Individual variability of wild juvenile Atlantic salmon activity patterns: effect of
flow stage, temperature and habitat use
Mathieu L. Roy1, André G. Roy1, James W. A. Grant2, Normand E. Bergeron3
1
Département de géographie, Université de Montréal, 520 Côte Ste-Catherine, Montréal,
QC, H2B 2V8, Canada.
2
Department of Biology, Concordia University, 7141 Sherbrooke St. West, Montréal,
QC, H4B 1R6, Canada.
3
INRS-Eau, Terre et Environnement, 490 rue de la Couronne, Québec, QC, G1K 9A9,
Canada.
1
Abstract
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The magnitude of variation of diel activity patterns and habitat use of wild
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Atlantic salmon parr was examined during the summer and autumn through a gradient of
4
declining temperature. Fish were marked with passive integrated transponders and
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tracked using a large network of flat-bed antennas. High inter-individual variability was
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observed, as some individuals were predominantly nocturnal whereas others frequently
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changed their daily activity pattern. Overall fish activity decreased with decreasing
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temperature and increasing flow stage, but most of these changes in daily activity
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occurred during crepuscular periods. Parr used habitats with lower velocity at night than
10
in the day during the summer, but not in the autumn. Furthermore, there was no
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difference between day and night habitats for fish that were cathemeral (active both day
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and night during a given day), so differences between day and night habitats were the
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result of individuals adopting different activity patterns. These results suggest that habitat
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interacts with activity pattern, as individuals using suboptimal habitats seem to increase
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daytime foraging to secure sufficient energy. Temporal and among fish variability of
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activity patterns illustrate the dynamic nature of foraging decisions that may partly result
17
from tradeoffs experienced at the microhabitat scale.
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Introduction
Juvenile salmonids exhibit complex and variable diel activity patterns (Reebs
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2002). During the summer, juveniles are active and feed during the day, but as
27
temperature drops to 8-12oC in the autumn and winter, they tend to become nocturnal and
28
shelter during the day (Rimmer et al. 1983; Cunjak 1988; Bremset, 2000). This switch to
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nocturnal activity at low temperature may be explained by reduced food requirements and
30
higher predation risk. However, between-population differences in seasonal activity
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patterns have been observed, including nocturnal summer activity (Gries et al. 1997;
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Johnston et al. 2004), and diurnal winter activity (Hiscock et al. 2002). Such variability
33
among populations may be related to differences in overall food availability, or its
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differential abundance during the day and night (Metcalfe et al. 1999; Orpwood et al.
35
2006), and in perceived predation risk (Orpwood et al. 2010). Variation in predation risk
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and food availability associated with flow stage can also cause differences in diel activity
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patterns at the reach level (Bradford and Higgins 2001).
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Previous field studies have shown variation in activity patterns between groups of
39
fish but also reported that behaviour was not uniform among all individuals in a group
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(e.g. Bremset 2000; Bradford and Higgins 2001). This variability may result from
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individual variation in the relative benefits of rapid growth for fish facing a complex
42
trade-off between growth and mortality risk (Metcalfe et al. 1999). The level of risk
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tolerated by individuals varies with their recent foraging success, but also on their state
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along their growth trajectory (i.e. above average fish taking less risk) (Imre and Boisclair
45
2004). Combined with dominance and hierarchy constraints, the state of a fish may
46
explain why different individuals react differently to environmental fluctuations (e.g.
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photoperiod and temperature) (Breau et al. 2007). Along the same lines, microhabitat use
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may have an important impact on the foraging behaviour (Nielsen, 1992), but also on the
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type of activity pattern adopted by an individual fish. For instance, a fish constrained to
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use suboptimal habitat may have to forage for longer periods during the day and night to
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meet its energy demands. Conversely, activity patterns may induce diel variation in
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habitat use, as salmonids tend to use different habitats for feeding and sheltering
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(Heggenes et al. 1999). For instance, at night young salmon generally use lower velocity
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habitats with shallower depths and coarser substrates, perhaps to offset a decrease in
55
foraging efficiency (Heggenes et al. 1993; Metcalfe et al. 1997; Valdimarsson et al. 1997;
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Mitchell et al. 1998).
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Juvenile salmonid activity has been a topic of great interest. Numerous controlled
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experiments have revealed the effects of particular variables on activity patterns,
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presumably through an indirect effect on the growth vs mortality risk tradeoff (e.g.
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Metcalfe et al. 1999). Concomitantly, field observation studies have described the
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seasonal trends in activity patterns of populations or groups of fish (Bremset, 2000;
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Johnston et al. 2004) as well as their habitat use (Heggenes et al. 1999; Armstrong et al.
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2003). Previous studies have described general patterns of activity and reported
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differences in habitat use between day and night (e.g. Metcalfe et al. 1997). However, the
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previous field research has focused on unmarked individuals. Therefore, it is uncertain if
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habitat use patterns are performed by most individuals within a given day or if they
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emerge as an average of individuals with dramatically different daily patterns.
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Furthermore, to our knowledge, a description of the temporal variation of individual
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activity patterns and its potential relation with microhabitat is lacking.
