Analysis of Partial Migration Strategies of Central European Raptors Based on Ring Re-Encounter Data

Analysis of partial migration strategies of Central European raptors based on ring re-encounter data

I n a u g u r a l d i s s e r t a t i o n

zur

Erlangung des akademischen Grades eines

Doktors der Naturwissenschaften (Dr. rer. nat.)

der

Mathematisch-Naturwissenschaftlichen Fakultät

der

Ernst-Moritz-Arndt-Universität Greifswald

vorgelegt von Daniel Holte

geboren am 06.10.1981

in Neuss

Greifswald, den 14.06.201828.03.2018

Dekan: Prof. Dr. Werner Weitschies

1. Gutachter : PD Dr. Martin Haase

2. Gutachter: Dr. Sven Renner

Tag der Promotion: 14.06.2018

Table of Contents

A. Synopsis 7 1. General introduction 7 1.1 Marking animals 7 1.1.1 Bird ringing 8 1.2 Bird migration 10 1.2.1 Partial migration 11 1.3 Target species 11 1.3.1 Common kestrel 12 1.3.2 Common buzzard 12 1.3.3 Eurasian sparrowhawk 13 1.4 Study aims and hypotheses 13 2. Methods 15 2.1 Partial migration 15 2.2 Predicting ring re-encounters considering spatial observer heterogeneity 16 3. Results and Discussion 18 3.1 Partial migration 18 3.2 Predicting ring re-encounters considering spatial observer heterogeneity 21 4. Cited literature 25

B. Manuscripts 29 Paper I 29 Paper II 47 Paper III 65 Contributions to manuscripts 95

C. Eigenständigkeitserklärung 97

D. Curriculum Vitae 99

E. Scientific contributions 101

F. Danksagung 103

A. Synopsis

1. General introduction

The history of ornithology goes far back in human history and its beginning is probably not clearly definable. Already in the fourth century B.C., Aristotle described about 140 bird species and he declared the study of birds to be a worthy activity for philosophers, which made ornithology become a science (Stresemann 1951). Currently, birds – more precisely their characteristics and capabilities as well as their ecology – are in these times of rapid environmental changes highly relevant objects for scientific research. The technological progress we have experienced during the last decades has also reached the ornithological research. It revealed for instance phylogenomic relationships among and between families, species and populations using DNA analyses (e.g. Fregin et al. 2012; Prum et al. 2015; Barani- Beiranvand et al. 2017), physiological traits, such as ultraviolet colours in plumage and beak colouration (e.g. Armenta et al. 2008; Schull et al. 2016), or aspects of ecology and behaviour, such as migratory flyways and wintering grounds, using satellite telemetry or isotope analyses (e.g. Strandberg et al. 2009; Seifert et al. 2016). Among many others, these findings help us to understand “the world of birds”, from an individual’s life cycle to complex interrelations within and between populations, which is a fundamental requirement to consult birds as indicators for ecology and to develop protection strategies.

1.1 Marking animals

Animals get marked all over the globe for different purposes. For instance, pets like cats and dogs are marked with collars, ear tattoos and microchips in order to identify them if they get lost and found again. Livestock get ear marks to differentiate individuals to e.g. control for their productivity. Female sheep get marked with different colours depending on the male with which they mated. However, the approach of marking animals is not only used for domestic, but also for wild animals. In scientific research, animals get marked for several reasons including tracking their movements (e.g. Strandberg et al. 2009), observing their mate choices (e.g. Shave & Waterman 2017), or defining home ranges (e.g. Šegvić-Bubić et al. 2018). Depending on the study aims, the technical progress and the monetary budget, animal marking is carried out using e.g. spray colours and dye, leg and wing tags, light-level geolocators or radio

7 transmitters including GPS tracking devices. Colour marking is applied mainly in studies in which behaviour of different individuals is observed (e.g. Shave & Waterman 2017). Leg and wing tags, geolocators as well as radio and GPS transmitters, however, can be used to study the spatio-temporal allocation and the movements of individuals, such as dispersal (e.g. Schmidt et al. 2017) and migration, including the determination of breeding and wintering sites (e.g. Omori & Fisher 2017), up to exact migration routes and fuelling sites (e.g. Strandberg et al. 2009; Lislevand & Hahn 2015; Hiemer et al. 2018). In order to avoid or at least to limit impairments that may influence the behaviour of a marked animal, the weight of the tag which is supposed to be applied must not exceed 5% of the animal’s body mass (at least in terrestrial vertebrates, see Murray & Fuller 2000). This requirement has led to a miniaturization of devices allowing even to mark insects in order to follow their flyways (e.g. Kissling et al. 2014). A less sophisticated but therefore cheap, hence widely used method of marking animals is the approach of bird ringing.

1.1.1 Bird ringing

The scientific bird ringing method was implemented more than 100 years ago by the Danish teacher Hans Christian Mortensen (Baillie et al. 2007). Birds are caught or taken from the nests and get marked with a metal and/or a plastic ring which carries an individual inscription consisting of a mixture of figures and letters (= ring number). This inscription functions like an ID card, which in case of being re-encountered by re-sighting or re-catching the ringed bird or by finding its carcass can be assigned to the respective bird unambiguously. In Europe, about 5 million birds get ringed each year (Baillie et al. 2007), providing the potential to create big databases with a huge amount of information. The quantity and quality of information that goes into these databases strongly depend on the kind of data that has been collected during ringing as well as reported as a re-encounter. While ringing is performed by expert and trained scientists and volunteers, a re-encounter can also be made by laymen. In this occasion, ring re- encounters mainly result from findings of dead birds. This can be a problem regarding the accuracy of information about the details of re-encounter and the circumstances that led to a bird’s death. In some cases even the time of dying is unknown or the re-encounter site is given only vaguely. The latter occurs mainly in reports that have been made before GPS and cell phone positioning became broadly available. In addition, uncertainties about age and sex of

8 ringed individuals may reduce the explanatory power of ring re-encounters. These uncertainties can result from trapping adult birds in which the age is not clearly determinable or from the absence of sexual dimorphisms at least at the ringing time. Finally, the probability to re- encounter a ringed bird is not equally distributed in space and time (e.g. Korner-Nievergelt et al. 2010a). It depends on the species – in terms of body size, habitat, ecology etc. – as well as on the potential observers. Observers may differ in knowledge and education, interest and social status as well as in the political situation and population density of the region where they live. Moreover, a re-encounter only goes into the databases if the observer contacts the corresponding ringing scheme and reports the ring. The willingness to report a re-encountered ring differs between observers as well, again depending on knowledge and interest, but also on the facilities to make a report. For example, web-based reporting applications have advanced ring reporting rates in the last two decades (see e.g. Boomer et al. 2013), whereas restrictions in hunting may reduce the reporting willingness, because illegal hunting activities that would be revealed by a report could be punished.

There have been some approaches implemented to correct for observer heterogeneity (e.g. Kania 2009; Korner-Nievergelt et al. 2010a, 2010b, 2012; Cohen et al. 2014; Thorup et al. 2014) but they all have one thing in common: additional information which supplements the ringing data is required. One of the most promising procedures combines ring re-encounters with data derived from tracking methods, such as satellite telemetry and geolocators (Korner-Nievergelt et al. 2010a; Thorup et al. 2014), which, however, cannot be applied retrospectively. Other approaches are based on socio-demographic factors, where observer distribution and re- encounter probabilities are estimated by human population densities or political borders (Korner-Nievergelt et al. 2010a). All this additional information is usually not available when starting an analysis of ring re-encounter data. It must be collected, if possible, as part of the study or obtained from other sources with much additional effort. Hence, these methods are only conditionally applicable. Due to the lack of adequate methods, the results of ‘classical’ ring re-encounter analyses which do not correct for observer heterogeneity should be interpreted carefully.

