Effect of geomagnetic field on migratory activity in a diurnal passerine migrant, the dunnock, Prunella modularis

Animal Behaviour 146 (2018) 79e85

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Effect of geomagnetic field on migratory activity in a diurnal passerine migrant, the dunnock, Prunella modularis Mihaela Ilieva a, b, Giuseppe Bianco a, Susanne Åkesson a, * a b

Department of Biology, Centre for Animal Movement Research, Lund University, Lund, Sweden Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria

a r t i c l e i n f o Article history: Received 14 May 2018 Initial acceptance 20 July 2018 Final acceptance 20 September 2018 MS. number: 18-00316R Keywords: bird migration computer vision diurnal migration endogenous migration program fuelling geomagnetic field magnetic displacement migratory restlessness songbird Zugunruhe

Migratory songbirds are guided by an endogenous programme during their first migration, encoding timing of migration, distance and direction. To successfully perform migration, birds have evolved phenotypic adaptations for flight, fuelling and navigation. Migratory distance in different species of birds is encoded as a period of expressed migratory restlessness, for which the length is correlated with migratory distance. Most of the work so far has been on nocturnal passerine migrants, while much less is known about the phenotypic adaptations for migration in diurnal migrants. Here we studied autumn migration fuelling and expression of migratory activity in caged diurnally migrating juvenile dunnocks in response to magnetic displacements. We kept one group (control) indoors at the location of capture in south Sweden, while the two experimental groups were gradually (over 5 days) displaced magnetically to locations to the northeast or to the wintering destination in southwest France. We found that all birds showed two peaks of activity during the day, for which the onset of activity was tightly timed to sunrise and sunset. The longest activity (2e3 h) occurred in the morning, coinciding with the natural period of migration for this species. For the control group, the migratory (flight) activity increased with the season (15 days), while it was strongly reduced for the dunnocks displaced to the wintering areas. Birds displaced to the north showed a stable, but slightly reduced migratory activity over time. The results support the finding that geomagnetic information expected to be met en route is important for triggering level of migratory activity in juveniles of a diurnal songbird migrant. © 2018 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Migratory songbirds are thought to rely on a genetic program encoding time, distance and direction of migration (e.g. Berthold, 1996; Berthold & Querner, 1981; Helbig, 1996, 2003; Liedvogel, Åkesson, & Bensch, 2011; Pulido, 2007), enabling billions of young birds (Hahn et al., 2009) to follow species-specific flyways during their first migration to distant wintering locations. There are several phenotypical adaptations needed to successfully perform long migrations, involving morphological, physiological and € m, 2007). Several of behavioural changes (Åkesson & Hedenstro those adaptations are involved in the timing of migration (Åkesson € m, et al., 2017) and navigation (e.g. Able, 1980; Åkesson, Bostro Liedvogel, & Muheim, 2014; Emlen, 1975), while others are €m & Alerstam, 1997). related to fuelling and flight (Hedenstro Most songbirds migrate at night and express nocturnal activity, originally termed ‘Zugunruhe’ or migratory restlessness (Wagner,

* Correspondence: S. Åkesson, Department of Biology, Centre for Animal Movement Research, Lund University, Ecology Building, SE 223 62 Lund, Sweden. E-mail address: [email protected] (S. Åkesson).

1930), during the migration periods. The migratory restlessness is characterized by stereotypic ‘wing-whirring’ (Czeschlik, 1974) and in some species migratory flight calls (Gwinner, 1975). The timing and length of the migratory restlessness period have further been shown to be correlated with migratory distance in songbirds (Berthold, 1973). Migratory restlessness in individual birds is controlled by two conspicuous environmental cycles, covering a full year or a day. The annual migration restlessness is controlled by an endogenous oscillator, which in absence of seasonal variations may free-run for several years (Gwinner 1977, 1986). While nocturnal migratory restlessness and compass orientation have been extensively studied in nocturnal songbird migrants (e.g. Åkesson, 1994; Gwinner, 1986; Helm, Schwabl, & Gwinner, 2009; Muheim, Moore, & Phillips, 2006), much less attention has been paid to studies of migration €ckman, adaptations in diurnal migrants (e.g. Åkesson et al., 2015; Ba Pettersson, & Sandberg, 1997; Bingman & Wiltschko, 1988; Ilieva, Bianco, & Åkesson, 2016; Muheim, R., Jenni, L., & Weindler, P., 1999). Fuelling to accumulate fat prior to migration flights is a process that has been predicted to take a substantial amount of time (7:1 €m & Alerstam, fuelling:flight ratio) during migration (Hedenstro

https://doi.org/10.1016/j.anbehav.2018.10.007 0003-3472/© 2018 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

