based blow sampling on blue whale

Notes MARINE MAMMAL SCIENCE, 00(00): 00–00 (January 2018) C 2018 Society for Marine Mammalogy V

DOI: 10.1111/mms.12482

Effect of drone-based blow sampling on blue whale (Balaenoptera musculus) behavior CARLOS A. DOMI´NGUEZ-SA´NCHEZ, Unit for Basic and Applied Microbiology, Autonomous University of Queretaro, Avenida de las Ciencias S/N, Juriquilla, Santiago De Queretaro, Queretaro 76230, Mexico; KARINA A. ACEVEDO-WHITEHOUSE , Unit for Basic and Applied Microbiology, Autonomous University of Queretaro, Avenida de las Ciencias S/N, Juriquilla, Santiago de Queretaro, Queretaro 76230, Mexico and the Marine Mammal Center, 2000 Bunker Road, Sausalito, California 94965, U.S.A.; DIANE GENDRON,1 Instituto Politecnico Nacional, Centro Interdisciplinario de Ciencias Marinas, Avenida IPN s/ n, Col. Playa Palo de Sta Rita, La Paz, Baja California Sur 23096, Mexico.

In the 21st century, scientific research that involves animals is under pressure to ensure it conforms as much as possible to the principles of the three Rs, that is, “replacement, reduction, and refinement” (Flecknell 2002, De Boo et al. 2005). While wildlife studies are no exception, it is often difficult to address biological questions without direct contact with the animals, often resulting in stressful capture and restraint protocols. In this sense, there have been various efforts to increase the number of minimally invasive sampling techniques available for wildlife and, in particular, cetaceans (e.g., Flores-Cascante and Gendron 2012, Hunt et al. 2013). Among these efforts, drone-based sampling has recently caught the attention of wildlife researchers (Ivosevi et al. 2015) as they have the potential to modernize the way their studies are conducted (Christie et al. 2016). Drones are robotic self-propelled aircraft that have no pilot onboard (Ivosevic´ et al. 2015, Sandbrook 2015) and are capable of flying autonomously (Marris 2013, Smith 2015) through the support of computers and sensors. While some authors (e.g., Finn and Wright 2012) make the distinction between devices capable of autonomous flight by following flight plans based on GPS coordinates (Van Gemert et al. 2015), and devices that need to be controlled by an operator with remote controls (Sandbrook 2015), throughout this paper we will use the term “drone” to refer to any unmanned aerial vehicle. The use of drones for monitoring or sampling marine vertebrates is rapidly gaining popularity, not least due to decreasing costs of purchase. Since the early proposal of using remotely controlled model helicopters to 1

Corresponding author (e-mail: [email protected]).

