Introduction

Annual cycles are adjusted to local environmental conditions in the breeding areas as those influence the availability of food resources (Briedis et al. 2016). Hence, appropriate timing of arrival in the breeding areas is considered essential in the migratory lifestyle as it has considerable implications on individuals’ fitness (Berthold 2001; Newton 2008). Arriving too early might expose individuals to environments with food shortages, thereby decreasing survival (e.g., Lerche‐Jørgensen et al. 2018). Arriving too late may incur fitness costs associated with reduced reproductive success (Kokko 1999). There tends to be a relatively large variation in migration timing (i.e., arrival and departure dates) between and within populations (e.g., Lourenço et al. 2011; Gill et al. 2014; Rotics et al. 2018). Potential factors affecting migration timing include sex- and age-related differences (Rubolini et al. 2004; Bildstein 2006), differences in the timing of the annual cycle events (Briedis et al. 2016), flexible responses to environmental conditions along the migratory route (Akesson and Helm 2020), and local conditions in the breeding site (Briedis et al. 2020).

Genetic and environmental factors play crucial roles in determining migration timing. During pre-breeding migration, when birds migrate from the non-breeding to the breeding grounds, long-distance (e.g., trans-Saharan) migrants primarily use internal clocks and photoperiod cues to decide when to depart from their non-breeding areas (Berthold 2001; Ramenofsky and Wingfield 2007; Bossu et al. 2022). However, local environmental conditions could also modulate these departure decisions (Haest et al. 2018; Burnside et al. 2021). Once migration begins, birds are expected to adjust their migration progress (e.g., travel speed and stopovers) in response to environmental conditions related to, for example, food availability or wind conditions encountered en route (Drent et al. 1978; Van der Graaf et al. 2006; Kölzsch et al. 2015). As birds approach their breeding destinations, decisions on arrival timing can be made based on expected local climatic conditions at their breeding areas (Rakhimberdiev et al. 2018; Briedis et al. 2020). Climatic variables, in particular temperature, which correlates with latitude, affects the onset of spring green-up in temperate regions (Newton 2008; Briedis et al. 2020), synchronizing the migration of herbivores and insectivorous birds due to the close relationship between plant and insect phenology (La Sorte et al. 2014; Usui et al. 2017). Conventional expectations for the pre-breeding arrival date of birds in relation to climate and latitude are that the arrival date may co-vary with geographical gradients in climate (Briedis et al. 2020). As a result, individuals breeding at lower latitudes are expected to arrive earlier than their conspecifics breeding at northern latitudes (Conklin et al. 2010; Briedis et al. 2016). In Eurasia, the climate varies along a west-to-east gradient ranging from a more oceanic climate with relatively small temperature fluctuations in the western part and a more continental climate in the eastern part with more substantial fluctuations (Metzger et al. 2005, 2013). If populations are capable of tracking spring at the breeding grounds, we may expect arrival dates to be timed with longitudinal climatic gradients from west to east (Briedis et al. 2020). Weather conditions in spring, such as wind direction and strength, may also generate differences in migration timing by influencing departure and stopover decisions (Thorup et al. 2006), with more favorable wind conditions allowing birds to arrive earlier (Haest et al. 2020).

Further questions arise regarding how individuals manage to reach their breeding grounds on time. Migrants could achieve this by departing earlier, increasing travel speed or reducing stopover duration during the pre-breeding migration (Nilsson et al. 2013). Recent bio-logging studies revealed that an early departure from the non-breeding area significantly influences a timely arrival (Lemke et al. 2013; Ouwehand and Both 2017; Rotics et al. 2018). However, other findings highlight that travel speed is important for a timely arrival to the breeding grounds (McKinnon et al. 2016; Schmaljohann et al. 2016). Indeed, reducing stopover duration appears to be the most effective strategy for promoting early arrival (Nilsson et al. 2013). Previous tracking studies have reported different migratory behaviors across and even within populations (Carneiro et al. 2020). For example, some Icelandic Eurasian Whimbrels (Numenius phaeopus islandicus) performing seasonal migrations to and from West Africa undertook a direct migration (fly non-stop to Iceland), while others made stopovers during their pre-breeding migration (Alves et al. 2016). Undertaking a direct flight may guarantee a timely arrival, whereas prolonged stopovers may help individuals arrive in better body condition to their breeding areas.

