Skip to main content
Download PDF
Download ePub

A 30,000-km journey by Apus apus pekinensis tracks arid lands between northern China and south-western Africa

Movement Ecology volume 10, Article number: 29 (2022) Cite this article

Abstract

Background

As a widely distributed and aerial migratory bird, the Common Swift (Apus apus) flies over a wide geographic range in Eurasia and Africa during migration. Although some studies have revealed the migration routes and phenology of European populations, A. a. apus (from hereon the nominate apus), the route used by its East Asian counterpart A. a. pekinensis (from hereon pekinensis) remained a mystery.

Methods

Using light level geolocators, we studied the migration of adult pekinensis breeding in Beijing from 2014 to 2018, and analysed full annual tracks obtained from 25 individuals. In addition, we used the mean monthly precipitation to assess the seasonal variations in humidity for the distribution ranges of the nominate apus and pekinensis. This environmental variable is considered to be critically relevant to their migratory phenology and food resource abundance.

Results

Our results show that the swifts perform a round-trip journey of ca 30,000 km each year, representing a detour of 26% in autumn and 15% in spring compared to the shortest route between the breeding site in Beijing and wintering areas in semi-arid south-western Africa. Compared to the nominate apus, pekinensis experiences drier conditions for longer periods of time. Remarkably, individuals from our study population tracked arid habitat along the entire migration corridor leading from a breeding site in Beijing to at least central Africa. In Africa, they explored more arid habitats during non-breeding than the nominate apus.

Conclusions

The migration route followed by pekinensis breeding in Beijing might suggest an adaptation to semi-arid habitat and dry climatic zones during non-breeding periods, and provides a piece of correlative evidence indicating the historical range expansion of the subspecies. This study highlights that the Common Swift may prove invaluable as a model species for studies of migration route formation and population divergence.

Background

Bird migration, connecting remote places, has captured the attention of humans for thousands of years, yet systematic scientific research only started about a hundred years ago [1]. It is generally believed that evolution of migration routes is driven and constrained by both external (ecological and biogeographic factors) and inherited factors (genetic components) [2]. This may lead to population divergence and speciation in long-distance migratory organisms [3], and some evidence of this has been found in several species, such as a long-distance migratory passerine, the Barn Swallow (Hirundo rustica) [4, 5]. Glaciation periods caused by historical climate change may have led to fluctuations in population distributions (e.g. expansion) [6], thus playing an important role in shaping current migration routes and strategies [7, 8], and further have an impact on population divergence [9]. Modern climatic conditions (e.g. temperature, rainfall and wind) may also affect the movement and survival of long-distance migrants [10, 11], of these, precipitation can affect the migration performance and survival rate of migrants, especially for aerial insectivorous birds, by changing the abundance and distribution of food resources [12, 13].

As a typical long-distance migratory and aerial insectivore, the Common Swift (Apus apus) is widely distributed in the Palearctic and often nests in cavities in buildings [14]. It has two subspecies: the nominate apus which breeds more northerly, from Europe and north-western Africa through to northern Asia, and the eastern subspecies pekinensis (also known as ‘Beijing Swift’) breeds from western/central Asia to eastern Asia [14, 15]. The easternmost breeding range of pekinensis overlaps with two other migratory swifts, i.e. the Pacific Swift (Apus pacificus), and the White-throated Needletail (Hirundapus caudacutus), whose migratory paths have been recently elucidated by light-level geolocators [16, 17], virtually following the East Asian-Australasian Flyway (EAAF) [18].

The Common Swift is an emerging model organism for the study of bird migration. In particular, several studies using different types of data loggers in European populations have revealed their migration routes, migration strategies and their inter-annual variation, unusual chain-migration patterns and possible drivers [19,20,21,22,23,24]. In contrast, the knowledge of migration behaviour and route choices of the eastern subspecies pekinensis is poorly understood. Documented specimens and field records have been collected from arid south-west Africa to southern Angola, Namibia and Botswana, and a small extent as far north as Zaire, Uganda, Sudan and United Arab Emirates [25, 26]. This evidence suggests a southern non-breeding distribution in Africa, also raising the possibility that pekinensis could follow an Asian-African flyway [27]. Yet the information hardly provides details on the migratory pathway en route to their breeding grounds in East Asia.

It is believed that the nominate apus and pekinensis can be reliably separated by some morphological features [27]. The breeding ranges of the two subspecies are parapatric, and a contact zone is traditionally considered to stretch from east of Lake Baikal to Iran through central Kazakhstan [28]. For long-distance migrants, seasonal migrations pose big challenges not only due to the high energy cost of flight, but also due to phenotypic and physiological adaptations to likely changeable environments between breeding and non-breeding habitats. Divergence in migratory routes, phenology and strategies can act as a prezygotic isolation mechanism to population differentiation between closely related migratory taxa [3, 29]. This may well be the case for the Common Swift. A recent study revealed that the northern population of nominate apus winters in west and central sub-Saharan Africa, whereas the southern European apus swifts spend winters in contiguous regions of central and southeastern Africa [22]. It suggests that, even within the European breeding populations of the nominate apus, there are substantial spatiotemporal separation in wintering grounds, as well as migratory phenology [22]. Therefore, uncovering the approximate wintering ground and detailed migratory route of pekinensis can help us understand the migration patterns of the East Asian population of the Common Swift, which is the prerequisite to allow comparisons of migration phenotypes and to make inferences of divergence between the nominate apus and pekinensis.