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sample whale exhaled breath condensate or blow (Acevedo-Whitehouse et al. 2010) to the more modern drones used to assess population parameters (Hodgson et al. 2013, Vermeulen et al. 2013, Bevan et al. 2015, Adame et al. 2017), researchers have been quick to appreciate the data-collecting opportunities that drones can offer to study free-ranging species (Pomeroy et al. 2015), particularly as functionality, autonomy, and availability increase (Goebel et al. 2015). The use of drones in conservation programs is still in the initials stages of development (Ivosevic´ et al. 2015, Christie et al. 2016). This new technology represents a low-cost and low-impact tool that can complement many ongoing conservation programs (Koh and Wich 2012). However, drones could also have negative effects on wildlife (e.g., in wild bears, the presence of drones increases their heart rate; in penguins, changes in the state of alertness of the entire colony have been recorded), the risk of which we currently have limited understanding (Ditmer et al. 2015, Christiansen et al. 2016, R€ummler et al. 2016, Weimerskirch et al. 2017). Wildlife can respond to drones in different and somewhat unpredictable ways depending on various factors, including the species being sampled, the environment, drone proximity to the individual, the type of drone, and the operator’s piloting skills (Hodgson and Koh 2016, Smith et al. 2016, Mulero-Pazmany et al. 2017). There are already proposed guidelines for approaching wildlife with drones, to mitigate disturbance (Vas et al. 2015, Hodgson and Koh 2016). In this context, as the use of drones becomes more popular for marine mammalogists, it is imperative to recognize whether such sampling methods are stressful for the species of interest and if so, to quantify the effect (Smith et al. 2016). Animal behavior in a broad sense should be evaluated before, during, and after drone flights (Hodgson and Koh 2016), and be measured by specific parameters. While reported observations of animal responses to drones are becoming available (e.g., Ditmer et al. 2015, Pomeroy et al. 2015, Vas et al. 2015), there is a need to acquire data across a range of animals and environments (Smith et al. 2016, Hodgson and Koh 2016), as effects are unlikely to be equal. These studies are also becoming essential to inform marine park managers, government authorities that issue research permits, and the whale-watching industry, to ensure better management of research and whalewatching activities. Here, we examined the effects of drone-based sampling of exhaled breath condensate (blow) on the behavior of blue whales, Balaenoptera musculus. Specifically, we investigated whether the use of a drone for collecting blow samples altered surface and diving behavior that may affect vital activities (e.g., feeding) as reported for other species (Williams et al. 2006, Lusseau and Bejder 2007, Senigaglia et al. 2016). The study was carried out in Loreto Bay National Park, situated in southwestern Gulf of California (Mexico) between 16 February and 19 March 2016 onboard a 7 m outboard skiff. The behavior data of blue whales was collected by continuous individual focal sampling (Altmann 1974) adapted to blue whales. Data from previous seasons were used in the meta-analysis study on cetaceans and whale-watching interactions (Senigaglia et al. 2016). For the purpose of our study, we only used data obtained during focal follows when no whale-watching boat was visible in the area. This is because the study area is used for whale-watching activities (Avila-Foucat et al. 2017) and presence of whale-watching boats could modify the behavior of the blue whales (Gendron and Busquets-Vass, unpublished data). During this procedure, the whale was photoidentified (Gendron and Ugalde de la Cruz 2012) and observed at a distance 100 m (range: 100–800 m), most often with the motor off, while its behavior was recorded (general activity, displacement patterns, diving behavior, and

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Figure 1. The quadracopter drone Phantom 2 with the camera GoPro Hero 31 (A) and the remote controller, used to collect blows samples. Photograph shows the floaters (B), Petri dishes (C), and microscope slides (D) attached to the base of the drone.

presence of other whales) as well as whether our boat engine was running or not. These data were recorded with a HP 95LX (Hewlett-Packard, Palo Alto, CA) palmtop computer. Dive and surfacing locations (to the nearest second) were obtained automatically by linking a 12X GPS (Garmin International, Olathe, KA) to the palmtop computer. To collect blue whale blows, we used a Phantom 2 quadracopter drone (DJI Innovations, Shenzhen, China) with a digital camera (GoPro Hero 31) attached to a Zenmuse H3-3D gimbal (Fig. 1). The drone relied on an iOSD Mark II flight data recorder and used wireless video transmission. Communication range with the flight controller was 300–500 m and was remotely operated from the deck of the boat. We attached two floaters, one on each side of the base of the drone, as a precautionary measure in case it fell into the ocean. We also attached one 10 cm Petri dish to each floater, as well as one microscope slide (Fig. 1). The added items weighed less than 50 g combined. We collected 4–5 blows during each flight. Take-off and landing were achieved by an operator, other than the pilot, that released and caught the drone by hand at the bow of the boat. The pilot positioned the drone above the whale by viewing live analog video-feed transmitted to a portable monitor. The approach to the whale with the drone was made from the caudal fin heading toward the head. An independent observer provided additional guidance of the drone’s position by using 8 3 10 field binoculars. Individual blue whales were followed for about 1 h prior to drone release, time during which the diving pattern was ascertained and it was possible to predict the timing of the whale’s next surfacing. Thus, the drone was prepared for flight 1 min before the whale was expected to surface. During the flight we started the boat’s engine and followed the drone in order to stay relatively close to it (approximately 200 m). The whale was further observed for at least 1 h after sampling. Because the altimeter of the drones is slightly inaccurate, we made daily ground flights prior to sailing to determine the height error. Flight height was adjusted to 1 m lower than the altimeter reading during sampling, as we aimed to sample the blow at roughly

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Figure 2. Drone-based blow collection from a blue whale in Loreto National Park, in the Gulf of California, Mexico. The red circle shows the position of the drone relative to the whale.