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M. Ilieva et al. / Animal Behaviour 146 (2018) 79e85

€berg et al., 2015), and had finished their postObservatory (Sjo juvenile moult. Until the start of the experiment, the birds were kept in individual cages indoors, in natural photoperiod for several days with food and water provided ad libitum. Birds were kept and tested in the facilities of Stensoffa Ecological Field Station at Lund University, and all captured birds survived and were released into the wild close to the point of capture after the experiment. All were released in good condition with body mass substantially higher than at capture (mean ¼ 7.89 ± 1.58 g). The birds spent on average 23 days (range 21e25 days) in captivity.

1997). An increase in fuelling rate and body mass may further be timed to coincide with the approach to predicted barriers (Fransson et al., 2001). Fransson et al. (2001) showed that thrush nightingales, Luscinia luscinia, increased mass gain in response to geomagnetic information available at a predicted stopover site in Egypt compared to control birds kept in cages near the site of capture in autumn. This result suggests that external information from the geomagnetic field, combining field intensity and angle of inclination, associated with a certain latitude, may trigger ecophysiological changes in young migratory birds. This experimental paradigm, using simulated geomagnetic displacements and observations of caged bird migrants, has thereafter been used to study fuelling responses and mass gain as a response to changes in the geomag€m et al., netic field in several species of caged songbirds (e.g. Bostro € m, Kullberg, & Åkesson, 2012; Kullberg, Henshaw, 2010; Bostro Jakobsson, Johansson, & Fransson, 2007; Kullberg, Lind, Fransson, Jakobsson, & Vallin, 2003; Henshaw et al., 2008), including the recent work on diurnally migrating dunnocks (Ilieva et al., 2016). Most of the magnetic displacement studies performed so far, however, lack measurements of migratory activity, which may be important for evaluation of the results (Ilieva et al., 2016). Ilieva et al. (2016) found no difference in mass increase between first-year dunnocks kept in a local magnetic field and those kept in a field resembling their wintering area. However, the birds that were magnetically displaced to northwest Russia (northernmost limit of the European species distribution) showed a slightly, but significantly, lower rate of fuelling in comparison to the control group. Possible explanations for these results could be (1) an anomalous response due to unrealistic geomagnetic parameters (i.e. tested birds might originally be from a more southern population than northern Russia) or (2) elevated activity reducing the fuelling rate (i.e. the birds felt they were late in their migratory schedule and tried to reduce the delay by increasing activity) which was also pointed out by Ilieva et al. (2016). Furthermore, it cannot be excluded that a combination of both these phenomena caused the different fuelling rates observed. To investigate whether the magnetic field manipulation has any effect on the level of migratory activity in dunnocks, we repeated the same experiment as was performed in autumn 2015, with an identical setting and initial stepwise magnetic displacement as in Ilieva et al. (2016), but with the addition of a camera set-up that allowed us to quantify the level of migratory restlessness of the individual birds. In this paper we report the complete results from these measurements, as well as fuelling (mass gain) in our young dunnocks. We set up the following predictions. (1) Diurnally migrating dunnocks will show a clear and repeatable daily activity pattern in relation to sunrise and sunset times, with the highest activity in the morning, when they naturally migrate. (2) Morning activity is associated with migration, and will be affected by simulated magnetic displacements, resulting in reduced morning activity as the birds are exposed to magnetic fields found at the wintering areas in southwest Europe. (3) Mass increase will be positively associated with the level of evening foraging activity.