Despite all those limitations and uncertainties, ring re-encounters have filled our databases with more than 2 million records until 2007 only in Europe (Baillie et al. 2007). In contrast, studies 9 that use new technologies (e.g. GPS tracking) are often limited in sample size because budgets for scientific research are usually small and, thus, confine the number of devices that can be applied. The huge numbers in ring data bases result from the facts that bird ringing is conducted mainly by many well trained volunteers (~ 10,000 ringers in Europe organized by the national ringing schemes; Baillie et al. 2007) and material costs are relatively low. Accordingly, about four million birds are ringed in Europe each year (Baillie et al. 2007). Many of the resulting datasets have been analysed and large voluminous ring recovery atlases have emerged (e.g. Fransson & Hall-Karlsson 2008; Bairlein et al. 2014) augmenting our knowledge about birds. Analyses have been made for instance in order to investigate dispersal (e.g. Galarza et al. 2016), community structures (e.g. Broughton et al. 2015) or migration patterns (e.g. Bai & Schmidt 2012; Finch et al. 2017), and analysing ringing data still plays an important role in ornithological research. Hence, bird ringing is – even in times of new technical achievements – a worthy approach to obtain scientifically useful data.

1.2 Bird migration

Bird migration is a worldwide occurring phenomenon and describes regular movements between regions in order to avoid unfavourable seasonal conditions (such as low temperatures and limited food supplies) followed by subsequent returning movements to the breeding sites (Berthold 2007; Newton 2008). In the northern hemisphere, these seasonal movements mainly occur in autumn (which is related to a southwards direction to avoid harsh winter conditions = autumn migration) and in spring, when birds return for the breeding season (= spring migration). In Central Europe, the predominant direction for autumn migration is southwest, according to the continent’s topography in which the Alps and the Mediterranean may be obstacles to migrating birds (Berthold 2007). Migration has been subject to many studies and there are several ways to classify migratory birds. For instance, birds can be differentiated (1) by their migration distances into short-, medium- and long-distance migrants, (2) according to their intrinsic migratory constraints into obligate and facultative migrants (e.g. Newton 2012) or (3) according to the proportion of migrants within a population into residents (showing no migrating individuals), complete migrants (= all individuals migrate) and partial migrants, which is a combination of the two previous forms.

1.2.1 Partial migration

In partial migratory birds, some individuals of a population migrate while others stay in or close to the breeding area (e.g. Newton 2008; Chapman et al. 2011). This phenomenon is very common and was described for many species, such as Great tits Parus major (Pakanen et al. 2018), Eurasian siskins Spinus spinus and Common redpolls Carduelis flammea (Newton 2012), Dupont’s larks Chersophilus duponti (Suárez et al. 2006) or Goshawks Accipiter gentilis (Meller et al. 2013) and many more. It is known that in partial migratory birds from Europe, a stronger tendency to migration is mainly shown in higher latitudes decreasing to the south (see Bauer et al. 2005), whereas differences between migratory tendencies can also be found between sexes and age classes (e.g. Belthoff & Gauthreaux 1991; Pakanen et al. 2018). While the former can be explained by harsher weather conditions in winter with increasing latitude, for the latter different hypotheses have been developed. For instance, the “body-size hypothesis” claims that individuals with smaller body size have a higher tendency to migrate because they possibly can tolerate thermal extremes less than larger conspecifics (Ketterson & Nolan 1976). According to the “dominance hypothesis” dominant individuals can better compete for limited food resources and promote migration in subordinates (Gauthreaux 1982). Contrasting to these and some other hypotheses (see Chapman et al. 2011 for an overview) migratory tendency has also been related to genetic drivers (e.g. Chakarov et al. 2013). Although partial migratory behaviour has been reported for several species, there are still many others for which details of their migration patterns are still unclear. This study aims to investigate partial migration of birds from Central Europe by analysing ringing and re-encounter data of three raptor species.

1.3 Target species

In Central Europe, most of the species are well described and common migration patterns have been reported (see e.g. Bauer et al. 2005; Bairlein et al. 2014). However, in some species these patterns are not sufficiently studied yet, especially in terms of differences between and within populations. This is the case, for example, for some partial migratory raptor species of the former order Falconiformes (Brown & Amadon 1989; for a recent classification see Jarvis et al.

2014; Prum et al. 2015) including the Common kestrel Falco tinnunculus, the Common buzzard Buteo buteo and the Eurasian sparrowhawk Accipiter nisus.

1.3.1 Common kestrel

The Common kestrel is a small bird of prey distributed all over Europe, parts of Asia and Northern Africa (Glutz v. Blotzheim et al. 1971). It shows a sexual dimorphism in plumage colouration of adult individuals as well as in size, with males (~200 g) being smaller than females (~260 g) (Bauer et al. 2005). Kestrels prefer small rodents, particularly voles, as food, but also other birds, reptiles and insects are eaten (Glutz v. Blotzheim et al. 1971). The nesting season lasts from April to July and juveniles hatch after 27-32 days (Bauer et al. 2005). They fledge at an age of ~30 days and become independent after additional four weeks. Kestrels get mature at the end of their first year (Bauer et al. 2005). The migratory strategies of kestrels are variable with predominant long-distance migration in Northern Europe toward shorter distances and residency in Southern and Western Europe; juveniles migrate more often than adults (Bauer et al. 2005).

1.3.2 Common buzzard

Common buzzards are medium-sized raptors distributed almost across the entire Palaearctic (Glutz v. Blotzheim et al. 1971). A clear sexual dimorphism in size or plumage colouration is not visible. The prey of buzzards consist mainly of ground-dwelling mammals, predominantly voles and other rodents (Bauer et al. 2005). The nesting season starts in March or latest in May, depending on the region (Bauer et al. 2005). Juveniles hatch after ~33 days and fledge at the age of 42-49 days. Independency is reached after additional 40-55 days. Usually, buzzards get mature at the end of their second year (Bauer et al. 2005). Buzzards are reported to be short-distance migrants or non-migratory with a higher migratory tendency in juveniles and immatures (second-year birds). They show a northeast-southwest gradient in migration intensity with less migratory tendencies towards southwest. Immatures usually do not return to the breeding areas in their second summer (Bauer et al. 2005).

1.3.3 Eurasian sparrowhawk

Eurasian sparrowhawks are small birds of prey with a distribution in almost all parts of Europe as well as in Asia and areas surrounding the Mediterranean (Bauer et al 2005). A sexual dimorphism is mainly displayed by clear differences in body size, with females (~290 g) being larger than males (~150 g) (Bauer et al. 2005). Sparrowhawks predominantly feed on birds (90 %), but also on small mammals (Glutz v. Blotzheim et al. 1971). The nesting season starts at the end of April in which juveniles hatch after ~34 days and fledge at the age of 24-31 days. Juveniles become independent after additional 20-30 days (Bauer et al. 2005). They reach sexual maturity at the end of their first year (Glutz v. Blotzheim et al. 1971). Wintering areas of sparrowhawks from Europe range from Central Scandinavia to Northern Africa, while migratory tendencies are highest in northern and eastern regions decreasing to south and west (Bauer et al. 2005). Juveniles and males are reported to migrate more often and over longer distances than adults and females, respectively (Bauer et al. 2005).

1.4 Study aims and hypotheses

Even though we know about migration patterns of kestrels, sparrowhawks and buzzards in general, strategies of the Central European populations are not well described in detail, yet. These species are common in Central Europe and an adequate amount of ringing data is available. The populations from Germany are of a particular interest because of Germany’s spatial location. Lying in the centre of Europe, Germany is expected to represent a transitional area from predominantly migratory populations in Northern and Eastern Europe to mainly resident populations in the South and West of the continent. Hence, I wanted to determine whether the German populations of kestrels, sparrowhawks and buzzards are partially migratory or not. I aimed to investigate possible differences between sexes and age classes (differential migration) as well as between regions within Germany. Moreover, I wanted to analyse migratory responses to weather conditions and adaptations to long-term changes. My hypotheses were:

1. the German populations of kestrels, sparrowhawks and buzzards are partially migratory 2. in all three species, juveniles and females migrate more often and with longer distances than adults and males, respectively 3. birds from North-eastern Germany tend more frequently to migration than individuals from the Southwest 4. migration occurs as response to weather conditions 5. the proportion of migrants has declined in recent years due to long-term changes, such as climate change

As a consequence of the limitations that come with regular ringing data, classical analyses are restricted in terms of meaningfulness and interpretation scope. Hence, I introduce a new approach with the potential of being used for different bird species. It corrects for observer heterogeneity in order to make re-encounters more comparable between regions on a continental scale. This might allow for more detailed investigations of ring re-encounter data and more robust inferences and conclusions.

