Effect of Weather Conditions on the Spring Migration of Eurasian Woodcock and Consequences for Breeding

Ibis (2018) doi: 10.1111/ibi.12657

Effect of weather conditions on the spring migration of Eurasian Woodcock and consequences for breeding

KEVIN LE REST,1* ANDREW HOODLESS,2 CHRISTOPHER HEWARD,2 JEAN-LOUIS CAZENAVE3 & YVES FERRAND1 1Ofﬁce National de la Chasse et de la Faune Sauvage, 8 boulevard Albert Einstein, Nantes, 44300, France 2Game & Wildlife Conservation Trust, Burgate Manor, Fordingbridge, Hampshire SP6 1EF, UK 3Club National des Becassiers, 105 rue Louis Pergaud, Villeneuve, Champniers, 16430, France

Migration is a critical period of time with fitness consequences for birds. The develop- ment of tracking technologies now allows researchers to examine how different aspects of bird migration affect population dynamics. Weather conditions experienced during migration are expected to influence movements and, subsequently, the timing of arrival and the energetic costs involved. We analysed satellite-tracking data from 68 Eurasian Woodcock Scolopax rusticola fitted with Argos satellite tags in the British Isles and France (2012–17). First, we evaluated the effect of weather conditions (temperature, humidity, wind speed and direction, atmospheric stability and visibility) on migration movements of individuals. Then we investigated the consequences for breeding success (age ratio) and brood precocity (early-brood ratio) population-level indices while accounting for climatic variables on the breeding grounds. Air temperature, wind and relative humidity were the main variables related to migration movements, with high temperatures and northward winds greatly increasing the probability of onward flights, whereas a trend towards greater humidity over 4 days decreased the probability of movement. Breeding success was mostly affected by climatic variables on the breeding grounds. The proportion of juveniles in autumn was negatively correlated with temperature in May, but positively correlated with precipitation in June and July. Brood precocity was poorly explained by the covariates used in this study. Our data for the Eurasian Woodcock indi- cate that, although weather conditions during spring migration affect migration movements, they do not have a major influence on subsequent breeding success. Keywords: age ratio, Argos transmitter, breeding ground conditions, carry-over effects, early- brood ratio.

The most common way for birds to cope with sea- wintering in habitat with higher resources, lower sonal changes in weather and feeding conditions is predation risk and/or less competition (Alerstam to make ﬂights between separate wintering and et al. 2003, McKinnon et al. 2010). Undertaking breeding grounds, a mechanism which has a strong migration, however, carries potential costs. It genetic basis (Berthold et al. 1990, Berthold & encompasses an important part of the annual cycle Helbig 1992, Pulido et al. 1996, Pulido 2007, (up to 40–60% for some long-distance migrants, Liedvogel et al. 2011, Liedvogel & Lundberg Newton 2011) and comprises a period during 2014). The expression of this behaviour in a popu- which birds cross unknown areas with uncertain lation is related to the ﬁtness of migrating individ- food resources, weather conditions and predation uals vs. resident ones, e.g. by breeding and/or risk. Conditions experienced during migration may thus have major effects on the timing and energetic costs of the journey (Jenni & Schaub *Corresponding author. Email: [email protected] 2003), as well as on individual survival (Klaassen