In this study, we characterized the migration route and phenology of pekinensis using light-level geolocators that were deployed during five breeding seasons at the Summer Palace, Beijing, China (a UNESCO World Heritage site). The study population is located almost at the easternmost part of the geographical range, and we expect that they might travel the longest migration distance between its potential breeding and nonbreeding areas as compared to the nominate apus. In turn, we expected that pekinensis exhibit a different migration strategy in terms of route and penology in comparison to the nominate subspecies. Because long-distance bird migrants commonly track seasonal resources between breeding and wintering grounds [30], we further hypothesized that it is highly likely pekinensis and apus experience very different environmental conditions throughout their migration. To this end, we tested the differences in precipitation along the migratory flyways between pekinensis and the nominate apus, since the amount of rainfall has been regarded as one key climatic factor that negatively correlate with swift’s survival [31, 32]. Together, this study complements our knowledge of the migratory strategies of an aerial long-distance migrant, and towards understanding population divergence in long-distance migratory birds.

Methods

Field procedure

We carried out fieldwork at the Kuoru Pavilion (116.2726°E, 39. 9891°N) situated in the Summer Palace in Beijing, China. Once per breeding season from 2014 to 2018, we captured breeding swifts using mist nets arranged around the pavilion between 03:45 and 06:00 on a single day around May 22th. Upon first capture, randomly selected individuals were fitted with light-level geolocators (0.67 g, Intigeo-W65C1, Migrate Technology Ltd.) using a full body harness made of soft braided flat nylon string (1 mm wide, average harness weight 0.09 g) around the neck and both wings [19], the total mass of the geolocators and harness is 2.04 ± 0.09% of the body mass of the birds. Using blood lancet and capillary, an extraction of 50 μl of blood was also collected from each individual’s brachial vein, and stored in 99% ethanol and a -80℃ freezer for the following molecular sex determination.

In this study, a total of 66 swifts were outfitted with geolocators of which 22 were retrieved the following year. We also banded 250 birds without attaching geolocators and recaptured 131 after one year. The average recovery rate with geolocator across four years was 30.0% (range 20.0–41.9%), which was lower than that in Sweden (ca 50%), but approximately close to those from other colonies (ca 30%) [33], and lower than the average recapture rate without geolocator in this study (53.4%, range 36.2–65.5%). Although there is evidence that light-level geolocators have a negative impact on the survival rate of swifts during migration [33], we assume that the very limited banding effort (i.e. we were allowed one 3-h capture period on a single day per year at this site), may be the most important reason for the low recovery rate in this study.

Finally, we successfully recaptured a total of 25 geolocators by 2018 including five birds with two-years of tracking data (three retrieved in the third year and two retrieved twice in the following two years). To allow consistent comparisons [24], we retained only the first year of data for the five individuals. Finally, 25 tracks covering both complete autumn and spring migrations were included in the analysis. In addition, the sex of fifteen males and six females were identified by a standard molecular sexing method [34], and four individuals were not sexed because no blood samples were taken at capture (Additional file 1: Table S1).

Data analysis

Position calculation

The light-level data were analysed using GeoLight 2.0.0 [35], an easy-to-adjust, intuitive and easy-to interpret package in R 4.0.2 [36]. More importantly, it allows comparisons of results generated with other packages in similar principals [19, 21, 37]. We set the threshold to 2 for log-transformed light-level data to identify the twilight events (dawn-sunrise and sunset-dusk) minimizing the latitude variation around the equinox [19, 20, 22]. In the pre-analysis, we found that the swifts stayed in the breeding site for a very short and variable period before and after breeding. Thus we used the “Hill-Ekstrom” procedure [38] to determine the single sun elevation angle for position estimating through the whole migration cycle for each individual [19, 20], which ranged from −4.8 to −6.6° (Additional file 2: Table S6). We excluded latitude estimates from two weeks before and after the spring and autumn equinoxes, as accuracy for latitude determination during this time is low. The movement state (i.e. stationary, directed flight) during these periods was estimated by changes in longitude and positions before and after [20, 37]. The final position data were imported into QGIS 3.14 for further analysis.

Stopover sites

Given the error margin of the light-level geolocators [39, 40], especially for the fast-flying species such as swifts, we used the changeLight function (quantile = 0.95, days = 3) [35] to determine the stopovers (local ranging). To avoid the inaccuracy of some positions caused by the occasional abnormal twilight events, we carried out manual correction for outliers with QGIS. Since the reported travel speed of Common Swift was above 250 km/day, and the stopovers below 2 days were considered indistinguishable from slow movement due to the inaccurate positioning [19, 21, 37], locations changing less than 500 km in at least 3 days were grouped as ‘stopover sites’ in our study. Then we selected the first position after the swifts reaching a stopover site to define the arrival date, and the last point before they leaving to define the departure date, and calculated the stopover time from that.

Autumn migration, winter quarters and spring migration

During the wintering period, the swifts were constantly moving around covering short distances per day, forming restricted “residence areas” with distinct arrival and departure paths, which were defined as wintering areas. The pekinensis swifts might move northeast in the later part of winter, often causing the central position of wintering area and median time of wintering period to be out of sync. Therefore, we chose the first position after the swifts entering the wintering area to define the end of autumn migration, and the last position before leaving to define the start of spring migration. The dates of arrival and departure from the wintering area were simultaneously determined in this step.

Fast east–west movements change the perceived day and night lengths the swifts experienced, interfering with accurate positioning, particularly latitude, which was more obvious during spring migration, so the end of the spring migration was determined by the first point less than 500 km away from Beijing, or at 116°E (longitude of Beijing).

Total migration distance

In order to reduce the effects of uncertainty associated with location estimates using geolocator data, we used the distances connecting 3-day average positions to calculate the total migration distance [19]. We also calculated the great circle route distance between the start and end points of each migration as the direct distance. Positions of stopover sites were replaced by the average locations during the stopover periods. The average migration speed was calculated including the stopover periods, while the travel speed was calculated excluding them. Most of the swifts in our study showed relatively long stays in or near the Congo Basin, which was considered by several studies to be part of the overwintering period [21], as the tracking of migratory birds for seasonal resources [30]. In order to describe the whole migration process, we selected the farthest and longest overwintering sites as endpoints to calculate the parameters of migration phenology.