2. Methods

2.1 Partial migration

In order to investigate migratory strategies of the three raptor species Common kestrel, Common buzzard and Eurasian sparrowhawk, I analysed ringing and re-encounter data of birds that have been ringed between 1924 and 2011 (kestrels) and between 1926 and 2010 (buzzards and sparrowhawks), respectively, in Germany. The data was collected and maintained by the three German ringing schemes “Vogelwarte Helgoland”, “Vogelwarte Radolfzell” and “Beringungszentrale Hiddensee”. The data was edited and provided by the “Institute of Avian Research” in Wilhelmshaven. Since records of ringing and re-encounter data differ in the way they have been obtained and the quality of information they cover, the analysed dataset was filtered according to the requirements of my investigation. For instance, I only used information coming from birds that were ringed as nestlings or as just fledged because I was interested in the spatial origin of the birds’ movements, which cannot be inferred from data coming from individuals ringed as adults (Berthold 2007). I wanted to investigate migratory behaviour, thus I excluded all re-encounters of non-independent juveniles in or around the nests, which was inferred based on the time difference between ringing and re-encounter day. I excluded records of birds that died more than one week before re-encounter because for these individuals (as opposed to ‘freshly dead’ birds) the exact time of death is unknown (Bai & Schmidt 2012). Records of birds that had been trapped intentionally were excluded as well, because, for instance sparrowhawks are often trapped in certain regions in winter, e.g. for ringing purposes, which can lead to biases due to overrepresentation of re-encounters for these regions. Thus, considering these factors constitutes an indispensable part of ring re-encounter analyses. Focussing on autumn migration and wintering, I analysed distances between ringing and re- encounter sites (hereafter: distances) travelled by different sexes and age classes. In short- and medium-distance migrants, a distinction between dispersal and migratory movements is not always clear, especially in data derived from ring re-encounters. Hence, I determined birds as migratory (as opposed to resident) if their distances exceeded a certain threshold (60 km in sparrowhawks, 100 km in kestrels and buzzards). These binomial categories (migratory or resident) were tested in terms of dependence on different factors, such as age class, sex, ringing region in Germany, re-encounter period (1950-1970, 1971-1990 and 1991-2011) and 15 weather, using Generalized Linear Models. Information on weather was included in form of NAO (North Atlantic Oscillation = differences in air pressure between the Icelandic Low and the Azores High) indices obtained by the Climate Prediction Centre of the NOAA (http://www.noaa.gov). The NAO is known to affect temperature and precipitation in most parts of Northern, Western and Central Europe as well as of the Mediterranean (see e.g. Hurrel 1995).

2.2 Predicting ring re-encounters considering spatial observer heterogeneity

The analyses of migratory strategies of kestrels, buzzards and sparrowhawks were conducted in the ‘classical way’ without correcting for observer heterogeneity, which is reasonable as long as this kind of limitation is considered for interpretation. In order to correct for observer heterogeneity, I predicted ring re-encounters by analysing a dataset consisting of ringing and re-encounter data of the three raptor species ringed in the area of responsibility of 16 European ringing schemes (Fig. 1), provided by the Euring Data Bank (EDB). Data was restricted to the period 1995-2015. I also collected total ringing numbers of the target species from the contributing ringing schemes. The study area was divided into raster cells of 200 x 200 km and in each cell re-encounter rates of three types of re-encounter were calculated for each species separately: same cell re-encounters (SCR), neighbour cell re-encounters (NCR) and foreign cell re-encounters (FCR). These basal re-encounter rates (BRRs) can be applied to predict re-encounters for any population of one of the target species from the study area. I applied the re-encounter rates to the Fig. 1. Study site: European countries from which ringing and re-encounter German populations which I analysed previously. For data as well as at least total ringing numbers were available. Raster grid with prediction of re-encounters, the re-encounter rates resolution of 200 x 200 km. were equalized (adjusted to the same level) by

16 bringing them to highest value observed within the respective home ranges and multiplied by the respective ringing numbers. For FCR, the results were additionally limited by a rounding argument, which follows a binomial distribution, in order the prevent over-prediction in foreign cells. The results of prediction are the number of potential re-encounters that could have been made if probabilities of re-encounter in each raster cell (represented by the re-encounter rates) were as high as the highest observed probability. For each species, I predicted re-encounters for three exemplary periods: (I) breeding season (May to July), (II) three winter months (December to February) and (III) the entire year (January to December). The distances of these predicted re-encounters were compared to the observed distances of the uncorrected dataset using Mann-Whitney-U tests. Because Korner-Nievergelt et al. (2010a) suggested to correct for observer heterogeneity by means of human population density, I conducted a correlation test on human population density in the year 2000 in Europe and my findings on re-encounter rates (of sparrowhawks exemplarily).

3. Results and Discussion

3.1 Partial migration

The analysed re-encounters of kestrels, buzzards and sparrowhawks showed no distinct wintering area but a wide distribution across Western and Central Europe (Fig. 2 and 3). I found differences in migratory behaviour between age classes and between sexes. For instance, juvenile and female kestrels travelled significantly larger distances than adults and males, respectively, depending on the ringing region (Fig. 2). However, also migratory adults and males were detected. While for Belgian and Polish kestrels only dispersal movements or single outlying cases of migration have been reported (Adriaensen et al. 1997; Śliwa et al. 2009), I found strong evidence for migration in German kestrels, which might be due to the larger sample size in general and the longer time span over which my data had been collected. In sparrowhawks and buzzards I detected larger distances in juveniles than in adult and immature individuals as well (see Holte et al. 2017: Fig. 3). In buzzards, however, the analysis of sexual differences was omitted because of the small proportion of reports in which the sex of the re- encountered birds was identified (< 6%). Migratory behaviour was not only affected by age and sex but in most cases also by weather and in buzzards additionally by ringing region (see Holte et al. 2016: Tab. 4 and 5; Holte et al. 2017: Tab. 3 and 4). The latter reflects the North-South gradient in which northern birds show stronger tendencies to migrate, whereas effects of weather conditions on migration choices may indicate the presence of facultative migration, at least in single individuals. Especially in buzzards from southern regions, I detected so called winter escapes. In contrast, migrating individuals from all of the three species have been observed independently of sex, age, weather and ringing region. This supports the hypothesis of obligate migration which is supposed to be driven by intrinsic factors (Newton 2012). Chakarov et al. (2013), for example, found candidate genes that correlate with migratory behaviour and simultaneously control for plumage colouration in buzzards. All these findings indicate facultative and obligate migration in individuals from one and the same population. In kestrels and buzzards, I also found differences between time periods (of 20 years each), showing less migratory behaviour in more recent periods (see Holte et al. 2016: Tab. 4 and 5; Holte et al. 2017: Tab. 3 and 4). These can be rated as responses to long-term changes (including climate change), as it has been reported for several species (e.g. Filippi- 18

Fig. 2. Autumn and winter re-encounter sites of Common Kestrels outside their regions of natal origin (left) and boxplots of re-encounter distances by sex and age classes (right). Reported P-values result from comparisons of respective classes using negative binomial GLMs with no predictors. Note the log-scale on y-axes. A — Northwest German Lowlands, B — Northeast German Lowlands, C — Central German Highlands, D — Southwest German Highlands incl. Alps and the Foreland of the Alps. Boxplots: horizontal bar = median, whiskers = last observation within 1.5 times the interquartile range, circles = observations outside whiskers. AC1 — juveniles, AC2+ — adults.

