Partial migration in European blackbirds: a study on alternative phenotypes
Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.)
der Universität Konstanz,
Mathematisch-Naturwissenschaftliche Sektion, Fachbereich Biologie
vorgelegt von
Adam Michael Fudickar Konstanz, 2012
Tag der mündlichen Prüfung: 21.11.12 1. Referent: Dr. Martin Wikelski 2. Referent: Dr. Karl-Otto Rothhaupt
As for me, all I know is that I know nothing.
Table of contents ___________________________________________
Chapter 1. General introduction...9
Chapter 2. Tracking migratory songbirds: accuracy of light level loggers (geolocators) in forest habitats………...…19
Abstract………..………21
Introduction………..………..………25
Methods………..………27
Results………..……….….31
Discussion………..………35
Acknowledgments..………39
Supplementary...41
Chapter 3. Co-occurrence of facultative and female biased obligate strategies in a partially migratory population………..…...………...45
Abstract………..………47
Introduction………..………..………51
Methods………..………56
Results………..………….….61
Discussion………..………66
Acknowledgments..………70
Chapter 4. Consistency of behavior of free living partial migrants……..………...73
Abstract………..………75
Introduction………..………..…77
Methods………....………..…79
Results………...….81
Discussion………..…83
Acknowledgments..………86
Chapter 5. Comparison of flight apparatus within a partially migratory population
of songbirds………...………...………89
Abstract………..………91
Introduction………..………..…93
Methods………..95
Results……….…...99
Discussion………100
Acknowledgments..………..104
Chapter 6. General discussion………...…….……..107
Summary……….117
Zusammenfassung (German summary)………..…121
Record of achievement……….….127
References………...……129
Acknowledgements………145
List of publications……….147
Curriculum vitae………149
Chapter 1 ___________________________________________
General introduction
Migration is a common behavior throughout the animal kingdom that results in seasonal movements by billions of animals across the globe annually (Holland et al. 2006, Wilcove 2008, Bridge et al. 2011). Migratory animals move between habitats: tracking resources, evading predators, and reproducing. Selective events that lead to seasonal migration are incompletely known, however migratory behavior at the population and species level can be gained and lost in a relatively short time (Pulido and Berthold 2010, Zink 2011).
Partial migration, when a population consists of both migratory and sedentary
individuals, is an intermediate stage between fixed migratory and sedentary life histories (Berthold 1996). Captive studies on partially migratory songbirds have provided evidence for endogenous control of individual strategies (Pulido et al. 1996, Partecke and Gwinner 2007, Pulido and Berthold 2010). Results of lab studies suggest that, given sufficient selective forces and evolutionary time, partially migratory populations can become completely migratory or completely sedentary (Berthold and Querner 1982, Berthold 2001). Studies on free-living partially migratory songbirds have mainly addressed questions of ecological relevance (Schwabl 1983, Adriaensen and Dhondt 1990, Jahn et al. 2010). The major aim of the current thesis was to combine the strengths of lab (individual measures, precise timing) and field studies (environmental contribution) on partial migration in an attempt to illuminate the complex nature of partial migration.
The central question in the study of partial migration remains: Why do some individuals migrate while others remain at breeding areas year round? Theoretical work over the past
two and a half decades has provided insight into potential answers for this question. One of the earliest theoretical contributions to partial migration by Per Lundberg (1987) described how individual strategies within partially migratory populations could be conditional with frequency-dependent choice. Frequency dependence has predominated theoretical models since this early work. Kaitala et al. (1993) advanced Lundberg’s ideas with a game-theoretical approach. The authors show how the evolutionarily stable strategy (ESS) in a partially migratory population should be sensitive to differences in reproductive success and survival between migrants. Further, differences in reproductive success between strategies, independent of age, should promote individuals to change strategies throughout their lifetimes. More recent theoretical work has emphasized the importance of assumptions on habitat patchiness and territory acquisition (Kokko and Lundberg 2001, Taylor and Norris 2007, Kokko 2011).
Empirical work has primarily addressed two questions, creating a dichotomy in partial migration research. Due to methodological limitations, field studies have focused on ecologically relevant answers to the question of “why?” Commonly, researchers observe demographic differences between migrants and non-migrants in free-lving populations of partial migrants. These observations have led to the proposal of three main hypotheses concerning the factors that lead to individual differences in strategies. The ‘Arrival Time’
hypothesis of partial migration states that if the reproductive fitness of one sex is partly influenced by the acquisition of a territory in early spring, then it is to the advantage of individuals of that sex to be at breeding areas as early as possible in spring (Ketterson and Nolan 1976). Therefore, if lifetime fitness increases by remaining at breeding areas year
round, then selection could lead to residency among the territorial sex (Table 1). The
‘Dominance’ hypothesis of partial migration proposes that competition for limited food requires subordinate individuals to leave the breeding grounds during the non breeding season (Gauthreaux 1982). If a dominance hierarchy of a population is structured demographically (e.g. adults dominant over juveniles, males dominant over females), then selection could result in migration as an ontogenetic or sex biased trait. The
‘Thermal Tolerance’ hypothesis of partial migration predicts that individual differences in thermal efficiency, due to variation in body size, results in differences in migratory tendency (Able & Belthoff 1998). If one sex is always smaller, then selection could lead to a sexual dimorphism in migratory behavior. Predictions of all three hypotheses are straightforward, although typically not mutually exclusive. However, the proximate cause of individual differences in migration strategy is not apparent. Although differences in body size, age, and sex are commonly found as correlates of migration strategies in free living partially migratory populations, it is still not clear if differences in strategy are the result of genetic determination or some aspect of individual condition.
Table 1. Hypotheses and predictions of partial migration.
Hypothesis Description Arrival time
(Ketterson and Nolan 1976)
Intrasexual competition for breeding territories promotes residency among the territorial sex.
Thermal tolerance
(Ketterson and Nolan 1976)
Individual differences in thermal efficiency lead to migration among the least efficient.
Dominance
(Gauthreaux 1982)
Competition for limited food leads to migration among subordinates.
Laboratory studies, which use nocturnal locomotor activity (i.e. “Zugunruhe”) of
individuals in cages as a measure of migratory propensity, have primarily focused on the heritability of migration in partially migratory populations. These studies have provided evidence for genetic control of the incidence and amount of Zugunruhe (Biebach 1983, Berthold 1988). However, it is still not clear what Zugunruhe corresponds to in wild populations. Several studies have found seasonal patterns of Zugunruhe in non-migratory populations (Smith et al. 1969, Chan 1994, Helm and Gwinner 2006, Coverdill et al.
2011). Helm and Gwinner suggested that Zugunruhe could be a regular feature of the endogenous program of birds that facilitates periodic movements even in non-migrants.
