Acoustic species identification of shrews: Twittering calls for monitoring

Ecological Informatics 27 (2015) 1–10

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Acoustic species identification of shrews: Twittering calls for monitoring Sándor Zsebők a,b,c,⁎, Dávid Czabán a, János Farkas a, Björn M. Siemers c,1, Sophie von Merten c,d a

Department of Systematic Zoology and Ecology, Eötvös Loránd University, H-1117 Budapest, Pázmány Peter sétány 1/c, Hungary Université Paris-Sud, Centre de Neurosciences Paris-Sud, UMR 8195, Orsay, France c Sensory Ecology Group, Max Planck Institute for Ornithology, Eberhard-Gwinner-Straße, 82319 Seewiesen, Germany d Department for Systematic Zoology, Adam Mickewicz University, ul. Umultowska 89, 61-614 Poznan, Poland b

a r t i c l e

i n f o

Article history: Received 12 November 2014 Received in revised form 7 February 2015 Accepted 8 February 2015 Available online 14 February 2015 Keywords: Twittering sound Passive acoustic monitoring Acoustic species discrimination Support Vector Machine Shrew

a b s t r a c t The acoustic signals of shrews (Soricidae) are largely understudied. As shrews are very vocal animals it may be feasible to use acoustic methods in field studies to assess ecological and behavioral data. In this study, we present the first detailed analysis of the twittering calls of six Central European shrew species (Sorex minutus, Sorex araneus, Neomys fodiens, Neomys anomalus, Crocidura russula and Crocidura leucodon). The analysis is based on over 6000 recorded calls from 121 individuals. Our results indicate that there is a large inter-individual variance and a large inter-specific overlap in the acoustic parameters of the calls. Each species uses a large spectral variety of calls without clear species specific call types. A species identification using the Support Vector Machine method on six species shows 66.2% accuracy; however, a pairwise comparison indicates accuracy between 68.5 and 97.3%. We propose to use acoustic monitoring of shrews in comparative studies to estimate the overall shrew activity. Moreover we suggest using the acoustic identification method in areas with few shrew species where the accuracy of the technique can be eligible. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Assessing biodiversity and behavioral data with the help of acoustic methods is an important data collecting technique that can support environmental research and protection of nature (Digby et al., 2013; Laiolo, 2010). Acoustic survey is often the only possible method for obtaining information about nocturnal or aquatic animals that are hidden from our eyes (Ahlen and Baagoe, 1999; Hastings and Au, 2012). Further, it can be especially profitable when we intend to study the animals' activity without causing disturbances (Blumstein et al., 2011; Depraetere et al., 2012). Two main acoustic survey techniques can be applied: active and passive. Active acoustic techniques are widespread in aquatic environment using sonar instruments to estimate the density of animals and identify them based on the echoes reflecting from them (Bosch et al., 2013). Passive acoustic techniques record the sounds coming from the animals with hydrophone or microphone systems (Marques et al., 2013; Sousa-Lima et al., 2013). In many studies, researchers record the acoustic signals (calls, songs) that animals produce for communication, navigation and prey detection. By attaching active speakers on the animals, even the activity of non-vocal species can be followed (Thums et al., 2013; Werry et al., 2014). In passive acoustic survey, the identification ⁎ Corresponding author at: Eötvös Loránd University, Department of Systematic Zoology and Ecology, Pázmány Péter sétány 1/c, H-1117 Budapest, Hungary. E-mail address: [email protected] (S. Zsebők). 1 Deceased.

http://dx.doi.org/10.1016/j.ecoinf.2015.02.002 1574-9541/© 2015 Elsevier B.V. All rights reserved.

of species or higher taxa is usually essential. In the last few years many automated systems have been developed for the identification of insect (Ganchev and Potamitis, 2007), anuran (Dorcas et al., 2010), bird (Acevedo et al., 2009), bat (Armitage and Ober, 2010) and cetacean species (Andre et al., 2011). One considerably large group of mammals, hidden from human sight, are shrews (family: Soricidae) with 385 species (Wilson and Reeder, 2011). Shrew species are endangered and protected in many countries; hence it is essential to follow the population changes of shrew communities and obtain sufficient information about their biology. Shrews can also serve as bio-indicators for sustainable forest management (Pearce and Venier, 2005). Moreover, studying shrews' activity provides information about the effect of urbanization (Chernousova, 2010) as well as the evolutionary processes and co-existence of different species (Rychlik, 2005). Numerous techniques for the assessment of shrew population density and structure are known; however most of them cause a significant disturbance not only of the shrews, but of the entire community of small, ground-dwelling mammals in the studied area. Box live-traps (Anjum et al., 2006; Flowerdew et al., 2004; Kalinin and Shchipanov, 2003; Lima et al., 2002; Rychlik, 2005), as well as pitfall traps (Nicolas et al., 2009), are commonly used to follow the long term changes in size and structure of shrew populations in different habitats. Most of these trapping techniques require continuous surveillance because the traps must be checked frequently to prevent high-metabolic shrews from starving. However, despite every precaution, shrews can die of stress when confined in a trap (Churchfield, 1990). Several

