bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
1 Characterising the vocal repertoire of the Indian wolf
2 (Canis lupus pallipes)
3 Sougata Sadhukhana, Lauren Hennellyb and Bilal Habiba*
4 a Department of Animal Ecology and Conservation Biology, Wildlife Institute of India, 5 Dehradun, India;
6 b Mammalian Ecology and Conservation Unit, Veterinary Genetics Laboratory, 7 University of California, Davis, USA
8 * Corresponding Author
9 Dr Bilal Habib, Scientists-E, Department of Animal Ecology and Conservation Biology, 10 Wildlife Institute of India, Dehradun-248001, India, Email ID- [email protected]
11
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13 Characterising the vocal repertoire of the Indian wolf
14 (Canis lupus pallipes)
15 Vocal communication in social animals plays a crucial role in mate choice, 16 maintaining social structure, and foraging strategy. The Indian grey wolf, among 17 the less studied subspecies, is a social carnivore that lives in groups called packs 18 and has many types of vocal communication. In this study, we characterise 19 harmonic vocalisation types in the Indian wolf using howl survey responses and 20 opportunistic recordings from captive and nine packs (each pack contains 2-9 21 individuals) of free-ranging Indian wolves. Using principal component analysis, 22 hierarchical clustering, and discriminant function analysis, we found four vocal 23 types using 270 recorded vocalisations (Average Silhouette width Si = 0.598) 24 which include howls and howl-bark (N=238), whimper (N=2), social squeak 25 (N=28), and whine (N=2). Although having a smaller body size, Indian wolf 26 howls have an average mean fundamental frequency of 0.422KHz (±0.126), 27 which is similar to other Holarctic clade subspecies. The whimper showed the 28 highest frequency modulation (37.296±4.601 KHz) and the highest mean 29 fundamental frequency (1.708±0.524 KHz) compared to other call types. Less 30 information is available on the third vocalisation type, i.e. ‘Social squeak’ or 31 ‘talking’ (Mean fundamental frequency =0.461±0.083 KHz), which is highly 32 variable (coefficient of frequency variation = 18.778±3.587 KHz). Our study’s 33 characterisation of the Indian wolf’s harmonic vocal repertoire provides a first 34 step in understanding the function and contextual use of vocalisations in this 35 social mammal.
36 Keywords: Communication, Social Canid, Howl, Harmonic Vocalisation, India
37 Introduction
38 Vocalisation plays a critical role in social animals for conveying information on
39 foraging, reproductive, and social behaviours [1–7]. Characterising the vocal repertoire
40 of a species provides a base for understanding the behavioural significance of different
41 vocalisations and studying how vocal communication varies across the populations,
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42 subspecies, and taxa [8–10]. The wolf (Canis lupus) is a social mammal and uses a
43 variety of vocalisations for communication. Being present throughout Eurasia and North
44 America, the wolf is one of the most widely distributed land mammals and occupies a
45 wide range of different habitat types [11]. The Indian wolf is among the smallest [12]
46 and one of the most evolutionarily distinct wolf subspecies, having diverged around
47 270,000 and 400,000 years ago based on mitochondrial DNA [13–15]. Studying the
48 vocal repertoire of the Indian wolf can aid in future studies on the function of different
49 vocal signals in Indian wolves and, more broadly, the variation in vocalisation across
50 subspecies and taxa within the Canis clade.
51 The best-known wolf vocalisation – the howl – is a long-range harmonic call used for
52 territorial advertising and social cohesion [1,16–18]. Recent studies have shown 21
53 different howl types across various canid subspecies based on quantitative similarity in
54 modulation pattern [8]. Along with howl, wolves also communicate using 7 to 12 other
55 harmonic calls, which is a clear pitch sound wave that possesses multiple integral
56 frequencies [19–21]. Many of these other harmonic vocalizations are short-ranged, and
57 due to difficulties in recording these calls, remain less studied compared to the wolf
58 howl [22]. These short-ranged calls are important for communicating passive or
59 aggressive behaviour among social canids [22–24].