et al. 2014). For instance, food abundance and This study focuses on the effect of weather con- availability at stopover sites are known to be ditions on the pre-breeding migration of a cryptic important variables influencing the duration of bird, the Eurasian Woodcock Scolopax rusticola stopovers for refuelling (Newton 2006). Moreover, (hereafter Woodcock). It is a widespread species the weather conditions such as wind strength and living in woodland habitats. It has a broad diet, direction (Liechti 2006, Shamoun-Baranes et al. composed mainly of lumbricids, myriapoda and 2017) experienced during the trip strongly influ- arthropods. The spring migration of the Woodcock ence migration progress, depending on the flight alternates between flights of several hundred kilo- strategy of the species, i.e. soaring or flapping metres at night and stopovers for one or several flight, fuel deposit capacity and altitude of flight days (https://Woodcockwatch.com; http://www. (Alerstam 2011). becassesmigration.fr; Crespo et al. 2016). Food Pre-breeding migration has profound conse- abundance and availability play an important role quences for the breeding season. Indeed, it sets in habitat selection during winter (Duriez et al. important characteristics for breeding, such as the 2005) but are not expected to influence migration date of arrival at the breeding ground and body movements because most of the habitats crossed condition on arrival. The latter determines the during migration are suitable for Woodcock (Cre- capacity of the bird to invest energy directly in spo et al. 2016). However, weather conditions are breeding activity (Sandberg & Moore 1996) and expected to have a large influence on stopover the former constrains the timing of breeding, e.g. duration during Woodcock migration and, conse- breeding success may depend on the relationship quently, on the duration of the complete spring between hatching date and seasonal availability of migration. food for chicks (Both et al. 2005, Møller et al. To test this, we linked satellite tracking data 2008, Saino et al. 2011). The energetic cost of from 68 individuals with remote sensing datasets migration is especially important for species using available daily at a global scale. The effect of stored resources for breeding, i.e. capital breeders 14 climatic variables expected to affect bird (Stephens et al. 2009), but the timing of migration migration, related to wind, temperature, visibil- has consequences for breeding whatever the breed- ity, humidity and atmospheric conditions ing strategies of the species (Both & Visser 2001, (Richardson 1990a, Shamoun-Baranes et al. Newton 2006). 2017), and when relevant their trends over a The study of bird migration has been revolu- few days, were first tested to explain the migra- tionized by the miniaturization of geolocation tion movements of Woodcock at the individual technologies, which now enable the tracking of level. We then investigated the consequences of most birds throughout their travels. The large weather conditions experienced during migration amount of geolocation data recorded has revealed on the breeding success (proportion of juve- unexpected migration routes and behaviours (Bur- niles) and brood precocity (proportion of juve- ger & Shaffer 2008, Klaassen et al. 2014, niles having completed their post-juvenile Weimerskirch et al. 2015). This technology also moult) during the subsequent breeding season offers new opportunities to study the mechanisms at the population level. However, because the and factors driving migration strategy at the indi- energetic needs of breeding are thought to be vidual level. One of the main challenges lies in gained mainly from local resources, especially linking these geolocation data to relevant informa- for an income breeder such as Woodcock (see tion on the factors expected to influence bird Stephens et al. 2009), the timing and success of movements at relevant spatial and temporal scales breeding will also depend on climatic variables (e.g. meteorology, Shamoun-Baranes et al. 2010). on the breeding grounds (Guzman & Arroyo Remote sensing data appear to be the best solu- 2015). Temperature and precipitation on the tion, because it is almost impossible to realize breeding grounds were thus calculated and field surveys at each bird position (Gordo 2007). included in the analyses. We discuss the impli- Worldwide data are now easily available from cations of our findings for Woodcock population public datasets, e.g. land cover classification, vege- dynamics and, more generally, for our under- tation indices and climate datasets, which offer standing of carry-over effects of spring migration valuable information on parameters affecting bird conditions on the reproductive success of migra- migration. tory birds.

