Days to Visit an Offshore Island: Effect of Weather Conditions on Arrival Fuel Load and Potential Flight Range for Common Blackbirds Turdus Merula Migrating Over the North Sea

doi:10.21203/rs.3.rs-695821/v1

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Days to Visit an Offshore Island: Effect of Weather Conditions on Arrival Fuel Load and Potential Flight Range for Common Blackbirds Turdus Merula Migrating Over the North Sea

https://doi.org/10.21203/rs.3.rs-695821/v1

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Background: Crossing open water instead of following the coast(line) is one way for landbirds to continue migration. However, depending on prevailing weather and the birds’ physiological condition, it is also a risky choice. To date, the question remains as to which interplay between environmental conditions and physiological condition force landbirds to stop on remote islands. We hypothesise that unfavourable winds would affect lean birds with low energy resources, while poor visibility would affect all birds regardless of their fuel load.

Methods: To test this hypothesis, we used data of 1312 Common Blackbirds Turdus merula, caught while stopping over on Helgoland during autumn and spring migration. Arrival fuel load was measured using quantitative magnetic resonance technology. Weather parameters (wind and relative humidity as a proxy for visibility) were interpolated for the night before arrival. Further, we calculated whether caught individuals would have managed to successfully cross the North Sea instead of landing on Helgoland, depending on wind conditions.

Results: Both wind and relative humidity the night before arrival were correlated with arrival fuel load. After nights with strong headwinds, birds caught the following day were mostly lean, most of which would not have managed to cross the sea if they had not stopped on Helgoland. On the other hand, fat birds that could have successfully travelled on were caught mainly after nights with high relative humidity (≥ 80%). Furthermore, the rate of presumably successful flights was lower due to wind: although only 9% of all blackbirds captured on Helgoland had insufficient fuel loads to allow safe onward migration in still air, real wind conditions would have prevented 30% of birds from successfully crossing the sea during autumn and 21% during spring migration.

Conclusions: We were able to decipher how physiological condition, wind and relative humidity force blackbirds to stop on a remote island. Adverse winds tend to affect lean birds with low energy resources, while poor visibility affects all birds, regardless of whether the arrival fuel load was sufficient for onward flight. Our findings will help to understand different migratory strategies and explain further questions like migration timing.

Terrestrial Ecology

Behavioral Ecology

arrival fuel load

body condition

EchoMRI

environmental condition

flight ranges

flow-assistance

migratory strategy

quantitative magnetic resonance

offshore bird migration

weather conditions

During migration, resting birds can be found in large numbers within coastal areas [1]. From there, crossing the sea is often one possibility to continue migration, providing advantages such as a significant reduction of flight distance and the time spent on migration [2, 3]. But, depending on prevailing weather conditions, it is also a risky choice with unique challenges, especially for terrestrial birds, as it covers a large, inhospitable area with limited opportunities for resting and refuelling [4, 5, 6, 7].

Depending on the migratory strategy used, the birds’ physiological condition (mainly fuel load) and environmental conditions, the open sea may even act as an ecological barrier [6, 8, 9, 10]. For bird migration along the East Atlantic flyway, the North Sea represents such a possible but small ecological barrier [11, 12]. Here, wind conditions in autumn are usually unfavourable for birds migrating south-west with predominantly westerly and south-westerly winds experienced as head- or crosswinds [13, 14]. In contrast, prevailing westerly winds in spring have a supportive direction for migrants [15, 16].

Crossing the open sea can bear a high mortality risk if land birds make navigational errors, deplete their fuel loads too early and/or face sudden unfavourable weather conditions [17, 18]. Successful crossing of a barrier is closely related to the bird’s fuel load and supportive weather conditions such as tailwinds [5, 6, 8, 9, 19]. On the other side, unfavourable weather conditions or dramatic changes in weather patterns can lead to poor or deteriorating flight conditions. They can even hinder the continuation of migration by causing “Zugstau”, i.e. local accumulation of migrants in a certain area due to an interruption of migration forced by obstructive weather [12, 20, 21, 22, 23].

In this context, weather conditions together with the bird’s fuel load lead to a wide range of observed strategies when birds are confronted with such barriers, i.e. crossing or circumventing [24, 25]. For example, birds with larger fuel loads tend to cross the barrier, while individuals with lower fuel loads tend to avoid it [26]. Further, weather conditions have been shown to shape, e.g., migratory timing [27, 28, 29, 30] and the bird’s departure decision (timing and direction) from the stopover site [28, 31, 32, 33, 34, 35]. These weather conditions, which have proven to influence the individuals’ migratory decision, include, e.g., temperature [33, 35, 36, 37, 38], precipitation [28, 29, 30, 39] and cloud cover [14, 35, 36, 40, 41, 42, 43]. However, the most influential weather covariates are pressure changes, wind and relative humidity (i.e. as a proxy for visibility [10, 23, 34, 43, 44, 45, 46]. In particular, visibility and wind directly influence the success of a flight [47]. Wind support is one of the most interesting, as it affects flight duration and range, the birds’ orientation and groundspeed [48, 49, 50, 51]. It also indirectly influences the birds’ fuel reserves: strong headwinds or drift increase fuel consumption [52], while greater wind support minimises travel time and fuel expenditure [53, 54].

The fuel load, in turn, determines departure probability [35], flight orientation [55, 56] and possible flight ranges [57, 58, 59]. If fuel loads are too low to reach the final destination, birds may opt for a migratory strategy involving a series of short flights rather than covering longer distances in non-stop flights [57]. During oversea crossings, the presence of islands provides the only natural opportunity for land birds to rest and refuel to then successfully reach the coast.

Especially remote islands on the route across the sea frequently exert a strong attraction on large numbers of birds [60, 61, 62]. This is also true for the small island of Helgoland [63], which lies about 45 km from the nearest island or coast. It is the only offshore/remote stopover site for land birds in the North Sea, often resulting in high concentrations of resting migrants [12, 20]. The number of birds stopping there is influenced by the weather conditions at the respective site: in good weather, only a small proportion of migrants stop on Helgoland, while most birds continue [64]. In deteriorating weather conditions, on the other hand, the number of birds that are “forced” to land, e.g., by the onset of fog or drizzle and/or drifted by wind, increases sharply [12, 63]. To what extent dwindling energy reserves force the birds to interrupt their journey is little known; especially in combination with weather conditions. Generally, fuel load is not considered a strong factor forcing birds to land on Helgoland, as their further journey across the sea, especially in autumn, is comparatively short, seems to require only low or moderate fuel loads and is therefore not considered a major problem for migrating birds [12, 65]. But how small or large is the “problem” that lean and/or fat birds have to face in reality when they choose not to stop on Helgoland but to travel on even though weather conditions are not conducive?