We compared the sexual and seasonal variations of migration parameters using t-tests. In addition, we compared the migration phenology of two groups of individuals from different subspecies, using a detailed set of published data from Sweden (N = 25) [19, 37],representing northernmost populations of apus. In addition, we also compared our data with some general information on movement patterns from several European populations of apus in published studies which also used farthest sites as migration endpoints [22].

Precipitation in the distribution regions

We assessed the condition of aridity in both reported and randomly selected positions throughout the distribution ranges using monthly precipitation from 2014 to 2018. Since the populations of the nominate apus were known to have different migration patterns in the northern and southern European populations, we compared them with pekinensis from Beijing respectively. Firstly, we extracted the precipitation data of four breeding sites of pekinensis with phenological records [41,42,43], including Beijing and three sites less than 500 km from Beijing, and that of 24 breeding sites of nominate apus in central and western Europe (8 northern, and 16 southern) [22] (Additional file 1: Table S5). In addition, given the limited number of sites with reported phenology information, we randomly took 100 points respectively in the breeding ranges of the two subspecies with a distance greater than 2 degrees between each two adjacent points to assess the precipitation in their entire breeding areas [15, 44] (Additional file 3: Fig. S1A; Additional file 4: Table S7). According to our study and the literature records [19, 22], we selected two time periods, April to July for pekinensis and May to August for nominate apus, to compare the mean monthly precipitation experienced by the two subspecies in breeding areas. Secondly, since the positions of Common Swifts are not fixed during the wintering period, we used the precipitation of the corresponding month at each position [22] to calculate the average value (Additional file 5: Table S8). Since the location information available for populations of nominate apus is incomplete during the wintering period, we only take the precipitation experienced by the three populations in November and December for comparison. Similarly, we used the random point method to measure the precipitation in the whole wintering areas, and determined the wintering periods and ranges of the two subspecies: November to February for pekinensis, October to April for northern and November to February for southern populations of nominate apus [19, 21, 22]. We randomly and evenly took 50 points respectively in the wintering ranges of three geographical populations (Additional file 3: Fig. S1B; Additional file 6: Table S9). Finally, we compared the mean monthly precipitation during the breeding and wintering periods at both “reported sites” and “random sites” between the two geographical populations of nominate apus and pekinensis as well as the annual precipitation at “random sites”, and compared the mean monthly precipitation during the wintering period of the three populations between the “reported sites” and “random sites”. The historical precipitation was obtained from WorldClim 2.1 [45] (https://www.worldclim.org), with a resolution of 2.5 min.

Key parameters of migration and precipitation were extracted, and maps were made in QGIS 3.14. All statistics were carried out in R 4.0.2 [36].

Results

Migration route

Around July 17th (range: July 3rd–25th) (Table 1), the tagged swifts left Beijing after breeding and initially departed towards the northwest into Mongolia, and thereafter moved westwards. After this initial migration period they re-entered China, passing through northern Xinjiang, and entered central Asia through the Junggar Basin between the Altai and Tienshan Mountains (Fig. 1; Additional file 7: Fig. S2).

Table 1 Key phenological parameters of migration of A. a. pekinensis breeding in Beijing (N = 25)
Full size table
Fig. 1
figure 1

Maps showing the migration routes and stopover sites of A. a. pekinensis. N = 25 (for details see Additional file 7: Fig. S2). In A (autumn) and B (spring): the gray hatching shows the breeding distribution of pekinensis [15, 44], the dotted lines indicate the lack of data in the two weeks before/after the equinoxes, and the base map shows the global annual precipitation distribution. The circles in C (autumn) and D (spring) represent the stopover sites. Different colour symbols represent different individuals. The pentagram represents the pekinensis breeding site and fieldwork location in Beijing.

Full size image

From central Asia, the swifts migrated to north-eastern Africa with three main stopovers explored in this region. They crossed the Red Sea around August 16th (± 11 days, range: July 27th–September 9th). Thereafter they moved to central Africa, reaching the approximate longitude of the eastern Congo Basin in early September, where they remained for around 39 ± 16 days (mean ± SD) before slowly moving south. Due to the Autumnal Equinox, we were not able determine how the locations of most individuals during this period relate to the Congo Basin. The swifts reached the Southern African Plateau (on average 1000 m asl) around November 5th. There, the swifts stayed for 100 ± 16 days, roaming the area before moving northeast around February 13th (Table 1, Fig. 2).

Fig. 2
figure 2

Map showing the locations of A. a. pekinensis in wintering area. The circles represent the 3-day average positions of different individuals during the wintering period. Different colour symbols represent different individuals

Full size image

Soon after the onset of spring migration, the pekinensis swifts reached the eastern Congo Basin in mid-February, and stayed there for about one month (25 ± 20 days). Then they crossed the Red Sea, leaving Africa in early April, and flew back to the breeding area almost nonstop, arriving in Beijing on average at April 18th (± 9 days, range: April 7th–May 14th, with only one individual arriving in May) (Table 1).

The migration distance in autumn was 14,733 ± 775 km, which was significantly longer than in spring (13.572 ± 999 km) (t = 4.59, df = 24, p < 0.001) (Table 1). The average detour during autumn migration was 25.56 ± 6.71%, which was also bigger than that during spring migration (14.85 ± 8.00%) (t = 5.23, df = 24, p < 0.001).