Fig. 3. Re-encounter sites of (A) Eurasian Sparrowhawks and (B) Common Buzzards ringed in Germany

(including climate change), as it has been reported for several species (e.g. Filippi-Codaccioni et al. 2010). These long-term changes may result from global warming which might reduce the necessity to leave the breeding areas, but also from improved habitats (e.g. due to nature conservation) which might provide more suitable winter territories and a better access to food resources. However, due to possible changes in re-encounter probabilities in space and time, these findings might be biased. Although I did not find any responses to long-term changes in ‘decision making’ of sparrowhawks, Fiedler et al. (2004) reported decreasing migration distances in recent years.

In general, the German populations of kestrels, sparrowhawks and buzzards 1. are partially migratory and consist of both migratory and resident individuals; 2. show differential migration in terms of sexes (unclear for buzzards) and age classes; however, some migrants as well as some residents have been detected independently of age and sex; 3. do not reflect a clear northeast-southwest gradient in migration tendency; only buzzards show a higher migration tendency in northern than in southern regions while a west-east gradient was not found; 4. respond to weather conditions; however, migration and residency was detected independently of weather as well; 5. show adaptations to long-term changes (except for sparrowhawks which, however, are likely to at least reduce migration distances).

3.2 Predicting ring re-encounters considering spatial observer heterogeneity

The number of predicted re-encounters of kestrels, buzzards and sparrowhawks was higher than that of the uncorrected observations, independently of the prediction period, while applying the prediction process on a large time period (e.g. 12 months, example III) is more conservative than a prediction for only few or single months. The spatial distribution of observed and predicted re-encounters is represented in Figures 4, 5 and 6. Human population density correlates with re-encounter rates of sparrowhawks (P < 0.001, ρ = 0.291 (SCR), 0.326 (NCR), 0.511 (FCR)). The correlation coefficients, however,

Fig. 4. Spatial distribution of real observed and additionally predicted re-encounters of Common Kestrels (Falco tinnunculus) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year

Fig. 5. Spatial distribution of real observed and additionally predicted re-encounters of Common Buzzards (Buteo buteo) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year

Fig. 6. Spatial distribution of real observed and additionally predicted re-encounters of Eurasian Sparrowhawks (Accipiter nisus) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year show that a correction only based on 22 should be carried out with caution, show that a correction only based on population density should be carried out with caution because the coefficients are low and differ between the BRRs. Thus, a prediction only based on human population density might disregard the details that are provided by the different BRRs. Distances of observed and predicted re-encounters differ significantly in all cases (P < 0.001, sparrowhawks (I): P = 0.002; Holte et al. (submitted): Fig. 5-7), showing that distances might be underestimated in uncorrected data. This also may lead to underestimations in the proportion of migrants when migratory status is determined by a certain threshold, i.e. a certain distance that birds had travelled. Yet, these results still have several limitations. Because of missing data, the study area was restricted to the contributing countries (Fig. 1) which leads to underestimations of home ranges, especially in Eastern Europe and Northern Africa, which also affects the home ranges at the boundaries of contributing countries. The information on total ringings only consists of ringing numbers without information on locations (which have been estimated, see Holte et al. (submitted): Methods), age classes and sexes etc. Thus, a differentiated prediction for age and sex classes is not yet possible. However, reliable information on age and sex ratios in ringing data might improve the prediction process substantially and could allow for more detailed analyses concerning differences (for instance in migrant proportions or dispersal distances) between age and sex classes. Compared to other methods, such as consideration of human population densities or political borders (Korner-Nievergelt et al. 2010a) or use of GPS tracking, this approach is based on observed re-encounter probabilities on a continental scale and can be directly applied to re- encounter data of other Euring member schemes that belong to the study area. Similarly to uncorrected data, predictions do not reflect the absolute reality (e.g. in terms of exact re- encounter locations). This is because I do not exclusively predict bird behaviour. I rather correct for observer heterogeneity. I combine empirical information on bird behaviour derived from observed re-encounters, while correcting the information on observer activities including the willingness to report. My approach makes data comparable by predicting re-encounters that could have been observed if the probability of observing an individual was equal throughout the respective home ranges. The comparability on large spatial scales might improve analyses of dispersal or migration, because uncertainties about re-encounter probabilities are minimized and bird abundances in different regions could be assessed more profound. This allows for a

23 direct comparison of regions in terms of estimations of migrating individuals or dispersal movements. Since some parameters are selected randomly at specific stages during the prediction process, the results of prediction may differ in certain details between several repetitions. However, the general result is not affected (see Holte et al. (submitted): Methods). In continuing projects, this method might also be extended for other species (or species groups) and other time periods, which are not considered yet, as well as for ringing data of other continents. More detailed information on total ringings, such as coordinates, sex and age classes or information on marking type (e.g. wing tags or colour rings), collected by the contributing schemes would be of much help to improve the analyses. This information cannot be elaborated retrospectively in some cases because ringing data have been recorded for instance only for individuals that have been re-encountered or the records have not been digitized yet and thus are not available for analyses. However, digitalization of already recorded data and comprehensive collection of recent and future information should be done in order to enable continuous studies and support the importance of bird ringing.

In general, bird ringing will play an essential role also for future studies, especially when focussing on responses to long-term changes. This huge amount of data that can be obtained over long periods and with relatively low budgets cannot be achieved by other methods, yet. However, ringing schemes must improve the availability of data (such as for analogue records or total ringings) and keep up to date in providing reporting facilities. The international collaboration among schemes could be enhanced in terms of standardized procedures, data formats and the information reported to international databanks like Euring.

4. Cited literature

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Armenta JK, Dunn PO & Whittingham LA (2008). Effects of specimen age on plumage color. Auk 125: 803-808

Bai ML & Schmidt D (2012). Differential migration by age and sex in central European Ospreys Pandion haliaetus. J Ornithol 153: 75-84

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B. Manuscripts

Paper I Holte D, Köppen U & Schmitz-Ornés A (2016) Partial migration in a Central European raptor species: an analysis of ring re-encounter data of Common Kestrels Falco tinnunculus Acta Ornithologica 51: 39-53

Paper II Holte D, Köppen U & Schmitz-Ornés A (2017) A comparison of migratory strategies of partial migratory raptors from Germany Journal of Ornithology 158: 579-592

Paper III Holte D, Köppen U & Schmitz-Ornés A (submitted) Predicting ring re-encounters considering spatial observer heterogeneity on a continental scale

ABSTRACT

Bird ringing is an important and proven method to investigate bird ecology. Its explanatory power, however, is limited by low re-encounter rates and temporal and spatial heterogeneity in re-encounter probability. Spatial heterogeneity mainly depends on the distribution of observers as well as on their willingness to report a re-encountered ring to the corresponding ringing scheme. We analyzed a data set of ringing and re-encounter data of Common Kestrels Falco tinnunculus, Common Buzzards Buteo buteo and Eurasian Sparrowhawks Accipiter nisus provided by the EURING Data Bank. We calculated monthly re-encounter rates across Europe and, for different time periods, we predicted re-encounters for individuals of these species ringed in Germany, on the assumption that re-encounter probabilities are evenly distributed at the highest value observed within the respective home ranges. Subsequently, we tested for correlation between re-encounter rates and human population density. The number of predicted re-encounters exceed the observed by 50-300 %. We found differences between monthly re- encounter rates and between different prediction periods. Distances (between ringing and re- encounter sites) differ significantly between observations and predicted re-encounters, with higher distances in predictions. Correlation between re-encounter rates and human population density is significant, but correlation coefficients are low (ρ = 0.291-0.511). Correcting for observer heterogeneity can help to analyze ring re-encounter data e.g. in terms of dispersal and migration. However, a comprehensive data collection and a digitalization of possible prior data records by the respective ringing schemes may allow advances in this method even further.