60 The whimper, whine and yelp are various calls for communicating passive and friendly
61 behaviour among wolves [18,23], whereas noisy calls, which don’t have a clear pitch or
62 distinct frequency band in their spectrograms, communicate different levels of
63 aggression [18,23]. The whimper and whine vocalization is similar to a crying sound
64 with the whimper having a comparatively shorter duration than whine [18,25]. The
65 whine vocalization is mostly used for submissive behaviour whereas the whimper is
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66 primarily used for greeting [18]. Yelp is a short and sharp cry and is used in submissive
67 behaviour with body contacts [18,25]. To communicate different levels of aggression
68 behaviors, wolves use noisy calls which consist of the growl, woof, and bark. Growl is a
69 laryngeal sound to show dominance in any interaction, whereas the woof vocalization is
70 a non-vocal sound cue (without involvement of vocal cords) used by adults for their
71 pups [18,25,26]. The bark is a short low pitched sound with rapid frequency modulation
72 and is used during aggressive defence [19,26], such as defending pups or defending a
73 food resource. Wolves also express communication through mixed vocalisation either
74 by ‘successive emission’ or by ‘superimposition’ of two or more sound types [21]. A
75 recent study on Italian wolf (Canis lupus italicus) suggests six other types of calls may
76 combine with howls to make a complex chorus vocalisation [27].
77 This study investigates the acoustic structure of harmonic vocalizations of Indian
78 wolves and classifies harmonic vocalizations using a statistical approach. We
79 accumulated the vocalisation data from free-ranging and captive Indian wolves, which
80 will be the first study to evaluate different types of vocalisations of this wolf subspecies.
81 Using multivariate analyses, we describe and classify different harmonic calls to
82 develop a vocal repertoire of the Indian wolf.
83 Materials and methods
84 Study Species
85 The Indian wolf (Canis lupus pallipes) is among the smallest subspecies of wolf with an
86 average body weight is 20.75 kg [12]. Indian wolves are mostly found in grasslands and
87 the edges of dense forest on the Indian subcontinent [12,28–31]. We recorded
88 vocalisations from nine packs of free-ranging wolves and ten captive wolves from Jaipur
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89 Zoo. For captive wolves, we collected vocalisation data from 10 wolves: two adult pairs
90 and six subadults. One adult male was recently captured from wild near the city of Jaipur,
91 Rajasthan, India . The rest of the Indian wolves are descendents of captive breeders at
92 Jaipur Zoo.
93 Study Sites
94 The study was conducted in the state of Maharashtra (Figure 1) and Jaipur Zoo of Jaipur,
95 Rajasthan, India. The study site of Maharashtra includes the semi-arid area of central
96 Deccan Plateau [32], which consists overlapping habitat of Tropical Dry Deciduous
97 Forest, Grassland, Savanna (Western Part) and Tropical Moist Deciduous Forest (Eastern
98 Part) [33].
99 Figure 1. Map of survey sites of the free-ranging wolves.
100 Data Collection
101 Vocalization of free-ranging wolves was recorded through acoustics survey from
102 November 2015 to June 2016. Most of the long-distance vocalisation data were
103 collected through howling surveys to elicit howl behaviour. For other types of
104 vocalisation data, we relied on opportunistic recordings from free-ranging wolves and
105 captive wolves, in which installed microphones near the enclosure was used to record
106 vocalizations. Howl surveys were performed during early morning and evening hours
107 using pre-recorded howls that were previously recorded from the Jaipur Zoo Indian
108 wolves. Each howling session consisted of five trials with three-minute intervals. A 50-
109 second-long pre-recorded solo howl was played thrice using JBL Xtreme speakers [34]
110 in the order of increasing volume. The session was followed by two 50-second-long
111 chorus howls with an increase in volume level. In the case of howling response, the
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112 session was terminated and repeated after 15 to 20 minutes. Responses were recorded
113 using Blue Yeti Pro Microphone [35] attached with Zoom H4N Handheld Audio
114 Recorder [36] at a sampling rate of 44.1KHz on 16-bit depth with 80 Hz noise filter.
115 Along with survey responses, the opportunistic recording sessions were conducted near
116 Indian wolf den sites and rendezvous sites. In addition to howl surveys at Jaipur Zoo,
117 vocalisations of captive wolves were recorded by installing microphones in the front of
118 cages during closing hours (6:30 pm-7: 30 am).
