Climate in Africa sequentially shapes spring passage of Willow Warbler Phylloscopus trochilus across the Baltic coast

Abstract

Background: Many migrant birds have been returning to Europe earlier in spring since the 1980s. This has been attributed mostly to an earlier onset of spring in Europe, but we found the timing of Willow Warblers' passage to be influenced by climate indices for Africa as much as those for Europe. Willow Warblers' spring passage through northern Europe involves populations from different wintering quarters in Africa. We therefore expected that migration timing in the early, middle and late periods of spring would be influenced sequentially by climate indices operating in different parts of the winter range.

Methods: Using data from daily mistnetting in 1 April-15 May over 1982-2017 at Bukowo (Poland, Baltic Sea coast), we derived an Annual Anomaly (AA, in days) of Willow Warbler spring migration. We decomposed this anomaly into three main periods (1-26 April, 27 April-5 May, 6-15 May); one-third of migrants in each period. We modelled three sequential time series of spring passage using calendar year and 15 large-scale climate indices averaged over the months of Willow Warblers' life stages in the year preceding spring migration as explanatory variables in multiple regression models. Nine climate variables were selected in the best models. We used these nine explanatory variables and calculated their partial correlations in models for nine overlapping sub-periods of AA. The pattern of relationships between AA in these nine sub-periods of spring and the nine climate variables indicated how spring passage had responded to the climate. We recommend this method for the study of birds' phenological responses to climate change.

Results: The Southern Oscillation Index and Indian Ocean Dipole in Aug-Oct showed large partial correlations early in the passage, then faded in importance. For the Sahel Precipitation Index (PSAH) and Sahel Temperature Anomaly (TSAH) in Aug-Oct partial correlations occurred early then peaked in mid-passage; for PSAH (Nov-March) correlations peaked at the end of passage. NAO and local temperatures (April-May) showed low correlations till late April, which then increased. For the Scandinavian Index (Jun-Jul) partial correlations peaked in mid-passage. Year was not selected in any of the best models, indicating that the climate variables alone accounted for Willow Warblers' multiyear trend towards an earlier spring passage.

Discussion: Climate indices for southern and eastern Africa dominated relationships in early spring, but western African indices dominated in mid- and late spring. We thus concluded that Willow Warblers wintering in southern and eastern Africa dominated early arrivals, but those from western Africa dominated later. We suggest that drivers of phenological shifts in avian migration are related to changes in climate at remote wintering grounds and at stopovers, operating with climate change in the north, especially for species with complex and long-distance migration patterns.

Keywords: Annual anomaly; Climate change; IOD; Large-scale climate indices; Migration timing; NAO; Phylloscopus trochilus; SOI; Sequential migration; Spring phenology.

Figure 1. Approximate migration routes of Willow Warblers that pass through Bukowo and areas influenced by the large-scale climate variables.

Symbols of the climate indices and the sources we used to visualise the regions they influence are presented in Table 1. The ranges of months are the main periods when Willow Warblers occur within their breeding, migration and wintering areas. The westernmost route in the Sahel region reflects geolocator tracks of birds breeding in Denmark (Lerche-Jørgensen et al., 2017), the eastern route considers genotyping (Zhao et al., 2020). This figure is based on Remisiewicz & Underhill (2020, modified), and is derived from “Phylloscopus trochilus Range Map.png” by Keith W. Larson, licensed under CC-BY-SA-3.0 by Magdalena Remisiewicz.

Figure 2. Division of the overall spring migration curve into thirds of average passage 1982–2017 and the division of Annual Anomaly (AA) for 2012 into three main periods using the derived ranges of dates.

(A) Division of the multiyear average migration curve into three non-overlapping main periods of spring (MP1, MP2, MP3). Red line = 1982–2017 average migration curve, black lines = division into thirds of average passage. (B) Ranges of dates from Fig. 2A to decompose AA for 2012 into three periods. Blue line = migration curve for 2012. The areas in the three main periods (blue and white patterns), total to overall AA for 2012. MP1, main period 1; MP2, main period 2; MP3, main period 3; ranges of percentiles and symbols of periods as in Table 2.

Figure 3. Trends for the Annual Anomaly in three main periods (A)–(C) and the whole season (D) for Willow Warbler spring migration at Bukowo, Poland, 1982–2017.

Symbols of spring periods as in Table 2 and Fig. 2; **–p significant after Benjamini-Hochberg correction for multiple comparisons. More statistics for regression equations in Table S7.

Figure 4. (A–I) Partial correlation coefficients for AA in nine overlapping sub-periods of spring against the nine main climate indices for Willow Warbler spring migration at Bukowo, Poland, 1982–2017.

The signs of partial correlation coefficients (Table S14) were inverted, so the positive values at Y-axis indicate negative correlation to better visualise their changes with the progress of migration; source data in Table S15. Symbols of climate indices in Table 1, symbols of spring sub-periods in Table 2.

Figure 5. Relationships between AA in three main periods (A–C) and the whole season (D) of Willow Warbler spring migration in 1982–2017 and the count of juveniles caught the previous autumn at Bukowo, Poland.

White circle = the outlier value in autumn 1982 excluded from the regression. **–p significant after Benjamini-Hochberg correction for multiple comparisons. Details of regressions in Table S16.