5 m above the whale (Fig. 2). The average sampling height (4.56 m, SD 5 0.77) was calculated from photographs of the drone taken during eight sampling attempts, in which we used the drone dimensions as a reference (Fig. 2). In total, we conducted 14 drone flights under the same environmental conditions (Beaufort scale 0–1). Average duration of each flight was 5.3 min (range: 4.1–11.4 min) and average distance covered during a flight was 300 m (range: 150–720 m). In total, we recorded 194 events (equal to surface and diving behaviors) that included presampling, sampling, and postsampling observations for the 14 drone flights. For each whale, we averaged the observations recorded for each sampling period. Observation periods ranged from 97 to 345 min (0 5 242 6 SD 77) for the whales sampled. Duration of time at the surface (“surface time”), length of each dive (“dive time”), and number of blows during surfacing (“blows per surfacing”) were obtained directly from the recorded data. The time between blows (“blow interval”), duration of surface time plus dive time (“full cycle length”), number of blows per minute per full cycle (“blow rate”), and proportion of time at the sea surface within a full cycle (“surface time proportion”) were calculated (see Dorsey et al. 1989). Prior to data analysis, the distribution of the data was examined. None of the variables deviated from a normal distribution, and there was no evidence of heteroscedasticity. Parametric tests were used to examine differences in the mean of each of the variables among sampling times (ANOVAs: comparisons among presampling, sampling, and postsampling values of surface time, number of blow intervals and blows per minute; t-tests: comparisons between presampling and postsampling values of diving duration, full cycle length, and surface proportion). Statistical significance was established at a 5 0.05. All analyses were conducted in R v.3.3.4 (R Core Team 2016). Graphs were built using package ggplot2 in R. There were no significant changes in surface (ANOVA, F2,34 5 1.16, P 5 0.32) and dive times (t 5 21.96, df 5 23, P 5 0.06) among sampling periods (Table 1).

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Table 1. Average values (mean 6 SD) of blue whale surface and diving behavior variables measured before, during, and after drone-based blow sampling. Behavior Surface time (min) Dive time (min) Blows per surfacing Blow interval (s) Blow rate (blows/min) Full cycle length (min) Surface proportion (%)

Before 2.53 6 0.34 10.80 6 0.81 11.08 6 1.39 13.73 6 1.65 1.21 6 0.13 13.38 6 0.91 19.29 6 1.94

During 2.54 6 0.44 — 11.61 6 1.53 13.39 6 1.13 — — —

After 2.43 6 0.23 9.97 6 1.10 10.75 6 1.49 13.62 6 1.44 1.18 6 0.13 12.49 6 1.34 19.53 6 1.64

Similarly, there was no difference in the number of blows per surfacing (ANOVA, F2,34 5 1.27, P 5 0.29), blow interval (ANOVA, F2,35 5 0.83, P 5 0.82), full cycle length (t 5 21.83, df 5 21, P 5 0.08), blows rate (t 5 20.47, df 5 20, P 5 0.64), and surface proportion (t 5 0.31, df 5 21, P 5 0.75). Surface and dive times were similar to results obtained during our field seasons of 2014–2016 from blue whales observed in the same study area (1.8 6 0.8 min and 8.3 6 3.6 min, n 5 586; DG, unpublished data), although our power was low for all tests (the highest being for dive time, where the power of the t-test was 0.59 and the effect size 0.859). The mean and variability of the data were extremely similar between groups, and in order to have adequate power to detect significant differences (1 2 b 0.8) we would have required a sample size in excess of 850 individuals, which would not have been feasible. Decreases in diving and surface times have been observed in response to anthropogenic activities such as whale watching of fin whales, Balaenoptera physalus (Stone et al. 1992) and blue whales (Gendron and Busquets-Vass, unpublished data). In the latter case study, the same method was used for data collection between 2009 and 2012. Thus, we believe diving and surface times are valid parameters to determine behavior change of whales in response to anthropogenic activities. It was interesting to note that the dive time was stable for up to six immersions after sampling (Fig. 3). After that (80.22 min later) we observed a decrease in the diving time that could plausibly be related to an unknown factor, but was unlikely related to the drone. There are many factors that can influence the duration of a whale’s dive. For instance, whale-watching traffic (see review in Parsons 2012), the behavior of the whale (foraging vs. not foraging), dive depth (Croll et al. 2001), and prey depth distribution (Croll et al. 1998). The apparent decrease in dive time that we observed 80 min after blow collection is unlikely to be related to the actual drone-based sampling, but will need to be investigated in more depth in future studies. As Smith et al. (2016) pointed out, little is known about the responses of individual marine mammals to the presence or absence of drones, and prior to this study, no quantitative study has been published on this issue for any cetacean, However, a few studies have briefly mentioned that no alteration in the whale behavior was observed in response to drone activity (Acevedo-Whitehouse et al. 2010; Durban et al. 2015, 2016; Goebel et al. 2015). On the other hand, Pomeroy et al. (2015) clearly showed that the platform used, behavioral state of the subjects, and flight altitude affect behavioral responses of seals to drones. As mentioned above, we approached the whales with the drone from the caudal fin, heading toward the head. However, on one occasion, due to a piloting problem, the drone approached the