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In this study, we monitored the habitat use and activity of 66 Atlantic salmon parr
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marked with passive integrated transponders (PIT) using a reach scale antenna grid in a
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natural environment providing both high temporal and spatial resolution. The main goal
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was to quantify the magnitude of variation in activity patterns, habitat use and their
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interaction and to identify if individuals were adopting different activity patterns on a
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daily and seasonal scale. The study was carried out during the summer and autumn
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through a gradient of declining temperature ranging from 18 to 3oC. Specifically, we
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tested the predictions that: 1) parr are predominantly nocturnal during both the summer
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and the autumn; 2) individual activity patterns (i.e. nocturnal, diurnal, cathemeral - day
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and night active, or crepuscular) vary on a short term basis, such that individuals are more
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nocturnal in higher flows and in colder temperatures; 3) parr habitat use interacts with
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activity patterns – fish use lower velocity habitat at night because their reaction distance
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is reduced, whether they are nocturnal or cathemeral.
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Materials and methods
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Study site
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This study was undertaken on Xavier Brook, a tributary of the Ste-Marguerite
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River in Saguenay, Québec, Canada (48°2591799 N; 69°5394899 W). The stream was
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10-15 m wide and flowed in a forested watershed. Data were collected in a channel
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characterized by two pools separated by a steep riffle, providing high habitat diversity
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(Fig. 1). In the thalweg at low flow (0.4 m3/s), depth ranged from approximately 0.1 m in
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the riffle to 1.65 m in the upstream pool. Median substrate size (B-axis, i.e. particle
92
width) varied from cobble-boulders in the riffle, to gravel-cobble (classification, after
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Wolman 1954) in the deep portion of the pools, and gravel-sand in the pool recirculation
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zones.
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Fish tracking system
96
A large flatbed antenna grid was used to monitor fish locations during 97 days, from 24
97
July to 1 November 2008. The tracking system consisted of an array of 149 circular
98
antennas of 50 cm in diameter. Each antenna, designed to detect the presence of PIT tags
99
(Texas Instruments (TIRIS) model RI-TRP-RRHP, 134 2 kHz, length: 23.1 mm,
100
diameter: 3.9 mm, weight in air: 0.6 g), was buried in the river bed and covered with
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substratum. The antennas were distributed systematically along transects of five antennas
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each. Overall, the detection field of the antenna grid covered 19% of the wetted area of
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the site at a discharge of 0.4 m3 s-1. Each group of five antennas was linked to a tuning
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capacitor, which was wired to a CYTEK multiplexer (JX/256 series, mercury wetted 256
105
single poles relay, www.cytec-ate.com). The multiplexer was connected to an Aquartis
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controller (custom made by Technologie Aquartis; www.aquartis.ca) composed of a
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TIRIS S-2000 reader, a datalogger and a custom-made controller unit. The system was
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powered by three solar panels connected to four 6V batteries plugged in series and two
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12V batteries plugged in parallel. Each antenna was activated every 34 s successively for
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the detection of PIT tag presence. When a PIT tagged fish was detected, the date
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(dd/mm/yy), time (hh/mm/ss), antenna ID (multiplexer card and port number) and fish ID
112
(tag number) were recorded. Detection range varied from 300-400 mm in height and 600-
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800 mm in diameter.
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During the study period, all antennas detected at least one individual. The failure
to distinguish two fish located in the same antenna field is a limitation of PIT tracking
116
systems (Armstrong et al. 1996; Linnansaari et al. 2007). Tag collision (i.e. multiple tags
117
blocking the detection of each other) can especially be a concern at high fish density.
118
However, the high density of antenna of this tracking system reduced the probability of
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having several fish simultaneously in the same antenna field. When fish are moving, they
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are likely to be detected sequentially by the same antenna or by adjacent antennas.
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Nevertheless, the presence of a sheltering fish might prevent other fish from being
122
detected by a specific antenna for a period of time. Although this is a potential issue for
123
consideration, previous frequent snorkeling and portable antenna surveys in the summer
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and autumn of 2007 never found multiple fish on the same antenna. Furthermore, errors
125
in tag reading of this tracking system for juvenile Atlantic salmon were 0.07% of the data
126
recorded at a similar tagged fish density (Johnston et al. 2009). For more detailed
127
information on the antenna grid, see Johnston et al. (2009).
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Fish tagging
130
A total of 69 Atlantic salmon parr (1+) were captured in the study reach on two
131
occasions using a backpack electrofishing device: 44 fish were caught on 24 July 2008,
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and 25 on 28 August 2008. In the first session, fish were captured at the tracking system
133
location and up to 50 m upstream. For the second session, fish were caught up to 200 m
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upstream from the reach to avoid recapturing tagged fish. Fish were brought from outside
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the boundary of the tracking system to increase the number of study individuals, as the
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study area had a low fish density in 2008. Juvenile Atlantic salmon of fork length < 80
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mm were not tagged to avoid potential effects on growth rates (Sigourney et al. 2005;
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Bateman and Gresswell, 2006) and survival (Roussel et al. 2000). Average and maximum
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tag size to body weight ratio were 5.9% and 7.8%, which is lower than 8%, recommended
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by Lacroix et al. (2004) to minimize effects on swimming performance.. Juvenile brook
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trout (Salvelinus fontinalis) and longnose dace (Rhinichthys cataractae) were released
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upon capture. The retained fish were anesthetised in a clove oil solution (3 ml/10 L) and
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were surgically implanted with 23 mm PIT tags (Texas Instruments) in the abdominal
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cavity secured with surgical tissue adhesive (Vetbond©). Tagged fish were kept for a
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maximum recovery period of two hours in fish tanks within the river before being
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released on the study site. Two and one individual perished during the first and second
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tagging sessions, respectively. On each occasion, fish were measured (fork length, mean
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± SD, LA: 98 ± 7.4 mm, LB: 109 ± 8.3 mm) and weighed (MA= 9.7 ± 1.7 g, 10.7 ± 2.3 g).