INTRODUCTION

The ringing approach which serves to collect information about an individually marked bird on at least two occasions (ringing and re-encounter, dead or alive), has been implemented for more than 100 years (Baillie et al. 2007). It has been improved throughout the decades (e.g. by introducing colour rings that can be read in the field without the need to re-catch the bird or to recover its carcass) and it produces huge data bases, such as the EURING Data Bank (EDB, www.euring.org). By means of a continuous development of statistical techniques, ring re- encounters contribute to our understanding of bird ecology, e.g. in terms of dispersion and migration or of population trends (e.g. Hosner & Winkler 2007, Kania 2009, Bairlein et al. 2014, Holte et al. 2016, 2017). This method, however, requires that the marked individual will be encountered by a person (observer) who (1) recognizes the ring and (2) is familiar with the processing of ring re- encounters, i.e. reporting it to the corresponding ringing scheme. While reporting a ring is only dependent on the knowledge and the willingness of the observer, finding a ring (or a ringed bird) depends on observer activities as well as on the bird’s ecology. Bird species with a relatively large body-size living in urban or cultural landscapes, such as Wood Pigeons Columba palumbus, Western Jackdaws Coloeus monedula or Mallards Anas platyrhynchos (see e.g. Bairlein et al. 2014 for ring reports), are exposed to a greater probability to be encountered by people than species occurring in hardly accessible and mainly human-free areas as well as species being smaller or showing more secretive habits, such as Little Owls Athene noctua (Naef-Daenzer et al. 2017), Nightingales Luscinia megarhynchos, Corncrakes Crex crex or European Nightjars Caprimulgus europaeus (see e.g. Bairlein et al. 2014). Ring re-encounter analyses are often based on data that has been collected in a wide geographical range (depending on the species) and on large time scales (e.g. over several decades). That means that a variety of observers has been involved in data collection and the majority of ringings does not result in any re-encounter, which leads to low reporting rates (Berthold 2007). Additionally, the variety of observers is not equally distributed. Observers differ in quantity and quality between continents and countries, depending on population densities, education and social status and there are even differences within countries (see Korner- Nievergelt et al. 2010a , Bairlein et al. 2014). Thus, ringing data covering large time periods and wide geographical ranges are sometimes incomparable and drawing conclusions without 68 correcting for observer heterogeneity should be avoided. Different approaches have been implemented to correct for observer heterogeneity (e.g. Kania 2009, Korner-Nievergelt et al. 2010a, 2010b, 2012, Cohen et al. 2014, Thorup et al. 2014). They all have one thing in common: additional information which supplements the ringing data is required. One of the most promising procedures combines ring re-encounters with data derived from tracking methods, such as satellite telemetry and geolocators (Korner-Nievergelt et al. 2010a, Thorup et al. 2014), which, however, cannot be applied retrospectively. Other approaches are based on socio- demographic factors, where observer distribution and re-encounter probabilities are estimated by human population densities or political borders (Korner-Nievergelt et al. 2010a). Kania (2009) inferred bird distribution from re-encounter distribution by calculating re-encounter ratios which depends on the knowledge of the total numbers of ringed birds (regardless of whether a ring results in a re-encounter or not). All these additional information is usually not available when starting an analysis of ring re-encounter data. It must be collected, when possible at all, as part of the study or obtained from other sources with much additional effort. In this study, we want to present another approach that is also based on re-encounter ratios but does not depend on sources of information other than ringing and re-encounter data collected by the different ringing schemes. We estimate spatially different re-encounter probabilities of three raptor species across Europe and predict potential re-encounters based on equalizing these re-encounter probabilities.

METHODS

We analysed ringing and re-encounter data of three raptor species that have been ringed and re-encountered between 1995 and 2015 provided by the EDB: Common Kestrels Falco tinnunculus, Common Buzzards Buteo buteo and Eurasian Sparrowhawks Accipiter nisus. Only reports of birds that were re-encountered freshly dead were taken into account. Reports of colour rings and wing tags were excluded in order to keep the data comparable. We only used data of ‘local’ birds (birds that have been ringed during the breeding season). As the ringing schemes contributing to the EDB operate differently in terms of the information being collected, only schemes that provided either data of all ringings (independently of whether a ring results in a re-encounter or not) or at least total ringing numbers per year and per species were taken into account. There were some schemes in which these ringing totals were missing for single years within the study period. In these cases, yearly ringing totals were estimated by dividing the total number of re-encounters in each of those years by a simple mean re-encounter rate (average of total number of re-encounters per year divided by the total number of ringings in the same year) of years in which ringing totals were given. Offshore and sea re-encounters were excluded, thus the study area consists of main land and islands that are maintained by those ringing schemes and was handled as a closed universe. The study area was divided into raster cells of 200 x 200 km (Figure 1). In order to avoid predictions in unsuitable areas, we excluded elevations of 2000 m and more during the prediction process, because Kestrels, Buzzards and Sparrowhawks usually do not occupy these elevations (Bauer et al. 2005). For each of the species the following procedure was applied. Re-encounters were determined as one of three categories:

. same cell re-encounter (SCR) = bird was ringed and re-encountered in the same raster cell . neighbour cell re-encounter (NCR) = bird was re-encountered in one of eight raster cells that directly surround the raster cell where it was ringed . foreign cell re-encounter (FCR) = bird was re-encountered in one of the other raster cells

In each raster cell, the number of ringings was counted, when the schemes provided complete ringing information. If only total ringing numbers were given, spatial allocation of ringings was estimated based on total ringing numbers and the coordinates of ringing locations that come with the corresponding re-encounter data. We tested this last method using ringing data of five different species including Kestrel, Dunlin Calidris alpina, Great spotted Woodpecker Dendrocopos major, Red backed Shrike Lanius collurio and Common Chiffchaff Phylloscopus collybita provided by the Hiddensee Bird Ringing Centre. Cell ranking of estimated ringings correlates in all cases with the real cell Figure 1. Study site: European countries from which ringing and re-encounter data as well as at least ranking of ringing (Spearman’s rank total ringing numbers were available. Raster grid with resolution of 200 x 200 km. correlation: ρ > 0.8, P < 0.01).

The re-encounter categories (SCR, NCR, FCR) were counted per cell as well and we calculated re-encounter rates (see Tabel 1 for abbreviations):

These basal re-encounter rates (BRRs) built the framework for prediction. To make predictions in any month of a year, we calculated BRRs for every month. Similarly, we determined a breeding season (BS; May to July), a non-breeding season (nonBS; November to February) and two transitional seasons (trans; March to April and August to October). For these seasons we defined home ranges of each species of each ringing scheme separately as the potential re- encounter area by using 70% (BS), 90% (trans) and 99% (nonBS) kernel density estimates of

71 all observations during the respective season. We applied different kernel density estimates because during the breeding season, birds are considered to show a higher fidelity to their breeding areas than during migration and wintering. This controls for over-prediction in FCR cells during the breeding season.

Prediction We used three data sets of ringing and re-encounter data of Kestrels, Buzzards and Sparrowhawks from Germany that has been analysed by Holte et al. (2016 and 2017) and were provided by the three German ringing schemes. These original data sets were considered as ‘uncorrected data’ or ‘real observations’. Since BRRs were derived from data between 1995 and 2015, re-encounters before 1995 were excluded from the data sets. Re-encounters were determined as SCR, NCR or FCR and the numbers of each category per cell were counted. The respective BRRs were used as basis for prediction. In the following we describe the prediction process based on Kestrel data. Subsequently, the same procedure was applied on Buzzard and Sparrowhawk data as well. First, because ringing totals are usually not provided in ring recovery data sets the number of ringings per cell ( i) was predicted by dividing the Kestrels’ SCRi by the basal rSCRi of Kestrels, giving the estimated number of Kestrels that potentially could have been re-encountered. Then, the number of ringings for neighbour cells ( iNC) was computed. The sum of i was taken as the number of ringings that could have been re-encountered in each foreign cell ( iFC). Because home ranges differ between seasons, the following prediction process was carried out for each season separately. We equalized the BRRs in all raster cells by setting them to the maximum of the actually observed BRRs within the respective area. For SCR and NCR, these maxima were multiplied with the respective i:

In order to consider home ranges per season in foreign cells, each cell belonging to the respective home range was assigned to a rounding argument (either ‘ceiling’ (↑; rounding up to the next integer) or ‘floor’ (↓; rounding down to the next integer)) chosen randomly following a 72

binomial distribution with probability , where is the length (i.e. the number of month) of the respective season (BS = 3 month, nonBS = 4 month, trans = 5 month) and is the number of months per season for which the prediction was carried out:

The rounding argument controls for over-prediction in foreign cells within the home range area. Although a random prediction may cause differences e.g. in maxima of distances between several repetitions of prediction, the average distances are not affected. We tested this by predicting re-encounters for an exemplary period (January to March) with 1,000 repetitions; medians did not differ significantly (Kruskal-Wallis-Test: P > 0.99). The difference between predicted (= corrected) and observed re-encounters is the number of Kestrels that would have been additionally re-encountered if the probabilities of a re-encounter in each raster cell was as high as the maximum of the observed probabilities in the respective BRRs. In order to visualize and process these potential re-encounters, estimated coordinates of ringing locations were determined by chance according to the actually observed ringing coordinates. Coordinates of predicted re-encounters were chosen coincidentally within the respective raster cells of re-encounter. In all three species, exemplary predictions were carried out for (I) the breeding season (May to July), (II) three winter months (December to February) and (III) the entire year (January to December). We compared the respective distances between ringing and re-encounter sites of real observations and predictions by means of Mann-Whitney-U tests. We draw the curves of the Empirical Cumulative Distribution Functions (ecdf) derived from the winter examples (II) of each of the three species in order to visualize possible differences between uncorrected and predicted data in regard to the cumulative distribution. Here, the cumulative distribution is the percentage of all individuals re-encountered up to a certain distance. Because Korner-Nievergelt et al. (2010a) found a correlation between human population density and the proportion of reported dead Blackbirds Turdus merula in Britain, we tested human population density in 2000 in Europe and our findings on re-encounter rates for correlation. Data on population density were downloaded as .csv-file from the NASA Earth Observation website

(https://neo.sci.gsfc.nasa.gov, access on Jan 22nd 2018) and transformed into a raster that exactly fits to the raster used for calculating re-encounter rates. Data that exceeded the study area were removed. The three BRRs for Sparrowhawks along all twelve months were calculated exemplarily and, subsequently, we performed a Spearman’s rank correlation test (because population density, as well as the BRRs, was not normally distributed) on population density and each of the three BRRs.

Table 1. Abbreviations

Symbol Meaning SCR same cell re-encounter NCR neighbour cell re-encounter FCR foreign cell re-encounter i cell number

SCRi number of SCR per cell i

NCRi number of NCR per cell i

Ri number of ringings per cell i

FCRi number of FCR per cell i rSCRi rate of SCR per cell i rNCRi rate of NCR per cell i rFCRi rate of FCR per cell i

ΣRNi sum of ringings in neighbouring cells of cell i

ΣRFi sum of ringings in foreign cells of cell i

BRRs basal re-encounter rates (rSCRi, rNCRi, rFCRi)

i estimated number of ringings in cell i

iNC estimated number of ringings for neighbour cell re-encounters in cell i

iFC estimated number of ringings for foreign cell re-encounters in cell i prSCRi predicted number of SCR in cell i prNCRi predicted number of NCR in cell i prFCRi predicted number of FCR in cell i BS breeding season (May to July) nonBS non-breeding season (November to February) trans transitional seasons (March to April, August to October)

RESULTS

The spatial distribution of observed and predicted re-encounters of Kestrels, Buzzards and Sparrowhawks is represented in Figure 2, 3 and 4, respectively. In all three species, the number of predicted re-encounters is higher than that of the uncorrected observations, independently of the predicted period (Table 2). The maximal re-encounter rates within the respective home ranges (based on the re-encounter probabilities in the entire study area; Appendix 1-9) show variations between the predicted periods as well as between the species (Table 2). The estimated numbers of ringings are 45,717 for Kestrels, 8,403 for Buzzards and 11,756 for Sparrowhawks. Distances of observed and predicted re-encounters differ significantly in all cases (P < 0.001, except Sparrowhawks (I): P = 0.002; Figure 5-7). In some raster cells, the number of observed re-encounters is higher than the predicted numbers (= trans-predictional re- encounters). These numbers differ between predicted periods as well as between species (Table 2). In predicted winter distances, the ecdf curves show a lower cumulative distribution at short and medium distances than in uncorrected winter observations (Figure 8). Human population density correlates with re-encounter rates of Sparrowhawks (P < 0.001, ρ = 0.291 (SCR), 0.326 (NCR), 0.511 (FCR)).

Figure 2. Spatial distribution of real observed and additionally predicted re-encounters of Common Kestrels (Falco tinnunculus) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year.

Figure 3. Spatial distribution of real observed and additionally predicted re-encounters of Common Buzzards (Buteo buteo) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year.

Figure 4. Spatial distribution of real observed and additionally predicted re-encounters of Eurasian Sparrowhawks (Accipiter nisus) from Germany between 1995 and 2015 in three different periods: (I) breeding season, (II) winter and (III) throughout the year.

Table 2. Prediction parameters and predicted numbers of re-encounters of a) Common Kerstrels (Falco tinnunculus), b) Common Buzzards (Buteo buteo) and c) Eurasian Sparrowhawks (Accipiter nisus) from Germany between 1995 and 2015. SCR = same cell re-encounter, NCR = neighbour cell re-encounter, FCR = foreign cell re-encounter, TPR = trans-predictional re-encounters (re- encounters whose number exceed the number of predictions in the respective raster cells), BS = breeding season (May to July), nonBS = non-breeding season (November to February), trans = transitional seasons (March to April, August to October)

Period Real observations (n) Maximum re-encounter rate Predicted re-encounters (n) TPR SCR NCR FCR Total SCR NCR FCR SCR NCR FCR Total (n) a) BS (I) 398 121 24 543 0.014 0.00129 0.000486 639 468 406 1513 51 Winter (II) 259 117 77 453 0.014 0.00121 3.01e-05 639 440 159 1238 35 Year (III) 941 374 174 1489 BS 0.007 0.00087 9.9675e-05 1498 1161 316 2975 262 nonBS 0.016 0.0013 3.0175e-05 trans 0.0098 0.001 3.985e-05 b) BS (I) 37 8 2 47 0.008 0.0022 4.88e-05 69 8 10 87 10 Winter (II) 96 53 35 184 0.018 0.0037 0.000182 152 246 79 477 14 Year (III) 279 116 54 449 BS 0.015 0.0021 0.000183 529 743 164 1436 25 nonBS 0.021 0.0044 0.000203 Trans 0.027 0.0047 0.000209 c) BS (I) 29 3 1 33 0.045 0 2.515e-05 53 0 4 57 5 Winter (II) 96 19 11 126 0.012 0.0011 7.47e-05 141 98 53 292 9 Year (III) 251 54 34 339 BS 0.012 0.0011 7.335e-05 646 316 238 1200 19 nonBS 0.02 0.0012 9.835e-05 Trans 0.023 0.0012 8.25e-05

