Bimodal distribution of inter-individual distance in free-ranging narrow-ridged finless porpoises—Potential use of visual and auditory cues

doi:10.21203/rs.3.rs-2993107/v1

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Bimodal distribution of inter-individual distance in free-ranging narrow-ridged finless porpoises—Potential use of visual and auditory cues

https://doi.org/10.21203/rs.3.rs-2993107/v1

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Animals must adjust their inter-individual distances (IIDs) by using several cues from other individuals; therefore, IIDs are expected to be restricted by the transmissive distances of such cues. In this study, the IIDs of free-ranging narrow-ridged finless porpoises (NRFPs) (Neophocaena asiaeorientalis) are shown to have two distributions that may correspond to the transmissive ranges or active spaces of visual and auditory cues. IIDs were measured using 2.8-h drone video data (DJI Air 2S) at a high altitude (ca. 400 m) for 12 d in two study areas in Ise Bay, Japan. The distribution of IIDs was divided into three categories based on the results of the Bayesian Information Criterion in both areas. The first (6.5 and 5.7 m, in Areas 2 and 1 on average, respectively) and second distribution (102.1 and 80.6 m in Areas 2 and 1 on average, respectively) were significantly shorter than the null model predicted by drone angle of view, whereas the third distribution (248.2 m in Area 2 and 203.2 m in Area 1 in average) did not significantly differ from the null model (Kolmogorov–Smirnov test; p < 0.01 and p = 0.48, respectively). The first distribution of IIDs corresponded to the visible range (water clarity in Ise Bay: approximately 5 m), and the second distribution corresponded to the audible range (active space of echolocation clicks in Ise Bay: median 72.15 m). Free-ranging NRFPs, which have a simple social system, may adjust their IIDs using visible and audible cues from other individuals.

active space

inter-individual distance

narrow-ridged finless porpoise

Neophocaena asiaeorientalis

Animals must adjust their inter-individual distances (IIDs) to increase their survival values. Sociality differences require different distances between individuals. For example, solitary or territorial animals, such as cheetahs (Acinonyx jubatus), maintain a distance between conspecifics to guard their resources within their home ranges or territories [1]. Gregarious animals, such as fishes (e.g. guppies, Poecilia reticulata), must maintain close distances from many conspecifics to decrease their predation risk [2], but close distances increase disease transmission between conspecifics [3]. Animals must use cues of other conspecifics to maintain IIDs (produced regardless of their intention to communicate), such as auditory, visual, olfactory, chemical, and tactile cues, with adaptive modalities in their habitat. For example, to not separate from stable group members, females of chacma baboons (Papio ursinus) produced more contact calls when they separated from members over 50 m away due to the risk of separating from the travelling direction of the group [4]. Male chimpanzees (Pan troglodytes verus) in Taï National Park remained outside the visible range of other conspecifics approximately half of the time (46%); however, they mostly remained within the auditory range (1 km) of other conspecifics (70%) [5]. Male chimpanzees suggestively maintain their distance using sounds, although they cannot observe other individuals. Japanese macaques (Macaca fuscata) frequently perform visual scans when the distance from an individual in proximity is longer [6]. In addition, they frequently produce contact calls when the visible distance is short [7], suggesting that they maintained spatial group cohesion through visual and auditory cues. Excluding primates, African savannah elephant (Loxodonta africana) orphans, which developed in a cohort for rehabilitation, maintained a strong relationship with orphans in the same cohort for an extended period after release [8]. These orphans retain IIDs within the distance where the stability of the spectral features of their rumbles can prevail (1.5 km) [9]. Male cheetahs may use olfactory marking posts to maintain IIDs, resulting in a rare overlap of their 50% home range [1]. Thus, the cue types used to maintain their IIDs can be predicted by measuring the distributions of the IIDs and analysing the active space of several cues.

