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Article

A Drone Study of Sociality in the Finless Porpoise Neophocaena asiaeorientalis in the Ariake Sound, Japan

by
Naruki Morimura
*,
Akihiro Itahara
,
James Brooks
,
Yusuke Mori
,
Yige Piao
,
Hiroki Hashimoto
and
Itsuki Mizumoto
Wildlife Research Center, Kyoto University, 990 Ohtao, Misumi-machi, Uki 869-3201, Kumamoto, Japan
*
Author to whom correspondence should be addressed.
Drones 2023, 7(7), 422; https://doi.org/10.3390/drones7070422
Submission received: 15 May 2023 / Revised: 21 June 2023 / Accepted: 23 June 2023 / Published: 25 June 2023
(This article belongs to the Special Issue Drone Advances in Wildlife Research)
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Abstract

:
The social structure of animal populations is a fundamental component of their biology, influencing gene flow, habitat use, competition and co-operation around resources, and communication. However, ecological and social relationships can be challenging to describe in most marine mammals, who spend the majority of their lives underwater. The finless porpoise (Neophocaena asiaeorientalis) is one such cetacean species with a largely unknown social structure. Recent advances in drone technology enable more systematic surveys, photogrammetry, and photo-identification for diverse animal species. The present study aimed to validate new survey methods and provide a preliminary description of the spatiotemporal distribution of free-ranging finless porpoises in the coastal open-sea area of Ariake Sound, Japan. A vertical take-off and landing (VTOL) drone equipped with an action camera yielded GPS location datasets through line and area surveys, covering a total sea area of 120 km2. The results suggest highly flexible and varied aggregation sizes in finless porpoises. Distance analysis across individuals and aggregations revealed a cohesive tendency among groups, compared to solitaries and in pairs. Therefore, the present VTOL drone surveys both elucidated some social aspects of the study population and confirmed the efficacy of these standardized research protocols involving automated, programmed, and repeatable flight missions.

1. Introduction

Knowledge of the distribution and abundance of both terrestrial and marine mammals is crucial for planning and evaluating conservation strategies. This knowledge informs our understanding of intrinsic and extrinsic factors influencing species’ ecology, behavior, and reproduction [1,2]. Body size, age, sex, life history, prey availability, predation risk, and intraspecific competition could all be major influential factors on the distribution of species [3]. Moreover, some species, such as African elephants, some primates, and bottlenose dolphins, form social structures with fission–fusion societies, which can flexibly respond to the dynamic interaction of ecological variables, such as resource availability and predation risk [4,5,6]. For example, one previous study [4] illuminated that the size and presence of male alliances in a population of bottlenose dolphins (Tursiops aduncus and Tursiops truncatus) were correlated with predation risk and population density factors. Anthropogenic impacts, such as harvesting activity, fisheries by-catch, sound and other forms of pollution, and disease, can also have significant impacts on the distribution of marine mammals [2,7]. These studies illustrate that surveys of animal spatial distributions are crucial for understanding species’ sociality, and, likewise, that understanding species’ sociality is necessary to make informed conservation and management decisions.
Unmanned aerial vehicles (UAVs), or drones, enable more systematic surveys, photogrammetry, and photo-identification with unprecedented spatial and temporal resolutions, alongside lower cost and greater operational flexibility than traditional methods for wildlife research [8,9,10,11,12]. Cetacean observation using UAVs, for example, has major advantages compared to standard ship-based surveys through preventing the negative impacts of ship presence and noise on cetacean behavior patterns, activity budgets, and group size [13]. Fixed-wing Vertical Take-Off and Landing (VTOL) drones, in particular, can both perform flight missions from vessels or land without the need for extended takeoff or landing runs, as well as fly over target species groups with greater range/duration compared to multirotor drones. Ref. [14] point out that the ability to examine and review UAV video recordings of whale behavior post-flight and away from the water facilitates more accurate and trustworthy analyses of whale behavioral states, especially social behavior and nursing.
The narrow-ridged finless porpoise (Neophocaena asiaeorientalis sunameri) is distributed throughout the shallow (usually <50 m deep) coastal waters of Japan. It is small, endangered, and has no dorsal fin, seriously reducing its visibility and, therefore, the feasibility of detailed population monitoring from surface or vessel observations alone [15]. Their behavioral repertoire and sociality in the wild are largely unknown. One study, which was based on an aircraft survey, found that stable social bonds form at least between mothers and calves [16]. Drone observations also revealed coincident diving of aggregated groups of finless porpoises near boat traffic [17]. These studies suggest potentially substantial undescribed sociality in the species.
Therefore, the present study surveyed the presence and distribution of finless porpoises in the open-sea area of Ariake Sound. A VTOL drone with a camera and GPS device collected location data, and we quantified distances between individuals and aggregations. If stable social relationships are rare, individuals and aggregations of a few individuals are expected to be sparsely distributed, with large inter-aggregation distances [18]. Considering feeding resources, such as prey-fish schools, occasionally attract many porpoises, the distribution of aggregation sizes and inter-aggregation distances would, therefore, be expected to be bimodally skewed [19]. In contrast, if stable social relationships are more common, aggregation variables would be expected to vary more gradually from single individuals to larger social groups. Therefore, the present study aimed to confirm the efficacy of VTOL drone monitoring for this species and describe social aggregation patterns, comparing the distributions of free-ranging individuals across a series of aerial surveys with an automated protocol.