Using a combination of state of the art animal tracking techniques and individual
measures of condition and morphology, this dissertation addresses questions concerning both proximate and ultimate causes of partial migration in a population of European blackbirds, Turdus merula, in southwestern Germany. By understanding the forces that lead to partially migratory behavior in individuals, we stand to gain insight into the evolution of obligate migration and residency at the population and species level.
In chapter 1 I developed a system for tracking passerines year round. I combined manual and automated radio telemetry with light level loggers (geolocators) to track individuals in breeding and non breeding areas. Geolocators have been used extensively to track large animals at sea (Hill 1994) however accuracy of geolocators for tracking terrestrial vertebrates was unknown until this study. Most importantly for the current dissertation, it was unclear if geolocators could be informative for tracking the movements of short
distance migrants. The results of chapter 1 validate the novel method that is presented in the remaining chapters.
In chapter 2 I tested predictions of The ‘Arrival Time’, ‘Thermal Tolerance’, and
‘Dominance’ hypotheses of partial migration (Table 1). Age and sex related differences in local movements during the non-breeding season can bias migration estimates towards juveniles and non-territorial individuals. Using tracking techniques described in chapter 1, I was able to distinguish between true migration events and local dispersal. By comparing physiological measures of the energetic state of migratory and sedentary individuals in the weeks prior to autumn migration, I aimed to test whether individual condition is an indicator of the decision to migrate. Further, by comparing age, sex, body size and energetic state of migrants and non-migrants my aim was to identify if migratory behavior is a fixed or condition dependent strategy within the population.
Despite the prevalence of partial migration, little is known about the proximate control of individual migratory behavior in the wild. Further, it is unclear how flexible partial migrants are in the timing and decision to migrate throughout their lifetime. In chapter 3 I tested the consistency of annual movement strategies and phenologies in the population over three consecutive autumn and spring migrations. The main objectives of chapter 3 were to identify: 1) if migration strategies of individuals are consistent across years and 2) if population and individual departure and arrival dates are consistent across years? If obligate migration or residency is preceded by partial migration, rigid programs should first be present for selection of phenotypes in the wild.
Migratory birds typically possess morphological, physiological, and behavioral
adaptations which help them cope with the energetic demands of sustained long distance flight. While increasing the efficiency of forward flight, adaptations to migration of the external flight apparatus decrease maneuverability at slow speeds (Thomas 1996, Bowlin and Wikelski 2008). Selection has led to both interspecies and population differences in these traits in relation to migration distance (Winkler and Leisler 1992, Fiedler 2005, Rolshausen 2009). If these principles are general, how is the external flight apparatus within a partially migratory bird species shaped in which some individuals migrate and others remain at breeding grounds year round? In chapter 4 I compared two primary indices of external flight apparatus of migrant and resident blackbirds. Blackbirds from the study area migrate on average 800 km away from the breeding site during the non breeding season (Schwabl 1983, Fudickar and Partecke unpublished manuscript). If partial migration is a dimorphism within the population, it would be advantageous for migratory individuals to have flight apparatus suitable for migration. Given the relatively long distance that migrant blackbirds from the region move seasonally compared to their non-migratory counterparts, I predicted that migrant blackbirds would have flight
apparatus more adapted to long distance flight than year round residents in the population.
Chapter 2 ___________________________________________
Tracking migratory songbirds: accuracy of light- level loggers (geolocators) in forest habitats
Adam M. Fudickar, Martin Wikelski, and Jesko Partecke
Methods in Ecology and Evolution (2012) 3:47-52
Abstract
Tracking return migrations in songbirds has been impossible until recently when miniaturization of light-level loggers enabled observation of the first complete round trip. Although geolocators are extensively used on animals at sea, little is known about how accurate geolocators are for tracking terrestrial or forest-dwelling
migrants. To test the accuracy of geolocators for tracking migratory songbirds living in forested habitat, we calibrated geolocators to a source population located in central Europe and collected location estimates based on the source population calibration from stationary geolocators deployed over an 800 km NE to SW gradient in Western Europe. Additionally, we fit non-migratory songbirds (European blackbirds, Turdus merula) with geolocators for 12 months to compare known locations of individuals with locations estimated by geolocators. We found an average error ± 95% CI of 201
± 43 km in latitude for stationary geolocators in forest habitat. Longitude error was considerably lower (12 ± 03 km). The most accurate geolocator was on average 23 km off target, the worst was on average 390 km off. The winter latitude estimate error for geolocators deployed on sedentary birds was on average (± 95% CI) 143 ± 62 km when geolocators were calibrated during the breeding season and 132 ± 75 km when they were calibrated during the winter. Longitude error for geolocators deployed on birds was on average (± 95% CI) 50 ± 34 km. Although we found error most likely due to seasonal changes in habitat and behavior, our results indicate that geolocators can be used to reliably track long-distance forest-dwelling migrants. We also found that the low degree of error for longitude estimates attained from geolocators makes
this technology suitable for identifying relatively short distance movements in longitude.
Introduction
Songbirds are model study systems in many biological disciplines and provide important ecosystem services (Newton 2008). Many songbird species are critically endangered, especially migrants (Wilcove 2008). However, the destinations of migrants among seasons are largely unknown. It is clear that events in one season influence life history, morphological and behavioral traits in subsequent seasons (Marra et al. 1998; Webster et al. 2002). The annual survival of migratory songbirds is potentially impacted by events occurring during migration (Sillett and Holmes 2002). It is thus of great scientific but also societal importance to better understand songbird migrations on the level of the individual, particularly to fully understand their annual cycle (Webster and Marra 2005;
Wilcove and Wikelski 2008).
Until recently, the tracking of individual migrants has been limited to large animals capable of bearing the load of satellite transmitters and GPS loggers. However, Stutchbury et al. (2009), a landmark study in movement ecology, reported the first complete migratory track for a songbird using light-level loggers (geolocators).
Significant findings of this study include an individual purple martin (Progne subis) traveling 577 km day-1.
Geolocators have rapidly become small enough (0.6g) to fit a significant number of small songbird species. Tracking migrants with light-level loggers is achieved using archived light intensity levels to estimate sunrise and sunset times to calculate day length and the time of midday (Wilson et al. 1992; for complete review of theory see Hill 1994).
Sunrise and sunset times are identified using a light intensity threshold determined during a pre-deployment calibration. Error in location estimates during deployment can be due
to shading at sunrise and sunset from clouds and landforms, atmospheric aberrations and low variation in day length around the equinoxes. Uncertainties in archived light values are known to cause smaller errors in longitude than latitude, regardless of the season (Ekstrom 2004). Species that live in flat, open habitat with unobstructed views of the horizon are well suited for geolocators (Croxall et al. 2005; Egevang et al. 2010).