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non-invasive studies based on collected scat (Churchfield et al., 2000) or hair samples (Pocock and Bell, 2011) from baited tubes or analysis of owl pellets (Tosh et al., 2008) provide distributional data on a larger scale; however, these techniques provide only poor time-resolution data. To our knowledge, no study has yet been published that utilizes a passive acoustic method for surveying shrews. Such a technique could provide data with a better resolution in time and space than trapping or sample collection, while causing considerably less disturbance. Unfortunately, we know very little about the shrews' acoustic behavior. Taking into account the number of species in Soricidae, we believe that from an acoustic viewpoint, they are one of the least studied mammals. The number of acoustic related shrew publications in the last 50 years is around 30, a number that is surprisingly low given the number of papers about primates (N800), bats (N 2000), dolphins and whales (N 1300) listed under this topic (ISI Web of Knowledge with the keywords “vocalization”, “acoustic” and “echolocation”, in December 2013). In shrews, many different call types have been described. Churchfield (1990) distinguished 12 types of acoustic signals that shrews produce in general; Schneiderova (2014) identified 7 tonal and 10 non-tonal call types in Asian house shrews (Suncus murinus). These sounds are related mainly to exploration, aggressive encounter situations, mother–young communication, collective resting, and courtship behavior. The duration of calls ranges from milliseconds to seconds. The minimum frequency lies around some hundreds of Hz for most species; however the maximum frequency shows large differences among species and depends on whether the respective study found ultrasonic calls in shrews (Buchler, 1976; Forsman and Malmquist, 1988; Gould et al., 1964) or not (Catania et al., 2008; Grunwald, 1969; Irwin and Baxter, 1980; Siemers et al., 2009). The signals used for acoustic survey should be produced by the studied animals frequently enough to make the recording of most individuals in the observed area possible with a high probability. The echolocation calls of bats are an example of such regularly produced calls. In shrews, the so-called twittering calls, which are produced regularly in their exploration behavior (Churchfield, 1990) can serve this purpose. Thus, in this study we focus on shrew twittering calls. Twittering calls are low intensity, short (0.01–0.2 s), sonic laryngeal sounds (Churchfield, 1990). Some authors (Churchfield, 1990; Köhler, 1998; Siemers et al., 2009) provide general information about the twittering call parameters, but up to now there is no detailed comparative acoustic description of twittering call characteristics. Our study's first objective is to provide the first quantitative and comparative analysis of shrew twittering calls. For this, we studied the twittering calls of six shrew species from three genera in Central Europe: Sorex minutus, S. araneus, Neomys fodiens, N. anomalus, Crocidura russula and C. leucodon. We search for the main call characteristics and parameters in which the species differ. The second objective of our study is to investigate how the species differences in twittering calls might be used for acoustic species identification and how it can be applied in field studies. The species identification in our study is conducted with the Support Vector Machine (SVM) method based on the averaged acoustic parameters in a cross-validation framework. The advantage of SVM over the widely-used Discriminant Function Analysis (DFA) (e.g. Azzolin et al., 2014; Britzke et al., 2011; Furey et al., 2009; Xia et al., 2012) is that no special assumptions regarding the distribution of data have to be met (Rexstad et al., 1990). Moreover, in methodological comparative studies, SVM performed better (Acevedo et al., 2009; Armitage and Ober, 2010; Redgwell et al., 2009). The SVM method is applied in many fields in visual and acoustic studies (e.g. Prashanth et al., 2014; Tanaka and Campbell, 2014), and has already been successfully used in acoustic species identification of bats (Armitage and Ober, 2010; Redgwell et al., 2009), birds (Cheng et al., 2010; Fagerlund, 2007) and amphibians (Acevedo et al., 2009; Huang et al., 2009).

2. Materials and methods 2.1. Animals and sound recording Data were collected in Hungary and Germany. In Hungary, shrews were caught in Kis-Balaton and Borsodi-Mezőség areas, from March to October between 2007 and 2009 (licenses by the Hungarian National Inspectorate for Environment, Nature and Water: 195-4/2007, 6541-1/ 2009). In Germany, shrews were caught in the area surrounding the Max Planck Institute for Ornithology in Seewiesen, and along the river Würm in Gauting, between April 2008 and November 2009 (license by Regierung von Oberbayern: 55.1-8642-8-2007), as well as around Tübingen (license by Regierungspräsidium Tübingen: 56-2/8852.15 and 56-6/8852.15). All shrews were caught with well-tested live traps that were equipped with meat or mealworms as bait and provisioning. Trap checking intervals were short (maximal three hours) to minimize stress on shrews. Pregnant or lactating females were released immediately. We additionally note, that in Hungary, 10 trapping sessions were performed in 4819 trap nights (number of traps multiplied by number of nights) with checking the traps every four hours. During this period 67 N. anomalus (1 dead), 19 N. fodiens (0 dead), 107 S. araneus (13 dead), 38 S. minutus (11 dead) and 26 C. leucodon (2 dead) were captured. This level of trap mortality is unfortunately common when trapping shrews. We recorded the calls of 20 Neomys anomalus, 17 N. fodiens, 44 Sorex araneus, 10 S. minutus, 9 Crocidura russula and 21 C. leucodon in total (Table 1). During sound recording, animals were held in plastic or glass cages (size between 22 × 40 cm and 40 × 50 cm, height between 30 and 40 cm), that were put a non-echoic room to isolate them from external environmental noise. The cages were equipped with soil and some hay as litter. After completion of the sound recording and individual marking (by tattooing in Hungary and bleaching of a small patch of fur in Germany) all shrews were released at the site of capture. We used 3 different recording systems: (1) microphone EMY-62N4 (EKULIT, Germany; frequency response 20–18,000 Hz, ± 3 dB) with TCD-D100 DAT recorder (SONY, Japan; 16 bit depth, sampling rate 48 kHz) in Kis-Balaton area; (2) microphone 26HH (G.R.A.S., Holte, Denmark; flat frequency response up to 20 kHz) with external A/D converter (Department of Animal Physiology, University of Tübingen, Germany; 16 bit depth, sampling rate 256 kHz) in Seewiesen; and (3) custom made ultrasonic condenser microphone (frequency response ± 3 dB between 4 and 20 kHz) with custom A/D converter (both from the Department of Animal Physiology, University of Tübingen; 16 bit depth, sampling rate 480 kHz) in Tübingen. The recordings were made from above at a distance of about 30 cm above the animals. We found no ultrasound component in the calls. We thus converted all the recordings down to a sampling rate of 48 kHz, resulting in the same recording quality for the following analyses. 2.2. Acoustic analysis In total, 11,797 good quality calls (clearly distinguishable from background noise) from 121 individuals were collected. 50 calls per individual were selected randomly for statistical analysis resulting in 6050 calls in total. Calls were measured on spectrograms using Table 1 Number of recorded animals in the two countries for each species. Species