119 Feature Extraction
120 Spectrogram of each vocalisation was generated through the Raven Pro 1.5 software
121 [37] using the Discrete Fourier Transform (DFT) algorithm. The discrete Fourier
122 function transforms same length sequence of equally spaced sample points (N), (N is a
123 prime number) with circular convolution being implemented on the points [38]. Hann
124 windows were used at the rate of 1800 samples on 35.2 Hz 3dB filter. A total of 270
125 spectrograms were selected for further analysis based on clarity (i.e. clearer spectrogram
126 with low noise and without external sound overlap). Web plot digitiser v3 [39] was used
127 to digitise fundamental frequency from the spectral images. This digitised data was
128 obtained at 0.1sec resolution. From this data, eleven acoustic variables (Table 1) were
129 obtained based on their performance from previous studies [40,41].
130 Table 1. Acoustic variables based on fundamental frequency (f0) that were extracted for 131 this study
Variable Name Definition of Variable Min f The minimum frequency of the fundamental (f0) Max f The maximum frequency of f0 Range f Range of f0; f0= Max f – Min f Mean f Mean frequency of f0 at 0.1 s interval over the duration Duration Duration of Howl measured at f0; Duration = tend- tstart
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Abrupt0.025 Number of abrupt changes in f0 more than 25Hz at single time step (0.1sec) Abrupt0.05 Number of abrupt changes in f0 more than 50Hz at single time step (0.1sec) Abrupt0.1 Number of abrupt changes in f0 more than 100Hz at single time step (0.1sec) Stdv Standard Deviation of f0. Co-fm Coefficient of frequency modulation of f0 = Σ|f(t)–f (t+1)|/(n–1) Χ 100/Mean f0 Co-fv Coefficient of frequency variation of f0 = (SD/mean) Χ 100
132 Statistical Analysis
133 Principal Component Analysis (PCA)
134 To obtain a smaller set of representative variables, we used principal component
135 analysis (PCA), which extracts linearly uncorrelated variables from a suite of
136 potentially correlated variables. PCA is advantageous to building a robust model more
137 quickly than would be possible with raw input fields [42]. To simplify the interpretation
138 of factors, we performed varimax rotation using Kaiser normalisation. From our dataset
139 of 270 vocalisations, we used eight scalar variables that are related to spectral structure
140 (Range f, Duration, Abrupt0.025, Abrupt0.05, Abrupt0.1, Stdv, Co-fm, Co-fv) for PCA
141 analysis through the software SPSS (v22). The first principal component (PC1) and
142 second principal component (PC2) were used in the subsequent clustering analyses.
143 Cluster Analysis
144 To classify the recorded vocalisations from the Indian wolf, we used agglomerative
145 hierarchical clustering through the R package AGglomerative NESting (AGNES). The
146 agglomerative hierarchical clustering algorithm measures the dissimilarity between
147 single and groups of observations using a “bottom-up” approach, thereby constructing
148 clusters [43]. Agglomerative hierarchical clustering was performed using Euclidean
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149 distances with PC1 and PC2 from the 270-vocalisation data using eight scalar variables.