An added problem when using geolocators in terrestrial habitats, many of which are at least partially forested, is that foliage causes increased shading. Location estimates could thus be systematically biased if the archived light intensity at sunrise and sunset is different from the threshold identified during calibration. Therefore, geolocators should be calibrated in the typical habitat of the species being tracked. Further, it has long been recognized that the daily activity patterns of birds can change throughout the annual cycle (Daan and Aschoff 1975). Daan and Aschoff identified seasonal changes in the timing of the onset and end of daily activity for several temperate avian species. These differences in behavior resulted in seasonal differences in subjective day length. Seasonal changes in subjective day length could result in increased error in latitude estimates during the winter if calibrations are performed during the summer. Additionally, seasonal changes in habitat shading either because of leaf loss, or if the bird changes habitat types as it migrates, could result in increased error.
To test the accuracy of geolocators for tracking terrestrial and partially forest- dwelling migratory birds, we calibrated geolocators to a source population located in central Europe and collected location estimates based on the source population
calibration from stationary geolocators deployed over an 800 km NE to SW gradient in Western Europe. Additionally, we analyzed location estimates for six non-migratory
European blackbirds (Turdus merula merula) we fitted with geolocators to identify error caused by individual and seasonal behavioral differences and seasonal changes in forest shading.
Material and Methods
We used MK 10S geolocators (1.2 g) developed by the British Antarctic Survey. All stationary geolocators were deployed between 07-Dec-2008 and 28-Apr-2009 from southern Germany (47.88° N, 11.10° E) to south-western France (44.02° N, 2.20° E) at 15 locations (locations were roughly 50 km apart). Two replicates were placed within 5 m of each other in similar forest structure. Geolocators were attached 2-3 m high in trees and shrubs in mixed coniferous/deciduous forests, always with the light sensor facing upward. Forest locations were selected that were suitable roosting habitat for blackbirds (Hill and Creswell 1997). The transect was selected because it is within the wintering range of migratory blackbirds from southern German.
We captured two blackbirds in September 2008 and four in the spring of 2009 (total of six blackbirds) at two locations in southern Germany (47.77° N, 9.00° E; 47.77°
N 9.04° E) using mist nets. Prior to capture, Mk 10S geolocators were connected to radio transmitters (2.6 g, Sparrow Systems, Fisher, IL, USA) with heat shrink tubing (0.4 g) and leg-loop harnesses were connected to the transmitters through pre-fabricated tubing in the transmitters. A range of harness sizes were built from 1-mm elastic beading cord to fit the naturally occurring body masses of blackbirds in the study population using the method of Naef-Daenzer (2007). All transmitter-geolocator backpacks weighed < 5% of the body mass of the individual that it was deployed on. Once a harness was fitted to a
bird, it was inspected for appropriateness of fit. All birds were observed for as long as possible immediately after release and throughout deployment whenever possible to ensure normal flight behavior.
Birds were confirmed to be present throughout deployment by automated receiving units (Sparrow Systems, Fisher, IL, USA) and manual tracking with a hand- held three element Yagi antenna (AF Antronics, Inc., Urbana, IL, USA) and an AR 8200 MKIII hand-held receiver (AOR U.S.A., Inc., Torrance, CA) from the first date of capture until recapture. In 2009 and 2010, birds were located 2-3 times per week until December and then at least 1 time per week until recapture. Once a bird was located by manual tracking, latitude and longitude were obtained using a hand-held GPS. All birds were also monitored by automated receiving units throughout deployment. The
automated receiving units searched for each frequency every 60 s throughout the testing period. The automated receivers were connected to H antennas (ATS, Isanti, MN, USA), mounted 3-6 m high. The maximum distance at our study site that an automated receiver was capable of receiving a signal from a transmitter was 850 m. The two individuals that were originally captured in 2008 were not tracked manually on a regular basis but were monitored daily with an automated receiving unit. Upon recapture, birds were
immediately released after removal of their backpack.
Before deployment, all geolocators were placed outdoors with a similar view of the horizon for 7 days to affirm similar light sensitivity. After deployment, raw data were corrected for clock drift using Bastrak (British Antarctic Survey). To calculate a light threshold and sun elevation angle for sunrise and sunset transitions for stationary geolocators, light data from six stationary geolocators placed in forest habitat at three
locations (47.44°N, 8.7°E; 47.77°N, 9.00°E; 47.88°N, 11.10°E) were analyzed. These six geolocators were deployed prior to the others at the northeast end of the 800 km transect and were meant to simulate the source population. We visually inspected sunrise and sunset transitions for the six geolocators from 26-Nov-2008 to 10-Dec-2008 (pre- deployment calibration) and from 1-Apr-2009 to 15-Apr-2009 (post-deployment calibration) using TransEdit2 to identify the average light-level value when the light changed most rapidly, excluding any anomalous transitions. Using typical transition events during the calibration period, a light-level threshold value of 16 was identified.
The corresponding average sun elevation angle for the six stationary geolocators during the calibration period (26-Nov-2008 to 10-Dec-2008 and 1-Apr-2009 to 15-Apr-2009) was -2.84° ± 0.18 (SE; n = 12).
To correct for behavioral and habitat choices of wild birds, we calibrated
geolocators deployed on blackbirds during deployment. To identify the effect of seasonal changes in shading, we calibrated all geolocators during two periods. The breeding calibration period started on the first date that we observed a migratory blackbird from the population return to the breeding site (March 24) and ended on the first date that we observed a migrant blackbird depart during the autumn (Oct 2). All six individuals in the current study were sedentary throughout the deployment. The winter calibration period started on November 1 and ended on February 15. All six geolocators on blackbirds were deployed throughout the entire winter calibration period. Capture and recapture dates varied, and so, calibration data were not available for the entire breeding calibration period for all individuals. The breeding calibration dates for the six individuals were the following (15-May-2009 to 2-Oct-2009, 24-Mar-2010 to 24-Apr-2010; 12-Jun-2009 to 2-
Oct-2009, 24-Mar-2010 to 21-Apr-2010; 24-Jun-2009 to 2-Oct-2009, recaptured before spring; 18-Jun-2009 to 2-Oct-2009, 24-Mar-2010 to 24-Apr-2010; 10-Sept-2008 to 2- Oct-2008, 24-Mar-2009 to 21-Apr-2009; 11-Sept-2008 to 2-Oct-2008, recaptured before spring). Using a light threshold of 16, the average sun elevation angle for the breeding
calibration period was -3.45° ± 0.19 (SE; n = 10) and -3.57° ± 0.25 (SE; n = 6) for winter.
Transitions for all geolocators were calculated using TransEdit2, and anomalous transitions were rejected from analysis (mean 40 days ± 26.3 SD, excluding 3 weeks before and after equinox events). Sunsets were retarded by 10 min. Latitude and longitude were calculated using Locator (British Antarctic Survey). Both midnight and noon locations were used. We did not compensate for movement because all geolocators were either stationary or deployed on non migratory birds throughout deployment.