Hungary

Germany

Neomys anomalus Neomys fodiens Sorex araneus Sorex minutus Crocidura leucodon Crocidura russula

20 17 28 3 13 0

0 0 16 7 8 9

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256-point Fast Fourier Transforms (FFT) with a 97% overlapping Hanning window, giving a frequency resolution of 187.5 Hz. We determined the start and the end of each call at those points where the intensity of the call was 20 dB higher than the background noise based on the spectrogram. As temporal parameters, we measured the duration of the calls (Tcall) and the time interval between the start of 2 consecutive calls (inter-call interval, Tint). For frequency analysis, we measured the peak frequency of each call at 5 points equally distributed along the first harmonic of the call (see M1–M5 measuring points in Fig. 1): at the start (F1), the end (F5) and 3 intermediate points (F2, F3, F4). Preliminary inspection of the calls' spectrograms indicated that 5 points were sufficient to track the time course of frequencies in the calls. From the measured frequency points we calculated 11 frequency parameters. We derived the mean (Fmean = (ΣFi)/5), maximum (Fmax = max(Fi)) and minimum (Fmin = min(Fi)) frequencies (where i goes from 1 to 5), and the frequency bandwidth (Fband = Fmax − Fmin). We also calculated the frequency differences: ΔFj = Fj + 1 − Fj (where j goes from 1 to 4). From these four frequency differences, three more parameters were derived that describe the shape of the call: the “slope” (Fslope = Σ(ΔFj)), the “waviness” (Fwaviness = Σabs(ΔFj) where j goes from 1 to 4) and the “v-shape” (Fv-shape = −ΔF1 − ΔF2 + ΔF3 + ΔF4). Fslope is positive for calls that are upward modulated in frequency and negative for downward modulated calls. Fwaviness is high in calls with strong frequency modulations. Fv-shape is positive for calls that have a v-shape and negative for calls that have an inverse v-shape. We used these 13 parameters (2 temporal and 11 frequency parameters) in the further statistical analyses and the classification procedure. Call sequences were segmented and calls were measured by the same person, with the help of self-written scripts in the MATLAB environment (R2008b, The MathWorks Inc.). 2.3. Statistical analysis For the descriptive statistics of the calls, we used the classical (Tcall, Tint, Fmean, Fmax, Fmin, Fband) and the shape related (ΔF1–4, Fslope, Fwaviness, Fv-shape) variables, all in all 13 parameters. First, we calculated the mean of the call parameters in each individual, and then we computed the mean, standard deviation and range for each shrew species based on the individuals' average data (Table 2). To avoid redundancy and keep the statistical power of the comparative analysis high, we inspected the correlation structure of the variables, and then chose five representative non-highly correlating variables (r ≤ 0.34) to use in the further analysis (Tcall, Tint, Fmean, Fslope, Fwaviness). The comparative analysis was twofold. First, we tested whether the species differ from each other based on the individually

Fig. 1. Example of a twittering call with the five measuring points (M1–M5) superimposed. The five measuring points are evenly distributed along the twittering call at the strongest frequency in each time point on the first harmonic.

