Conservation biology is entering a new era of innovation, with unprecedented growth across a range of techniques, from genetics and genomics to telemetry and remote sensing (Allendorf et al., 2010; Hussey et al., 2015). Rapid advances in the technology underpinning Unmanned Aerial Vehicles (UAVs also known as Unmanned Aircraft Systems or drones), are driving new and innovative environmental applications (Koh and Wich, 2012; Anderson and Gaston, 2013; Christie et al., 2016; Smith et al., 2016; Duffy et al., 2017). The application of UAVs in conservation science makes it possible to collect information from dangerous and inaccessible environments and answer research questions that were previously limited to the hypothetical (Harvey et al., 2016). UAVs also provide an alternative, safer, quieter and often cost-effective option for monitoring fauna and flora, from individuals and populations to entire ecosystems, and in so doing are replacing expensive manned systems such as helicopters and fixed-wing aircraft (Christiansen et al., 2016; Christie et al., 2016). UAV applications in wildlife research now encompass almost all environments, from arid deserts, through rainforests, oceans to polar regions (Linchant et al., 2013, 2015; Durban et al., 2015; Goebel et al., 2015; Duffy et al., 2017).

UAVs are transforming the way scientists monitor and conserve wildlife (Gonzalez et al., 2016). In the terrestrial world, UAVs have been used for a wide variety of conservation applications (van Gemert et al., 2014; Gonzalez et al., 2016). Some examples include, counting elephants (Loxodonta africana) (Linchant et al., 2013; Vermeulen et al., 2013), UAV surveillance (anti-poaching tools) for elephants and rhinoceros (Diceros bicornis and Ceratotherium simum) (Marks, 2014; Mulero-Pázmány et al., 2014; Hahn et al., 2017), locating chimpanzee nests (Pan troglodytes) (van Andel et al., 2015) and mapping Sumatran orangutan (Pongo abelii) habitat, distribution and density (Wich et al., 2015; Szantoi et al., 2017). UAV applications now extend to the polar regions where they have been used to monitor and estimate abundance of penguin populations (gentoo, Pygoscelis papua, and chinstrap, Pygoscelis antarctica) and estimate size and condition of leopard seals (Hydrurga leptonyx) (Goebel et al., 2015; Ratcliffe et al., 2015). In the marine environment, UAVs are revolutionizing the way marine species can be studied due to their small size, apparent minimal disturbance of wildlife and improved safety for both operators and animals (Nowacek et al., 2016; Fiori et al., 2017). UAVs have been utilized for a wide variety of applications including aerial surveys, monitoring, habitat use, abundance estimates, photogrammetry and biological sampling e.g., whale “blow” (Hogg et al., 2009; Acevedo-Whitehouse et al., 2010; Hodgson et al., 2013; Durban et al., 2015; Pomeroy et al., 2016; Schofield et al., 2017).

There are widespread concerns about the health of marine mammal populations in the face of global anthropogenic stressors (Gulland and Hall, 2007). Yet health assessments typically involves collecting samples from stranded animals, which are often biased as these animals are most likely to be health-compromised (Geraci and Lounsbury, 2005). Sampling exhaled breath or ‘blow' from wild whales may therefore provide a more representative assessment of the health status of individuals because samples can be randomly taken from the population. From a single sample of whale blow, scientists may be able to collect respiratory bacteria, lipids, proteins, DNA and hormones (Hogg et al., 2005, 2009; Schroeder et al., 2009; Acevedo-Whitehouse et al., 2010; Hunt et al., 2013, 2014; Thompson et al., 2014; Burgess et al., 2016; De Mello and De Oliveira, 2016; Raverty et al., 2017). This information is important for whale conservation, as it can be collected over time to help monitor the recovery of whale populations post-whaling. Early approaches to sampling whale blow involved passing a cotton gauze or nylon stocking on the end of a carbon fiber pole through the blow when the animal surfaced (Hogg et al., 2009; Hunt et al., 2014). Recent advancements on this method have seen the use of a pole with a number of petri dishes with lids to sample wild killer whales (Raverty et al., 2017). However, this method requires extremely close vessel approaches to whales (Hogg et al., 2009). Given the large size, mass and power of whales, this approach involves high risk to both researchers and to the whale itself. Even under ideal circumstances this method is likely to disturb the animal, potentially compromising the validity of some of the measures such as stress hormones which elevate rapidly (Harcourt et al., 2010). Accordingly, alternative approaches have long been sought. Acevedo-Whitehouse et al. (2010) deployed a single-rotor UAV (a remote-controlled helicopter) to sample whale blow. Their study demonstrated the feasibility of the approach but loss of samples from the UAV as it careers through the sea air proved a potential issue as did contamination from airborne particulate not expired by the whale.

