Where do wintering cormorants come from? Long

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Received: 27 June 2017 Accepted: 21 December 2017 DOI: 10.1111/1365-2664.13106

RESEARCH ARTICLE

Where do wintering cormorants come from? Long-term changes in the geographical origin of a migratory bird on a continental scale Morten Frederiksen1

| Fränzi Korner-Nievergelt2 | Loïc Marion3 | Thomas Bregnballe4

1

Department of Bioscience, Aarhus University, Roskilde, Denmark

Abstract

2

1. Populations of migratory birds often mix to a considerable extent in their winter-

Swiss Ornithological Institute, Sempach, Switzerland

3

UMR CNRS ECOBIO, Rennes1 University, Rennes cedex, France 4

Department of Bioscience, Aarhus University, Rønde, Denmark Correspondence Morten Frederiksen Email: [email protected] Funding information European Commission, Grant/Award Number: 070307/2013/6577079/ETU/B3 Handling Editor: Philip Stephens

ing areas. Knowledge about the composition of wintering populations is highly relevant to management, not least for species such as the great cormorant Phalacrocorax carbo sinensis, prone to conflicts with human interests. However, few studies have been able to estimate long-term changes in winter population composition. 2. We use 30 years of ringing and recovery data (1983–2013) from all major breeding populations of cormorants in continental Europe (except the Black Sea region) to estimate partitioning probabilities (i.e. the probabilities of moving to specific wintering areas) using a Bayesian capture–mark–recovery model. Combining these results with information on breeding numbers and reproductive output in a population model, we estimate the size and composition of wintering populations in Europe and North Africa. 3. Partitioning probabilities showed some variation over time, but were similar for first-winter and older birds. Cormorants from the western part of the breeding range tended to winter progressively further west over time. This may be a density-dependent response to the recent growth of more easterly breeding populations. 4. All wintering populations grew rapidly over the study period, and their composition showed pronounced changes. All wintering populations were composed of birds from many different breeding populations, but the proportion of cormorants of more easterly origin increased markedly over time in most wintering areas. 5. Policy implications. Cormorant wintering populations in Europe consist of mixtures of birds of different breeding origins. These mixtures are also highly variable over time. These factors reduce the chances of successfully limiting conflicts in specific wintering areas through, for example, regulation of breeding numbers in one breeding area. The dynamic nature of cormorant winter populations means that conflicts are best addressed when and where the conflict occurs, or on the scale of the entire continental population. It is unlikely that the latter will be cost-effective and politically realistic.

J Appl Ecol. 2018;1–14.

wileyonlinelibrary.com/journal/jpe © 2018 The Authors. Journal of Applied Ecology | 1 © 2018 British Ecological Society

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Journal of Applied Ecology 2

FREDERIKSEN et al.

KEYWORDS

Bayesian model, capture–mark–recovery, great cormorant, human–wildlife conflict, migratory birds, Phalacrocorax carbo, population model, winter ecology

1 | I NTRO D U C TI O N

the non-breeding season have considerable interest in knowing the breeding origin of the birds present, particularly because manage-

Many animal species perform seasonal migrations and thus inhabit

ment interventions may take place both in the breeding and non-

different areas during different seasons (Milner-Gulland, Fryxell, &

breeding areas.

Sinclair, 2011; Newton, 2008). Among birds, the degree to which in-

Historically, most studies of bird migration and connectivity

dividuals belonging to different breeding populations of the same

between breeding and non-breeding areas have been based on

species mix during the non-breeding season varies widely, from al-

recoveries of dead ringed birds. While cheap and widely applica-

most complete separation (Madsen, Tjørnløv, Frederiksen, Mitchell,

ble, this technique suffers from several important biases. Notably,

& Sigfússon, 2014) to one single shared wintering area (Petersen,

variable chances of recovery and reporting of dead ringed birds

Larned, & Douglas, 1999). These scenarios have very different impli-

in different non-breeding areas lead to an incomplete picture of

cations, for example for population genetics, conservation and the

the non-breeding distribution of specific breeding populations

spread of contagious diseases. The degree of connectivity between

(Korner-Nievergelt, Sauter, et al., 2010; Wernham & Siriwardena,

breeding and non-breeding areas, and specifically the composition of

2002), and variable ringing effort in different breeding areas

wintering populations in terms of their breeding origin, is therefore

complicates the assessment of the composition of specific non-

an important research question in both fundamental and applied

breeding populations (du Feu, Clark, Schaub, Fiedler, & Baillie,

ecology (Bauer & Hoye, 2014; Finch, Butler, Franco, & Cresswell,

2016). Colour ringing avoids some of these biases, but observations

2017), not least considering expected changes in migration patterns

are limited by the distribution of observers. Recently, several other

due to climate change. Quantification of migratory connectivity has

techniques have been used to investigate migratory connectivity,

thus become a central theme in ecology and conservation biology

including stable isotopes (Arizaga et al., 2016; Guillemain, Van

(Webster, Marra, Haig, Bensch, & Holmes, 2002).

