Delimitation of Call Types of Common Crossbill (Loxia Curvirostra)

Home , Crossbill, Parrot crossbill, Red crossbill

1 Delimitation of Call Types of Red Crossbill (Loxia curvirostra) in the 2 Western Palearctic

3 Ralph Martin1, Julien Rochefort2, Roger Mundry3, Gernot Segelbacher1 4

5 1 University of Freiburg, Tennenbacher Strasse 4, 79106 Freiburg, Germany; 2 12 rue de

6 la Tourelle, 91600 Savigny-sur-orge, France; 3 Max Planck Institute for Evolutionary

7 Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany

9 Correspondence adress: [email protected]

10 Delimitation of Call Types of Red Crossbill (Loxia curvirostra) in the Western Palearctic

11 1 Abstract 12

13 Vocal signals are important in many animal species for communication, coordination, and

14 pair bonding and are especially well studied in birds. In the Red Crossbill (Loxia

15 curvirostra) calls are an important trait for mate choice. In this species, calls having the

16 same function (e.g., flight calls or excitement calls) are known to be clustered in distinct

17 groups, so called 'call types'. Each individual utters only calls of one call type. The driving

18 force for the differentiation of Red Crossbill call types in the Palearctic remains unknown,

19 as call types often overlap in space, and time and birds can be seen feeding on the same

20 seeds. In this study, we investigated calls of crossbills, recorded within seven years in the

21 Western Palearctic. We found at least 17 distinct call types of Red Crossbill and at least

22 two call types of Parrot Crossbill (Loxia pytyopsittacus). There were obvious differences

23 in call type delimitation between the northern and southern part of the study area. We

24 argue this is in conflict with the ecological differentiation hypothesis and propose that

25 there are other or further driving forces for this differentiation process.

27 2 Keywords 28

29 Red Crossbill; Loxia; curvirostra; pytyopsittacus; Population delimitation; Call types

31 3 Introduction 32

33 Vocal signals are used by many different animals for inter- and intraspecific

34 communication and serve many different purposes such as coordination within groups

35 (Lemures (Ramanankirahina et al., 2016)), group cohesion (Lemures (Braune, Schmidt,

36 & Zimmermann, 2005)), individual recognition (dolphins (Sayigh et al., 1998)), and pair

37 bonding (budgerigars (Hile, Plummer, & Striedter, 2000)). One of the most vocal groups

38 of animals are birds. Their vocalizations are grouped into songs and different kinds of

39 calls (Catchpole & Slater, 2008). Songs are usually complex vocal signals, mainly used

40 by males to establish a territory and to attract females. Calls are usually short and simple

41 structured vocal signals used in various contexts like threat, alarm or flight. The

42 distinction between song and calls is not always unambiguous, as many calls can be more

43 complex, and there seems to be a continuum between the two categories (Catchpole &

44 Slater, 2008). Most vocal signals are thought to be innate in birds, but some vocal signals

45 are known to be learned by members of three bird orders: hummingbirds (Trochilidae),

46 parrots (Psittaciformes) and songbirds (Passeriformes) (Catchpole & Slater, 2008). Birds

47 learn vocalizations during a sensitive period in the first few months of life (closed ended

48 learners, e.g., the White-crowned Sparrow (Zonotrichia leucophrys) (Marler, 1971)) or

49 throughout their lifetime (open ended learners, e.g., the Pine Siskin (Carduelis pinus)

50 (Mundinger, 1979)) with intermediate forms in between those two learning systems.

51 While song learning is well studied (Nottebohm, 1971), (Konishi, 1965), (Thorpe, 1954),

52 call learning has gained less attention because calls have been thought to be mostly innate

53 (Marler, 1963). However Mundinger (1979), compared the calls of social pairs of

54 different finch species and concluded that call learning is widespread within Carduelinae,

55 a subfamily of Finches (Fringillidae).

56 The Red Crossbill (Loxia curvirostra) is one of these call learning Carduelinae species

57 (Sewall, 2011). Crossbills give different calls in different situations and two calls are

58 particularly striking: excitement calls (ECs, sometimes also called ‘toops’ (Nethersole-

59 Thompson, 1975)) and flight calls (FCs). ECs are mostly used when a perched bird is

60 agitated. Flight calls are the most commonly uttered calls and are probably best

61 considered contact calls used both in flight and when perched. The Red Crossbill occurs

62 in coniferous forests (Picea spp., Pinus spp., Larix spp., Tsuga spp. and Pseudotsuga sp.)

63 in the northern Hemisphere (Clement & Christie, 2017). Their occurrence and movements

64 are closely linked to the availability of conifer seeds as their main food (Newton, 1972).

65 South of about 44°N, coniferous forests are dominated by different species of Pine (San-

66 Miguel-Ayanz et al., 2016). There, seed production of many Pine species is fairly even

67 and reliable year to year, such as in Mountain Pine (Pinus mugo uncinata) (Clouet, 2000),

68 Scot’s Pine (Pinus sylvestris) (Harper, 1977), Black Pine (P. nigra) (Tapias et al., 2011;

69 own evaluation of ICP Forests (www.icp-forests.net)) and Aleppo Pine (P. halepensis)

70 (Tapias et al., 2011; own evaluation of ICP Forests (www.icp-forests.net)). Thus, in this

71 region Red Crossbills live and feed in these trees all year round (Senar et al., 1993) being

72 resident or moving only short distances (Alonso et. al., 2016; SEO BirdLife, 2018). North

73 of 44°N the main food resource of Red Crossbills are seeds of spruce (Picea spp.,

74 especially Norway Spruce (P. abies)) and larch (Larix spp.) (Alps (Glutz von Blotzheim

75 & Bauer, 2001), Germany (Thies, 1996), Great Britain (Summers & Broome, 2012),

76 Netherlands (Bijlsma, 1994)). In contrast to many pine species, the number of seeds in

77 spruce and larch varies considerably from year to year (Thies, 1996). Therefore, crossbills

78 in these coniferous forests are forced to leave areas during a cone crop failure and cover

79 long distances when searching for coning trees. They are thought to be nomadic (Perrins

80 & Cramp, 1998).

81 Many bird species, especially those learning calls, show geographical variation in their

82 vocalisations. Close relatives of crossbills with a linkage to a geographical area such as

83 Chaffinch (Fringilla coelebs) (Bergmann et al., 1988), Pine Grosbeak (Pinicola

84 enucleator) (Adkisson, 1981), and Bullfinch (Pyrrhula pyrrhula) (Constantin & The

85 Sound Approach, 2006) display such clear spatial variation in their calls. Other nomadic

86 species, like Siskins (Carduelis spinus and C. pinus), seem to show flock specific calls

87 which birds change when they join a new flock (Mundinger, 1979). Calls of Red

88 Crossbills vary too. Weber (1972) described two ‘call dialects’ of Red Crossbills in the

89 same area in Germany. In fact, FCs and ECs are clustered in distinct groups, the so called

90 ‘call types’, which are recognizable by their distinct spectrograms (Groth, 1993a). The

91 call type of an individual is therefore recognizable by its distinctive FCs as well as its

92 ECs (individuals sharing the same FC type usually also share the same EC type (Summers

93 et. al., 2002)). Individuals probably keep the same call type for their entire life, at least

94 for several years (Keenan & Benkman, 2008). In fact, a change in the call type of an

95 individual has been rarely described, and it is only documented for the FCs (Keenan &

96 Benkman, 2008). Calls are important cues for mate choice of Red Crossbill (Snowberg

97 & Benkman, 2007); hence, birds with different call types show more or less pronounced

98 genetic differences (Parchman et al., 2016). There are ten call types described in Red

99 Crossbill for North America (Groth (1993b), Benkman et al. (2009a) and Irwin (2010)),

100 and seven to nine call types in central and northern Europe (Clouet & Joachim (1996),

101 Robb (2000), Summers et. al. (2002), Edelaar et al. (2004), and Constantine & The Sound

102 Approach (2006)). However, these European studies are mostly restricted to single

103 countries, and a large-scale analysis of crossbill calls is lacking in the Western Palearctic.

104 Actually, within one forest, birds with different call types can often be found breeding

105 next to each other (Robb, 2000) and feeding in the same trees (Summers et al., 2010).

106 This differentiation without obvious spatial or ecological differences in the Western

107 Palearctic makes Red Crossbills a unique study species within birds, raising the question

108 about the drivers for the differentiation process. Edelaar et al. (2012) analysed crossbills

109 from Spain and found differences in morphology, genetics, and vocalizations between

110 crossbills foraging in different tree species and therefore argued, that differentiation of

111 crossbill calls is an effect of isolation by ecology.

112 In our study, we investigated call variation in crossbills throughout the Western

113 Palearctic. We clustered calls in groups, and tried to connect them with the different

114 species of Crossbills and the call types of Red Crossbill which had been published in

115 Europe before. If similar ecological preferences shape call types, we expect to find calls

116 to be clearly clustered in distinct call types all over the study area as result of a possible

117 speciation process. We expect only differences in call type delimitation between northern

118 (nomadic) and southern (resident) birds, if geographic origin promotes call type

119 differentiation due to the different movement patterns.

120

121 4 Material and Methods 122 4.1 Recordings and Classification

123 To analyse variation and number of call types of European Red Crossbill, we collected

124 8,216 recordings across the Western Palearctic (west of 60°E and north of 25°N) recorded

125 between June 2010 and May 2016. The recordings were made in 33 countries (Table S

126 1), and we acquired them from different sources (Table S 2). Crossbills (Loxia spp.) are

127 often hidden high up in the trees, which complicates visual species identification. Thus,

128 we included all recordings of all species of the genus Loxia spp. in this study. We did not

129 discriminate between males and females nor between adult and young birds as no

130 differences in calls have been found between sexes. Similarly, young birds utter the same

131 calls like their parents at an age of several weeks (Groth, 1993a). In adult crossbills, calls

132 typically change only slightly during the mating season to develop pair distinctive calls

133 (‘call matching’; Sewall 2009). For each recording we collected the GPS location and

134 recording date. We estimated the minimum number of calling individuals by looking for

135 temporally overlapping calls within a recording and for distinct frequency or amplitude

136 patterns of the different individuals (Figure S 1; Keenan & Benkman, 2008). We also

137 checked for differences in the distance and position to the microphone, expressed by

138 differences in volume level between calls and between the two channels of the stereo

139 recordings, to distinguish the different individuals. We used the two easily recognizable

140 calls, the FC and the EC for this study. FCs are appropriate for comparative analyses

141 because they are short and their structure is simple (Farnsworth, 2005). Similarly, ECs

142 are short and not very complex in their acoustic structure, and they are important as a

143 second character in the analysis to help classification (Groth, 1993b). We did not use

144 further calls like the quietly uttered ‘chitter calls’ because variation within an individual

145 is larger than in FCs and ECs (Hynes & Miller, 2014). As crossbills songs are complex

146 with about 40 different strophes for each call type (personal observation) we did not use

147 these in our analysis.

148 For our own recordings (n=4,485) we used self-constructed parabolas

149 (avesrares.wordpress.com) in combination with four Primo EM-172 microphone capsules

150 (two for each channel). We used an Olympus LS-3 recorder with a sampling rate of 96

151 kHz / 24 bit or 44 kHz / 16 bit. For the analysis, we converted all recordings to 44 kHz

152 wav-files (16 bit). First, we used only the recordings of crossbills from which FCs as well

153 as ECs of the same individual were available (n = 384 ind.). Thus, we had two

154 independent calls from each individual for the classification. Two crossbill vocalization

155 experts (named experts in the following) categorized FCs and ECs into groups with

156 respect to sound (by listening), amplitude (comparing wavograms (amplitude over time;

157 Bradbury & Vehrencamp, 2011)) and frequency pattern (comparing spectrograms). The

158 wavograms and spectrograms were created with Raven Pro 1.5 (Bioacoustics Research

159 Program, 2014) using a 512-sample discrete Fourier Transform with a 350-sample Kaiser

160 window and a time overlap of 95 %. Second, the two experts assigned the calls from all

161 recorded birds of all recordings (n = 20,707, only FCs or ECs recorded of these birds) to

162 one of the previously identified groups. We counted the number of individuals that could

163 be assigned to each of these groups. To avoid multiple sampling of the same individual,

164 we only counted recordings that were separated from all others of the same call group by

165 at least two kilometres. In case there were two or more recordings from approximately

166 the same location (less than two kilometres distance) and the same call type, we only

167 counted the recordings separately, if more than 100 days elapsed between them (crossbills

168 need about 39-47 days from nest building to the young fledgling (Glutz von Blotzheim &

169 Bauer, 2001)). If there were several recordings of the same call group within this

170 geographical distance (less than 2 km) and time frame (less than 100 days), we used the

171 recording with the largest number of individuals.