Throughout the experimental period (9e25 October), birds were kept individually in nonmagnetic cylindrical cages (550 mm in diameter and 700 mm high) placed in six wooden sheds (15 m2 internal surface) with semitransparent plastic roofs, in which the temperature was recorded continuously. In each shed four circular cages were placed in the middle of an electromagnetic coil allowing manipulation of the magnetic field inside the coil. For more details on the experimental facilities, see Ilieva et al. (2016). The cameras had no measurable effect on the local Earth's magnetic field (control group) nor on the simulated magnetic field generated by the coils (magnetically displaced groups). We extensively tested these effects using Honeywell HMR2300 magnetometers before the experiment. We tested two different models of camera and accurately positioned the cameras at the furthest possible distance from the cages. We constantly monitored and recorded the geomagnetic field in all houses to ensure the correct geomagnetic parameters were measured throughout the experiment. In addition, we are also confident that no radio frequencies that could disrupt the birds' magnetic sense (Engels et al., 2014) were €berg, and Pinzonemitted by the cameras. Indeed, Muheim, Sjo Rodriguez (2016) in a similar camera set-up and identical location could demonstrate that only introducing an artificially generated radiofrequency in addition to a digital camera caused the loss of magnetic orientation in tested birds. For the experiment we randomly divided the dunnocks into three groups (one control and two magnetically displaced) comprising eight individuals each. In a 5-day stepwise displacement, group 1 was magnetically displaced southwest to the expected wintering grounds in southern France (Fransson & HallKarlsson, 2008; hereafter south), group 2 to the northeast reaching the magnetic field of northwest Russia, representing the northern limit of the European species distribution (north), and group 3 was kept in the local geomagnetic field of southern Sweden, where the birds were caught (control; Fig. 1a, Table 1). The displaced birds (groups 1 and 2), were kept in their destination magnetic field for 10 more days while the control group was kept in the local magnetic field for the whole 15-day period. The values of the magnetic field were calculated using the Fortran code of the 12th International Geomagnetic Reference Field model (IGRF-12, bault et al., 2015). The

METHODS

Food and Body Mass

Experimental Birds and Housing Conditions

At the time the birds were moved to the experimental cages, most had slightly increased in weight since capture (mean ¼ 0.89 ± 1.44 g) and had fat scores between 2 and 4. Every day during the experiment, the birds received fresh water and mealworms, Tenebrio molitor, at 1000 hours local summer time (þ1200 UTC). At the same time they were weighed using electronic balances (Precisa BJ410C; Precisa Gravimetrics AG, Dietikon, Switzerland) situated under each cage and connected with the

First-year dunnocks were captured with mist-nets near Stensoffa Ecological Field Station (55
410 N, 13
260 E) in southwestern Sweden in October 2015. At capture we selected 24 birds that were in migratory condition, that is, had fat scores between 1 and 3 according to the visual scale for fat classification of Pettersson and Hasselquist (1985), and extended to 0e9 at Falsterbo Bird

Test Facilities and Magnetic Field Exposure

M. Ilieva et al. / Animal Behaviour 146 (2018) 79e85

I (µT)

81

Displacement

DIP (°)

North

Control

South

(a) 16

(b)

12

65°N

8 4 60°N weight (g)

(c)

55°N

27 24 21 18

50°N

(d) 7.5 5

45°N

2.5 0 0°

10°E

20°E

30°E

40°E

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Day Figure 1. (a) Map showing the simulated 5-day stepwise magnetic displacements (black arrows) of first-year migratory dunnocks from the experimental site (Lund, Sweden) towards both southern France (population-specific wintering grounds) and northwest Russia (northern limit of European species distribution). All displacements are 319 km long and lie on the rhumb line passing through the experimental site. The map is in Mercator projection and reports magnetic intensity (I) and inclination (DIP) isolines. Locations' coordinates and magnetic values are given in Table 1. Mean ± SD (N ¼ 8 for each group) of (b) food intake, (c) body mass and (d) body mass increase for the control and magnetically displaced birds. Dashed vertical lines indicate the end of the 5-day stepwise displacements before displaced birds were kept in constant geomagnetic parameters corresponding to specific geographical locations: southern France (south) and northwest Russia (north) as illustrated in (a).