Wilgenburg, Legagneux, & Hobson, 2014; Hobson & Wassenaar,

The composition of non-breeding populations is often of in-

2008), population genetics (Sonsthagen, Tibbitts, Gill, Williams, &

terest in applied ecology. For species of conservation concern and

Talbot, 2015) and tracking using electronic devices (Bächler et al.,

for species exposed to hunting, it is highly relevant to know which

2010; Frederiksen et al., 2016; Kjellén, Hake, & Alerstam, 1997).

breeding populations are affected by human activities in specific

Although tracking provides direct data on connectivity, it is often

non-breeding areas, for example harvest, habitat change or active

difficult to achieve sufficient coverage and sample size to estimate

management (Norris & Marra, 2007). Brünnich’s guillemots Uria lom-

the composition of non-breeding populations. On the other hand,

via are hunted in south-west Greenland in autumn and winter, and

indirect methods such as stable isotopes and population genetics

range-wide tracking has demonstrated that adult birds shot in this

often suffer from considerable uncertainty in assignment of indi-

area mainly originate from declining breeding populations in Iceland

viduals to locations, because breeding populations overlap in al-

and Svalbard (Frederiksen et al., 2016). Both Eurasian Scolopax rus-

lele frequencies (Cadiou et al., 2004) and interpretation of stable

ticola and American woodcock Scolopax minor are heavily hunted

isotope measurements can be difficult (Hahn, Hoye, Korthals, &

migratory species, and considerable research effort has focused on

Klaassen, 2012).

identifying the breeding origin of the birds hunted in autumn, to as-

For many bird species, traditional ringing remains the most

sess sustainability of these hunted populations (Bauthian, Gossmann,

useful technique for the study of migratory connectivity, not least

Ferrand, & Julliard, 2007; Sullins et al., 2016). Similarly, for species

because this method allows examination of long-term changes in

involved in human–wildlife conflicts, it is important to know which

migratory behaviour (Thorup, Korner-Nievergelt, Cohen, & Baillie,

breeding populations will be affected by management interventions

2014). This is particularly the case when large-scale ringing has

in a given non-breeding area, or vice versa. A good example of the

taken place throughout the breeding range over long periods, and

latter is the great cormorant Phalacrocorax carbo (hereafter cormo-

where many re-encounters have accumulated. However, it is nec-

rant) in Europe. This species has experienced a large increase in

essary to employ statistical methods that can provide unbiased

abundance following improved protection in the 1970s, and is now

estimates in the presence of spatial variation in recovery probabil-

involved in widespread conflicts with fishery and aquaculture inter-

ity and ringing effort. Improved statistical methods for estimating

ests (Marzano, Carss, & Cheyne, 2013). Cormorants breed mainly

the proportion of birds from one breeding area moving to specific

in northern Europe and spend the winter in central and southern

wintering areas, while correcting for spatial variation in reporting

Europe, although breeding and winter areas show some overlap

probability, have recently become available (Bauthian et al., 2007;

(Bregnballe, Frederiksen, & Gregersen, 1997). Management agencies

Korner-Nievergelt, Liechti, & Thorup, 2014; Thorup & Conn, 2009),

and stakeholders in countries mainly occupied by cormorants during

based on the pioneering work of Kania and Busse (1987). The next

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FREDERIKSEN et al.

step involves combining these methods with robust estimates of population size in the breeding areas to derive estimates of winter population composition.

2.2 | Data Cormorants have practically only been ringed as unfledged chicks

The cormorant provides a good case study for this next step.

in Europe, and in this study, we did not include the very few birds

Since the 1980s, more than 220,000 cormorants have been ringed

ringed as adults. Ringing and recovery data were obtained from the

throughout the European breeding range, and large numbers of

EURING database (www.euring.org) and from the national ringing

these birds have been recovered as dead during winter, allow-

schemes. We used data from 1983 until 2013, including recoveries

ing estimation of movement probabilities. Furthermore, cormo-

from the 2013/2014 winter. A total of 222,467 cormorant chicks

rants are easy to count, and robust annual estimates of breeding

were ringed in the study area during this period (Appendix S2). A

population size exist for most breeding areas, along with more

substantial proportion of these chicks also received one or more

sporadic information on breeding productivity. Here, we com-

plastic colour rings; the presence of colour ring(s) may have en-

bine these sources of information to estimate size and composi-

hanced the probability that these birds were reported if found dead.