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Habitat survey
A pressure transducer was used to monitor flow stage and water temperature
152
fluctuations every 15 min. Flow stage was estimated by subtracting atmospheric pressure
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fluctuations from the signal using data obtained from the closest meteorological station.
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Then, we subtracted the minimum value recorded during the study period, so flow stage
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was defined as the water level above minimum summer low flow (Fig. 2). Considerable
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precipitation was received during the summer and autumn 2008 (486 mm from July to
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October). A high magnitude flow event occurred at the beginning of August, followed by
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a stage decrease in the following month. A prolonged low-flow period lasted for 10 days
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until the end of October, but was interrupted by a few precipitation events and remained
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about 10 cm above minimum between these events, which corresponded approximately
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to the median flow stage.
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During the study period, water temperature decreased from 19.0 oC to 2.8 oC (Fig.
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2). In the first part of the study period, water temperature remained relatively stable, with
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slight fluctuations around 15 oC. Around 3 September, water temperature started
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decreasing, reaching 12 oC on 11 September, which corresponds to the upper boundary
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of the temperature range when juveniles suppress their daytime activity (Rimmer et al.
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1983; Fraser et al. 1993). Therefore, this date was chosen to define the boundary between
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the summer (12-18 oC) and the autumn (3-12 oC) periods.
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The physical characteristics of the study reach were characterized in detail.
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Micro-topography was surveyed using a robotic total station (Trimble 5600DR) by
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combining a systematic sampling along transects 1m apart, with the characterization of
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individual roughness elements that protruded 10 cm above the local mean bed elevation.
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This strategy optimized sampling effort, as sampling point density increased
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proportionally with bed complexity. From 6250 sample points, we created a digital
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elevation model (DEM) using a triangular irregular network (TIN) interpolation with
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pixel size of 10 cm. Depth was obtained by removing the general longitudinal slope of
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the channel using linear regression, and then by subtracting the water level at average
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flow (Q = 0.07 m3·s-1, stage: 10 cm) to all values of the bed elevation DEM. The general
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slope being removed, the remaining differences in depth within the reach were attributed
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to the channel shape, particularly to the steep pool riffle sequence. At median flow, the
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maximum depth of 1.8 m was recorded in the upstream pool and the minimum depth in
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the thalweg was 0.30 in the riffle (Fig. 1).
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Flow velocity measurements were sampled extensively along the reach on three
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occasions, close to median flow periods (stages of 13, 14 and 16 cm, Fig. 2). Three-
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dimensional flow velocity measurements were sampled using four acoustic Doppler
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velocimeters (ADVs, Sontek, San Diego) simultaneously at 10 cm above the bed at a
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density of 3 samples.m-2. An aluminum frame was used as a support and reference grid
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for ADV measurements. Each flow measurement was referenced using the total station
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and mapped. At median flow, mean flow velocity was approximately 0-30 cm·s-1 in the
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pool recirculation zones, 0.40-0.60 cm·s-1 in the pool tails and from 75-125 cm·s-1 in the
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riffle.
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At each antenna location, velocity and depth were averaged within the antenna
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detection range (0.35 m radius). Although depth and velocity fluctuated during the study
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period, in this study we focused on habitat use at close to base flow and removed fish
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detections that were recorded during flood events (flow stage > 20 cm). We assumed that
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a particular habitat would be ranked similarly in terms of depth and velocity across minor
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flow stage fluctuations. Since flow velocities were measured at median flow stage,
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reported habitat use values are slightly higher than previously reported values, generally
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estimated at base flow.
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Diel activity patterns
First, the data set was explored for temporal trends in occurrences of detection of
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each individual in different periods of the day. For each individual, for each hour, we
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examined the periods during which fish were continuously detected every 34 s (tracking
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frequency) without interruptions for more than 30 minutes and removed this hour for this
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fish from the analysis, as it would be likely indicating that a fish was inactive. Large parr
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are often mobile over short time scales (Ovidio et al. 2007) and have feeding territories
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exceeding the detection range of the antennas (Armstrong et al. 1997). Hence, foraging
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parr should be detected moving at least once in a 30 minute period. For all fish, a total of
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175 h were removed this way over the entire study period, including 48 hours for a single
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fish, and 127 h for 15 other individuals. Afterwards, for each day that a fish was detected
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for at least 1h, each individual was assigned a “presence/absence” status for each hour.
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When a fish was not detected during a specific day, no ‘presence/absence’ status was
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recorded because we could not assess if the fish had emigrated from the reach or was
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sheltering. To examine within-individual variability, we first selected the individuals that
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were detected on the reach a minimum of five days in either the autumn or the summer
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period or both. We then assumed that presence recordings reflect periods of fish activity,
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as movements are likely to increase the frequency of fish detection by the system.
