Drones have emerged as a popular wildlife research tool, but their use for many species and environments remains untested and research is needed on validation of sampling approaches that are optimised for unpiloted aircraft. Here, we present a foreword to a special issue that features studies pushing the taxonomic and innovation boundaries of drone research and thus helps address these knowledge and application gaps. We then conclude by highlighting future drone research ideas that are likely to push biology and conservation in exciting new directions.

Radio-tracking tagged wildlife remains a critical research technique for understanding the movements, behaviours and survival of many species. However, traditional hand-held tracking techniques on the ground are labour intensive and time consuming. Therefore, researchers are increasingly seeking new technologies to address these challenges, including drone radio-tracking receivers. Following the implementation of drone radio-tracking techniques for five different threatened species projects within different habitat and landscape types, we identified the need to quantify the relative spatial extent of surveys using both drone and hand-held techniques for each project. This was undertaken using viewshed analyses. These analyses demonstrated that survey coverage with drone-based radio-tracking was substantially greater than that of hand-held radio-tracking for all species and landscapes examined. Within mountainous landscapes, drone radio-tracking covered up to four times the area of hand-held tracking, whereas in flat to undulating landscapes, drone surveys covered up to 11.3 times the area that could be surveyed using hand-held techniques from the same locations on the ground. The viewshed analyses were also found to be a valuable visualisation tool for identifying areas for targeted surveys to reduce the risk of ‘losing’ tagged animals, which has traditionally been one of the biggest radio-tracking challenges.

Context. West African crocodylian populations are declining and in need of conservation action. Surveys and other monitoring methods are critical components of crocodile conservation programs; however, surveys are often hindered by logistical, financial and detectability constraints. Increasingly used in wildlife monitoring programs, drones can enhance monitoring and conservation efficacy.

Aims. This study aimed to determine a standard drone crocodylian survey protocol and evaluate the drones as a tool to survey the diverse crocodylian assemblage of West Africa.

Methods. We surveyed crocodile populations in Benin, Côte d’Ivoire, and Niger in 2017 and 2018, by using the DJI Phantom 4 Pro drone and via traditional diurnal and nocturnal spotlight surveys. We used a series of test flights to first evaluate the impact of drones on crocodylian behaviour and determine standard flight parameters that optimise detectability. We then, consecutively, implemented the three survey methods at 23 sites to compare the efficacy of drones against traditional crocodylian survey methods.

Key results. Crocodylus suchus can be closely approached (>10 m altitude) and consumer-grade drones do not elicit flight responses in West African large mammals and birds at altitudes of >40–60 m. Altitude and other flight parameters did not affect detectability, because high-resolution photos allowed accurate counting. Observer experience, field conditions (e.g. wind, sun reflection), and site characteristics (e.g. vegetation, homogeneity) all significantly affected detectability. Drone-based crocodylian surveys should be implemented from 40 m altitude in the first third of the day. Comparing survey methods, drones performed better than did traditional diurnal surveys but worse than standard nocturnal spotlight counts. The latter not only detected more individuals, but also a greater size-class diversity. However, drone surveys provide advantages over traditional methods, including precise size estimation, less disturbance, and the ability to cover greater and more remote areas. Drone survey photos allow for repeatable and quantifiable habitat assessments, detection of encroachment and other illegal activities, and leave a permanent record.

Conclusions. Overall, drones offer a valuable and cost-effective alternative for surveying crocodylian populations with compelling secondary benefits, although they may not be suitable in all cases and for all species.

Implications. We propose a standardised and optimised protocol for drone-based crocodylian surveys that could be used for sustainable conservation programs of crocodylians in West Africa and globally.

Context. Unmanned aerial vehicles or drones are powerful tools for wildlife research. Identifying the impacts of these systems on target species during operations is essential to reduce risks of disturbance to wildlife, to minimise bias in behavioural data, and to establish better practices for their use.

Aims. We evaluated the responses of captive Antillean manatees to the overhead flight of a small aerial drone.

Methods. We used aerial and ground videos to compare manatee activity budgets and respiration rates in three 15-min sampling periods: ‘before’, ‘during’ and ‘after’ flights with a DJI Phantom 3 Advanced. The drone was hovered stationary for 3 min at five altitudes (100 m, 40 m, 20 m, 10 m, 5 m) to determine whether manatees display behavioural responses compared with the control period, and whether they respond more at lower altitudes. Only one flight was performed per manatee group to avoid bias owing to habituation to the drone.

Key results. Manatees responded to drone flights by (1) increasing their activity levels during and after flights, therefore signalling after effects; (2) decreasing their respiration rate during flights; and (3) displaying behavioural reactions including grouping, tail-kicking, fleeing from their original position and moving under submerged structures. From the 11 individuals displaying behavioral reactions, 9 reacted in the first ∼2 min of flight, preventing assessments of altitude effects and suggesting manatees responded to the drone sound at take-off.