Before analysis, all error values were converted to distance (km) in latitude and longitude based on the actual location of the individual geolocator to make the results transferable to global applications. To identify location estimate error because of seasonal and individual behavioral differences, we calculated the average latitude and longitude estimate ± 95% CI for each month of deployment for geolocators deployed on blackbirds using the breeding calibration angle. Additionally, we calculated the monthly coefficient of variation (CV) of latitude and longitude estimate error for sedentary birds using the average breeding calibration angle. For stationary geolocators, we calculated the monthly coefficient of variation of latitude and longitude estimate error using a sun elevation angle of -2.84. Estimates for the equinoxes included three weeks before and after (autumnal equinox = 22-Sept, vernal equinox = 20-Mar). Data were only used for one time unit; therefore, days in October, February and April that were included in an
equinox event were not used to calculate the error for that month. To compare regional differences in geolocator accuracy, we calculated the average winter location estimate ± 95% CI (10-Dec to 31-Jan) for stationary geolocators deployed along the migratory trajectory. We also calculated the average winter error ± 95% CI of latitude and
longitude for stationary geolocators over the same period. For geolocators deployed on sedentary blackbirds, we calculated the average winter error ± 95% CI (blackbird winter
= 1-Nov to 15-Feb) using both the breeding and winter calibrations.
Results
The average monthly location estimate error (± 95% CI) of latitude and longitude for individual geolocators deployed on blackbirds is presented in Table 1. The average latitude winter error (± 95% CI) for sedentary blackbirds using the breeding calibration was 143 ± 62 km and 132 ±75 km using the winter calibration. The average winter error for longitude for sedentary blackbirds (± 95% CI), using both the breeding and winter calibration, was 50 ± 34 km. The average winter location error (± 95% CI) for stationary geolocators was 201 ± 43 km for latitude and 12 ± 03 km for longitude (Fig.
1). The monthly coefficient of variation of error for latitude and longitude for sedentary birds and stationary geolocators is presented in Fig. 2. One stationary geolocator was removed from the analysis because it was a statistical outlier.
April n 184 ±65 e 31 ±12 n 186 ±64 w 12 ±18 n 311 ±118 e 23 ±33 n 219 ±53 e 5 ±13
Vernal equinox s 1429 ±1039 e 46±13 s 80 ±777 w 13 ±16 s 1015 ±819 w 0 ±11 s 144 ±845 e 35 ±19
February s 318 ±99 e 59 ±16 s 287±100 e 98 ±26 s 22 ±150 e 34 ±30 s 94 ±149 e 72 ±31 s 506 ±120 e 20 ±15 n 134 ±153 e 107 ±29
January s 154 ±36 e 61 ±15 s 107 ±38 e 95 ±16 n 96 ±61 e 34 ±31 s 92 ±39 e 15 ±19 s 122 ±37 e 37 ±17 n 137 ±36 e 107 ±24
December s 159 ±32 e 49 ±14 s 139 ±45 e 97 ±21 n 167 ±56 w 32 ±36 s 39 ±33 e 37 ±16 s 96 ±37 e 29 ±25 n 115 ±40 e 74 ±23
November s 258 ±43 e 23 ±16 s 202 ±47 e 62 ±18 n 231 ±92 w 16 ±29 s 76 ±69 e 50 ±20 s 165 ±56 e 18 ±17 n 61 ±63 e 69 ±19
October s 375 ±193 e 70 ±36 s 455 ±144 e 46 ±15 n 756 ±483 w 41 ±103 n 15 ±113 e 51 ±24 s 216 ±162 e 49 ±26 n 343 ±145 e 67 ±28
Autumnal equinox s 582 ±916 e 73 ±16 s 723 ±837 e 75 ±15 n 23 ±1511 e 16 ±44 s 1343 ±894 e 31 ±23 s 455 ±755 e 50 ±17 s 1781 ±847 e 30 ±22
August s 9 ±79 e 40 ±27 s 127 ±94 e 108 ±28 n 40 ±57 e 80 ±20 s 511 ±115 e 4 ±35
July s 108 ±49 e 148 ±33 s 95 ±45 e 170 ±23 n 113 ±34 e 41 ±24 s 16 ±30 e 45 ±40
June s 76 ±92 e 162 ±47 s 19 ±49 e 173 ±38 n 55 ±51 e 145 ±30 s 71 ±33 e 191 ±42
Table 1. Average monthly error (km) ± 95% CI of latitude (upper row) and longitude estimates (lower row) for six geolocators on sedentary blackbirds in southern Germany. Equinox calculations include 3 weeks before and 3 weeks after (22-Sept-2008&2009and20-Mar-2009&2010). n, north; s, south; e, east; w, west.
Fig. 1. Error of winter locations for stationary geolocators in forest habitat. Shown are average location estimates of 30 geolocators distributed in Western Europe from 10-Dec- 2008 to 31-Jan-2009 (winter). One outlier had an average winter location error of 565 km (*). Locations (black dots) along the transect are the actual locations of two
replicates (A). Winter location estimates with 95% CI are connected to actual locations
Fig. 2. Monthly coefficient of variation for stationary geolocators and geolocators deployed on sedentary blackbirds in latitude (A) and longitude (B). The monthly error was pooled for all geolocators in each category (geolocators on blackbirds = solid lines, stationary geolocators = dotted lines).
Discussion
Tracking individual migratory songbirds across seasons will provide researchers with the opportunity to answer important questions about the basic biology of a multitude of species, which have already been studied exclusively on either their breeding or wintering grounds. Unfortunately, there is still a large gap in our understanding of the movement ecology of migrant songbirds and the effect of varying movement strategies on future life-history stages. Most importantly, little is known about the connectivity of breeding and wintering populations (Webster et al. 2002). The introduction of
geolocators as a method for tracking songbirds throughout their annual cycle has understandably received considerable attention (Robinson et al. 2009; Bowlin et al.
2010a,b). However, as we proceed with any new method, we must also learn its limitations.
By analyzing location estimates for stationary geolocators calibrated to a source population and deployed along an 800 km terrestrial transect through Western Europe, we have identified location estimate error potentially because of landscape and habitat differences. Variation in topography and vegetation density across south-western Europe could result in different intensities of shading at sunrise and sunset. These differences can occur because of slight changes in microhabitat structure and large-scale changes in the landscape. If the light-level threshold used for trajectory calculations is reached at a different sun elevation angle during deployment than identified during calibration owing to differences in shading, error is expected. Alternatively, the variation in accuracy of geolocators along the transect could be due to regional differences in shading by clouds.
This is unlikely given that all stationary geolocators were analyzed over the same time
frame and that the distance between individual geolocators was relatively short (roughly 50 km).