Migration phenology

The difference in migration duration, speed and stopovers were significant between autumn and spring. In autumn, the average duration of migration was 111 ± 13 days, significantly longer than 64 ± 11 days in spring (t = 11.85, df = 24, p < 0.001). Therefore, the average total migration speed in autumn, 134 ± 17 km/day, was significantly slower than 217 ± 34 km/day in spring (t = −9.52, df = 24, p < 0.001). Excluding the stopover periods, the average travel speed in autumn was 423 ± 171 km/day, which was slightly slower than 528 ± 171 km/day in spring (t = −1.88, df = 24, p = 0.07) (Tables 1 and 2).

Table 2 Results of comparative analysis of characteristic parameters between autumn and spring using paired t-test
Full size table

We found that the swifts on average used four stopover sites in autumn and only one site in spring, most of them in the central part of Africa, possibly in or near the Congo Basin. The locations of the other stopover sites were clustered, of which the southwest coast of the Red Sea and the south coast of the Caspian Sea were two areas with highly used stopover areas with several individuals (Fig. 1C and D). The average total stopover time in autumn was 71 ± 15 days, significantly longer than 36 ± 12 days in spring (t = 7.66, df = 24, p < 0.001) (Tables 1 and 2). Moreover, our study revealed no difference in timing, distance, duration, or speed of migration between sexes (Additional file 1: Table S2 and S3).

Temporal and spatial distribution of precipitation

At breeding sites, where information about breeding and migration has been reported [22, 41,42,43], we did not find a significant difference in rainfall between groups (pekinensis vs. northern nominate apus: t = −0.43, df = 26.25, p = 0.67, and pekinensis vs. southern nominate apus: t = −1.45 df = 26.61 p = 0.16). Based on random sampling throughout the whole breeding areas, pekinensis experienced significantly lower rainfall (t = 11.40, df = 763.37, p < 0.001), as compared to ranges explored by nominate apus (Fig. 3). During the wintering period, pekinensis remained in significantly drier areas than nominate apus, shown both for the restricted sample of reported sites (pekinensis vs. northern nominate apus: t = 12.30, df = 86.30, p < 0.001; pekinensis vs. southern nominate apus: t = 13.97, df = 85.04, p < 0.001) and when the complete wintering ranges were considered (pekinensis vs. northern nominate apus: t = 2.27, df = 432.82, p = 0.02; pekinensis vs. southern nominate apus: t = 7.62, df = 390.53, p < 0.001) (Fig. 4).

Fig. 3
figure 3

Distribution of monthly precipitation during breeding season of the two subspecies. A: reported sites; B: random sites

Full size image
Fig. 4
figure 4

Distribution of monthly precipitation during wintering season of the two subspecies. A: reported sites; B: random sites

Full size image

The subspecies pekinensis tracked from Beijing spend 46.1% of their non-breeding period (123 ± 17 days, range 83–168 days) in areas with less than 600 mm of annual precipitation. This figure is much higher than that in the nominate apus from Sweden, which spent only 25 days mainly in the Sahara Desert (10.5% of non-breeding period) [19, 20]. In fact, the average annual precipitation for the distribution of pekinensis is also significantly lower than that for the nominate apus throughout its breeding range (284.01 ± 231.67 mm vs. 629.34 ± 292.66 mm; t = 9.25, df = 188.09, p < 0.001) and wintering area (pekinensis: 617.79 ± 421.54 mm; northern nominate apus: 1608.27 ± 396.29 mm; southern nominate apus: 1262.69 ± 378.70 mm; pekinensis vs. northern nominate apus: t = 12.05, df = 96.84, p < 0.001; pekinensis vs. southern nominate apus: t = 8.05, df = 96.90, p < 0.001), as calculated from random positions. When comparing the mean monthly precipitation between "reported sites" and "random sites" during the wintering period, we found that the precipitation experienced by these known individuals was different from that of the whole wintering area, with higher precipitation for northern nominate apus (t = 5.76, df = 243.39, p < 0.001), and lower for pekinensis (t = −8.46, df = 213.27, p < 0.001), but similar for southern nominate apus (t = -0.03, df = 147.03, p = 0.98).

Discussion

Differences in migration characteristics for pekinensis and nominate apus

Compared to the northern population of nominate apus breeding in Sweden for which migration routes and phenology have been well described [19, 22, 37], pekinensis from Beijing had a significantly longer breeding period, stopover time, migration distance and duration, shorter wintering period and lower movement speed, except not significantly for travel speed in spring (Additional file 1: Table S4). And compared to southern populations of nominate apus from several different sites, the migration distance of pekinensis in our study was nearly twice that of theirs [22]. Meanwhile, pekinensis spent 64% and 54% of their total migration duration at stopover sites in autumn and spring, respectively, which were both significantly higher than nominate apus from Sweden. As food resource and climate conditions at stopover sites are important for flight performance and survival during bird migration [46, 47], we assume it is likely that pekinensis spend more time accumulating fat reserves or ranging locally during migration in order to manage a longer migration journey [48]. As for the long suspected stopover in central Africa of pekinensis, more accurate tagging methods, such as GPS-tracking, are needed to pinpoint the location of the swifts and thus to understand their spatial use and ecology in the Congo Basin and surrounding plateaus.

The spring migration duration of pekinensis was much shorter than autumn with both faster migration and travel speeds, as found in nominate apus and many other migratory birds [19, 49]. The difference in migration speed between spring and autumn is usually explained as selection for early arrival due to intraspecific competition for mates and resources during the breeding season [50], which could be mainly mediated by seasonal variations in stopover duration [50, 51].

