account for temporal dependence between successive days.

266Similarly, to examine differences in habitat use in terms of depth and bed roughness in

267relation to behavioural types, two distinct mixed-effects models were performed using

268fish as subjects, days as repeated measures, behavioural types as a fixed factor and depth

269and roughness as a dependant variable. Again, first order autoregressive covariance of the

270repeated measurements was chosen. Best suited models were selected based on lowest

271Akaike’s information criterion (AIC) and quasi AIC (QIC) values (Burnham et al. 2011).

272For both types of statistical models, the effect of fish mass and length was tested. No

273significant effect was observed (p > 0.05), perhaps because of a low statistical power

274resulting from the need to treat the fish from the two tagging periods separately and of the

275overall low variability of values observed among fish. Therefore, mass and length were

276not incorporated in the models.

23 12 24 277

278Results

279Fish tracking

280 Of the 66 fish that were PIT-tagged and released in the reach, 4 individuals (6%)

281were never detected by the tracking system and 12 individuals (18%) were detected for

282either less than 24 hours following release or less than three hours in a single day. These

283individuals were not included in further analyses. Of the remaining fish, 10 individuals

284(15%) were detected in the reach during a single day, 16 individuals (24%) were detected

285for 1 to 5 days and 24 individuals (37%) remained between 6 and 70 days in the reach.

287Behavioural types

288 The daily distance travelled, the number of movements, the number of sites visited

289and the extent of the reach used by fish were positively correlated, which allowed for data

290reduction. Indeed, 90% of the variability was explained by the two first axes of a PCA

291(Table 1). The primary ordination axis (PCA1), which accounted for 70% of the

292variability, was positively correlated with all mobility variables, but was least strongly

293correlated with extent (Fig. 2). Therefore, low values of PCA1 represented lower mobility

294in smaller home ranges, whereas higher values represented higher mobility in larger

295home ranges. In contrast, the secondary axis explained 20% of the variability and was

296negatively correlated to the number of movements and positively correlated to the extent

297of fish movement. Data ordination illustrated the high variability of overall mobility

298exhibited by fish during the entire study period (Fig.2.).

25 13 26 299 Based on a cluster analysis, fish spatial behaviour was categorized into four types:

300Stationary, Sedentary, Floater and Wanderer (Fig. 2). Stationary behaviour was

301characterized by low mobility, being detected most often by a single antenna (Table 2).

302Fish 15 on Day 26 (Aug 18) adopted typical stationary behaviour (Fig. 3). On some

303occasions, stationary behaviour also included the use of more than one location during

304the day. However, these locations were adjacent to each other and no back and forth

305movements were observed.

306 When fish were detected at a few locations in a day, their behaviour was

307characterized as sedentary (Table 2). Fish exhibiting sedentary behaviour travelled on

308average 10m daily and moved three to four times between locations for an average extent

309of 5.7 m. For example, on Day 61 (22 Sep), Fish 50 exhibited typical sedentary behaviour

310by using four locations located throughout half the channel length and moved only once

311between each location (Fig. 3).

312 Cluster analysis also discriminated two types of higher mobility behaviour (high

313PCA1 scores) along the extent-number of movement gradient (PCA2 axis) (Fig. 2). When

314individuals used a relatively restricted home range (average extent: 5.7m), but made

315many movements between locations (mean =36), their behaviour was defined as floater

316(Table 2). For instance, Fish 37 on Day 32 (24 Aug) was detected at only five nearby

317locations in the downstream pool, but switched 34 times between these locations (Fig. 3).

318During the study period, the most extreme floater made 525 movements, resulting in a

319daily travelled distance of 2449 m on an extent of 5.3 m.

320 In contrast, when a fish exhibited a high distance travelled (avg: 115 m), but over

321a larger extent (avg: 25.3 m), their behaviour was defined as wanderer (Table 2). Typical

27 14 28 322wanderer behaviour involved travelling across the entire reach, from one pool to the

323other. Wanderer behaviour was characterized by a similar number of sites visited as the

324floater. However, the number of movements between locations was generally lower and

325the locations visited were farther away. The number of sites visited by fish adopting

326wandering behaviour was not higher than for floaters, likely because individuals moving

327long distances travelled rapidly and were thus difficult to detect. For example, on Day 61

328(22 Sep), Fish 66 travelled from the downstream pool almost to the upstream pool, then

329back again, but was detected at only 11 sites for a total of 16 movements (Fig. 3).