tion of wintering cormorant populations in Europe for a 30-year

However, we did not have access to information about the colour

period. The specific aims of this study were: (1) to estimate the

ring status of each bird, so we were unable to include this potential

composition, in terms of breeding origin, of wintering cormorant

effect in our model.

populations; (2) to quantify long-term changes in composition of

We included dead recoveries from the winter period (15

these populations; and (3) to assess whether any such changes

November–28 February), when cormorants were assumed to

have mainly been caused by changes in migratory behaviour or

have reached their wintering areas and remain fairly stationary

by non-h omogenous population growth in the breeding areas. In

(Bregnballe et al., 1997; Frederiksen, Bregnballe, van Eerden, van

addition, we compare model estimates to winter counts for one

Rijn, & Lebreton, 2002). Records where only the ring was found and

important wintering area, France, and discuss the implications for

those with a highly uncertain finding date were excluded. We re-

management.

tained 4,511 recoveries (Appendix S2), of which 2,003 referred to shot birds, and 2,508 to birds reported dead from other or unknown

2 | M ATE R I A L S A N D M E TH O DS 2.1 | Study population and spatial coverage

causes. Recoveries were allocated to wintering areas in ArcGIS 10.2. Appendix S3 shows observed numbers of winter recoveries split by breeding and wintering areas. We collated all available data on breeding numbers (Appendix S4)

Two traditional subspecies of cormorants breed and win-

and productivity (Appendix S5), and filled any gaps using interpola-

ter in Europe: Ph. c. carbo (here including the third subspe-

tion and expert judgement.

cies Phalacrocorax carbo norvegicus, Marion & Le Gentil, 2006) breeds along rocky coasts in north-western Europe, whereas Phalacrocorax carbo sinensis breeds in trees or on the ground along shallow coasts and inland in continental Europe and further east. This study concentrated on the population of Ph. c. sinensis

2.3 | Bayesian multistate CMR model 2.3.1 | Overall structure

breeding in northern and central Europe. We therefore excluded

We developed a Bayesian multistate capture–mark–recovery model

breeding areas populated exclusively or mainly by Ph. c. carbo (the

to estimate the proportions of cormorants from each breeding area

British Isles including the Channel Islands, Iceland, Norway ex-

that wintered in each wintering area. The model was an extension to

cept the south-e astern region, Arctic Russia), as well as the large

a long-lived species of that employed by Korner-Nievergelt, Schaub,

breeding population of Ph. c. sinensis around the Black Sea (see

Thorup, Vock, and Kania (2010) and Korner-Nievergelt et al. (2014),

Figure 1). Ph. c. carbo and Ph. c. norvegicus also breed in western

which again was based on the division coefficient method of Kania

France mixed with Ph. c. sinensis, and these populations are in-

and Busse (1987). Under this model, these proportions (here termed

cluded here.

partitioning probabilities) are only estimable if the number of breed-

We included all known wintering areas of our target population.

ing areas is ≥ the number of wintering areas, and if birds from dif-

However, some of these areas are shared with Ph. c. carbo (e.g. in

ferent breeding areas have different wintering distributions. It is

Norway, Sweden, Denmark, the British Isles, western France, the

assumed that all individuals spend the winter season in one of the

Iberian peninsula; Bakken, Runde, & Tjørve, 2003; Wernham, Ekins,

wintering areas considered, that is, no individual should leave the

& Sellers, 2002) or Black Sea Ph. c. sinensis (the Black Sea region,

study area. Furthermore, it is assumed that all marked birds dying in

Greece, eastern North Africa), and our estimates of wintering popu-

a given wintering area have the same probability of being found and

lation size and composition thus do not reflect all cormorants pres-

reported to the ringing scheme, regardless of their breeding origin.

ent in these wintering areas. We defined 12 breeding areas and 11 wintering areas (Figure 1, Appendix S1).

We estimated different survival and partitioning probabilities for first-year and adult birds, and assumed that the recovery probability (see definition below) was independent of age.

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FREDERIKSEN et al.

F I G U R E 1 (top panel) Map of breeding areas. White triangles show ringing locations of the 4,511 recovered cormorants included. (bottom panel) Map of wintering areas. For precise definitions, see Appendix S1. Black dots show recovery locations of the 4,511 cormorants included

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FREDERIKSEN et al.

Parameters, variables, and sub- and superscripts are defined in Table 1.

limitations, the partitioning probabilities were modelled as period- dependent rather than year-dependent, using six 5-year periods (with a final period of 6 years).