Migration and subspecies divergence

Divergence in migratory routes in closely related populations/subspecies has been considered to be associated with population divergence [3, 52].The most important difference revealed is that the migratory range of pekinensis covers to a large extent semi-arid habitats in Continental Asia (Fig. 1). In this study, the wintering area of pekinensis and the nominate apus from central Europe partially overlapped in the Katanga Plateau [22]. But in general, pekinensis overwinters in areas with less rainfall. Even so, pekinensis still followed drier habitat while nominate apus followed wetter habitat in areas with more rainfall, which may indicate that pekinensis might have adapted to different climatic zones or have different patterns of food resource utilization during non-breeding period [53]. Some small parts of the breeding range of pekinensis have variable seasonal precipitation. For instance, the narrow areas in the far east near the ocean and the southwestern foothill of the Himalayas, have relatively higher rainfall from July to August. However, pekinensis here usually begin their autumn migration by this time, suggesting that this subspecies may have a relatively strong attachment to arid habitats, especially during non-breeding periods.

Taking a direct route from the wintering grounds meant that pekinensis would have to pass over the Arabian Sea and the Tienshan-Himalaya mountains. To avoid the two physical barriers, i.e. ocean and mountains [54,55,56], there is another similar-length potential route passing around the Arabian Sea and turning northeast below the Himalayas, which is used by other bird species that have similar breeding and wintering areas to pekinensis in eastern Asia, such as Cuculus canorus [57] and Falco amurensis [58], with abundant rainfall from spring to early autumn in its latter part. The route taken by pekinensis, after leaving Africa, however, detours through the semi-arid regions of southwest, central and eastern Asia, thus flying through almost their entire distribution range. Along the way, they bypass the mountainous barrier from the Tienshan Mountains to the Himalayas through the Junggar Basin – an important corridor for birds using Central Asian and East African flyways, e.g. the threatened MacQueen's bustard (Chlamydotis macqueenii) [59]. This detour through suitable environments and tracing the subspecies’ range may support the hypothesis that the migration route might reflect the historical expansion of pekinensis eastward along this same route, which has been hypothesised but will need more evidence to be verified [60]. On this premise, they may have remained isolated from the nominate apus in an arid refugium during the glacial period before the eastward population expansion. In the case of the nominate apus, we speculate that its ancestral populations colonized Europe via the west coast of Africa, from a relatively wet refugium, bypassing their greatest barrier – the Sahara Desert. However, these scenarios must be inferred by coupling genomic-based historical demographic analysis and paleoclimate reconstruction[8]. Such an approach seizes a good opportunity to understand the roles of population expansion of pekinensis along this route and prior isolation from the nominate apus in an arid refugium during the evolution of the two subspecies.

At present, only a single population of pekinensis has been studied. We suggest that future work should be directed to investigate migration patterns in multiple populations of pekinensis throughout its geographical range in central and western Asia. While exploring these populations, parallel studies allow us to compare whether chain migration is also present in pekinensis [22],and to what extent nonbreeding grounds of other pekinensis populations would geographically overlap with the nominate apus or the Beijing pekinensis population studied here. Moreover, it would be extremely intriguing to delineate migration routes of populations at the contact zones (i.e. from Iran to central Kazakhstan), in order to determine whether a migratory divide can act as barrier to maintain population divergence between the nominate apus and pekinensis [5].

Using light level geolocation data not only sheds new lights on the migration of East Asian pekinensis of the Common Swift, also advances our knowledge of other migratory swift species in East Asia. Only recently, migration patterns of two East Asian breeding swifts,i.e. Pacific Swift Apus pacificus [16] and White-throated Needletail Hirundapus caudacutus [17] have been uncovered. It is not surprising that their migratory routes and wintering grounds follow the East Asian-Australasian Flyway given their non-breeding ranges in SE Asia and Australia. Unlike pekinensis, it is worth noting that their non-stop flight involves crossing the airspace over oceans. The formation of migratory patterns (e.g. the choice of migratory routes and wintering grounds) of Pacific Swift and White-throated Needletail may reflect an adaptation to explore forest habitats en route in a tropical climate [16, 17].

Conclusions

In conclusion, our results painted a picture of the poor-known migration progress of pekinensis tracked from Beijing, which travelled the 14,000 km one-way route through eastern and central Asia to south-western Africa each year. Compared with the nominate apus, the tracked pekinensis experienced more arid environmental conditions and habitats during the non-breeding periods. This pattern might indicate that the subspecies pekinensis of the Common Swift has adapted to explore arid regions at certain stages of their annual cycle, and might be correlative with the historical colonisation route by tracking preferred habitats from sub-Saharan Africa into Asia. However, whether this specialization in habitat preference, together with other possible environmental variables, could be part of the explanation as to why pekinensis diverged from nominate apus in their evolutionary history remains to be tested. The two subspecies may thus prove to be good models for further comparative studies of the specialized intrinsic genetic, behavioural and physiological mechanisms that allow swifts to manage a highly mobile life-style spending substantial part of the year constantly on the wing [61].

Availability of data and materials

The datasets supporting the study of this article were included within the additional files. The raw light data are available from the corresponding author.

References

  1. Rappole JH. The avian migrant: the biology of bird migration. New York: Columbia University Press; 2013.

    Book  Google Scholar 

  2. Alerstam T, Hedenstrom A, Åkesson S. Long-distance migration: evolution and determinants. Oikos. 2003;103:247–60.

    Article  Google Scholar 

  3. Turbek SP, Scordato ESC, Safran RJ. The role of seasonal migration in population divergence and reproductive isolation. Trends Ecol Evol. 2018;33:164–75.

    Article  PubMed  Google Scholar 

  4. Scordato ESC, Smith CCR, Semenov GA, Liu Y, Wilkins MR, Liang W, et al. Migratory divides coincide with reproductive barriers across replicated avian hybrid zones above the Tibetan Plateau. Grether G, editor. Ecol Lett. 2020;23:231–41.