perch (Ilieva et al., 2016). The amount of food eaten by each bird was estimated by weighing the remaining food left from the previous feeding. Mealworms that had escaped and were found were included in the measurements. Measure of Activity To measure individual birds' activity during the experimental procedure we implemented a custom computer vision system. We continuously recorded videos at 6 frames/s from above the four cages in each house with an Axis P1427-LE network camera. The cameras recorded colour video during daytime and automatically switched to night vision mode when natural light was too weak using the internal infrared light which is not visible to the birds. All videos were stored on hard drives and successively analysed using the Open Source Computer Vision Library (OpenCV) version 3.2 (http://opencv.org) as follows. First, we determined the position and the circular edge of each of the four cages with the Hough transform technique (Duda & Hart, 1972) using the initial frames of the video. Then, each cage was cropped in the video sequence and the behaviour of each bird analysed independently in a parallel process. For each frame of the video sequence, the position of the birds was extracted using the Gaussian mixture model background subtraction described in Zivkovic et al. (2006). In short, the algorithm dynamically models the background of the video, analysing the variance of individual pixels, and detects moving objects in the scene as the group of pixels that do not fit the model. A moving bird will thus be represented by the group of adjacent pixels that do not belong to the background, that is, the foreground pixels. We

determined the position of the bird as the centre of the ellipse fitted to the foreground pixels and normalized the positions' coordinates relative to the radius of the circle describing the cage (Bianco et al., 2016). To avoid false positive detections, the part of the image outside the cage's edge was masked out before the analysis. A quality control of the tracking algorithm was independently performed by two persons who randomly watched subsets of videos with overlays of the features extracted by the algorithm, that is, the correct detection of the cage and bird (i.e. no false positive detections), the bird's coordinates and the bird's speed. A short 1 min example of a video with overlays is given in the Supplementary Material. All the raw data extracted by the algorithm for the six houses and 24 birds throughout the experiment are given in Appendix Fig. A1 (see also below). We considered a bird to be in flight mode when it moved more than 1/10th of the cage radius between successive frames. This fixed distance used to define the flight mode was heuristically defined by looking at the distribution of speeds during active intervals. The distribution presented a net separation between little motion (i.e. the bird walking on the cage floor or twirling around its own body axis on the perch) and flying. These two motion categories were always substantially lower or higher than the selected threshold, respectively. We summarized the bird's flying activity as the fraction of frames spent in flying mode over a 20 min interval and we report it either as a ratio (Fig. A1) or as a percentage (Fig. 2). The overall activity was then computed as the number of 20 min intervals in which the birds were in flying mode for more than 5% of the time (i.e. 1 min). The 5% threshold was necessary to not include occasional activity, such as when an operator was introducing food/

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M. Ilieva et al. / Animal Behaviour 146 (2018) 79e85

Table 1 Location, geographical coordinates and geomagnetic parameters used during the 5-day simulated stepwise displacements Day

Location

Latitude (
)

Longitude (
)

Intensity (mT)

Inclination (
)

5 4 3 2 1

Northwest Russia Finland West Finland Baltic Sea Southeast Sweden Experimental site

67.80 65.38 62.96 60.54 58.12 55.70

30.01 26.12 22.57 19.31 16.28 13.45

53.8 53.1 52.4 51.7 51.0 50.3

77.7 76.3 74.9 73.4 71.8 70.1

Northern Germany Germany Northeast France France Southern France

53.28 50.86 48.44 46.02 43.60

10.79 8.28 5.89 3.62 1.44

49.6 48.9 48.0 47.2 46.2

68.3 66.3 64.1 61.7 59.1

e 1 2 3 4 5

Stepwise displacements were performed towards the destination in southern France (population-specific wintering grounds) and northwest Russia (northern limit of European species distribution) of first-year migratory dunnocks. Geomagnetic field was changed at noon of days 1e5, and thereafter kept constant for the next 10 days to the end bault of the experiment (day 15). Geomagnetic values were calculated using the 12th International Geomagnetic Reference Field model (IGRF-12) for 20 October 2015 (The et al., 2015).

water in the cages, or false positives introduced by noise in the videos (mainly during the automatic switch of the cameras from daylight to IR light and vice versa). Finally, active intervals were analysed for the full day (from 1200 to 1200 of the successive day), and separately in morning activity (0000e1200) and evening activity (1200e0000), respectively based on local time (þ0200 UTC; Fig. 3). Data Analysis Owing to a technical problem with one of the cameras, activity data for day 4 (morning) and day 5 (morning and evening) for one house (N ¼ 4 birds; north) are not included (see Fig. A1). The missing activity data, however, occurred during the stepwise displacement, and thus before birds were moved to their