Appendix S1 for details) and, when relevant, their METHODS trends over 4 days were extracted. The accuracy of these variables was not available, which may have Migration movements introduced additional uncertainty into the analysis From 2012 to 2017, 87 Woodcock were fitted (see Baker et al. 2017). All weather variables were with Argos satellite tags (solar PPT Microwave available four times daily, but only information at Telemetry, Inc., Columbia, MD, USA; 9.5 g) in 18 h (UTC) was extracted because Woodcock February–March in Western Europe (63 in the Bri- migration movements started at dusk. Moreover, all tish Isles in 2012–17 and 24 in France in 2015– altitude-dependent weather variables were 16). Tags were attached to the birds using a leg- extracted near the ground surface because GPS log- loop harness (Rappole & Tipton 1991) composed gers fitted on Woodcocks showed that they mainly of 1.6-mm-diameter, UV-resistant, marine-grade migrate at low altitude (A. Hoodless unpubl. data). rubber cord (EPDM cord; Polymax Ltd, Bordon, Weather variables were averaged over 3 days (time UK) passed through biomedical tubing (Silastic t, t + 1 and t + 2) to fit the tracking data time reso- tubing; Cole Palmer, St Neots, UK). The tag lution (time t corresponded to the first 10-h ON schedule alternated between a 10-h ON period period; time t + 1 and t + 2 to the 48-h OFF per- (Argos messages transmitted every 60 s) and a 48- iod) and their trends were assessed over 4 days h OFF period (no messages transmitted). Some of (adding the previous day, t À 1). the 87 tags failed prematurely and some others failed to transmit any signal during spring migra- Age and early-brood ratios tion. Moreover, several Woodcock were resident in the UK (no migration data) and some others died The effect of weather conditions experienced dur- (hunted or unknown causes). Consequently, only ing spring migration on subsequent breeding ide- 68 tags from the 87 used provided meaningful ally would be investigated at the individual level. data for studying spring migration. However, tracking data did not provide reliable The spring tracking data were converted to a information about breeding success. Frequent binary variable indicating whether the bird had movements of females during the breeding period continued its migration (1) or not (0) between indicated incomplete incubation, but this was two ON periods (first 10 h ON + 48 h OFF + insufficient to assess breeding success confidently. next 10 h ON, i.e. 68 h elapsed time). A buffer of However, the proportion of juveniles (hereafter 50 km was defined to assess whether the distance age ratio) and the proportion of juveniles hav- travelled between the two ON periods corre- ing finished their post-fledging moult before migra- sponded to local movement or a migration flight. tion (hereafter early-brood ratio) were available The threshold of 50 km was chosen according to each year from French and Russian monitoring the home-range of Woodcock during the breeding schemes. period, when movements were typically < 50 km Hunting of Woodcock wintering in Western (mean Æ sd = 14 Æ 23 km, n = 12). Such binary Europe is popular, especially on the wintering data thus described the probability of migration grounds and during autumn migration. In France, movement where at least two accurate locations a non-profit organization of Woodcock hunters were transmitted during a 68-h period. In total, (Club National des Becassiers) collected wings of 729 spring locations from 68 individuals were used hunted Woodcock in order to determine the age (Fig. 1). Spring migration movements in more and progress of moult (8000–10 000 wings col- than 1 year were recorded for 31 individuals, with lected each year). At the same time, a ringing pro- some birds yielding data for up to five consecutive gramme was managed by a governmental years. organization (Office National de la Chasse et de la Weather variables were extracted from the Faune Sauvage – ONCFS) and hunter organiza- NCEP Reanalysis (Kalnay et al. 1996) 2.5° 9 2.5° tions (Federation Nationale des Chasseurs – FNC, gridded data provided by the NOAA/OAR/ESRL Federations Departementales des Chasseurs – FDC). PSD (Boulder, CO, USA) website (http://www. Thousands of birds (5000–7000) were ringed in esrl.noaa.gov/psd/). Fourteen weather variables France each year through this programme and the related to wind, temperature, humidity, visibility age and moult of individuals were assessed. Tens and atmospheric conditions (see Table S1 in to a few hundred Woodcock were also caught in

