Closing the air gap: the use of drones for studying wildlife ecophysiology

Techniques for non-invasive sampling of ecophysiological data in wild animals have been developed in response to challenges associated with studying captive animals or using invasive methods. Of these, drones, also known as Unoccupied Aerial Vehicles (UAVs), and their associated sensors, have emerged as a promising tool in the ecophysiology toolkit. In this review, we synthesise research in a scoping review on the use of drones for studying wildlife ecophysiology using the PRISMA-SCr checklist and identify where efforts have been focused and where knowledge gaps remain. We use these results to explore current best practices and challenges and provide recommendations for future use. In 136 studies published since 2010, drones aided studies on wild animal body condition and morphometrics, kinematics and biomechanics, bioenergetics, and wildlife health (e.g. microbiomes, endocrinology, and disease) in both aquatic and terrestrial environments. Focal taxa are biased towards marine mammals, particularly cetaceans. While conducted globally, research is primarily led by institutions based in North America, Oceania, and Europe. The use of drones to obtain body condition and morphometric data through standard colour sensors and single camera photogrammetry predominates. Techniques such as video tracking and thermal imaging have also allowed insights into other aspects of wildlife ecophysiology, particularly when combined with external sampling techniques such as biologgers. While most studies have used commercially available multirotor platforms and standard colour sensors, the modification of drones to collect samples, and integration with external sampling techniques, have allowed multidisciplinary studies to integrate a suite of remote sensing methods more fully. We outline how technological advances for drones will play a key role in the delivery of both novel and improved wildlife ecophysiological data. We recommend that researchers prepare for the influx of drone-assisted advancements in wildlife ecophysiology through multidisciplinary and cross-institutional collaborations. We describe best practices to diversify across species and environments and use current data sources and technologies for more comprehensive results.

Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community.

Harbour porpoises respond to recreational boats by speeding up and moving away from the boat path

Recreational boats are common in many coastal waters, yet their effects on cetaceans and other sensitive marine species remain poorly understood. To address this knowledge gap, we used drone video footage recorded from a recreational boat to quantify how harbour porpoises (Phocoena phocoena) responded to the boat approaching at different speeds (10 or 20 knots). Furthermore, we used a hydrophone to record boat noise levels at full bandwidth (0.1-150 kHz) and at the 1/3 octave 16 kHz frequency band for both experimental speeds. The experiments were carried out in shallow waters near Funen, Denmark (55.51° N, 10.79° E) between July and September 2022. Porpoises were more likely to move further away from the path of the boat when approached at 10 knots, but not when approached at 20 knots. In contrast, they swam faster when approached at 20 knots, but not when approached at 10 knots. The recorded received sound level did not depend on how fast the boat approached, suggesting that differences in porpoise responses were related to the speed of the approaching boat rather than to sound intensity. In addition, porpoises generally reacted within close proximity (<200 m) to the approaching boat and quickly (<50 s) resumed their natural behaviour once the boat had passed, indicating that the direct impact of small vessels on porpoise behaviour was most likely small. Nevertheless, repeated exposure to noise from small vessels may influence porpoises' activity or energy budget, and cause them to relocate from disturbed areas. The approach used in this study increases our understanding of recreational boats' impact on harbour porpoises and can be used to inform efficient mitigation measures to help focus conservation efforts.

Confirmation of significant sea turtle nesting activity on a remote island chain in the Gulf of Mexico

Globally, six of the seven sea turtle species are threatened or endangered and as such, monitoring reproductive activity for these species is necessary for effective population recovery. Remote beaches provide a challenge to conducting these surveys, which often results in data gaps that can hamper management planning. Throughout the summer of 2022, aerial surveys were conducted over the Chandeleur Islands in the Gulf of Mexico. Turtle crawls were photographed for subsequent review by 10 expert observers. Whenever possible, ground surveys were conducted, and samples of unhatched eggs or dead hatchlings were collected. A summary of historic reports of sea turtle nesting activity at this site was also compiled. On 11 days between May 4, 2022, and July 30, 2022, photographs of 55 potential sea turtle crawls were taken. Observers identified 54 of those as being made by a sea turtle. There was high-to-moderate certainty that 16 of those crawls were nests, that 14 were made by loggerheads, and that two were made by Kemp's ridleys. Observers were least certain of species identification when surveys were conducted during rainy weather. Genetic analyses based on mitochondrial and nuclear DNA were conducted on samples from five nests and those analyses confirmed that three nests were laid by Kemp's ridleys and two were laid by loggerheads. Historic records from the Chandeleur Islands substantiate claims that the Chandeleurs have supported sea turtle nesting activity for decades; however, the consistency of this activity remains unknown. Our aerial surveys, particularly when coupled with imaging, were a useful tool for documenting nesting activity on these remote islands. Future monitoring programs at this site could benefit from a standardized aerial survey program with a seaplane so trends in nesting activity could be determined particularly as the beach undergoes restoration.