    Article  PubMed  Google Scholar 

  5. Turbek P, Schield D, Scordato ESC, Contina A, Da XW, Liu Y, et al. A migratory divide spanning two continents is associated with genomic and ecological divergence. Evolution. 2022;76:722–36.

    Article  PubMed  Google Scholar 

  6. Milá B, Smith TB, Wayne RK. Postglacial population expansion drives the evolution of long-distance migration in a songbird. Evolution. 2006;60:2403–9.

    Article  PubMed  Google Scholar 

  7. Zink RM, Gardner AS. Glaciation as a migratory switch. Sci Adv. 2017;3: e1603133.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Thorup K, Pedersen L, da Fonseca RR, Naimi B, Nogués-Bravo D, Krapp M, et al. Response of an Afro-Palearctic bird migrant to glaciation cycles. Proc Natl Acad Sci USA. 2021;118: e2023836118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gu Z, Pan S, Lin Z, Hu L, Dai X, Chang J, et al. Climate-driven flyway changes and memory-based long-distance migration. Nature. 2021;591:259–64.

    Article  CAS  PubMed  Google Scholar 

  10. Both C, Bouwhuis S, Lessells CM, Visser ME. Climate change and population declines in a long-distance migratory bird. Nature. 2006;441:81–3.

    Article  CAS  PubMed  Google Scholar 

  11. Sanz JJ, Potti J, Moreno J, Merino S, FRÍAS O. Climate change and fitness components of a migratory bird breeding in the Mediterranean region: pied Flycatcher responses to climate change. Global Change Biol. 2003;9:461–72.

    Article  Google Scholar 

  12. Sinclair ARE. Factors affecting the food supply and breeding season of resident birds and movements of Palaearctic migrants in a tropical African savannah. Ibis. 1978;120:480–97.

    Article  Google Scholar 

  13. Rockwell SM, Wunderle JM, Sillett TS, Bocetti CI, Ewert DN, Currie D, et al. Seasonal survival estimation for a long-distance migratory bird and the influence of winter precipitation. Oecologia. 2017;183:715–26.

    Article  PubMed  Google Scholar 

  14. Chantler P, Boesman P, Kirwan GM. Common Swift (Apus apus). Handbook of the Birds of the World Alive. Barcelona: Lynx Edicions; 2020.

    Google Scholar 

  15. Dementiev GP, Gladkov NA. The Birds of the Soviet Union. Jerusalem: Israel Program for Scientific Translation; 1966.

    Google Scholar 

  16. Ktitorov P, Heim W, Kulikova O, Gibson L. Cross the sea where it is narrowest: migrations of Pacific Swifts (Apus pacificus) between Sakhalin (Russia) and Australia. J Ornithol. 2022;163:19–26.

    Article  Google Scholar 

  17. Yamaguchi NM, Mori S, Yonekawa H, Waga D, Higuchi H. Light-level geolocators reveal that White-throated Needletails (Hirundapus caudacutus) follow a figure-eight migration route between Japan and Australia. Pac Sci. 2021;75:75–84.

    Article  Google Scholar 

  18. Yong DL, Heim W, Chowdhury SU, Choi C-Y, Ktitorov P, Kulikova O, et al. The state of migratory landbirds in the East Asian Flyway: distributions, threats, and conservation needs. Front Ecol Evol. 2021;9: 613172.

    Article  Google Scholar 

  19. Åkesson S, Klaassen R, Holmgren J, Fox JW, Hedenström A. Migration routes and strategies in a highly aerial migrant, the Common Swift Apus apus, revealed by light-level geolocators. Willis SG, editor. PLoS ONE. 2012;7:e41195.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Åkesson S, Bianco G, Hedenström A. Negotiating an ecological barrier: crossing the Sahara in relation to winds by common swifts. Philos Trans R Soc B Biol Sci. 2016;371:20150393.

    Article  Google Scholar 

  21. Klaassen R, Klaassen H, Berghuis A, Berghuis M, Schreven K, Horst Y, et al. Migration routes and wintering areas of Dutch Common Swifts Apus apus revealed using geolocators. Limosa. 2014;2:173–81.

    Google Scholar 

  22. Åkesson S, Atkinson PW, Bermejo A, Puente J, Ferri M, Hewson CM, et al. Evolution of chain migration in an aerial insectivorous bird, the common swift Apus apus. Evolution. 2020;74:2377–91.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Jacobsen LB, Jensen NO, Willemoes M, Hansen L, Desholm M, Fox AD, et al. Annual spatiotemporal migration schedules in three larger insectivorous birds: European nightjar, common swift and common cuckoo. Anim Biotelemetry. 2017;5:4.

    Article  Google Scholar 

  24. Wellbrock AHJ, Bauch C, Rozman J, Witte K. ‘Same procedure as last year?‘ Repeatedly tracked swifts show individual consistency in migration pattern in successive years. J Avian Biol. 2017;48:897–903.

    Article  Google Scholar 

  25. Brooke RK. Seasonal distribution of the migratory European swift Apus apus (Linnaeus)(Aves: Apodidae) in the Ethiopian region. Durban Museum Novitates. 1975;10:239–49.

    Google Scholar 

  26. Roberts H, Campbell O. Proving the occurrence of Common Swift Apus apus pekinensis in the United Arab Emirates. Sandgrouse. 2015;37:79–86.

    Google Scholar 

  27. Reyt S, Roques S. Identification of pekinensis Common Swift. British Birds. 2021;115:10–27.

    Google Scholar 

  28. Cramp S, Simmons KEL. The birds of Western Palearctic, vol. 4. Oxford: Oxford University Press; 1985.

    Google Scholar 

  29. Tang Q, Burri R, Liu Y, Suh A, Sundev G, Heckel G, et al. Seasonal migration patterns and the maintenance of evolutionary diversity in a cryptic bird radiation. Mol Ecol. 2022;31:632–45.