The Lesser Kestrel (Falco naumanni) is a migratory insectivorous raptor with a reversed sexual size dimorphism (females being ~ 15% larger in body mass) (Cramp and Simmons 1980). It breeds in colonies across southern Europe, northern Africa to China, and migrates to the Sahel and up to South Africa, although a minority of individuals are resident in Europe year-round (Negro et al. 1991). Arrival in southern Europe typically occurs from mid-March to early-April (Sarà et al. 2019). Due to their size and morphology, they are less energy-limited than other long-distance raptors from the Palearctic to use flapping flight (Agostini et al. 2015). As a result, Lesser Kestrels can extend daily travel time into the night when thermal updrafts are not available. They also achieve higher speeds during nocturnal than diurnal migration, enabling them to cross vast ecological barriers (Sarà et al. 2019; Lopez-Ricaurte et al. 2021). Lesser Kestrels also show much longer and more frequent stopovers during the pre-breeding than the post-breeding migration, primarily situated in the northern regions of Africa (Lopez-Ricaurte et al. 2021).

In this study, we used data from Lesser Kestrels tracked with GPS tags to explore between-individual variation in the pre-breeding migration timing of birds traveling from sub-Saharan Africa to various breeding sites across a gradient of latitude (37°–42° N) and longitude (6.5° W–16.5° E). We first investigated spatiotemporal variation in pre-breeding migration, testing the relationship between the geographical location of breeding sites, departure locations from non-breeding sites, and migration timing, along with any sex-related differences. We then examined whether the variation in arrival dates correlates with the local breeding climate, using local spring temperature and winter rain at the breeding colony as proxies for the onset of spring. Birds breeding in warmer locations (southwestern sites) were expected to arrive earlier than those breeding in colder locations (northeastern sites) (Briedis et al. 2020). Finally, we assessed the impact of migration traits, including departure date, duration of stopovers, and travel speed, on early arrivals, taking into consideration potential sex differences. Sex-biased timing is well documented in many animals (see review: Morbey and Ydenberg 2001) and protandry, the early arrival of males to the breeding areas relative to females, has been demonstrated in birds (Rubolini et al. 2004; Coppack and pulido 2009). However, a previous study using a much smaller sample of the same individuals from the Iberian Peninsula that we consider here (15 vs. 53 individuals, Sarà et al. 2019) showed no sex differences in timing. We expected that the departure date would play a significant role, similar to other long-distance migrants (Bildstein 2006; Schmaljohann et al. 2016; Rotics et al. 2018; Pancerasa et al. 2022). Stopover duration might be influenced by the phenology of plants (e.g., NDVI) and wind conditions along their migratory route. Birds crossing inhospitable areas, such as individual Lesser Kestrels moving across deserts or semi-deserts during pre-breeding migration, are indeed expected to prolong stopover duration when they find areas with suitable drinking/fueling conditions or encounter strong adverse winds (Thorup et al. 2006).

Methods

Tagging and tracking

Fieldwork was conducted in Spain and Italy during the breeding seasons of 2014 to 2020 (Fig. S1 and Table S1). A total of 227 adults were captured near to the colony using bal-chatri, spring nets or mist nets. They were also captured within the nest (such as nestboxes or other cavities) before egg laying, at the end of the incubation period or during the chick-rearing phase. Different solar GPS-UHF models from different manufacturers (Ecotone, Microsensory LS, and Pathtrack Ltd.) were used. The GPS-UHF loggers weighing 5.5 g (including harness, ~ 3.8% of the mean weight at capture, males = 146.0 g ± 35 SD; females = 148.0 g ± 29) were attached as backpacks with a Teflon harness. Locations were stored on-board and downloaded via a UHF base station placed in the vicinity of the colony. Details on logger programming are described in the Online Resources.

Identifying the pre-breeding migration trip

We resampled all data to 1-h intervals to have a uniform temporal resolution (with deviations of 20 min). After resampling, we analyzed 24,335 locations corresponding to the pre-breeding migration. For each migratory trip, we searched for a group of first and last 3 consecutive days with an average daily distance of at least 150 km, preceded (if onset) or followed (if end) by 5 stationary days with mean daily traveled distance < 70 km (cf. Rotics et al. 2018). We assigned as the start of migration day to the first of the 3-day set and the end of migration day to the last of the 3-day set. We confirmed those dates visually using QGIS.