Understanding these (local) environmental and intrinsic arrival conditions that lead to the decision to interrupt flight is crucial for understanding birds’ movement and for theoretical predictions regarding different migratory strategies [47]. Yet, to our knowledge, few studies have focussed on arrival fuel load [33, 57], and even fewer have studied this intrinsic arrival condition in combination with possible interacting environmental effects [57, 65]. Therefore, the essential question remains unanswered, which environmental conditions actually force birds with which physiological condition to stop on a remote island like Helgoland. We hypothesise that unfavourable winds rather affect lean birds with low energy resources, while poor visibility effects all birds regardless of the sufficiency of their arrival fuel load.

To investigate this, we directly measured the arrival fuel load (using quantitative magnetic resonance technology; QMR) of a nocturnal short-distance migrant, the Common Blackbird Turdus merula. From this, we were then able to analyse the influence of local wind and relative humidity, as two of the most important weather parameters effecting migratory decisions during spring and autumn migration [27, 47, 66]. Additionally, we analysed other intrinsic factors, i.e. age and sex, that can influence how birds respond to weather conditions [67]. Lastly, we evaluated the fat score in comparison to the actual arrival fuel load to make our data more comparable to other studies (e.g. [10, 57, 65, 68]) that used fat scoring (cf. [69]).

Bird data

1312 Common Blackbirds of both sexes and age classes were caught, ringed and measured (cf. [12]) in the trapping garden of the Institute of Avian Research on Helgoland (54.18°N, 7.88°E) during autumn and spring migration 2017–2019 (Fig. 1). The blackbird is one of the most frequent species stopping on Helgoland during migration. These birds breed mainly in southern Fennoscandia and travel via the East-Atlantic flyway to reach their wintering grounds, particularly in the UK and western France [63, 70]. For a better understanding of the following overall method description, a supporting flow chart can be found as supplementary Fig. 1 in Additional file 1, chapter 1.

Body mass (±0.1 g), using an electronic scale, and the QMR fat mass (±0.01 g; hereafter: “absolute fuel load”), using the EchoMRI™ (EchoMRI Body Composition Analyser E26-262-BH, Zinsser Analytic GmbH, Frankfurt am Main, Germany; for more details on functionality, software settings, validation and accuracy see Kelsey & Bairlein [71], Kelsey et al. [72] and Additional file chapter 2), were measured for each bird on the day of first capture. This is considered to be the day of arrival [35], as most ringed blackbirds only stay on Helgoland for one or two days [73]; but median of four to five days in radio-telemetered blackbirds [35, 74]. Furthermore, it can be assumed that birds are most mobile on the first day after arrival and therefore the probability of capture is highest [75,76,77]. Fat score (levels 0 to 8) was estimated according to Kaiser [69]. Sex and age (latter only during autumn: “first-years”, i.e. first-calendar year birds, and “adults”, i.e. birds older than first-calendar year) of the blackbirds were classified based on plumage characteristics [78]. After measuring and ringing, the birds were released.

We estimated the individuals’ lean body mass by subtracting the absolute fuel load from the actual body mass. We then divided the absolute fuel load by the estimated lean body mass to calculate the birds’ relative fuel load (hereafter “arrival fuel load”).

Weather data and tailwind components

Spatiotemporal weather data were obtained using the National Centers for Environmental Prediction (NCEP) Reanalysis I dataset from the National Oceanic and Atmospheric Administration (NOAA/OAR/ESRL PSD, Boulder, Co, USA [79]) via the function “NCEP.gather” of the R-package “RNCEP” [80]. The extracted spatial grid covers Helgoland (nearest weather data point: 55°N and 7.5°) and the surrounding area from 50° to 60°N and 0° to 10°E with a spatial resolution of a grid cell with 2.5° × 2.5°, resulting in 25 grid cell intercepts along the covered area (Fig. 1). For each grid, the relative humidity (“rhum”; %) as well as u- and v-wind components (“uwnd”: east/west, “vwnd”: north/south; m/s) of each day during the time period 2017-2019 were used. These parameters were obtained for the pressure levels 1000 (near surface), 925 (~760 m above sea level) and 850 (~1500 m) hPa at 00:00 UTC (supplementary Table 1 in Additional file chapter 3), as these represent the most likely migration altitudes [15, 81] and are temporally close to the peak of the nocturnal migration after midnight [23, 35]. Information on other weather parameters that were considered but not taken into account for the final analyses can be found in Additional file, chapter 3.

We used the u- and v-wind components to calculate the pressure level-dependent tailwind components (hereafter TWC [m/s]; supplementary Table 1 in Additional file chapter 3) for each individual by multiplying wind speed by the cosine of the angular difference between the wind direction and the assumed flight direction, cf. Fransson [82] and Eikenaar et al. [74]. For this step, we used the function “NCEP.Airspeed” of the R-package “RNCEP” [80], which requires u- and v-wind components as well as the assumed airspeed and preferred flight directions of the bird. For our blackbirds, we assumed an airspeed of 10 m/s [83] and different flight directions depending on the season and oriented towards the selected potential flight paths (explained in the following chapter). Negative TWC values describe obstructive winds (i.e. headwinds) and positive values assisting winds (i.e. tailwinds).

As migratory birds are most likely to “choose” the appropriate flight altitude to seek more supportive winds [15, 53,54, 84,85], increasing survival probability when crossing an ecological barrier [8, 86], we had NCEP.Airspeed select the flight altitude (including associated relative humidity values) with the highest TWC for each individual (supplementary Table 2 in Additional file chapter 3). If more than one flight altitude had the highest value, the lower altitude of these was selected, as birds generally concentrate around the lowest altitude with supportive winds [15]. These altitude-dependent weather conditions were selected for each of the flight paths aligned with the selected coastal destinations (explained in the following chapter).

Flight distance and direction

For blackbirds stopping on Helgoland, four different exemplary coastal destinations with increasing flight distances and different flight directions were defined for each season (Fig. 1). The coastal destinations were selected depending on their location along the coastline lengthways of the assigned migration route of blackbirds, based on the descriptions in the avifauna of Helgoland [63] as well as in the ringing atlases of Germany [87], Norway [88] and Sweden [89]. In autumn, we selected the coastal destinations Wangerooge (Germany), Juist (Germany), Terschelling (Netherlands) and Caister-on-Sea (United Kingdom), while in spring, we chose St. Peter-Ording (Germany), Amrum (Germany), Blåvand (Denmark) and Mandal (Norway) as coastal destinations (Fig. 1). To determine flight distances (km) and directions (°) between Helgoland and the corresponding coastal destinations, we used the function “NCEP.flight” of the R-package “RNCEP” [80].