0

destination location for the next 10 days (Fig. 3). In addition, one bird (ring 2KR32146; control) was unusually hyperactive during the first 1.5 days of the experiment (see Fig. A1) and feeding much less than the other birds. The exclusion of this bird from the analysis did not change the results or our conclusion, so we kept the bird in the analysis presented below. All the plots and statistical tests were done in the software R version 3.4.2 (R Core Team, 2017). One-way and repeated-measures ANOVA were used for temperature, food intake and body mass changes; whereas activity was analysed with a series of linear mixed-effect models, owing to the unbalanced and unequal withinsubject variance in the data. We used the R package lme4 version 1.1e14 (Bates, Maechler, Bolker, & Walker, 2015) to fit the models for our data and select the best model based on the Akaike information criterion (AIC) and likelihood ratio test.

5

10

15

20

Flying time (%) Full day (1200-1200)

Evening

North Control

Day

Morning

South

09 Oct 10 Oct 11 Oct 12 Oct 13 Oct 14 Oct 15 Oct 16 Oct 17 Oct 18 Oct 19 Oct 20 Oct 21 Oct 22 Oct 23 Oct 09 Oct 10 Oct 11 Oct 12 Oct 13 Oct 14 Oct 15 Oct 16 Oct 17 Oct 18 Oct 19 Oct 20 Oct 21 Oct 22 Oct 23 Oct 09 Oct 10 Oct 11 Oct 12 Oct 13 Oct 14 Oct 15 Oct 16 Oct 17 Oct 18 Oct 19 Oct 20 Oct 21 Oct 22 Oct 23 Oct 0000

1200

0000

1200

0000

Local time (+0200 UTC)

Figure 2. Actogram for first-year migratory dunnocks monitored from 9 to 24 October 2015 under simulated geomagnetic conditions (north and south displacements) and the local geomagnetic condition (control). Each horizontal line shows the mean time spent in flying mode by eight birds per group. Data are shown for 2 consecutive days: the second day is repeated as the first day on the successive line (except for first and last half-days). Dashed vertical lines indicate 1200 hours local time (þ1400 UTC), that is, the time when geomagnetic displacements took place during the first 5 days of the experiment (see Fig. 1).

M. Ilieva et al. / Animal Behaviour 146 (2018) 79e85

North

12

83

Control

South

10 Full day

8

6

8 7

Morning

Active intervals

9

6 5 4

4 3.5 Evening

3 2.5 2 1.5 1

3

5

7

9 11 13 15

1

3

5

7

9 11 13 15

1

3

5

7

9 11 13 15

Day Figure 3. Average daily activity of first-year migratory dunnocks for the control and two magnetically displaced groups (north and south; N ¼ 8 for each group) during 15 days of recordings. One group (control) was always kept to the local geomagnetic field (Sweden); for the other two groups, the magnetic field was changed daily to a different location for the first 5 days (until the dashed vertical line) and then kept to constant geomagnetic parameters corresponding to a defined geographical location (Fig. 1a): population-specific wintering grounds in southern France (south) and the northern limit of the European species distribution in northwest Russia (north). The daily activity was measured as the average number of 20 min active intervals (i.e. intervals when birds were flying for more than 1 min). It is presented for the full day and divided into morning and evening periods (see Fig. 2). Linear fitting lines are also reported to visualize trends in the data.