150 Subsequently, silhouette clustering was combined with AGNES to validate the number
151 of clusters in our vocalisation data. Silhouette clustering measures the similarity of
152 observation within its cluster compared to other clusters [44]. The average silhouette
153 value (0 represents poor fit, 1 depicts the highest fit) describes the evaluation of
154 clustering validity [44]. Average Silhouette width (Si) was calculated for 14 different
155 solutions (2 to 15 clusters). The “solution” that provided the best fit was selected upon
156 the maximum average silhouette value. The dendrogram was plotted using ‘Circlize
157 Dendrogram’ in the package ‘Dendextend’ in program R.
158 Discriminant Function Analysis (DFA)
159 Discriminant function analysis (DFA) was performed using PC1 and PC2 as an
160 independent variable under the program SPSS v22 to cross-validate the obtained
161 clusters from AGNES. Predicted clusters that were determined by the maximum
162 silhouette value were then used as a grouping variable to see within-group covariance in
163 DFA analysis.
164 Box plot using simple scalar variable
165 From these clusters, we then used box plot to show the overall pattern and distribution
166 characteristics of different vocal clusters.
167 Results
168 Principal Component Analysis
169 Two principal components (PC1 and PC2) were generated from the 8-simple scalar
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170 variables through PCA based on Kaiser-Guttman Rule (Eigenvalue >1) [45]. PC1 and
171 PC2 together explained 70.6% variance. PC1 was based on the variances of six acoustic
172 parameters (Abrupt0.025, Abrupt0.1, Abrupt0.05, Co-fv, Range f, Stdv) whereas PC2 is
173 explained by the variances of five parameters (Abrupt0.1, Co-fm, Duration, Range f,
174 Stdv) (Table 2)
175 Table 2. The loadings of PC1 and PC2. Communalities are the proportional factors by 176 which the importance of each variable is explained. Acoustics PC1 Loading after PC2 loading after Varimax Communalities Parameters varimax rotation Rotation Abrupt0.025 .858 0 0.741
Abrupt0.1 .602 .415 0.535
Abrupt0.05 .825 0 0.732 Co-fm 0 .945 0.898 Co-fv .862 0 0.750 Duration 0 -.382 0.231 Range f .852 .392 0.880 Stdv .759 .556 0.885 % of Variance 48.946 21.692
177 Cluster Analysis
178 The highest silhouette value (Si = 0.598) was obtained at the 4-group solution in the
179 cluster analysis using PC1 and PC2 from PCA analysis (Figure 2). The average
180 silhouette value was 0.62 for the first cluster (N=238), 0.37 for the second cluster
181 (N=2), 0.38 for the third cluster (N=28) and 0.73 for the fourth cluster (N=2) (Figure 3).
182 The 4 clusters were formed at 3.9 clustering scale through agglomerative hierarchical
183 clustering (Figure 4).
184 Figure 1. Average silhouette width plotted against 14 different solutions (2-15 185 cluster). Average Silhouette width represents the significance level (0 represents poor 186 fit, 1represents best fit). We obtained maximum silhouette width in 4 cluster solutions
187 i.e. Si= 0.598.
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188 Figure 2. Silhouette plot is a validation method for the consistency among the
189 clusters. It provides information about the similarity or difference of each call from its
190 clusters. A negative value indicates the chance of a call to fall under the neighbouring
191 cluster. The average silhouette value of 4 groups are 0.62 (N=238), 0.37 (N=2), 0.38
192 (N=28), 0.73 (N=2) respectively.
193 Figure 3. Cluster Diagram obtained from Agglomerative hierarchical clustering
194 using Euclidean Distance as matric function. Four clusters were formed at 3.9
195 Clustering scale. Cluster 1 (Howl), Cluster 2 (Whimper), Cluster 3 (Social Squeak) and
196 Cluster 4 (Whine) are denoted by Blue, Red, Green and Brown Respectively.
197 Discriminant Function Analysis (DFA)
198 DFA achieved 95.9 % accuracy of vocal group identification using two PCA values
199 (Table 3). Each of the four groups has a distinct group centroids. Graphical
200 representation using two discriminant functions (DF1 and DF2) shows that vocal
201 clusters do not overlap (Figure 5).