Movement metrics

We calculated the daily straight-line distance (i.e., the shortest orthodromic path) between the first and last daily position for each bird (for 24-h intervals, sunrise to sunrise, Lopez-Ricaurte et al. 2021). Next, we differentiated between travel days and stopover days using a threshold of daily straight-line distance of 50 km (Klaassen et al. 2011; Limiñana et al. 2013). We segmented each pre-breeding migratory trip into travel vs stopover segments and calculated for each stopover event the total duration (in days) and the median latitude and longitude for the stopover location (n = 125 events, ranging from 1 to 17 days). Finally, travel speed (km/day) was calculated as the ratio between the total migration distance and the migration duration (excluding stopover days).

Environmental data

For all breeding colonies, we extracted monthly weather data for average minimum air temperature (°C), average maximum air temperature (°C), and total precipitation (mm) from the WorldClim database at 0° 2.5″ (~ 21 km2) resolution. These data are derived by downscaling the CRU-TS-v4.06 gridded data (Climatic Research Unit gridded Time Series) (Fick and Hijmans 2017). We extracted the average minimum and maximum temperatures from February to April (hereafter, TminFeb, TmaxFeb, TminMarch, TmaxMarch, TminApr, TmaxApr) and the mean minimum and maximum temperature of the breeding sites (hereafter, TmeanMin, TmeanMax). We considered these values as proxies for forecasted spring temperatures. We calculated the cumulative average monthly precipitation by summing up the precipitation from December to March (which measures winter rain) and then finding the average across the years 2010 to 2021 (hereafter, CumPrec).

To determine the relative influence of food availability and wind on stopover decisions, we used the normalized difference vegetation index (NDVI) as a proxy of food abundance and wind direction and strength in relation to migratory direction. The NDVI is a vegetation index indicative of vegetation cover and photosynthetic activity in an area and a proxy of food/insect abundance for insectivorous birds (Schlaich et al. 2016; Morganti et al. 2019; Åkesson and Helm 2020). We annotated each GPS location using the Env-DATA track annotation tool of Movebank (Dodge et al. 2013). We obtained NDVI from MODIS (NASA’s Moderate Resolution Imaging Spectroradiometer) provided every 16 days at 250 m spatial resolution. Wind direction and strength come as U (west–east) and V (north–south) wind components (km/h) at a spatial resolution of 0.75 degrees and temporal resolution of 6 h from the ECMWF (European Center for Medium-Range Weather Forecast). We interpolated wind components from the 925 mb pressure level (approx. 750 m.a.s.l.) (Schmaljohann et al. 2012; Limiñana et al. 2013). To compute hourly tailwind, V-wind and U-wind components were combined in a single vector adding hourly flight direction in degrees to the north and wind strength (Vansteelant et al. 2015). We determined tailwind strength relative to the bird’s direction of travel during migration. We averaged those per day and computed daily wind speed and direction. We selected the bilinear interpolation method for all variables (Dodge et al. 2013).

Statistical analyses

We analyzed between-individual variation in migration timing, specifically the day of the year of departure and arrival (hereafter, DOY), in relation to sex using linear mixed models (LMMs). To account for geographic influences, we added departure latitude, departure longitude, arrival latitude, and arrival longitude as fixed effects. After inspecting residual plots, we fitted a Gaussian error distribution. In all models, we included bird identity as a random effect to account for 23 repeated migratory trips corresponding to the same individual in different years. To examine how arrival DOY varied with local climatic conditions, we used separate LMMs. For each climatic variable (TminFeb, TmaxFeb, TminMarch, TmaxMarch, TminApril, TmaxApril, TmeanMin, TmeanMax, CumPrec), we constructed individual models while accounting for bird identity as a random effect. To investigate the relative importance of climate and geographic variables on the arrival date, we included all variables in a single model. For variable pairs exhibiting high correlation (r > 0.60, < − 0.60), we included the variable with the higher predictive power in the arrival date when assessed individually (Table S2; Hinkle et al. 2003).