A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones

BACKGROUND

Large-scale species monitoring remains a significant conservation challenge. Given the ongoing biodiversity crisis, the need for reliable and efficient methods has never been greater. Drone-based techniques have much to offer in this regard: they allow access to otherwise unreachable areas and enable the rapid collection of non-invasive field data. Herein, we describe the development of a drone-based method for the estimation of population size in Galápagos marine iguanas, Amblyrhynchus cristatus. As a large-bodied lizard that occurs in open coastal terrain, this endemic species is an ideal candidate for drone surveys. Almost all Amblyrhynchus subspecies are Endangered or Critically Endangered according to the IUCN yet since several colonies are inaccessible by foot, ground- based methods are unable to address the critical need for better census data. In order to establish a drone-based approach to estimate population size of marine iguanas, we surveyed in January 2021 four colonies on three focal islands (San Cristobal, Santa Fe and Espanola) using three techniques: simple counts (the standard method currently used by conservation managers), capture mark-resight (CMR), and drone-based counts. The surveys were performed within a 4-day window under similar ambient conditions. We then compared the approaches in terms of feasibility, outcome and effort.

RESULTS

The highest population-size estimates were obtained using CMR, and drone-based counts were on average 14% closer to CMR estimates-and 17-35% higher-than those obtained by simple counts. In terms of field-time, drone-surveys can be faster than simple counts, but image analyses were highly time consuming.

CONCLUSION

Though CMR likely produces superior estimates, it cannot be performed in most cases due to lack of access and knowledge regarding colonies. Drone-based surveys outperformed ground-based simple counts in terms of outcome and this approach is therefore suitable for use across the range of the species. Moreover, the aerial approach is currently the only credible solution for accessing and surveying marine iguanas at highly remote colonies. The application of citizen science and other aids such as machine learning will alleviate the issue regarding time needed to analyze the images.

Design and fabrication of a stereo-video camera equipped unoccupied aerial vehicle for measuring sea turtles, sharks, and other marine fauna

The recent commercialization of unoccupied aerial vehicles (UAVs) has facilitated their incorporation into a variety of ecological studies. While UAVs are able to provide accurate visual data of marine species from an aerial perspective, these devices have some limitations that make measuring marine animals below the surface challenging. Many marine organisms are often visible from the air, but are deeper in the water column, and current methods cannot measure animals below the surface. Here, we developed and tested a stereo-video camera (SVC) system that was mounted onto a commercially-available UAV. We used the SVC-UAV to conduct remote body-size measurements for two marine species: the green sea turtle (Chelonia mydas) and the nurse shark (Ginglymostoma cirratum). When comparing SVC measurements to those taken by hand, the SVC-UAV had a mean absolute error (MAE) of 4.44 cm (n = 6; mean percent error (MPE) = 10.6%) for green sea turtles and 7.16 cm absolute error (n = 1; PE = 3.6%) for the nurse shark. Using a linear model, we estimated the slope of the SVC versus hand measurements for green sea turtles to be 1.085 (±0.099 SE), and accounting for the standard error, a measurement bias was not apparent. Using model selection, based on a global model predicting MAE from animal distance to the SVC and body size, the top ranked model was the intercept-only model. This indicates that neither animal distance nor body size strongly influenced measurement error. Incorporating SVC systems into UAVs can allow for relatively accurate measurements of near surface-dwelling marine species. To our knowledge, there is no other stand-alone SVC for UAVs available that offers similar accuracy and utility.