2. Materials and Methods

2.1. Study Area

Surveys were conducted in an area of 120 km2 off the Japanese coast in the marine waters of Ariake Sound (Figure 1) between June 2022 and January 2023. The launch location for the VTOL drone was set at an off-limit area of 30 m × 30 m at Ohtao Port (32°38′23.4″ N, 130°28′39.9″ E).

2.2. Drone Use and Flight Routes

The presence/absence of finless porpoises at the sea surface was recorded using a fixed-wing VTOL drone (makeflyeasy Co., Ltd., Fighter VTOL, Mianyang, China). The aircraft was automatically controlled with the QGroundControl (ver. 4.2.0) for Android OS (Dronecode Project, Inc.). Photographic data with GPS location were collected at 1-s intervals throughout the flights, using the time-lapse function of GoPro Hero 5 (GoPro, Inc.). The flight height of the drone was set at 149 m above sea level (ASL) throughout all flights. A 90° angle of view had an approximate area of 99 m by 74 m under these conditions. All flights were conducted from 9 a.m. until 2 p.m. The presence and noise of the aircraft had a limited impact on the animal’s surface behavior [20]. The observation was repeated on a schedule but canceled in unsuitable weather conditions.
The flight routes for the survey consisted of two line routes, namely the North and Yushima routes, and a single area route (Figure 1). The North route was a straight flight of 12 km from Ohtao Port to a turning point (32°43′53.4″ N, 130°33′04.8″ E) near the Kumamoto Ferry Terminal. The Yushima route was another straight flight of 15 km from Ohtao Port to a turning point (32°36′39.8″ N, 130°19′15.7″ E) near Yushima Island. The area route covered coastal water of 6 km by 11 km surrounded by the coordinates of 32°37′28.4″ N 130°24′55.0″ E, 32°39′49.2″ N 130°22′24.5″ E, 32°43′23.4″ N 130°28′04.7″ E, and 32°41′02.1″ N 130°30′35.6″ E. The aircraft repeated a 6-km straight flight 12 times with a 1-km-long interval from between the southern and northern ends. The total flight length of the area route was 95 km. The turning point on the North route was reduced to an 8-km distance from the launch location, while maintaining the same direction as the original route, because of the local fishery activity and radio wave interference when approaching a high-density human population. The other two routes remained constant throughout the surveys. All flights were monitored for surveillance by authors based on land and boat.