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averaged call parameters. We assumed this analysis would reveal the differences between hypothetical species specific calls. Second, we tested the differences between species based on the standard deviation calculated for each individual. We assumed using this analysis we would be able to compare the variability of calls, where a higher standard deviation might indicate large call variability in the individual. Because the assumptions of the one-way analysis of variance (ANOVA) were not met for any call parameter, we used the Kruskal– Wallis test to test the differences between species. In cases of significant results, we used Tukey-HSD post-hoc test for multiple comparisons of species. To consider a possible effect of having samples from two countries, we also conducted the Kruskal–Wallis test mentioned above after separating the Hungarian and German samples in those two species that we had recorded in both countries, namely S. araneus and C. leucodon. In S. araneus (with sample sizes 28 and 16), we didn't find significant differences in any of the variables between the samples from the two countries. In C. leucodon only one temporal variable, namely Tint, proved to be significantly different in the samples from the two countries. However, the low sample sizes (13 and 8) don't provide a result robust enough to confidently interpret this finding. Considering these results, we decided to conduct all statistical analyses combining the samples from both countries. For inspecting the multivariate relatedness between species, we conducted a hierarchical classification based on all 13 acoustic parameters. First, we conducted a Principal Component Analysis (PCA), and then chose the first three principal components (explaining more than 95% of the variance) as new variables. Second, we calculated the mean values for each species and then computed the Euclidean distance between species. Last, we hierarchically clustered the species using average linkage function in an agglomerate procedure (Unweighted Pair Group Method with Arithmetic Mean, UPGMA) and presented the results in a dendrogram. All statistical computations were conducted in MATLAB Statistics Toolbox 7.0 (MathWorks). 2.4. Species identification We conducted three classification calculations to show the possibilities for species discrimination of twittering calls. In the first calculation, we used the data of all six species in one classification procedure to mimic a situation where all species are present (six-species classification). In the second calculation, we used all possible combinations of pairs of species and conducted the analysis for each of the twospecies situations (pairwise classification). In the third calculation, we conducted the classification separately for the two countries, using only data of those species with sample sizes equal or higher than seven (i.e. N. anomalus, N. fodiens, S. araneus, C. leucodon in the Hungarian species classification, and S. minutus, S. araneus, C. leucodon and C. russula in the German classification). In all three calculations, the procedure of the teaching-testing process was the same and is described as follows. In the first step, we prepared the set of data with the mean values and the standard deviation of all 13 call parameters for all individuals. This procedure resulted in 26 parameters describing each individual. We used both the means and standard deviations in the classification because the comparative analysis revealed species specific differences in both measurements. Based on these 26 call parameters, we conducted a 10-fold cross-validation procedure (Hsu et al., 2000), in the classification process, in which we divided the data randomly into a training dataset (0.9 of the data) and a testing dataset (0.1 of the data) 10 times. In each round, we first reduced the data dimensions using Principal Component Analysis (PCA) and keeping the first 10 principal components (responsible for more than 95% of variance) in the process. As the sample sizes (number of individuals) differed between species, we used oversampling (Japkowicz, 2000) to make the training dataset

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Table 2 Descriptive statistics of the twittering call parameters. Each column represents a given species. Each parameter has two rows: the mean ± standard deviation in the first row and the minimum and maximum values in brackets in the second row. For explanation of call parameters see Materials and Methods section Acoustic analysis (Section 2.2).

Tcall (ms) Tint (s) Fmin (kHz) Fmax (kHz) Fmean (kHz) Fband (Hz) ΔF1 (Hz) ΔF2 (Hz) ΔF3 (Hz) ΔF4 (Hz) Fslope (Hz) Fwaviness (Hz) Fv-shape (Hz)

N. anomalus

N. fodiens

S. araneus

S. minutus

C. leucodon

C. russula

15.0 ± 5.0 (10.1–32.0) 1.55 ± 0.97 (0.23–3.36) 5.58 ± 0.92 (3.25–7.56) 6.20 ± 0.88 (4.12–7.99) 5.84 ± 0.90 (3.58–7.76) 617 ± 129 (430–870) −150 ± 90 (−401–−13) −82 ± 63 (−183–50) −12 ± 94 (−173–166) 143 ± 164 (−89–461) −100 ± 335 (−563–620) 746 ± 163 (534–1181) 363 ± 219 (71–832)

13.3 ± 3.3 (10.0–22.9) 0.95 ± 0.51 (0.16–1.65) 4.96 ± 0.67 (4.30–6.66) 5.48 ± 0.71 (4.73–7.39) 5.17 ± 0.69 (4.46–6.99) 523 ± 116 (315–733) −74 ± 47 (−149–6) −40 ± 42 (−118–19) 20 ± 51 (−74–106) 159 ± 104 (−27–369) 65 ± 203 (−339–379) 618 ± 122 (381–818) 293 ± 128 (62–487)

12.7 ± 2.7 (8.2–20.9) 1.03 ± 0.62 (0.17–2.49) 4.87 ± 0.84 (2.60–6.50) 5.54 ± 0.93 (2.91–7.54) 5.16 ± 0.89 (2.74–6.89) 672 ± 207 (312–1146) −36 ± 112 (−301–220) 6 ± 87 (−206–180) 49 ± 100 (−129–236) 165 ± 170 (−186–542) 183 ± 373 (−613–778) 773 ± 231 (354–1233) 244 ± 253 (−189–930)

11.2 ± 2.5 (8.7–15.8) 1.05 ± 0.68 (0.21–2.71) 6.45 ± 1.19 (4.77–8.58) 7.18 ± 1.16 (5.56–9.14) 6.77 ± 1.17 (5.13–8.83) 728 ± 163 (475–1069) −52 ± 137 (−232–148) 19 ± 73 (−73–130) 57 ± 63 (−29–183) 182 ± 110 (0–338) 207 ± 287 (−87–701) 864 ± 193 (551–1271) 272 ± 187 (−30–508)

12.8 ± 3.2 (8.8–21.7) 0.81 ± 0.53 (0.14–2.00) 5.49 ± 0.55 (4.39–6.27) 6.03 ± 0.55 (4.90–6.96) 5.75 ± 0.56 (4.61–6.60) 540 ± 260 (205–1344) 79 ± 111 (−123–239) 54 ± 69 (−72–242) 37 ± 80 (−88–297) 114 ± 162 (−108–685) 284 ± 309 (−163–1249) 645 ± 271 (245–1408) 18 ± 249 (−350–717)