Warm beach, warmer turtles: Using drone-mounted thermal infrared sensors to monitor sea turtle nesting activity

For decades sea turtle projects around the world have monitored nesting females using labor-intensive human patrolling techniques. Here we describe the first empirical testing of a drone-mounted thermal infrared sensor for nocturnal sea turtle monitoring; on the Osa peninsula in Costa Rica. Preliminary flights verified that the drone could detect similar sea turtle activities as identified by on-the-ground human patrollers – such as turtles, nests and tracks. Drone observers could even differentiate tracks of different sea turtle species, detect sea turtle hatchlings, other wildlife, and potential poachers. We carried out pilot flights to determine optimal parameters for detection by testing different thermal visualization modes, drone heights, and gimbal angles. Then, over seven nights, we set up a trial to compare the thermal drone and operators’ detections with those observed by traditional patrollers. Our trials showed that thermal drones can record more information than traditional sea turtle monitoring methods. The drone and observer detected 20% more sea turtles or tracks than traditional ground-based patrolling (flights and patrols carried out across the same nights at the same time and beach). In addition, the drone operator detected 39 other animals/predators and three potential poachers that patrollers failed to detect. Although the technology holds great promise in being able to enhance detection rates of nesting turtles and other beach activity, and in helping to keep observers safer, we detail challenges and limiting factors; in drone imagery, current cost barriers, and technological advances that need to be assessed and developed before standardized methodologies can be adopted. We suggest potential ways to overcome these challenges and recommend how further studies can help to optimize thermal drones to enhance sea turtle monitoring efforts worldwide.

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

sUAS Monitoring of Coastal Environments: A Review of Best Practices from Field to Lab

Coastal environments are some of the most dynamic environments in the world. As they are constantly changing, so are the technologies and techniques we use to map and monitor them. The rapid advancement of sUAS-based remote sensing calls for rigorous field and processing workflows so that more reliable and consistent sUAS projects of coastal environments are carried out. Here, we synthesize the best practices to create sUAS photo-based surveying and processing workflows that can be used and modified by coastal scientists, depending on their project objective. While we aim to simplify the complexity of these workflows, we note that the nature of this work is a craft that carefully combines art, science, and technology. sUAS LiDAR is the next advancement in mapping and monitoring coastal environments. Therefore, future work should consider synthesizing best practices to develop rigorous field and data processing workflows used for sUAS LiDAR-based projects of coastal environments.

UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.

A drone-based survey for large, basking freshwater turtle species

Conservation concerns are increasing for numerous freshwater turtle species, including Pseudemys gorzugi, which has led to a call for more research. However, traditional sampling methodologies are often time consuming, labor intensive, and invasive, restricting the amount of data that can be collected. Biases of traditional sampling methods can further impair the quality of the data collected, and these shortfalls may discourage their use. The use of unmanned aerial vehicles (UAVs, drones) for conducting wildlife surveys has recently demonstrated the potential to bridge gaps in data collection by offering a less labor intensive, minimally invasive, and more efficient process. Photographs and video can be obtained by camera attachments during a drone flight and analyzed to determine population counts, abundance, and other types of data. In this study we developed a detailed protocol to survey for large, freshwater turtle species in an arid, riverine landscape. This protocol was implemented with a DJI Matrice 600 Pro drone and a SONY ILCE α6000 digital camera to determine P. gorzugi and sympatric turtle species occurrence across 42 sites in southwestern Texas, USA. The use of a large drone and high-resolution camera resulted in high identification percentages, demonstrating the potential of drones to survey for large, freshwater turtle species. Numerous advantages to drone-based surveys were identified as well as some challenges, which were addressed with additional refinement of the protocol. Our data highlight the utility of drones for conducting freshwater turtle surveys and provide a guideline to those considering implementing drone-mounted high-resolution cameras as a survey tool.

Within-season shifts in multiple paternity patterns in mass-nesting olive ridley sea turtles

Multiple paternity is common to all sea turtle species, but its causes and consequences are hard to ascertain and the behaviors and success of males difficult to observe. This study aims to describe patterns of multiple paternity for olive ridley turtles Lepidochelys olivacea at Playa de Escobilla, an ‘arribada’ (mass-nesting) site on the Mexican Pacific coast with over a million clutches laid each reproductive season. A total of 15 females and their hatchlings were sampled during 3 arribada events which occurred over the 2016-2017 nesting season. Females and hatchlings (N = 329) were genotyped at 5 microsatellite loci, from which we inferred the alleles of 46 contributing males. Multiple paternity was detected in 60% of the analyzed clutches, which were sired by a range of 2 to 7 males. Multiple paternity rates differed significantly across arribada events, suggesting more males achieved fertilizations earlier in the breeding season. Paternal contribution in 6 of the clutches with multiple paternity was skewed towards a single male; the remaining clutches had a homogeneous male contribution. However, our results are based on relatively small within-arribada sample sizes. The frequency of multiple paternity among turtle clutches laid on this arribada beach could be related to the density of breeding individuals in the reproductive patch off Playa de Escobilla, rather than to the nesting population size or female size.