Cetaceans use several modalities to communicate with conspecifics (reviewed by [10]). For example, the common bottlenose dolphin (Tursiops truncatus), beluga whale (Delphinapterus leucas), and Pacific white-sided dolphin (Lagenorhynchus obliquidens) produce contact calls to maintain relationships when isolated from group members [11, 12, 13]. Most Indo-Pacific bottlenose dolphin (T. aduncus) pairs synchronise their behaviour before and after a synchronous breathing event [14]. Calves of some species prefer the right side of their mothers to see their mothers with their left eyes (killer whales (Orcinus orca); [15], beluga whale; [16]). Bruck et al. [17] revealed that common bottlenose dolphins could recognise other individuals from their urine using only gustation. Tactile stimuli are primarily used for social behaviour [18] and/or care of the body surface [19]. Olfaction may not be used to detect other individuals because approximately all of their olfactory nervous systems have been evolutionarily lost [20], and limited information is provided on the chemical and electric senses of toothed whales, except for one study on the chemical sense of common bottlenose dolphins [21] and another on the electric sense of the Guiana dolphin (Sotalia guianensis) [22]. Audition, vision, and tactile stimuli may be used to detect other cetacean individuals. Tactile communication can easily be detected because of the need to touch other individuals. Therefore, the active spaces of audition and vision should be measured to understand the cue types used to maintain IIDs in cetaceans.

Some studies have investigated the relationship between inter-individual and transmission distances of acoustic/visual cues in cetaceans. The mean separation distance (115 m) of the mother–calf pair (MCP) in common bottlenose dolphins was shorter than the active space of whistles (more than 230 m) [23], suggesting that they maintain their IIDs using sounds. The maximum IID of Amazon river dolphins (Inia geoffrensis geoffrensis) from the signaller to the closest receiver was approximately 0.15–1.5 km, suggesting that Amazon river dolphins may adjust their IIDs acoustically, not to separate from each other, and not to come closer to other individuals [24]. Indo-Pacific bottlenose dolphins breathe synchronously in proximity (88% lateral distance and 85% of the longitudinal distance in analysed data was approximately less than 1.5 m and 1.1 m, respectively) in Mikura Island, Japan [14]. The transparency in this study area was longer than the IID (10–15 m maximum clarity in Mikura Island [25]); therefore, this species may maintain its distance using vision. Thus, IIDs and their cues are estimated to be closely related.

Narrow-ridged finless porpoises (NRFPs) (Neophocaena asiaeorientalis), reside in coastal areas shallower than 50 m in depth and have a simple social system, generally residing alone or as an MCP [26]. Scarce social relationships have been predicted to exist between the individuals of this species. However, biologging techniques revealed that two adult males of Yangtze finless porpoises (N. a. asiaeorientalis) in semi-wild conditions synchronised their dive depth with that of an immature female and juvenile male, suggesting that they selected non-mother–calf associates [27]. The behavioural observation of NRFPs in an aquarium also suggested long-lasting affiliative non-MCPs that ended despite social disturbances (a group member change) [28]. These studies imply a more complex society than previously theorised. Therefore, it can be hypothesised that NRFPs maintain their IIDs through cues.

NRFPs produce echolocation click trains at 5.1 s intervals [29]. Captive finless porpoises use two sound types; packet sounds as contact calls and burst pulses, possibly related to affiliative sexual behaviours [30]. Therefore, they may use sounds to maintain IIDs. No study on the visual ability of this species exists, but they might have similar visual ability compared to other ‘visual’ cetaceans, such as common bottlenose dolphins, common dolphins (Delphinus delphis), harbour porpoises (Phocoena phocoena), but larger than several terrestrial animals, estimated by their ganglion cell size (reviewed by [31]). Common bottlenose dolphins can observe a 24-cm square target from at least 2.5 m underwater [32]. Therefore, finless porpoises can visually detect other individuals effectively. Therefore, NRFPs would possibly maintain their IIDs by using visual and acoustic cues. To test this prediction, whether IIDs of the porpoises would have bimodal distributions that correspond to the range in which porpoises can see each other, and the range in which porpoises can hear the sounds of each other, was investigated. IIDs were measured by drone. The visible range was defined as the transparency of the study area [33], and the audible range was measured using a hydrophone array. Behavioural status observations were also conducted to investigate the possibility that IIDs were gathered for foraging.

Study area

This study was conducted in two areas: Areas 1 (St. 1 and 2) and 2 (St. 3) in Ise Bay, Japan (Fig. 1). Ise Bay is a primary location of NRFPs in Japan [26]. Both areas are sharper than 20 m, and the sea bottom consists of silt, coarse sand, and sand [34]. Area 1 was substantially noisier than Area 2. Drone video footage recorded from one flight was defined as a ‘unit’. The average number of boats (primarily for fishing) per unit in each area was calculated as the total number of boats in all units divided by the number of units in each area. The water depth in both study areas was under 50 m. Water temperature and salinity were 8.8–26.2 ℃ and 21.6–31.4, respectively, in Ise Bay from 2006 to 2015 [33].