172 In order to verify the expert’s call group classification, we presented call spectrograms to

173 volunteers (here named reliability classifiers) for classification (Figure 1). We only

174 showed them the spectrograms, as listening to the calls is not as useful as comparing the

175 spectrograms, as birds have a higher temporal resolution as humans (Dooling et. al.,

176 2002). Also, wavograms are not as valuable as spectrograms as transmission of the

177 different frequencies is highly influenced by distance (Bradbury & Vehrencamp, 2011),

178 habitat, and even recording gear. Spectrograms however are less influenced by these

179 parameters but only by the Doppler-effect, which can shift frequencies in extremes for

180 200 Hz, but the frequency course of the call stays more or less the same.

181 [Figure 1 near here]

182 For the classification of the reliability classifiers, we printed cards with call spectrograms

183 (same spectrogram settings than before) and submitted them to the reliability classifiers.

184 Each card showed the spectrogram of one FC and one EC of a specific single individual.

185 Time axis and frequency axis were fixed (8 kHz / 7.3 cm and 1 sec. / 22.27 cm). To

186 compare the call delimitation between southern and northern birds and to simplify the

187 classification process due to fewer call groups in each data set, we split the data set into

188 a northern (north of 44°N) and a southern data set (south of 44°N), based on changes in

189 the food supply of crossbills (San-Miguel-Ayanz et al., 2016) and therefore likely with

190 their movement pattern. Forty-four degrees is roughly the southern distribution limit of

191 spruce and larch (Figure S 2), which forces crossbills to live nomadic (due to their erratic

192 seed production). The set of spectrograms of the pine dominated region (south of 44°N)

193 consisted of cards from all birds from which we recorded FCs and ECs (n = 61). For the

194 northern set of spectrograms, we randomly selected a maximum of five individuals for

195 each call group determined previously by the two experts – resulting in a final set of n =

196 78 spectrograms (note that not always five individuals were available in each call group).

197 To ensure that call classification into groups was reproducible, both card sets were

198 presented to six reliability classifiers and again to the two experts without any additional

199 information about recording site or date. The eight classifiers were asked to form groups

200 of similar looking calls with these cards by comparing the spectrograms of the FCs and

201 ECs on each card (same method as in Peri (2018)). We counted the number of groups

202 formed by each classifier and compared the prior classification of the experts with the

203 classification that resulted from the sets of cards. We determined the percentage

204 agreement and effective percentage agreement (Jones et. al., 2001) between the

205 classifications made by the experts and reliability classifiers. We accepted a call group as

206 confirmed if it was clustered identically by more than half of the reliability classifiers and

207 experts. After the classification process, we compared spectrograms of the call groups

208 from the northern and the southern data set, to determine wether there are identical calls.

209 As there is no clear definition of ‘call type’, we only named a group of calls a call type if

210 call groups were confirmed by more than the half of the reliability classifiers and experts.

211 It is known that calls can deviate in sick birds and in offspring of pairs of mixed call types

212 (Groth, 1993b), but then, they are only used by a single bird or by members of a single

213 family. To exclude such variation, we only defined a distinct group of calls as a ‘call type’

214 if calls were recorded from at least five birds with a distance of more than 10 km between

215 the recordings.

216

217 4.2 Species identification of the Call Groups

218 In the call group classification process in 4.1, all European species of crossbills (Red

219 Crossbill, Parrot Crossbill, Scottish Crossbill (Loxia scotica), and Two-barred Crossbill

220 (L. leucoptera bifasciata) were taken into account. To confirm species identity of the

221 different call groups and exclude potentially misidentified birds, we counted the number

222 of recordings with individuals, which were identified by morphological features, for each

223 call group. We monitored this counting for unambiguous hints towards one species.

224

225 4.3 Accordance of Call Types / Species of our Data Set with other Publications

226 We also compared our data with already published call types. For this, the two experts

227 compared the call groups classified in 4.1 with published call types of Red Crossbills, as

228 well as calls from the other species of crossbills in Constantine & The Sound Approach

229 (2006), Summers et. al. (2002), and Robb (2000). We did not compare with the call

230 groups of Clouet & Joachim (2015), Förschler & Kalko (2009), and Summers & Jardine

231 (2005) since they either published only FCs (first two) or it was not clear whether the

232 calls were uttered by the same individuals (latter one). The experts compared the calls

233 with respect to frequency pattern (spectrograms with the same settings as in 4.1). They

234 classified the calls into three categories (identical, similar and dissimilar).

235

236 4.4 Acoustic Measurements of Calls

237 To confirm the classification of our data with a cluster analysis, we used a Random Forest

238 algorithm (Shi & Horvath, 2006). Random Forest was already used for different kinds of

239 acoustic clustering (birds (Keen et al., 2014), bats (Armitage & Ober, 2010), and dolphins

240 (Henderson, Hildebrand, & Smith, 2011)) and has proven to work well with robust results

241 (for a comparison of Random Forest with ‘mclust’ and ‘fuzzy clustering’ see

242 supplementary material, 13.1).

243 We used calls of all available crossbills with FCs and ECs recorded of the same individual

244 (61 individuals for the southern study area and 327 individuals for the northern study

245 area), and we also reduced the data set to calls of the same individuals, that we presented

246 to the reliability and expert classifiers in 4.1 (61 individuals for the southern study area

247 and 78 individuals for the northern study area). Calls were imported to the software

248 ‘Luscinia’ (Lachlan, 2016) to extract data points of the fundamental frequency of the

249 calls. For each call, we determined every 0.25 ms the median frequency and the amplitude

250 of the fundamental (Figure S 3 and Figure S 4). In case both parts of the syrinx formed

251 different sounds (temporally overlapping elements of a call, not explainable by a

252 harmonic), we measured them separately. For the statistical analysis, we imported the

253 data into R (R Core Team, 2016). From the derived contour of the fundamentals, we

254 determined 46 different acoustic parameters such as the maximum frequency and its

255 position in the call (Table S 3) for the FCs as well as 23 parameters for the ECs (Table S

256 4). Because five FC parameters and three EC parameters had skewed distributions, we

257 log-transformed them. For all FC and EC parameters, we determined Variance Inflation

258 Factors (VIF; (Field, 2005)), which quantify the extent of redundancy among them, using

259 the package ‘car’ (Fox & Weisberg, 2011). We removed all parameters with a VIF-value

260 greater than 3 (Zuur, Ieno, & Elphick, 2010) from our analysis, leaving 20 FC and 13 EC

261 parameters (Table S 3 & Table S 4). For the clustering with ‘random forest’, we used the

262 following specifications: 100 forests with 4,000 trees (forest and tree numbers were

263 decreased and increased by 50% to check stability), ‘Addcl1’ was used to randomly

264 sample from the product of the empirical marginal distribution of the variables (as

265 recommended by Shi and Horvath (2006)) and the ‘UPGMA-method’ was used for

266 distance calculation.

267 We compared this algorithm-based clustering of all calls with the classifications of the

268 two experts and the six reliability classifiers. Calls were separated into two data sets (north

269 and south of 44°N as in 4.1) to compare the results between both cluster analyses. For the

270 evaluation, the proportion of identical decisions (same / different cluster) of all decisions

271 and the proportion of calls assigned to the same call groups of all calls were counted.

272 11

273 4.5 Call Consistency and Individuality

274 We examined call stability of an individual within a short time frame and also examined

275 individuality of calls from individuals, as the latter has not been investigated in the

276 Palearctic before. We recorded single perched birds as long as possible between June

277 2010 and January 2017. For this analysis, we used only recordings of individuals which

278 could be seen calling during the whole recording. We inspected all calls in the

279 spectrogram for call group stability. To analyse individuality of single birds within a call

280 group, we reduced the data set to the call groups of which we had more than one individual

281 available. We analysed FCs of 14 individuals representing four call groups (recording

282 durations: 54 to 582 sec; mean 217 sec) and ECs of 16 individuals belonging to six call

283 groups (recording durations 78 to 727 sec; mean 378 sec). We randomly extracted ten

284 calls from the whole recording for each individual. These calls were processed with the

285 software ‘Luscinia’, and we extracted and transformed parameters in R as in 4.1. We

286 calculated VIF (see 4.4) and chose the nine parameters (one less than the number of calls

287 for each individual to prevent overfitting) with the smallest VIF. We evaluated the

288 remaining parameters with a Discriminant Function Analysis combined with a

289 permutation test (pDFA; (Mundry & Sommer, 2007) based on the function lda of the R

290 package MASS (Venables & Ripley, 2002)). We then tested for call group stability and

291 for individuality of the calls. We set the number of random selections to 100 (as

292 recommended) and the number of permutations to 1,000. The number of calls, selected

293 for the derivation of the discriminant functions, we set to eight (one less than the number

294 of call parameters).

295 To examine, wether the individuality of the calls can be recognised by a visual inspection

296 of the spectrograms, we printed a spectrogram of each call used in the pDFA (settings as

297 in 4.1) and presented the spectrograms to one of the crossbill vocalization experts. The 12

298 person was advised to detect the ten calls from each individual (most similar calls with

299 respect to frequency pattern) within all calls. We counted the number of calls he assigned

300 to the correct individual to evaluate the results.

301

302 5 Results 303 5.1 Classification of Call Groups

304 Calls north of 44°N

305 We found the calls to be distinctly grouped north of 44°N (Figure 2 & Figure 3), with the

306 grouping of calls easily recognizable by the experts and the reliability classifiers (Table

307 1). Accordance between all classifications was very high as 20 of 21 call groups were

308 confirmed by most of the reliability classifiers and experts (N16 was separated into two

309 groups by most reliability classifiers). 19 of these 20 groups did fulfil the condition of a

310 wider usage of the calls, as described in 4.1. Ninety-eight percent (n = 8,682) of the calls

311 of all recorded birds could be classified to these groups by the experts. Within the

312 remaining 139 calls, two further groups involving 44 calls (group N22 and N23) were

313 defined by the experts. 95 calls (1 %) did not fit in any group. Most of the call groups

314 could be found across several thousand kilometres (Table S 5 and Figure S 5).

315 [Figure 2, 3 near here]

316 [Table 1 near here]

317

318 Calls south of 44°N

319 There was a large discrepancy in call classifications of southern calls: all reliability

320 classifiers and experts reported difficulties to group these calls at all (Figure 4 and Figure

321 5). None of them was totally confident about his or her classification. Neither were the

322 experts able to repeat their prior classification nor were the reliability classifier

323 classifications similar to the expert’s ones (Table 1). Only five call groups (S1, S2, S8,

324 S11 and S14) with a total of 48 individuals (7 % of all birds) were classified identically

325 by the majority of the reliability classifiers and the experts to the initial classification of

326 the experts. S1, S2 and S8 were the only call groups, which fulfilled the other conditions

327 we set for a call type, as described in 4.1. Forty-six percent of southern crossbill calls

328 (338 over a total of 734 ind.) were assigned by the experts to the pre-defined 14 groups

329 (Table S 6; but remember, that delimitation between these groups was not confirmed by

330 the reliability classifiers and experts). Within the 396 calls left, the experts defined seven

331 further groups of calls, however given the variability of the calls, no further conclusions

332 can be made without additional recordings of ECs. 131 calls were assigned to these further

333 groups. The experts recognized six call groups from north of 44°N (Table S 7) by

334 comparing the visual characteristics in the spectrograms. The remaining 265 calls (36 %)

335 were intermediate or did not fit in any group. Geographical distances between calls

336 classified to the same group were typically much smaller than in the north and rarely

337 crossed country boarders (Table S 6 and Figure S 5).

