Amanda Hodgson

Understanding the fine-scale behavioural and feeding ecology of marine megafauna is imperative for effective management of their habitat areas; however, obtaining the relevant data can be both costly and challenging. Here we integrate the use of small drones for dugong surveys with underwater benthic habitat assessment techniques at the local spatial scale (∼30 km2), to determine the drivers of dugong (Dugong dugon) distribution across three locations in the Pilbara, Western Australia. Paired assessment data was collected three times over two years. Benthic habitat (percent cover), seagrass nutritional quality and environmental parameters (temperature, water clarity, water current, water depth) were tested as predictor variables using generalised linear models, to examine drivers of both dugong presence/absence and abundance. We found that low cover (typical for this region; 2–10 %) of colonising seagrass is a key driver of the presence and abundance of dugongs. Halophila ovalis and Halodule uninervis were the main predictors of dugong presence and abundance across the three locations surveyed. Where both seagrass species simultaneously occurred, the likelihood of dugongs being present increased by over 60 times. The presence of H. uninervis alone was predicted to increase the abundance of dugongs by 1.4 times across all locations and by 6.8 times in one location, Exmouth Gulf, compared to when no seagrass was present. This study provided evidence of critical seagrass habitat, which is important knowledge for the protection and conservation of dugongs and their foraging habitat. The methods developed in this study could be employed in environmental impact assessments to predict and confirm potential seagrass forage habitat.
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Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols.
This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of deep learning for animal detection, localisation, recognition, and their combinations. The paper also discusses the challenges, limitations, and future research directions of this field. A key message from this review is the need for representative training data covering the various scenarios in which target animals may appear, image annotation difficulties, and the comparability of DL model results across studies. Many studies have focused on single species, locations, or images with a high density of common target species. Assessments of models are potentially biased from using a single test set; many studies lack metrics to evaluate model efficiency, feasibility, and generalizability, and there are uncertainties regarding the optimal number of training images and required ground sample distance (GSD) for different animal detection tasks in drone imagery.
The potential applications of deep learning in wildlife monitoring, conservation, and ecological research using drone imagery are substantial. By enhancing the accuracy and efficiency of animal detection in imagery, this technology could contribute to the understanding and protecting animal populations. To expand the applicability of deep learning to diverse species, environments, and spatial scales, researchers should create standardised benchmark datasets and prioritise open collaboration and data sharing, which would aid in addressing the current challenges.

Can you picture cows grazing on a meadow of grass? Did you know that there are also “cows” under the sea that graze on seagrass meadows? Dugongs—a type of sea-cow—are threatened with extinction, mainly as a result of human activities and loss of their main food source, seagrass. Seagrasses are a group of flowering plants that grow in the ocean! Seagrasses are important not only as a food source for dugongs, but they provide a home for many animals, absorb carbon dioxide aiding in climate change mitigation, and so much more! However, seagrasses are declining globally, which is bad news not only for dugongs, but for humans as well. Luckily, dugong presence can aid scientists in understanding the health of seagrasses in an area, as well as help scientists locate and protect our important seagrass ecosystems.

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone’s range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98–1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31–88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [−28–57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals to visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel-launched, multi-rotor Unoccupied Aerial Vehicles (UAVs, or drones) to collect video of dolphins to characterize availability and investigate error surrounding group size estimates. We collected over 20 h of aerial video of dive-surfacing behaviour across 32 encounters with Australian humpback dolphins Sousa sahulensis off north-western Australia. Mean surfacing and dive periods were 7.85 sec (se = 0.26) and 39.27 sec (se = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non-instantaneous availability estimates, assuming a 7 sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions and environmental conditions. Through data re-sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow-water, small cetaceans, and will directly inform a government-run program of aerial surveys in the region.

The Journal of Unmanned Vehicle Systems—led by its founding editor, David Bird—published its first issue in December 2013. It was the first peer-reviewed scientific journal entirely dedicated to research relating to all types of remotely piloted or autonomous robotic vehicles, including those that operate in the air, on the ground, or on or below the water’s surface. Although rare currently, it could also expand to include those that operate in outer space environments (Potter 2020). This is a uniquely eclectic field of research that encompasses multiple engineering and design aspects of the vehicles themselves in addition to a diverse and ever-growing array of practical applications of the technology...

The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based on independent experiments. We performed focal follows of known dolphin species and distributed our imagery amongst 13 trained observers. Then, we investigated the effects of reviewer-related variables and image attributes on the accuracy of species identification and level of certainty in observations. In addition, we assessed the number of reviewers required to produce reliable identification using an agreement-based framework compared with the majority rule approach. Among-reviewer variation was an important predictor of identification accuracy, regardless of previous experience. Image resolution and sea state exhibited the most pronounced effects on the proportion of correct identifications and the reviewers’ mean level of confidence. Agreement-based identification resulted in substantial data losses but retained a broader range of image resolutions and sea states than the majority rule approach and produced considerably higher accuracy. Our findings suggest a strong dependency on reviewer-related variables and image attributes, which, unless considered, may compromise identification accuracy and produce unreliable estimators of abundance.