Calibration and accuracy of the drone distance measurement

To maximize the accuracy of distance measurements, the distortion of the camera attached to the drone was calibrated (DJI Co. Ltd, Air 2s, and Phantom 4 Advanced, Shenzhen, China) using a Single Camera Calibrator application in MATLAB (The MathWorks, Natick, MA), and the measurement errors were calibrated using length-known scales, following the methods of Burnett et al. [35]. To calibrate the camera distortion, a 4 K video was captured while rotating the X- and Y-axes of the chequerboard at various angles. Fifty frames were extracted from the video and imported into the Single Camera Calibrator application to estimate a three-parameter polynomial model that optimally estimates the lens distortion for a given camera model and to calibrate the distortion.

The scale, located at St. 3, was measured aerially by drone to calculate the measurement error. Scales of 5, 10, 50, 100, and 200 m were video-recorded five times using a drone from each height (100, 200, 300, and 400 m). After selecting a good image from the video, a black circle was marked on each end of the scale, and pixel size between the centres of the two black circles was automatically measured using the Image Processing Toolbox in MATLAB. To calculate the measurement length of the scales, the following formula from Torres et al. [36] was used:

$$GSD = \frac{Altitude \left(m\right)}{Focal length \left(mm\right)} \times \frac{Sensor width \left(mm\right)}{Image width \left(pix\right)}$$

$$Length \left(m\right)= GSD \times Number of pixels measured \left(pix\right)$$

where GSD represents the Ground Sampling Distance, and pix represents the mean number of pixels.

The measurement error at each scale length was calculated as follows:

$$Measurement error \left(m\right)= Length measured \left(m\right) - Scale length \left(m\right)$$

Because the measurement error increased linearly with the scale length, back-calculation using a linear relationship was conducted to minimise the measurement error.

Sampling rate for image selection from the video

The sampling rate of IIDs of NRFPs has trade-offs between accuracy and study effort; a higher sampling rate results in more accurate IIDs but consumes more time for analysis, and vice versa. The efficient sampling rate (‘sampling interval’) was preliminarily assessed by comparing the distribution of IIDs of NRFPs at sampling intervals of 1, 5, 10, 15, 20, 30, 40, and 60 s. Five over 3 min video footages of NRFPs at St. 3 on 10 and 15 June 2021, using Phantom 4 Advanced, were used. The first, second, and third quartiles of IID distributions with a 1 s sampling interval were set as correct, and the absolute error of each sampling interval and quartile was calculated using the following equation:

Absolute error = | each quartile at each sampling interval $-$each quartile at a 1-s sampling interval | (4)

To discover longer sampling intervals with similar accuracy to 1-s sampling, the sampling interval with lower absolute errors in all three quartiles and a longer sampling interval was sought. Results indicated that the 15-s interval had lower absolute errors in all quantiles despite its longer intervals (Fig. 2). Therefore, the sampling interval was set to select an image from the video at 15-s intervals.

Data collection

Behavioral states and inter-individual distances

A drone (Air 2s) was used to record a video of NRFPs from 8 February to 11 March 2022 in Area 1 and from 24 May to 13 July 2022 in Area 2. Porpoise neonates were stranded from late April to mid-July in Ise Bay [37], and the pregnancy period was approximately one year [26], which suggests that the drone survey in Area 2 was during a reproductive season, whereas in Area 1, it was not. A neutral-density filter (ND64/PL; Freewell Industry Co. Ltd., Kowloon, Hong Kong) was attached to the drone camera. The surveys were conducted under ideal conditions: without rain and strong winds and with a Beaufort Sea State of less than 3. The drones were removed from the same location in each area. The altitudes were calculated above sea level, considering the tide level.

First, NRFPs were sought while maintaining a 150-m drone altitude, and their behavioural states were recorded for 1 min. Behavioural states were classified into five categories according to Lusseau [38]: socialising, milling, foraging, resting, and travelling (Table 1). Behavioural states were recorded during a 1-min observation period at 150 m if behavioural states changed during this period and if several porpoises exhibited different behavioural states. After recording their behaviour, the drone recorded at an altitude of 400 m until the battery or recording time expired (approximately 10 min). To minimise the possibility of recording the same NRFPs between the units, the drone was flown to different points during the same time period (e.g. morning and evening).