338 [Figure 4 and 5 near here]

339

340 Overlap between northern and southern call groups

341 Six call groups occurred both, north and south of 44°N. Four of these groups, were

342 common in the north but rare in the south (Table S 7; N1/S1, N4/S16, N8/S21 and

343 N11/S18), and two call groups were more common in the south than in the north (Table

344 S 7; N12/S7, N16/S15). Additionally, the latter two occurred further south of 44°N than

345 north (N12/S7: maximum 3.7° south of 44°N but just 1.4° north of 44°N; N16/S15:

346 maximum 6.7° south of 44°N but just 0.4° north of 44°N), while the first four groups

347 occurred by far further north of 44°N than south (2.2° to 6.1° south of 44°N, but 10.5° to

348 24.7° north of 44°N). FCs and ECs of one further group in the south (S4) were classified

349 as very similar to a group in the north (N2). Nevertheless, there were consistent

350 differences between S4 and N2 (see caption of Figure 4).

351

352 5.2 Species Identification of the Call Groups

353 While all well seen birds south of 44°N were identified as Red Crossbill based on their

354 morphology and colouration (however Parrot Crossbills or Scottish Crossbills would be

355 even more difficult to distinguish in this area because of the thicker bills of the local

356 birds), birds north of 44°N could be identified as either Red, Two-barred, Parrot or thick-

357 billed (Scottish / Parrot) Crossbills. There were visual identifications to species level

358 available for individuals of all recorded call groups, except for N18 (individuals not seen

359 well enough) (Table S 8). Hence, most call groups were directly assigned to a single

360 species. However, individuals of the groups N3, N4 and N8 were mainly identified as

361 Red Crossbill and in few cases as Parrot Crossbill.

362 Birds of the group N20 were noted to be ‘thicker billed’ than Red Crossbills but the

363 observer was not sure, whether the birds were Scottish or Parrot Crossbills. All visually

364 identified birds of this group were recorded in Scotland and therefore either species are

365 possible. Within Scandinavia, European Russia, and in northern Europe, we found two

366 distinct groups of calls (N21 and N22) that were identified as Parrot Crossbill by sight on

367 a regular basis. To summarise, call type N1 to N17 and N23 belonged to Red Crossbills,

368 N20 was likely Scottish or Parrot Crossbill, N21 and N22 were Parrot Crossbills, and N19

369 was Two-barred Crossbill.

370

371 5.3 Accordance of Call Types / Species of Data Set with other Publications

372 Comparing the call groups of this study with published call types of Red Crossbill and

373 the remaining three crossbill species in Europe, we could identify two of six call types

374 published in Robb (2000) (Table S 9). Calls from Two-barred Crossbill coincided with

375 our calls found for this species. FCs of Parrot Crossbill in the recordings of Robb (2000)

376 vary a lot with some FCs being similar to N21 and some calls being identical with N22.

377 ECs were identical with N21 (ECs of N22 were not available). We did not compare the

378 calls with the recordings of Scottish Crossbill in this publication, as they were mixed up

379 with Parrot Crossbill calls (Constantin & The Sound Approach, 2006). Compared with

380 Summers et. al. (2002), we could retrieve one of three call types described by them (Table

381 S 10). Results for the other call types and crossbill species were not conclusive. Compared

382 with Constantine & The Sound Approach (2006) we could retrieve four of the seven call

383 types described for Red Crossbill (Table S 11). Calls described for Two-barred Crossbill

384 were identical to the ones found in our study. Results for the other crossbill species were

385 not conclusive.

386

387 5.4 Acoustic Measurements of Calls

388 We applied the random-forest-algorithm to our dataset. The results did not change by de-

389 or increasing tree number and forest number in ‘random forest’ by 50 %. While northern

390 calls showed a good accordance between experts as well as reliability classifiers

391 classification and random forest clustering (Table 2 and Table S 12), the accordance was

392 by far lower in southern areas than north of 44°.

393 [Table 2 near here]

394

395 5.5 Call Consistency and Individuality

396 In a visual inspection of the spectrograms, the experts assigned all ten calls from every

397 individual to just one call group confirming call group stability within short periods of

398 time. In the pDFA, FCs and ECs showed highly significant differences between the call

399 types (both p < 0.001) and individuals (both p < 0.001). Individuality could be also

400 confirmed when visually inspecting the spectrograms. Here the expert assigned 120 of

401 140 FCs (86%) and 121 of 160 ECs (76%) to the correct individual (n=14 and n=16

402 individuals, respectively; for an example, see Figure S 6).

403

404 6 Discussion 405

406 In this study, we analysed calls of crossbills in the Western Palearctic. While former

407 analyses were restricted to smaller areas such as north-western Europe (Robb, 2000),

408 Great Britain (Summers et al., 2002), or Finland (Lindholm, 2011), this study included

409 most of the Western Palearctic. Reliability classifiers, experts, and cluster algorithms

410 clustered the FCs and ECs into groups. We found 16 call types of Red Crossbill north of

411 44°N, one (maybe two) call type south of 44°N, and calls of six call types were found

412 both, north and south of 44°N. Additionally, we found call types in Parrot Crossbill. Call

413 groups were clearly distinct in the north, while the assignment was often unclear in the

414 south.

415

416 6.1 Call Types of Crossbills in the Western Palearctic

417 Of the calls recorded of Red Crossbill north of 44°N, a specific FC always determined a

418 specific EC. No deviations were found to this rule. ECs of N1 and N11 were sometimes

419 hard to distinguish, however there were no intermediate FCs. ECs of N4 and N5 were

420 often similar, too, but in combination with the FCs an identification was always possible.

421 N16 and N18 were the only groups which did not meet the criteria defined for a call type

422 in 4.1. Hence, we found at least 16 (maybe 17 with N23) different call types of Red

423 Crossbill in the north. Contrastingly, call variation was continuous and thus calls more

424 difficult to categorize in the south. S1, S2, and S8 were the only call groups which fitted

425 the conditions set for a call type. S1 was actually a call identical to a call type common

426 in the north (N01), S2 was a call type restricted to Corsica Island. S8 is the only group

427 from the mainland south of 44°N, which was classified similarly by most of the reliability

428 classifiers and experts. However, it was represented in the reliability analysis by a single

429 individual; therefore, its status as a call type remains uncertain. In the Mediterranean, the

430 experts often found calls that were intermediate to the pre-defined groups. This impeded

431 call classification, which is one of the reasons why there were so many unassigned calls.

432 Variation, within the ECs of birds with similar FCs, was actually so extensive that

433 individuals would have been clustered in different call groups when considering FCs or

434 ECs (see for example similarity of EC2 of S7 and EC1 of S8, but not with the other ECs

435 of S7. See also variation within ECs of S10) and vice versa. Therefore, clustering into

436 groups is very uncertain. Also, at a single site a variety of calls including intermediate

437 calls, was often found. But calls which were identical to the calls from a given site were

438 rarely found far apart. To conclude, similarity seemed to decrease with distance. The only

439 exception to this rule was S7 / N12. It was mainly recorded in the Pyrenees, the

440 southernmost occurrence of spruces and larches in south-western Europe (San-Miguel-

441 Ayanz et al., 2016 and Figure S 2). While these birds were clearly assignable to the same

442 call group in the Pyrenees and north of 44°N, there were many more or less similar calls

443 in central Iberia (S8, S10, S12), and as a result, it was considered as a call type north of

444 44°N (N12) while it was not south of 44°N (S7) (S17 might be similar in this respect to

445 S7 / N12, however there were no sufficient data available). The reason for the calls

446 reminding S7 / N12 in central Iberia might be the same, as with many other calls in this

447 region, which resembled call types from populations further in the north-east (for example

448 S15 and N11, or S4 and N2, or S3 and N10): These birds might be leftovers of former

449 invasions of individuals from the North-east, whose calls converged with the local birds

450 via call matching processes (Keenan & Benkman, 2008). In North America, there is a

451 similar situation published for Newfoundland (Hynes & Miller, 2014) and Anticosti

452 Island (Tremblay et. al., 2018); however, only a small percentage of the recorded birds

453 was involved. Summarised, almost all calls from northern birds could be assigned to

454 distinct call types as expected. But in the southern areas, we could assign only a low

455 percentage of birds to distinct call groups and there were many intermediate calls and

456 even nonmatching FC and EC combinations (FCs and ECs which would have been

457 assigned to different call groups) present. This contradicts the expectations we had for an

458 isolation by ecology hypothesis, and therefore we conclude, that isolation by ecology is

459 not the only driving force for call type differentiation within western Palearctic Red

460 Crossbills. Our results suggest that crossbill call types might be associated with a

461 geographical region as proposed by Knox (1992) but spatial and ecological data are

462 needed to analyse this in detail.

463 The only distinct call type (S2) in the Mediterranean was restricted to Corsica Island, and

464 it may be typical for Loxia curvirostra corsicana. Isles promote the evolution of distinct

465 populations (for example L. c. percna on Newfoundland (Benkman, 1989) or different

466 subspecies of Chaffinch (Fringilla coelebs) living on the Canary Islands, Madeira, and

467 Azores (Glutz von Blotzheim & Bauer, 2001)). There might be further call types on the

468 other Mediterranean isles with resident crossbills (Balearics, Sicily, maybe Crete and

469 Cyprus). However, we had only access to recordings from Cyprus for the study period:

470 The calls from birds on Cyprus were very variable in our recordings, many calls also

471 resembled calls from populations further in the north as in Iberia, suggesting strong

472 influence by northern influxes as well. Recordings from the Balearics from Pim Edelaar

473 in 2007 and Stefan Wehr in 2009 suggested also the existence of a call type on the

474 Balearics; however no recordings were available for the study period.

475 Although there was less sampling effort in the south, similar calls from Red Crossbill

476 were obviously restricted to smaller areas, where they were typically very common (it is

477 hardly recognisable in Table S 5 and Table S 6, as we excluded recordings within a radius

478 of 2 km and with less than 100 days elapsed between them (see 4.1)). This difference

479 between northern and southern birds was even more pronounced when we analysed

480 recordings from the whole Palearctic region. We did not find calls belonging to southern

481 call groups anywhere further east. However, we could detect five of the northern call

482 types (N3, N4, N5, N8, and N9) in areas spanning distances of more than 5,000 km

483 (maximal distances between recordings of these five types ranged from 5,410 to 9,945

484 km). The difference in recording distances between individuals belonging to the same call

485 group in northern and southern areas as well as the limited exchange of crossbill call types

486 confirmed the border of 44°N to be a good choice. This result of limited exchange

487 between the north and south is comparable to Arizaga et. al. (2015) who analysed stable

488 isotopes within Iberian birds and also concluded that there was little exchange between

489 northern and southern birds. Furthermore, we found in our study, that this exchange was

490 imbalanced: Northern call types occurred more often in southern areas, and they occurred

491 in far southern areas. On the contrary, the two call groups, which were more common in

492 the south (suggesting a southern origin), occurred just slightly north of 44°N. This

493 supports the notion of northern birds being nomadic and southern birds being more

494 sedentary.

495 When comparing species identifications by morphological features for the different call

496 types, some contradicting results were found for N3, N4, N7, and N8. N3, N4, and N8

497 were identified mostly as Red Crossbill but few times as Parrot Crossbill. Most of these

498 contradicting results shared the fact that several call types were present in the original

499 recordings, including birds of the group N21 (always identified as Parrot Crossbills,

500 measurements also fitting Parrot Crossbills (Buckx in prep.) and distribution range

501 matches the range of Parrot Crossbill (Svensson, Mullarney, & Zetterström, 2012)). This

502 suggests that not all members of the flock had been visually identified, which can explain

503 the contradiction. N7 was typically identified as Red Crossbill but once as Two-barred

504 Crossbill. Its identification was controversially discussed on the basis of photos and sound

505 recordings in the German birding community. It was finally accepted by the German

506 Rarities Committee (DAK) as a Two-barred Crossbill (Deutsche Avifaunistische

507 Kommission, 2015).

508 Within Two-barred Crossbill, we found no call types, however we found two distinct call

509 groups of Parrot Crossbill calls in Scandinavia (but we had no ECs available of the second

510 call group (N22) of Parrot Crossbills). Therefore, there are likely similar drivers for

511 differentiation present in Parrot Crossbill as in Red Crossbill. Call types in Parrot

512 Crossbill were unexpected, because none had been described previously. As in some call

513 types of Red Crossbill, results of the comparison of call of Parrot Crossbills with calls

514 published earlier were not conclusive (however no ECs of N22 were available). But while

515 comparing calls from Parrot Crossbill in Constantine et al. (2006), Summers et. al. (2002),

516 and Robb (2000) we noticed that calls from Parrot Crossbills recorded in Scotland by

517 Constantine et al. (2006) and Summers et. al. (2002) were similar while they differed

518 from calls from Parrot Crossbills recorded in Scandinavia by Robb (2000). These results

519 suggest a further call type of Parrot Crossbill, distributed in Scotland. The only ‘thick-

520 billed’ Crossbills we recorded in Scotland (N20) could not be clearly assigned to species

521 level (Scottish or Parrot Crossbill). FCs of N20 were similar to the FC1 recorded by

522 Summers et. al. (2002) of Red Crossbills, whereas ECs were identical to the ECs recorded

523 by Constantine et al. (2006) and Summers et. al. (2002) of Parrot Crossbills in Scotland.