Here we describe a purpose-built UAV designed to sample whale blow in the field with minimal contamination. Our goal was to provide a snapshot of whale health. We specifically targeted northward migrating humpback whales (Megaptera novaeangliae) off the East coast of Sydney, Australia for the collection of baseline microbiota information. The UAV used in our study has a unique combination of features that represent a significant advance over existing UAVs. It is fast, highly maneuverable, durable, waterproof, low-cost (< $USD 1000) and provides flexible payload mounting options. The UAV is scaled to the sampling gear (in this case a 100 mm petri dish), which is held in a mechanism that allows the dish to be opened/closed during flight–minimizing sample contamination or loss.

A total of 74 flights were conducted over 4 days of sampling. Each pod was considered independent as all whales were on their annual northern migration (Pirotta et al., 2016). Overall, 59 successful samples were collected from at least 48 different whales (11 whales were sampled but not identified via video due to occasional failure of the GoPro® camera e.g., low battery or maximum storage capacity reached). Sample volume varied between 50 and 150 μl of exhaled breath. The average opening time of the flip lid was 4 s (min 2 s, max 6 s). The UAV had a maximum flight time (battery time) of 15 min and sampling attempts on average were 4 min 28 s long (range: 27 s to 7 min). The majority of flight time was used to search for the whale's next surfacing position. The time that the UAV was in close proximity to a whale (UAV approximately within 5 m horizontal distance) varied but was on average 53 s (range: 2 s to 2.36 min or 141 s). The most number of samples collected in 1 day was 38. In all cases, there was no behavioral response to the drone (level 1, n = 48). Twice there were strong social interactions that occurred prior to the drone approaching the whales (one tail slap, one breach) but sampling was continued on the group in each case and samples successfully collected.

UAVs are rapidly transforming the way scientists collect information on their study species (Christie et al., 2016; Lowman and Voirin, 2016; Nowacek et al., 2016; Duffy et al., 2017). In whale research, UAVs have enabled sampling methods to be refined and have eliminated the need for close vessel approaches. To our knowledge, this study is the first to successfully demonstrate the use of a purpose-built UAV designed to sample humpback whale blow in Southern Hemisphere waters. The minimal behavioral disturbance observed suggests this method is an excellent, low-impact alternative to pole sampling methods for large, migrating whales. Humpback whales may have been aware of the UAV and did not react or, mostly likely, were not even aware of the UAV's presence. Underwater noise generated from the UAV was likely to be very low level at the heights flown (< 10 m), as it is smaller, lighter and has a lower disc loading than comparable off-the-shelf UAVs shown to transmit minimal noise transmission underwater (e.g., SwellPro Splashdrone and the DJI Inspire 1 Pro) (Christiansen et al., 2016). The combination of the waterproof design and the remotely operated flip lid petri dish designed to minimize airborne contamination, is a significant improvement over existing UAV types.