RESULTS Fuelling The mean daily temperature in the houses varied by a few degrees between days depending on weather conditions and cloud cover. Minimum temperature in the houses on day 10 (mean of all houses ± SD) was 7.9 ± 0.2
C and the maximum temperature on day 15 was 14.0 ± 0.3
C with an average of 11.4 ± 1.8
C. However, there was no difference in the houses' daily mean temperatures for the three treatments (repeated measures ANOVA, days 1e15: F2,3 ¼ 0.874, P ¼ 0.502). There was no significant difference between groups in food intake throughout the experiment (repeated measures ANOVA, days 1e15: F2,21 ¼ 0.641, P ¼ 0.537) or in initial body mass (north: mean ± SD: 20.9 ± 1.1 g; south: 21.1 ± 1.8 g; control: 19.9 ± 1.5 g; one-way ANOVA: F2,21 ¼ 1.437, P ¼ 0.260; Fig. 1b). All birds increased their body mass by approximately 30% during the experiment (Fig. 1b), with a small but significant difference between groups (repeated measures ANOVA, days 1e15: F2,21 ¼ 4.492, P < 0.05). A series of post hoc multiple comparisons of means showed that the difference was between the group displaced south and the control group (Tukey's test, days 1e15: P < 0.01), but only during the first 5 days of stepwise displacement (Tukey's test, days 1e5: P ¼ 0.01; Fig. 1b). Indeed, when we

recalculated the body mass increase over the 10 days of the experiment, that is, when the magnetic parameters were kept constant, there was no difference in mass increase between groups (repeated measures ANOVA, days 6e15: F2,21 ¼ 0.095, P ¼ 0.909). Activity Visual inspection of the actogram (Fig. 2) for the entire period revealed two clear and distinctly repeated daily activity periods: a longer one in the morning (lasting 2e3 h) and a shorter one (0.5e1 h) in the evening. Morning and evening peaks of flight activity were very finely tuned in the diel cycle with local sunrise and sunset time, respectively. Moreover, the flying activity appeared to vary over time both in intensity (Fig. 2) and in duration (Fig. 3). To investigate whether there were differences in migratory restlessness we analysed activity duration (i.e. number of active intervals; Fig. 3) between groups as outlined below. We built a simple (null) model with day as a fixed effect and allowing a random intercept for each bird to explain the number of active interval changes over time. Then we tested the null model against more complete models and selected the best one based on AIC values and a likelihood ratio test. The best model for the activity data included displacement and the interaction between day and displacement as fixed effects (lower AIC and likelihood ratio test against the null model: c24 ¼ 28.013, P < 0.001). This complete

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M. Ilieva et al. / Animal Behaviour 146 (2018) 79e85

model was also valid for morning activity (c24 ¼ 36.658, P < 0.001), whereas for evening activity the best model was simpler, without displacement (c22 ¼ 0.0183, P ¼ 0.991) or its interaction with day (c24 ¼ 0.1796, P ¼ 0.996) as fixed effects. As a result, the source of differences over time between displaced groups was only in morning activity, whereas during the evening all birds showed a similar decrease in activity (mean ± SE: 1.6 ± 0.3 min/day) without any effect of displacement (see also Fig. 3). Finally, the fixed-effects estimate of the complete model for morning activity showed that only the control group increased in activity over the course of the experiment (5.1 ± 1.2 min/day), whereas south and north displacements both had significantly different rates of activity change. Specifically, the south displacement showed a decrease in rate of activity (5.0 ± 1.7 min/day; P < 0.001) and the north displacement kept morning activity at relatively constant levels (0.8 ± 1.7 min/day; P < 0.001). DISCUSSION In the previous experiment with juvenile dunnocks (Ilieva et al., 2016), it was observed that the group displaced to the north gained less mass than the control group, which puzzled us. We hypothesized that the reason for this difference may be related to the level of activity expressed by the birds in the cages: highly active birds may not be able to increase their mass as much as less active ones (Ilieva et al., 2016). Level of migratory activity has been shown to be affected by food availability for caged songbirds, low levels of food leading to higher activity (Gwinner, Biebach, & Kries, 1985). The higher level of activity has been interpreted as increased searching for food or dispersal from bad foraging sites (Gwinner et al., 1985). However, in our case the birds had food ad libitum and we did not record a significant difference in food intake between groups (Fig. 1b). In the current work, we did not observe any difference in mass gain between the displaced and control groups but there were differences in activity, suggesting that other reasons such as the experimental setting and potentially the time of season may have affected fuelling and mass gain in the birds. Time of season has been shown to affect fuelling decisions and mass gain in caged bird migrants (Kullberg et al., 2003, 2007), the rate being higher in late birds. Indeed, the current study was performed earlier in the season (start date 9 October) than that of Ilieva et al. (2016; 20 October), and even though our experiment lasted 4 days longer, the birds were released earlier (24 October versus 31 October). In the current study, therefore, the dunnocks were ‘less late’ than in the previous study (Ilieva et al., 2016), and the group displaced to the north presumably did not need to speed up and waste their fuel load to advance their autumn migration. We further noted that the south displaced group was gaining significantly less body mass than the control group in the first 5 days of the experiment (the stepwise phase), but not during the last 10 days (with constant magnetic parameters), when the difference was nonsignificant. We speculate therefore that researchers could use a stepwise (more natural) displacement to trigger ecophysiological changes in caged birds. However, it is also possible that mass gain is triggered by a different mechanism than migratory activity (cf. Eikenaar & Bairlein, 2014; Gwinner et al., 1985). We found two peaks in activity in our recordings, which were present in all groups, with the highest level of activity in the morning, when dunnocks normally migrate (Dorka, 1966). The onset of activity was regular relative to the time of local sunrise and sunset, suggesting that the activity was controlled by the changing light conditions and an endogenous oscillator controlled by daylength (Gwinner, 1986, 1996). In contrast, the length of activity periods is likely to be regulated by internal factors as in other