Figure 1. The 729 locations from 68 individuals where spring migration movement of Woodcock could be assessed in a 68-h elapsed time. autumn near their breeding grounds in Central extracted from 1996 to 2017 for each location and Russia, thanks to a partnership between ONCFS date. As in the migration movement analysis, and a Russian organization (BirdsRussia). weather variables were averaged over 3 days (time Age (juvenile or adult) can be assessed easily t, t + 1 and t + 2) and their trends assessed over for Eurasian Woodcock using plumage characteris- 4 days (adding t À 1). All values were then aver- tics (Clausager 1973, Ferrand & Gossmann 2009), aged yearly, which resulted in a unique set of and age ratio (proportion of juveniles) provided an weather conditions each year and enabled a correl- index of breeding success at the population level. ative analysis with the breeding indices. We con- The age ratio of birds caught in Central Russia in sidered three scenarios of change in migration early autumn was especially valuable because it phenology from 1996 to 2017: (1) no change; (2) gave a measure of breeding success at the start of a linear shift of ¼ day per year (based on mean autumn migration. However, the number of indi- spring passage at Helgoland; Huppop€ & Huppop€ viduals caught was low in comparison with the 2003); and (3) a non-linear shift driven by spring thousands of birds collected each year in France. precocity. Scenario (2) was evaluated by retrospec- The moult stage of juveniles in autumn/winter tively shifting migration dates by 1 day every gave important information on their hatching 4 years because weather conditions were only date because early-hatched juveniles (early extracted in the evening. Scenario (3) was broods) moulted all their secondary coverts, achieved using air temperature data extracted for whereas late-hatched juveniles (late broods) scenario (1) to give an index of rise in spring tem- showed some juvenile coverts (autumn migration perature at the flyway scale. An annual delay or stops moult of the wing feathers, Ferrand & Goss- advancement in spring temperatures was then cal- mann 2009). The proportion of early-brood juve- culated. This variable was scaled, as the overall niles therefore provided information on breeding trend from 1996 to 2017 corresponded to a gen- phenology and its possible consequences for eral shift of ¼ day per year, as in scenario (2). The breeding success, e.g. higher mortality rate in difference in temperature values was then con- chicks reared later. verted into a number of days, which allowed a To link such population-level indices with non-linear shift of dates to be considered. spring migration weather, the tracking data loca- Age and early-brood ratios were also expected tions and dates were used as a sample to represent to be influenced by weather on the breeding Woodcock flyway movements. Important weather grounds. If so, failing to account for these vari- variables found to affect migration movement were ables may lead to fallacious results due to

spurious correlations between weather during during migration on age and early-brood ratios, spring migration and the breeding period. were investigated using linear models with normal Monthly mean temperature and precipitation in errors. Model residuals were visually checked for May, June and July from 1996 to 2017 were normality. A linear model accounted for overdis- therefore extracted from known breeding loca- persion due to the independent variance parame- tions of 63 tracked Woodcocks. Total precipita- ter r of the error term, whereas the use of a tion from January to April was extracted as an binomial model would imply that variance was a index of pre-breeding soil–water conditions each function of the mean, which ignores overdisper- year. sion. An alternative would be to use quasi-binomial models, but AIC could not be strictly evaluated with such an approach. Note that the Statistical analyses results remained equivalent using either quasi- The effect of weather conditions on spring binomial or linear models. The source of data migration movements was examined using gener- (birds ringed in Russia, ringed in France or hunted alized linear mixed models, with a binomial in France) was used as a factor to account for the error term and a logit link function. Weather differences between these sources, ensuring that variables were added as fixed effects and the covariates explained relative changes. Results individual bird was specified as a random effect. remained unchanged whether data source was Candidate covariates (fixed effects) were chosen considered as a random or a fixed effect. Parame- according to prior knowledge or their expected ters were estimated by weighted least squares to effect on migration movements. It was not possi- account for the differences in the number of indi- ble to consider all subsets of these candidate viduals ni used to calculate age and early-brood covariates (2k possibilities with linear effects). ratios. Covariate selection was thus done in several Candidate climatic variables were those steps starting from a model including only tem- selected in the migration movement analysis perature and wind effects (strong support from (eight variables from the best model selected) prior knowledge). In total, about 150 subsets of and conditions at the breeding grounds (seven covariates were considered (weather conditions variables, see above). These covariates could not and individual-based variables, e.g. age and all be considered in the same model because the breeding location) while avoiding collinearity number of covariates considered was high rela- between covariates (cross correlation < 0.7, see tive to the number of observations, and because Dormann et al. 2013). All subsets of covariates of collinearity between some of them. The analy- were then classified according to Aka€ıke’s infor- sis was thus carried out considering two main mation criterion (AIC) and we retained the best potential drivers of variation in observed index model and alternative models having DAIC < 2, values: (1) the climatic variables extracted on i.e. those having ‘substantial support’ according the breeding grounds and (2) the weather condi- to Burnham and Anderson (2002). Row coeffi- tions experienced during migration. The second cients of the models and their uncertainty are potential driver of variation was investigated by provided in Appendix S2 (see Table S2) and testing the three scenarios detailed above on the averaged parameters were calculated from this changes in migration phenology since 1996, i.e. subset of models (Symonds & Moussali 2011). (scenario 1) no change in migration dates, Model selection was performed on a subsample (scenario 2) a shift of ¼ day per year and of 668 observations for which there were no (scenario 3) an annual change in migration dates missing values for any of the covariates. How- depending on air temperature. Covariate selection ever, coefficients were estimated from the maxi- was done independently for each of these four mum number of observations with no missing sets of covariates (see Tables S3 and S7, in value in the set of covariates considered (from Appendices S3 and S4, respectively). Model selec- 668 to 729 depending on the covariates consid- tion was carried out using a corrected AIC (AICc) ered). Model residuals were checked for spatial with n = 23 (number of studied years). AICc was correlation. compared between the different scenarios, which The effects of climatic variables extracted on allowed assessment of the main cause of variation the breeding grounds, and the weather conditions in age ratio and early-brood ratio.