524 Therefore, results are not conclusive and do not help for species identification.

525 Surprisingly, we found in our recordings more than twice the number of call types

526 compared to Constantine & The Sound Approach (2006), Summers et. al. (2002), and

527 Robb (2000). However, we could only recognize about half of the published call types in

528 our data set. Of the missing published call types, we found extremely similar ECs to

529 almost all of them (Table S 13). However, FCs did not fit in these cases to the missing

530 types. We propose three not mutually exclusive explanations for this result. The first is

531 that FCs of crossbills are much more variable than ECs (perhaps geographical variation

532 of just few calls, as it was reported for Chaffinch (Glutz von Blotzheim & Bauer, 2001)

533 and Pine Grosbeak (Adkisson, 1981) or as shown for different song phrases of White-

534 crowned Sparrow (Nelson, 2017)). Another possibility is that some call types became

535 extinct or very rare and we just found call types with similar ECs by accident. The last

536 explanation is a temporal change of FCs over the years, as it has been described for song

537 in other species like Great Tit (Lehtonen, 1983) or Three-wattled Bellbird (Procnias

538 tricarunculatus) (Kroodsma et al., 2013). In a further study, we shall examine this in more

539 detail.

540 Our last analysis confirmed earlier studies of call group stability of individuals within

541 short periods of time (Sewall (2009), Summers et. al. (2002), and Groth (1993b)). It also

542 shows the individuality of FCs and ECs which enables crossbills to recognize their mates

543 during the breeding season. It would be interesting to get a bigger sample of recordings

544 of individuals before and after mating and especially for the whole lifetime of a bird.

545 Unfortunately, it is very difficult to conduct such studies because of the nomadic life of

546 Red Crossbills. Until today, there has been only one American study that demonstrated

547 call stability across several years (Keenan & Benkman, 2008).

548

549 7 Conclusion 550

551 In our study, we found 17 (maybe 18) call types of Red Crossbill and at least two call

552 types of Parrot Crossbill within the Western Palearctic. FCs and ECs of individuals

553 proved to be stable within the analysed time period with respect to the call type as well

554 as the individuality of calls. Most call types were found in Red Crossbill, but two call

555 types were found in Parrot Crossbill. While we found similar ECs to most of the already

556 published call types and crossbill species, flight calls were commonly dissimilar to the

557 published calls. Without further data, we cannot identify the reasons.

558 In a comparison of calls between northern and southern birds, northern nomadic

559 populations were distinctly clustered in call types, while calls of the southern, resident 23

560 birds were not. Instead, these birds showed continuous variation at single sites and

561 similarity seemed to decrease with distance. This indicates that isolation by ecology is

562 unlikely across the whole study area and further driving forces for call type differentiation

563 need to be explored.

564

565 8 Acknowledgements 566

567 We wish to thank for all the support we encountered from people, who recorded crossbills

568 for us, sent us recordings, or uploaded them to online platforms (see supplementary

569 material, 13.4). Without their extraordinary help and effort, it would have been impossible

570 to realize this project. We want to thank especially Johannes Honold and Magnus Robb

571 for discussions and motivation in the beginning of the project, Severin Hauenstein for

572 discussions about the methodical approach of the study, and the reliability classifiers

573 Balduin Fischer, Johannes Honold, Steve Klasan, Paula Martin, Lukas Pelikan, and

574 Daniela Züfle. We also thank Florent Figon for commenting on the manuscript and the

575 ‘Konrad-Adenauer Stiftung’ for the financial funding of this work.

576

577 9 Declaration of Interest Statement 578

579 Here we declare that we have no conflict of interests.

580

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830

831 11 Tables 832

833 Table 1: Results of the classification of the set of cards of Crossbill calls north of 44°N (n = 78 individuals) and south of 44°N N (n = 61 individuals). Classification is compared to the prior 834 classification of the experts.

Number of groups Percentage agreement of Effective percentage agreement of formed matching decisions (same / matching decisions (same group) [mean (range)] different group) of the initial of the initial experts classification experts classification [mean (range)] [mean (range)] Northern study Experts (n=2) in advance 21 - - area Experts (n=2) set of cards 21 (21) 1.0 (1.0) 1.0 (1.0) (north of 44°) Reliability classifiers (n=6) set of cards 23 (16-27) 0.99 (0.98 – 1.0) 0.74 (0.52 – 0.95) Southern study Experts (n=2) in advance 15 - - area Experts (n=2) set of cards 13 (12-14) 0.85 (0.85) 0.54 (0.49 - 0.59) (north of 44°) Reliability classifiers (n=6) set of cards 26 (13-38) 0.82 (0.70 – 0.86) 0.52 (0.39 – 0.62)

835

836 Table 2: Results of the classification with the random forest algorithm. Only the calls present in the set of cards in the 837 verification process were used.

Origin of Calls Percentage agreement of Effective percentage agreement the calls matching decisions (same / of matching decisions (same different group) of the group) of the algorithm and algorithm and the second second experts’ and reliability experts’ and reliability classifiers’ classification (n = 8) classifiers’ classification (n = [mean (range)] 8) [mean (range)] North FCs 0.81 (0.68 – 0.86) 0.97 (0.95 – 0.98) (n = 78) ECs 0.69 (0.58 – 0.73) 0.95 (0.93 – 0.96) South FCs 0.37 (0.22 – 0.47) 0.80 (0.47 - 0.94) (n = 61) ECs 0.36 (0.24 – 0.49) 0.80 (0.52 – 0.94) 838

839 12 Figures 840

841

842 Figure 1: Schematic chart showing how the data sets were used. One data set contained only recordings of individuals 843 with FCs and ECs recorded, the other data set contained recordings of individuals with either FCs or ECs 844 recorded.

845

846 Figure 2: Spectrograms of the groups of calls N1 to N16 of crossbills north of 44°N. For each group, ECs are shown on 847 the left and FCs on the right. For each call group, ECs and FCs of three different individuals are shown (if 40

848 available in decent quality). Note, that assignment of N16 and N18 could not be confirmed by the reliability 849 and expert classifiers (see 5.1). Recording data: N1, ECs, 1: Miesbach, Germany, 30.01.2014, J. Honold; 2: 850 Kleines Walsertal, Austria, 21.08.2014, R. Martin; 3: Ravensburg, Germany, 27.12.2015, R. Martin; FCs, 1: 851 Kleines Walsertal, Austria, 10.06.2014, J. Honold; 2: Ravensburg, Germany, 27.12.2015, R. Martin; 3: 852 Ravensburg, Germany, 28.12.2015, R. Martin; N2, ECs, 1: Forêt domaniale de Saint-Prix Centre, France, 853 19.11.2014, J. Rochefort, 2: Finiels, France, 29.08.2015, J. Rochefort, 3: Finiels, France, 29.08.2015, J. 854 Rochefort; FCs, 1: Haut-Folin, France, 21.11.2014, J. Rochefort; 2: Klingenbrunn, Germany, 07.02.2015, M. 855 Péron; 3: Finiels, France, 28.08.2015, J. Rochefort; N3, ECs, 1: Morvan Mountains, France, 19.11.2014, J. 856 Rochefort; 2: Rastatt, Germany, 29.11.2014, R. Martin; 3: Tiveden National Park, Sweden, P. Åberg; N3, FCs, 857 1: Yvelines, France, 13.6.2015, J. Rochefort; 2: Mölltorp, Sweden, 28.02.2016, P. Åberg; 3: Dänkritz, 858 Germany, 09.03.2016, R. Martin; N4, FCs, 1: Rambouillet, France, 20.08.2013, J. Rochefort; 2: Mlawa, 859 Poland, 06.01.2014, J. Matusiak; 3: Klingenbrunn, Germany, 01.02.2015, M. Péron; FCs, 1: Kattila, Finland, 860 31.03.2013, A. Lindholm; 2: Senart, France, 17.02.2014, J. Rochefort; 3: Rastatt, Germany, 25.11.2014, R. 861 Martin; N5, ECs, 1: Fontainebleau, France, 28.02.2013, J. Rochefort; 2: Rambouillet, France, 17.04.2014, J. 862 Rochefort; 3: Bad Segeberg, Germany, 20.01.2015, P. Schleef; FCs, 1: Fontainebleau, France, 04.03.2013, J. 863 Rochefort; 2: St. Peter-Ording, Germany, 22.10.2013, P. Schleef; 3: Konstanz, Germany, 09.02.2014, R. 864 Martin; N6, ECs, 1: Harwood Northumberland, Great Britain, 01.02.2012, S. Elliot; 2: Rambouillet, France, 865 13.11.2012, J. Rochefort; 3: Aviemore, Great Britain, 07.12.2014, R. Martin; FCs, 1: Rambouillet, France, 866 21.09.2012, J. Rochefort; 2: Rambouillet, France, 21.09.2012, J. Rochefort; 3: Abernethie, Great Britain, 867 08.12.2014, R. Martin; N7, ECs, 1: Karlsbad, Germany, 14.02.2014, R. Martin; 2: Calw, Germany, 17.10.2014, 868 R. Martin, 3: Biberach, Germany, 05.11.2015, R. Martin; FCs, 1: Schaffhausen, Switzerland, 25.02.2015, R. 869 Martin; 2: Schaffhausen, Switzerland, 25.02.2015, R. Martin; 3: Ravensburg, Germany, 27.12.2015, R. Martin; 870 N8, ECs, 1: Biberach, Germany, 29.12.2015, R. Martin; 2: Dänkritz, Germany, 09.03.2016, R. Martin, 3: 871 Landébia, France, 17.04.2016, J. Rochefort; FCs, 1: Rambouillet, France, 18.05.2014, J. Rochefort; 2: 872 Winterthur, Switzerland, 27.06.2014, B. Keist; 3: Bavarian Forest, Germany, 21.07.2015, R. Martin; N9, ECs, 873 1: Fontainebleau, France, 11.03.2011, J. Rochefort; 2: Fontainebleau, France, 11.03.2011, J. Rochefort; 3: 874 Plöttwitz, Germany, 17.03.2016, R. Martin; FCs, 1: Fontainebleau, France, 01.04.2011, J. Rochefort; 2: 875 Werdau, Germany, 06.01.2016, R.Martin; 3: Plöttwitz, Germany, 17.03.2016, R. Martin; N10, ECs, 1: Morvan 876 Mountains, France, 21.08.2012, B. Dallet; 2: Morvan Mountains, France, 02.12.2013, J. Rochefort; 3: Massif 877 Central, France, 30.08.2014, J. Rochefort; FCs, 1: Forez, France, 30.08.2014, J. Rochefort; 2: Morvan 878 Mountains, France, 21.11.2014, J. Rochefort; 3: Forez, France, 19.01.2016, J. Rochefort; N11, ECs, 1: Tatra, 879 Slovakia, 06.05.2014, R. Martin: 2: Massif central, France, 01.09.2014, J. Rochefort: 3: Finsterau, Germany, 880 08.11.2014, M. Péron; FCs, 1: Klingenbrunn, Germany, 07.02.2015, M. Péron; 2: Jura, France, 11.03.2015, J. 881 Rochefort; 3: Oberstdorf, Germany, 23.03.2015, R. Martin; N12, ECs, 1: Gavarnie, France, 08.08.2014, J. 882 Rochefort; 2: Finiels, France, 31.08.2015, J. Rochefort; 3: Finiels, France, 31.08.2015, J. Rochefort; FCs, 1: 883 Gavarnie, France, 08.08.2014, J. Rochefort; 2: Finiels, France, 28.08.2015, J. Rochefort; 3: Finiels, France, 884 31.08.2015, J. Rochefort; N13, ECs, 1: Rambouillet, France, 31.07.2013, J. Rochefort; 2: Bad Segeberg, 885 Germany, 17.02.2014, P. Schleef; 3: Morvan Mountains, France, 20.11.2014, J. Rochefort; FCs, 1: 886 Rambouillet, France, 31.07.2013, J. Rochefort; 2: St. Peter-Ording, Germany, 25.08.2013, P. Schleef; 3: 887 Gelderland, Netherlands, 15.09.2013, J. Veeken; N14, ECs, 1: Queyras, France, 31.07.2011, J. Rochefort; 2: 888 Queyras, France, 06.08.2011, J. Rochefort; 3: Savoie, France, 22.08.2012, J. Rochefort; FCs, 1: Queyras, 889 France, 31.07.2011, J. Rochefort; 2: Queyras, France, 06.08.2011, J. Rochefort; 3: Saint-Paul-sur-Ubaye, 890 France, 11.07.2015, J. Rochefort; N15, FCs, 1: Rambouillet, France, 06.11.2012, J. Rochefort; 2: Rambouillet, 891 France, 06.11.2012, J. Rochefort; 3: Fontainebleau, France, 04.03.2013, J. Rochefort; FCs, 1: Rambouillet, 892 France, 30.09.2012, J. Rochefort; 2: Fontainebleau, France, 04.03.2013, J. Rochefort; 3: Saint-Cadou, France, 41