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To examine the overall temporal patterns of fish activity, we estimated relative
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frequency of detection for each hour of the day for each individual. For instance, if a fish
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was detected during 20 days, but only five times between 5h00 and 6h00, its average
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relative frequency of detection for 5h00 would be 25%. The frequency of detection for
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each hour were square root transformed and used as datum in the analysis (n = 34 for
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summer and n = 14 for autumn). Activity patterns of fish captured during the first and
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second capture sessions were compared for a period during which a maximum of fish
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from the two capture sessions were simultaneously tracked (25 Aug. – 25 Sep.). Repeated
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measures general linear model with hour as a within-individual factor and capture
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session as a between individual factor showed no significant difference in activity
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patterns between the two groups of fish for that period (F=1.82, df=1,23, P=0.191).
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Therefore, individuals from the two capture sessions were combined in further analyses.
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To test for the presence of an overall diel activity pattern and for a seasonal interaction,
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we used a repeated measures general linear model using hour as a within-individual
233
factor and season, mass and fork length as between-individual factors or covariates. Only
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hour and season remained in the final model, as the other variables did not have
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significant effects (i.e. p>0.05).
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To examine activity patterns of each individual, we used logistic regression with
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hour as the explanatory variable and average relative frequency of fish detection as a
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response variable. Quadratic terms were added to the regression, as the variable hour was
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ordered from 1 to 24; the hours of darkness occurred in two non-contiguous portions.
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Then, based on the circadian cycle and on visualization of activity patterns, periods of
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24h were divided into four daily periods: night (0100-0300 and 2200-2400), dawn (0300-
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0700), day (0700-1800) and dusk (1800-2200). To present the within-individual variation
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in activity pattern for each individual, for each day, each fish was categorized as 1)
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nocturnal: active during night and possibly dawn and dusk, 2) diurnal: active during day
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and possibly dusk and dawn, 3) cathemeral: active both night and day and possibly dusk
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and dawn 4) crepuscular: active only during dusk and dawn periods.
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Activity patterns vs. flow and temperature
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Next, we investigated the temporal variation and the effect of flow stage and
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temperature on daily activity (i.e. daily frequency of fish detection), diurnal activity,
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nocturnal activity and crepuscular activity. The range of values for daily averaged
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temperature and flow stage were divided into nine and seven discrete categories,
253
respectively. We estimated the percentage of time an individual was active at a particular
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daily period at a particular temperature and flow level. We used four distinct mixed-
255
effects models to test for the effects of flow and temperature as fixed factors on fish
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activity (daily, diurnal, nocturnal and crepuscular). Comparison of different fixed effect
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structures was done using AIC values (Burnham et al. 2011).
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Habitat use diel patterns
For each hour of each day an individual was considered active, an average depth
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and velocity values were estimated. To test for the presence of an overall habitat use
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effect and a seasonal interaction effect, we used a general linear model with repeated
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measures using daily period (i.e. night, day, crepuscular) as a within-individual factor and
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season as a factor . Then, to examine and visualize the individual daily habitat use pattern
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of each fish, for each individual for each season, quadratic regressions were carried out
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using hour as an explanatory variable and average habitat use for that hour as the
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response variable.
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Diel habitat use vs. diel activity patterns
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For each of the habitat variables, four distinct mixed-effects models were
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performed using fish as subjects, days as repeated measures and diel activity pattern and
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season as fixed factors. Hence, we compared 1) day and night habitat use within days
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when individuals were cathemeral, 2) night habitat use when individuals were nocturnal
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to night habitat use when individuals were cathemeral, 3) day habitat use when
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individuals were diurnal to when individuals were cathemeral. All analyses were carried
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out using Matlab R2010a (The Mathworks Inc., Natick, Massachussets) and SPSS 17.0
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(SPSS Inc., Chicago, Illinois).
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Results
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Fish recordings
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In this study, a total of 66 fish were captured, PIT-tagged and released in the
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reach. From the total, four individuals (6%) were never detected by the tracking system
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and 12 individuals (18%) were detected in the study reach for less than 24 hours, as they
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most likely left the study site. Ten individuals (15%) were detected on site for a single
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day, 13 individuals (24%) stayed in the reach between one and four days, and 27
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individuals remained in the reach between 5 and 70 days (37%). As we were interested in
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individual variability, only fish present for five days or more were investigated herein. Of
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those fish, 13 and two individuals were only detected during the summer and autumn
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periods, respectively, and 12 were detected during both periods.
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During the study period, each antenna of the reach detected between 2 and 20
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different individuals during the study period, with an average of 7.5 individuals. The
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upstream shallow recirculating zone was used by the fewest individuals, followed by the
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downstream part of the riffle (Fig. 3). The transition between the upstream pool and the
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riffle, and the entire downstream pool were visited by the highest number of tagged
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individuals. The highest frequency of fish detections was recorded in the upstream pool
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tail, the transition between the upstream pool and the riffle and in the margins of the
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downstream pool. Relatively few fish were detected in riffle habitats, the upstream
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recirculation zone and the deep pool.