wetness. Precipitation rate had a skewed distribu- RESULTS tion, which prevented a good evaluation of its effect on migration movements. Migration movements Migration movements more frequently occurred Variable selection based on AIC resulted in reten- in the presence of positive air surface temperatures tion of 15 models with DAIC < 2. The models (> 3 °C), stable atmospheric conditions (positive had from 10 to 15 parameters (Table 1, best four-layer lifted index) and favourable winds Appendix S2). Air surface temperature, winds (especially northward winds, Fig. 2). A quadratic (especially northward wind), atmospheric stability effect was fitted to atmospheric conditions, such and relative humidity trend were the main that the highest values resulted in a decrease in weather variables associated with migration move- movement probability, but the number of observa- ments (Table 1, Fig. 2). These variables were pre- tions with such extreme conditions was small and, sent in all of the 15 competing models, and their hence, the uncertainty was high. Conversely, coefficients were very similar across them (see migration movements occurred less frequently in Appendix S2). Air pressure and relative humidity the presence of an increasing trend in relative were selected in most of the alternative models, humidity. Movements were most frequent in the but the estimated coefficients showed high uncer- presence of a slightly decreasing or stable relative tainties. Neither cloud cover nor cloud ceiling humidity trend. Uncertainty was very high for height (visibility) were selected in the models. The conditions with a strongly decreasing relative same was true for vertical wind speed and soil humidity trend (Fig. 2). Bird breeding origin (longitude and/or latitude of breeding area, linear migration distance; Table 1) also showed weak Table 1. Averaged parameters from the best set of models positive correlations with probability of migration (15 models with DAIC < 2, see Table S2 in Appendix S2) on movement. Longitude of the breeding area (Fig. 2) the probability of migration movement. was the variable which best highlighted this correlation, with reduced uncertainty in most of the Averaged Covariates Averaged standard models considered (Table 1, Appendix S2). (scaled) coefficients errors t-values Selected interactions (lifted index 9 air pressure trend and eastward 9 northward wind) enhanced Intercept 0.60 0.15 3.95 AIC values, but coefficient estimates showed high Longitude of 0.23 0.11 2.03 breeding area uncertainty. Air temperature 0.74 0.13 5.73 Eastward wind 0.15 0.09 1.66 (u-component) Age and early-brood ratios Northward wind 0.50 0.10 4.90 Theproportionofyoungamonglateautumn (v-component) Air pressure 0.15 0.10 1.46 andwinterbirdscollectedinFrancewaslower Air pressure trend 0.14 0.10 1.40 than that of Russian birds caught in early Best four-layer 0.23 0.12 1.90 autumn. Moreover, age ratio differed between lifted index ringed and hunted individuals in France, but (Best four-layer À0.25 0.07 À3.55 ² both showed synchrony over time (Fig. 3a, lifted index) = = Relative humidity 0.15 0.12 1.27 r 0.79, n 21 years). All the models consid- Relative humidity À0.14 0.09 À1.53 ered accounted for these differences through trend inclusion of the factor ‘type of data’.Variable (Relative humidity À0.16 0.06 À2.71 selection showed that the lowest AIC was ² trend) achieved by a model accounting for the climatic (Eastward wind 0.13 0.09 1.55 9 Northward wind) variables during the breeding period (Table 2, Best four-layer lifted 0.14 0.10 1.44 Table S3 in Appendix S3). The alternative best index 9 Air pressure model, accounting only for weather conditions trend during spring migration, was that of the non- À À Latitude of breeding 0.12 0.11 1.03 shifted migration hypothesis, but it performed area D > Migration distance 0.17 0.12 1.42 very poorly ( AIC 30, Table S4). The most important variables correlating with age ratio