Successful flights (individual flight ranges and required fuel load)

As we wanted to identify possible causes for the blackbirds’ stopover on Helgoland, we calculated whether caught individuals would have managed the required flight distances to cross the North Sea to the coastal destinations instead of landing on Helgoland. This individual flight range depends on the experienced wind conditions corresponding to the different coastal destinations and the individual’s fuel load at arrival. Therefore, we 1) simulated flight trajectories and 2) calculated the required fuel load.

With point 1), we determined on which observation days the wind conditions along the possible flight paths would allow birds to reach the respective coastal destinations. For this purpose, we used the function “NCEP.flight” of the R-package “RNCEP”: This function simulates single flight trajectories according to the specified behavioural rules and thus determines how the animal will behave and move in relation to the flow-assistance (i.e. wind support, considering TWC and cross winds [90]). The simulation was set to start at midnight (see above) on Helgoland and end locations were defined as each selected coastal destination. After the start of the simulation, the altitude, from which flow-assistance was to be obtained (1000, 925, 850 hPa), was automatically recalibrated every half hour. The simulations stopped when 1) the final destination was reached, 2) flow-assistance fell below -12 m/s, or 3) simulated flights ran for 18 hours; the first case means that the model bird reached the final destination, the last two cases mean that it did not reach the destination. For the successful cases, NCEP.flight estimated the flight duration.

This estimate was used in point 2) to calculate the fuel load required for a successful crossing of the sea on each individual day. For this, we used equation two from Delingat et al. [58]: flight duration [h] = 100 × ln(1 + arrival fuel load) and rearranged:

Required fuel load [rel.] = e^{(flight duration [h]/100)} – 1 (1)

We defined a bird as being successful in reaching a coastal destination (hereafter “successful flight”) when the bird travelled on days with supportive flow-assistance (point 1) and the bird’s arrival fuel load was greater than the required fuel load (point 2).

In order to disentangle the effects of wind and fuel load of the birds, we additionally investigated whether the birds already had sufficient fuel loads at arrival that would theoretically allow them to reach the coastal destinations under still air (zero flow-assistance). To do this, we used equation three from Delingat et al. [58]: flight range [km] = 100 × airspeed [km/h] × ln(1 + arrival fuel load) [h], where airspeed is the birds’ own speed in still air. This equation was then rearranged again:

Sufficient fuel load (rel.) = e^{(flight range [km]/(100} ^{× airspeed [km/h])] [h]} – 1 (2)

where the flight range corresponds to the various coastal destinations (Fig. 1) and the airspeed was 10 m/s (= 36 km/h), as described above.

Statistical analyses

We conducted the data analyses using R [91] and provided uncertainty measures (2.5% and 97.5% quantiles of the symmetric 95% credible intervals, CrI) for each model using the function “sim” from the R-package “arm” (cf. [92]). An effect was specified as strong if the associated 95% CrI did not include zero or if the 95% CrI did not overlap between comparing groups.

Differences in body mass, lean body mass and absolute fuel load (dependent variables) between birds of different sex (two-level explanatory factor: male and female) or age (first-years and adults) or between the migratory seasons (autumn and spring) were tested with linear models (LM), including all corresponding interactions between the explanatory variables. Another LM was used to analyse absolute fuel loads (dependent variable) between different fat score levels (six-level explanatory factor: 0 to 5).

Since the arrival fuel load (dependent variable) is given in relative proportions, we used a generalised linear model with a binomial error distribution (GLM; family “quasibinomial”) to test for effects of sex, age and season (explanatory variables). The GLM family “quasibinomial” transforms the original data with a logit function (=logit^-1(x)). In order to compare the original arrival fuel load values between the groups, we back-transformed the data with the inverse logit function (=e^x/(1+ex)) provided by the R-function “plogis”. This way, we also correlated arrival fuel load (dependent variable) with fat score levels (six-level explanatory factor: 0 to 5).

The models used to analyse the relationship between the birds’ arrival fuel load and altitude-dependent weather conditions (see above) also consisted of GLMs separately for each season. TWC and relative humidity were included as continuous explanatory variables, sex and age (including the two-way interaction in autumn) as two-level explanatory factors, and arrival fuel load as dependent variable. For relative humidity, we used orthogonal polynomials up to a quadratic degree, which are uncorrelated and, therefore, allow the estimated linear and quadratic effects to be interpreted as purely linear and purely quadratic influences of the predictor on the outcome [92]. TWC was only included for a linear regression, as quadratic regressions were never significant in initial models.

As TWC depended on the assumed flight direction, for which we had chosen different possibilities (see above), we calculated models separately for each of the flight paths oriented towards the coastal destinations in spring and autumn. Using the function “vif” from the R-package “car” [93], we tested for multicollinearity using the variance inflation factors (VIF) of the variables. All continuous explanatory variables of the models were found to be non-collinear with VIF < 2 [94]. While GLM-transformed data are provided to compare effects between the explanatory variables and arrival fuel load (CrI do not include zero), back-transformed data (see above) were used to visualise the results.

In addition, we investigated the arrival condition of migrants on Helgoland in relation to the weather experienced in the night before arrival by distinguishing between migrants caught after “unfavourable weather” nights and migrants caught otherwise (after “favourable weather” nights). We defined unfavourable weather nights as either nights with TWC ≤ -5 m/s (following Erni et al. [47] on flow-assistance; hereafter “adverse winds”) and/or a relative humidity ≥ 80% (“high” relative humidity; hereafter “h-rhum”), as the latter is closely associated with poor visibility and orientation due to heavy cloud cover, fog, drizzle and precipitation [23, 95,96]. Favourable weather nights included TWC > -5 m/s (hereafter “favourable winds”) and/or relative humidity < 80% (“normal” relative humidity; hereafter “n-rhum”). The arrival fuel loads (dependent variable) of the individual blackbirds were compared between favourable and unfavourable weather nights as two-level explanatory factors (relative humidity: n- and h-rhum; TWC: favourable and adverse winds) using GLMs. Again, the models were considered separately for both seasons and flight paths aligned with the coastal destinations, and model outputs provided here include back-transformed values to allow comparisons of group means. Initially, the models included the interaction between relative humidity and TWC, but as the interaction did not show an effect in any of our models, it was excluded from further analysis.