For many migrant species, changes in habitat use throughout the year are expected (Rivera et al. 1998; Pagen et al. 2000). This is particularly true for-long
distance migrants. Estimates of accuracy on breeding grounds might not reflect accuracy at wintering grounds if animals change habitat seasonally, such that sun elevations calculated from breeding habitat bias locations estimated in wintering habitat. To better interpret winter location estimates for migratory wood thrush, a species which has high site fidelity to their winter territory, Stutchbury et al. (2011) compared the wintering locations of individual wood thrushes over multiple years.
The average winter location error for stationary geolocators that we report for latitude (201 ± 43 km) is similar to the error reported previously for geolocators in forest habitat during the spring and summer; the average winter location error for longitude (12
± 3 km) reported is considerably lower than previous estimates (Stutchbury et al. 2009, 2011). However, we should be cautious when interpreting results from geolocators not attached to live birds, as presented as a part of this study, because of potential influences of behavior on location estimates. The most realistic measure of error is recorded from animals at known locations, as Stutchbury et al. (2009, 2011) did for the summer months only, when their individuals were still on the breeding grounds. In this study, we found lower average error for latitude estimates derived from geolocators deployed on live birds than stationary geolocators. Latitude estimates are sensitive to differences in shading between deployment and calibration. Non-migratory blackbirds roost in similar habitat throughout their annual cycle (A. Fudickar, pers. obs.) and therefore experience relatively
similar shading throughout the year. However, migratory birds could experience a multitude of different levels of shading from stopover to wintering grounds. The results from our 800 km transect might reflect the potential variation in shading experienced by a bird over a relatively short distance.
We found slightly lower average winter error for latitude estimates derived from winter calibrations than estimates derived from summer calibrations for sedentary blackbirds. This difference in error could be due to seasonal changes in forest shading.
Additionally, the decrease in error could be partially due to behavioral differences between the breeding season and the winter, which are compensated for when
calibrations are performed during the winter. It is important to note that for long-distance migrants, potentially experiencing a much broader range of habitats and climates, larger seasonal differences in sun elevations should be expected. Error reduction should be possible for long-distance migrants by calibrating geolocators in typical wintering habitat for the focal species. Stutchbury and co-authors are now performing winter calibrations on wood thrush in tropical forests in Costa Rica and Belize to better estimate the winter territory locations of birds that breed in temperate deciduous forests in eastern Canada and the United States (B. Stutchbury pers. comm.).
There was a sharp increase in the CV of latitude estimate error for geolocators deployed on blackbirds in August and February (Fig. 2a). In August, blackbirds in southern Germany transition from breeding to molting (Partecke et al. 2005). During this transition, blackbirds often change their daily foraging behavior. During breeding, blackbirds leave the roost in early morning in order to sing from the tops of trees and fly to the forest edge to forage. During molt, blackbirds are inconspicuous and forage in
dense vegetation most likely to avoid predators during a period of high vulnerability.
During February, resident blackbirds in southern Germany begin to prepare for the oncoming breeding season (Partecke et al. 2005). Males start to establish territories, and females can begin nest building. These seasonal changes in behavior could result in variations in location estimate error if individuals are transitioning between behaviors at different times and if these changes in behavior are accompanied by changes in habitat use. Interestingly, we also report a high CV for longitude estimates in August for
blackbirds. Error in longitude estimates results from the archived midday diverging from the actual midday.
As long as satellite transmitters and GPS loggers are limited to large animals, the potential for the use of geolocators to track long-distance songbird migrants is astounding given how little we actually know about these seasonal movements. However, we think that this technology could also be used for identifying seasonal movements of medium- and short-distance migrants (400-1000 km). Limiting location estimates to data collected during months with lowest error (December and January) could provide the resolution to distinguish differences in overwintering sites within a relatively short range. Further, researchers interested in studying the movements of individuals with seasonal movements directed at least partially east or west could benefit from the low error inherent in
longitudinal estimates. Migratory barriers that hinder direct northerly or southerly movements, such as the Himalayas, can create such migratory pathways.
To optimize future studies utilizing geolocator technology, we recommend tests to address methods for reducing error, such as those currently being performed by
Stutchbury and colleagues mentioned earlier. Ideally, future tests would also be
conducted on forest-dwelling migratory species during migration. One approach would be to combine satellite telemetry or GPS loggers with geolocators in order to compare estimated locations. Given the rapid decrease in the mass of gps loggers, this might be possible on the largest songbirds very soon. Although the monetary expense would be high, additional verification of this method would be valuable for the future of movement ecology. We are optimistic that with increased effort to verify and optimize this powerful tracking technology, long-standing questions about songbird migration, and thus our understanding of the phenomenon of migration in general, can be addressed.
Acknowledgments
We thank Catarina Miranda, Davide Dominoni and Carrie Fudickar for help capturing birds and deploying geolocators and Wolfgang Fiedler for help building the maps. Jeff Kelly, Carlos David Santos and Sjouke Kingma provided helpful comments on previous versions of the manuscript. We especially thank Bridget Stutchbury and Heiko
Schmaljohann for providing very thoughtful and thorough reviews of the manuscript.
Funding was provided by the Max Planck Institute for Ornithology, Department of Migration and Immuno-Ecology.
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Supp. Fig. 1. Monthly mean of location estimates with 95% CI for six (Panel A-F) sedentary European blackbirds. All birds were observed at the breeding site (square) in Southwest Germany throughout the entire deployment.
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Chapter 3 ___________________________________________
Co-occurrence of facultative and female biased obligate strategies in a partially migratory
population
Adam M. Fudickar and Jesko Partecke
In Review
Abstract
Partial migration occurs when a breeding population consists of seasonal migrants and year round residents. Although it is common among birds, the basis of individual movement decisions within partially migratory populations is still unresolved.
Due to the existence of both migratory and sedentary strategies within partially migratory populations, partial migration is considered an intermediate stage between fixed migratory and sedentary life histories at the population and species level.
Over four years we used state of the art tracking techniques, a combination of
geolocators and radio transmitters, to follow individual European blackbirds, Turdus merula, year round from a partially migratory population to determine individual strategies and departure and arrival dates. The individual-based tracking combined with measures of energetic and hormonal (corticosterone) state enabled us to test several classical hypotheses of partial migration: the ‘Arrival time’-, ‘Dominance’- and ‘Thermal Tolerance’-hypotheses. Two distinct migratory periods were observed during the study; one in early autumn, and another during the midst of winter. While blackbirds that migrated in autumn were never observed overwintering within 300 km of the study site, four individuals that departed in the winter were observed within 40 km. Females were significantly more likely to migrate in autumn than males but there was no difference in the age or body size of migrants and non-migrants in autumn.