Table 1

Definitions of behavioral state; foraging, miling, resting, socialising and travelling.
Behavioral state	Definition
Foraging	Individuals dive for long intervals and perform “steep dives”, arching their back at the surface to increase their speed of descent. Individuals turn counterclockwise, which is foraging behaviors stated in Amano et al. [61].
Miling	Individuals often change of direction. Dive intervals are variable but short.
Resting	Individuals are staying at the same position without waving their flukes.
Socialising	Interactive behavioural events are observed such as body contact and synchronous swimming.
Travelling	Individuals are moving steadily in a constant direction. Swimming with short, relatively constant dive intervals.

Sound recordings

Sound recordings were conducted from a boat at the Fisheries Research Laboratory, Mie University (ZAGA2, 21 ft) in Area 1 during the daytime on 9 February 2022 for ambient noise recordings and 1 March 2022 for sound recordings of NRFPs. Ambient noise was recorded at five different points when no fishing boats were present within 500 m. The pulsed sounds of NRFPs were recorded using a hydrophone array consisting of two acoustic data loggers: an A-tag (ML-200-AS2: Marine Micro Technology, Saitama, Japan) and a sound recorder (SoundTrap; ST300-HF: Ocean Instruments, Auckland, New Zealand). The A-tag had two ultrasonic hydrophones with a sensitivity of 201 dB re 1 V/ 1 µPa between 100 and 160 kHz ± 5 dB [39], and the SoundTrap had a hydrophone with a sensitivity of 172 dB re. 1 µPa clipping level (high gain) between 20 and 150 kHz ± 3 dB with 16-bit resolution. Two acoustic data loggers were fixed to a rope (14 m) at a distance of 1 m, and the sound recorder was placed between the two dataloggers at 0.5 m distances each. The boat was stopped when NRFPs were found. The array was suspended vertically with a terminal weight (2 kg). The hydrophone of the upper data logger was positioned approximately 1.5–3 m below the water surface.

Data analysis

Behavioral and distance data

A unit comprised several behavioural states; therefore, the total number of each behavioural state was averaged using the number of units per area. Fisher’s exact test was used to investigate whether the total number of each behavioural state in all units in each area differed between Areas 1 and 2.

IIDs were measured at 15-s sampling intervals. Data was omitted if land or invisible areas due to reflection from the sun were excluded from the analysis. To classify IID distribution into several Gaussian distributions, Gaussian mixture modelling (GMM) was applied using the R package ‘mixtools’ (version 1.2.0) [40]. The distribution was converted into a common logarithmic scale before applying the GMM. The IIDs were fitted to the Gaussian mixture model using the Expectation-Maximization Algorithm. The estimated parameters were λi (mixing ratio), µi (average), and σi (SD). The number of Gaussian distribution components that should be applied to the IIDs was determined by selecting the lowest Bayesian Information Criterion of the models using 1–20 at each study site. IIDs less than 2 m were discarded from the GMM analysis because of the software measurement limitation.

Pseudo-distributions of IIDs were expected owing to the same height and angle of the drone camera. To discard pseudo-distributions, a null model of the distribution was created by calculating the distances between two sets of random points of the viewing angle that were randomly determined 100,000 times on the x- (0–631 m) and y-axis (0–355 m). Owing to technological limitations, one porpoise was always in the centre at the observation starting point from an altitude of 400 m, which was the expected bias of IIDs between porpoises. To overcome this problem, measurements were initiated 4 min after the drone rose from an altitude of 150 m. The average swimming speed of the Yangtze finless porpoises was 0.89 m/s [41]; therefore, the porpoises could exit from the angle of view within 4 min.

After fitting the GMM, the data were re-transformed into frequency distributions with a 5-m bin size. The distributions of Areas 1 and 2 were compared to the null model using the Kolmogorov–Smirnov test with a Bonferroni correction.

Acoustic analysis

Underwater sounds for ambient noise level measurements were recorded at five points in Area 1. From these five recordings, five 5-s duration sounds were randomly extracted without any artificial sounds and the ambient noise level was measured. The sound pressure levels (SPL; dB re 1 µPa RMS) of the ambient noise in the 1/3rd -octave bands with center frequencies of 125 kHz were calculated using MATLAB.