    Article  PubMed  Google Scholar 

  30. Thorup K, Tøttrup AP, Willemoes M, Klaassen RHG, Strandberg R, Vega ML, et al. Resource tracking within and across continents in long-distance bird migrants. Sci Adv. 2017;3: e1601360.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Boano G, Pellegrino I, Ferri M, Cucco M, Minelli F, Åkesson S. Climate anomalies affect annual survival rates of swifts wintering in sub-Saharan Africa. Ecol Evol. 2020;10:7916–28.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rajchard J, Procházka J, Kindlmann P. Long-term decline in Common Swift Apus apus annual breeding success may be related to weather conditions. Ornis Fennica. 2006;2:66–72.

    Google Scholar 

  33. Morganti M, Rubolini D, Åkesson S, Bermejo A, de la Puente J, Lardelli R, et al. Effect of light-level geolocators on apparent survival of two highly aerial swift species. J Avian Biol. 2018;49:jav-01521.

    Article  Google Scholar 

  34. Fridolfsson A-K, Ellegren H. A simple and universal method for molecular sexing of non-ratite birds. J Avian Biol. 1999;30:116.

    Article  Google Scholar 

  35. Lisovski S, Hahn S. GeoLight—processing and analysing light-based geolocator data in R. Hodgson D, editor. Methods Ecol Evol. 2012;3:1055–9.

    Article  Google Scholar 

  36. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org/

  37. Åkesson S, Bianco G. Wind-assisted sprint migration in northern swifts. Science. 2021;24:102474.

    Google Scholar 

  38. Lisovski S, Hewson CM, Klaassen RHG, Korner-Nievergelt F, Kristensen MW, Hahn S. Geolocation by light: accuracy and precision affected by environmental factors. Methods Ecol Evol. 2012;3:603–12.

    Article  Google Scholar 

  39. Phillips R, Silk J, Croxall J, Afanasyev V, Briggs D. Accuracy of geolocation estimates for flying seabirds. Mar Ecol Prog Ser. 2004;266:265–72.

    Article  Google Scholar 

  40. Fudickar AM, Wikelski M, Partecke J. Tracking migratory songbirds: accuracy of light-level loggers (geolocators) in forest habitats: Accuracy of geolocators. Methods Ecol Evol. 2012;3:47–52.

    Article  Google Scholar 

  41. Duan W, Shi J, Ma Y, Li H, Zhang S. Study on biology of Common Swift in Huhhot. J Inner Mongolia Terchers Univ (Natural Science Edition). 1990;1:53–9.

    Google Scholar 

  42. Cao Y, Tian F, Zhang Z, Sun L. Observation of the breeding habit of Common Swift. Chinese J Zool. 1997;32:46–9.

    Google Scholar 

  43. Li H. Study on the breeding ecology of Common Swift in Qingzhou of Shandong Provinve. J Weifang Educ Coll. 2004;17:37–8.

    Google Scholar 

  44. Liu Y, Chen S. The CNG field guide to the birds of China. Changsha: Hunan Science & Technology Press; 2021.

    Google Scholar 

  45. Fick SE, Hijmans RJ. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;12:4302–15.

    Article  Google Scholar 

  46. Newton I. Population limitation—conditions on stopover. The Migration Ecology of Birds. Amsterdam: Elsevier; 2008. p. 777–803.

    Google Scholar 

  47. Hewson CM, Thorup K, Pearce-Higgins JW, Atkinson PW. Population decline is linked to migration route in the Common Cuckoo. Nat Commun. 2016;7:12296.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vincze O, Vágási CI, Pap PL, Palmer C, Møller AP. Wing morphology, flight type and migration distance predict accumulated fuel load in birds. J Exp Biol. 2019;222:jeb183517.

    PubMed  Google Scholar 

  49. Tøttrup AP, Klaassen RHG, Strandberg R, Thorup K, Kristensen MW, Jørgensen PS, et al. The annual cycle of a trans-equatorial Eurasian-African passerine migrant: different spatio-temporal strategies for autumn and spring migration. Proc R Soc B Biol Sci. 2012;279:1008–16.

    Article  Google Scholar 

  50. Nilsson C, Klaassen RHG, Alerstam T. Differences in speed and duration of bird migration between spring and autumn. Am Nat. 2013;181:837–45.

    Article  PubMed  Google Scholar 

  51. Schmaljohann H. Proximate mechanisms affecting seasonal differences in migration speed of avian species. Sci Rep. 2018;8:4106.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Rolshausen G, Segelbacher G, Hobson KA, Schaefer HM. Contemporary evolution of reproductive isolation and phenotypic divergence in sympatry along a migratory divide. Curr Biol. 2009;19:2097–101.

    Article  CAS  PubMed  Google Scholar 

  53. Norevik G, Boano G, Hedenström A, Lardelli R, Liechti F, Åkesson S. Highly mobile insectivorous swifts perform multiple intra-tropical migrations to exploit an asynchronous African phenology. Oikos. 2019;128:640–8.

    Article  Google Scholar 

  54. Newton I. Incredible journeys. The Migration Ecology of Birds. Amsterdam: Elsevier; 2008. p. 139–61.

    Google Scholar 

  55. Newton I. Weather effects and other aspects. The Migration Ecology of Birds. Amsterdam: Elsevier; 2008. p. 67–94.

    Google Scholar 

  56. Ydenberg RC. The Himalayas as an ecological barrier for avian migrants: high and dry, but also dangerous? In: Prins HHT, Namgail T, editors. Bird Migration Across the Himalayas. Cambridge: Cambridge University Press; 2017. p. 283–98.