Just prior to autumn migration, migrants had higher fat scores than non-migrants and tended to have higher levels of baseline corticosterone. Unlike autumn migrants, we found no difference between the tendency for males and females to migrate in winter, nor did we find any difference in body size or age of winter migrants. Autumn
migration was sex biased and resembled obligate migration. Our results provide strong support for The Arrival Time hypothesis for partial migration in the autumn.
By tracking individuals year round, we were able to identify a second period of departures. Overall, these results suggest the co-occurrence of obligate autumn migrants, facultative winter migrants and sedentary individuals within a single population.
Introduction
Seasonal migration is a common trait in a diverse range of taxa that facilitates the exploitation of temporally pulsing resources in geographically distant habitats. Seasonal migration optimizes reproductive fitness during the critical phase of offspring rearing while increasing survival the remainder of the year. Even in the tropics, where seasonal environmental variability is less extreme compared to temperate latitudes, many species migrate annually (Greenberg 1980; Joseph 1997; Jahn et al. 2006). Migrations are often anticipatory: animals start migrating at times when environmental conditions are still benign where they are, escaping before conditions deteriorate. Despite the predictive nature of migration, many migratory species are flexible in their migratory behaviour, allowing for adjustments to unpredictable environmental conditions during migration (Jenni and Schaub 2003; Schmaljohann et al. 2009; Schmaljohann and Naef-Daenzer 2011).
Ongoing changes in habitat phenology and broad scale changes in weather patterns have resulted in variable responses by migratory populations. Some migratory populations have adapted to these changes and flourished while others are apparently limited in their ability to respond (Wilcove 2008). For migratory birds at north temperate latitudes, some populations have advanced the timing of their spring arrival in response to changing spring phenologies and in some cases the frequency, distances and even
directions of migrations are changing (Hüppop and Hü ppop 2003; Bearhop et al. 2005;
Pulido and Berthold 2010). Given the rate at which the earth’s biosphere is changing, it is clear that individual flexibility and population variability are critical for the survival of migratory populations globally.
Migratory patterns within populations can be variable. One exceptional case of variable migration strategies within a population is partial migration. Partially migratory populations consist of both seasonal migrants and year round residents. Therefore, partial migration is thought of as an intermediate stage between fixed migratory and fixed sedentary life histories (Berthold 2001). Increased knowledge on the interaction of migrants with the environment and the selective pressures that shape migratory life histories is important for our basic understanding of the evolution of migration but also for informing policy decisions directed at preserving migratory populations.
The mechanisms responsible for individual strategies within partially migratory bird populations are still mostly unresolved. Although the mechanisms most likely vary between species and populations, two general hypotheses have been advanced:
genetically controlled and condition dependent partial migration. Genetic determination of migratory behaviour within partially migratory bird populations was proposed almost as early as the phenomenon was identified in the literature (Thomson 1921; Nice 1933;
Lack 1944). Controlled lab studies over the past four decades have provided estimates for the heritability of nocturnal locomotor activity “Zugunruhe” which coincides with the timing of autumn and spring migration for free-living counterparts. Heritability estimates for onset, termination and intensity of Zugunruhe for partially migratory populations range from 0.16 to 0.67 (Newton 2008). Although these results are intriguing, it is difficult to interpret what Zugunruhe corresponds to in wild populations. Helm and Gwinner (2006) found that resident African stonechats, Saxicola torquata, express seasonal Zugunruhe in captivity. The author’s interpretation of their findings was that
Zugunruhe could be a regular feature of the endogenous program of birds that might act to facilitate periodic movements in non migrants.
If migratory behaviour is a genetic dimorphism, then in order for both migratory and sedentary morphs to be maintained within a population, differences in survival and reproduction should vary across years. If one strategy were to maintain a fitness
advantage, it could become fixed within the population relatively fast (Pulido and Berthold 2010). Further, if genetic determination of migratory strategy is a true dimorphism in a population, then there should be equal age and sex ratios of migrants unless the trait is either 1) a sexual dimorphism or 2) an ontogenetic trait that changes throughout an individual’s lifetime.
The ‘Arrival Time’ hypothesis of partial migration posits that if the reproductive fitness of one sex is partly influenced by the acquisition of a territory in early spring, and quality territories are limited, then it is to the advantage of individuals of that sex to be at the breeding grounds as early as possible (Ketterson and Nolan 1976). If lifetime fitness is on average increased by remaining at breeding grounds year round, then selection could lead to residency among the sex that establishes a territory. Conversely, if over- winter survival of the sex which chooses a mate based on territory quality is increased by leaving the breeding grounds during the non breeding season and no net fitness advantage results from residency, then selection could promote migratory behaviour in that sex.
Conditional differences that result in differences in overwintering strategies within a population could occur if breeding habitat is only sufficient enough to support a fraction of the population through the non breeding season. In this case, partial migration results from individual asymmetries where migration is a conditional strategy and
migrants are making the “best of a bad job” (Lundberg 1987; Lundberg 1988). The
‘Dominance’ hypothesis of partial migration, also referred to as The ‘Competitive Release’ hypothesis, proposes that competition for limited food requires subordinate individuals to leave the breeding grounds during the non breeding season. Field based studies on temperate breeding birds have provided support for The ‘Dominance’
hypothesis (Schwabl 1983; Lundberg 1985; Smith and Nilsson 1987; Nilsson et al.
2008). Individual asymmetries that might lead to variation in migratory strategy are not limited to competitive ability; the ‘Thermal Tolerance’ hypothesis of partial migration, also referred to as The ‘Body Size’ hypothesis, predicts that individual differences in thermal efficiency result in differences in migratory tendency (Able and Belthoff 1998).
Previous studies at north temperate latitudes have found correlations between body size and migratory tendency, with smaller individuals most likely to migrate (Chapman et al.
2011). The interpretation of these results has been that larger individuals are more capable of surviving extreme temperatures and limited food during the winter due to greater metabolic efficiency and are therefore less likely to migrate than smaller individuals.
In the current study, we tested predictions of The ‘Arrival Time’, ‘Thermal Tolerance’ and ‘Dominance’ hypotheses of partial migration by tracking European blackbirds from a partially migratory population in southern Germany year-round using automated radio telemetry and light-level loggers (geolocators) (Table 1). Male
blackbirds compete for quality territories during the early spring (Lundberg 1985).
Therefore, our prediction for The ‘Arrival Time’ hypothesis was that female blackbirds would migrate more frequently than males (Table 1). During the non-breeding season a
Table 1. Hypotheses and predictions of partial migration.