893 09.12.2015, J. Rochefort; N16, ECs, 1: Finiels, France, 29.08.2015, J. Rochefort; 2: Lozère, France, 894 31.08.2015, J. Rochefort; 3: Lozère, France, 31.08.2015, J. Rochefort; FCs, 1: Lozère, France, 28.08.2015, J. 895 Rochefort; 2: Lozère, France, 28.08.2015, J. Rochefort; 3: Lozère, France, 29.08.2015, J. Rochefort;

896

897

898 Figure 3: Spectrograms of the groups of calls N17 to N23 of Crossbills north of 44°N. For each group, ECs are shown 899 on the left and FCs on the right. For each call group, ECs and FCs of three different individuals are shown (if 900 available in decent quality). We do not have recordings of ECs of N22 and N23. Recording data: N17, ECs, 1: 901 Forêt du Seuil, France, 07.08.2015, J. Rochefort; 2: Forêt du Seuil, France, 07.08.2015, J. Rochefort; 3: Saint- 902 Pierre-de-Chartreuse, France, 26.09.2015, B. Drillat; FCs, 1: Forêt du Seuil, France, 07.08.2015, J. Rochefort; 903 2: Crolles, France, 08.08.2015, B. Drillat; 3: Saint-Pierre-de-Chartreuse, France, 26.09.2015, B. Drillat; N18, 904 ECs, 1: Werdau, Germany, 05.01.2016, R. Martin; FCs, 1: Werdau, Germany, 05.01.2016, R. Martin; 2: 905 Werdau, Germany, 05.01.2016, R. Martin; N19, ECs, 1: Västergötland, Sweden, 27.08.2011, P. Åberg; 2: 906 Podlaskie Voivodeship, Poland, 26.01.2014, T. Tumiel; 3: Västerås, Sweden, 14.02.2015, M. Litsgård; FCs, 907 1: Västergötland, Sweden, 04.08.2011, P. Åberg; 2: Länsi-Suomen Lääni, Finland, 20.08.2013, A. Lindholm; 908 3: Almnäs, Sweden, 14.02.2015, P. Åberg; N20, ECs, 1: Aviemore, Great Britain, 12.12.2014, R. Martin; 2: 909 Aviemore, Great Britain, 13.12.2014, R. Martin; 3: Aviemore, Great Britain, 13.12.2014, R. Martin; FCs, 1: 910 Abernethie, Great Britain, 08.12.2014, R. Martin; 2: Aviemore, Great Britain, 13.12.2014, R. Martin; 3: 911 Aviemore, Great Britain, 13.12.2014, R. Martin; N21, ECs, 1: Uppsala, Sweden, 09.03.2013, J. Poelstra; 2: 912 Vännäs, Sweden, 21.04.2013, J. Grahn; 3: Uppsala, Sweden, 31.03.2013, J. Poelstra; FCs, 1: Uppsala, Sweden, 913 07.09.2012, J. Poelstra; 2: Uppsala, Sweden, 23.02.2013, J. Poelstra; 3: Arnheim, Netherlands, 25.10.2013, H. 914 van Oosten; N22, FCs, 1: Porkkalanniemi, Finland, 04.09.2010, A. Lindholm; 2: Nauvo, Finland, 23.06.2013, 915 A. Lindholm; 3: Spithami, Estonia, 01.09.2014, A. Lindholm: N23, FCs, 1: Gelderland, Netherlands, 916 22.10.2013, H. van Oosten; 2: Gelderland, Netherlands, 22.10.2013, T. de Boer; 3: Gelderland, Netherlands, 917 25.10.2013, H. van Oosten;

918

919

920 Figure 4: Spectrograms of the groups of calls S1 to 16 of Crossbills south of 44°N. For each group, ECs are shown on 921 the left and FCs on the right. For each call group, ECs and FCs of three different individuals are shown (if 44

922 available in decent quality). We do not have recordings of ECs of S17. Note, that assignment of most groups 923 (except S01, S02, S08, S11 and S14) could not be confirmed by the reliability and expert classifiers (see 5.1). 924 Recording data: S1, ECs, 1: Grosseto, Italy, 08.11.2012, M. Dragonetti; FCs, 1: Grosseto, Italy, 08.11.2012, 925 M. Dragonetti; 2: Grosseto, Italy, 08.11.2012, M. Dragonetti; 3: La Caume, France, 28.05.2013, P. C. 926 Rsmussen; S2, ECs, 1: Corsica, France, 26.09.2014, S. Werner; 2: Corsica, France, 26.09.2014, S. Werner; 3: 927 Corsica, France, 26.09.2014, S. Werner; FCs, 1: Corsica, France, 02.04.2011, T. Linjama; 2: Corsica, France, 928 16.09.2014, J. Honold; 3: Corsica, France, 26.09.2014, S. Werner; S3, ECs, 1: Cyprus, 30.08.2013, M. 929 Feuersenger; 2: Cyprus, 30.08.2013, M. Feuersenger; 3: Cyprus, 30.08.2013, M. Feuersenger; FCs, 1: Cyprus, 930 30.08.2013, M. Feuersenger; 2: Cyprus, 26.05.2016, H.-H. Bergmann; S4, ECs: Very similar to N2, however 931 ECs of S4 show in the second half of the call a more pronounced decreasing part of the call than N2. 1: Sierra 932 Gredos, Spain, 10.02.2015, R. Martin; 2: Sierra Gredos, Spain, 10.02.2015, R. Martin; 3: Sierra Gredos, Spain, 933 11.02.2015, R. Martin; FCs: Very similar to N2, however the starting frequency of the first rising part of the 934 FCs of S4 is lower than in N2. There is also a more pronounced kink in the decreasing, second part of the call 935 in N2. S4 typcially shows a small additional element right above the highest frequency of the main element of 936 the call. 1: Sierra Gredos, Spain, 13.01.2015, R. Martin; 2: Sierra Gredos, Spain, 10.02.15, R. Martin; 3: Sierra 937 Gredos, Spain, 11.02.2015, R. Martin; S5, ECs, 1: Hoyos del Espino, Spain, 14.01.2015, R. Martin; 2: Hoyos 938 del Espino, Spain, 14.01.2015, R. Martin; 3: Hoyos del Espino, Spain, 14.01.2015, R. Martin; FCS, 1: Hoyos 939 del Espino, Spain, 14.01.2015, R. Martin; 2: Hoyos del Espino, Spain, 13.01.2015, R. Martin; 3: Hoyos del 940 Espino, Spain, 13.01.2015, R. Martin; S6, ECs, 1: Toufliht, Morocco, 19.02.2014, R. Martin; 2: Oukaimeden, 941 Morocco, 11.03.2014, R. Martin; 3: Oukaimeden, Morocco, 11.03.2014, R. Martin; FCs, 1: Issoumar, 942 Morocco, 19.02.2014, R. Martin; 2: Oukaimeden, Morocco, 11.03.2014, R. Martin; 3: Oukaimeden, Morocco, 943 11.03.2014, R. Martin; S7, ECs, 1: Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 2: Espot, Spain, 944 26.01.2015, R. Martin; 3: Sant Marti de Tous, Spain, 29.01.2015, R. Martin; FCs, 1: Sant Marti de Tous, Spain, 945 29.01.2015, R. Martin; 2: Ripoll, Spain, 29.01.2015, R. Martin; 3: Fiscal, Spain, 25.01.2015, R. Martin; S8, 946 ECs, 1: Espot, Spain, 26.01.2015, R. Martin; FCs, 1: Espot, Spain, 26.01.2015, R. Martin; 2: Sierra de 947 Guadarrama, Spain, 12.01.2015, R. Martin; 3: Espot, Spain, 26.01.2015, R. Martin; S9, ECs, 1: Sierra de 948 Guadarrama, Spain, 12.01.2015, R. Martin; 2: Ripoll, Spain, 29.01.2015, R. Martin; 3: Ezcaray, Spain, 949 18.01.2015; FCs, 1: Sierra de Guadarrama, Spain, 12.01.2015, R. Martin; 2: Ripoll, Spain, 29.01.2015, R. 950 Martin; 3: Ezcaray, Spain, 18.01.2015; S10, ECs, 1: Ezcaray, Spain, 18.01.2015, R. Martin; 2: Sierra de 951 Guadarrama, Spain, 13.01.2015, R. Martin; 3: Valencia, Spain, 02.02.2015, R. Martin; FCs, 1: Sierra de 952 Gredos, Spain, 11.02.2015, R. Martin; 2: Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 3: Valencia, 953 Spain, 02.02.2015, R. Martin; S11, ECs, 1: Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 2: Sierra de 954 Guadarrama, Spain, 13.01.2015, R. Martin; FCs, 1: Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 2: 955 Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 3: Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 956 S12, ECs, 1: Sierra de Gredos, Spain, 11.02.2015, R. Martin; 2: Sierra de Guadarrama, Spain, 13.01.2015, R. 957 Martin; 3: Ojacastro, Spain, 18.01.2015, R. Martin; FCs, 1: Sierra de Gredos, Spain, 11.02.2015, R. Martin; 2: 958 Sierra de Guadarrama, Spain, 13.01.2015, R. Martin; 3: Ojacastro, Spain, 18.01.2015, R. Martin; S13, ECs, 1: 959 Sierra de Gredos, Spain, 15.01.2015, R. Martin; 2: Sierra de Gredos, Spain, 15.01.2015, R. Martin; 3: Sierra 960 de Gredos, Spain, 15.01.2015, R. Martin; FCs, 1: Sierra de Gredos, Spain, 11.02.2015, R. Martin; 2: Sierra de 961 Guadarrama, Spain, 12.01.2015, R. Martin; 3: Sierra de Gredos, Spain, 15.01.2015, R. Martin; S14, ECs, 1: 962 Ezcaray, Spain, 18.01.2015, R. Martin; FCs, 1: Ezcaray, Spain, 18.01.2015, R. Martin; 2: Santurde de Rioja, 963 Spain, 18.01.2015, R. Martin; S15, ECs, 1: Valsain, Spain, 12.01.2015, R. Martin; 2: Valsain, Spain, 964 13.01.2015, R. Martin; FCs, 1: Valsain, Spain, 12.01.2015, R. Martin; 2: Valsain, Spain, 13.01.2015, R. 965 Martin; 3: Valsain, Spain, 12.01.2015, R. Martin; S16, FCs, 1: Gavarnie, France, 08.08.2014, J. Rochefort;