2.3. Data Analysis

2.3.1. The Size of the Animal Aggregations

Aggregations were defined based on photo sequences and collected at 1-s intervals. The flight speed was planned at 15 m/s throughout the survey (though the actual speed varied according to wind conditions), which indicates that the sea area in the angle of view had no overlap between photographs taken at a delay of ˃7 s. We, therefore, set a threshold for characterizing individuals/aggregations as distinct when there was no animal detected over seven consecutive images. Individuals that appeared in multiple photographs were counted only once, and the number of individuals in each aggregation was also counted to estimate the total number of individuals. Individual recognition was not feasible with this survey method, and, therefore, some individuals might have been counted twice (especially in the area survey). Water visibility during the study period was usually less than 2 m, but varied depending on the weather and tide conditions.

2.3.2. Distances across Solitaries and Aggregations

The GPS location data for each individual and aggregation were collected from a single photograph. When individuals or small aggregations were in captured in multiple photographs, the location of the photograph with the individual or aggregation closest to the center of the image was used. In the case of large aggregations spread out over more than one image, the location was chosen as the photograph with the largest number of individuals in a single image. Based on the location datasets, we quantified distances between the single individuals, dyads, and aggregations observed in each flight.

2.4. Statistical Analysis

The numbers of finless porpoises and aggregation sizes were compared between the line and area surveys to evaluate the performances along different flight routes using a Welch two-sample t-test. The detection effort was operationally defined as the flight distance per single individual detected, and the line and area surveys were compared using Welch two-sample t-tests. To analyze seasonal differences throughout repeated sessions in both surveys, the number of observed individuals was analyzed using Pearson’s product-moment correlation. Moreover, the distances between distinct individuals and aggregations were compared to explore a possible cohesive tendency using one-way analysis of variance (ANOVA), followed by Bonferroni’s post hoc tests. Statistical tests were performed using R statistical software, version 3.5.0 [21]. We considered values of p < 0.05 to be statistically significant. The null hypothesis was that there would be no tendency for social cohesion.