We analyzed the relative contribution of departure date, stopover duration, and travel speed to the variation in arrival date by means of LMMs. Since we aimed at determining which of these primarily characterized timely arrivals, we fitted separated LMMs with arrival date as a response variable, including individual identity as a random effect. Furthermore, departure date, duration of stopovers, and travel speed were included in a single model as fixed effects, controlling for sex.

To identify whether NDVI and wind conditions influence stopover decisions, we compared NDVI and daily tailwind strength between stopover and traveling days. We constructed LMMs of daily tailwind conditions and NDVI. We included the variable “travel” (with two levels: travel, and stopover) as a fixed factor and bird identity as a random effect.

We tested for multicollinearity by calculating variance inflation factors (VIF) for all our predictors. Values of these were in all cases below 2.5. Estimates of parameters whose 95% confidence interval (CI) does not overlap zero were considered significant. We used lmerTest R package to estimate degrees of freedom using the Satterthwaite’s method and obtain p values for fixed effects (Kuznetsova et al. 2017). To get comparable effect sizes, all continuous predictors were scaled by their standard deviation as recommended by Schielzeth (2010). We assessed the variance explained by the model and by fixed factors using marginal (r2mar), conditional (r2cond) and semi-partial r-squared (sr2) (i.e., the proportion of variance explained by fixed factors, by both random and fixed factors, and by each fixed effect, respectively) following Nakagawa and Schielzeth (2013).

All data analyses were conducted in R (v.4.2.2., R Core Team 2022), and all figures were produced with ggplot2 (Wickham 2009).

Results

We analyzed 84 pre-breeding migratory trips of 61 adults breeding in Spain (53 individuals, 27 females and 26 males) and Italy (8 individuals, 5 females and 3 males) (Fig. 1). There were 21 individuals with 2 and 1 individual with 3 migratory trips from different years. We found considerable between-individual variation in the timings of Lesser Kestrels (Fig. 2). Departure dates from sub-Saharan non-breeding areas spanned across 3 months between 27 January and 24 April (mean: 28 February ± 17 days). Arrival dates to breeding colonies ranged from 2 February to 13 May (mean: 14 March ± 20 days).

Fig. 1
figure 1

Pre-breeding migration trips (n = 84) of Lesser Kestrels and stopover duration (legend). The geographic extent of our breeding sites stretched between 37 and 42° N latitude and between 6.5° W and 16.5° E longitude, which falls in a climatic transition among the hot-summer Mediterranean, continental Mediterranean, steppe to warm temperate climate (according to the Köppen-Geiger climate classification and orographic conditions Koppen 1936). Yellow stars indicate clusters of breeding colonies near each other included in our study (maximum distance between colonies in a cluster = 60 km) (color figure online)

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Fig. 2
figure 2

Pre-breeding migration timing of Lesser Kestrels. The histograms show: A the distribution of departure DOY from non-breeding grounds in sub-Saharan Africa and B the distribution of arrival DOY to their breeding grounds in southern Europe (color figure online)

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Geographic and sex-related patterns

We found a significant positive relationship between departure DOY and breeding latitude and longitude (Table 1). Birds breeding at more northern and eastern locations departed increasingly later, at a rate of c. 5 days delay per 1° increase in arrival latitude and c. 1 day delay per 1° increase in arrival longitude. Similarly, we found a significant positive relationship between arrival DOY and breeding latitude and longitude (Table 1). Most northeastern breeding migrants reached their breeding grounds later, at a rate of c. 6 days delay per 1° increase in arrival latitude and c. 2 days delay per 1° increase in arrival longitude. We did not find significant differences in migration timing between males and females (Table S3).