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Diel and seasonal activity pattern
In both summer and autumn, the frequency of fish detection exhibited a clear
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daily pattern, with fish activity peaking at dawn and dusk, and being higher at night than
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during the day (Two-way repeated measures GLM (hours); F = 11.854, df = 23, 851,
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p < 0.001) (Fig. 4a). During the summer (stars), frequency of fish detection peaked at
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approximately 0.45 between 0500 and 0600 and between 2000 to 2100, which
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corresponded with sunrise and sunset. Average frequency of fish detection was 0.25
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during the night and decreased to 0.15 to 0.20 during the day. A similar pattern was
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observed during the autumn period (Fig. 4a, circles), except the peaks in fish detections at
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sunrise and sunset occurred one hour later (between 0600 and 0700) and earlier (between
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1900 and 2000), respectively. Mean frequency of fish detection was lower in autumn than
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in summer (GLM (season), F = 4.652, df = 1, 37, p = 0.038), particularly during the dawn
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and dusk peaks of activity.
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Although the above results suggested an overall predominance of nocturnal
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behaviour with activity peaks at twilight, substantial variability among parr was observed.
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Logistic regressions with added quadratic terms revealed three types of daily patterns:
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nocturnal, concave quadratic curves; diurnal, convex quadratic curve; or, equally active
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during day and night, not significant (Fig 4b). During the summer, only 11 of 25
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individuals exhibited the predominant nocturnal pattern, whereas five individuals (20%)
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exhibited diurnal behaviour, and nine individuals (36%) showed no significant daily
320
pattern. During the autumn, nocturnal behaviour was adopted by 10 of 14 individuals
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(71%), with four individuals showing no clear pattern (29%) and no parr exhibiting
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diurnal behaviour. However, the difference in frequencies of diel patterns between
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summer and autumn was not significant (χ2 = 4.20, df = 2, p = 0.12).
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Although some individuals exhibited a predominant diel activity pattern, others
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exhibited considerable within-individual variability. Fish that did not exhibit a significant
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nocturnal or diurnal pattern were either active both day and night within the same day or
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were active only at night on some days and only during the day on other days (Fig. 5).
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Juveniles differed in how they adopted different activity patterns over time. Several
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individuals were only nocturnal or crepuscular, whereas others frequently changed daily
330
patterns between days (Fig. 5).
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During the summer, all fish adopted a nocturnal pattern on at least 10% of the
332
days, but the majority of fish were nocturnal on more than 50% of the days. While seven
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individuals were never detected during daylight hours, 15 individuals (60%) were active
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only in the day at least once during the summer. Three individuals (12%) were highly
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crepuscular and were only detected at twilight periods on more than 50% of the days.
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Within individual variability of activity pattern decreased during the autumn, as most fish
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were nocturnal and crepuscular. However, four individuals (30%) still exhibited daytime
338
activity (i.e. either diurnal or both) over 15% of the days.
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Daily activity patterns vs flow stage and temperature
Daily patterns of activity varied among fish within days, with all types of activity
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patterns being observed most days. Daily activity increased significantly with
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temperature and was the highest at 13 oC (Fig. 6a; GLM, F = 2.08, df = 8, 104.50, p =
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0.046). This relationship was mainly due to a significant increase in crepuscular activity
345
(GLM, F = 3.81, df = 8, 107.03, p = 0.001), as no significant effect of temperature was
346
observed on diurnal and nocturnal activity (GLM, F = 1.63, df = 8, 103.87, p = 0.126; F
347
= 0.791, df = 8, 106.75, p = 0.612). Daily activity also decreased significantly with flow
348
stage (Fig. 6b GLM, F = 3.98, df = 6, 107.10, p = 0.001), primarily due to a decrease in
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nocturnal (GLM, F = 2.58, df = 6, 110.39, p = 0.022 ) and crepuscular (GLM, F = 5.46,
350
df = 6, 110.27, p < 0.001) activity, with no significant effect of flow on diurnal activity
351
(GLM, F = 2.14, df = 6, 106.87, p < 0.055).
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Diel and seasonal habitat use patterns
Average velocity used by parr varied significantly between day, night, and
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crepuscular periods (Fig. 7a; GLM within fish repeated measures, F = 3.76, df = 3,111
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p=0.013); parr used lower velocity habitats during the night than during dawn, day and
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dusk periods. During the summer, velocity used ranged from 50 to 60 cm˖s-1 during hours
358
of darkness (2000 to 0500) and from 65 to 86 cm˖s-1 during the rest of the daily cycle.
359
However, this diel pattern of habitat use was not observed during the autumn period
360
(GLM within fish days x season interaction, F = 3.78, df = 3,111 p = 0.013), as average
361
velocity used varied irregularly between 64 and 70 cm·s-1 for the four daily periods.
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Nevertheless, overall average velocity used did not differ between the summer and
363
autumn season (between fish GLM, F=0.033, df = 1, 3, p = 0.857). Although the use of
364
lower velocity at night was the predominant pattern, substantial variability among
365
individuals was observed. During the summer, 10 individuals (40%) exhibited a convex
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daily pattern of current velocity used, whereas three individuals (12%) adopted the
367
opposite behaviour and 12 (48%) did not show a clear pattern (Fig. 7c). During the
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autumn period, only three fish used lower velocity habitats at night, whereas the
369
remaining 10 individuals did not exhibit significant daily patterns (Fig.7e).