Turtles and Tortoises Are in Trouble

Turtles and tortoises (chelonians) have been integral components of global ecosystems for about 220 million years and have played important roles in human culture for at least 400,000 years. The chelonian shell is a remarkable evolutionary adaptation, facilitating success in terrestrial, freshwater and marine ecosystems. Today, more than half of the 360 living species and 482 total taxa (species and subspecies combined) are threatened with extinction. This places chelonians among the groups with the highest extinction risk of any sizeable vertebrate group. Turtle populations are declining rapidly due to habitat loss, consumption by humans for food and traditional medicines and collection for the international pet trade. Many taxa could become extinct in this century. Here, we examine survival threats to turtles and tortoises and discuss the interventions that will be needed to prevent widespread extinction in this group in coming decades.

Comparison of UAV and Boat Surveys for Detecting Changes in Breeding Population Dynamics of Sea Turtles

Surveying the breeding population of a given species can be difficult for many logistic reasons. Marine turtles are a challenging taxon for the study of reproductive ecology and breeding strategies, because turtles aggregate off-shore and males remain exclusively at sea. For successful management of sea turtle populations, determining operational sex ratios (OSRs) on a continuing basis is critical for determining long-term population viability, particularly in the context of changing hatchling sex ratios due to temperature-dependent sex determination in a warming climate. To understand how survey technique and stage of the breeding season might influence the ability to detect turtles and determine OSRs, we surveyed the presence and identified the sex of adult male and female green sea turtles (Chelonia mydas) using a boat and small commercial unoccupied/unmanned aerial vehicle (UAV), at the start (October) and peak (December) of a nesting season at an important breeding site at Heron Island, Great Barrier Reef, Australia. The ratio of males to females within the breeding ground detected by both survey methods changed from being male-biased in October to heavily female-biased in December, indicating that most males cease their reproductive effort and depart before the peak of the nesting season. Surveying with a UAV more than doubled the rate of turtles seen per minute of survey effort compared with surveying solely from the boat and allowed surveys to be conducted at times and/or places unsafe or inaccessible for boats. The sex of a slightly greater proportion of turtles seen could not be identified by observers using a UAV versus a boat, although more turtles were detected using the UAV. The departure of many males during the peak of the nesting season is likely due to an increasing biological cost of residency in the area because males encounter fewer receptive females as the season progresses and the limited foraging opportunity is insufficient to support the number of males present. Overall, we found that UAVs are an effective tool for studying important but difficult to observe aspects of sea turtle biology.

Application of a Drone Magnetometer System to Military Mine Detection in the Demilitarized Zone

We propose a magnetometer system fitted on an unmanned aerial vehicle (UAV, or drone) and a data-processing method for detecting metal antipersonnel landmines (M16) in the demilitarized zone (DMZ) in Korea, which is an undeveloped natural environment. The performance of the laser altimeter was improved so that the drone could fly at a low and stable altitude, even in a natural environment with dust and bushes, and a magnetometer was installed on a pendulum to minimize the effects of magnetic noise and vibration from the drone. At a flight altitude of 1 m, the criterion for M16 is 5 nT. Simple low-pass filtering eliminates magnetic swing noise due to pendulum motion, and the moving average method eliminates changes related to the heading of the magnetometer. Magnetic exploration was conducted in an actual mine-removal area near the DMZ in Korea, with nine magnetic anomalies of more than 5 nT detected and a variety of metallic substances found within a 1-m radius of each detection site. The proposed UAV-based landmine detection system is expected to reduce risk to detection personnel and shorten the landmine-detection period by providing accurate scientific information about the detection area prior to military landmine-detection efforts.