Conclusions. Behavioural changes of responding manatees were similar to previous reports of disturbance responses to boats and drones in this species. Our use of a control period showed shifts in respiration rates and activity budgets that persisted after flights. Several manatees reacted to the drone from the time of take-off and first minutes of flight, indicating that the sound of the electric rotors could be a strong negative stimulus to manatee and highlighting the importance of establishing safe distances for take-off.

Implications. Future studies should consider that drones could elicit conspicuous and inconspicuous responses in manatees. Our results emphasise the need for control data on animal behaviour to better assess the impact of drones on wildlife and to design non-invasive protocols.

Context. Baleen whale calves rapidly increase in size and improve locomotion abilities, while on their low-latitude breeding ground, allowing them to undertake a successful migration to high-latitude feeding grounds.

Aims. We investigated energy expenditure and resting behaviour of humpback whale (Megaptera novaeangliae) mother–calf pairs in regard to changes in calf length on an undisturbed breeding/resting ground off Exmouth Gulf, Western Australia.

Methods. Data were collected from August to October in 2018 and 2019 on lactating mothers that were predominantly resting on the surface with their calf. Focal follows on mother–calf pairs (n = 101) were conducted using an unmanned aerial vehicle to obtain detailed video of behaviours and respirations (23.7 h). Body length measurements of individual whales were calculated from aerial still frames.

Key results. Results on calves ranging in length from ∼4–8 m demonstrated that calf respiration rate decreased with an increase in calf length and increased with presence of activity (P < 0.001). Calf inter-breath intervals became longer in duration with an increase in calf length (P < 0.01). Calf activity level and resting behaviour remained constant, with calves logging for 53% of the time their mothers were logging. Maternal respiration rate remained low and did not differ with respect to maternal or calf length.

Conclusions. Results highlighted the importance of resting grounds for energy preservation, which benefits the calves’ rapid growth before migration to polar waters.

Implications. Findings from the present largely undisturbed population serve as a baseline for understanding the impacts of anthropogenic disturbance on resting behaviour and energy expenditure in humpback whale mother–calf pairs globally.

Context. Monitoring is an essential part of managing invasive species; however, accurate, cost-effective detection techniques are necessary for it to be routinely undertaken. Current detection techniques for invasive deer are time consuming, expensive and have associated biases, which may be overcome by exploiting new technologies.

Aims. We assessed the accuracy and cost effectiveness of automated detection methods in comparison to manual detection of thermal footage of deer captured by remotely piloted aircraft systems.

Methods. Thermal footage captured by RPAS was assessed using an algorithm combining two object-detection techniques, namely, YOLO and Faster-RCNN. The number of deer found using manual review on each sampling day was compared with the number of deer found on each day using machine learning. Detection rates were compared across survey areas and sampling occasions.

Key results. Overall, there was no difference in the mean number of deer detected using manual and that detected by automated review (P = 0.057). The automated-detection algorithm identified between 66.7% and 100% of deer detected using manual review of thermal imagery on all but one of the sampling days. There was no difference in the mean proportion of deer detected using either manual or automated review at three repeated sampling events (P = 0.174). However, identifying deer using the automated review algorithm was 84% cheaper than the cost of manual review. Low cloud cover appeared to affect detectability using the automated review algorithm.

Conclusions. Automated methods provide a fast and effective way to detect deer. For maximum effectiveness, imagery that encompasses a range of environments should be used as part of the training dataset, as well as large groups for herding species. Adequate sensing conditions are essential to gain accurate counts of deer by automated detection.

Implications. Machine learning in combination with RPAS may decrease the cost and improve the detection and monitoring of invasive species.

Context. Ungulate populations are subject to fluctuations caused by extrinsic factors and require efficient and frequent surveying to monitor population sizes and demographics. Unmanned aerial systems (UAS) have become increasingly popular for ungulate research; however, little is understood about how this novel technology compares with conventional methodologies for surveying wild populations.

Aims. We examined the feasibility of using a fixed-wing UAS equipped with a thermal infrared sensor for estimating the population density of wild white-tailed deer (Odocoileus virginianus) at the Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota, USA. We compared UAS density estimates with those derived from faecal pellet-group counts.

Methods. We conducted UAS thermal survey flights from March to April of 2018 and January to March of 2019. Faecal pellet-group counts were conducted from April to May in 2018 and 2019. We modelled deer counts and detection probabilities and used these results to calculate point estimates and bootstrapped prediction intervals for deer density from UAS and pellet-group count data. We compared results of each survey approach to evaluate the relative efficacy of these two methodologies.