966 45

967

968

969 Figure 5: Spectrograms of the groups of calls S17 to N21 of Crossbills south of 44°N. For each group, ECs are shown on 970 the left and FCs on the right. For each call group, ECs and FCs of three different individuals are shown (if 971 available in decent quality). We do not have recordings of ECs of S16 to S21. Note, that without excitement 972 calls recorded, we could not finally conclude about distinctness of the shown call groups. Recording data: S17, 973 FCs, 1: Borjomi-Kharagauli National Park, Georgia, 11.06.2015, B. Fischer; 2: Borjomi-Kharagauli National 974 Park, Georgia, 11.06.2015, B. Fischer; 3: Borjomi-Kharagauli National Park, Georgia, 11.06.2015, B. Fischer; 975 S18, FCs, 1: Stribugliano, Italy, 08.11.2012, M. Dragonetti; 2: Durmitor National Park, Montenegro, 976 14.09.2014, R. Martin; 3: Corsica, France, 01.10.2014, S. Werner; S19, FCs, 1: Rilski manastir, Bulgaria, 977 02.06.2014, B. Jahnke; 2: Rilomanastirska gora, Bulgaria, 20.08.2014, L. Pelikan; 3: Durmitor National Park, 978 Montenegro, 15.09.2014, R. Martin; S20, FCs, 1: Kirka, Turkey, 15.05.2016, M. Reimann; 2: Alanyurt, 979 Turkey, 30.05.2016, M. Reimann; 3: Alanyurt, Turkey, 30.05.2016, M. Reimann; S21, FCs, 1: Córdoba, Spain, 980 11.09.2013, J. Grahn;

981

982 13 Supplementary material 983 13.1 Comparison of three cluster analysis

984 We compared the performance of three cluster algorithms on a test data set. As a test data set,

985 we used the established data set from the CD of Robb (2000), which was already used in a

986 similar study (Tanttu et. al., 2006). We removed two tracks labelled as Red Crossbill as they

987 were likely uttered by a Parrot Crossbill (Loxia pytyopsittacus) (Magnus Robb, personal

988 communication). We searched for temporally overlapping calls and individual frequency

989 patterns within the recordings as described in 4.1, to determine the number of birds within the

990 recordings. We extracted three calls of each individual if possible (n=146 extracted calls from

991 49 individuals). Calls were then imported to the software ‘Luscinia’ (Lachlan, 2016). For each

992 call, we determined every 0.25 ms the median frequency and the amplitude of the fundamental.

993 In case both parts of the syrinx formed different sounds (temporally overlapping elements of a

994 call, not explainable by a harmonic), we measured them separately. We imported the data to R

995 (R Core Team, 2016). From the derived contour of the fundamental, we determined 46 different

996 acoustic parameters such as the maximum frequency and its position in the call for the FCs

997 (Table S 3) as well as 23 parameters for the ECs (Table S 4). Because five FC parameters and

998 three EC parameters had skewed distributions, we log-transformed them. For all FC and EC

999 parameters, we determined Variance Inflation Factors (VIF; (Field, 2005)), which quantify the

1000 extent of the multicollinearity, using the package ‘car’ (Fox & Weisberg, 2011). We removed

1001 all parameters with a VIF-value greater than 3 (Zuur et al., 2010) from our analysis, leaving 20

1002 FC and 13 EC parameters (Table S 3 and Table S 4).

1003 We applied three different methods of unsupervised clustering to the remaining parameters.

1004 These methods have been used in bioacoustics analysis: a model based clustering provided by

1005 ‘mclust’ (Fraley et. al., 2012), a fuzzy clustering provided by ‘fanny’ of the ‘cluster’-package

1006 (Maechler et. al., 2016), and a tree based algorithm provided by Random Forest (Shi & Horvath,

1007 2006). ‘Mclust’ fits statistical models, consisting of finite Gaussian distributions, to the data.

1008 Within Mclust, there are ten different models available and they are fitted with an expectation-

1009 maximization algorithm. Mclust automatically uses the best fitting model for clustering. It was

1010 used for acoustical analysis of African Elephants (Loxodonta africana) (Wood et. al., 2005).

1011 ‘Fanny’ is a type of fuzzy clustering (Zadeh, 1965). It is an algorithm designed to describe

1012 systems with not strictly separated categories (Wadewitz et al., 2015). Every observation can

1013 have a partial membership to each cluster (Kaufman & Rousseeuw, 2008), therefore allowing

1014 imperfect membership. It was used to describe the graded structure of a vocal repertoire of

1015 chacma baboons (Papio ursinus) (Wadewitz et al., 2015). Random Forest constructs a specified

1016 number of trees using different bootstrap samples of the data (Breiman, 2001). Moreover, the

1017 nodes are split using the best predictor among a subset randomly chosen at that node (Liaw &

1018 Wiener, 2002). We used Random Forest for hierarchical clustering because the hierarchical

1019 structure is typically more informative than a fixed output of clusters (Große Ruse et. al., 2016).

1020 Random Forest was already used for different kinds of acoustic clustering (birds (Keen et al.,

1021 2014), bats (Armitage & Ober, 2010), and dolphins (Henderson et al., 2011)) and has proven

1022 to work well and with robust results.

1023 For the fuzzy clustering with ‘fanny’, we used the following specifications: Euclidean distance

1024 (cf. Groth (1988) for calculating dissimilarities between observations), 4.000 iterations

1025 (iterations were decreased and increased by 50% to check stability) and a membership exponent

1026 of 1.5 for a smooth membership degree (Klawonn & Höppner, 2003). For the clustering with

1027 ‘random forest’, we used the following specifications: 100 forests with 4,000 trees (forest and

1028 tree numbers were decreased and increased by 50% to check stability), Addcl1 was used to

1029 randomly sample from the product of the empirical marginal distribution of the variables (as

1030 recommended by Shi and Horvath (2006)) and the UPGMA-method was used for distance

1031 calculation. We forced all three cluster analyses to form 7 clusters, which corresponds to six

1032 call types (named Type A to F), as defined in Robb (2000), and one additional cluster because

1033 our experts had defined another call group within FC and EC recordings of Robb (2000) (Table

1034 S 9)). For the evaluation, the proportion of identical decisions (same / different cluster) of all

1035 decisions and the proportion of calls assigned to the same call groups of all calls were counted

1036 (Table S 14).

1037 13.2 Additional tables

1038 Table S 1: Number of recordings available from the different countries

Country No. of recordings Austria 146 Belgium 41 Bulgaria 5 Croatia 17 Cyprus 8 Czech 58 Republic Denmark 8 Estonia 9 Finland 118 France 2545 Georgia 12 Germany 2451 Great 138 Britain Greece 1 Hungary 2 Iceland 1 Italy 16 Montenegro 5 Morocco 46 Netherlands 826 Norway 19 Poland 125 Portugal 1 Romania 4 Russia 26 Slovakia 42 Slovenia 15 Spain 796 Sweden 540 Swiss 185 Tunisia 3 Turkey 13 Ukraine 3 1039

1040 Table S 2: Sources of recordings and numbers of recordings we used from them.

Source Number of used recordings www.avocet.zoology.msu.edu 2 www.club300.de 2 www.ornitho.de 15 www.ornitho.ch 6 www.otus-bayern.de 12 www.waarnemingen.nl / 803 www.waarnemingen.bl / www.observation.org www.xeno-canto.org 577 1041

1042 Table S 3: List of parameters, used for the analyses of the FCs.

Name of the Parameter Description Value for the example in Figure S 3 Used for Log-trans- evaluation formed No. of elements No. of separate elements of the call 2 (red and blue element) x Intercept We fitted a weighted regression line in the spectrogram of 3823 Hz the call, using the data points of the median frequency we had for all elements for every 0.25 ms. Therefore we set time to zero in the centre of the call. For weighting, we used the value of the amplitude of each data point.

Slope Slope of the weighted regression line of the previous 4.0 parameter

SD.Residuals Standard deviation of the residuals from the weighted 998 Hz x regression line of the previous parameters

Call.length Duration of the call in ms 53 ms x x

Freq.Median Median frequency at which half of the energy of the call 3768 Hz was uttered

Freq.Quantile.25P Frequency at which 25 % of the energy of the call was 3570 Hz uttered

Freq.Quantile.75P Frequency at which75 % of the energy of the call was 3977 Hz uttered

Freq.range.80P Frequency difference between the 10% and the 90% 765 Hz x quantile

Time.Quantile.25P 25 % of the energy of the call was uttered at this moment -14 ms

Time.Quantile.75P 75 % of the energy of the call was uttered at this moment 8 ms

Time.range.80P Time difference between the 10% and the 90% quantile 27 ms x

Slope.1 Same parameter as Slope, but using only the first half of 74 x the call SD.Residuals.1 Same parameter as SD.Residuals, but using only the first 419 Hz x half of the call Freq.Median.1 Same parameter as Freq.Median, but using only the first 3739 Hz half of the call Freq.Quantile.25P.1 Same parameter as Freq.Quantille.25P, but using only the 3569 Hz x first half of the call Freq.Quantile.75P.1 Same parameter as Freq.Quantille.75P, but using only the 3943 Hz first half of the call Freq.range.80P.1 Same parameter as Freq.range.80P, but using only the 691 Hz x first half of the call Time.Quantile.25P.1 Same parameter as Time.Quantille.25P, but using only -16 ms the first half of the call Time.Quantile.75P.1 Same parameter as Time.Quantille.75P, but using only -11 ms the first half of the call Time.range.80P.1 Same parameter as Time.range.80P, but using only the 10 ms x first half of the call Slope.2 Same parameter as Slope, but using only the second half -58 x of the call SD.Residuals.2 Same parameter as SD.Residuals, but using only the 848 Hz second half of the call

Freq.Median.2 Same parameter as Freq.Median, but using only the 3805 Hz second half of the call Freq.Quantile.25P.2 Same parameter as Freq.Quantille.25P, but using only the 3579 Hz x second half of the call Freq.Quantile.75P.2 Same parameter as Freq.Quantille.75P, but using only the 4042 Hz second half of the call Freq.range.80P.2 Same parameter as Freq.range.80P, but using only the 952 Hz x second half of the call Time.Quantile.25P.2 Same parameter as Time.Quantille.25P, but using only 8 ms x x the second half of the call Time.Quantile.75P.2 Same parameter as Time.Quantille.75P, but using only 11 ms the second half of the call Time.range.80P.2 Same parameter as Time.range.80P, but using only the 9 ms x second half of the call Neg.chang.sign We fitted a weighted regression line to the spectrogram 1 x for two consecutive 4.5 ms periods, using as data points the median frequency every 0.25 ms. For weighting, we used the value of the amplitude of each data point. We compared the slope of both elements, checked whether there was a switch from a positive to a negative slope value. If there was, we counted it as a negative slope switch. Then we moved further for 2.25 ms and fitted again a weighted regression for the next two consecutive 4.5 ms periods. The value is the number of negative slope switches. Pos.chang.sign Same parameter as previous but we counted slope 1 x switches from a negative to a positive slope value Call.length.MainEL Duration of the main element (the element with the 48 ms longest duration) of the call

Max.Freq.Main.EL Maximum frequency of the main element of the call 5076 Hz

Max.Freq.MainEL.1/2 Ratio of the maximum frequency of the first half of the 0.97 main element to the second half of the main element

Pos.Max.Freq.Main.EL Relative time when the maximum frequency of the main 0.53 element was reached

Pos.Max.Ampl.Main.EL Relative time when the maximum amplitude of the main 0.27 x element was reached

Overlap.F.FC Frequency range covered by both elements of the call. If 2193 Hz there was no overlap or no additional element, it was set to 0

FC.Rat.Overlap.F.Main.EL Ratio of the whole frequency range of the main element, 0.69 which overlaps with the additional element. If there was no additional element or if there was no overlap, it was set to 0

Overlap.T.FC Time period for which the main element and the 31 ms additional element were uttered at the same time. If there is no additional element or if there was no overlap, it was set to 0

FC.Rat.Overlap.T.Main.EL Ratio of the duration of the main element which overlaps 0.65 with the additional element. If there was no additional element or if there was no overlap, it was set to 0

Dist.Freq The average difference between the mean frequencies of 1299 Hz x the elements (determined every 2 ms) for the temporal

overlap period of the elements. If there was just one element, we set the value to 0

Slope.Dist.Freq We fitted a regression line for the frequency differences 18 x (see previous parameter) and determined its slope. If there was only one element in the call present, we set the value to 0

S1.S2 Slope.1 / Slope.2 -1.3 x

Freq.range.1.2 Freq.range.1 / Freq.range.2 0.73 x x Freq.Median.1.2 Freq.Median.1 / Freq.Median.2 0.98 1043

1044 Table S 4: List of parameters, used for the analyses of the ECs.

Name of the Parameter Description Value for the example in Figure S 4 Used for Log-trans- evaluation formed Intercept See Table S 3 2698 Hz Slope See Table S 3 4.2 Call.length See Table S 3 66.6 ms Freq.Median See Table S 3 2830 Hz X Freq.Quantile.90P 90 % of the energy of the call was uttered 2886 Hz

Freq.range.80P Frequency difference between the 10% and the 90% 833 Hz quantile

Freq.range.50P Frequency difference between the 25% and the 75% 133 Hz X quantile

Time.Quantile.90P 90 % of the energy of the call was uttered at this time 11.1 X period

Time.range.80P See Table S 3 22.9 Slope.1 See Table S 3 15.9 X Freq.range.80P.1 See Table S 3 839 Hz Slope.2 See Table S 3 -22.4 X Freq.range.80P.2 See Table S 3 825 Hz P.Overlap.T.EC Ratio of the duration of the main element which 0.97 overlaps with the additional element. If there was no additional element or if there was no overlap, it was set to 0.