331Individual variability in behaviour

332 Individuals exhibited a variety of types of mobility behaviour rather than

333‘specializing’ on one type over the study period. Among the 26 individuals that were

334detected on the site for less than six days, their behaviour was sedentary, stationary and

335wanderer on average for 36%, 30% and 30% of their time, respectively (Fig. 4a). Floater

336behaviour was only observed in five fish, which represented on average 8% of their time

337(Fig. 4a).

338 Among the individuals that stayed more than six days on the study site, high intra-

339individual variability of behaviour was observed (Fig. 4b). Out of 24 individuals, 15

340showed all four types of behaviour during the study period. Low-mobility behaviour was

341most frequently observed, as individuals were sedentary and stationary for 33% and 28%

342of the days, respectively, during which they were detected in the reach. Floater and

343wanderer behaviour were less frequent, with an average of 19% and 20% of the days,

344respectively. However, six fish were more mobile than the others, exhibiting floater or

29 15 30 345wanderer behaviour for more than 50% of the days. For fish that stayed more than six

346days, there was no significant trend between the duration in the reach and the proportion

347of days each behaviour type was adopted by an individual (p > 0.1).

348 All fish were sedentary most of the time, but many individuals exhibited

349occasional bouts of high mobility. Indeed, all fish that stayed more than 6 days in the

350reach showed a low median distance travelled, but most moved more than 90 m. The

351daily number of movements displayed a similar pattern, with a relatively low median

352number of movements and numerous extreme values. Although the number of sites and

353the extent did not show as many extreme values, there was high intra-individual

354variability. Decomposing the components of variation of the four mobility variables

355indicated that intra-individual variation accounted for 83 to 88% of the total variation,

356compared to 12 to 17% for the inter-individual variation (Table 3). Similarly, average

357ordination scores for individuals that stayed more than six days suggested that a

358considerable number of individuals had relatively similar average mobility (Fig. 2). For

359instance, 17 individuals (70%) had their average ordination scores categorized as

360sedentary while the remaining fish was categorized as floater or wanderer.

362Temporal variability

363 The frequency of behaviour exhibited over the course of the season suggested that on

364most days, a mixture of behavioural types was observed (Fig. 5). Following the tagging

365of 42 parr on 24 July, 14 individuals were present on the reach. The number of

366individuals dropped drastically on 4 Aug. following a major flood event, then fluctuated

367between 4 and 8 before the second tagging session on 28 Aug., after which the number

31 16 32 368peaked at 22 and then constantly decreased until the end of the observation period.

369Despite the variability of behaviour observed on a daily basis, some periods were

370dominated by specific behaviour types. For instance, between 14 Aug. and 28 Aug.,

371wandering behaviour was observed only four times, whereas most fish exhibited

372wandering behaviour from Day 16 Oct. to 20 Oct. Similarly, from day 22 Aug. to 29

373Aug., floater behaviour was most frequent.

374 Examining the frequencies of occurrence of behavioural types in relation to flow

375stage and season using GEE showed a general decrease in mobility with increasing flow

376stage (Wald χ2=7.974, df=3, p=0.047). Pooled frequencies of occurrence illustrated an

377increase in the proportions of sedentary behaviour from 30% to 45% with an increase in

378flow stage (Fig. 6). Conversely, wandering behaviour decreased from 22% of occurrence

379to 7% from low flow to a very high flow. Over the season, parr used slightly different

380habitats in terms of depth when adopting different behavioural types (F = 5.46, df =

3813,514, p = 0.001). Daily average depth used was 0.2 m higher for the fish exhibiting

382floater behaviour than the other behavioural types (Fig. 7a). However, this trend was

383observed for only five individuals (Fig. 7b). In fact, a mixed effect model accounting for

384individual and temporal dependence indicated that wanderer behaviour was associated

385with lower depths used than the three other behavioural types (confidence interval on

386depth difference, df = 222-249, p < 0.03 for all comparisons, Bonferroni adjustments). In

387contrast, no difference in bed roughness used was observed for the different behaviour

388types (F = 1.235, df = 3, 549, p = 0.296).