€seler, & Bairlein, 2017). Birds are known diurnal migrants (Stey, Ro to show a morning peak in activity associated with increased foraging (Morton 1967). The extended morning activity in our study was expressed as increased flight activity, but not slow jumping and foraging movements; therefore, we believe it is associated with migration flight activity (Dorka, 1966). The evening activity in dunnocks, on the other hand, may be associated primarily with foraging or exploratory behaviour and not departure €mpfer, time as in nocturnal migratory birds (Schmaljohann, Ka Fritzsch, Kima, & Eikenaar, 2015). In fact, evening activity decreased throughout the experimental period for all groups, and was correlated with an increase in body mass, suggesting the evening activity may be associated primarily with foraging. The difference in overall activity (full day) between groups was due to the difference in morning activity (Fig. 3), suggesting that our magnetic displacements primarily affected the level of migratory activity between the different groups. The highest morning activity was observed in dunnocks kept at the site of capture, increasing with the season, while the migratory activity declined strongly over time for birds displaced to the expected wintering areas in southwest France (Fransson & Hall-Karlsson, 2008). The level of activity was kept at an even but somewhat reduced level for the north displaced group, suggesting the magnetic field parameters out of their expected range had no or limited effect on the birds' activity and ecophysiological responses, in contrast to the weight difference previously found by Ilieva et al. (2016). A similar experiment to ours was recently performed by Bulte, Heyers, Mouritsen, and Bairlein (2017) with a nocturnal passerine migrant: captive-bred northern wheatears, Oenanthe oenanthe. They measured activity with microphones over 1 month and found that the amount of night-time migratory restlessness increased in birds exposed to a local magnetic field in Germany, whereas the activity decreased when the magnetic field was experimentally changed as predicted along the birds' natural flyway (Bulte et al., 2017). The results from our study and that of Bulte et al. (2017) suggest that the geomagnetic field parameters, that is, angle of inclination and field intensity, expected to be met along predicted flyways act as an important trigger for migratory activity in both diurnal and nocturnal songbird migrants. Future studies, however, need to address how important these magnetic parameters are for migratory compass orientation, including inherited course shifts (e.g. Beck & Wiltschko, 1988; Gwinner & Wiltschko, 1978; Helbig, Berthold, & Wiltschko, 1989), but also how photoperiod may influence fuelling rate and migratory activity. Author contributions M.I., G.B. and S.Å. conceived the study, M.I. performed the fieldwork with support from G.B. and S.Å., G.B. evaluated the activity data, M.I., G.B. and S.Å. discussed the outcome of the results and wrote the manuscript. Declaration of interest We have no competing interests. Acknowledgments € holm for assistance during We are grateful to Christoffer Sjo fieldwork. We thank Andres Vigren and Johan Diedrichs from Axis Communications AB for technical support with camera equipment. The project received financial support from the Swedish Research Council to S.Å. (621-2013-4361) and the Centre for Animal Movement Research (CAnMove) financed by a Linnaeus grant (3492007-8690) from the Swedish Research Council and Lund

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