3. Results

The presence/absence survey of finless porpoises was conducted for seven sessions during the study period (Figure 2 and Table 1). The numbers of finless porpoises in the line (North and Yushima routes) and area surveys, on average (±standard error of the mean [SEM]), were 9.6 ± 2.5 individuals and 46.7 ± 16.1 individuals (t = −2.28, df = 6.29, p-value = 0.06), respectively. The mean aggregation size, including solitaries, in the line surveys was 1.3 ± 0.1 individuals, whereas that of area surveys was 1.6 ± 0.3 individuals (t = −0.62, df = 7.93, p-value = 0.55). In both survey types, the distribution of the observed individuals varied drastically between the seven sessions, indicating substantial variation in finless porpoise distributions over time in the survey area. Moreover, the values of detection effort, when defined operationally, were 12.8 ± 5.7 km and 9.6 ± 7.0 km in the line and area surveys, respectively (t = −0.34, df = 9.68, p-value = 0.74). Thus, the line and area surveys generally yielded quite similar detection and grouping patterns (Figure 3). However, the observed number of individuals increased as the area surveys were repeated from session #1 to #7 (r = 0.82, t5 = 3.23, p-value = 0.02), although those figures in the line surveys did not show such tendency (r = 0.57, t5 = 1.62, p-value = 0.16).
The area survey clarified the spatiotemporal distribution characteristics of finless porpoises on the sea surface in the study area (Figure 4). Solitaries and various sizes of aggregations widely appeared across the survey area. In sessions #1, #3, #5, and #7, the aggregation sizes varied gradually between one and nine individuals (Table 1). In contrast, in session #6, we observed over 50 individuals in relatively close proximity, segregating some as single individuals and some as aggregations (of 2, 16, and 33 individuals). Notably, no prey-fish school was observed on this occasion. Only solitary individuals and pairs were observed on the rest of the survey route in that session, suggesting that the distribution of aggregation sizes was bimodally skewed to extremely large and small values on this day. Thus, both possible distribution patterns described in the introduction were observed, on different days, over the course of the seven area survey sessions (Table 1 and Figure 4). In sessions #2 and #4, few individuals were observed, i.e., zero and two, respectively.
For the size frequency of aggregations in the area surveys (N = 157), solitaries were the most common (54.1%), followed by pairs (29.3%) and three individuals (7.0%), with decreasing proportions (Figure 5a). Solitary individuals and pairs were, therefore, the most common group size observed. Contrastingly, by focusing on the number of individuals (N = 327), the proportion of solitary individuals reduced to 26.0%, indicating that the majority of observed finless porpoises (N = 242) belonged to some type of aggregation (Figure 5b). We focused on the area surveys for these aggregation size analyses, as only six aggregations with more than three individuals were observed in the line surveys. Visual comparison of the aggregation distribution between the line and area surveys revealed a similar overall tendency, where the observed aggregation sizes varied with decreasing frequency from single individuals to larger social groups. In both survey types, the most common grouping size was solitary, though the majority of individual porpoises were observed in aggregations (Figure 6).
Inter-individual/aggregation distances can provide a basis for exploring characteristics of sociality, such as social behavior, group structure, and cohesion [22,23]. The distances across all inter-individual/aggregation combinations in the area surveys were calculated and compared. Observations were split into two types for comparison: singles and pairs (the sprinkling type) and aggregations with more than three individuals (the group type). Again, these analyses focused on the area surveys. The comparison revealed that the group types were more closely distributed (Figure 7), whereas the sprinkling types were relatively more dispersed throughout the survey area (one-way ANOVA; F2, 5403 = 52.90, p-value < 0.01). The mean distance across sprinkling-to-sprinkling types was 3581 ± 36.5 m, ranging from 18–10,857 m. The distance across sprinkling-to-group types was 3042 ± 50.0 m, on average, ranging from 33–10,519 m, although that of group-to-group types was the closest, being 2527 ± 104.0 m, on average, ranging from 31–5810 m. Thus, the group types showed more cohesion than the sprinkling types (post hoc tests: the distances of sprinkling-to-sprinkling vs. group-to-group, p < 0.01), indicating potentially distinct relationships between the group types compared to sprinkling types.