Sirenian social and reproductive behaviors lack much complexity or diversity. Whereas sirenians are usually sighted as solitary, or as cows with single calves, aggregations of many individuals can occur. Persistent social groupings are unknown. Home ranges are widely overlapping. Mating systems of dugongs (Dugong dugon) have been variously described as leks or as scramble promiscuity (mating herds ) and lone mating pairs have been observed in areas of low density, but further research into the hypothesized leks is needed (especially because scramble promiscuity has been observed in the same region). Dugongs and all manatees (Trichechus) show scramble promiscuity, wherein males form groups that escort single females with much physical contact for many days. The strongest social bonds are between females and nursing calves. Florida manatees (Trichechus manatus latirostris) show natal philopatry for years after weaning. Socially transmitted knowledge (tradition) appears important to Florida manatees and perhaps all species of sirenians, particularly in regions where seasonal movements during winter are necessary for survival, such as in winter for Florida manatees, and dugongs at the high latitude limits of their range. Some populations of Antillean, Amazonian, and African manatees have regular movements in response to seasonal flooding and access to food, which also may be learned through tradition . Dugongs may rely on group movements based on traditional knowledge in response to regional loss of food supply from extreme weather events. Communication is most obvious through vocalizations, which can show individual distinctiveness. Vocal communication is most prevalent between mothers and calves. Allomaternal care occurs in Florida manatees at shared aggregation sites. Florida manatees occupying a given region can consist of multiple matrilines that develop through the early bonding of calves to mothers and subsequent natal philopatry. Population genetics research supports male-biased dispersal and possible female-based philopatry in other trichechids, but perhaps not as strongly in dugongids. Considerable further research is needed on these and related topics to more comprehensively understand sirenian social and reproductive behavior.

Understanding species’ distribution patterns and the environmental and ecological interactions that drive them is fundamental for biodiversity conservation. Data deficiency exists in areas that are difficult to access, or where resources are limited. We use a broad-scale, non-targeted dataset to describe dolphin distribution and habitat suitability in remote north Western Australia, where there is a paucity of data to adequately inform species management. From 1,169 opportunistic dolphin sightings obtained from 10 dugong aerial surveys conducted over a four-year period, there were 661 Indo-Pacific bottlenose dolphin (Tursiops aduncus), 191 Australian humpback dolphin (Sousa sahulensis), nine Australian snubfin dolphin (Orcaella heinsohni), 16 Stenella sp., one killer whale (Orcinus orca), one false killer whale (Pseudorca crassidens), and 290 unidentified dolphin species sightings. Maximum Entropy (MaxEnt) habitat suitability models identified shallow intertidal areas around mainland coast, islands and shoals as important areas for humpback dolphins. In contrast, bottlenose dolphins are more likely to occur further offshore and at greater depths, suggesting niche partitioning between these two sympatric species. Bottlenose dolphin response to sea surface temperature is markedly different between seasons (positive in May; negative in October) and probably influenced by the Leeuwin Current, a prominent oceanographic feature. Our findings support broad marine spatial planning, impact assessment and the design of future surveys, which would benefit from the collection of high-resolution digital images for species identification verification. A substantial proportion of data were removed due to uncertainties resulting from non-targeted observations and this is likely to have reduced model performance. We highlight the importance of considering climatic and seasonal fluctuations in interpreting distribution patterns and species interactions in assuming habitat suitability.

Manatees and dugongs live in tropical and semi-tropical regions around the world. Their preferred habitats are seagrass beds, rivers, lakes, and estuaries. Manatees live in both freshwater and marine systems although habitat preferences vary across the three species, while the dugong is entirely marine. Sirenians are shallow water divers, and their dive durations are short compared to most other marine mammals. The maximum recorded manatee dive duration is 24 min, with the maximum recorded duration of a dugong dive being about half that. Even though the durations of dugong dives are shorter than those of manatees, current data indicate that dugongs dive deeper than manatees. Dive depths for manatees generally do not exceed 5 m, other than during occasional travel over deeper water; however, this may be an artifact of water depth in areas where diving data were recorded, or where manatees live. In some parts of their range, dugongs are found over deep-water seagrass beds and dives have been recorded to more than 30 m. All extant sirenians eat diverse plant-based diets: collectively they have been documented feeding on at least 55 genera of marine and freshwater plants. Although not confirmed for the Amazonian manatee, it is likely that all extant sirenians eat animal, as well as plant, matter. West Indian and African manatees have been documented eating marine and freshwater fish and invertebrates , and for African manatees, these food resources are a regular part of their diet, arguably making them omnivores . Dugongs have been recorded targeting invertebrates at the high latitude limits of their range in winter. All manatees like to drink fresh water, in contrast to dugongs, which live entirely in marine systems and apparently meet the water requirements from their food.