Figure 2. Effect of air temperature (°C), atmospheric stability (best four-layer lifted index), wind speed (m/s), relative humidity trend and longitude of breeding area (decimal degree) on migration movements. The grey line with shaded area represent the mean and its 95% conﬁdence interval. The dashed horizontal line corresponds to a probability of migration movement of 0.5. were temperature in May and precipitation in positively related and stabilized in the presence July. The former was negatively related to the of high precipitation (Table 2, Fig. 4). Age ratio proportion of juveniles, whereas the latter was was also higher in the presence of high

Figure 3. Trends in (a) age ratio and (b) early-brood ratio from 1996 to 2017. Each source of data is drawn using a different colour (see key). temperature and precipitation in June. Overall, n = 21 years). Values in the Russian data were climatic variables on the breeding grounds slightly higher (Fig. 3b) but between-year variation explained 65% of the observed variation in age was high, so selection based on AIC favoured ratio (calculated from the deviance ratio of the models without the factor ‘type of data’ model including only the factor ‘source of (Table S4_1 in Appendix S4). The lowest AIC in data’). In contrast, the models accounting for the models examining early-brood ratio was weather conditions during migration only achieved by a model accounting for weather con- explained 10% (non-linear shifted migration), ditions under the hypothesis of non-shifted migra- 15% (linear shifted migration) and 37% (non- tion. Air pressure and relative humidity showed shifted migration) of the variation in age ratio negative correlations with early-brood ratio (Tables S3_1 to S3_3 in Appendix S3). More (Table 3), but their effects on migration move- importantly, the main weather variables corre- ments had high uncertainty (Table 1, lated with age ratio were not the ones that Appendix S2). The models evaluated under the were strongly related to migration movements. alternative hypotheses of linear and non-linear In particular, coefﬁcients estimated from air shifted migration phenology performed poorly pressure and air pressure trend exhibited high (Appendix S4). Of the climatic variables extracted uncertainties (Table 1). at the breeding grounds, only temperature in May Early-brood ratio was quite similar between was selected. Early-brood ratio was higher in the hunted and ringed individuals in France (r = 0.48, presence of higher temperatures in May.