All models initially included year as a random factor; however, as the variance of this parameter had an effect of (close to) zero, we excluded it from our models. Time of day-effects were not considered as relative fuel load showed high variability during the entire day with coefficients of determination (R²) close to zero in both seasons (spring: R² = 0.0003; autumn: R² = 0.0007), which is in accordance with Dierschke & Bindrich [65]. We assessed model assumptions (e.g. normal distribution of residuals, Tukey-Anscombe Plot) according to Korner-Nievergelt et al. [92]. Inspections of residuals plots presented no violation of the model assumptions for any model.

Percentage distribution and group comparison of arrival fuel load

In both seasons, 91% of the blackbirds studied (autumn: n = 919; spring: n = 393) already carried a sufficient fuel loads (≥ 1%) on arrival that would theoretically allow them to continue their journey immediately and reach the nearest coastal destination in still air (autumn: Wangerooge, 43 km; spring: St. Peter Ording, 49 km; Fig. 2). 36% of the birds in both seasons even had sufficient fuel loads (≥ 13%) to allow them to continue the migration over sea and reach the furthest coastal destination (autumn: Caister-on-Sea, UK, 442 km; spring: Mandal, Norway, 429 km).

In autumn, where we were able to compare age classes, we found a lower average arrival fuel load for first-year birds than for adults in both sexes (Table 1). Sexes did not differ, neither in first-year birds nor in adults. On the contrary, the sexes differed in their spring arrival fuel load by about two percentage points in favour of fatter males (Table 1). Females caught in spring had an arrival fuel load about two percentage points lower than female autumn birds; this was not the case for males. In all age and sex groups, there were individuals that had sufficient fuel load (≥ 13%) to reach the furthest coastal destination (AuMad: 52%, SpMad: 42%, AuFad: 41%, SpFad: 30%, AuMf and AuFf: 25%) and individuals that had insufficient fuel loads (< 1%) for a successful oversea crossing in still air (AuMf: 15%, AuFf: 12%, SpFad: 11%, SpMad and AuFad: 6%, AuMad: 3%).

Table 1: Average body mass, lean body mass, absolute and relative arrival fuel load.

Season

Sex

Age

Acronym

Body mass [g]

Lean body mass (g)

Absolute fuel load (g)

Arrival fuel load (rel.)

Autumn

Male

First-years

AuMf

212

98.1

(96.9 – 99.3)

90.61

(89.85 – 91.40)

7.47

(6.62 – 8.30)

0.082

(0.073 – 0.091)

Adults

AuMad

219

105.7

(104.5 – 106.9)

93.26

(92.45 – 94.01)

12.45

(11.59 – 13.25)

0.134

(0.124 – 0.145)

Female

First-years

AuFf

249

95.6

(94.5 – 96.7)

87.83

(87.13 – 88.56)

7.73

(6.94 – 8.50)

0.088

(0.080 – 0.096)

Adults

AuFad

239

100.4

(99.2 – 101.5)

89.98

(89.24 – 90.70)

10.36

(9.56 – 11.14)

0.115

(0.106 – 0.125)

Spring

Male

Adults

SpMad

200

102.8

(101.5 – 104.0)

91.65

(90.82 – 92.43)

11.12

(10.19 – 11.97)

0.121

(0.111 – 0.132)

Female

Adults

SpFad

193

95.0

(93.7 – 96.3)

86.79

(85.98 – 87.65)

8.22

(7.35 – 9.18)

0.095

(0.085 – 0.104)

Data are given for all migrating Common Blackbirds (n=1312), broken down by sex, age and season. For each group, sample size (n), mean values and 95% credible intervals (2.5% and 97.5%) are reported.

Arrival fuel load and weather

TWC, calculated for the flight path aligned with Wangerooge during autumn migration and with St. Peter-Ording and Amrum during spring migration (Fig. 1), showed a positive regression with arrival fuel load (for all, CrI did not include zero; Table 2, Fig. 3a, c), i.e. the lower the TWC during the night, the lower the arrival fuel load of birds caught on Helgoland the following day. Relative humidity was consistently correlated with arrival fuel load (positive linear and/or positive or negative quadratic regressions; Table 2, Fig. 3d), with the exception of the flight path that was directed towards the nearest coastal destination Wangerooge during autumn migration (Fig. 3b). Age (Fig. 3a, b) and the interaction of sex and age, i.e. that the effect of sex can change with age and vice versa, in autumn as well as sex in spring (Fig. 3c, d) influenced arrival fuel load in all models.

Table 2: Correlation between arrival fuel load, tailwind components (TWC), relative humidity, sex and age.

Autumn migration

Wangerooge

Juist

Terschelling

Caister-on-Sea

Intercept

-2.415

(-2.521 – -2.302)

-2.405

(-2.517 – -2.301)

-2.411

(-2.523 – -2.307)

-2.413

(-2.159 – -2.307)

Tailwind components

(linear)

0.017

(0.009 – 0.025)

0.003

(-0.006 – 0.012)

-0.004

(-0.012 – 0.005)

-0.006

(-0.014 – 0.003)

Relative Humidity

(linear)

1.499

(-0.104 – 3.215)

2.136

(0.592 – 3.630)

1.887

(0.359 – 3.516)

1.832

(0.290 – 3.474)

Relative Humidity

(quadratic)

-0.526

(-2.089 – 1.033)

1.600

(0.031 – 3.080)

1.512

(0.064 – 3.046)

1.445

(-0.013 – 2.971)

Sex

0.078

(-0.066 – 0.222)

0.071

(-0.070 – 0.214)

0.073

(-0.068 – 0.215)

0.073

(-0.069 – 0.216)

Age

0.517

(0.379 – 0.654)

0.532

(0.393 – 0.672)

0.522

(0.385 – 0.661)

0.517

(0.377 – 0.658)

Interaction

(Sex:Age)

-0.229

(-0.417 – -0.034)

-0.230

(-0.419 – -0.036)

-0.231

(-0.420 – -0.043)

-0.227

(-0.417 – -0.036)

Spring migration

St. Peter-Ording

Amrum

Blåvand

Mandal

Intercept

-2.199

(-2.236 – -2.004)

-2.042

(-2.157 – -1.930)

-1.985

(-2.094 – -1.879)

-1.970

(-2.077 – -1.866)

Tailwind components (linear)