4. Discussion

The findings of the present study highlight dynamic spatiotemporal variation in narrow-ridged finless porpoises’ distribution and provide novel data on both the total number of individuals and the frequencies of their aggregation in this area. Using a fixed-wing VTOL drone, comparison of distribution patterns in different time periods could be conducted with automated and programmed flights over a total sea area of 120 km2. Those methods clarified that finless porpoises did form aggregations of a variety of sizes and, thus, were not living strictly solitarily or in pairs (Figure 4). Aggregations typically ranged in size from 1 to 9, but could occasionally reach sizes of more than 30 individuals. Furthermore, the majority of individual porpoises were observed in some type of aggregation. This observation suggests a potentially rich diversity of social interactions. The presence/absence surveys, therefore, hint at the possibility of highly flexible social relationships and grouping structures in the finless porpoise.
The average aggregation sizes of both line and area surveys in the present study (1.3 and 1.6 individuals, respectively) were similar to those of previous studies, such as 1.97 individuals recorded in a previous study of finless porpoises [16] and 2.32 individuals recorded in a study of harbor porpoises (Phocoena phocoena) [24]. Solitaries were the most frequent, followed by pairs, seemingly supporting the characterization of finless porpoise grouping structures as mostly individualistic (with some very small group sizes). However, when focusing on the number of individuals, a striking 76.0% of the observed individuals in the area surveys were observed in some kind of aggregation. Aggregation sizes were calculated based on an operational definition of the number of individuals photographed in a single image or a stream of sequential photographs with conspecific individuals. These aggregation size distributions can be interpreted in at least two ways. Firstly, porpoise aggregations could be assumed to typically represent small, stable groups with fixed membership. In this case, aggregation sizes ranging gradually from one to nine individuals would seem to indicate that some porpoises have the behavioral competence to form long-lasting social bonds among many individuals. The sharp discontinuity in aggregation size frequencies beyond nine individuals suggests against these aggregations representing mere resource-based groupings. This interpretation would, therefore, suggest a complex pattern of intra-group relations and the presence of unknown factors in determining group size. Alternatively, and possibly more parsimoniously given the present findings, porpoise aggregations could instead be more representative of flexible and transient association patterns without stable membership. Under this interpretation, finless porpoises may then develop more varied and dynamic inter-individual relationships that change across time. Future work will be necessary to verify these possibilities, but in either case, the current results suggest a higher degree of social complexity than has typically been assumed in this species.
The mean group-to-group distance (≥3 individuals) was smaller than the sprinkling-to-sprinkling distance in the open sea area (Figure 7), potentially suggesting a cohesive tendency across groups. In contrast, the sprinkling types (including solitaries and pairs) were widely dispersed throughout the survey area. The mechanism of this effect remains unclear. A recent acoustic study revealed pulsed signals in the communication of captive finless porpoises [25], but long-distance communication remains undetermined. The fact that the medium-sized groups (three to nine individuals) disappeared entirely in session #6 of the area survey (when the largest aggregation was detected) also indirectly supports the possibility of social communication (regardless of intentionality) in catalyzing the formation of large aggregations. Additional studies, especially those focusing on a structural analysis of groups and their ranges (using a VTOL drone at a relatively large scale), as well as cognitive experiments targeting captive groups, will be necessary to better understand the behavioral flexibility and variation underlying group formations, social structure, and communication in finless porpoises.