2.3.2 | Likelihood and estimated parameters Recovery data were summarised as a four-dimensional m-array (by breeding area i, year of ringing k, wintering area j and year of recov-

2.3.3 | Priors For the means of the logit of first-year and adult survival, we used

ery t), R i=1,…,12,k=1,…,31,j=1,…,11,t=1,…,31. We assumed that the recoveries

flat normal distributions (M = 0, SD = 10) as priors. For the standard

of birds ringed during the same year and in the same breeding area

deviation of the random year effects, we used folded-t distribution

(Nik) were multinomially distributed, R ik,j=1,…,11,t=1,…,31 ~ Multinom

(M = 0, SD = 1, df = 2) priors (Gelman, 2006).

(pik,j=1,…,11,t=1,…,31,Nik).

Cell

probabilities

pik,j=1,…,11,t=1,…,31

were

The priors for the intercept in the logistic regression for recovery

then modelled as functions of survival, partitioning and recovery

probabilities (i.e. area-specific recovery probabilities for 1998, the

probabilities.

central year of the study) were constructed so that their order was

Sjuv and Sad represent annual (year-dependent) survival probabilities t t

partly fixed. Based on a general understanding of patterns of ring

for, respectively, the first year of life and subsequent years. For both,

recovery activity, we assumed that recovery probabilities were high-

we included a normal random between-year variance on the logit scale.

est in the UK and lowest in North Africa, with other wintering areas

The recovery probability rjt represents, for a bird migrating to

ranked in between (see definitions of wintering areas in Figure 1 and

wintering area j and dying in year t, the probability that its date of

Appendix S1): rD > rA , rE > rC , rF > rB, rG , rH > rI, rJ > rK . The prior for rD

death is during the winter season, that it is found, and that the ring

was normal (M = 0, SD = 5) on the logit scale, and for each subsequent

number is reported to the ringing scheme. Recovery probabilities

level of the series, a uniform prior between −12 and the previous level

in each wintering area were modelled with independent logit-linear

was used. Normal priors (M = 0, SD = 5) were used for the linear trends.

trends over time (z-transformed).

For the partitioning probabilities, we used beta(1,1) priors which

The first-year and adult partitioning probabilities mjuv and mad ijt ijt

were then scaled to sum to 1 for each cohort of birds during one time

represent the (in principle year-dependent) probability that a bird

period. Some movements were regarded as highly unlikely, for example

“belonging” to breeding area i (i.e. fledged there) spends the winter in

birds moving north in winter, or very extensive east–west movements

wintering area j. The partitioning probabilities for one cohort of birds ∑11 = 1), that is the 11 during one time period sum to one (e.g. j=1 mjuv ijt

(Bregnballe et al., 1997; Reymond & Zuchuat, 1995). For these, a priori

wintering areas include the whole winter distribution of the studied

We also used a beta(1,10) prior for all movements to the marginal win-

population. The partitioning probabilities are not transition probabil-

tering areas D (British Isles) and J (Black Sea) (see Appendix S3).

rare movements, we used a beta(1,1,000) prior before scaling as above.

ities in the usual sense, because these parameters are not dependent on where the bird was during the previous year, but on where it was ringed (there is no Markovian relationship). The parameters mjuv and ijt

2.3.4 | Model fitting

mad are population rather than individual characteristics. Due to data ijt

To fit the model, we used Markov chain Monte Carlo simulations

TA B L E 1 Notation used for the CMR and population models (parameters, variables, and sub- and superscripts)

using R2jags (Su & Yajima, 2015). Two chains of length 60,000 were

as implemented in JAGS (Plummer, 2003) via r (R Core Team, 2015) simulated and from the last 50,000 iterations, every 10th was used to describe the posterior distributions of the model parameters.

ad

superscript indicating adult age class (older than one year)

B->W

period from breeding season until winter

BS

breeding success, mean number of chicks fledged per pair

i

subscript indicating breeding area

shot and non-shot birds produced qualitatively similar results (pre-

j

subscript indicating wintering area

liminary analyses, data not shown).

juv

superscript indicating first-year age class

k

subscript indicating year of ringing

m

partitioning probability

NB

breeding population size (number of pairs)

NW

winter population size (number of individuals)

r

recovery probability

ing numbers and productivity in the 12 breeding areas. Assuming

S

annual survival probability

equal survival probability before and after the winter census, we

t

subscript indicating year of recovery in CMR model, and year in population model

calculated survival of adults (1 Y+) from breeding season until winter √ ̂ ad as the square root of the annual estimate of survival: Sad,B→W = Ŝ

Convergence was assessed graphically, by the r-hat value (Brooks & Gelman, 1998) and the number of effective samples. The model was validated using posterior predictive checking and prior-posterior overlap, see Appendix S6. Fitting the model separately to the data of

2.4 | Winter population model Winter population size and composition in the 11 wintering areas was estimated by combining parameter estimates (survival and partitioning probabilities) from the Bayesian CMR model with data on breed-

t

t

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FREDERIKSEN et al.