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Figure 3. Duration of diving of the blue whales, recorded before and after drone-based sampling. The plot shows the median (thick line), first and third quartile (box), and 95% confidence interval of the median (whiskers).

whale from the head. On that occasion, the blue whale must have been able to see the drone, as a video of the episode clearly showed the whale turning on its side to take a look at the drone before diving prematurely. During the drone-based sampling, our boat stayed at least 200 m from the whales and only moved if the distance to the drone was greater than 200 m. Thus, in accordance with Pomeroy et al. (2015), the approach of the drone is probably the most important factor that may change a marine mammal’s behavior. In addition, the distance of the launching platform (vessel following the drone) could alter the behavior of a whale (Garrod and Fennell 2004, Parsons 2012). As for altitude of the drone, our use of a Phantom 2 quadracopter drone flown at about 5 m behind the whale’s head did not appear to cause changes in diving behavior, a finding that concurs with that reported by Christiansen et al. (2016), who studied the potential negative effect of noise from drones. These authors found that although drone noise may be heard by some marine mammals underwater, the effect of such underwater noise is probably small. As noted above, to the best of our knowledge, ours is the first study to quantify parameters (surface and diving time, blows per surfacing, blow

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interval, full cycle length, blow rate, and surface time proportion) to assess the effects of drone-based blow sampling in large cetaceans. We found no evidence that the observed diving behaviors were modified due to blow sampling, although our power to detect changes was low. Cetacean blow sampling has become popular in the past decade, as it can provide a myriad of information on stress (Thompson et al. 2014), reproductive status (Hogg et al. 2009), genetics (Fre`re et al. 2010), energetic cost of reproduction (Christiansen et al. 2016), microbiomes, and health (Acevedo-Whitehouse et al. 2010, Raverty et al. 2017). While the benefits of drone-based blow sampling of species that are otherwise virtually impossible to sample appear to outweigh potential negative effects, it is nevertheless necessary to monitor behavioral responses to different types of drones in order to generate guidelines for using this sampling technology for different species. Future studies should aim to increase sample sizes to attain greater statistical power and help determine whether behavioral responses to drone-based sampling are unequivocally null or minimal. This is particularly important, as not only is the use of drones for whale research growing (Hunt et al. 2013), but whale-watching tours have also started to incorporate the use of drones to film blue whales in our study area. Particular attention should be given to the approach path, which should only be done from tail to head, and should avoid flying in front or over the head. Furthermore, the minimum flight height should be determined based on the type of drone, and a minimum distance from the launching platform to the whale should be maintained when approaching the individual. Finally, we do not believe our results should be extrapolated per se to other species. Rather, species-specific drone flight and sampling protocols should be developed and implemented wherever possible. Only in this way will it be possible to reap the benefits of drone-based sampling and thus expand our knowledge of whales, while minimizing sampling stress to the animals.

ACKNOWLEDGMENTS This study was carried out in accordance with the recommendations and methodologies for approaching blue whales provided by Mexican legislation (NOM-059-SEMARNAT2010). All sampling was conducted under permit SGPA/DGVS/00255/16 issued by the Direccion General de Vida Silvestre. We thank Antonio Zamarron for his assistance during navigation, and Ana Sofia Merino, Madeleine Gauthier, Daniel Valdivia, and Ricardo Mirsha Mata Cruz for their help during fieldwork. CADS was funded by CONACYT (PhD Studentship 558253). Field work (sampling and navigation) was funded by The Instituto Politecnico Nacional (SIP20160496) and the Comision Nacional de Areas Naturales Protegidas (Programa de Conservacion de Especies en Riesgo).

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