While the social system and social intelligence of dolphins and some other cetacean species have been more extensively discussed [26,27,28], finless porpoises have been thought to develop only rare and transient social relationships in a simpler social structure [16,29]. However, the present study found population distributions that suggested unexpected complexity, highlighting the need for both increased attention to finless porpoise sociality and re-evaluation of the social complexity of other marine mammals that may have been overlooked through traditional observation methods [30,31]. Nonetheless, there are several important limitations to this study in characterizing finless porpoise sociality. The drastic change in finless porpoise distributions between two types of group structure (skewed vs. gradual) in a relatively short period raises the question of how each individual contributes to the formation of large aggregations, as well as how these patterns are impacted by extremely localized valuable resources, such as a large prey-fish school, which we could not assess here. More generally, inability to identify individuals involves a risk of double counting individuals. This issue may be especially serious in the area surveys, where individuals and pairs may swim across the survey area over the course of a drone flight. Nonetheless, we do not have reason to suspect double counting may be more likely to occur for larger aggregations. On the other hand, some porpoises may have been associated with others who were too deep in the water for the drone to detect and, thus, counted as solitary (or as a smaller aggregation size). Actual aggregation sizes, as well as the proportion of individuals found in aggregations, may, thus, have been underestimated. In either case, our main findings are unlikely to be explained by these limitations alone. However, limited knowledge of the size and structure of home ranges, prey-fish resource distributions, and how other unmeasured variables may affect habitat use seriously limits the generalizability of our results, and this issue will require explicit research attention.
Furthermore, the drone flight techniques employed in the present study were primitive and will need refinement and improvement in future studies. The seasonal angle of sunlight on the water surface is one possible influential factor on visual detection [11]. Notably, the number of individuals observed in the last three sessions was four times larger than that observed in the first three sessions (303 vs. 75 individuals). This increasing tendency may, therefore, be related, in part, to the angle of sunlight on the water surface influencing the visibility of finless porpoises (due to the seasonal change in sun elevation, which is higher from ASL in summer than in winter in Japan). It is also possible this result is driven by ecological factors across seasons. Drone research protocols have the potential for further improvement by increasing flight range and duration, as well as decreasing intervals between flights. More frequent aerial surveys can also provide novel opportunities for evaluating and adjusting methods towards more sophisticated flight protocols. Comparing a variety of flight routes, sizes of survey areas, speeds, altitudes, and times of day can suggest more optimal techniques for efficient detection of finless porpoises, other animal species, and even other objects at the sea surface [32,33,34]. In any case, our observation techniques, which were facilitated by the use of a VTOL drone, enabled us to precisely quantify and visualize the spatiotemporal dynamics of overlapping activity between finless porpoises and humans at unprecedented resolution in a broad sea area. We hope future work can extend this approach and improve population monitoring efforts for endangered and understudied species.
In sum, the current study validated the efficacy of fixed-wing VTOL drone flights for population monitoring of narrow-ridged finless porpoises and, in so doing, discovered highly complex and undescribed social aggregation patterns. This outcome reinforces the feasibility of advanced drone technology as a tool for monitoring large coastal areas and probing the sociality of understudied species, all through entirely automated, programmed, and repeatable flight routes. Conservation and management programs crucially depend on our understanding of species’ distribution and social structures, and with high ocean biodiversity [35,36,37], coastal areas are prime targets for intensive monitoring. These efforts will undoubtedly be essential for protecting narrow-ridged finless porpoises and other endangered species, as well as for understanding the diversity and evolution of animal sociality [38,39].