For first-year birds, we assumed that survival was similar to adults

and southerly wintering areas (e.g. Italy, area H, and western Balkan,

from the first winter on (see Appendix S7), and calculated survival

area I). Overall, first-year and adult partitioning probabilities were

from fledging until winter as: √ ̂ juv ad = Ŝ ∕ Ŝ Sjuv,B→W t t t

quite similar (Appendix S10) and highly correlated (Pearson’s correlation coefficient = .87). The estimated total winter population was 91,000 in 1983,

The counts only included breeding birds (pairs), whereas cormo-

and increased rapidly over the next 15–20 years. During 2003–

rants typically start breeding at an age of 2–4 years (Frederiksen

2013, the estimated mid-w inter population was relatively

& Bregnballe, 2001). In the model, we assumed that all cormorants

stable at 600,000–690,000 cormorants (wintering birds orig-

started to breed at age 3 years. The estimated numbers of adult and

inating from the UK and most of Norway are not included, see

first-year cormorants from breeding area i wintering in wintering

Section 2). Wintering populations increased in all winter areas

area j in year t were then calculated as respectively:

(Appendix S11), although populations in areas D (UK) and J (Black Sea) were quite small. The composition of wintering pop-

( √ √ √ juv ̂ ̂ ̂ ̂ ̂ ad ad ad ad ad = m ∗ 2 ∗ NB ∗ + NB ∗ BS ∗ S ∕ ∗ + NW S S Ŝ it it−1 it−1 ijt ijt t t t−1 t−1 √ ) √ √ juv ad ad ad and ∗ Ŝ ∗ Ŝ NBit−2 ∗ BSit−2 ∗ Ŝ ∕ Ŝ t t−2 t−1 t−2 √ juv juv juv ̂ ̂ ad ∗ NBit ∗ BSit ∗ Ŝ ∕ Ŝ =m NW t ijt ijt t For adults, this calculation was not possible for the first two study years due to missing estimates of breeding productivity and survival before 1983, and we therefore assumed that 3 adults (1 Y+) were present in winter for each breeding pair (i.e. ̂ ̂ ad ad =m ∗ 3 ∗ NB ), a value close to the average for the early part NW ijt

ijt

ulations changed considerably over time (Figure 3). Breeding populations around the eastern Baltic (areas 2 and 3) increased later than further west (Appendix S4), and thus made up an increasing proportion of several wintering populations (Figure 3, e.g. areas B, H and I). Europe-w ide counts of wintering cormorants were only available for January 2003 (van Eerden, Marion, & Parz-G ollner, 2011); these counts were strongly correlated with regional model estimates from the same winter (Appendix S12).

it

of the study. Estimates of the annual total numbers present in each wintering area were then obtained by summing across breeding areas and age

4 | D I S CU S S I O N

classes. All these calculations were carried out at each Markov chain

4.1 | Cormorant migration and wintering behaviour

Monte Carlo iteration, and we thus obtained full posterior distribu-

Cormorants from all European breeding areas showed a wide va-

tions of the winter population estimates.

riety of migration behaviour, distributing themselves across many

The r and JAGS code used for the Bayesian multistate CMR model and the winter population model is shown in Appendix S8.

different wintering areas (Figure 2). This finding is consistent with previous studies at the national or more local level (Antoniazza, Korner-Nievergelt, & Keller, 2012; Bregnballe et al., 1997; Herrmann,

3 | R E S U LT S

Wendt, Köppen, Kralj, & Feige, 2015; van Eerden & Munsterman, 1995). There was thus no one-to-one correspondence between breeding and wintering areas, and migratory connectivity sensu

Posterior predictive checking indicated a good agreement between

Webster et al. (2002) was relatively weak. Nevertheless, there was

model predictions and the observed geographical distribution of re-

a clear tendency that birds breeding further east also wintered fur-

coveries (Appendix S6). High prior-posterior overlap indicating high

ther east (compare e.g. breeding areas 2 and 5 in Figure 2), a pattern

prior influence appeared for some partitioning probabilities related

already identified by Reymond and Zuchuat (1995). At any point in

to rarely observed movements (Appendix S6). Estimated winter

time, the various wintering populations thus differed in composition

population composition is expected to be quite robust to these prior

(Figure 3).

influences, because they largely concern relatively low absolute numbers of birds. Survival probabilities were estimated with a fairly high precision

We found that for some populations, choice of wintering area changed relatively little over the 30-year period, at least on the scale studied here (Figure 2). However, other populations showed consis-

(Appendix S9). Mean estimated survival (geometric mean of the an-

tent patterns of change. Birds from the more westerly breeding areas

nual posterior medians) was 0.410 for first-year birds and 0.804 for

(e.g. areas 1 and 5 holding a large proportion of the entire breeding

adults. Recovery probabilities were estimated as declining over time

population) tended to shift their wintering areas westwards, so that

in most wintering areas (Appendix S9), with the exception of France

for example the proportion wintering in east-central Europe (area

and the Iberian Peninsula.