DISCUSSION

Scientific bird ringing is still an important method for analysing bird ecology, even though more modern techniques are developed, which however cannot be applied retrospectively. Ring re- encounter data often provide information only on a low accuracy level compared to data derived by other methods (e.g. satellite tracking), but it can produce a high number of records which are not restricted to only few individuals. Our approach of predicting ring re-encounters based on spatial heterogeneity of re-encounter probabilities can help to analyse ring re-encounter data, for instance in terms of dispersal or migration. Similarly to uncorrected data, predictions do not reflect the absolute reality (e.g. in terms of exact re-encounter locations), either. This is because we do not exclusively predict bird behaviour rather we correct for observer heterogeneity. We combine empirical information on bird behaviour derived from observed re-encounters, while correcting the information on observer activities including the willingness to report. Our approach makes data comparable by predicting re-encounters that could have been observed if the probability of observing an individual was equal throughout the respective home ranges. The comparability on large spatial scales might improve analyses of dispersal or migration. Since some parameters are selected randomly at specific stages during the predicting process, the results of prediction may differ in certain details between several repetitions. However, the general result is not affected (see Methods). The exemplary predictions show an increase of re-encounters compared to the real observations (Table 2), whereas applying the prediction process on a large time period (e.g. 12 month, example III) is more conservative than a prediction for only few or single months. This is supported by the higher numbers of trans-predictional re-encounters in III (Table 2), meaning that in some raster cells re-encounters are underestimated by the prediction, because the number of real observations even exceeds the number of predictions. The estimated numbers of ringings are smaller than the real total ringing numbers (Kestrels: estimated=45,717, real=116,768; Buzzards estimated=8,403, real=28,173; Sparrowhawks: estimated=11,756, real=26,938) which seems to be highly conservative as well. That is because the data used for prediction was restricted (e.g. by considering only freshly dead birds and by excluding colour rings and records with high coordinate inaccuracy etc.; see Methods), whereas BRRs have been computed from total ringing numbers without any restriction. 78

Figure 5. Distances of real observed (ro) and Figure 6. Distances of real observed (ro) and additionally predicted (pr) re-encounters of additionally predicted (pr) re-encounters of Common Kestrels (Falco tinnunculus) from Common Buzzards (Buteo buteo) from Germany between 1995 and 2015 in three Germany between 1995 and 2015 in three different periods: (I) breeding season (May- different periods: (I) breeding season (May- July), (II) winter (December-February) and (III) July), (II) winter (December-February) and (III) throughout the year (January-December). throughout the year (January-December).

Figure 7. Distances of real observed (ro) and additionally predicted (pr) re-encounters of Euraisian Sparrowhawks (Accipiter nisus) from Germany between 1995 and 2015 in three different periods: (I) breeding season (May- July), (II) winter (December-February) and (III) throughout the year (January-December).

The correlation between human population density and re-encounter rates of Sparrowhawks supports the approach of Korner-Nievergelt et al. (2010a) to correct ringing data for observer heterogeneity by means of human population densities. The correlation coefficients, however, show that a correction only based on population density should be carried out with caution, because the coefficients are low and differ between the BRRs. Thus, a prediction only based on human population density might disregard the details that are provided by the different BRRs. Holte et al. 2016 and 2017 defined birds as migratory if their distances exceeded a certain threshold (100 km in Kestrels and Buzzards, 60 km in Sparrowhawks). The lower cumulative distributions in winter predictions than in uncorrected data (Figure 8) show that more re- encounters at larger distances should be expected than it is revealed by uncorrected data. This may lead to underestimations of e.g. the proportion of migrants in a population when not correcting for observer heterogeneity and, thus, supports the application of such a correction. Observer heterogeneity in space and time is an issue not only in Europe. For instance, Boomer et al. (2013) reported changes in recovery probabilities in hunted mallards in North America over time due to rewards, reporting facilities and ring (= band) inscriptions. Zimmerman et al. (2009) showed lower reporting rates in hunted goose species in Canada than in the USA, while re-encounter probabilities in non-hunted bird species are not well described for North America yet. Cohen et al. (2014) showed the importance of considering differences in re-encounter probabilities for analyses of migratory connectivity in three American Tern species. However, tern re-encounter probabilities were assessed on basis of re-encounters of other waterfowl species and only on large scales. Since our method is not constrained only to Europe, it can be implemented on other regions where bird ringing is applied and it should improve ringing data analyses there as well by correcting for spatial observer heterogeneity. Yet, our results still underlie several limitations. Because of missing data, the study area was restricted to the countries shown in Figure 1 which leads to underestimations of home ranges, especially in Eastern Europe and Northern Africa, which also affects the home ranges at the boundaries of contributing countries. The information on total ringings only consists of ringing numbers without information on locations (which have been estimated, see Methods), age classes and sexes etc. Thus, the number of estimated ringing totals is constrained to be too low and a differentiated prediction of age and sex classes is not yet possible. However, reliable information on age and sex ratios in ringing data might improve the prediction process and

Figure 8. Emperical Cumulative Distribution Function curves of predicted (= corrected; black) and real observed (= uncorrected; grey) winter (December-February) re-encounters of Common Kestrels (Falco tinnunculus), Common Buzzards (Buteo buteo) and Eurasian Sparrowhawks (Accipiter nisus) from Germany between 1995 and 2015.

could allow for more detailed analyses concerning differences (for instance in migrant proportions or dispersal distances) between age and sex classes. Compared to other methods like the consideration of human population densities or political borders (Korner-Nievergelt et al. 2010a) this approach is based on observed re-encounter probabilities on a continental scale and can be directly applied to re-encounter data of other EURING member schemes that belong to the study area. In continuing projects, this method might also be developed for other species (or species groups) and other time periods, because local re-encounter probabilities can also change over time (e.g. Guillemain et al. 2011, Boomer et al. 2013), which is not considered yet, as well as for ringing data of other continents. More detailed information on total ringings, such as coordinates, sex and age classes or information on marking type (e.g. wing tags or colour rings), collected by the contributing schemes would be of much help to improve the analyses. This information cannot be elaborated retrospectively in some cases, because ringing data have been recorded for instance only for individuals that have been re-encountered or the records have not been digitalized yet, which make them inaccessible for analyses. However, digitalization of already recorded data and comprehensive collection of recent and future information should be done in order to enable continuous studies and support the importance of bird ringing.

ACKNOWLEDGEMENTS

We are grateful to all volunteer ringers that spend so much time and effort on ringing birds and to the observers that reported the re-encounters to the schemes. We thank the contributing ringing schemes for collecting the data and providing total ringing numbers. We thank Wolfgang Fiedler and Chris du Feu for their support during the data collection process. The data sets were supplied by EURING.

LITERATURE CITED

Baillie S.R., Bairlein F., Clark J., du Feu C. et al. 2007. Vogelberingung für Wissenschaft und Naturschutz. EURING

Bairlein F., Dierschke J., Dierschke V. et al. 2014. Atlas des Vogelzugs – Ringfunde deutscher Brut- und Gastvögel. Aula, Wiebelsheim

Bauer H.G., Bezzel E., Fiedler W. 2005. Das Kompendium der Vögel Mitteleuropas. (Bd. 1: Nonpasseriformes). Aula-Verlag, Wiebelsheim

Berthold P. 2007. Vogelzug – Eine aktuelle Gesamtübersicht, 5th edn. Wissenschaftliche Buchgesellschaft, Darmstadt

Boomer G.S., Zimmerman G.S., Zimpfer N.L. et al. 2013. Band Reporting Probabilities for Mallards Recovered in the United States and Canada. J Wildlife Manage 77: 1059-1066

Cohen E.B., Hostetler J.A., Royle J.A. & Marra P.P. 2014. Estimating migratory connectivity of birds when re-encounter probabilities are heterogeneous. Ecol Evol 4: 1659-1670

Guillemain M., Devineau O., Gauthier-Clerc M. et al. 2011. Changes in ring recovery rates over the last 50 years: shall we continue to ring ducks? J Ornithol 152: 55-61

Holte D., Köppen U. & Schmitz-Ornés A. 2016. Partial migration in a Central European raptor species: an analysis of ring re-encounter data of Common Kestrels Falco tinnunculus. Acta Orn 51: 39-53

Holte D., Köppen U. & Schmitz-Ornés A. 2017. A comparison of migratory stratesgies of partial migratory raptors from Germany. J Ornithol 158: 579-592

Hosner P.A. & Winkler D.W. 2007. Dispersal distances of Tree Swallows estimated from continent-wide and limited-area data. J Field Ornithol 78: 290-297

Kania W. 2009. Relative re-encounter ratio – an alternative approach to infer bird distribution from the re-encounter distribution. In: EURING analytical meeting 2009, Estimation, Modelling and Conservation of Vertebrate Populations Using Marked Individuals, p. 89. http://www.wkania.eu/publikacje_z_zakresu,89.html