0.024

(0.014 – 0.034)

0.017

(0.003 – 0.030)

-0.002

(-0.017 – 0.015)

-0.011

(-0.026 – 0.005)

Relative Humidity

(linear)

1.518

(-0.185 – 3.328)

2.135

(0.425 – 3.999)

2.443

(0.710 – 4.347)

2.117

(0.394 – 3.998)

Relative Humidity

(quadratic)

-1.896

(-3.685 – -0.024)

-0.533

(-2.290 – 1.232)

-0.617

(-2.398 – 1.167)

-0.245

(-1.993 – 1.474)

Sex

-0.238

(-0.394 – -0.083)

-0.264

(-0.422 – -0.108)

-0.283

(-0.438 – -0.128)

-0.285

(-0.441 – -0.131)

Separate models were run for both seasons, with each flight path aligned with a coastal destination (Fig. 1). During autumn migration, the two-way interaction of sex and age was included. Mean estimates and CrI are presented for each explanatory variable (transformed values; see methods). Effects (CrI do not include zero) are shown in bold. Reference category for age was first-years and for sex males.

When comparing nights with favourable (n-rhum and/or favourable winds) and unfavourable (h-rhum and/or adverse winds) weather, birds arriving during autumn migration did not differ in arrival fuel load regardless of the flight path (estimate means: 0.097-0.108; CrI overlapping between groups). An exception was the flight paths aligned with the nearest coastal destination, Wangerooge (Fig. 4a), where regardless of relative humidity, birds arriving after nights with adverse winds had lower arrival fuel load (n-rhum: n = 115, estimate mean = 0.085, CrI = 0.075 – 0.095; h-rhum: n = 76, 0.086, CrI = 0.076 – 0.098) than after nights with favourable winds (n-rhum: n = 280, estimate mean = 0.109, CrI = 0.101 – 0.117; h-rhum: n = 448, 0.110, CrI = 0.103 – 0.117).

In spring, the relationship between arrival fuel load and previous nights of favourable and unfavourable weather differed depending on the selected flight path: taking into account TWC for the flight path oriented towards the nearest coastal destination, St. Peter-Ording (Fig. 4b), birds had greater arrival fuel loads after nights with favourable winds (n-rhum: n = 111, estimate mean = 0.109, CrI = 0.101 – 0.117; h-rhum: n = 227, 0.110, CrI = 0.103 – 0.117) than after nights with adverse winds (n-rhum: n =45, estimate mean = 0.056, CrI = 0.042 – 0.072; h-rhum: n = 10, 0.062, CrI = 0.046 – 0.082). When modulating spring flight paths aligned with the other three coastal destinations, no effects were found when comparing the birds’ arrival fuel load between previous nights of favourable and unfavourable weather. Instead, these flight paths showed weak (Amrum – n-rhum: n = 114, 0.095, CrI = 0.083 – 0.109; h-rhum: n = 252, 0.118, CrI = 0.108 – 0.129; Mandal – n-rhum: n = 101, 0.093, CrI = 0.080 – 0.109; h-rhum: n = 255, 0.112, CrI = 0.104 – 0.122) or strong effects (Blåvand – n-rhum: n = 134, 0.091, CrI = 0.079 – 0.103; h-rhum: n = 242, 0.112, CrI = 0.106 – 0.127) when comparing relative humidity conditions on nights with favourable winds. On the other hand, no differences were found between relative humidity conditions on nights with prevailing adverse winds, possibly due to the small sample sizes caught after such nights (between 10 and 24 individuals; even zero for Blåvand).

Weather effects on percentage distribution and simulated successful flight rate

Wind effect

Under the directional flow-assistance simulated for individual birds during the presumed migration night before stopping on Helgoland and their individual arrival fuel load, 70% of the blackbirds caught in autumn would have successfully reached the nearest coastal destination (Wangerooge, Germany, 43 km; Fig. 5), while only 12% of the birds would have reached the furthest coastal destination (Caister-on-Sea, UK, 442 km). For spring migration, we found higher success rates: 79% of blackbirds would have been able to reach the nearest coastal destination (St. Peter-Ording, Germany, 49 km) and 27% the furthest coastal destination (Maland, Norway, 429 km).

Depending on the associated flow-assistance, there were birds in all groups that could have reached the most distant coastal destinations (SpMad = 34%, SpFad = 20%, AuMad = 14%, AuFad = 13%, AuFf = 11% and AuMf = 10%) and individuals that would not even reach the nearest coastal destination (AuMf = 36%, AuFf = 33%, AuFad = 29%, SpFad = 25%, AuMad = 21%, SpMad = 17%).

Combined effect of wind and relative humidity

During autumn migration, the fuel loads of blackbirds stopping on Helgoland differed after nights with favourable winds from nights with adverse winds, regardless of relative humidity: considering the flight path directed towards the nearest coastal destination Wangerooge, 24% and 25% of the birds stopping after nights with n- or h-rhum, respectively, carried less than 5% arrival fuel load when winds were favourable (Fig. 6b). All but two blackbirds with arrival fuel loads > 25% would have been able to successfully reach the nearest coastal destination. The three individuals with an arrival fuel load above 30% were caught after nights with h-rhum. Overall, i.e. across all fuel load classes, successful flights were possible in 73% (n-rhum) and 77% (h-rhum) of blackbirds caught during nights with favourable winds. When adverse winds prevailed, the percentage of lean birds captured the next day increased to 30% and 37% (Fig.6a). The percentage of successful flights was always lower on n-rhum nights than on h-rhum nights. Overall, 48% and 59% of the travelling birds would have managed to continue to the nearest destination Wangerooge in adverse winds, regardless of arrival fuel load. The difference between nights with favourable and adverse winds was also evident in birds with arrival fuel loads ≤10%, where success rates were higher on nights with favourable winds (n-rhum: 65% and h-rhum: 64%) than on nights with adverse winds (30% and 33%). Furthermore, only three of all 28 birds with arrival fuel loads >25% were caught after adverse wind nights.

Looking at the other three coastal destinations during autumn, the overall rate of simulated successful flights decreases with flight distance (Juist – favourable winds: 74%, adverse winds: 38%; Terschelling – 53% and 15%; Caister-on-Sea – 17% and 1%). None of the lean birds (fuel load ≤ 5%) would have successfully reached Terschelling or Caister-on-Sea on nights with adverse winds. Astonishingly, only two of the 247 individuals caught after such nights would have successfully reached Caister-on-Sea, independent of the arrival fuel load.