P.Overlap.F.EC Ratio of the overlap between the main and the additional 0.27 element and the frequency range of the main element. If there was no additional element or if there was no overlap, it was set to 0.

Overlap.T.EC Time period for which the main element and the 62.9 ms x x additional element were uttered at the same time. If there was no additional element or if there was no overlap, it was set to 0.

Overlap.F.EC Frequency range for which the main element and the 74.5 Hz x additional element overlaps. If there was no additional element or if there was no overlap, it was set to 0. 64

R.u.t.l.band Duration of upper call element divided by the duration 1.01 x of the lower call element

Freq.Median.1.Freq.Median.2 Freq.Median.1 / Freq.Median.2 0.99 x

ECs.Dist.Freq See Table S 3 549 Hz x Ecs.Slope.Dist.Freq See Table S 3 -2.5 X S1.S2 Slope.1 / Slope.2 -0.7 x Freq.range.1.2.ECs Freq.range.1 / Freq.range.2 1.02 x x 1045

1046 Table S 5: Number of the recorded individuals of the different groups of calls classified by the two experts north of 44°N 1047 and maximum geographical and temporal distance between the recordings. Italic are the call groups that have 1048 been also found south of 44°N (Table S 7). The last column lists the countries in which they were recorded. AT 1049 = Austria, BE = Belgium, CH = Switzerland, CZ = Czech Republic, DE = Germany, DK = Denmark, EE = 1050 Estonia, FI = Finland, FR = France, GB = Great Britain, HR = Croatia, HU = Hungary, IS = Iceland, IT = Italy, 1051 NL = Netherlands, NO = Norway, PL = Poland, RO = Romania, RU = Russia, SE = Sweden, SI = Slovenia, 1052 SK = Slovakia, UA = Ukraine

Grou Number of Number of Maximal Maximal Countries with p individuals individuals distance time lag individuals recorded with FCs and with FCs or between the between the (ISO 3166 ALPHA-2) N1 ECs recorded27 ECs recorded374 recordings1301 recordings2155 AT, CH, CZ, DE, FR, [km] [days] IT, NL, SI N2 4 126 1366 675 AT, BE, CH, CZ, DE, FR, HR, NL, SI, SK N3 18 565 3533 2154 AT, BE, CH, DE, EE, FI, FR, GB, NL, PL, RU, SE, SK, UA N4 48 2646 4167 2184 AT, BE, CH, CZ, DE, DK, EE, FI, FR, GB, IS, IT, NL, NO, PL, RU, SE, SK N5 9 167 2637 1931 AT, CZ, DE, DK, FI, FR, GB, NL, NO, PL, SE N6 7 113 1270 1565 BE, DE, FR, GB, NL N7 60 888 2722 2163 AT, BE, CH, CZ, DE, FI, FR, NL N8 38 1094 3631 2182 AT, BE, CH, CZ, DE, DK, EE, FI, FR, GB, HR, HU, NL, NO, PL, RU, SE, SI, SK N9 4 27 1937 2035 DE, FI, FR, NL N10 21 1000 960 1177 FR, DE N11 33 918 2187 2102 AT, CH, CZ, DE, FR, HR, HU, IT, NL, PL, RO, SK, SI N12 5 76 210 26 FR N13 4 73 2714 1249 DE, EE, FI, FR, NL, NO, PL, SE N14 2 65 539 1708 FR N15 3 9 631 1206 FR N16 3 25 25 3 FR N17 1 18 21 349 FR N18 1 2 0 0 DE N19 22 62 3228 1558 DE, DK, FI, GB, NL, NO, PL, RU, SE N20 3 17 83 300 GB N21 14 278 2150 1865 BE, DE, EE, FI, GB, NL, NO, PL, SE N22 0 30 1519 1822 EE, FI, NL, SE 66

N23 0 14 215 1595 BE, NL Unass 0 95 2757 1717 BE, CZ, EE, FI, CH, igned DE, FR, GB, NL, SI Total 327 8682 1053

1054 Table S 6: Number of the recorded individuals of the different groups of calls classified by the two experts south of 44°N 1055 and maximum geographical and temporal distance between them. Italic are the call groups that have been also 1056 found north of 44°N (Table S 7). The last column lists the countries in which they were recorded. BG = 1057 Bulgaria, CY = Cyprus, ES = Espania, FR = France, GE = Georgia, GR = Greece, IT = Italy, MA = Morocco, 1058 ME = Montenegro, PT = Portugal, TN = Tunisia, TR = Turkey

Group Number of Number of Maximal Maximal Countries with recorded individuals individuals distance time lag individuals (ISO 3166 with FCs with FCs or between the between ALPHA-2) S1 and1 ECs ECs6 recordings545 687the FR (Corsica), IT S2 recorded1 recorded21 [km]75 recordings1278 FR (Corsica) [days] S3 1 9 7 189 CY S4 4 23 484 1352 ES, PT S5 5 6 6 28 ES S6 5 28 1929 1396 MA, TN S7 3 100 659 1319 ES, FR S8 1 9 467 14 ES S9 6 0 543 17 ES S10 23 50 813 1177 ES S11 2 1 0 0 ES S12 1 2 118 29 ES S13 3 21 10 30 ES S14 1 5 5 0 ES S15 0 8 113 2 ES S16 0 2 506 187 FR, ES S17 0 84 160 267 GE S18 0 14 1556 1258 FR, IT, ME S19 0 5 368 105 BG, ME S20 0 13 275 763 TR S21 0 5 479 143 BG, GR, ME Unassigned 0 265 3410 1550 BG, CY, ES, FR, IT, ME, TR Total 57 677 1059

1060 Table S 7: Groups of crossbills recorded both, north and south of 44°N. Number of recorded individuals of the different 1061 groups in the north and south and maximum geographical and temporal distance between the recordings.

Nort Sout Nr. of Nr. of individuals Maximal Maximal Southern- and hern hern individuals recorded south of distance time lag northernmost grou grou recorded north of 44°N and between the between recorded p p 44°N and percentage of all recordings the latitude name name percentage of all southern birds [km] recordings [south / north] northern birds [n / %] [days] [n / %] N1 S1 401 / 4.5 7 / 1.0 1437 2155 41.8 / 54.5 N4 S16 2694 / 29.9 2 / 0.3 4872 2184 40.3 / 68.7 N8 S21 1132 / 12.6 5 / 0.7 3631 2182 37.9 / 68.1 N11 S18 951 / 10.6 14 / 1.9 1063 1533 41.8 / 54.9 N12 S7 81 / 0.9 103 / 13.9 2187 2102 40.3 / 45.4 N16 S15 28 / 0.3 8 / 1.1 761 230 37.3 / 44.4

1062

1063 Table S 8: Number of recordings and individuals per group of calls from northern crossbills in which birds were identified 1064 by colour and structure. ‘Thick-billed’-Crossbills were reported from Scotland, as the observer was not sure, 1065 whether the birds belonged to Scottish or Parrot Crossbill.

Red Crossbill Parrot Crossbill ‘thick-billed’- Two-barred Crossbill Crossbill No. of No. of No. of No. of Group recordings birds recordings birds recordings birds recordings birds N1 49 182 N2 10 20 N3 56 89 6 8 N4 154 506 8 12 N5 12 15 N6 8 28 N7 48 101 1 1 N8 97 190 2 2 N9 5 5 N10 21 112 N11 64 151 N12 13 31 N13 4 20 N14 5 16 N15 3 4 N16 5 8 N17 3 10 N18 N19 18 29 N20 7 15 N21 36 89 N22 7 12 N23 2 3 1066

1067 Table S 9: Similarity of calls published in Robb (2000) (Red Crossbill Type A to F, Two-barred Crossbill and Parrot Crossbill) with the groups of calls in this analysis. RC = Red Crossbill, TBC 1068 = Two-barred Crossbill, PC = Parrot Crossbill. ‘S’ means similar, ‘I’ means identical, empty cell means dissimilar. Values in brackets refers to some of the example calls given in Robb 1069 (2000).

CC CC CC CC CC CC CC TBC PC

Type A Type B Type B Type C Type D Type E Type F (Scandinavia)

FC EC FC EC FC EC FC EC FC EC FC EC FC EC FC EC FC EC (Track (Track 16d) 17d) N1 / S1 S S S N2 I N3 I S N4 / S16 S I N6 S N7 S I N10 S N11 / S18 S N13 S I N15 S N17 S N19 I I N21 (S) I N22 (I) - 1070 71

1071 Table S 10: Similarity of calls published in Summers et. al. (2002) with the groups of calls in this analysis. The different 1072 flight calls were named with numbers, the excitement calls were named with letters. A call type therefore is 1073 named by a combination of a number and a letter. The combination ‘3 C’ is Scottish Crossbill and ‘2 D’ is 1074 Parrot Crossbill (Summers et al., 2002). According to Edelaar, Summers, & Iovchenko (2003) the call 1075 combination ‘1 A’ is equal to Type E in Table S 9 (Robb, 2000), ‘4 E’ to Type C, ‘2 B’ to Type A. ‘S’ means 1076 similar, ‘I’ means identical, empty cell means dissimilar, ‘-‘ means no calls available. The recording of the FCs 1077 of Parrot Crossbills seemed to contain two call types of Parrot Crossbill with some birds being similar to N21 1078 and some birds being identical with N22.

FC EC 1 2 3 4 A B C D E N2 S S N3 I N4 / S16 S I N6 S S N8 / S21 I N10 S N20 S I 1079

1080 Table S 11: Similarity of calls published in Constantine et al. (2006) with the groups of calls in this analysis. TBC = Two-barred Crossbill, PC = Parrot Crossbill, ScoC = Scottish Crossbill. ‘S’ 1081 means similar, ‘I’ means identical, empty cell means dissimilar. The recording of the flight calls from Type E is the same as that published in Robb (2000).

Type A Type B Type C Type D Type E Type F Type X TBC PC ScoC (Scotland) FC EC FC EC FC EC FC EC FC EC FC EC FC EC FC EC FC EC FC EC N1 / S1 S I N2 S N3 I S N4 / S16 I I N6 I N7 S I N8 / S21 S I N13 S I N19 I I N20 I S3 S 1082

1083 Table S 12: Results of the classification of our own dataset with the random forest algorithm. All available calls were 1084 classified and compared with the initial classification of the experts.

Origin of Calls Percentage agreement of matching Effective percentage agreement of

the calls decisions (same / different group) of matching decisions (same group) of

the initial experts classification the initial experts classification

North FCs 0.82 0.97 (n = 327) ECs 0.70 0.96

South FCs 0.35 0.83 (n = 61) ECs 0.38 0.83 1085

1086 Table S 13: Number and percentage of published call types that were assessed in 5.2 as similar / identical to already 1087 published call types (see Table S 9 - Table S 11).

ECs FCs

Identical Similar Not Identical Similar Not similar

similar

[n] [%] [n] [%] [n] [%] [n] [%] [n] [%] [n] [%]

Robb (2000) 7 78 2 22 0 0 3 33 4 44 2 22 Summers et al. (2002) 3 60 2 40 0 0 0 0 3 75 1 25 Constantine et al. 9 100 0 0 0 0 2 22 4 44 3 33

(2006) Mean 6. 79. 1. 20. 0.0 0.0 1. 18. 3. 54. 2.0 26.