Our results demonstrate that whale blow can be effectively sampled while minimizing species associated with likely sources of contamination, i.e., air and seawater, to define microbes specifically associated with whales. Amplification of DNA extracted from UAV-captured air highlights the sensitivity of PCR-based approaches for detecting microbiota, even from low amounts of extracted DNA, while also demonstrating the sensitivity of this approach to contamination from external sources. The development of a flip-lid sampling system using sterile petri-dishes enabled us to effectively reduce contamination from typical seawater bacteria, which may exist in aerosols above the sea surface. While the presence of abundant seawater species (Alphaproteobacteria SAR11 and cyanobacteria) in air and whale blow samples is not surprising, the source of some major species detected in air samples is less clear. Some of the most abundant species detected in air samples, Propionobacteria, Arthrobacter, and Staphylococcus, are common commensal organisms of mammalian (human) skin and nasal cavities (Human Microbiome Project Consortium, 2012; Prussin and Marr, 2015). A potential source of some non-marine material may have been contamination during the DNA extraction or amplification procedure, especially when the amount of captured material was low (i.e., for air or near-miss samples). In the context of developing indicators of whale health the presence or absence of species that are common in humans should be interpreted cautiously. Nevertheless, in the UAV-sampled blow where a sufficient amount of material was collected, our analyses indicate that ~70% of the total sequences were specific to whales, a group of whale associated sequences accounted for a further ~12% and the remainder could be confidently identified as seawater- or air-specific.

To our knowledge this is the first study to utilize a long-term seawater dataset to identify and subtract seawater bacteria from community profiles of field-captured mammalian samples. The seawater data provided a comprehensive, temporal assessment of the composition of microbial communities present in sea water off Sydney. Critically, a much larger quantity of seawater was collected (2 L) and analyzed in comparison to the whale samples. This method minimized the impact of external sources of contamination and allowed for the greater coverage of the seawater community diversity. We used this resource to filter out all sequences characteristic of seawater to produce a whale blow dataset that could be used as a diagnostic for whale health. The distinct differences observed between statistically-defined bacteria in whale, sea water and air samples indicates that this method was effective for collecting whale microbiota with minimal contamination.

The successful collection of bacterial DNA in this study provides baseline information of microbiota found in migrating humpback whale blow. Due to the infancy of sampling whale breath as an assessment of whale health (Acevedo-Whitehouse et al., 2010; Hunt et al., 2013), it is not clear as to the type of microflora/bacteria species that are considered ‘normal' for northward migrating humpback whales off Sydney. Despite this, there are similarities in our collection of bacterial genera from the few studies that have collected blow for the assessment of microbiota (Acevedo-Whitehouse et al., 2010; Denisenko et al., 2012; Hunt et al., 2013). For example, Streptococcus and Staphylococcus genera were detected in our samples and have been detected in the blow of blue whales (Balaenoptera musculus), gray whales (Eschrichtius robustus) and Southern resident killer whales (Acevedo-Whitehouse et al., 2010; Denisenko et al., 2012; Hunt et al., 2013; Raverty et al., 2017). Bacteria from the Streptococcus genus is common in mucous membranes of animals (and humans) and is known to be found in the upper respiratory tract (Krzyściak et al., 2013). Streptococcus bacteria has previously been responsible for pneumonia causing death in cetaceans (Acevedo-Whitehouse et al., 2010). Bacillus sp. was also identified via blow collection from western North Pacific gray whales and Southern resident killer whales (Denisenko et al., 2012; Hunt et al., 2013; Raverty et al., 2017).

Next generation sequencing identified Cardiobacteriaceae (family) and Tenacibaculum (genus) to be the most abundant bacterial rRNA genes in whale blow. Cardiobacteriaceae has previously been isolated as a dominant taxa in the respiratory system of “healthy” captive bottlenose dolphins (Tursiops aduncus and, T. truncates) and free-ranging species (T. truncates) (Johnson et al., 2009; Lima et al., 2012). These findings may indicate that these genes are part of the normal microflora of dolphins, whilst presence in whales until now was unknown. Cardiobacteriaceae are abundant on humpback whale skin (Gammaproteobacteria genus), as is Tenacibaculum (Apprill et al., 2011, 2014). It may be possible that bacteria found on whale skin also occur within the respiratory tract or epithelial cells. Tenacibaculum has been associated with the microbiome of other marine species such as southern bluefin tuna (Thunnus maccoyii castelnau) (Valdenegro-Vega et al., 2013), while Psychrobacter is part of the thresher shark and rainbow trout skin microbiome (Lowrey et al., 2015; Doane et al., 2017).