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Average flow depth used did not differ significantly between day, night and
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crepuscular periods (F = 1.46, df =3, 111 p = 0.23) or between summer and autumn
372
(GLM, F=0.85, df = 1, 37, p=0.364), nor was there a significant interaction between daily
373
period and season (Fig. 7b; GLM season x daily periods interaction, GLM, F = 0.25, df =
374
3,111 p = 0.863). However, average patterns obscured interesting variability between
375
fish: six fish (24%) used shallower areas at night than during the day; six others
376
exhibited the opposite pattern; and, half did not exhibit any clear daily depth use pattern
377
(Fig. 7d). During the autumn, six individuals out of 14 (43%) exhibited a different depth
378
use between day and night periods: two using deeper and four using shallower habitats at
379
night than during the day (Fig. 7f). Again, a large proportion of individuals (57%) did not
380
display any particular pattern of depth use.
381
382
383
Diel habitat use pattern vs. activity pattern
Contrary to the average daily patterns of habitat use, cathemeral individuals did
384
not use lower velocities at night than in the day (within the same day) in the summer (F =
385
2.58, df = 1, 145, p = 0.11) nor autumn (F = 1.31, df = 1, 40, p = 0.26). Therefore,
386
average differences in daily habitat use were caused by individuals exhibiting different
387
daily patterns (diurnal vs. nocturnal). During the summer, nocturnal individuals used
388
night habitats with significantly lower velocities and shallower depths than cathemeral
389
individuals (U: F = 31.97, df = 1,271 p < 0.001; F = 15.94, df = 1,263 p < 0.001).
390
However, diurnal individuals did not use different day habitats than cathemeral fish (U F
391
= 0.95, df = 1, 84, p = 0.33; Y F = 2.57, df = 1, 82 p = 0.113). In the autumn, fish
392
exhibiting different daily activity patterns did not differ significantly in their habitat use
393
during the day and night (data not shown).
394
395
Discussion
396
Diel and seasonal activity pattern
397
Our results showed that the probability of detecting a fish, which is assumed to be
398
closely associated with fish activity, peaked during twilight periods, particularly during
399
the summer. Activity was also higher at night than during the day during both summer
400
and autumn. Although parr have been observed switching from a predominantly diurnal
401
to a nocturnal behaviour as temperature drops in the autumn between 8-12 oC (Rimmer et
402
al. 1983; Fraser et al. 1993; Bremset 2000), our results support more recent field studies
403
reporting a predominance of nocturnal behaviour during the warmer temperatures in
404
summer (Gries et al. 1997, 13-23oC; Imre and Boisclair 2004, 16-21 oC) and in the
405
autumn (Johnson et al. 2004). Nocturnal behaviour might be associated with a size
406
dependant trade-off between growth and predation risk. Post young-of the year parr tend
407
to be more nocturnal to minimize predation risk from diurnal predators, whereas young-
408
of-the-year tend to maximize food intake to increase their growth rate and chances of
409
surviving the winter (Johnston et al. 2004; Imre and Boisclair 2004). While we observed
410
only post YOY in this study, body size had no significant effect on diel activity pattern,
411
suggesting that small differences in size might be less important than other factors in
412
inducing a particular activity pattern.
413
Although the crepuscular activity of juvenile salmonids is generally
414
acknowledged (Brittain and Eikeland 1988), most studies compare only day and night
415
activity. However, our results suggest that dawn and dusk are important, often
416
representing the only periods of activity in a daily cycle for some individuals.
417
Furthermore, the overall low probability of fish detection of 23% suggests low activity
418
levels, even in the summer. Similarly, 1+ Atlantic salmon are active only 37% of the time
419
in the summer (Breau et al. 2007). Low activity in the summer may be explained by
420
individuals gaining enough energy during short periods of activity to survive the next
421
winter without unduly increasing their predation risk (Cunjak et al. 1998). Low activity
422
levels might also have been induced by an abundance of food (Metcalfe et al. 1999;
423
Orpwood et al. 2006).
424
In this study, fish activity was most likely underestimated due to several
425
limitations of the tracking system. First, fish could sometimes be active in areas that were
426
not covered by the antenna grid. However, this issue is shared with other methods, such
427
as snorkeling, that assume “absent” fish to be inactive (e.g. Breau et al 2007). Hence,
428
probability of detection in this study, as in others, was primarily a way of comparing
429
activity between periods, rather than an estimate of absolute activity. Secondly, multiple
430
individuals simultaneously located on the same detector for extended periods of time
431
could not be recorded due to tag collision. However, since large parr are generally mobile
432
and territorial (unpublished data), it is unlikely that multiple fish would stay for extended
433
periods at the same location. Though it was unlikely that an active fish would be recorded
434
at a location without interruption, this possibility should be considered when interpreting
435
our results.
436
437
438
Individual variability of parr activity
The high within-individual variability in activity pattern confirmed our second
439
hypothesis that parr frequently changed activity patterns. Despite a predominantly
440
nocturnal activity pattern, important variability between individuals was observed,
441
confirming previous studies (Breau et al. 2007). In our study, only 44% of the fish
442
followed the average concave pattern in the summer, which was associated with lower
443
activity in the day than in other periods. These results highlight the importance of
444
examining individual variation in salmonid behaviour, rather than reporting average
445
trends. Our results also suggest that fish behaviour was more homogeneous in the
446
autumn, as the majority of individuals adopted a predominantly nocturnal activity pattern
447
and no fish were predominantly diurnal.