Hypothesis Description Prediction
Arrival time
(Ketterson and Nolan 1976) Intrasexual competition for breeding territory promotes residency among the territorial sex.
male blackbirds should migrate less frequently than females
Thermal tolerance
(Ketterson and Nolan 1976) Individual differences in thermal efficiency leads to migration among the least efficient.
the tendency to migrate among blackbirds should increase with decreasing body size
Dominance
(Gauthreaux 1982) Competition for limited food
leads to migration among subordinates.
resident --- migrant adult ♂ > adult ♀ > 1st year ♂ > 1st year ♀
clear dominance hierarchy exists in European blackbird foraging flocks. Adults are dominant over juveniles and males are dominant over females during aggressive
encounters over food (Lundberg and Schwabl 1983; Lundberg 1985). Our prediction for The ‘Dominance’ hypothesis was that first year birds would migrate more frequently than adults and females would migrate more frequently than males (Table 1). For The
‘Thermal Tolerance’ hypothesis we predicted that smaller individuals would migrate more frequently than larger individuals (Table 1). Age and sex related differences in local movements during the non-breeding season could bias migration estimates towards juveniles and non-territorial individuals. Using a combination of year-round radio
telemetry and geolocators we were able to distinguish between true migration events and local dispersal. We also compared physiological correlates of energetic state of autumn migrants and non-migrants in the weeks preceding autumn departures from the breeding grounds. By comparing the energetic state of migratory and sedentary individuals in the weeks prior to autumn migration, we aimed to test whether individual condition, as measured by energetic state, is an indicator of the decision to migrate. Further, by
comparing age, sex, body size and energetic state of migrants and non-migrants our aim was to identify if migratory behaviour is a fixed or condition dependent strategy.
Materials and methods
European blackbirds, Turdus merula, were captured over four years (2009 – 2012) in a mixed coniferous/deciduous forest in southern Germany (N 47° 47’, E 9° 2’). Birds were initially captured in spring and summer using mist nets. 5-12 mist nets (12 m wide x 3 m tall) were opened between civil twilight and 12:00, when weather permitted. Nets were placed on the edge of breeding habitat next to known foraging areas. Nets were checked every 30 min. Age and sex of individuals was determined based on plumage differences (Svensson 1992). Prior to first pre-basic molt, the sex of hatch-year blackbirds cannot be determined based on plumage differences. 50 μl of blood was collected from hatch-year birds from the brachial vein by venipuncture for molecular sex determination. Tarsus length has been shown to be a reliable measure of body size in blackbirds as in many other species (Alatalo and Lundberg 1986; Richner 1989, Merilä 1997). We used tarsus length as a measure of structural body size and it was measured to the nearest 0.5 mm using dial calipers to compare the body size of migrants to non migrants.
Mk 10S and Mk 12S geolocators (≤ 1.2 g; British Antarctic Survey, Cambridge, UK) connected to radio transmitters (≤ 2.6 g; Sparrow Systems, Fisher, IL, USA) with heat shrink tubing (≤ 0.4 g), were attached to birds via leg-loop harnesses. A range of harness sizes were built from 1 mm elastic beading cord to fit the naturally occurring body sizes of blackbirds in the population (Naef-Daenzer 2007). Each backpack weighed
< 5% of the mass of the individual that it was deployed on. Once a harness was fitted to a
bird, it was inspected for appropriateness of fit. All birds were observed for as long as possible after release and throughout deployment to ensure normal behaviour. All transmitters and geolocators were manufactured to last at least one year. Beginning each March, birds tagged the previous year were recaptured for backpack removal and
deployment of a new backpack consisting of a new transmitter and geolocator. In 2010 and 2011, newly captured birds were also tagged with backpacks.
To identify presence of individuals at the breeding site and departure dates, all birds were tracked using radio telemetry whenever present at the study site. In the first year, all birds were located twice per week from the date of capture until 1 December 2009. Beginning 1 December, birds were tracked once per week by ground until recapture the following spring. In the second and third years, birds were tracked twice per week by ground after capture until recapture the next spring except from 20
December – 10 January when birds were monitored from automated receivers
exclusively. Ground tracking was done using the combination of either a handheld three element Yagi antenna (AF Antronics, Inc., Urbana, IL, USA) and AR 8200 MKIII handheld receiver (AOR U.S.A., Inc., Torrance, CA, USA) or a handheld H antenna (Andreas Wagener Telemetry Systems, Köln, DE) and a Yaesu VR 500 handheld receiver (Vertex Standard USA, Cypress, CA, USA). If an individual could not be located by ground tracking, aerial searches encompassing a 20 km radius (minimum) of the study site were performed using a Cessna airplane equipped with two H-antennas, one per wing, and two Biotrack receivers, one per antenna (Lotek, Newmarket, ON, Can).
Individuals were classified as migrants after at least two searches from the air without a signal.
Three to five stationary automated receivers (Sparrow Systems, Fisher, IL, USA) were present at the study site throughout the study to monitor the presence of individuals, and departure and arrival dates (Kays et al. 2011; Mitchell et al. 2012). Each automated receiver searched for 16 frequencies every 60 seconds. Automated receivers were connected to H antennas (ATS, Isanti, MN, USA), mounted 3 – 6 m high. Not all birds were captured within range of an automated receiver; therefore, manual tracking was the only means for monitoring their presence. After a departure was identified, extensive ground and air tracking was done to confirm absences. After recapture, geolocators were used to confirm departure and arrival dates (see Fudickar et al. 2012 for detail of methods for geolocator analysis).
In each year we observed a bimodal distribution in post breeding departures from our study site (one in early autumn and one in winter). We performed a k-means cluster analysis with an a priori criterion of two clusters to classify migrants into one of the two categories (“autumn” or “winter”). We justified separating migrants into two periods given that the periods were bimodal with autumn departures occurring immediately after post-breeding molt and before the onset of winter conditions while winter departures occurred in the midst of winter during periods of extreme minimum temperatures.
To compare the energetic state of migrants and non-migrants just prior to autumn departure, we recaptured tagged individuals (14 migrants and 22 non migrants) between 20 September and 20 October in 2010 and 2011 (referred to as the “pre-migratory period”). The same catching procedure was used during the pre-migratory period as in spring and summer however, only 1 – 6 mist nets were opened. Based on 2009 departure dates, this period was chosen because it provided the best opportunity to capture migrants
just prior to departure. To ensure that all individuals were in a similar resorptive state, only individuals captured between civil twilight and 09:30 were used for comparison.
Migratory birds undergo a suite of endogenously controlled physiological changes prior to migration to prepare for the energetically taxing behaviour of migration.
Endogenously controlled fat accumulation, prior to migration, provides migrants with the fuel reserves required for their journey (Berthold 1996). All tagged individuals recaptured during the “pre-migratory period” received a subcutanteous furculum fat score based on a scale of 0 – 5 (Helms and Drury 1960). To rule out the potential for observer differences in fat scores, all scores were assigned by AMF.