B) and western Balkan (area I) declined over time. Such a shift was

Partitioning probabilities showed some temporal variation

described for Danish cormorants wintering in France by Marion

(Figure 2, Appendix S10). Cormorants from more westerly breeding

(1995). Other studies have shown that individual cormorants are

areas (e.g. Denmark, area 5 and the Netherlands, area 7) showed

fairly consistent in their choice of wintering areas from year to year

an increasing tendency to winter further north and west (e.g. the

(Frederiksen et al., 2002; Lekuona & Campos, 2000). The observed

Baltic, area A, and France, area F) at the expense of more easterly

changes in partitioning probabilities were thus most likely caused

FREDERIKSEN et al.

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F I G U R E 2 Partitioning probabilities for the 12 breeding areas, 1983–2013. For each breeding area (highlighted in red), a map shows the estimated partitioning probability to each of the 11 wintering areas (A–K) over six 5-year periods (1983/1984–1987/1988 through 2008/2009–2013/2014) for adults (bars). The yellow dots linked by a black line show the mean location of recoveries used in the analysis for each breeding area and 5-year period, with an arrow indicating the direction of time. Mean locations were corrected for variation in recovery probability between wintering areas (for method, see Appendix S15)

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FREDERIKSEN et al.

F I G U R E 2 (Continued) by young individuals on average choosing different wintering areas

that density-dependent mechanisms thus could be responsible for

than their parents. It is likely that choice of first wintering area is

the observed shift in wintering areas for some populations; however,

affected by the number of conspecifics already using that area, and

our results do not provide any direct evidence for this.

FREDERIKSEN et al.

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F I G U R E 3 Estimated proportional composition of the winter population of cormorants (juveniles and adults) in each of the 11 wintering areas (A–K), in terms of breeding origin. Each colour (1–12) refers to a specific breeding area, as in Figure 1. For definition of wintering and breeding areas, see Figure 1 and Appendix S1. For wintering areas D and J, the estimates are based on very few recoveries and thus subject to considerable random fluctuations There was considerable variation over time in the composition

in winter composition are mainly linked to differential growth of

of wintering cormorant populations (Figure 3). Such wide-r anging,

the various breeding populations rather than a change in migration

long-term changes in composition of non-breeding bird popula-

behaviour. For example, the recent increase in the number of cor-

tions have, to our knowledge, not previously been demonstrated,

morants breeding in Finland and the Eastern Baltic (Appendix S4)

due to lack of data and a suitable analytical framework. The vari-

has led to an increasing proportion of birds originating from these

able composition of wintering populations over time contrasts to

breeding areas (2 and 3) in most wintering areas (Figure 3). Similarly,

the relatively stable winter distribution of most breeding popula-

the proportion of birds originating from breeding areas 5 and 7

tions. Therefore, we conclude that the observed long-term trends

(Denmark and the Netherlands) has declined in most wintering

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FREDERIKSEN et al.

by some causes of death (e.g. Bregnballe & Frederiksen, 2006), and the chance of the ring being reported varies between causes , , , , ,

of death (here primarily shot, drowned or found dead). Our model allows different recovery probabilities in different wintering areas, which is likely to partly address this issue because the distribution of causes of death is spatially variable. We have also assumed that survival probability is uniform across all breeding and wintering areas. If mortality in fact is higher in some wintering areas (which seems likely), recovery probabilities in those

,

areas might be expected to be overestimated. However, a simula-

,

tion study with spatial variation in survival showed that estimates of recovery probabilities were in fact unbiased, while survival estimates not including spatial variation in survival were biased. Most

F I G U R E 4 Number of recoveries recorded, model estimates of winter population size and the results of coordinated winter cormorant counts (Marion, 2014) in France 1983–2013. The year given indicates the first year of a winter (e.g. 1985 = 1985/1986). Counts and model estimates of population size were positively, but not very highly, correlated (r = .57)

importantly, estimates of partitioning probabilities were unbiased (Appendix S13). Available information on breeding productivity was limited, so we used expert judgement of spatio-temporal variation (Appendix S5). A simple sensitivity analysis indicated that the specific values used for breeding productivity had a minor effect on the estimated winter population composition, relative to partitioning probabilities and

areas, because breeding numbers stabilised earlier in these areas

breeding population sizes (Appendix S14). We are thus confident

than elsewhere in Europe. Shorter term variation in winter com-

that our main conclusions regarding the composition of wintering

position reflects temporal variation in partitioning probabilities

populations are robust to these data limitations.