In spring, annual sample sizes of blackbirds caught after nights with adverse winds are small (n-rhum: 0-45 individuals; h-rhum: 10-24; Fig. 6c); this was due both to few nights with adverse winds (on 3-10 days) and to few individuals being caught the following day (only 1-4 days with more than 5 individuals caught). Looking at the weather conditions for the flight path aligned with the nearest coastal destination, St. Peter-Ording, the proportion of lean birds after such nights was 47% (n-hrum) and 80% (h-rhum: including 8 out of 10 individuals; Fig. 6c). Only five of the 55 blackbirds would have managed to fly on regardless of the arrival fuel load. After nights with favourable winds (Fig. 6d), 23 and 22% of the blackbirds arriving on Helgoland were lean after nights with n- or h-rhum, respectively. All birds with higher arrival fuel loads would have successfully reached St. Peter-Ording if n-rhum had also prevailed. 19 out of 22 individuals with arrival fuel load > 25% were found after nights with additional h-rhum, of which all but two birds would have been successful. Overall, the rate of successful flights during nights with favourable winds was 94 and 89%.

Looking at the other three coastal destinations in spring, we can again observe changes with increasing flight distance: The overall rate of successful flights decreased with flight distance (Amrum: 75 and 79%; Blåvand: 51 and 70%; Mandal: 20 and 32%). Similar to St. Peter-Ording, the number of successful flights on nights with adverse winds towards the other three coastal destinations was very low with only two to five individuals; most of them had more than 15% arrival fuel load.

Relationship between fat score, fuel loads and potential flight range

Absolute and relative arrival fuel loads differed between the fat score levels (Table 3), except for the comparison between fat score level “0” and “1”. Here, CrI overlapped in term of absolute and relative arrival fuel loads; most likely due to the small sample size for level “0”. All of the blackbirds classified in fat score level “5” had sufficient arrival fuel loads enabling them to fly between 446 km and 1,185 km in still air without further refuelling, theoretically allowing them to reach all possible coastal destinations. On the other hand, none of the blackbirds with a fat score of “0” would have been able to reach the next coastal destination in still air.

Table 3: Fat score levels, absolute and (relative) arrival fuel load and potential flight range.

Fat Score

Sample Size

Absolute fuel load (g)

Arrival fuel load (rel.)

Potential flight range (km)

0.06

(-3.7 – /3.88)

0.001

(0 – 0.127)

104

1.69

(0.78 – 2.58)

0.019

(0.015 – 0.025)

305

4.89

(4.33 – 5.42)

0.055

(0.050 – 0.060)

193

486

9.37

(8.95 – 9.78)

0.104

(0.099 – 0.109)

356

392

14.93

(14.49 – 15.30)

0.165

(0.159 – 0.172)

549

23.24

(21.04 – 25.14)

0.251

(0.218 – 0.286)

806

We have described the estimated mean (in bold) and CrI (in brackets). Corresponding potential flight ranges (km) are given considering still air (groundspeed = 10 m/s).

In this study, we were able to decipher in detail how physiological condition, wind and relative humidity force blackbirds to stop on a remote island. By directly measuring the individuals’ arrival fuel load using QMR, we were able to simulate the birds’ successful flight, assuming that they would not have stopped on Helgoland. This allowed us to separately consider two important aspects (energy limitation and weather as a flight obstruction) that influence different landing decisions in birds. As expected, we were able to show that adverse winds tended to affect the lean birds with low energy resources, while poor visibility, i.e. high relative humidity, affected all birds, regardless of whether the arrival fuel load was sufficient to allow onward flight. Furthermore, we could show that part of the variance found could be explained by age, sex and season, suggesting differentiated migratory strategies. Finally, the relationship between arrival fuel, measured by QMR, and visually estimated fat scores provides a better framework for more accurate estimations, e.g. of flight range, for future studies using fat scoring to assess fuel load.

Effect of arrival fuel load as energy limitation

In still air, over 90% of the birds stopping on Helgoland in both seasons carried sufficient fuel loads (≥ 1%) that would have allowed them to continue their travel to the nearest selected coastal destinations (Fig. 2) and would therefore not have needed to land. 36% would even have managed to reach the furthest coastal destination in the UK or Norway directly in a non-stop flight (sufficient fuel loads ≥ 13%). Therefore, although ¼ of the blackbirds arriving on Helgoland can be considered lean (≤ 5% arrival fuel load), most birds would not have needed to land on Helgoland due to limited energy reserves. Our results under still air support the statement of Dierschke & Bindrich [65] that fuel load alone does not have a strong influence on the birds’ decision of birds to land on Helgoland, as the further sea crossing represents only a short hop. Thus, the questions to be discussed are why birds that are not constrained by insufficient energy reserves for a successful onward flight stop on Helgoland, and to what extent the weather influences this choice.

In our study, we consider the fuel load to be equal to the birds’ fat amount. However, it is well known that migrating birds can also store proteins, which are predominantly catabolised from muscles and digestive organs (e.g. [97,98,99,100]), as an energy reserve for flight. Excluding proteins as fuel load may lead to an underestimation of the total fuel loads of the birds and maximum flight ranges calculated from them. Salewski et al. [101] showed that including muscle mass as fuel load resulted in a 35% higher mean potential flight range of four Garden Warblers in the desert. Therefore, a higher percentage of the blackbirds studied here may have been potentially able to reach coastal destinations than estimated. Yet, as Garden Warblers are long-distance migrants, simply adopting these assessments for the blackbirds in our study would almost certainly be incorrect, especially as proteins contribute only around 5% of the energy needed [102]. In general, the inclusion of protein as fuel load would only lead to a relative shift of our results without affecting the basic statement of our study.

Effect of winds as flight obstruction

Similar to studies on departure decisions [14, 30, 35], we were able to highlight wind as an important driver of decision to stop on Helgoland, as in both seasons, TWC experienced the night before landing on Helgoland was positively correlated with the arrival fuel load of blackbirds caught during the following day (Table 2, Fig. 3a, c). Thereby, our results indicate that especially lean birds are influenced by strong headwinds. As strong head- and crosswinds make orientation difficult, reduce the birds’ groundspeed and thus increase fuel consumption per unit distance [36, 52, 103], lean birds may not have the energy reserves to compete against strong headwinds and compensate for drift by strong crosswinds. Therefore, they are likely blown offshore towards Helgoland if winds were too strong [36, 104,105]. In addition to these already lean birds, it is also conceivable that previously better conditioned blackbirds suffered large energy losses due to increased energy consumption under strong head- and crosswinds and thus perceived Helgoland as an unplanned stopover to refuel.