448
Several individuals exhibited the consistent diel activity pattern of being active
449
only during crepuscular periods and at night. This activity pattern likely provides basic
450
energetic needs while minimizing predation risk: dusk and dawn are typically times of
451
high food abundance (Brittain & Eikeland 1988), without the high predation risk of
452
daytime (Clark and Levy 1988). However, many individuals exhibited considerable
453
variability in activity patterns, sometimes being active at night only, during the day only,
454
during both the day and night, and during twilight periods only.
455
Numerous controlled laboratory experiments have examined how various factors
456
affect the tradeoff between growth and mortality risk and how this translates to changes
457
in the activity patterns of juvenile salmonids (e.g. Fraser et al. 1993; Alanärä and
458
Brannas, 1997). These studies have shown that an abundance of food and cover favour
459
night foraging, as it provides sufficient growth rates while minimizing the risk of
460
predation. Hence, the spatial and temporal variability of food and cover will contribute to
461
the variation in activity pattern observed in the reach. Furthermore, when food and space
462
is limited, dominance status may influence activity patterns; dominant individuals feed at
463
the most beneficial times, whereas subordinates feed at other time periods (Alanärä and
464
Brannas 1997; Alanärä et al. 2002). Social status and competition may combine with
465
short term variation in energy requirements and individual foraging success (Metcalfe et
466
al. 1999) to explain the observed high temporal and intra-individual variability of activity
467
patterns.
468
469
470
Activity vs. flow and temperature
Contrary to the prediction, temperature did not significantly affect diurnal and
471
nocturnal activity. However, total activity increased with temperature, mostly due to an
472
increase in crepuscular activity. Our results suggested, therefore, that older parr in the
473
wild are predominantly nocturnal in the summer regardless of temperature (Imre and
474
Boisclair 2004, Johnston et al. 2004), but also modulate their crepuscular activity levels,
475
perhaps to benefit from higher food availability at higher temperatures during dusk and
476
dawn and to fulfill higher metabolic costs during the summer Furthermore, at high
477
temperatures, a higher variability in activity patterns among individuals was observed
478
than at low temperatures, when fish were almost all nocturnal. This result is analogous to
479
those of Alanärä et al. (2002), who suggested that temporal segregation occurred at high
480
temperatures, resulting from higher competition due to higher energetic requirements.
481
Also contrary to the prediction, nocturnal and crepuscular activity decreased with flow
482
stage. Perhaps the increased food availability in faster flows decreased the time required
483
to fulfill energetic demands while keeping predation risk to a minimum. However, unlike
484
in Bradford and Higgins (2001), daytime activity did not decrease with flow stage.
485
486
487
Seasonal and diel habitat use pattern
On average in the summer, parr were detected in lower flow velocities at night
488
than during the day, perhaps to offset a decrease in their ability to catch prey at night in
489
fast flows (Metcalfe et al. 1997). Interestingly, in the autumn, there was no difference
490
between the flow velocities where fish were detected during the day and night. Similarly,
491
Riley et al. (2006) reported that young-of-the-year salmon used lower flow velocity at
492
night than during the day, but not for older juveniles. Therefore, differences in day/night
493
habitat use might be related to differences in day and night foraging. Young-of-the-year
494
salmon continue to feed later in the year than older juveniles (Johnston et al. 2004, Breau
495
et al. 2007). If diel patterns in velocity-related habitat use were associated with diel
496
activity patterns, a suppression of activity during daylight would affect the type of
497
habitats used during the day and night. In contrast with previous studies reporting the use
498
of low-velocity habitats at low temperatures (Metcalfe et al. 1997; Cunjak et al. 1998),
499
we observed no significant differences between summer and autumn. Consistent with
500
previous studies (Riley et al. 2006), parr did not exhibit a significant diel pattern in the
501
use of flow depth. Flow depth may be of less importance than velocity in habitat
502
selection.
503
504
Parr exhibited high inter-individual variability, as only 40% of individuals
exhibited the predominant pattern of a shift to slower flows at night, with some
505
individuals exhibiting the reverse pattern. Such behaviour might arise as a result of lower
506
velocity habitats being limited by competition. Also, habitat selection might affect short-
507
term foraging success, which indirectly affected activity pattern decisions.
508
509
510
Activity pattern and habitat use patterns
In contrast to the general pattern of lower flow velocity used at night, cathemeral
511
fish did not use different habitats at different times of day. This observation contrasts
512
with previous experiments that showed a preference for lower velocity at night (Metcalfe
513
et al. 1997; Valdimarsson and Metcalfe 1998). However, these studies were conducted
514
under controlled conditions in the absence of competition, which might prevent some
515
individuals from using already occupied habitats. The overall differences between day
516
and night habitat use observed in this study can therefore be attributed to individuals
517
being nocturnal or diurnal, which supports the hypothesis that habitat use influences the
518
activity pattern adopted by individuals.