Author Contributions

Conceptualization, funding acquisition, methodology, N.M.; investigation N.M., A.I., J.B., Y.M., Y.P., H.H. and I.M.; formal analysis, N.M.; Writing—original draft, Writing—review and editing, N.M., A.I., J.B. and Y.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Mitsui and Co. Environment Fund, Grant/Award (K18-0098) to Naruki Morimura, and the Collaboration Research Program of IDEAS, Chubu University (IDE-AS201714/IDEAS201818/IDEAS201918/IDEAS202119/IDEAS202216).

Institutional Review Board Statement

The study protocol was approved by the Institutional Committee of the Wildlife Research Center of Kyoto University (permit no. WRC-2022-004). The registration ID of the aircraft was JU42269C1562. Drone flights were approved by the Osaka Regional Civil Aviation Bureau (permit no. 31326). Kumamoto Coast Guard supported the security of boat traffic control and related human activity.

Data Availability Statement

The dataset generated during the current study is not publicly available, but is available from the corresponding author on reasonable request.

Acknowledgments

We thank Yujiro Kamimura and Wataru Hamasaki from the Kumamoto Drone Technology and Development Foundation; Makoto Kitano and Kyouko Myose from CLIMAX Co., Ltd.; and Tatsuya Sato from Mirai Legal Service Office Co., Ltd. for supporting the aircraft development, flight practices, and administrative proceedings required for the research. We thank Daisuke Fukushima and the local fishers from the Japan Fisheries Cooperative, Misumimachi, for carrying out drone surveillance by boat. We also thank Hiromichi Fukui, Satoru Sugita, and Hiroshi Inoue for providing technical drone expertise at the early stage of development, as well as all staff of the Kumamoto Sanctuary of Kyoto University for their support during daily observations.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The study area in Ariake Sound, Japan. Solid lines indicate the North, Yushima, and Area flight routes on the Digital Map (Basic Geospatial Information) published by the Geospatial Information Authority of Japan.
Figure 1. The study area in Ariake Sound, Japan. Solid lines indicate the North, Yushima, and Area flight routes on the Digital Map (Basic Geospatial Information) published by the Geospatial Information Authority of Japan.
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Figure 2. Enlarged sample picture of finless porpoises in the present study (taken during session #6). Arrows indicate finless porpoises.
Figure 2. Enlarged sample picture of finless porpoises in the present study (taken during session #6). Arrows indicate finless porpoises.
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Figure 3. Comparisons of the average number of individuals (bar chart) and detection efforts (line chart; mean ± SEM) between the area and line surveys.
Figure 3. Comparisons of the average number of individuals (bar chart) and detection efforts (line chart; mean ± SEM) between the area and line surveys.
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Figure 4. The distributions of solitaries and aggregations for each session in the area survey. Heatmaps corresponding to the aggregation sizes and the numbers across sessions are drawn on the satellite images of © OpenStreetMap. No individual was observed in session #2.
Figure 4. The distributions of solitaries and aggregations for each session in the area survey. Heatmaps corresponding to the aggregation sizes and the numbers across sessions are drawn on the satellite images of © OpenStreetMap. No individual was observed in session #2.
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Figure 5. Proportions (%) of aggregation size frequency (a) and individual numbers (b) belonging to solitaries and aggregations of 2, 3, 4, 5, and ≥6 individuals in the area surveys.
Figure 5. Proportions (%) of aggregation size frequency (a) and individual numbers (b) belonging to solitaries and aggregations of 2, 3, 4, 5, and ≥6 individuals in the area surveys.
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Figure 6. Comparison of size frequency of aggregations between area (a) and line (b) surveys.
Figure 6. Comparison of size frequency of aggregations between area (a) and line (b) surveys.
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Figure 7. Comparisons of distance-class distributions of group-to-group, group-to-sprinkling, and sprinkling-to-sprinkling aggregations. The distance class was set at a 100-m interval, with D000 representing the distance range from 0–99 m.
Figure 7. Comparisons of distance-class distributions of group-to-group, group-to-sprinkling, and sprinkling-to-sprinkling aggregations. The distance class was set at a 100-m interval, with D000 representing the distance range from 0–99 m.
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Table
Table 1. Observation of conditions and performances for each session in the area and line surveys. Start times in the line surveys indicate the North and Yushima routes, respectively.
Table 1. Observation of conditions and performances for each session in the area and line surveys. Start times in the line surveys indicate the North and Yushima routes, respectively.
AB Frequency of Aggregation Sizes (#Individuals)
Survey Type
-Session#		Start Time-DateRoute
Length
(km)		#IndividualsDetection
Effort
(A/B; km)		Mean
Aggregation
Size		#1#2#3#4#5#6#7#8#9#16#33
Area #0110:56, 27 June 202295.0293.31.96612
Area #0210:56, 4 August 202295.00n/an/a
Area #0310:40, 30 August 202295.0273.51.41451
Area #0410:57, 22 September 202295.0247.51.02
Area #0510:17, 27 December 202295.0741.31.9181234 1
Area #0610:23, 6 January 202395.0911.02.82011 11
Area #0710:20, 11 January 202395.01040.92.125126121 11
Line #019:39 and 12:56, 27 June 202256.7511.31.05
Line #029:41 and 12:56, 4 August 202256.7318.91.03
Line #039:43 and 12:36, 30 August 202256.7115.21.4611
Line #049:50 and 12:54, 22 September 202256.7144.11.6531
Line #059:35 and 12:21, 27 December 202244.4144.41.01
Line #069:38 and 12:15, 6 January 202344.4182.51.67211
Line #079:34 and 12:28, 11 January 202344.4153.01.9332
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MDPI and ACS Style

Morimura, N.; Itahara, A.; Brooks, J.; Mori, Y.; Piao, Y.; Hashimoto, H.; Mizumoto, I. A Drone Study of Sociality in the Finless Porpoise Neophocaena asiaeorientalis in the Ariake Sound, Japan. Drones 2023, 7, 422. https://doi.org/10.3390/drones7070422

AMA Style

Morimura N, Itahara A, Brooks J, Mori Y, Piao Y, Hashimoto H, Mizumoto I. A Drone Study of Sociality in the Finless Porpoise Neophocaena asiaeorientalis in the Ariake Sound, Japan. Drones. 2023; 7(7):422. https://doi.org/10.3390/drones7070422

Chicago/Turabian Style

Morimura, Naruki, Akihiro Itahara, James Brooks, Yusuke Mori, Yige Piao, Hiroki Hashimoto, and Itsuki Mizumoto. 2023. "A Drone Study of Sociality in the Finless Porpoise Neophocaena asiaeorientalis in the Ariake Sound, Japan" Drones 7, no. 7: 422. https://doi.org/10.3390/drones7070422

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