390Discussion

33 17 34 391 In this study, most individuals exhibited low mobility (stationary and sedentary

392behaviour) on most days, but most individuals also showed occasional bouts of high

393mobility, either by carrying out frequent movements in a relatively restricted area

394(floater) or by travelling across the reach, from one pool to the other (wanderer). Our

395results suggest that most fish in the reach switched behaviour on a daily basis.

396 Daily behaviour switching contrasts with the common view that fish population

397are composed of fractions of sedentary and mobile individuals (Rodriguez 2002,

398Morrissey and Ferguson 2011). However, comparisons of behavioural categories across

399study designs and spatial scale is complicated. Telemetry and recapture techniques can

400provide a very large spatial scale, which is useful to characterize habitat switches

401sometimes involving migrations of several kilometres to lacustrine (Hutchings 1986,

402Ryan 1986) or estuarine environments (Cunjak et al. 1989). Our relatively restricted

403study area (65 m reach) most likely underestimated the mobility of fish exhibiting

404wandering behaviour. Nevertheless, reach extent likely had a minor effect on the majority

405of fish, which adopted sedentary behaviour most of the time and therefore should not

406affect our conclusion about intra-individual variability in behaviour. Hence, our proposed

407classification of mobility behaviour is dependent on the spatial scale of the study design,

408which focused on the fine scale movements of parr in a single reach.

409 Most studies confirming the presence of mobile and sedentary fractions of a

410population have used recapture techniques with relatively low temporal sampling

411frequency (e.g. Heggenes et al. 1991; Roghair 2005). Such techniques require sampling

412over a long duration to obtain individual variability without underestimating fish

413mobility. For instance, if an individual moves 40 meters upstream over a short period of

35 18 36 414time and then back to its original location, the following recapture could lead to the

415biased conclusion of sedentary behaviour Rather than being strictly sedentary or mobile,

416individual brown trout switched behaviour over the course of a two-year study (Harcup et

417al. 1984). Our data support a similar flexibility of the mobile or sedentary fractions, but

418over shorter time scales.

419 Generally, there was less variation in behaviour when environmental conditions

420were more homogeneous. Despite a baseline amount of variation, some types of

421behaviour tended to dominate during particular periods of environmental stasis (i.e. more

422sedentary at high flow, more floating at low flow). Furthermore, the most drastic

423environmental fluctuations might have triggered changes in behaviour. For instance, the

424steady declines in water temperature from 16-20 October were accompanied by

425wandering behaviour by most fish. Perhaps, this was the trigger that winter or spawning

426is imminent, leading to a search for overwintering habitat by females and spawning

427opportunities for males. Similarly, during the major floods the few fish that were detected

428were sedentary rather than mobile, and the most sedentary fish were probably even not

429detected.

430 Salmon parr in our study exhibited a decrease in mobility with an increase in flow

431stage. Similar results have been observed in previous studies (Kemp et al. 2006), whereas

432others found no effect of flow stage on mobility (Robertson et al. 2004; Heggenes et al.

4332007). Because salmon parr are well adapted to using flow refuges to maintain station on

434the bed, most of the increased swimming costs that accompany higher flows (Hill and

435Grossman 1993) are likely to be associated with foraging movements or longer range

436movements from one foraging location to another (Liao 2007). Moreover, when

37 19 38 437velocities are higher, parr tend to reduce their foraging territory size in response to the

438increased swimming costs and prey density (Hughes and Dill 1990; Piccolo et al. 2008).

439 We found no difference in movement behaviour between the summer and autumn,

440despite the decrease in temperature. A decrease in mobility was expected due to a

441decrease in metabolic rate (Jonsson et al. 2001) and the expected decrease of diurnal

442activity (Fraser et al. 1993). However, parr can remain active even when water

443temperature is close to zero (Bremset 2000). Indeed, a radio-telemetry study showed that

444parr home ranges were as large during the autumn as during the summer (Okland et al.