202 Table 3. Classification Results of Discriminant Function Analysis (DFA). 95.9% Vocal 203 clusters (estimated from Agglomerative hierarchical clustering) are identified correctly. cluster Predicted Group Membership Total 1 2 3 4 Count 1 229 0 8 1 238 2 0 2 0 0 2 3 0 0 26 2 28 4 0 0 0 2 2 % 1 96.2 .0 3.4 .4 100.0 Correct 2 .0 100.0 .0 .0 100.0 3 .0 .0 92.9 7.1 100.0
Predicted Predicted in Cluster analysis 4 .0 .0 .0 100.0 100.0 204
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205 Figure 4. The plot of Discriminant Function Analysis (DFA) using PCA value for
206 270 vocalisation data from Indian Wolf. Different colours represent different call
207 type.
208 Box plot using a simple scalar variable
209 The whisker box plot represents the variation among the acoustic variables within the
210 four identified call types (Figure 6). Call type 1 had the longest duration (5.214±2.49
211 Sec) whereas call type 2 showed the shortest duration among the four recognised groups
212 (0.4±0) (N=2). Type 2 calls are also high frequency modulated (37.296±4.601)
213 (variation in frequency per unit time). However, frequency variation (around the mean)
214 is highest in type 3 calls (18.778±3.587) (Table 4Error! Reference source not found.).
215 Figure 5. Box plot of variation among acoustic variables between different call 216 type. a. Variation among minimum frequency, b. Variation among maximum 217 frequency, c. Variation among Mean frequency, d. Variation among duration of the call, 218 e. Variation among coefficient of frequency modulation, f. variation among coefficient 219 of frequency variation.
220 Table 4. Variation among important acoustic variables within the four identified call 221 types.
Clus Min f0 Max f0 Mean f0 Range f0 Duratio Co-fv Co-fm ter (KHz) (KHz) (KHz) (KHz) n (Sec) 1 0.359±0 0.469±0 0.422±0 0.11±0. 5.214±2 7.17±3.6 4.444±4.4 .116 .141 .126 065 .49 89 63 2 1.632±0 1.77±0. 1.708±0 0.137±0 0.4±0 3.407±2. 37.296±4. .578 5 .524 .077 632 601 3 0.327±0 0.623±0 0.461±0 0.295±0 3.471±1 18.778±3 9.071±4.8 .051 .077 .083 .05 .855 .587 02 4 0.733±0 1.1±0.2 0.906±0 0.367±0 1.2±0.7 13.649±5 20.694±1 .19 2 .242 .029 07 .526 7.347
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222 Discussion
223 This study provides a quantitative assessment of the vocalisations of the Indian wolf
224 subspecies. Our results show that there are four statistically classified groups of Indian
225 wolf vocalisations based on ten captive individuals and nine free-ranging Indian wolf
226 packs. Though the Four to Six solution groups showed a narrow difference in their
227 average silhouette values based on silhouette plot analysis, the four cluster solution was
228 found to be the most significant based from the global maxima. This characterisation of
229 vocalisations provides a first step to evaluating the function and contextual use of
230 different types of vocalisations in these canids.
231 The first call type is identified as a howl (Figure 7a), which is the most dominant and
232 prolonged (5.214±2.49 sec) call type in our dataset. The fundamental frequency of howl
233 ranged from 0.359 KHz (±0.116) minimum to 0.469 KHz (±0.141) maximum (N=238).
234 Besides having smaller body size, the mean Fundamental Frequency of the Indian wolf
235 howl (0.422±0.126 KHz) was similar as compared to other Holarctic clade subspecies
236 reported by Hennelly et al. [46]. Since the howl is the most detectable vocalisation used
237 in long-range social cohesion and territorial advertisement [8,22], our high howl sample
238 size shouldn’t be considered as the most dominant vocalisation. Barking-howl, which was
239 mentioned by many authors as a common type of mix vocalisation in wolves [22,27,47],
240 falls under the same cluster along with howling (Figure 7b). From our field observations,
241 wolves bark in defence to an immediate threat. In one such occasion, the she-wolf of a
242 pack started barking at nearby villagers to protect her cubs and did not stop until all the
243 three pups ran away to a safer distance from the villagers.
