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Supplementary Information For: Authors Affiliations This Supplementary Information for: Weather at the winter and stopover areas determines spring migration onset, progress, and advancements in Afro-Palearctic migrant birds Authors Birgen Haest1, 2, *, Ommo Hüppop1, Franz Bairlein1 Affiliations 1 Institute of Avian Research „Vogelwarte Helgoland‟, Wilhelmshaven, 26386, Germany 2 Swiss Ornithological Institute, Sempach, CH-6204, Switzerland * Corresponding author: [email protected]; +49 176 749 786 82 This PDF file includes: Supplementary Figures S1 to S5 Supplementary Tables S1 to S14 SI References 1 www.pnas.org/cgi/doi/10.1073/pnas.1920448117 Supplementary Figures Fig. S1 Per species ΔAICc, adjusted R2, and regression coefficient maps of the identified best time windows for temperature. The ΔAICc values are the difference of the AICc value of the model with the selected best time window for each pixel with the AICc value of the baseline model for that species (see Table S3). ΔAICc values are only shown for pixels that had a probability Pc value < 0.3, i.e., pixels for which the relation between the identified time window and mean spring passage date had a ΔAICc that is less likely to obtain due to chance. The adjusted R2 values are for the models that have as independent variables both the best identified time window for temperature and the „year‟ terms to account for trends (see Table S3). The regression coefficient maps show the regression coefficient (days/°C) of the best identified time window for temperature when „year‟ terms to account for trends are also included in the model (see Table S3). Yellow dots with annotated numbers indicate the candidate pixels selected as potentially influencing mean spring migration phenology at Helgoland (see Table S8). The ID values are used consistently throughout all supplementary figures and tables for ease of reference. 2 European Pied Flycatcher 3 Common Redstart 4 Willow Warbler 5 Common Whitethroat 6 Garden Warbler 7 Spotted Flycatcher 8 Fig. S2 Per species ΔAICc, adjusted R2, and regression coefficient maps of the identified best time windows for precipitation. The ΔAICc values are the difference of the AICc value of the model with the selected best time window for each pixel with the AICc value of the baseline model for that species (see Table S3). ΔAICc values are only shown for pixels that had a probability Pc value < 0.3, i.e., pixels for which the relation between the identified time window and mean spring passage date had a ΔAICc that is less likely to obtain due to chance. The adjusted R2 values are for the models that have as independent variables both the best identified time window for precipitation and the „year‟ terms to account for trends (see Table S3). The regression coefficient maps show the regression coefficient (days/mm) of the best identified time window for precipitation when „year‟ terms to account for trends are also included in the model (see Table S3). Yellow dots with annotated numbers indicate the candidate pixels selected as potentially influencing mean spring migration phenology at Helgoland (see Table S8). The ID values are used consistently throughout all supplementary figures and tables for ease of reference. 9 European Pied Flycatcher 10 Common Redstart 11 Willow Warbler 12 Common Whitethroat 13 Garden Warbler 14 Spotted Flycatcher 15 Fig. S3 Per species ΔAICc, adjusted R2, and regression coefficient maps of the identified best time windows for the number of days with midnight winds coming from the direction of Helgoland. The ΔAICc values are the difference of the AICc value of the model with the selected best time window for each pixel with the AICc value of the baseline model for that species (see Table S3). ΔAICc values are only shown for pixels that had a probability Pc value < 0.3, i.e., pixels for which the relation between the identified time window and mean spring passage date had a ΔAICc that is less likely to obtain due to chance. The adjusted R2 values are for the models that have as independent variables both the best identified time window for number of days with midnight wind coming from Helgoland and the „year‟ terms to account for trends (see Table S3). The regression coefficient maps show the regression coefficient (days/day) of the best identified time window for the number of days with midnight wind coming from Helgoland when „year‟ terms to account for trends are also included in the model (see Table S3). Yellow dots with annotated numbers indicate the candidate pixels selected as potentially influencing mean spring migration phenology at Helgoland (see Table S8). The ID values are used consistently throughout all supplementary figures and tables for ease of reference. 