Korner-Nievergelt F., Liechti F. & Hahn S. 2012. Migratory connectivity derived from sparse ring reencounter data with unknown numbers of ringed birds. J Ornithol 153: 771-782

Korner-Nievergelt F., Sauter A., Atkinson P.W. et al. 2010a. Improving the analysis of movement data from marked individuals through explicit estimation of observer heterogeneity. J Avian Biol 41: 8-17

Korner-Nievergelt F., Schaub M., Thorup K. & Kania W. 2010b. Estimation of bird distribution based on ring re-encounters: precision and bias of the division coefficient and its relation to multi-state models. Bird Study 57: 56-68

Naef-Daenzer B., Korner-Nievergelt F., Fiedler W. & Grüebeler M.U. 2017. Bias in ring-recovery studies: causes of mortality of little owls Athene noctua and implications for population assessment. J Avian Biol 48: 266-274

Thorup K., Korner-Nievergelt F., Cohen E.B. & Baillie S.R. 2014. EURING analytical meeting 2009, Estimation, Modelling and Conservation of Vertebrate Populations Using Marked Individuals: Large-scale spatial analysis of ringing and re-encounter data to infermovement patterns: A review including methodological perspectives. Methods Ecol Evo 5: 1337-1350

Zimmerman G.S., Kendall W.L., Doherty P.F., White G.C. & Caswell D.F. 2009. Factors Influencing Reporting and Harvest Probabilities in North American Geese. J Wildlife Manage 73: 710-719

Appendix

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Falco tinnunculus Falco

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Appendix9 93

Contributions to manuscripts

Paper I: Partial migration in a Central European raptor species: an analysis of ring re- encounter data of Common Kestrels Falco tinnunculus

Holte D, Köppen U & Schmitz-Ornés A (2016)

The basal idea and question were suggested by Ulrich Köppen (90%) and finally formulated by Angela Schmitz-Ornés (5%) and Daniel Holte (5%). Ulrich Köppen (75%) and Daniel Holte (25%) were involved in obtaining the datasets. The preparation of data was conducted by Daniel Holte (100%) and finally analysed by Daniel Holte (75%) and Angela Schmitz-Ornés (25%). Daniel Holte (75%) and Angela Schmitz-Ornés (25%) wrote the manuscript.

Paper II: A comparison of migratory strategies of partial migratory raptors from Germany

Holte D, Köppen U & Schmitz-Ornés A (2017)

Paper III: Predicting ring re-encounters considering spatial observer heterogeneity on a continental scale

Holte D, Köppen U & Schmitz-Ornés A (submitted)

The basal idea and question were conceived by Daniel Holte (70%), Ulrich Köppen (15%) and Angela Schmitz-Ornés (15%). Daniel Holte (80%) and Ulrich Köppen (20%) collected the datasets and information on total ringings. The preparation of data was conducted by Daniel Holte (100%). Daniel Holte (90%) and Angela Schmitz-Ornés (10%) developed the methodology and conducted the analyses. Daniel Holte (70%), Angela Schmitz-Ornés (25%) and Ulrich Köppen (5%) wrote the manuscript.

C. Eigenständigkeitserklärung

Hiermit erkläre ich, dass diese Arbeit bisher von mir weder an der Mathematisch- Naturwissenschaftlichen Fakultät der Ernst-Moritz-Arndt-Universität Greifswald noch einer anderen wissenschaftlichen Einrichtung zum Zwecke der Promotion eingereicht wurde. Ferner erkläre ich, dass ich diese Arbeit selbstständig verfasst und keine anderen als die darin angegebenen Hilfsmittel und Hilfen benutzt und keine Textabschnitte eines Dritten ohne Kennzeichnung übernommen habe.

D. Curriculum Vitae

Name Daniel Holte Citizenship German Date of birth 06.10.1981 in Neuss since 2015 Scientific assistant – AG Vogelwarte Hiddensee, Zoological Institute and Museum, University of Greifswald since 2010 Accountant – Rechtsanwalt Lichtblau, Greifswald (secondary employment) 2013-2015 Scholarship “Landesgraduiertenförderung Mecklenburg-Vorpommern” for PhD students 2013 Diploma thesis: “Who stays, who goes? Partial migration in East German banded Common Kestrels Falco tinnunculus” 2011 Internship – Untere Landschaftsbehörde Kreis Siegen-Wittgenstein 2009 Internship – NABU Mecklenburg-Vorpommern, Greifswald 2008-2013 Study course Landscape Ecology and Nature Conservation – University of Greifswald 2008 Internships – Biologische Station Rothaargebirge; Städtisches Umweltamt Siegen 2005-2008 Dispatcher in forwarding agency – Spedition Bender GmbH, Freudenberg 2005 Industrial management assistant – OTTO QUAST GmbH & Co KG, Freudenberg 2002-2005 Vocational apprentice: Industrial management assistant – OTTO QUAST GmbH & CO. KG, Siegen 2001-2002 Military service 2001 Abitur – Ev. Gymnasium, Siegen-Weidenau

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E. Scientific contributions

Scientific papers

Stermin AN, David A, Holte D & Schmitz-Ornés A (submitted). Nest site and nesting habits in Water Rail Rallus aquaticus and Little Crake Zapornia parva on large wetlands.

Holte D, Köppen U & Schmitz-Ornés A (submitted). Predicting ring re-encounters considering spatial differences in observer heterogeneity on a continental scale.

Holte D, Köppen U & Schmitz-Ornés A (2017). A comparison of migratory stratesgies of partial migratory raptors from Germany. J Ornithol 158: 579-592

Holte D, Köppen U & Schmitz-Ornés A (2016). Partial migration in a Central European raptor species: an analysis of ring re-encounter data of Common Kestrels Falco tinnunculus. Acta Orn 51: 39-53

Conference contributions

Holte D, Köppen U & Schmitz-Ornés A (2017). Todesursachen in Ringfundmeldungen unter Berücksichtigung der Landnutzung in Deutschland. Poster at the annual meeting of the German Ornithologists‘ Union, Stralsund.

Holte D, Köppen U & Schmitz-Ornés A (2016). Partielle Migration deutscher Greifvögel. Oral presentation at Bird Ringers‘ Conference of Beringungszentrale Hiddensee, Güstrow.

Holte D (2014). Partielle Migration in Ostdeutschland beringter Turmfalken. Oral presentation at the annual meeting of the German Ornithologists‘ Union, Bielefeld.

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F. Danksagung

Zu allererst danke ich Angela und Martin, die mir über die Zeit meiner Arbeit zwei großartige Betreuer gewesen sind. Sie hatten für alle Fragen und Probleme stets ein offenes Ohr und haben sich bei Bedarf mit aller Kraft für mich eingesetzt. Des Weiteren bedanke ich mich bei Uli, der mit seinen Ideen den Stein überhaupt ins Rollen brachte. Auch seine Unterstützung bei Rückfragen und insbesondere der Beschaffung von Daten und Informationen waren für mich unverzichtbar. Ein ganz besonderer Dank gilt meiner gesamten Arbeitsgruppe. Eure Fragen, kritischen Anmerkungen, Ideen und Antworten haben mir geholfen, das Ziel nicht aus den Augen zu verlieren. Die angenehme Arbeitsatmosphäre und nicht zuletzt unsere Teichrunden, die Essens-Tombola und der Dessert-Freitag haben mich trotz mancher Widrigkeiten bei Laune gehalten. Ich bedanke mich auch bei Wolfgang Fiedler für die angenehme Kommunikation und insbesondere bei Chris du Feu, der mich mehr als einmal motivierte, stets hartnäckig zu bleiben (“Do not give up on making trouble.“). Zuletzt, aber wohl am grundlegendsten, gilt mein Dank allen ehrenamtlichen Beringerinnen und Beringern, die aus Interesse an der Ornithologie Zeit, Geld und Mühe investiert haben, um diese und andere Untersuchungen überhaupt zu ermöglichen. Euch allen meinen aufrichtigen Dank!

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