The distance from the phonating NRFP to the recorder was estimated using two A-tags. The angle was calculated using the time-of-arrival differences between the two A-tag hydrophones. The distance from the recorder and the intersection of the two angles calculated from the two A-tags (sound sources) were estimated. The sound velocity in the water was calculated from the salinity and water temperature using the Medwin equation [42]. Salinity and water temperature were measured before the sound recordings using a portable conductivity meter (CM-31P: DKK-TOA Corporation, Japan). The criteria for identifying on-axis clicks were the same as those of Kyhn et al. [43]: 1) recorded on all five hydrophones, 2) the maximum amplitude in an echolocation, and 3) the amplitude of the click was stronger than any reflections from the bottom or surface. The bandpass function in the Signal Processing Toolbox in MATLAB was used to pass from 112 to 140 kHz with a steepness of 0.85.

The apparent sound source levels (ASLs) of the peak-to-peak sounds were estimated according to Møhl et al. [44]. ASLs were used because the possibility that the sounds were indirectly and accurately produced by the sound recorder could not be eliminated. ASLs were calculated using the sonar equation: ASL = received level (RL) + transmission loss (TL). RL is the sound level recorded using a sound recorder. TL was estimated as follows; TL = 20 log (R) + Rα. R is the estimated distance from the sound source to the sound recorder, and α is the absorption coefficient that depends on the sound frequency. The α at the 100 kHz signal was calculated from the salinity and water temperature using the method described by Francois & Garrison [45]. The detectable distance was estimated to be 25 m from the result of Morisaka et al. [46], which was estimated to be 50 m (double the distance to our system) using a system with a 2.2 m distance between hydrophones. Other studies estimated 65 m recorded by 1.5-m apart hydrophones [43] and 60 m recorded by 2-m apart hydrophones [47]; therefore, the estimation of this study was conservative compared to other studies. Therefore, only clicks whose distances were estimated to be less than 25 m were used for further analyses.

The active space, which is the distance at which individuals of a species can detect conspecific signals, was estimated using the following equation [48]: SE = ASL – TL – NL – DT + DI + 10 × log₁₀ (T × W). SE indicates signal excess. Animals could detect a signal with a 50% probability at zero SE. ASL is the apparent source level of the sound of NRFPs, and NL is the average noise level of the five study points. The detection threshold (DT) was set to 68 dB according to Wang et al. [49]; this value was the average detection threshold of the four finless porpoises at 128 kHz. Kyhn et al. [50] suggested that the directivity index (DI) in Phocoenidae was similar; therefore, the DI was regarded as 25 dB. T × W refers to the time-bandwidth product of -10 dB duration (T) and − 3 dB bandwidth (W) of each pulsed signal. The − 10 dB duration was defined as between − 10 dB points on the envelope and calculated as the absolute value of the analytical waveform according to Kyhn et al. [51].

The measurement error steadily increased with scale length rather than height (Fig. S1). The error equation was as follows: measurement error = 0.033 × scale length – 0.189, and the correction equation was as follows: length after correction = measured length – (measured length × 0.033 + 0.189). The error after the correction was less than 1% (Fig. S2).

Videos were recorded for 2 h 35 min 24 s from 40 units (20 units from both Areas 1 and 2) (Table S1). The average number of fishing boats recorded in a video unit of the drone was 1.60 and 0.05 in Areas 1 and 2, respectively.