13.1 ± 2.2 (10.2–17.0) 0.99 ± 0.27 (0.60–1.32) 5.15 ± 0.67 (4.39–6.69) 5.93 ± 0.74 (5.09–7.51) 5.47 ± 0.70 (4.66–7.02) 776 ± 136 (604–1050) −234 ± 127 (−362–−19) −57 ± 93 (−195–116) 28 ± 72 (−81–135) 239 ± 84 (127–349) −23 ± 330 (−500–532) 965 ± 176 (806–1305) 558 ± 154 (337–856)

balanced. In the next step, we trained a Support Vector Machine (LIBSVM Toolbox of Matlab (Chang and Lin, 2001)) with radial basis function on the training dataset, and then tested the derived model on the testing dataset. After repeating the whole 10-fold cross validation process 100 times, we summarized the classification results in a confusion matrix and applied the commonly used F-measure value (Ozgur et al., 2005) to evaluate the overall performance of the classification. 3. Results 3.1. Description of the twittering calls and differences between species A visual inspection of the sonograms revealed a large variation of twittering calls. In each species, we found calls with increasing and decreasing frequency modulation (Fig. 2), with V-shaped and reversed

V-shaped (Fig. 3) frequency graphs, as well as with simple quasiconstant frequency and with complex shape (Fig. 4). We found no clearly recognizable species specific calls. Moreover, the calls varied gradually; therefore, no apparent call types were found. By showing the categories mentioned above, we wish to call attention to the variability of these calls. The calls were emitted in sequences with inter-call intervals (Tint) of 0.1 to 3.4 s. The individual mean length of the calls (Tcall) was about 8–32 ms (Table 2). The calls occurred in the frequency range between 2.6 kHz and 8.8 kHz based on the individual mean values (Fmean). The mean bandwidth of calls ranged from about 0.2 to 1.3 kHz. The mean frequency differences (ΔF1–ΔF4) varied between − 0.4 and 0.7 kHz. The shape parameters derived from the frequency differences showed also large variety: the slope (Fslope) varied from − 0.5 to 1.2 kHz, the waviness (Fwaviness) was between 0.2 kHz and 1.4 kHz, while Fv-shape varied from −0.3 to 0.9 kHz based on the individual means.

Fig. 2. Twittering calls with upward frequency modulation (A) and downward frequency modulation (B). Each column shows spectrograms of twittering calls in a given species. For each species two call examples with different length or shape are shown. All spectrograms are in the same frequency (0 to 24 kHz, linear) and time scale shown in the top left corner.

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Fig. 3. Twittering calls with V-shape (A) and reversed V-shape (B). Further explanations as in Fig. 2.

We found a large overlap in the values of the call parameters between species. First, we tested the differences between the mean values for different species of the five chosen non-correlating call parameters: Tcall, Tint, Fmean, Fslope and Fwaviness (Fig. 5). We found significant differences in the frequency parameters (Kruskal–Wallis test, df = 5, Fmean: χ2 = 26.25, p b 0.001; Fslope: χ2 = 14.79, p = 0.011; Fwaviness: χ2 = 21.71, p b 0.001), but no differences in time parameters (Tcall: χ2 = 9.63, p = 0.086; Tint: χ2 = 9.60, p = 0.087). The post-hoc tests revealed few significant pairwise species differences. We found that S. minutus has higher Fmean values than N. fodiens and S. araneus; N. anomalus has lower Fslope values compared to C. leucodon; and C. russula produce calls with higher values of Fwaviness than N. fodiens and C. leucodon (Tukey-HSD post hoc tests, p b 0.05). In addition, we tested the differences between species in the individually calculated standard deviation of the call parameters to reveal differences in the variability of calls (Fig. 6). If the standard deviation of a call parameter is high in an individual we expect higher variability of calls regarding the given call parameter. We found significant differences in four parameters (Kruskal–Wallis test, df = 5, Tcall: χ2 = 15.67, p = 0.008; Fmean: χ2 = 51.79, p b 0.001; Fslope: χ2 = 20.67, p b 0.001; Fwaviness: χ2 = 20.44, p b 0.001), but no differences in Tint (χ2 = 9.47, p = 0.091). The post-hoc tests showed significant differences in a few cases. We found that N. anomalus has higher standard deviation (SD) of Tcall than S. araneus and S. minutus; both Crocidura

species have lower SD of Fmean than the other species; C. leucodon has lower SD of Fslope than the two Sorex species; and C. leucodon has lower SD of Fwaviness than N. anomalus and the two Sorex species (Tukey-HSD post-hoc tests, p b 0.05). Besides the univariate analyses, we inspected the multivariate relationship between species. The hierarchical cluster analysis of species revealed no genus-based structure (Fig. 7). Based on the acoustic space differences, S. araneus and N. fodiens seemed the closest species. 3.2. Species identification First, we attempted the classification on all six species simultaneously (six-species-classification). The accuracy of this species identification attempt was 66.2% (F-measure value); a random classification would be 16.7% for six species. The discrimination tested best for C. leucodon and C. russula with an accuracy of at least 84%. It was worst for S. minutus and for N. fodiens with accuracy below 50%; both species were confused most often with S. araneus (Table 3). Secondly, we attempted to classify species testing only two species at the same time in all species combinations (pairwise classification). In this way we wanted to mimic a situation in which only two species are present in a given area. The best results were for C. leucodon and C. russula with accuracies greater than 90% for every species combination. The lowest accuracy was achieved for the discrimination of

Fig. 4. Twittering calls with quasi constant frequency (A) and rather complex shape (B). Further explanations as in Fig. 2.

