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Migration confers winter survival benefits in a partially migratory songbird

  1. Daniel Zúñiga
  2. Yann Gager
  3. Hanna Kokko
  4. Adam Michael Fudickar
  5. Andreas Schmidt
  6. Beat Naef-Daenzer
  7. Martin Wikelski
  8. Jesko Partecke  Is a corresponding author
  1. Max Planck Institute for Ornithology, Germany
  2. University of Konstanz, Germany
  3. University of Zurich, Switzerland
  4. Indiana University, United States
  5. Swiss Ornithological Institute, Switzerland
Short Report
Cite as: eLife 2017;6:e28123 doi: 10.7554/eLife.28123
3 figures, 1 table and 4 additional files

Figures

Diagram representing the partial migratory system of the population under study.

Migrants and residents of a breeding population of European blackbirds are in sympatry during the summer months (March - October). During the wintering months (November – March) migrants and residents overwinter in different habitats.

https://doi.org/10.7554/eLife.28123.003
Overwintering locations and migratory distance of migrant European blackbirds (Turdus merula) between 2009 and 2014.

(A) Mean overwintering locations (red symbols) and 25% kernel utilization distribution (red lines) of 22 blackbirds were calculated using the light-level data acquired by geolocators during the wintering months (November – February). Raw light level data were processed using the R package ‘GeoLight’(Lisovski and Hahn, 2012) and Kernel utilization distributions were calculated to estimate the error of each location. Filled red circles represent 16 individuals with one single measurement. The other symbols represent six individuals with at least two repeated measurements in different years. (B) Histogram of the migratory distance of migrants. (C) Female radio-tagged blackbird.

https://doi.org/10.7554/eLife.28123.004
Figure 2—source data 1

Zip file contains five files: ‘locations_data.csv’; ‘kud2009.

Rdata’; ‘kud2010.Rdata’; ‘kud2011.Rdata’; ‘kud2012.Rdata’; ‘kud2013.Rdata’. ‘locations_data.csv’ contains the mean overwintering estimated locations (lat, long) of 22 blackbirds derived from Geolocators during the years 2009–2014 and plotted in Figure 2 panel a (red symbols). Estimated locations were calculated using the ‘geolight’ function from the Geolight R package. Files: ‘kud2009.Rdata’; ‘kud2010.Rdata’; ‘kud2011.Rdata’; ‘kud2012.Rdata’; ‘kud2013.Rdata’: 25% kernel utilization distribution representing the error of each estimated overwintering locations for the different years in R data format and plotted in Figure 2 panel a (red circles).

https://doi.org/10.7554/eLife.28123.005
Figure 2—source data 2

Lat and long and distance to the breeding grounds (km) to the 29 overwintering locations of European blackbirds (2009–2014) used to generate histogram of Figure 2 panel b.

https://doi.org/10.7554/eLife.28123.006
Seasonal survival probability of migrants and residents European blackbirds.

Survival probability (Φ) and 95% confidence intervals of migrants (red) and residents (yellow) birds estimated using the best ranked multievent capture - mark recapture model (Φ [season +migr].P[migr]). Detection probability (P) was estimated as 0.74 for residents and 0.19 for migrants. 262 birds were included in this analysis (192 were classified as residents and 70 were classified as migrants).

https://doi.org/10.7554/eLife.28123.008
Figure 3—source data 1

Results of the best ranked model (Model 1).

Survival estimates and confidence interval values predicted by model 1 of migrants and residents during winter and summer. Values are plotted in Figure 3.

https://doi.org/10.7554/eLife.28123.009

Tables

Table 1
Models examining effects of various covariates (Season, migratory strategy, sex, age) on survival (Φ) and detection probabilities (P) of a partially migratory population of European blackbirds between 2009 and 2016.

All models were compared to the base model using Akaike’s Information Criterion (AICc), Delta AICc, and changes in model deviance (Dev).

https://doi.org/10.7554/eLife.28123.007
ModelNumber of parametersQAICcDelta AICc (Δi)Weights
(ωi)
Deviance
(1) Φ [season + migr.].P[migr]51408.30.000.591398.2
(2) Φ [season + migr + sex.].P[migr]61409.20.950.361397.1
(3) Φ [season].P[migr]41414.36.040.021406.2
(4) Φ [season + juv + ad.].P[migr]51416.17.790.021406.0
(5) Φ [migr].P[migr]41447.138.820.011439.0
(6) Φ [.].P[migr]31448.239.980.001442.2
(7) Φ [sex].P[migr]41449.941.60.001441.9
(8) Φ [season + migr].P[season]51504.095.750.001493.9
(9) Φ [season].P[season]51504.295.890.001494.1
(10) Φ [season + sex + migr].P[season]61504.996.600.001492.7
(11) Φ [season + sex].P[.]31530.3122.020.001524.3
(12) Φ [season + sex]. P[.]41530.6122.43220.001522.6
(13) Φ [season + migr].P[.]41531.0122.70.001522.9
(14) Φ [season + juv + ad.]. P[.]41523.2123.00.001523.2
(15) Φ [season + sex + migr].P[.]51532.0123.70.001521.9
(16) Φ [.].P[Season]31528.9126.70.001528.9

Additional files

Source data 1

Raw presence data derived from capture-recapture and radio telemetry data of 262 European blackbirds used for the survival analysis.

https://doi.org/10.7554/eLife.28123.010
Source data 2

Presence-absence matrix during winter and summer of 262 European blackbirds between 2009–2016.

This matrix was built with Source data 1 .

https://doi.org/10.7554/eLife.28123.011
Source data 3

Zip file containing the necessary files to run the a session of the E-surge software (estimation of multi-events survival models).

More information on E-surge see Gimenez et al., 2014. Download for free here: https://www.cefe.cnrs.fr/fr/ressources/films/34-french/recherche/bc/bbp/264-logiciels File called "seasonalfms.mod", contains the actual session to be opened in E-surge. This session contains all the models fitted (Table 1). File called "GEPAT-blackbirds.pat", contains the GEPAT file. Input file for E-surge necessary to estimate the transition matrix between presence absences events. File called "matrix.fm-s.males.females.non.winter.winter2.txt" contains the raw presence absence data in E-surge format used to fit the multi-events survival models.

https://doi.org/10.7554/eLife.28123.012
Transparent reporting form
https://doi.org/10.7554/eLife.28123.013

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