16 European Pied Flycatcher 17 Common Redstart 18 Willow Warbler 19 Common Whitethroat 20 Garden Warbler 21 Spotted Flycatcher 22 Fig. S4 Per species ΔAICc, adjusted R2, and regression coefficient maps of the identified best time windows for the number of days with midnight winds going into the direction of Helgoland. The ΔAICc values are the difference of the AICc value of the model with the selected best time window for each pixel with the AICc value of the baseline model for that species (see Table S3). ΔAICc values are only shown for pixels that had a probability Pc value < 0.3, i.e., pixels for which the relation between the identified time window and mean spring passage date had a ΔAICc that is less likely to obtain due to chance. The adjusted R2 values are for the models that have as independent variables both the best identified time window for number of days with midnight wind going to Helgoland and the „year‟ terms to account for trends (see Table S3). The regression coefficient maps show the regression coefficient (days/day) of the best identified time window for the number of days with midnight wind going to Helgoland when „year‟ terms to account for trends are also included in the model (see Table S3). Yellow dots with annotated numbers indicate the candidate pixels selected as potentially influencing mean spring migration phenology at Helgoland (see Table S8). The ID values are used consistently throughout all supplementary figures and tables for ease of reference. 23 European Pied Flycatcher 24 Common Redstart 25 Willow Warbler 26 Common Whitethroat 27 Garden Warbler 28 Spotted Flycatcher 29 Fig. S5 Histograms of the adjusted and predictive R2 values for: (a) the „best‟ model (in terms of AICc and irrespective of the number of independent variables); and (b) the model with the four „most important‟ (albeit falsely identified) variables, in each of the two hundred repetitions of the full procedure using randomized versions of each species‟ spring migration phenology. The full red, full orange, dashed, and dotted horizontal lines are drawn at values of 0.80, 0.70, 0.50 and 0.30, respectively. 30 (a) 31 (b) 32 Supplementary Tables Table S1 Per-species overview of the spring migration passage timing across years, including average 5th and 95th percentile, and mean spring passage date (MSPD) as Gregorian calendar date, standard deviation of MSPD (days), difference between the 5th and 95th percentile (days), the total amount of ringed birds and the average number of birds ringed per year. Species are ordered by average MSPD. Difference Average 5th Average Average 95th Standard deviation 95th and 5th Total birds ringed Average Species name Scientific name percentile MSPD percentile MSPD (days) percentile (days) 1960-2014 birds / year European Pied Flycatcher Ficedula hypoleuca 03 May 15 May 28 May 5.80 25 2753 50 Common Redstart Phoenicurus phoenicurus 01 May 15 May 30 May 5.49 29 12038 219 Willow Warbler Phylloscopus trochilus 01 May 15 May 29 May 6.11 28 12620 229 Common Whitethroat Sylvia communis 08 May 19 May 02 Jun 5.03 26 6843 124 Garden Warbler Sylvia borin 13 May 24 May 04 Jun 4.10 22 19028 346 Spotted Flycatcher Muscicapa striata 13 May 26 May 09 Jun 4.07 26 4355 79 Table S2 Properties and pre-processing of the weather data that were acquired from the NCEP Reanalysis I database. Spatial resolution Number of Weather variable NCEP variable (in degrees) analyzed grid cells Data pre-processing and comments temperature 'air.2m' 1.875° 558 We calculated daily mean temperatures from the four 6-hour temperature values. precipitation 'prate.sfc' 3.75° 159 Precipitation rate data were converted to mm/day. Spatial resolution is half that of the temperature data, because we took the mean over four grid cells. wind direction (East-West) 'uwnd' and 2.5° 342 The 925 hPa pressure level roughly corresponds to 750 m altitude. We used the (North-South) 'vwnd' at midnight values (UTC) of the u- and v-wind components to calculate the midnight 925 hPa wind direction. 33 Table S3 Per‐species overview of the selection of the best mean spring passage date (MSPD) temporal trend model, including the coefficients and their standard errors (SE) of the selected best trend model. Trend coefficients Species Scientific name Selected MSPD adj. R2 Int. SE Lin. SE Quad. SE Cubic SE trend model European Pied Flycatcher Ficedula hypoleuca Linear 0.33 566.73 80.13 -0.21 0.04 - - - - Common Redstart Phoenicurus phoenicurus Linear 0.25 497.84 80.04 -0.18 0.04 - - - - Willow Warbler Phylloscopus trochilus Quadratic 0.62 145.31 0.51 -34.35 3.79 -9.54 3.79 - - Common Whitethroat Sylvia communis Cubic 0.39 149.01 0.53 -21.53 3.93 -5.80 3.93 8.83 3.93 Garden Warbler Sylvia borin Linear 0.56 535.13 46.14 -0.19 0.02 - - - - Spotted Flycatcher Muscicapa striata Linear 0.40 480.65 53.05 -0.16 0.03 - - - - Table S4 Per species overview of the Augmented Dickey-Fuller test results for stationarity in the residuals of the mean spring passage date trend models (see Table S3).
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