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Fig. 5. Species differences in the mean call parameter values. The figure shows how the mean values of each parameter calculated for the individuals distributed within the species. Larger distribution of the values means larger differences between individuals in the actual parameter. The different letters above the box plots indicate significant differences. For example, S. minutus has significantly higher mean frequency than S. araneus.

Fig. 6. Species differences in the variability of call parameters. The figure shows how the standard deviations calculated for each individual are distributed in the parameters within the species. Higher values indicating larger call variation in the given species. The different letters above the box plots indicate significant differences. For example, in mean frequency, the two Crocidura species have significantly lower call variation than the other species.

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Table 4 Pairwise classification results. In this analysis only two species were included into the SVM classification at the same time. The numbers give the percentage of the correct classification for the corresponding species. A random classification would give 50% accuracy.

N. fodiens S. araneus S. minutus C. leucodon C. russula

Fig. 7. Acoustic relatedness of species. The dendrogram shows the hierarchical clustering result based on Euclidian distance. The acoustic distance was measured in the parameter space of the first three principal components that explain more than 95% of the variance.

N. fodiens and S. araneus at a level 68.5%. In this pairwise classification, a random classification would have given 50% accuracy (Table 4). We also conducted a multi-species classification for each country separately with four species each. We got similar results of 72% accuracy for the Hungarian samples and 70% for the German samples, where the random classification would be 25% in both cases. To study the influence of the number of calls for the identification accuracy, we conducted the six-species and the pairwise classification using 1 to 50 calls from each individual (Fig. 8). We observed a large increase for both classification schemes up to call numbers about 15; and above that number the curves show only a moderate increase in accuracy. In the six-species classification the identification accuracy was just above 30% when one call from each individual was used and reached 60% when the sample size was increased to about 35. In the pairwise classification using one call the average accuracy was about 70% and about 83% when 15 calls were used in the calculations. 4. Discussion 4.1. Call characteristics, species differences and possible functions of twittering Our study indicates that shrew twittering calls have diverse frequency structures without distinct call types. Despite the large inter-specific overlap, we found statistically significant differences between six European shrew species in many call parameters. These differences can partly be explained by physical constraints. It has been shown that body size, through vocal tract length (Fitch, 2000), correlates well with acoustic features, especially the mean frequency, as a general rule in acoustic communication of different taxa (Fletcher, 2004) and in echolocation signals of bats (Jones, 1999). Our results confirm this generalization: N. fodiens (body length, BL: 70–90 mm; weight, W: 9–18 g) has a slightly lower mean frequency (although not significant in the multiple comparison) than the typically smaller N. anomalus (BL: 60–83 mm; W: 8–14 g), and the call of S. araneus (BL: 50–85 mm;

N. anomalus

N. fodiens

S. araneus

S. minutus

C. leucodon

78.7 86.0 85.4 97.3 95.8

68.5 90.2 96.6 92.7

70.7 94.1 95.2

91.4 94.7

90.2

W: 6–14 g) has a significantly lower mean frequency than the call of the smaller S. minutus (BL: 40–60 mm; W: 2.5–6.5 g). We found no significant difference in the mean frequencies of the calls between species with largely overlapping sizes in the genus of Crocidura, namely C. leucodon (BL: 54–82 mm; W: 6.5–13 g) and C. russula (BL: 58–86 mm; W: 9–13 g). All body size data based on Kraft (2008). We found significant differences in several call parameters between the species we tested, however the range of these parameters largely overlap. Our best species identification testing six species simultaneously reached only 66.2% accuracy. Assuming that the call parameters we used for this classification describe well the true variance of twittering call structure, the large inter-specific overlap in these call parameters suggest that shrew twittering calls did not evolve for clear species recognition. Up to now, knowledge about the function of shrew twittering calls is scarce. In previous studies it has been observed that shrews produce twittering calls more often in new environments, compared to accustomed environments (Crowcroft, 1957), and so have been named exploration calls. Possible functions of twittering are echolocation and active conflict avoidance (Gould et al., 1964). Different forms of conflict avoidance behavior (active by e.g. keeping distance and passive by e.g. freezing) between shrews have been previously described (Rychlik and Zwolak, 2005). This conflict avoidance behavior is not only relevant in intra- but also in inter-specific contexts as territories can be partitioned between species (Hawes, 1977; Neet and Hausser, 1990; Spencer and Pettus, 1966). If twittering calls are used in conflict avoidance, the large inter-specific overlap in the acoustic call parameters might facilitate the communication between species. However, the role of twittering calls in these situations has been not studied so far. From behavioral and acoustic measurements, Siemers and his colleagues (Siemers et al., 2009) concluded that twittering calls could possibly facilitate a basic form of echolocation (echo-orientation). However, real behavioral evidence for the echo-orientation hypothesis has not been published. The echo-orientation hypothesis suggests that the parameters of the twittering calls should reflect adaptation to the structure of the environment in a similar way to the calls of bats which are used for echolocation (Barclay, 1999). According to this hypothesis, one would not expect large differences among the shrew species in twittering calls, because their habitats often largely overlap (Rychlik, 2000). Indeed, our cluster analysis revealed no clear acoustic separation between species based on taxonomy.