The effect of such adverse winds on the probable mortality risk of terrestrial sea-crossing migrants is clearly shown in our study by the reduced proportion of successful simulated flights based on the prevailing winds during the night (Fig. 5): while only 9% of all blackbirds captured on Helgoland did not have sufficient fuel loads to allow them to safely travel on in still air (see previous chapter), wind conditions would have prevented 30% of birds during autumn migration and 21% during spring migration from successfully reaching the nearest coastal destination (Fig. 5). The lower success rate during autumn migration results from the prevailing westerly and south-westerly winds typical of Central Europe during this season, which are experienced as head- or crosswinds [13, 106] by blackbirds migrating mainly WSW from Scandinavia [63, 87, 107,108]. In our study, such winds inhibited successful flights on 24 to 65% of the 123 autumn nights observed. Spring migration showed a more supporting pattern with 57 to 92% of the 83 observed nights allowing successful flights. This reflects the more profitable and beneficial spring winds as blackbirds cross the North Sea with the main direction of migration in NE in birds [63, 87, 107,108]. Thereby, the wind conditions in spring of our observed years were more unfavourable than typically observed for Helgoland during this season [63]. This could therefore mean that, on average over the years, successful flights are possible on more days and thus also for a larger proportion of the birds.

Erni et al. [47] hypothesised that a flow-assistance ≤ -5 m/s can be considered stressful for migrating birds and a limiting threshold. Although TWC does not account for crosswinds, it is striking that birds caught on days following nights with such adverse winds (≤ -5 m/s) carried on average lower arrival fuel loads than when winds were favourable (Fig. 4), which also resulted in a higher proportion of lean birds (Fig. 6). Furthermore, the simulated possibility of successfully continuing on such nights and reaching at least the nearest coastal destination was greatly reduced, especially for lean birds. Therefore, our results strongly support the hypothesis of Erni et al. [47], as they indicate a higher mortality risk for blackbirds flying under adverse winds.

Interestingly, correlations between TWC experienced the night before landing on Helgoland and arrival fuel load of blackbirds stopping on Helgoland the next day were only found when analysing flight paths directed towards Wangerooge (178°; autumn migration) as well as St. Peter-Ording (72°) and Amrum (27°; spring migration; Table 2, Fig. 3, 4). Since we do not know where our birds actually come from and flight paths were selected on the basis of ringing data, we cannot clearly explain why wind influences were only found for the flight paths that were aligned with the less distant coastal destinations (Fig. 1). It is conceivable that the majority of our observed blackbirds stopping on Helgoland actually flew on these flight paths around the time of the weather measurements (00:00 UTC) before landing. This would only be possible if a) the birds actually departed from a starting point that was on the opposite side of the theoretical flight path (for autumn, Norway and for spring, UK or German-Dutch border) or/and b) the birds travelled along the coast and drifted into our flight path at some point before Helgoland. In both cases, fuel loads of some of the birds will have been dramatically depleted by strong headwinds [52,53,54]. (Nocturnal) birds in such a situation should try to reorient themselves parallel to the coastline [27, 66, 109], towards the nearest coastal destination. Therefore, birds driven by adverse winds to a point in front of Helgoland might have actually been oriented towards Wangerooge and St. Peter-Ording (possibly also Amrum) before deciding to stop over at Helgoland, as these are the quickest to reach with only 43 to 61 km flight distance (Fig. 1) and the success flight rate is the highest. This would be in line with results from Eikenaar et al. [74], who observed a departure direction of 181° of radio-tagged blackbirds on Helgoland. Radar observations on Helgoland during autumn migration also revealed that, besides the main south-westerly direction, a considerable proportion of nocturnal migrants fly in a southerly direction [110]. Interestingly, Hüppop & Hilgerloh [23] calculated a different autumn migration direction of 235° from recaptures within one month after ringing on Helgoland. However, during such a long-time interval, birds are able to fly detours and adjust flight directions several times. Thus, this mean total flight direction can be divided into two (or more) travel intervals: blackbirds (1) departing towards the nearest coastal destination, which would be consistent with Eikenaar et al. [74], part of the radar observation [111] and our results; (2) adjusting their flight direction towards the next stopover or final destination after reaching “safe” terrain. Further studies tracking blackbirds caught in their breeding areas are needed to identify preferred flight paths and from this the reasons why blackbirds fly over Helgoland.

Our findings on the influence of TWC complement the statements of previous studies: while for blackbirds and other thrushes the decision to depart (and probably also to continue or fly over) is favoured for an oversea crossing in weak and/or tailwind conditions [14, 23, 35], landing on an island en route across the sea should be favoured in strong headwinds – especially when energy reserves are low and mortality risk thus is high, as wind is considered the greatest determinant of annual apparent survival [112]. However, Dierschke & Bindrich [65] caught the heaviest birds on Helgoland in unfavourable winds. One reason for the differences with our study could be that this tendency towards heavier birds was based on big fall days (i.e days with > 200 individuals caught). As these events were combined with high overcast conditions, this could be the actual driver behind this effect. Understanding the influence of relative humidity, being closely related to fog or (low-lying) cloud cover and precipitation, in addition to wind conditions is thus important.

Additional effect of relative humidity as flight obstruction

In our study, all birds were affected by higher levels of relative humidity, regardless of the individuals’ arrival fuel load (Table 2, Fig. 3). Furthermore, comparisons between migration nights with n-rhum and h-rhum indicate that fatter birds stopped on Helgoland after nights with h-rhum (Fig. 4). Additionally, most blackbirds carrying large fuel loads were found after nights with h-rhum (Fig. 6), from which the majority would have been able to successfully travel on. These individuals would therefore not have had to stop on Helgoland due to energy limitation or adverse winds, but rather due to high relative humidity. As h-rhum is strongly associated with heavy cloud cover, fog, drizzle and precipitation [23, 95,96], this condition coincides with poor visibility, which is detrimental for orientation of migrating birds in general [113], and “forces” even fat birds to land on Helgoland. Brust et al. [14] also found that 65% of the birds flew on days with relative humidity below 80%. As few birds fly in such conditions [27, 114], birds use Helgoland as an emergency stopover regardless of their physiological condition, as it seems wiser to take off again when visibility is restored by clear skies [14]. Other related aspects such as increased flight costs in very humid air, which might affect the birds’ flight capability and thermoregulation, can additionally be weighed for the landing decision of the birds [10, 47, 115].