The collection of bacterial microbiota is as an indicator of cetacean health is growing (Hogg et al., 2009; Schroeder et al., 2009; Acevedo-Whitehouse et al., 2010; Lima et al., 2012; Hunt et al., 2013; Nelson et al., 2015; Raverty et al., 2017). We were able to sample a number of individuals from a single population over a very short time frame. The use of the waterproof GoPro® camera made identification of different individuals reliable and therefore reduced repeated sampling. Our remotely operated “flip dish” design proved effective at reducing possible contamination from the pilot/research team (e.g., breath, touch, clothing) and vessel vapor/fumes. The placement of Parafilm® around the dish after sampling ensured that the sample remained unexposed until back in the laboratory for processing. Recently published work by Burgess et al. (2016) found polystyrene dishes (petri dish) to be the most effective surface for sampling whale blow in comparison to other sampling materials like veil nylon and nitex nylon mesh. In addition, the use of ice chilling of our samples for temporary storage was also consistent with Burgess et al. (2016), which found storage in cooler box with ice packs was appropriate for preserving samples (at least for hormones) for daylong fieldwork at sea (<6 h). Our samples only contained a fine mist [we estimated between 50 and 150 μL per sample, similar to amounts collected by Hogg et al. (2009)], and so we were unable to directly pipette samples but we found that swabbing the non-agar lid of the petri dishes to be effective. Variability in blow sample volumes appear to be a common issue (Hogg et al., 2009; Acevedo-Whitehouse et al., 2010) and therefore the need for repeated sampling is recommended. Sample success increased with effort/experience and we recommend effort be made early in any study to improve pilot skill, sample collection, quality and quantity.

While overall highly successful, UAVs still require a high level of skill and effort. Predicting when the whale is about to surface, positioning the UAV and opening the petri dish in time remains challenging. This may be complicated when a whale comes to the surface to breath but does not respire forcefully. When this happens, the plate is exposed to the air and so the UAV must return to the boat so the petri dish can be exchanged, our miss/near-miss rate was 11/59 = 20%. Second, not using an off-the-shelf product requires a high level of UAV competence both to fly and to fix problems as they arise. Third, the flight time for this UAV is 15 min, restricting the number of opportunities for sampling before the UAV must return to the vessel in order to replace the battery. Flight time will increase as battery technology progresses (Nowacek et al., 2016).

Our dataset details the diversity and abundance of the microbiota found in a migrating whale population which provides the baseline to identify pathogenic species. Ultimately, the isolation of pathogens from healthy or diseased animals will be an important step toward understanding the causes of disease and the factors that contribute to virulence. Culture-dependent techniques remain a viable option for the surveillance of pathogens in populations. In this study, nutrient agar was an effective way of culturing a subset of whale blow microbiota, including species commonly associated with respiratory disease in mammals. The use of both sides of the petri dish effectively doubled the chance of obtaining bacterial samples. While next generation sequencing has the capacity to probe the diversity of whale blow microbiota, at present, the isolation and identification bacteria from agar plates can be achieved within 3–5 days, compared to a practical timeframe of weeks for illumina sequencing. Selective media could be used to target potential pathogens in conjunction with opportunistic sampling of diseased or distressed animals.

Our purpose-built UAV proved highly successful in sampling whale blow for microbial community analysis. It is cost-effective, has low risk of contamination and greatly reduces disturbance of whales. Future applications include other free-ranging whale species (e.g., southern right whales, Eubalaena australis), as well as sampling smaller cetaceans (e.g., dolphins). Our UAV is useful addition to the conservation scientist's tool box, enabling collection of health information and therefore the ability to monitor changes in individual health as populations recover and to provide an early warning system for potential future changes.

Paper conception: VP, AS, RH, MO, IJ, and AG. Experiment design: VP, AS, RH, and IJ. Field work: VP, AS, and RH. Laboratory work: VP, DR, and MO. Analysis and interpretation of data: VP, MO, RH, IJ, AG, and DR. Wrote paper: VP, MO, RH, IJ, AG, DR, and AS.