4452004). Furthermore, a recent study undertaken under similar temperature ranges reported

446a higher mobility of parr in the winter (6.6-10.8 oC) than during the autumn (10.7-14.3oC)

447(Riley et al. 2006). The authors suggested that this behaviour might be unique to

448groundwater fed systems. Taken together with previous studies, our results suggest that

449mobility can remain relatively high even when water temperatures are low. In this study,

450the effect of lower metabolism on movement might have been offset by several factors

451including a change from sit and-wait drift foraging to benthic cruise foraging due to a

452decrease in drift abundance (Nislow et al. 1998). Interestingly, all individuals adopted

453wandering behaviour on two days in mid-October close to spawning season when

454temperature was between 4 and 6oC. The presence of spawning adults passing through the

455site may have increased the mobility of tagged fish, particularly the precocious parr.

456 We hypothesized that individuals would be more sedentary in shallow and

457heterogeneous habitats because high habitat heterogeneity is more likely to provide

458complementary feeding and sheltering habitats close together (Johnston et al. 2010) and

459because territory size tends to decrease with habitat heterogeneity (Kemp et al. 2005;

39 20 40 460Dolinsek et al. 2007; Venter et al. 2008). Our results did not support this prediction.

461However, for five individuals that remained over forty days in the reach, deeper habitats

462were associated with floater behaviour. Finding mobile fish in pools is in agreement with

463the assumption that foraging fish occupy a larger territory in lower velocity areas

464(Hughes and Dill 1990). Furthermore, although the term floater tends to refer to

465individuals deprived of a territory, such behaviour could be associated with multiple

466central place foraging, where fish frequently switch from one foraging territory to an

467adjacent one (Steingrimsson and Grant 2008). Nevertheless, when fish adopted wanderer

468behaviour, they used slightly shallower habitats than when adopting other behavioural

469types. These results contrast with our hypothesis and with previous observations on adult

470grayling (Thymallus thymallus (Nykanen et al. 2004). The presence of mobile fish in

471deeper areas might have implications for the accuracy of abundance surveys. As the

472catchability of juvenile salmonids decrease with mobility (Crozier and Kennedy, 1994),

473electrofishing might underestimate abundance in pools, which could be mistakenly

474considered as low quality or unused habitats (Linnansaari et al. 2010).

475 One possible mechanism explaining our observation of mostly sedentary

476behaviour interrupted by frequent bouts of high mobility might be the fish’s need to

477‘sample’ their spatially and temporally variable environment to evaluate drift abundance.

478Being aware of the quality of alternative habitats, an assumption of the ideal free

479distribution, is critical to determine which habitats or strategy will generate the greatest

480energy intake and growth (Power 1984), within the constraints of dominance hierarchies

481and predation risk.

41 21 42 482 In summary, the present study showed that Atlantic salmon parr are sedentary on

483most days, but also exhibit infrequent bouts of higher mobility. Movement behaviour

484appears to be plastic, allowing individuals to adapt to changing environmental conditions

485(Gowan et al. 1994). Even though movement behaviour was linked to variation in flow

486stage, a high between- and among intra-individual variation was observed, suggesting

487individuals undertake movements in reaction to other proximate factors operating at

488shorter time scales. Several studies on fish behaviour, movement and habitat use have

489reported a high inter-individual variation (Okland et al. 2004; Ovidio et al. 2007).

490However, studies are often conducted for a shorter duration and at a lower temporal

491frequency than our study. Furthermore, intra-individual variability is often overlooked by

492averaging values to estimate home ranges over the entire study period. Therefore,

493differences in individual behaviour may decrease as study duration increases. Because

494Atlantic salmon parr exhibit relatively high mobility, maintaining connectivity between

495different habitats (i.e. pools and riffles) should be considered a priority in salmon

496conservation practices.

498Acknowledgements

499 We would like to thank Francis Bérubé and Marc-André Pouliot for their help in

500the development of the tracking system and for essential logistical and technical

501assistance, and Jordan Rosenfeld for interesting discussions. We are also grateful to

502Patricia Johnston, André Boivin, Claude Gibeault, Marie-Êve Roy and René Roy for their

503help in the field and Geoide, NSERC and the Canada Research Chair Program for the

43 22 44 504funding of this research. Three anonymous reviewers provided useful comments that

505substantially improved the paper.