Table 1 Effect of geography on day of year of departure and arrival
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Local climate influencing arrival date

Examining variables with separate LMMs revealed that the climatic factor explaining most of the variation in arrival date was TmaxMarch (t67.4 =  − 5.90, p < 0.001, r2mar = 0.31; Table 2, Fig. 3), with the other climate variables accounting for some variation but to a lesser extent (TmaxApr: t64.02 =  − 5.20, p < 0.001, r2mar = 0.27; TmaxFeb: t64.05 =  − 4.74, p < 0.001, r2mar = 0.23; TmeanMax: t64.97 = − 5.10, p < 0.001, r2mar = 0.26; CumPrec: t64.97 =  − 3.29, p < 0.001, r2mar = 0.14). Our full model for climate and geography explained 53% of variation in arrival date, of which 35% was attributed to fixed effects. The model revealed a significant negative relationship between TmaxMarch and arrival DOY (sr2 = 0.23), indicating that Lesser Kestrels arrive earlier in locations with warmer spring temperatures (Table S4). Breeding latitude, breeding longitude and CumPrec had weaker effects (sr2 = 0.01, 0.02 and 0.01, respectively).

Table 2 Results of separate linear mixed models associating arrival DOY with different climatic variables at the breeding location averaged for the period (2010–2021)
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Fig. 3
figure 3

Correlation between arrival DOY and a max temperature of March in °C, b max temperature of April in °C and, c accumulated precipitation in mm. Each dot represents one pre-breeding migration trip. Shaded areas represent 95% confidence

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Migration traits

The departure date, stopover duration, and travel speed had significant effects on arrival date, with no sex effects (sr2 = 0.99, 0.94, 0.74, respectively, Table 3). Arrival date had a strong correlation with departure date (r2 = 0.84, n = 84, p < 0.001), a moderately positive correlation with stopover duration (r2 = 0.31, n = 84, p < 0.001), and a weaker correlation with travel speed (r2 = 0.15, n = 84, p < 0.001; Fig. 4, Table S5). Lesser Kestrels from the southwest arrive earlier, mainly due to early departures, with stopover duration also influencing arrival, albeit less significantly. Indeed, we found that stopover behavior was strongly influenced by conditions en route. Stopover days were associated with locations exhibiting higher NDVI values compared to travel days. In addition, stopover days were marked by significantly stronger headwinds, while travel days were predominantly characterized by supportive winds (Fig. S2 and Table S6).

Table 3 Full model for the effect of departure date, duration of stopovers, and travel speed on arrival DOY
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Fig. 4
figure 4

Linear relationships of a departure date (day of year, DOY), b stopover duration (days), and c travel speed (km/day) with arrival date. Each dot represents one pre-breeding migration trip. R-square (r2) and p values are reported. Males are shown in purple and females in yellow. Shaded areas represent 95% confidence (color figure online)

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Discussion

Our study describes considerable between-individual variation in the pre-breeding migration timing of Lesser Kestrels that breed within a geographic gradient in the south of Europe. Lesser Kestrels breeding at more southwestern colonies departed earlier from West African non-breeding grounds and arrived earlier in their Mediterranean colonies relative to their conspecifics breeding in more northeastern sites. This is likely due to climatic gradients between S–N and W–E European breeding colonies. Our results confirm previous findings that birds aim to synchronize their arrival date to match the local climatic conditions of their breeding sites (Briedis et al. 2020), with likely cascading consequences on the reproductive success of this species (Rodríguez and Bustamante 2003). On the other hand, we found evidence that Lesser Kestrels from warmer breeding areas manage to arrive earlier than their conspecifics by departing earlier. Furthermore, the duration of stopovers influences the arrival date, albeit to a lesser extent.

We aimed to understand the factors influencing variation in arrival dates. The geographical location of the breeding colony explained a good proportion of this variation (Table 1). We found a positive relationship between the latitude and the longitude of the breeding sites and arrival date, i.e., with each additional 1° latitude delaying arrival by c. 6 days and with each additional 1° longitude delaying arrival by c. 2 days. This revealed an earlier arrival among the southwest relative to the northeast colonies, echoing results from previous work on other taxa (Conklin et al. 2010; Panuccio et al. 2014; Briedis et al. 2016). This pattern likely results from annual cycles being synchronized with the spatiotemporal gradient in the local climate at the breeding sites, aiming to match spring temperatures or associated factors that promote the emergence of insects (Marra et al. 2005; Gunnarsson et al. 2006).