IIDs were measured from 633 images (275 and 358 in Areas 1 and 2, respectively) (Table S1). One NRFP was recorded in six units (three in Area 1 and three in Area 2), and five units were not recorded. The behavioural frequencies of foraging, milling, resting, socialising, and travelling were 45.0%, 5.0%, 50.0%, 45.0%, and 55.0% in Area 1 and 85.0%, 20.0%, 50.0%, 25.0%, and 65.0% in Area 2, respectively. The foraging frequency differed significantly between Areas 1 and 2 (Fisher’s exact test: p < 0.05), whereas others were not different (Fisher’s exact test: milling, p = 0.34; resting, p = 1; socialising, p = 0.32; travelling, p = 0.75). The distribution of the IIDs in both areas (Fig. S3) was divided into three clusters (Fig. 3; logarithmic scale) because the BIC converged to a similar value when the number of components was greater than three at both study sites (Fig. S4). The estimated parameters were (λi, µi, and σi) = (0.048, 0.758, and 0.213), (0.475, 1.906, and 0.318) and (0.477, 2.308, and 0.200) in Area 1, (0.094, 0.812, and 0.244), (0.311, 2.009, and 0.289) and (0.594, 2.395, and 0.154) in Area 2, respectively. The three peaks were p₁ (10^µ1) = 10^0.758 = 5.72 m, p₂ (10^µ2) = 10^1.906 = 80.60 m, and p₃ (10^µ3) = 10^2.308 = 203.24 m in Area 1, p₁ = 10^0.812 = 6.49 m, p₂ = 10^2.009 = 102.11 m, and p₃ = 10^2.395 = 248.19 m in Area 2. The mean ± SD of the null model was 262.64 ± 136.25 m. Kolmogorov–Smirnov tests showed a significant difference between the observed distance and the null model in both study areas in p₁ and p₂ (p₁: D = 0.901, p < 0.001, p₂ : D = 0.338, p < 0.001 in area 1, p₁: D = 0.958, p < 0.001, p₂: D = 0.296, and p = 0.004 in Area 2), but no difference in p₃ (D = 0.141, p = 0.482 in area 1, D = 0.141, and p = 0.482 in Area 2) (Fig. 4). There were no differences in the peak values between the two study areas (p₁: D = 0.056, p = 1; p₂ : D = 0.197, p = 0.127; p₃: D = 0.127, and p = 0.618) (Fig. 4). The p₁ and p₂ distributions in both areas, based on the probability distributions of the GMM, are shown in Fig. 5.

The average ambient noise level at the five points was 78.73 dB. Twelve on-axis clicks from 1.3 h of recording presented the mean ± SD of the ASL, -10 dB signal duration, -3 dB bandwidth, and peak frequency as 161.70 ± 5.30 dB, 13.01 ± 3.15 kHz, 72.92 ± 11.77 µs, and 124.59 ± 4.26 kHz, respectively. The active space was estimated as 72.15 m in the median (min: 37.80 m and max: 169.80 m) (Fig. 6).

IIDs in NRFPs were divided into three distributions. The first and second distribution peaks in both areas were significantly different from those in the null model, whereas the third peak did not show any significant differences. These results suggest that the first and second distributions were significantly closer in the IIDs than in the random estimation, and the third distribution reflects a random distribution within the limited angle of the camera. NRFPs may adjust their IIDs using two distributions. Moreover, the first and second peaks did not differ between areas, with different seasons and numbers of ships, implying that these IID peaks might be general trends in Ise Bay.

The first IID distribution peak was approximately 6 m (mean: 5.72 and 6.49 m in Areas 1 and 2, respectively) and might correspond to the maximum visual range in Ise Bay. The mean transparency in Ise Bay was reported as 1.9–5.0 m from mid-January to September, with season and location variations [33]. Some NRFPs may remain within visual range. In Yangtze finless porpoises, MCPs maintained proximity during lactation (approximately six months) [52]. In Ise Bay, neonate strandings occurred from late April to mid-July [37]; therefore, Area 1 had at least 7-month calves, and Area 2 had neonates and almost one-year individuals. Therefore, identifying MCPs by drone was problematic, but the data of this study included ‘possible MCPs’ within this range. However, several individuals of the same size were observed within this range, suggesting that individuals of non-MCPs remained in this range. In Yangtze finless porpoises, two adult males synchronised their dive depth with an immature female and juvenile male, suggesting close non-mother–calf associations [27]. Captive NRFPs show long-lasting affiliative non-MCPs [28]. Therefore, the first distribution or visual range peak possibly comprised not only the MCPs but also non-MCP individuals.

The second IID distribution peak was approximately 90 m (mean: 80.60 and 102.11 m in Areas 1 and 2, respectively) and might correspond to the audible range in Ise Bay. The active space was estimated as 72.15 m in median (min: 37.80 m, max: 169.80 m). NRFPs may use auditory cues to maintain IIDs. Captive NRFPs produce two types of communication sounds (burst pulses and packet sounds). Burst pulses are related to affiliative sexual behaviours, and packet sounds are produced as contact calls [30]. Such communication sounds were not recorded in Area 1. Communication sounds in captivity had a frequency above 100 kHz, similar to echolocation clicks [30]; therefore, the audible range estimation could be adapted for communication sounds. NRFPs may remain within the audible range of other individuals.