Immediately after capture, during the pre-migratory period, we collected 400 – 500 μl of blood from recaptured individuals from the brachial vein by venipuncture. All samples used for analysis were collected ≤ 180 seconds. Blood samples were
immediately stored on ice and transported to Vogelwarte Radolfzell for plasma extraction within 3 hours of collection. Plasma samples were stored at -70°C until they were
assayed. To assess the physiological state of migrants and non-migrants, we compared plasma levels of triglycerides and baseline corticosterone of migrants and non migrants during the “pre-migratory period” (20 September – 20 October).
Plasma levels of triglycerides (TRIG) have been used in avian studies to asses the physiological state and condition of free living individuals. Circulating plasma TRIG levels have been shown to increase during feeding and decrease during fasting (Jenni- Eiermann and Jenni 1998). Plasma TRIG levels were determined using a standard spectrophotometric assay (see Masello and Quillfeldt 2004). We added 6 μl of undiluted plasma to 600 μl Triglycerides reagent (n° 981786, Thermo Fisher Scientific) (warmed to
37°C). Standard curves were calculated for each run using the combination of 6 μl of sCal (n° 981831, Thermo Fisher Scientific) and 600 μl Triglycerides reagent. Plasma samples and standards were incubated for 5 minutes at 37°C after addition to the Triglycerides reagent. Absorptions were then measured at 540 nm wavelength. All samples were run on the same day but not in the same run. New standard curves were calculated for each run.
Glucocorticosteroid corticosterone (CORT) is produced in the adrenal cortex of birds and acts to increase carbohydrate metabolism and mediate the effects of stressful stimuli (Nelson 2005). Circulating levels of CORT have also been shown to be elevated in migrating birds (Piersma et al. 2000; Holberton et al. 2008). Migration is an
energetically demanding activity and CORT acts to mobilize energy reserves. We compared baseline CORT (≤ 180 seconds) of autumn migrants with residents during the pre-migratory period. Following methods in Ouyang et al. (2011), CORT concentrations were determined from 7 μl plasma samples after a diethyl-ether extraction using an enzyme immunoassay kit (Cat. No. 900-097, Enzo Life Sciences). The intra-plate coefficient of variation of two replicate standards was 8.3%.
For comparisons of migrants and non-migrants we performed tests comparing 1) autumn migrants with those who stayed behind through the autumn migration period and 2) winter migrants with those who stayed through the winter. To determine if different ages and sexes were more or less migratory as predicted by The ‘Arrival Time’ and The
‘Dominance’ Hypotheses we ran separate Fisher’s exact tests for autumn and winter periods. Separate multinomial logistic regressions were run to explore the correlates of migrating in either autumn or winter as a function of year, age, sex, tarsus and the
interactions between sex and tarsus and age and year using stepwise backward elimination. To explore differences in physiological measures of energetic state of autumn migrants and non-migrants we ran one multivariate general linear model with migratory strategy (yes/no) as a fixed factor and baseline CORT (log transformed), fat (log transformed) and TRIG (log transformed) as dependent variables. A priori, we excluded CORT and TRIG outliers from statistical analyses. All statistical analyses were performed in SPSS 15.0.
Results
Out of 153 blackbirds monitored, 68 (44.4%) were classified as migrants. 15 birds which were included as non-migrants in the autumn died between December and February and are therefore (conservatively) not included in the comparison of winter migrants and sedentary birds. The cluster analysis assigned 19 individuals to the winter migration group (mean: 30 December, range: 30 November – 7 February) and 47 to the autumn migration group (mean: 15 October, range: 19 September – 9 November) (Fig. 1).
Throughout the study, autumn migrants were never observed by aerial radio tracking less than 300 km from the study site after the date of departure and before arrival date. The results of aerial tracking of autumn migrants were confirmed by all geolocators that were recaptured. Unlike autumn migrants, four winter migrants were located by airplane tracking (range: 6 – 38 km from the study site). Four winter migrants, which were not located after departure, were recaptured the following spring. Geolocator estimates indicate that two of the recaptured winter migrants moved > 400 km overnight and then remained sedentary at their new location for the remainder of the winter. The winter
estimates of the other two recaptured winter migrants did not vary significantly from the study site. However, tracking by air indicates that they were at least 20 km from the breeding site. In contrast, sedentary birds were typically observed within 500 m of the breeding grounds but occasionally moved up to 1.5 km during the winter period.
We found no difference in the tendency to migrate between hatch-year birds and adults in the autumn (one-tailed Fisher’s exact test, P = 0.340) (Fig. 2). The model that best explained migratory tendency in the autumn excluded everything but sex, with females more likely to migrate than males (χ² = 6.969, d.f = 1, P = 0.008). Autumn migrants had higher subcutaneous furculum fat scores in the pre-migratory period than non migrants (F1,36 = 6.661, p = 0.030, adjusted R2 = 0.361) (Fig. 3). Autumn migrants tended to have higher levels of baseline CORT than non-migrants in the pre-migratory period (F1,14 = 4.319, p = 0.067, adjusted R2 = 0.249). There was no difference in TRIG between autumn migrants and non-migrants (F1,16 = 0.007, p = 0.933, adjusted R2 = - 0.110). In the winter, we found no difference between either age or sex in the tendency to migrate (one-tailed Fisher’s exact test, P = 0.487, P = 0.622). The final model that best explained migratory tendency in the winter excluded all of the predictor variables: age, sex, body size and interaction between sex and body size, indicating that none of the predictor variables contributed to the model.
Fig. 1. Departure and arrival dates of European blackbirds from a partially migratory population in southwestern Germany over four years. Cluster analysis assigned individuals to one of two post-breeding departure groups: autumn departures occurred from 19 September – 9 November (panel a; mean – 15 October, n = 47) and winter departures occurred from 30 November – 7 February (panel b; mean – 30 December, n = 19). Average return date for all migrants in the spring was 16 March (SD = 8.9 days, n = 24, range 23 February – 29 March). Winter departure and arrival dates are indicated with grey bars.
Sept Oct Nov Dec Jan Feb Mar Apr
0 5 10 15
Count
(a) (b) (c)
Fig. 2. Percent of European blackbirds from the study population that migrated (a) in the autumn (19 September – 9 November) and (b) winter (30 November – 7 February) separated by age and sex. Numbers in parentheses on the x-axis represent sample size.
Females were significantly more likely to migrate in autumn than males (one-tailed Fisher’s exact test, P = 0.007).
(a)
0 10 20 30 40 50 60
♂ ♀
% migrated in autumn
**
0 10 20 30 40 50 60
♂ ♀
(b)
Hatch-year (22)
Adult
(64) Hatch-year
(25) Adult (42)
Hatch-year (10) Hatch-year
(14)
Adult (46)
Adult (21)
% migrated in winter
500
400
300
100
0 (c) ● 4
1 5
0
***
(b)
2 8
6
4
2
0
●
●
● (a)
CORT (ng ml-1 )
(11) (6)
Fat score
(22) 3
(14)
Sedentary Migrant
TRIG (mg/ dl) 200
(10) (7)