The influence of sex on migration timing and traits of Lesser Kestrels was negligible. Sex differences in pre-breeding migration schedules, where males precede females, have been shown in various raptors (e.g., Bald Eagle, Haliaeetus leucocephalus; Red Kite, Milvus milvus; Northern Harrier, Circus hudsonius, Bildstein 2006, European Honey Buzzard, Pernis apivorus, Chiatante et al. 2024). In species with more pronounced sexual dimorphism such as the Montagu’s Harriers (Circus pygargus), where females are considerably heavier than males (360 g vs. 260 g), males departed significantly earlier than females from West Africa, although daily flight distance did not differ between sexes (Schlaich 2019). Tracking studies on Western Marsh Harriers (Circus aeruginosus) showed that females depart later than males but arrive slightly earlier, probably due to shorter stops in the Mediterranean (Vansteelant et al. 2020), and use flapping flight to a greater extent en route, increasing migration speed (Agostini et al. 2023). However, in our study, we were not able to find any temporal or behavioral segregation between male and female Lesser Kestrels outside the breeding period. Indeed, as previously suggested, it is likely that a long pre-breeding period at breeding sites and coloniality buffer against sex differences in timing in this study system. Moreover, uncovering such mechanisms may require a deeper study of the settlement phase and a larger sample of individuals from multiple populations over a broader spatial gradient. In line with our expectations and consistent with previous studies (Jahn et al. 2013; Schmaljohann et al. 2016; Ouwehand and Both 2017), Lesser Kestrels from southwestern colonies manage to arrive earlier than their conspecifics breeding in more northeastern colonies primarily by departing earlier. The departure date could be influenced by a mixture of endogenous cues, photoperiod, and local environmental factors such as wind, temperature, rain, and food supplies (Newton 2008; Burnside et al. 2021). For example, a study on the long-distance migrant Swainson’s Thrush (Catharus ustulatus), migrating from Canada to the Gulf Coast and back, revealed that departure decisions were best explained by a mixture of high daily temperatures (> 21 °C) and low wind speed (< 10 km/h) at the time of take-off (Bowlin et al. 2005). However, Lesser Kestrels in this sample are known to aggregate in communal areas before the onset of the pre-breeding migration (Pilard et al. 2011; Lopez-Ricaurte et al. 2023), experiencing similar environmental cues.

We found that birds had varying departure dates corresponding to the latitude and longitude of their breeding colony. We speculate that endogenous biological clocks play a more important role in kestrels’ departure decisions than local conditions (but see Ramellinie unpubl. data). The existence of timekeeping mechanisms or the use of biological clocks to time migration has been recently described in related species such as the Peregrine Falcon (Falco peregrinus), and the American Kestrel (Falco sparverius) (Gu et al. 2021; Bossu et al. 2022). In addition, environmental cues indicating the onset of suitable phenological conditions at the breeding sites, 3,000 km away and 1 month in the future, may be unlikely (Åkesson et al. 2017; but see Saino and Ambrosini 2008).

We also found that stopover duration and travel speed significantly affected the arrival date but were comparatively less influential than the departure date. Stopover duration was influenced by other factors such as conditions en route. Lesser Kestrels made more prolonged stopovers in areas with higher vegetation productivity and when experiencing stronger headwinds compared to days when they traveled, suggesting that food availability and winds may be important factors mediating stopover decisions.

There are some potential limitations to the interpretations of our results. First, we do not analyze other environmental variables en route that could also affect the progress (i.e., stopover duration, travel speed) of Lesser Kestrels (e.g., rainfall, sandstorms) (Haest et al. 2018, 2020). Thus, we cannot discount the effect of other environmental variables in mediating stopover decisions. Finally, our limited sample size for birds breeding at more easterly locations in Europe suggests that we should exercise more caution with the interpretations of these results. However, our findings align with those previously reported for a broader longitudinal pattern (Sarà et al. 2019).

In conclusion, these findings indicate that annual cycles are tuned with both local climate gradients at the breeding areas and conditions en route. The large effect of departure date on arrival date, in contrast to the more moderate influence of local arrival conditions, supports the idea that geographically uneven climate change may negatively affect fitness. This effect could be more pronounced in the temperate breeding range, as opposed to the tropical non-breeding range of Lesser Kestrels (Ponti et al. 2020). The potential ecological mismatches at breeding sites, influenced by climate change, could have a more significant impact on long-distance migrants, like the Lesser Kestrel, as compared to short-distance migrants (Saino et al. 2011).