The aim of the genetic part of the research program was to determine the genetic structure of European breeding populations [see (14) for additional details] and to assign autumn migrants captured on the western flyway (southwest France) and the eastern flyway (Kuwait and Israel) to genetic breeding groups/areas.

We used a shotgun sequencing approach on an ion PGM (Personal Genome Machine) platform (Life Technologies) to develop 24-microsatellite loci from muscle tissue obtained from one specimen found dead in Kuwait in September 2014. We extracted DNA from the tail feather collected on all individuals captured on breeding sites or along migration flyways. A total of 1127 samples, including 143 duplicates, were genotyped for the 24 loci. The resulting breeding dataset consisted of 575 individuals sampled at 26 breeding sites for population-level analyses. We carried out further analyses without three loci that displayed a high frequency of null alleles and deviated from the Hardy-Weinberg equilibrium (HWE). We performed a randomized G test with 1000 replicates to test for genetic differentiation among sites.

We used two methods to uncover genetic population structure. First, the Bayesian clustering program STRUCTURE (28) allowed the assignment of the 575 breeding individuals to K populations by minimizing deviations from the HWE. We obtained the optimal number of clusters K from ΔK, based on the rate of change in the log probability of data in successive K values (29). Second, we applied a discriminant analysis of principal components (DAPC) (30) to the breeding dataset. DAPC is free from population genetic assumptions with inferences made on allelic similarity. It summarizes genetic variability of individuals within groups while optimizing group discrimination. We used sampling sites as the grouping variable. We carried out analyses out using the ADEGENET 2.0.1 package in R 3.3.1. We retained the first 115 principal components in the data transformation step, corresponding to 84.2% of genetic variance, and we saved three discriminant functions for further analyses. We reached consensus on population structure based on results from these two methods. The magnitude and direction of contemporary gene flow occurring between the consensus populations were estimated using the program BAYESASS 3.0.1 (31).

We then performed a cross-validation of the population structure. We randomly split the full breeding dataset (575 individuals) into a training and a validation dataset by randomly assigning 70% of individuals from each sampling site to the training set (402 individuals) and the remaining 30% (173 individuals) to the validation set. The training set defined the genetic makeup of the clustering to be tested, and we assigned individuals from the validation set to one of these populations by the program GENECLASS (32) using the Bayesian method described in (28). We repeated the process 10 times. The cross-validation on the three populations supported the strength of the northern and eastern clusters with an average of 85 and 79% of individuals, respectively, correctly assigned (fig. S2). Moderate correct assignment to the western population (52%) with a large contribution of the eastern population (36%) suggests low differentiation between these populations.

We finally assigned the 396 individuals captured during migration along the western flyway (in southwest France) and the eastern flyway (Kuwait and Israel) to one of the clusters defined previously by the program GENECLASS using a Bayesian method (33). We first performed assignments of migrants to the three defined genetic clusters (western, eastern, and northern) to reflect both the higher level, and thus stronger, genetic structure and our knowledge of migratory flyways.

We grouped individuals captured in southwest France into three categories, as for isotopic analyses: (i) wild migrants (captured in the wild with mist nets by ringers and first-calendar-year individuals seized by the hunting police, hatched and grown the same year in the wild), (ii) dummies (adults obviously kept in captivity during their last molt because of aberrant colors, e.g., blackish plumage, or one or more totally white remiges), and (iii) status unknown (adult seized birds that could be dummies or wild migrants). Chi-square tests were used to compare the distributions of origin assignment and especially to compare wild individuals (n = 73), captive individuals (n = 21), and individuals of unknown status (n = 172). The number of wild and first-calendar-year individuals here is 73 and in isotopic analyses is 74, because we did not succeed in sequencing one of the 74 samples. We found no significant difference in the distribution of assigned individuals to breeding populations among the different categories of French migratory birds (χ2 test = 16.601, P = 0.165). For genetic analyses, we thus pooled all birds captured in France to represent the western flyway.

We found significant differences in assignments between individuals captured in the eastern and western flyways (χ2 test = 30.565, P < 0.001). Hence, the procedure assigned 67.69% of migratory birds using the eastern flyway to the eastern population and 23.84% to the western population (fig. S3). In contrast, the assignment of individuals from the western flyway was more equally distributed among populations, with 39.10% assigned to the western population, 38.72% to the eastern one, and 21.80% to the northern one.

Stable isotopes and light loggers data indicated that individuals caught in southwest France did not originate from the eastern population. We therefore reran the assignment for the western flyway and removed the eastern population as a potential breeding origin. Individuals originated mostly from the western (66.54%) and northern (33.08%) populations (fig. S4). Further details on the genetic study can be found in (14), including pairwise FST (fixation index) between populations and estimates of contemporary gene flow.

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