Table 2. Estimated parameters of the best model fitting age correlated with spring migration movements of ratio to the climatic variables at the breeding grounds (see Woodcock (Fig. 2), with individuals logically tak- D ≫ Appendix S3 for other models, all having AIC 2). ing advantage of tailwinds. Intriguingly, the effect of northward wind was much stronger than the Estimated Estimated standard effect of eastward wind, although there was no Covariates (scaled) coefficients errors t-values apparent advantage to Woodcock in heading north faster. Wind speed and direction may not only Intercept 59.27 0.83 71.41 facilitate migration flights but may also provide Source: ringed 16.94 3.40 4.98 in Russia some information on conditions at the following Source: hunted 7.35 0.73 10.07 stopover sites. In particular, northward winds may in France give important information about temperature Temperature May À2.50 0.41 À6.10 trend (and snowmelt) at a broad scale, whereas Precipitation July 2.89 0.40 7.22 eastward winds are associated with rainy weather Precipitation June 1.69 0.41 4.12 Temperature June 0.75 0.45 1.67 and atmospheric instability (which may generate (Temperature June)² 1.57 0.37 4.24 snow when reaching cold areas). Another explana- (Precipitation July)² À1.54 0.46 À3.35 tion might be related to the structure of winds in R²: 0.80 AICc: 397.2 the different layers of the atmosphere. Indeed, birds may adapt their flight altitude by a compro- mise between wind support and air temperature (Kemp et al. 2013). We need reliable data on the DISCUSSION flight altitude of tracked Woodcock to investigate this hypothesis. Migration movements Atmospheric conditions have not often been Spring migration movements of Eurasian Wood- shown to influence non-soaring bird migration cock were clearly associated with particular (Richardson 1990b). Yet, in theory, stable atmo- weather conditions. Air surface temperature spheric conditions could also be advantageous for showed the strongest positive correlation, but flapping-flight migration by reducing turbulence in wind, atmospheric stability and relative humidity the air and the risk of encountering storms, thus trend were also highly related to migration move- encouraging the bird to migrate. Atmospheric sta- ment probability (Fig. 2). Many studies of bird bility was evaluated here by the best four-layer migration have shown that temperature has a lifted-index and was the third most important vari- major role in migration schedules (e.g. Marra et al. able (after temperature and wind) explaining 2005, Huppop€ & Winkel 2006, Gordo 2007, Woodcock spring migration movements (based on Tøttrup et al. 2010). Woodcock in particular estimated coefficients, Table 1). Moreover, as avoided moving when temperatures were below or expected, Woodcock migration movements were near 0 °C, and migration movement frequency lower when relative humidity increased. We were reached 50% above 4 °C and 80% above 11 °C. unable to find any study in which this variable has Temperature may have an indirect effect on previously been related to migration movements. migration movements, i.e. increasing food abun- Both atmospheric stability and relative humidity dance and availability at new sites along the migra- trend should thus be given greater consideration in tion route, which consequently stimulates birds to future studies modelling bird migration movements. continue their travel. This would be analogous to Migration movements of Woodcock still the green wave hypothesis for goose migration (Si occurred when weather conditions were not opti- et al. 2015, and references therein). mal. Indeed, the movement probability was rarely Wind is also known to be an important factor lower than 0.2 for each single effect (Fig. 2). Thus, influencing migration movements (Liechti 2006, it seems that only a combination of unfavourable Sinelschikova et al. 2007). Biologging technologies elements could drastically reduce migration move- permitting the tracking of birds throughout their ment probability towards zero and, conversely, just migration travels have recently highlighted the a few favourable factors could encourage some importance of this variable at the individual level birds to continue spring migration. A large amount (Conklin & Battley 2011, Safi et al. 2013). In our (88%) of the observed variance (evaluated from study, wind direction and strength were strongly the null model with only the individuals

Figure 4. Effect of temperature (°C) in May and June and precipitation (mm) in June and July on age ratio. The grey line with shaded area represent the mean and its 95% conﬁdence interval.

Table 3. Estimated parameters of the best model fitting early- accuracy according to the density of weather sta- brood ratio to the weather conditions under the hypothesis of tions, which may create additional variance or non-shifted migration phenology (see Appendix S4 for other even bias in analysis (Baker et al. 2017). Another models, all having DAIC > 2). part of this unexplained variance results from variables and processes unaccounted for in the models. Estimated For instance, energy expenditure and body condi- Covariates Estimated standard (scaled) coefficients errors t values tion could affect the decision of Woodcock to stop or fly on at the individual level throughout the Intercept 63.89 0.47 135.94 migration. Variables related to visibility (cloud À À Air pressure 1.98 0.45 4.4 cover and cloud-ceiling height) were not shown to Relative humidity À1.56 0.47 À3.32 (Air pressure)² À0.97 0.30 À3.23 affect Woodcock migration movements. R²: 0.33 AICc: 379.01 We found no difference in migration movements between juvenile (second calendar year) and adult Woodcock (third or more calendar considered as a random effect) remained unex- year). However, Woodcock breeding further east plained by the best model. A part of this unex- had a higher rate of migration movements than plained variance may be related to the accuracy of birds breeding closer to the wintering grounds the variable used in the model. In particular, some (Table 1). This is mainly related to the migration variables may have spatial variation in their distance the birds have to cover, as the longitude