1088 3 3 3 7 7 5 7 6 9

1089

1090 Table S 14: Results of the classification of the three different clustering algorithms of Red Crossbill calls in comparison to 1091 expert classification. The data set was taken from Robb (2000).

Call Clustering Method Percentage agreement of Effective percentage Type matching decisions (same / agreement of matching different group) of experts decisions (same group) of classification experts classification FCs Fanny 0.82 0.41 (n = 101) Mclust 0.87 0.62 Random forest 0.93 0.90 ECs Fanny 0.89 0.61 (n = 45) Mclust 0.88 0.68 Random forest 0.90 0.69 1092

1093 13.3 Additional figures

1094

1095 Figure S 1: Spectrogram showing a recording with estimated FCs of five individuals (designated by numbers) belonging to four call types (designated by different colouration). Frequency pattern 1096 separates the different call types. The different individuals of the same call type (bird two and three) are recognizable by temporal overlap of the calls (for example at 0.05s) and by 1097 distinct frequency pattern (bird two with an additional vertical element above the part with the highest frequency) / direction to the microphone (bird two is in the focus of the parabola 1098 (same volume level in both channels) while bird three is further to the left (less volume on the right channel) or something between the microphone and the bird prevents sound 1099 propagation on the right channel). 77

1100

1101 Figure S 2: Map of the distribution of Picea spp. and Larix spp. within the study area. Distribution of P. abies, P. omorica, P. sitchensis and L. decidua following San-Miguel-Ayanz et al. (2016), 1102 distribution of P. obovata and L. sibirica following EUFORGEN (2009), distribution of L. sibirica following AgroAtlas (2008), overlap zone of P. obovata and P. abies following 1103 Andersson (2005) and distribution of P. orientalis following the American Conifer Society (2017). Map made with Natural Earth (2018).

1104

1105 Figure S 3: FC of a bird belonging to N1. On the left side is the spectrogram, the red line shows the median frequency of the main element, the blue line the median frequency of the additional 1106 element as exported by Luscinia. In the middle are the wavograms of the call (amplitude over time), for the main element (red) – the element with the longest duration – as well as the 1107 additional element (blue) and both combined (black). On the right is the power spectrum, the amplitude over the frequency for both elements combined.

1108

1109 Figure S 4: EC of a bird belonging to N3. On the left side is the spectrogram, with the red line showing median frequency of the ‘main element’ (lowest fundamental frequency), and the blue line 1110 showing median frequency of the additional element (upper fundamental frequency) as exported by Luscinia. In the middle are the wavograms of the call (amplitude over time), for the 1111 main element (red) – the element with the lowest frequency on average – as well as the additional element (blue) and both combined (black). On the right, there is the amplitude over 1112 the frequency for both elements combined.

1113

1114

1115 Figure S 5: Map of the distribution of the five most common call groups in this study (coloured dots) from north of 44°N and south of 44° N. If markers covered each other, we slightly shifted the 1116 points to retain visibility. The shaded green background shows the distribution of Red Crossbill (BirdLife International and Handbook of the Birds of the World, 2017). Map made with 1117 Natural Earth (2018). 81

1118

1119 Figure S 6: Spectrograms of ten calls of four different birds belonging to N7 recorded over an extended period of time. 1120 Each line shows a different individual. The upper two rows show FCs, the lower two lines show ECs. FCs of 1121 individual one were recorded for 110 seconds, FCs of individual two were recorded for 582 seconds. ECs of 1122 individual three were recorded for 259 seconds, ECs of individual four were recorded for 93 seconds. 1123 Differences in FCs between the different individuals are quite obvious, with a short decreasing part in the 1124 beginning of the call in the second individual and an interrupted additional element in the second half of the 1125 call of the same individual. Differences between ECs are less pronounced. ECs of the fourth individual are a 1126 little bit shorter with a weak rising part in the beginning and a weak decreasing part in the end. The first and 1127 the second element typically touch each other in the end of the call of the fourth individual, while they typically 1128 do not in the third individual.

1129 13.4 Number of Recordings of each Recordist

1130 A.-W. Faber (2), A. Ahtiainen (1), A. Burbidge (7), A. Dalton (1), A. Knychala (1), A.

1131 Lastukhin (25), A. Lees (1), A. Lindholm (102), A. Neu (1), A. Overweg (3), A. Postma

1132 (2), A. Schaftenaar (4), A. Strand (2), A. van den Berg (1), A. van Lubeck (1), A. van

1133 Reenen (1), A.Schaftenaar (1), B. Bousquet (3), B. Dallet (11), B. Drillat (44), B. Fischer

1134 (18), B. Gickel (1), B. Gnep (1), B. Gras (5), B. Haamberg (5), B. Jahnke (2), B. Keist

1135 (28), B. Klavr (1), B. Kok (2), 1B. Muis (1), B. Piot (8), B. Saadi-Varchmin (7), B. Steffen

1136 (5), B. ter Keurs (4), B. van de Meulengraaf (6), B. Verhoeven (1), B. Vogels (2), Birds

1137 of Pool Harbour (1), C. Batty (3), C. Groenewegen (1), C. Klein (4), C. Mroczko (4), C.

1138 Struijk (3), C. Timmers (1), C. van der Zanden (1), C. van Tuijl (1), C. Zuyderduyn (1),

1139 D. Beuker (2), D. Drukker (6), D. Eykemans (2), D. Groenendijk (23), D. Honold (75),

1140 D. Kok (6), D. Meijer (1), D. Meinen (1), D. Metsemakers (1), D. Mulder (1), D.

1141 Pennington (4), D. Preston (1), D. Šere (2), E. A. Ryberg (2), E. de Bruin (1), E. de Weerd

1142 (1), E. Ebels (1), E. Eggenkamp (4), E. Goutbeek (24), E. Klunder (4), E. Meijer (3), E.

1143 Paljakka (4), E. Reinstra (1), E. Scheinpflug (3), E. Schoppers (3), E. van Dijk (5), F.

1144 Anger (13), F. de Boer (2), F. Hidvegi (1), F. Holzapfel (56), F. Hoorn (1), F. J. Hoogstra

1145 (2), F. Lambert (2), F. Meijer (8), F. Neijts (4), F. Roos (11), F. Wichmann (2),

1146 F.Wichmann (8), G.-J. Pontenagel (1), G. Bakker (1), G. Catley (4), G. Colembie (1), G.

1147 Gelling (1), G. Kenter (2), G. O. Jeijl (1), G. Speranza (1), G. Strang (3), G. Tanis (3), G.

1148 van Duin (54), G. Wichers (1), H.-H. Bergmann (4), H.-J. van der Kolk (1), H. Bakkenes

1149 (1), H. Damen (1), H. Derks (4), H. Groot (3), H. Matheve (1), H. Noordkampf (1), H.

1150 Schekkerman (4), H. ter Haar (3), H. van der Berg (1), H. van der Borg (1), H. van der

1151 Meer (1), H. van Oosten (16), H. Wieleman (3), I. Kondratjew (1), I. Röhl (1), I. Rösler

1152 (1), I. Weiß (2), J.-O. Kriegs (2), J.-S. Rousseau-Piot (2), J. Betleja (1), J. Borst (3), J.

1153 Bosma (6), J. Breidenbach (24), J. Buckens (4), J. Buddemeier (1), J. Buhl (33), J. C.

1154 Sires (5), J. Calvet (3), J. de Gans (2), J. de Jong (7), J. Dierschke (1), J. Dijkhuizen (3),

1155 J. Fischer (12), J. Grahn (338), J. Halbauer (254), J. Hartog (1), J. Heeremans (1), J.

1156 Honkala (1), J. Honold (293), J. Jacobsen (2), J. Jansen (124), J. Jordaans (1), J. Kirkeby

1157 (1), J. Koster (4), J. Lotz (1), J. Matusiak (25), J. Poelstra (81), J. Rochefort (2416), J.

1158 Roeland (2), J. Rommens (1), J. Roos (9), J. Sohler (10), J. van Bruggen (8), J. van de

1159 Westeringh (2), J. van Deijk (4), 6J. van Dillen-Staal (1), J. van Erp (4), J. van Laerhoven

1160 (1), J. van t Bosch (1), J. Veeken (24), J. Walhout (1), K.-D. Moormann (1), K. Bramt

1161 (2), K. Deoniziak (9), K. Hendricks (1), K. Hendriks (1), K. Kuijper (1), L. A. Hansen

1162 (4), L. A. M. Benner (1), L. Bregman (1), L. Buckx (9), L. Buscà (1), L. Pelikan (15), L.

1163 Ramos (1), L. Thomas (5), L. Thomson (1), M. Anderson (1), M. Badack (1), M. Bongers

1164 (3), M. Bonte (24), M. Braun (6), M. Bull (8), M. Dijksterhuis (7), M. Dragonetti (12),

1165 M. Feuersenger (2), M. Förschler (5), M. Heincz (2), M. Hemprich (33), M. Hoffmann

1166 (4), M. Hoogteijling (1), M. Hornsveld (1), M. Jansen (18), M. Jonker (3), M. Koster (7),

1167 M. Kuschereitz (1), M. Litsgård (1), M. Loeve (3), M. Oehler (3), M. Olthoff (2), M.

1168 Peron (1), M. Péron (196), M. Rahm (1), M. Reimann (70), M. Schweitzer (1), M.

1169 Seehausen (9), M. Slaymaker (3), M. Sluijter (1), M. Tenhaeff (1), M. van de Wouw (1),

1170 M. van der Velde (1), M. van Oss (23), M. Verbeek (2), M. Vogels (1), M. Wielstra (5),

1171 N. Agster (4), N. Goulem (1), N. Krabbe (4), N. Uhlhaas (2), N. van Pelt (1), O. Juhnke

1172 (9), Vogelaars (2), P.-G. Gelderblom (16), P. Åberg (105), P. Boesman (4), P. C.

1173 Rasmussen (1), P. Cox (1), P. de Nobel (1), P. de Rouw (13), P. Franke (64), P. Gnodde

1174 (3), P. Gorissen (2), P. Heuts (1), P. Knolle (1), P. Kuijper (1), P. Nicolau (1), P. Parkko

1175 (1), P. Pirinen (1), P. Schleef (252), P. Spierenburg (1), P. Szczypsinski (72), P. van de

1176 Haar (1), P. van Sanden (1), P. Verbelen (1), P. Verhelst (1), P. Wolf (1), P. Zeller (10),

1177 P.G. Gelderblom (3), R. Altenburg (5), R. Brouwer (2), R. Dunn (9), R. Ekman (1), R.

1178 Jousma (7), R. Kima (9), R. Martin (2069), R. Mikusek (5), R. Offereins (2), R. Oving

1179 (5), R. Schols (1), R. Schonewille (1), R. Schwartz (9), R. Smabers (1), R. van Bemmelen

1180 (17), R. van Beusekom (4), R. Winters (2), 66S. Baeten (1), S. Bot (14), S. Cooleman (2),

1181 S. Elliot (9), S. Gobin (28), S. Hietkamp (1), S. Klasan (99), S. Lagerveld (1), S.

1182 Molværsmyr (1), S. Ø. Nilsen (4), 76S. Rieck (2), S. Schagen (3), S. Schilperoort (2), S.

1183 Scholten (1), S. Tautz (1), S. Valkenburg (4), S. Wehr (6), S. Werner (148), Sonnenburg

1184 (1), T. de Boer (10), 286T. Fijen (19), T. Jansen (1), T. Johansson (1), T. Kolaas (4), T.

1185 Linjama (5), T. Luijendijk (1), T. Luiten (3), T. Luther (13), T. Lüthi (3), T. Rocke (3),

1186 96T. Roeke (13), T. Sacher (13), T. Sirotkin (2), T. Tumiel (14), T. van Oerle (4), T.

1187 Visbeek (1), T. Walachowski (1), T. Wulf (41), Timos S. (1), U. Paal (5), 06V. Arnold

1188 (1), V. de Lenne (4), V. Serge van Bergen (1), W. Bakker (10), W. Hermus (1), 11W.

1189 Knap (2), W. Ramaekers (1), W. Stehen (2), W. Vergoossen (1), Z. Hinchcliffe (1).