(Figure 2), and for the wintering areas with a low number of recov-

A simplifying assumption in our model is that recovery prob-

eries (primarily areas D and J) this involves considerable random

abilities are only allowed to change monotonically over time, as

sampling fluctuation.

logit-linear trends (Appendix S9). Several studies have found de-

The pronounced mixing of cormorant breeding populations in

clining reporting probabilities over time (Frederiksen & Bregnballe,

winter may also have important implications, for example for disease

2000; Robinson, Grantham, & Clark, 2009), but the simple trends

spread between populations. However, the environmental prev-

may mask important temporal variation. Recovery probabilities are

alence and concentration of pathogens around cormorant winter

cause-specific, and affected by complex socioeconomic factors. For

roosts is poorly studied (but see Smolders, Smolders, Watkinson, &

instance, introduction of a national culling programme in a specific

Ryder, 2014), and thus, the potential for disease transmission out-

wintering area may lead to a temporary increase in recovery proba-

side the breeding season is unknown. Cormorants are known to be

bility. Because the logit-linear trend imposed does not allow for such

frequent hosts for avian paramyxovirus 1 (APMV1; Klimaszyk &

an erratic temporal pattern, the partitioning probabilities to an area

Rzymski, 2016), and several outbreaks have been documented in the

may be over- or underestimated for part of the study period. There

closely related double-crested cormorant (Phalacrocorax auritus) in

are indications that this occurred in France around 2000, where

North America (Dimitrov, Ramey, Qiu, Bahl, & Afonso, 2016; White

estimates of winter population size showed an abrupt increase and

et al., 2015). However, none of 208 cormorants shot in winter in

subsequent decrease (Figure 4), roughly in line with a peak in the

Switzerland tested positive for APMV1 (Albini et al., 2014). Other

number of recoveries recorded. This pattern of population change

pathogens such as avian influenza virus are seemingly infrequent in

was not consistent with the results of winter counts of roosting cor-

cormorants (review in Klimaszyk & Rzymski, 2016).

morants (Figure 4), and thus more likely a by-product of a temporary peak in recovery probability. There was a strong positive correlation

4.2 | Methodological issues The ringing effort has been variable over time and space, which has

between the annual number of recoveries and the number of shot birds until 2003 (rs = .93), but not later (rs = −.05), perhaps indicating declining interest in reporting ringed birds. However, this poten-

caused sample size to be quite low in some cases, with associated

tial bias is expected to affect birds from all breeding areas equally,

uncertainty in the estimates. However, these uncertainties mainly

and should therefore not affect the estimated winter composition.

affect relatively small breeding populations, and thus have a minor

For this reason, we consider the estimated winter composition

impact on the estimated winter compositions. Because only juveniles

(Figure 3) to be more robust than the estimated winter population

have been ringed, we have had to assume that recovery probabilities

size (Appendix S11).

are the same for all age classes. This is potentially a problematic as-

Although our Bayesian CMR model is quite data hungry, it

sumption (Catchpole, Freeman, & Morgan, 1995; Lakhani & Newton,

could be useful for other species which have been ringed in sub-

1983), because juveniles are likely to be disproportionately affected

stantial numbers over wide areas, and where recovery probability

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FREDERIKSEN et al.

is reasonably high (e.g. hunted species, gulls, certain raptors). Our

conflicts in specific wintering areas are thus inappropriate. Even

population model approach could be used for species where migra-

without changes in migration behaviour, wintering populations will

tory connectivity has been estimated in some way, and where good

change as a consequence of differential growth of the various breed-

estimates of regional breeding population sizes exist.

ing populations. Although density dependence in wintering populations is not well documented, it is highly likely that cormorants will

4.3 | Management implications

change their distribution in response to changes in local numbers, so that an influx of “new” wintering birds may lead to redistribution

The most striking overall result of this study is that all European

of birds previously wintering in that area. On the other hand, a local

wintering populations of cormorants are of mixed breeding origin,

decline, due to culling or reduced influx from birds from a given

and that the proportional composition of each wintering popu-

breeding area, is likely to make the wintering area more attractive

lation has changed considerably over time (Figure 3). Although

to other cormorants. These mechanisms not only will tend to buffer

these findings are not entirely new, this is the first time that data

temporary reductions in wintering numbers in a specific area over

from the entire European range of Ph. c. sinensis have been ana-

time, but may also lead to rapid changes in composition. In addition,

lysed together, and that methods correcting for spatially variable

cormorant choice of wintering areas is likely strongly affected by

ringing effort and recovery probability have been employed. Our

changes in food availability, which again are driven by a combination

clear and unequivocal quantitative demonstration of the complex

of climate, lake and river restoration activities, fish stocking and nu-

relationships between breeding and wintering cormorant popula-

trient loads (van Eerden, van Rijn, Volponi, Paquet, & Carss, 2012).