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59 30 60 676 677

678Table 1. Pearson correlation coefficients of mobility variables versus axis scores from an

679ordination of daily fish spatial behaviour in the study reach and proportion of total

680variance expressed by the two first ordination axes (n=681).

PCA1 PCA2 Distance travelled 0.56 - 0.52 Number of movements 0.51 - 0.14 Number of sites 0.52 0.04 Extent 0.38 0.83 Proportion of variance 0.70 0.20 681

694Table 2. Frequency of occurrence (n) and mean (range) of the four mobility variables for

695each behavioural type pooled for all individuals.

Stationary Sedentary Floater Wanderer N 161 213 134 111 Distance traveled (m) 2.2(0-12) 10.6(0.7-41) 70(7.6-2454) 115(15-2249) Number of movements 1.0(0-4) 3.4(1-11) 36(4-558) 10.9(3-154)

61 31 62 Number of sites 1.8(1-4) 3.3(2-6) 5.7(2-12) 5.8(3-17) Extent (m) 2.5(0-6) 7.23(1.2-35) 5.7(1.3-15) 28.2(16-43) 696

710Table 3. Geometric mean, total sum of squares, and within- and between-individual

711variation in four mobility variables and principle component 1 for 24 juvenile Atlantic

712salmon parr monitored for 6-97 days (619 observations).

Mean Total Intra Inter

(%) (%) PCA1 -0.1 1655.7 87 13 Distance travelled 0.93 167.7 88 12 Number of movements 0.68 92.5 87 13 Number of sites 0.69 18.9 85 15

63 32 64 Extent (m) 0.76 57.6 83 17 715

728Figure captions

729Fig. 1. Water temperature (upper curve) and stage (lower curve) recorded from 24 July to

73030 October 2008. The vertical dashed line divides the study into summer and autumn

731periods based on a threshold of 12oC. The horizontal dashed line shows the flow stage

732matching bankfull discharge. *indicates the two fish tagging sessions.

734Fig. 2. Principal component analysis (PCA) on 50 Atlantic salmon parr daily mobility

735variables (N=681) during 97 days. Each dot represents the mobility of an individual on a

736particular day. Open circles show individual average values for the 24 fish that remained

737in the reach for six days or more. Polygons delineate behavioural types (stationary,

738sedentary, floater and wanderer) discriminated by a cluster analysis (K-means) on the

65 33 66 739daily mobility data: Number of sites, Distance travelled (m), Number of movements and

740Extent (m).

742Fig.3. Typical daily mobility corresponding to four behavioural types. Examples were

743selected based on the closest average PCA1 and PCA2 scores for each type: 1- (star)

744Stationary: 0 movement (18 Aug., Fish 15); 2- (black) Sedentary: 3 movements, 4 sites

745(22 Sept., Fish 50), 3- (white) Floater: 37 movements, 5 sites (24 Aug., Fish 37), 4-

746(dashed) Wanderer 14 movements, 11 sites (22 Sept, Fish 66). Contour shows depth at an

747estimated discharge of 0.4 m3/s (flow stage: 15 cm).

751Fig. 4. a) Number of days Atlantic salmon parr stayed in the reach subdivided by

752behavioural type. Dashed line indicates the fish that stayed more than 6 days. b)

753Proportion of days fish showed each of the behavioural types. Most individuals exhibited

754all types of behaviour during the study period.

756Fig. 5. Time series of the number of individuals tracked on the study site decomposed by

757behavioural types.42 individuals were PIT tagged on 24 July and 24 more were added on

75828 Aug. 2008.

760Fig.6. Proportion of fish behaviour exhibited on a daily basis by all individuals in relation

761to flow stage: low (0-10 cm, n= 34days), median (10-15, n=34 days), high (15-20 cm,

762n=13 days), very high (>20 cm, n=16 days).

67 34 68 763

764Fig.7. a) Daily averaged flow depth used per behaviour types pooled for all fish (unequal

765number of days per fish). b) Daily averaged flow depth used, each line representing

766averages per behaviour types per individual. Five individuals (bold lines) exhibited a

767relatively higher depth used while adopting floater behaviour.

769 770 771 772 773 774 775 776 777 778 779 780Figures 1 to 7

69 35 70 782

71 36 72 785

73 37 74 787 788

75 38 76