Table 3 Confusion matrix of the six-species classification. The columns show the correct species, the rows show the species as identified by SVM, with the numbers giving the percentage of classifications into the respective species. The correctly identified cases are on the main diagonal. Correct species

Species identified by SVM

N. anomalus N. fodiens S. araneus S. minutus C. leucodon C. russula

N. anomalus

N. fodiens

S. araneus

S. minutus

C. leucodon

C. russula

73.0 16.5 3.9 5.8 0.3 0.6

22.3 49.9 27.2 0.6 0.0 0.0

8.9 22.5 53.7 12.4 2.5 0.0

2.8 7.6 34.8 47.8 0.0 7.0

0.0 0.4 14.9 0.3 84.4 0.1

5.2 0.3 2.0 5.8 2.6 84.0

8

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Fig. 8. Classification accuracy as a function of the call number used from each individual. The figure shows the averaged accuracy for both the six-species and the pairwise classifications. The accuracy of random classification would be 50% for the pairwise classification and 16.6% for the six-species classification.

According to our findings we can't confirm either the communication (conflict avoidance) or the echo-orientation hypothesis; however it is worthwhile to note that these hypotheses are not mutually exclusive. Bats' echolocation calls are not only used in echo-guided orientation and prey detection but also in communication (Fenton, 2003; Jones and Siemers, 2011). Bats can eavesdrop on echolocation calls of other individuals and by this gain information about food availability (Gillam, 2007), or the size, sex and age of the calling bat (Jones and Kokurewicz, 1994; Russo et al., 2001). Eavesdropping also allows the recognition of individuals (Kazial et al., 2001). Likewise, the shrew twittering calls might have a double function: conflict avoidance and echo-orientation. We conclude that there is need for more experimental studies to explore the function of shrew twittering calls. 4.2. Monitoring opportunities, species identification and applications In our study, all individuals of the six species in three genera of shrews (Sorex, Neomys and Crocidura) produced twittering calls during the recording trials. Beside of that, there is evidence that species of the genera Blarina, Cryptosis and Suncus also produce twittering sounds (Gould, 1969; Köhler, 1998; Schneiderova, 2014). We conclude that twittering calls are a general characteristic of shrews and thus may offer a feasible tool for the acoustic surveying of many shrew species. Churchfield (1990) mentioned that shrews produce twittering calls continuously during exploration and foraging. In a long-term acoustic laboratory experiment of Neomys anomalus (Czabán, personal communication) and in our long-term field monitoring study involving many species (Zsebok et al., unpublished results), it is noted that shrews produce twittering calls often and regularly. Our study is the first to provide information about acoustic species discrimination in shrews. The results show quite large inter-specific overlap in call parameters. The rather imperfect classification we observed is thus not surprising. Since faunistic investigations require accurate identification of species in order to identify new species in a given geographic area, our acoustic identification system for six species with 66.2% accuracy (random identification would be 16.6%) cannot be used for that purpose. In this case, catching of the specimens and identifying them based on their morphological or genetic characteristics seems inevitable. Based on our results, we propose two main areas for application of passive acoustic methods in shrews as follows. First, in specific ecological studies that compare the relative abundance of animals in different areas or time, it might be feasible to use

acoustic identification of shrews based on twittering calls. Comparative studies in bats, based on echolocation calls, provide many examples in which such imperfect identification systems have been successfully applied (Obrist et al., 2011; Russo and Jones, 2003; Vaughan et al., 1997). It is worth noting that previous knowledge about the species composition of the shrew community can influence the accuracy of our identification system. With fewer species presented on the studied area, the identification will obviously reach higher accuracy. According to our pairwise classification results, in two-species communities, the classification accuracy might reach a feasible level. We found 10 out of 15 pairwise classification accuracies higher than 90%, a precision that is used widely in bat studies (Obrist et al., 2011; Russo and Jones, 2003; Vaughan et al., 1997). The second proposed scope of the passive acoustic technique is the monitoring of shrews without species identification. To the best of our knowledge, no other animals produce acoustic signals with similar frequency and time parameters like shrew twittering. Therefore, these twittering calls can be reliably extracted from acoustic recordings and in this way the activity of shrews can be surveyed in the field. Several previous ecological studies, based on counting the number of calls, were conducted in bats to gather information about habitat and roost use without species identification (e.g. Humes et al., 1999; Karlsson et al., 2002; Parsons et al., 2003) or identifying only for acoustic groups (Flaquer et al., 2007). Our results indicate that this method could be used in shrew studies as well. It is however important to emphasize that while most of the bat species can be detected from a distance of several meters (possibly 50 m or more), the twittering of shrews can only be recorded from one or two meters. This has to be taken into account in future acoustic surveys on shrew. Our identification systems show continuously increasing accuracy using increasing number of calls from each individual. Using more than 15 calls, however, produces only a small increase in accuracy. The results of this study along with our own field observations (Zsebok et al., unpublished results) suggest that 15 calls can be easily recorded in the field. It is worth noting, that our database was established based on the acoustic data collected from four different areas (two in Hungary and two in Germany); therefore, it might be a good idea to build site specific sound libraries and identification systems to eliminate potential geographic differences between shrew populations (see the same problems in bats in (Gillam and McCracken, 2007; Jiang et al., 2010)) and thereby increase the species identification accuracy. However, by conducting the classification for the samples separated for the two countries we found similar accuracies (four species classifications with 72% accuracy for Hungary and 70% for Germany, where the random classification would be 25%). At the present time, our database is not large enough to analyze the geographical influences on the classification results. The main advantage of using passive acoustic techniques over other sampling methods is threefold: (1) highly decreased disturbance, (2) detailed time resolution, and (3) the possibility to process data automatically. There is evidence that both radio tracking and trapping cause mortality in shrews (Anthony et al., 2005; Rychlik et al., 2010). In contrast, the proposed acoustic method doesn't influence the survival of shrews and other small mammal species in a particular study area. This fact is important from both conservation and data sampling viewpoint especially for long-term studies. Apart from a potential harm to trapped animals, trapping and other sampling methods that need frequent surveillance in the field can cause small scale habitat degradation associated with the frequent control walks of the researchers. This habitat degradation is usually unwanted. For small ground-dwelling mammals, a human-caused foot path inside a meadow can be a barrier to normal movement. Further, the presence of researchers during these frequent control walks can influence the activity of small mammals. In that case, passive acoustic methods offer a