Remotely Piloted Aircraft System (RPAS)-Based Wildlife Detection: A Review and Case Studies in Maritime Antarctica

Simple Summary

Remotely piloted aircraft systems (RPAS) have been successfully applied in wildlife monitoring with imaging sensors to improve or to supplement conventional field observations. To effectively utilize this technique, we reviewed previous studies related to wildlife detection with RPAS. First, this study provides an overview of the applications of RPAS for wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. In terms of the RPAS payload, applying thermal-imaging sensors was determined to be advantageous in detecting homeothermic animals due to the thermal contrast with background habitat using case studies detecting southern elephant seal (Mirounga leonina) using RGB and thermal imaging sensors in King George Island, maritime Antarctica.

Abstract

In wildlife biology, it is important to conduct efficient observations and quantitative monitoring of wild animals. Conventional wildlife monitoring mainly relies on direct field observations by the naked eyes or through binoculars, on-site image acquisition at fixed spots, and sampling or capturing under severe areal constraints. Recently, remotely piloted aircraft systems (RPAS), also called drones or unmanned aerial vehicles (UAV), were successfully applied to detect wildlife with imaging sensors, such as RGB and thermal-imaging sensors, with superior detection capabilities to those of human observation. Here, we review studies with RPAS which has been increasingly used in wildlife detection and explain how an RPAS-based high-resolution RGB image can be applied to wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. The applicability of thermal-imaging sensors was also assessed with further information extractable from image analyses. In addition, RPAS-based case studies of acquisition of high-resolution RGB images for the purpose of detecting southern elephant seals (Mirounga leonina) and shape property extraction using thermal-imaging sensor in King George Island, maritime Antarctica is presented as applications in an extreme environment. The case studies suggest that currently available cost-effective small-sized RPAS, which are capable of flexible operation and mounting miniaturized imaging sensors, and are easily maneuverable even from an inflatable boat, can be an effective and supportive technique for both the visual interpretation and quantitative analysis of wild animals in low-accessible extreme or maritime environments.

Applications of Unmanned Aerial Vehicles in Mining from Exploration to Reclamation: A Review

Over the past decade, unmanned aerial vehicles (UAVs) have been used in the mining industry for various applications from mineral exploration to mine reclamation. This study aims to review academic papers on the applications of UAVs in mining by classifying the mining process into three phases: exploration, exploitation, and reclamation. Systematic reviews were performed to summarize the results of 65 articles (June 2010 to May 2020) and outline the research trend for applying UAVs in mining. This study found that UAVs are used at mining sites for geological and structural analysis via remote sensing, aerial geophysical survey, topographic surveying, rock slope analysis, working environment analysis, underground surveying, and monitoring of soil, water, ecological restoration, and ground subsidence. This study contributes to the classification of current UAV applications during the mining process as well as the identification of prevalent UAV types, data acquired by sensors, scales of targeted areas, and styles of flying control for the applications of UAVs in mining.

Use of unmanned aerial vehicles (UAVs) for mark-resight nesting population estimation of adult female green sea turtles at Raine Island

Nester abundance is a key measure of the performance of the world’s largest green turtle rookery at Raine Island, Australia, and has been estimated by mark-resight counts since 1984. Nesters are first marked by painting their carapace with a longitudinal white stripe. Painted and unpainted turtles are then counted by a surface observer on a small boat in waters adjacent to the reef. Unmanned aerial vehicles (UAV) and underwater video may provide more cost-effective and less biased alternatives to this approach, but estimates must be comparable with historical estimates. Here we compare and evaluate the three methods. We found comparatively little variation in resighting probabilities between consecutive days of sampling or time of day, which supports an underlying assumption of the method (i.e. demographic closure during sampling). This lack of bias in the location availability for detection of painted versus unpainted turtles and further supported by a parallel satellite tracking study of 40 turtles at Raine Island. Our results demonstrated that surface observers consistently reported higher proportions of marked turtles than either the UAV or underwater video method. This in turn yielded higher population estimates with UAV or underwater video compared to the historical surface observer method, which suggested correction factors of 1.53 and 1.73 respectively. We attributed this to observer search error because a white marked turtle is easier to spot than the non-marked turtle. In contrast, the UAV and underwater video methods allowed subsequent frame-by-frame review, thus reducing observer search error. UAVs were the most efficient in terms of survey time, personnel commitment and weather tolerance compared to the other methods. However, underwater video may also be a useful alternative for in-water mark-resight surveys of turtles.

Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance

While aerial shark spotting has been a standard practice for beach safety for decades, new technologies offer enhanced opportunities, ranging from drones/unmanned aerial vehicles (UAVs) that provide new viewing capabilities, to new apps that provide beachgoers with up-to-date risk analysis before entering the water. This report describes the Sharkeye platform, a first-of-its-kind project to demonstrate personal shark alerting for beachgoers in the water and on land, leveraging innovative UAV image collection, cloud-hosted machine learning detection algorithms, and reporting via smart wearables. To execute, our team developed a novel detection algorithm trained via machine learning based on aerial footage of real sharks and rays collected at local beaches, hosted and deployed the algorithm in the cloud, and integrated push alerts to beachgoers in the water via a shark app to run on smartwatches. The project was successfully trialed in the field in Kiama, Australia, with over 350 detection events recorded, followed by the alerting of multiple smartwatches simultaneously both on land and in the water, and with analysis capable of detecting shark analogues, rays, and surfers in average beach conditions, and all based on ~1 h of training data in total. Additional demonstrations showed potential of the system to enable lifeguard-swimmer communication, and the ability to create a network on demand to enable the platform. Our system was developed to provide swimmers and surfers with immediate information via smart apps, empowering lifeguards/lifesavers and beachgoers to prevent unwanted encounters with wildlife before it happens.

Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1–5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa.

New views of humpback whale flow dynamics and oral morphology during prey engulfment

The rise of inexpensive, user-friendly cameras and editing software promises to revolutionize data collection with minimal disturbance to marine mammals. Video sequences recorded by aerial drones and GoPro cameras provided close-up views and unique perspectives of humpback whales engulfing juvenile salmon at or just below the water surface in Southeast Alaska and Prince William Sound. Although humpback feeding is famous for its flexibility, several stereotyped events were noted in the 47 lunges we analyzed. Engulfment was rapid (mean 2.07 s), and the entrance through which the tongue inverts into the ventral pouch was seen as water rushes in. Cranial elevation was a major contributor to gape, and pouch contraction sometimes began before full gape closure, with reverberating waves indicating rebounding flow of water within the expanded pouch. Expulsion of filtered water began with a small splash at the anterior of the mouth, followed by sustained excurrent flow in the mouth's central or posterior regions. Apart from a splash of rebounding water, water within the mouth was surprisingly turbulence-free during engulfment, but submersion of the whale's head created visible surface whirlpools and vortices which may aggregate prey for subsequent engulfment.

Assessing climate change associated sea-level rise impacts on sea turtle nesting beaches using drones, photogrammetry and a novel GPS system

Climate change associated sea-level rise (SLR) is expected to have profound impacts on coastal areas, affecting many species, including sea turtles which depend on these habitats for egg incubation. Being able to accurately model beach topography using digital terrain models (DTMs) is therefore crucial to project SLR impacts and develop effective conservation strategies. Traditional survey methods are typically low-cost with low accuracy or high-cost with high accuracy. We present a novel combination of drone-based photogrammetry and a low-cost and portable real-time kinematic (RTK) GPS to create DTMs which are highly accurate (<10 cm error) and visually realistic. This methodology is ideal for surveying coastal sites, can be broadly applied to other species and habitats, and is a relevant tool in supporting the development of Specially Protected Areas. Here, we applied this method as a case-study to project three SLR scenarios (0.48, 0.63 and 1.20 m) and assess the future vulnerability and viability of a key nesting habitat for sympatric loggerhead (Caretta caretta) and green turtle (Chelonia mydas) at a key rookery in the Mediterranean. We combined the DTM with 5 years of nest survey data describing location and clutch depth, to identify (a) regions with highest nest densities, (b) nest elevation by species and beach, and (c) estimated proportion of nests inundated under each SLR scenario. On average, green turtles nested at higher elevations than loggerheads (1.8 m vs. 1.32 m, respectively). However, because green turtles dig deeper nests than loggerheads (0.76 m vs. 0.50 m, respectively), these were at similar risk of inundation. For a SLR of 1.2 m, we estimated a loss of 67.3% for loggerhead turtle nests and 59.1% for green turtle nests. Existing natural and artificial barriers may affect the ability of these nesting habitats to remain suitable for nesting through beach migration.