    Chapter  Google Scholar 

  57. Townshend T. The Beijing Cuckoo Project [Internet]. 2016. Available from: https://birdingbeijing.com/beijing-cuckoo-project/

  58. Meyburg B-U, Howey P, Meyburg C, Pretorius R. Year-round satellite tracking of Amur Falcon (Falco amurensis) reveals the longest migration of any raptor species across the open sea. From avian tracking to population processes, British Ornithologists’ Union Annual Conference. University of Warwick, UK; 2017.

  59. Tourenq C, Combreau O, Lawrence M, Launay F. Migration patterns of four Asian Houbara Chlamydotis macqueenii wintering in the Cholistan Desert, Punjab, Pakistan. Bird Conserv Int. 2004;14:1–10.

    Article  Google Scholar 

  60. Newton I. Biogeographical legacies. The migration ecology of birds. Amsterdam: Elsevier; 2008. p. 639–74.

    Google Scholar 

  61. Hedenström A, Norevik G, Warfvinge K, Andersson A, Bäckman J, Åkesson S. Annual 10-month aerial life phase in the Common Swift Apus apus. Curr Biol. 2016;26:3066–70.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to all the volunteers taking part in the fieldwork. We also appreciate the support from Management office of Summer Palace and Beijing Wildlife Rescue & Rehabilitation Centre, and the help from National Bird Banding Centre of China and Beijing Bird Banding Station. We are very grateful to the three anonymous reviewers and an associate editor for their constructive comments and suggestions.

Funding

This study was supported by SHAN SHUI Conservation Centre, Beijing, the research grants from the Swedish Research Council (621-2013-4361, 2016‐03625) to SÅ, and a Dulverton Trust grant from British Trust for Ornithology to CMH. The geolocators were funded by Action for Swifts (DN) and by SÅ at Lund University covered by funds listed above, with at least one logger donated by Migrate Technology Ltd.

Author information

Authors and Affiliations

  1. State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-Sen University, Guangzhou, 510275, Guangdong, China

    Yanyan Zhao & Yang Liu

  2. Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, Guangxi, China

    Yanyan Zhao

  3. Beijing Normal University, Beijing, 100875, China

    Xinru Zhao

  4. China Bird Watching Society, Beijing, 100097, China

    Xinru Zhao, Lan Wu, Fang Yu, Xuan Liang, Jianping Fu, Xiaoru Hou, Peng Peng, Xiaoyang Li, Tao Zhang & Terry Townshend

  5. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA

    Tong Mu

  6. Belora Vzw, Kloetstraat 48, B-9120, Melsele, Belgium

    Lyndon Kearsley

  7. Administrate Office of the Summer Palace, Beijing, 100080, China

    Su Yan

  8. Action for Swifts, Old Beach Farm, 91 Green End, Landbeach, Cambridge, CB25 9FD, UK

    Dick Newell

  9. British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP24 2PU, UK

    Chris M. Hewson

  10. Department of Biology, Center for Animal Movement Research, Lund University, Ecology Building, 223 62, Lund, Sweden

    Susanne Åkesson

Authors
  1. Yanyan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinru Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lyndon Kearsley
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xuan Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jianping Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaoru Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peng Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xiaoyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Su Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Dick Newell
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Chris M. Hewson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Terry Townshend
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Susanne Åkesson
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XZ, LW, LK, DN, CMH, TT and SÅ conceived and designed the study. XZ and LK led bird capturing and tagging with inputs from all co-authors. FY, XL, JF, XH, PP, XLi and TZ joined bird banding and acquired raw data of the study. SY obtained research permit from the Summer Palace. YZ and TM analysed the data. YZ wrote the initial draft of the manuscript with inputs from TM, LK, DN, CMH, TT, SÅ and YL. All authors agree with the content therein and approve the final version of the manuscript.

Corresponding authors

Correspondence to Xinru Zhao or Yang Liu.

Ethics declarations

Ethics approval and consent to participate

Bird capturing and tagging were approved by Beijing Gardening and Greening Bureau.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. Table S1

. Distribution of data over years. Table S2. Key phenological parameters of migration of different sex individuals of A. a. pekinensis breeding in Beijing. Table S3. Results of comparative analysis of characteristic parameters between female and male pekinensis using t-test. Table S4. Results of comparative analysis of characteristic parameters between pekinensis from Beijing (N=25) and nominate apus from Sweden (N=25) using t-test. Table S5. Monthly and annual precipitation (mm) at several sites reported in the breeding areas of both subspecies.

Additional file 2. Table S6

. Migration phenology of each individual in this study.

Additional file 3. Figure S1

. The breeding and wintering areas of two subspecies of Common Swift and the random sample points used in this study.

Additional file 4. Table S7

. List of random samples and monthly precipitation (mm) during the breeding season.

Additional file 5. Table S8

. Monthly precipitation (mm) at several sites reported within the wintering areas of both subspecies.

Additional file 6. Table S9

. List of random samples and monthly precipitation (mm) during the wintering season.

Additional file 7. Figure S2

. Map showing the tracks away from breeding site and across the Junggar Basin.

Additional file 8. Figure S3

. Maps showing the migration routes of five individuals in two years. The pentagram represents the bird breeding site and fieldwork location -Beijing. The dotted lines indicate the lack of data in the two weeks before/after the autumn/spring equinoxes. The yellow lines represent the autumn routes, the blue line represent the spring routes.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Y., Zhao, X., Wu, L. et al. A 30,000-km journey by Apus apus pekinensis tracks arid lands between northern China and south-western Africa. Mov Ecol 10, 29 (2022). https://doi.org/10.1186/s40462-022-00329-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40462-022-00329-2

Keywords

Movement Ecology

ISSN: 2051-3933