Interestingly, effects of relative humidity were often found for flight paths oriented towards coastal destinations that did not present effects of TWC (Table 2, Fig. 3, 4). Our results suggest that the effect of relative humidity can be partially superimposed by the effect of TWC. Here, the two weather parameters lead to effects on different sides of the arrival fuel load: while adverse winds affect the lean birds, most birds, including the fat ones, are additionally influenced by h-rhum. Thus, if TWC is the major influence [116], it may override “minor” effects of relative humidity.

Effects of season, age and sex

Although we were able to disentangle the effects of weather and arrival fuel load on the decision of blackbirds to stop, high variation remained. Therefore, sex and age classes need to be considered in a seasonal context, as they may have different migratory strategies and therefore may cope differently with different environmental situations [67].

Under assumed still air conditions, seasonal-dependent differences in arrival fuel loads were observed between sex and age groups (Table 1): adults carried larger fuel loads than first-years because they were possibly better prepared, most likely due to more experience [117, 118]. This would have enabled a higher proportion of the adults stopping on Helgoland to successfully reach a coastal destination without refuelling, indicating a reduction in the “mortality risk” compared to first-years. It should be kept in mind that birds with very large fuel loads usually do not stop on Helgoland, as shown by the high fat score levels of blackbirds that crashed with an offshore platform near the island [119]; therefore, our data represent a possible ‘capture bias’.

While no sex differences were evident during autumn migration, males carried larger arrival fuel loads than females during spring migration. Seasonal differences often represent different migratory strategies within a species, as in spring the timing of breeding and thus of arrival at the breeding areas is relevant for individual fitness due to carry-over effects between migration and breeding [120,121,122]: a later arrival at the breeding grounds leads to later breeding and ultimately to lower breeding success than earlier breeding conspecifics [112]. Therefore, larger fuel loads allow males in particular, which are under greater evolutionary pressure, to reach breeding areas earlier than females and establish themselves in prime territories [3, 123,124], as observed in many species [125,126,127]. Also, during spring migration on Helgoland, male blackbirds pass significantly earlier than females [110].

Once wind was included to simulate trajectories, the rates of successful flights of all blackbird groups were negatively affected by eleven to 14 percentage points in spring and eleven to 23 percentage points in autumn. Sex and age differences in still air (see above) remained evident, so that males had higher simulated success flight rates than females and adults had higher rates than first-years. We would have expected first-years to show either larger variance in simulated success flight rates than adults when wind effects were included, as they fly for the first time and ‘poor quality individuals’ were not yet ‘weeded out’ by natural selection, or to show larger decreases, as they are assumed to reach their final destination purely through endogenous programmes, including decisions about timing, flight direction and duration, whereas adults modify this programme by additionally developing a map through the experience of previous migration flight [62, 128,129,130,131]. Therefore, first-years, unlike adults, should lack the experience to correctly judge wind conditions as (not) supportive and thus select for certain more supportive winds and adjust their wind selection criteria [13, 132]. This hypothesis is supported by previous studies showing that first-years are less wind selective than adults at departure and have considerably less efficient migratory flights [74, 133,134,135]. However, we found no evidence for this in our study.

Relationship of fat score, fuel loads and potential flight range

Absolute and relative arrival fuel loads differed between fat score levels except between level “0” and “1”, and they varied greatly within each fat score level (Table 3). Our results show a way to validate the frequently used fat score and allow us to estimate the average flight range and, in particular, to narrow down the possible flight ranges. These correlations can be used by future studies to more accurately assess the blackbirds’ physiological condition despite the lack of direct QMR measurements.

Our results confirmed that blackbirds stop, at least partly, on Helgoland as the first suitable habitat when weather conditions become unfavourable. However, “unfavourable” seems to depend on the birds’ available fuel load: while headwinds, which increases fuel consumption of birds [52], lead to higher numbers of lean birds stopping on Helgoland, which could not continue flying in such winds, all birds regardless of fuel load, i.e. including birds carrying large amounts of fat, were affected by high relative humidity as a proxy for poor visibility compromising orientation. Our results provide a new perspective on previously statements that fuel load has no influence on the decision of birds to land on a remote island where a further sea crossing is only a seemingly short ecological barrier [65]. This may be the case if one considers purely still air conditions. But to successfully reach a destination, not only the fuel load but also the corresponding real wind conditions have to be considered. In this case, the fuel load has a clear influence on how the birds cope with certain wind conditions.

Of course, the Common Blackbird is only one of many passerine species stopping over on Helgoland, differing in their breeding and wintering areas, with some of them having to cross the Mediterranean Sea and/or the Sahara Desert as major ecological barriers. Here, other bird species might show different migratory strategies in dealing with unfavourable weather. Therefore, we encourage repeating this study in a multi-species approach, comparing different types like short/medium-distance migrants and trans-Saharan migrants or diurnal migrants versus nocturnal migrants.

In summary, understanding the local weather and individuals’ physiological conditions that lead to an interruption of migration, e.g. on an island like Helgoland as in our study, will help to understand the different observed migratory strategies and explain further migratory questions such as stopover duration, departure and total migration timing.

QMR: quantitative magnetic resonance technology used by the EchoMRI™

TWC: tailwind components

CrI: credible intervals

LM: linear models

GLM: generalised linear models

h-rhum: “high” relative humidity ≥80%

n-rhum: “normal” relative humidity < 80%

AuMad and AuFad: adult male and female blackbirds, respectively, during autumn migration

AuMf and AuFf: first-year male and female blackbirds, respectively, during autumn migration

SpMad and SpFad: adult male and female blackbirds, respectively, during spring migration

Ethics approval and consent to participate

Trapping, ringing and handling of the birds comply with the law of the Federal State of Schleswig-Holstein, Germany.

Consent for publication

Not applicable.

Availability of data and materials

The dataset analysed during the current study are available on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Ministry of Science and Culture of Lower Saxony (Grant No.

74ZN1477).

Authors’ contributions

FB conceived the project, OH and NK designed the study. NK conducted fieldwork, analysed the data (supported by OH) and drafted the manuscript. All authors contributed to reviewing and editing the manuscript. The authors read and approved the final manuscript.

Acknowledgments

We are grateful to Jochen Dierschke and Klaus Müller for logistical support on Helgoland.

We thank the staff of the island station for helping us.

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Days to Visit an Offshore Island: Effect of Weather Conditions on Arrival Fuel Load and Potential Flight Range for Common Blackbirds Turdus Merula Migrating Over the North Sea

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