tions has important implications for management of this conflict-

Although quantitative studies of winter population composi-

prone species. Any damage to fish populations in rivers and/or to

tion are lacking for other species, it is highly likely that many short-

the fishery industry in one wintering area is not due to cormorants

distance and partial migrants show similarly complex patterns over

from a specific breeding area, and there is thus little or no pros-

space and time. For species which are involved in human–wildlife

pect that regional cormorant-related conflicts can be solved or

conflicts in the wintering areas (e.g. double-crested cormorants

reduced by management interventions in specific breeding areas.

in North America, various goose species in Europe and North

Conversely, the impact of, for example culling in a specific win-

America), this has important implications for management. Ideally,

tering area will be spread over many breeding populations, and

population management of such species should be based on the

is thus unlikely to lead to localised declines in breeding numbers.

principles of adaptive management, where management decisions

Management of cormorant-related conflicts is thus best carried

are based on available scientific evidence in combination with

out either at the local scale, focusing on reduction of damage, or

clear goals agreed among stakeholders (Rist, Campbell, & Frost,

on the scale of the entire European flyway population. Given the

2013).

complexities involved at the flyway scale and the many opportunities for density-d ependent feedback, the local approach seems most realistic. A number of local approaches have been attempted

AC K N OW L E D G E M E N T S

in various parts of Europe. Shooting to scare cormorants is one

This study was mainly funded by the European Commission (con-

of the most frequently used management tools. Experience so far

tract no. 070307/2013/6577079/ETU/B3). We thank the EURING

shows that the efficiency of this tool is highest if shooting is coor-

Data Bank and the national ringing schemes in Belgium, Croatia,

dinated in time and space and partly directed towards night roosts

Czechia, Estonia, Finland, France, Germany (Vogelwarte Helgoland

(Bregnballe, Hyldgaard, Clausen, & Carss, 2015). However, few at-

and Hiddensee), Italy, Lithuania, Norway, the Netherlands, Poland,

tempts have been made to assess the impact of shooting outside

Russia, Slovenia, Sweden, Switzerland and Ukraine for supplying

the breeding season as a measure to reduce cormorant numbers

ringing and recovery data. We thank Mennobart van Eerden, Stef

locally where cormorants cause damage, or at larger geographical

van Rijn, Stefano Volponi, Jean-Yves Paquet and David Carss for

scales (Chamberlain, Austin, Newson, Johnston, & Burton, 2013;

valuable discussions, numerous people for assisting with popula-

Parrott, McKay, Watola, Bishop, & Langton, 2003). In France,

tion and breeding success data, and Lukas Jenni, Verena Keller,

large-s cale shooting has occurred since 1996 (increasing from 6%

Dan Chamberlain and Rob Robinson for helpful comments. Special

of the total number of cormorants counted in France in mid-winter

thanks to Kjeld T. Pedersen for help in data management, Daniel

to 43% in 2013), with no detectable effect on local numbers of

Clausen for GIS assistance, and Juana Jacobsen for graphics

wintering cormorants in the following winters (Marion, 2012).

design.

Other applied site-specific tools include protecting the fish by use of exclusion techniques, and reducing fish availability by changing fish stock management and/or by restoring natural habitats (see Russell, Broughton, Keller, & Carss, 2012 for a full review). The size and composition of wintering cormorant populations

AU T H O R S ’ C O N T R I B U T I O N S T.B. and M.F. conceived the study; F.K.-N. and M.F. developed the statistical and population models; T.B. collated the data from nu-

will probably continue to be dynamic in the next decades, and calls

merous contributors; L.M. provided winter counts and cull size in

for reducing specific breeding population to limit cormorant-related

France; M.F. analysed the data and led the writing of the manuscript.

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All authors contributed critically to the drafts and gave final approval for publication.

DATA AC C E S S I B I L I T Y Data available from the Dryad Digital Repository https://doi. org/10.5061/dryad.fd1tf (Frederiksen & Bregnballe, 2018).

ORCID Morten Frederiksen

http://orcid.org/0000-0001-5550-0537

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S U P P O R T I N G I N FO R M AT I O N Additional Supporting Information may be found online in the supporting information tab for this article.

How to cite this article: Frederiksen M, Korner-Nievergelt F, Marion L, Bregnballe T. Where do wintering cormorants come from? Long-term changes in the geographical origin of a migratory bird on a continental scale. J Appl Ecol. 2018;00:1–14. https://doi.org/10.1111/1365-2664.13106