Drones for Conservation in Protected Areas: Present and Future

Park managers call for cost-effective and innovative solutions to handle a wide variety of environmental problems that threaten biodiversity in protected areas. Recently, drones have been called upon to revolutionize conservation and hold great potential to evolve and raise better-informed decisions to assist management. Despite great expectations, the benefits that drones could bring to foster effectiveness remain fundamentally unexplored. To address this gap, we performed a literature review about the use of drones in conservation. We selected a total of 256 studies, of which 99 were carried out in protected areas. We classified the studies in five distinct areas of applications: “wildlife monitoring and management”; “ecosystem monitoring”; “law enforcement”; “ecotourism”; and “environmental management and disaster response”. We also identified specific gaps and challenges that would allow for the expansion of critical research or monitoring. Our results support the evidence that drones hold merits to serve conservation actions and reinforce effective management, but multidisciplinary research must resolve the operational and analytical shortcomings that undermine the prospects for drones integration in protected areas.

Unoccupied Aircraft Systems in Marine Science and Conservation

The use of unoccupied aircraft systems (UASs, also known as drones) in science is growing rapidly. Recent advances in microelectronics and battery technology have resulted in the rapid development of low-cost UASs that are transforming many industries. Drones are poised to revolutionize marine science and conservation, as they provide essentially on-demand remote sensing capabilities at low cost and with reduced human risk. A variety of multirotor, fixed-wing, and transitional UAS platforms are capable of carrying various optical and physical sampling payloads and are being employed in almost every subdiscipline of marine science and conservation. This article provides an overview of the UAS platforms and sensors used in marine science and conservation missions along with example physical, biological, and natural resource management applications and typical analytical workflows. It concludes with details on potential effects of UASs on marine wildlife and a look to the future of UASs in marine science and conservation.

Social responses of travelling finless porpoises to boat traffic risk in Misumi West Port, Ariake Sound, Japan

Anthropogenic effects have created various risks for wild animals. Boat traffic is one of the most fatal risks for marine mammals. Individual behavioral responses of cetaceans, including diving behavior such as changing swimming direction and lengthing inter-breath interval, to passing boats is relatively well known; however, the social function of cetacean responses to boat traffic in a natural setting remains poorly understood. We focused on describing the behavioral responses of single and aggregated finless porpoises to boats passing at Misumi West Port, Ariake Sound, Japan, by using a drone characterized with a high-precision bird's-eye angle. During the study period, we collected 25 episodes of finless porpoise responses to boats passing by. A mean (± SEM) of 5.1 ± 1.0 individuals were observed for each episode. The primary response to passing boats was avoidance by dive, which implies boat traffic is a substantial disturbance to finless porpoises that travel along the seawater surface daily. The diving duration decreased significantly with an increase in the number of aggregated individuals. The diving and floating reaction times were 10.9 ± 2.3 s and 18.7 ± 5.0 s, respectively. There was no significant difference between the reaction times indicating that each individual was motivated to keep the group cohesion consistent when floating even after the risk had dissolved, which is comparable to the behavior of porpoises that dive when riskier conditions are present, such as when a boat approaches an aggregation. Our findings provide new insights on the sociality of finless porpoises even though there were limitations, like an inability to identify a specific individual. The drone enabled us to observe the social behavior of finless porpoises and other cetaceans at an unprecedented resolution, which may lead to a better understanding of the evolutionary diversity of intelligence and sociality and the bridge to human evolution.

Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments

Although sea turtles face significant pressure from human activities, some populations are recovering due to conservation programs, bans on the trade of turtle products, and reductions in bycatch. While these trends are encouraging, the status of many populations remains unknown and scientific monitoring is needed to inform conservation and management decisions. To address these gaps, this study presents methods for using unmanned aerial systems (UAS) to conduct population assessments. Using a fixed-wing UAS and a modified strip-transect method, we conducted aerial surveys along a three-kilometer track line at Ostional, Costa Rica during a mass-nesting event of olive ridley turtles (Lepidochelys olivacea). We visually assessed images collected during six transects for sea turtle presence, resulting in 682 certain detections. A cumulative total of 1091 certain and probable turtles were detected in the collected imagery. Using these data, we calculate estimates of sea turtle density (km-2) in nearshore waters. After adjusting for both availability and perception biases, we developed a low-end estimate of 1299 ± 458 and a high-end estimate of 2086 ± 803 turtles per km-2. This pilot study illustrates how UAS can be used to conduct robust, safe, and cost-effective population assessments of sea turtle populations in coastal marine ecosystems.