519
When fish were nocturnal, they used habitats with lower flow velocity than when
520
they were cathemeral. Given the relatively high velocities observed on the reach at
521
median flow, we suggest that average velocity selected by nocturnal fish (53 cm·s-1) were
522
more profitable than the higher average velocity selected at night by cathemeral fish (68
523
cm·s-1). Perhaps low foraging success at night in fast flows induced a need to forage also
524
during the day. Individuals exhibiting nocturnal activity used lower velocity habitats than
525
other individuals during crepuscular periods, perhaps because low flow velocity habitats
526
were limited in the reach.
527
In this study, because the initial number of fish on the study site was relatively
528
low, a fraction of the study fish were brought into the detection area from upstream.
529
Furthermore, a second group of fish from upstream was added a month after the
530
beginning of the study period to balance a decrease in fish density over time. The causes
531
of the declining density may include mortality and emigration due to a density of large
532
parr exceeding the carrying capacity of the reach, a response to storm events and homing
533
behaviour to their capture locations (Huntingford et al. 1998). Moreover, large parr at this
534
location occasionally show bouts of high mobility, moving across the reach typically
535
from one pool to another (Roy et al. 2012). Such mobility likely increased the chances for
536
fish to relocate outside the boundary of the tracking system. However, despite no
537
significant difference in activity patterns between the groups of fish tagged on two
538
occasions, a potential impact of relocating fish and of combining resident and immigrant
539
fish on the behaviour noted here cannot be completely ignored.
540
In summary, most juvenile Atlantic salmon frequently changed activity patterns,
541
although some remained mostly nocturnal. Fluctuations in temperature and flow stage
542
were associated with changes in activity, mostly during crepuscular periods. Habitat use
543
differed between nocturnal fish and diurnal fish, but not between the night and day
544
habitats of fish that were active both day and night. Previous controlled laboratory studies
545
have shown how numerous factors can induce changes in day/night activity of individual
546
fish through a tradeoff between growth and mortality risk. While previous field studies
547
have reported nocturnal or diurnal activity trends at the reach scale, this study has shown
548
how variable activity patterns are among individuals and how part of this variability
549
might be associated by microhabitat scale processes. The high temporal variability in
550
activity patterns illustrated the dynamic nature of fish foraging decisions in a natural
551
system.
552
553
554
Acknowledgements
We are very grateful to Francis Bérubé and Marc-André Pouliot for their help in
555
the development of the tracking system and for essential logistical and technical
556
assistance. We also thank Patricia Johnston, André Boivin, Claude Gibeault, Marie-Eve
557
Roy and René Roy for help in the field. We would also like to thank Geoide, NSERC and
558
the Canada Research Chair program for the funding of this research, I. Imre and two
559
anonymous reviewers for their helpful comments.
560
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561
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562
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653
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657
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658
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Fig. 1. Flatbed antenna grid buried in the bed of a reach of Xavier Brook; 144 antennas
685
are displayed in transects (dots), connected to a controller and multiplexer on the bank
686
(rectangle) powered by solar panels (suns). Contours illustrate bathymetry at median flow
687
during the study period.
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Fig. 2. Time series of water level and water temperature. Gray shading indicates periods during
706
which river stage was 20 cm over the minimum base level observed during the study period. The
707
vertical dashed line denotes 12oC, defining a warmer summer period and a colder autumn period.
708
Asterisks and numbers indicate fish tagging sessions and the days of flow measurement,
709
respectively.
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711
712
713
714
715
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724
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Fig. 3. Maps of a) depth (stage:10 cm) b) the number of different tagged individuals detected at
726
each antenna over the entire study period c) the frequency of fish detections observed. Frequency
727
of fish detections was estimated from time decimated data (i.e. only one location per fish per hour
728
was kept at a single antenna location). Flow is from left to right.
729
730
731
732
733
734
735
736
737
738
Fig. 4. a) Average probability of fish presence per hour during the summer (stars, N=25) and
739
autumn (open circles, N=14). Horizontal line shows average periods of daylight during summer
740
and autumn. Quadratic logistic regression models of probability of fish presence as a function of
741
hours during the b) summer and c) autumn. Solid lines indicate significant curves, whereas
742
dashed lines non-significant curves.
743
744
Fig. 5. Number of days each fish was present in the reach subdivided into diel activity pattern
745
adopted by each individual in the (a) summer and (b) autumn.
746
747
748
749
750
751
752
753
754
755
756
757
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759
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Fig.6. Mean + SE activity (i.e. relative frequency of detection) of Atlantic salmon parr
764
during the entire day (24 h) (stars –solid line), crepuscular (diamonds – large dashed
765
line), night (circles – small dashed line) and day (square) periods in relation to a) water
766
temperature and b) flow stage above minimum recorded for a period of three months in
767
the summer and autumn. Lines indicate significant trends. Each datum was the mean
768
activity for each marked fish at each temperature and flow stage interval. For each
769
increasing temperature category, n = 3, 6, 14, 14, 16, 18, 22, 26, 17 and for flow category
770
n= 17, 27, 25, 25, 20, 12, 12.
771
772
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Fig. 7. Average ± SE (a) flow velocity and (b) depth used by Atlantic salmon parr in the summer
782
(stars) and autumn (open circles) per hour. Quadratic regressions of individual average (c) flow
783
velocity and (d) use (c) per hour during the summer (n=25) (significant relationship: solid, non
784
significant: dashed) and (e) and (f) are the same relationships for the autumn period (n=13).
785
786
787