Other factors may affect IIDs, such as population densities, environmental differences (including anthropogenic disturbances), and behavioural differences (especially feeding) between locations. Population density may not differ between Areas 1 and 2 because NRFP density did not differ at water depths of less than 50 m in Ise Bay [34]. Environmental factors, including anthropogenic disturbances, especially the number of boats, differed between Areas 1 and 2. The behavioural frequency of foraging also differed between areas. Despite the differences between the two areas, the IID distribution peaks were similar, implying that environmental factors and behavioural differences did not affect IID distribution, which are general trends in Ise Bay, as previously mentioned.

This study showed that NRFPs use two different modalities to select appropriate IIDs, implying a non-simple social system. NRFPs are theorized to have a simple social system, generally residing alone or as MCPs [26]. However, the observation of adult male associations with an immature female and a juvenile male in Yangtze finless porpoises [27] and long-lasting affiliative non-MCPs in captivity [28] strengthens the hypothesis that they have a more ‘complex’ society than theorized. Individual identification is needed to investigate the ‘complexity’ of their society compared to other odontocetes, such as a multilevel society in killer whales and a society with high fission-fusion dynamics in Indo-Pacific bottlenose dolphins. However, it is difficult to identify NRFPs because of their lack of dorsal fins. The group sizes of this species (approximately 1–3 individuals, Kasuya & Kureha [53] were substantially smaller than those of killer whales (18 individuals [54]) and Indo-Pacific bottlenose dolphins (25–30 individuals [55]), implying less gregariousness in this species compared to other odontocetes. NRFPs may prefer fewer individuals (MCPs, affiliative pairs, and pairs related to breeding) within the visual range and maintain distances within the auditory range of other individuals. Yoshida et al. [56] reported unusually large groups with 82 and 117 individuals of NRFPs from Tachibana Bay, western Kyushu, but this would be ‘aggregation’ rather than a large functional social group [26].

The group sizes of harbour porpoises belonging to the same family Phocoenidae as NRFPs are similarly small (2.3 individuals [57]). This species has a strong relationship between the mother and calf only [58]. However, recent drone observations have demonstrated that they collaboratively hunted with six individuals [59]. Thus, harbour porpoises have a similar sociality level to NRFPs. The degree of social complexity of these two phocoenids compared to other mammals could not be determined. Group-living but solitary-foraging yellow mongooses (Cynictis penicillata) may have a comparable society because they have intermediate vocal repertoire sizes between group- and solitary-living foraging species [60]. This study suggests that NRFPs may have ‘intermediate’ sociality between solitary to complex sociality by using several cues to maintain IIDs.

Two non-random distributions of the IIDs of wild NRFPs were discovered in Areas 1 and 2 by drone. One distribution in this study corresponded to the visual range (6 m) and the other to the audible range (80–100 m). This suggests that NRFPs actively select visual and audible cues to maintain appropriate IIDs from other conspecifics. This may challenge the theory that this species has a simple social system.

Ethics approval and consent to participate

This study complied with the “Guidelines for Wildlife Research issued by the Wildlife Research Center of Kyoto University.”

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

Not applicable.

Funding

This research was supported by the Fujiwara Natural History Foundation and the Grant-in-Aid for JSPS Research Fellow (Grant Number 22J11913) for T.T. and JSPS KAKENHI (Grant Number 22H05651) for T.M.

Authors' contributions

TT and MT conceived the ideas and designed methodology; TT, YG, KI, and OK collected the data; TT analysed the data; TT, MT and YM led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgment

We are grateful to Hirokazu Matsuda and Takumi Okabe at Fisheries Research Laboratory, Graduate School of Bioresources, Mie University for their supports at area 1. We would like to thank Ayumu Furuyama, and Noboru Koda at Mie University for their support of our field research, and Tamao Maeda at The Graduate University for Advanced Studies, SOKENDAI and Yuka Mishima at Tokyo University of Marine Science and Technology for their advice on analytical techniques. We would also like to thank the members of the Laboratory of Fish Stock Enhancement at Mie University for their helpful comments.

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Bimodal distribution of inter-individual distance in free-ranging narrow-ridged finless porpoises—Potential use of visual and auditory cues

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Behavioral states and inter-individual distances

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