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feasible option, as after a one-time installation of acoustic equipment in the field, no more disturbances are caused. Another disadvantage of trapping is the low temporal resolution of the trapping intervals, lasting usually up to 3 or 4 h. In contrast, acoustic monitoring can be continuous and thus be used to study shrew activity in more detail in field experiments. A good temporal resolution of sampling can be useful in studies on anthropogenic influence such as light pollution (Stone et al., 2012). The autonomous long-term collection of shrew twittering calls using automatic segmenting and measurement techniques in combination with subsequent automated species identification can be a powerful sampling method. In the field of bat research such complete systems are already in use (e.g. Plank et al., 2012). To put passive acoustic monitoring of shrews into practice, it will be necessary to use a large enough number of microphones (e.g. microphone arrays) to compensate for the rather low intensity of shrew calls. In this way, the movement of individuals can be traced and the number of individuals estimated (see a review from Blumstein et al., 2011). We are not aware that such readymade systems, combining a large number of microphones, recording devices and power supply with the possibility to be used in the field, are commercially available at this moment. However, we believe that in the near future, specifically designed systems can provide solutions to use passive acoustic techniques in shrew studies. 5. Conclusions Shrews of many taxa and geographic regions use twittering calls during the exploration of their environment. Even though the biological function of this type of call is still unclear, twittering calls offer a tool for passive acoustic monitoring of shrews. Our paper shows the theoretical application possibilities of using twittering calls in field studies. We believe that the development of databases containing twittering calls of many shrew species over large geographical areas will help to make a reliable acoustic identification, as well as monitoring, possible in the future. Acknowledgments We dedicate our paper to Björn M. Siemers, our inspiring colleague and friend. We thank Klemen Koselj and Yossi Yovel for their technical comments on the earlier versions of the manuscript. We also thank Sara Troxell and Robert E. Mattick for their grammatical edit of this paper. Finally, we thank the two anonymous reviewers for their useful comments. References Acevedo, M.A., Corrada-Bravo, C.J., Corrada-Bravo, H., Villanueva-Rivera, L.J., Aide, T.M., 2009. Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecol. Inform. 4, 206–214. Ahlen, I., Baagoe, H.J., 1999. Use of ultrasound detectors for bat studies in Europe: experiences from field identification, surveys, and monitoring. Acta Chiropterologica 1, 137–150. Andre, M., van der Schaar, M., Zaugg, S., Houegnigan, L., Sanchez, A.M., Castell, J.V., 2011. Listening to the deep: live monitoring of ocean noise and cetacean acoustic signals. Mar. Pollut. Bull. 63, 18–26. Anjum, F., Turni, H., Mulder, P.G.H., van der Burg, J., Brecht, M., 2006. Tactile guidance of prey capture in Etruscan shrews. Proc. Natl. Acad. Sci. U. S. A. 103, 16544–16549. Anthony, N.M., Ribic, C.A., Bautz, R., Garland, T., 2005. Comparative effectiveness of Longworth and Sherman live traps. Wildl. Soc. Bull. 33, 1018–1026. Armitage, D.W., Ober, H.K., 2010. A comparison of supervised learning techniques in the classification of bat echolocation calls. Ecol. Inform. 5, 465–473. Azzolin, M., Gannier, A., Lammers, M.O., Oswald, J.N., Papale, E., Buscaino, G., Buffa, G., Mazzola, S., Giacoma, C., 2014. Combining whistle acoustic parameters to discriminate Mediterranean odontocetes during passive acoustic monitoring. J. Acoust. Soc. Am. 135, 502–512. Barclay, R.M.R., 1999. Bats are not birds — a cautionary note on using echolocation calls to identify bats: a comment. J. Mammal. 80, 290–296. Blumstein, D.T., Mennill, D.J., Clemins, P., Girod, L., Yao, K., Patricelli, G., Deppe, J.L., Krakauer, A.H., Clark, C., Cortopassi, K.A., Hanser, S.F., McCowan, B., Ali, A.M., Kirschel, A.N.G.,

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