Key results. Our best-fitting model of certain deer detections derived from our UAS-collected thermal imagery produced deer density estimates (, 95% prediction interval = 4.32–17.84 deer km⁻²) that overlapped with the pellet-group count model when using our mean pellet deposition rate assumption (, 95% prediction interval = 4.14–11.29 deer km⁻²). Estimates from our top UAS model using both certain and potential deer detections resulted in a mean density of 13.77 deer km⁻² (95% prediction interval = 6.64–24.35 deer km⁻²), which was similar to our pellet-group count model that used a lower rate of pellet deposition (, 95% prediction interval = 6.46–17.65 deer km⁻²). The mean point estimates from our top UAS model predicted a range of 136.68–273.81 deer, and abundance point estimates using our pellet-group data ranged from 112.79 to 239.67 deer throughout the CCESR.

Conclusions. Overall, UAS yielded results similar to pellet-group counts for estimating population densities of wild ungulates; however, UAS surveys were more efficient and could be conducted at multiple times throughout the winter.

Implications. We demonstrated how UAS could be applied for regularly monitoring changes in population density. We encourage researchers and managers to consider the merits of UAS and how they could be used to enhance the efficiency of wildlife surveys.

Context. Drones, or remotely piloted aircraft systems, equipped with thermal imaging technology (RPAS thermal imaging) have recently emerged as a powerful monitoring tool for koala populations. Before wide uptake of novel technologies by government, conservation practitioners and researchers, evidence of greater efficiency and cost-effectiveness than with other available methods is required.

Aims. We aimed to provide the first comprehensive analysis of the cost-effectiveness of RPAS thermal imaging for koala detection against two field-based methods, systematic spotlighting (Spotlight) and the refined diurnal radial search component of the spot-assessment technique (SAT).

Methods. We conducted various economic comparisons, particularly comparative cost-effectiveness of RPAS thermal imaging, Spotlight and SAT for repeat surveys of a low-density koala population. We compared methods on cost-effectiveness as well as long-term costs by using accumulating cost models. We also compared detection costs across population density using a predictive cost model.

Key results. Despite substantial hardware, training and licensing costs at the outset (>A$49 900), RPAS thermal imaging surveys were cost-effective, detecting the highest number of koalas per dollar spent. Modelling also suggested that RPAS thermal imaging requires the lowest survey effort to detect koalas within the range of publicly available koala population densities (∼0.006–18 koalas ha⁻¹) and would provide long-term cost reductions across longitudinal monitoring programs. RPAS thermal imaging would also require the lowest average survey effort costs at a landscape scale (A$3.84 ha⁻¹), providing a cost-effective tool across large spatial areas.

Conclusions. Our analyses demonstrated drone thermal imaging technology as a cost-effective tool for conservation practitioners monitoring koala populations. Our analyses may also form the basis of decision-making tools to estimate survey effort or total program costs across any koala population density.

ImplicationsOur novel approach offers a means to perform various economic comparisons of available survey techniques and guide investment decisions towards developing standardised koala monitoring approaches. Our results may assist stakeholders and policymakers to confidently invest in RPAS thermal imaging technology and achieve optimal conservation outcomes for koala populations, with standardised data collection delivered through evidence-based and cost-effective monitoring programs.

Context. Aerial video surveys from unpiloted aerial systems (UAS) have become popular in wildlife research because of increased accessibility to remote areas, reduction of anthropogenic disruption to habitats and wildlife, low operating costs, and improved researcher safety. In shallow marine systems, they can provide opportunities to rapidly survey species that cannot easily be surveyed using boat- or land-based techniques. However, detectability of subsurface animals in marine habitats may be affected by environmental factors.

Aims. We investigated the effects of water depth, seagrass cover, surface glare, and observer numbers and expertise on the probability of detecting subsurface green turtles in UAS video surveys.

Methods. We deployed inanimate green turtle decoys at randomised intervals along 24 pre-determined transects across a depth gradient in a seagrass-dominated bay off Great Abaco, The Bahamas. We collected aerial videos of the transects by flying a DJI Phantom 3 Advanced quadcopter drone at an altitude of 10 m over each transect. Three independent observers watched each video and recorded decoy sightings to compare detection probabilities across observer experience levels. We used a generalised linear model to test for the effects of glare, water depth, wind speed, and seagrass cover on the detectability of turtle decoys. We also recorded glare conditions with aerial videos taken at 2-h intervals over a still body of water on cloudless days off North Miami, FL.

Key results. Individual observers performed similarly, but adding one additional observer increased detection by 11–12% and adding a third observer increased detections by up to 15%. Depth, seagrass cover, and glare significantly affected decoy detections. In both summer and fall, the optimal times and directions to minimise glare in aerial video surveys were 0800 hours, facing any direction other than north, and 1800 hours, facing any direction other than south.

Conclusions. The number of human observers and environmental variables, especially depth, seagrass cover, and glare, are important to explicitly consider when designing and analysing data from UAS surveys of subsurface animal abundances and distribution.

Implications. Our study draws attention to potential limitations of UAS-acquired data for subsurface observations if environmental conditions are not explicitly accounted for. Quantifying the effects of environmental factors, designing surveys to minimise variance in these factors, and having multiple observers are crucial for optimising UAS use in research and conservation of sea turtles and other marine fauna.