of the breeding area is highly correlated with the thus increase post-fledging mortality. Guzman and distance between the wintering and breeding areas Arroyo (2015) also showed a negative effect of tem- (r = 0.97, n = 82 spring travels, DAIC of the alter- perature in July and a positive effect of precipitation native model = 1.1, see Appendix S2). Woodcock on relative Woodcock abundance in Spain during breeding further east and undertaking longer the following winter, which would be partly related migrations thus compensate for their long-distance to the breeding success that summer. However, travel by increasing their rate of migration move- they also found a positive effect of precipitation in ment. Migration will have a greater impact on the May, but did not provide interpretation of this fitness of these individuals, and they may need to result. be more efficient at finding resources at stopover We evaluated breeding success using the age areas and/or their breeding grounds might be ratio, which only gave the proportion of young expected to be of higher quality. Woodcock in the population a few months after the breeding period. The limitations in the interpretation of results on age ratio must be borne in Age and early-brood ratios mind because, although breeding productivity will Age ratio was higher among hunted Woodcock have a strong influence on age ratio values, condi- than those captured for ringing, but both trends tions during autumn migration may affect adults across years were very similar. The higher age ratio and juveniles differently, e.g. differential mortality among hunted birds is likely to be due to differen- and/or migration routes. The abundance of Wood- tial distribution of young birds according to hunt- cock in Western Europe in a given winter is likely ing pressure, such that young birds reaching to depend strongly on weather conditions (espe- wintering areas for the first time are more likely to cially frost) in northern and eastern countries, settle in hunted areas by accident than are adults, which push Woodcock towards southern areas which almost always returned to the same winter- such as Spain (Peron et al. 2011). Unfortunately, ing location (Fadat 1991). Once accounting for the this was not taken into account by Guzman and factor ‘source of data’, annual age ratio was mostly Arroyo (2015), and hence the relative importance related to climatic variables on the breeding of breeding ground conditions and autumn/winter grounds. Conversely, weather conditions during weather (and their correlations) on the abundance spring migration were unlikely to influence the pro- of wintering Woodcock still need to be evaluated. portion of juveniles. Temperature in May and June In contrast to age ratio, the early-brood ratio was and precipitation in June and July showed strong poorly related to the covariates studied. The weather correlations with age ratio. In May, most female variables during spring migration that were Woodcock on the Russian breeding grounds are correlated with early-brood ratio were not those cor- incubating, so a higher temperature might be con- related with migration movements. Such inconsisten- strued as good for breeding. However, temperature cies were probably due to multicollinearity between and precipitation were positively correlated in May the covariates measured during spring migration and (r = 0.43, n = 23 years) and heavy precipitation other important variables unaccounted for in our might reduce hatching success. The negative corre- models. Of the variables extracted on the breeding lation between age ratio and temperature in May grounds, only temperature in May may have a weak suggests that unstable weather in May (high tem- positive influence on early-brood ratio. This is plausi- peratures and precipitation) was detrimental to ble, as high temperatures in May advance spring phe- hatching success. Conversely, in June, most young nology and hence hatching date (Hoodless & would have hatched and would benefit from high Coulson 1998). However, because temperature in temperatures and precipitation, which was consis- May was negatively correlated with age ratio, it tent with the correlations we found. In July, dry might produce antagonistic effects on breeding suc- weather will probably drive earthworms deeper into cess and breeding precocity. the soil and make it harder for Woodcock to probe, which could prevent them from feeding on soil Implications for population dynamics invertebrates (Peach et al. 2004, Hoodless & Hirons 2007). Low rainfall in July could particularly affect In a recent review, Shamoun-Baranes et al. (2017) Woodcock chicks, most of which will be full grown provided an overview of the effect of atmospheric by July but still relatively na€ıve foragers, and could conditions on bird migration at the individual level

and gave some insights into the consequences of by the Shooting Times Woodcock Club. ONCFS and these conditions at the population level. The CNB financed the tags in France. CNB financed the authors highlighted that the feedback of weather tags thanks to a membership fee as well as private conditions experienced during migration on popu- donations. We would like to thank Bruno Meunier fi (President of CNB) and all the members of CNB for lation dynamics has not yet been suf ciently inves- their input since the first stages of this project. Owen tigated. We have shown that the weather Williams (The Woodcock Network) and Luke Harman conditions experienced during spring migration (University College Cork) helped to fit tags in Wales had little influence on breeding success (age ratio and Ireland. Pierre Launay (CNB), Damien Coreau and after the breeding period) or breeding phenology Francßois Gossmann (ONCFS), as well as many ringers (early-brood ratio) of Woodcock. 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