244 Figure 6. Spectrograms of different type of vocalisation in Indian wolf a. Howl (type
245 1); b. Bark-Howl (type 1 subtype); c. Whimper (type 2), d. Social Squeak (type 3); e.
246 Whine (Type 4);
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247 The second call type has the highest frequency modulation (37.296±4.601 KHz) and is
248 commonly known as a whimper (Figure 7c). The whimper is low intensity but high-
249 pitched sound and unlike howls, it is used for short distance communication among pack
250 members. [20,22] (mean fundamental frequency = 1.708±0.524 KHz). This short duration
251 (0.4±0 KHz) vocalisation is reported to be associated with submissive or friendly greeting
252 behaviour [22,47]. Since it is not audible from more than one to two hundred yards away
253 [47], our dataset contains only a few observations (difference sources) of this type of call
254 (N=2). While our study provides some initial insight into the acoustic structure of this
255 vocalization, further sampling will be needed to robustly characterize the acoustic
256 structure of the whimper.
257 The third group of Indian wolf vocalisations can be termed as ‘social squeak’ (Figure 7c),
258 following observations by previous studies (Mech 1981, Crisler 1959, Fentress 1967).
259 This high-frequency variable vocalisation (18.778±3.587 KHz) in the Indian wolf is
260 similar to ‘talking’ as termed by Crisler [48]. Social Squeak has a minimum frequency of
261 0.327 KHz (±0.051) to Maximum of 0.623 KHz (±0.077) (N=28).
262 Lastly, our fourth group we identified as the whine (Figure 7e), which is characterized as
263 a short duration vocalisation (1.2±0.707 Sec). The whine in the Indian wolf (Canis lupus
264 pallipes) is longer than the whine reported in Italian wolf (0.13±0.10 Sec) [27]. Although
265 our data set is small (N=2; from two different individuals) to interpret robustly, the mean
266 fundamental frequency of Indian wolf whine (0.906±0.242 KHz) has a similar frequency
267 as the Italian wolf (0.979±0.109 KHz) [27].
268 Overall, our study identified four statistically classified groups of vocalisations of the
269 Indian wolf. While the howl has been extensively studied in its behavioural function and
270 variation across subspecies [8,46], less is known about the short-range communication
271 among wolves. For example, our study identified the ‘Social Squeak’, or the ‘talking’ like
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272 vocalisation in the wolf, yet little is known about its function in wolf packs and if it’s a
273 common communication across different canid species and within domestic dogs. Further
274 research on a larger dataset of Indian wolf vocalisations can develop a more robust
275 classification of the vocal repertoire and can be used for future studies on the contextual
276 use and variations across different wolf subspecies. More broadly, the species within the
277 Canis clade vary in their body sizes, social structure, and habitats. Describing the vocal
278 repertoires of various canid species can give insight into the factors influencing vocal
279 communication within the genus Canis.
280 Acknowledgements
281 We want to thank the authority and staffs of the Jaipur Zoo and Maharashtra Forest
282 Department for permitting to conduct this research. We sincerely acknowledge the
283 funding agencies, Department of Science and Technology, India and Forest Department
284 of Maharashtra. We appreciate all our field personals and wildlife enthusiast groups
285 (Pune Wolfgang, Mihir Godbole, Vineet Arora, R. V. Kasar, Rajesh Pardeshi, Sawan
286 Behkar and others) from Maharashtra who helped in local information gathering and
287 various logistic arrangement during data collection. The first author is grateful to Holly-
288 Root Gutteridge, who helped me in learning feature extraction during the initial days.
289 We acknowledge the effort of Shivam Shrotriya and Nilanjan Chatterjee for assistance
290 during the analysis. We would like to show our gratitude to our friends and colleagues
291 whose insight and comments considerably improved the manuscript. We are delighted
292 for having the continuous support and facility from Wildlife Institute of India,
293 Dehradun.
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420 Supporting Information
421 S1 File. The Variables, Clusters and Other Details information of every calls.
20 bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/612507; this version posted April 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.