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Efficient monitoring of organisms is at the foundation of protected area and biodiversity management. Such monitoring programs are based on a systematically selected set of survey locations that, while able to track trends at those locations through time, lack inference for the overall region being "monitored". Advances in spatially-balanced sampling approaches offer alternatives but remain largely untested in marine ecosystems. This study evaluated the merit of using a two-stage, spatially-balanced survey framework, in conjunction with generalized additive models, to estimate epifauna cover at a reef-wide scale for mesophotic reefs within a large, cross-shelf marine park. Imagery acquired by an autonomous underwater vehicle was classified using a hierarchical scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI). At a realistic image subsampling intensity, the two-stage, spatially-balanced framework provided accurate and precise estimates of reef-wide cover for a select number of epifaunal classes at the coarsest CATAMI levels, in particular bryozoan and porifera classes. However, at finer hierarchical levels, accuracy and/or precision of cover estimates declined, primarily because of the natural rarity of even the most common of these classes/morphospecies. Ranked predictor importance suggested that bathymetry, backscatter and derivative terrain variables calculated at their smallest analysis window scales (i.e. 81 m2) were generally the most important variables in the modeling of reef-wide cover. This study makes an important step in identifying the constraints and limitations that can be identified through a robust statistical approach to design and analysis. The two-stage, spatially-balanced framework has great potential for effective quantification of epifaunal cover in cross-shelf mesophotic reefs. However, greater image subsampling intensity than traditionally applied is required to ensure adequate observations for finer-level CATAMI classes and associated morphospecies.

Automatic Identification Systems (AIS) are a standard feature of ocean-going vessels, designed to allow vessels to notify each other of their position and route, to reduce collisions. Increasingly, the system is being used to monitor vessels remotely, particularly with the advent of satellite receivers. One fundamental problem with AIS transmission is the issue of gaps in transmissions. Gaps occur for three basic reasons: 1) saturation of the system in locations with high vessel density; 2) poor quality transmissions due to equipment on the vessel or receiver; and 3) intentional disabling of AIS transmitters. Resolving which of these mechanisms is responsible for generating gaps in transmissions from a given vessel is a critical task in using AIS to remotely monitor vessels. Moreover, separating saturation and equipment issues from intentional disabling is a key issue, as intentional disabling is a useful risk factor in predicting illicit behaviors such as illegal fishing. We describe a spatial statistical model developed to identify gaps in AIS transmission, which allows calculation of the probability that a given gap is due to intentional disabling. The model we developed successfully identifies high risk gaps in the test case example in the Arafura Sea. Simulations support that the model is sensitive to frequent gaps as short as one hour. Results in this case study area indicate expected high risk vessels were ranked highly for risk of intentional disabling of AIS transmitters. We discuss our findings in the context of improving enforcement opportunities to reduce illicit activities at sea.

Illegal, Unreported and Unregulated (IUU) fishing activities pose one of the most significant threats to sustainable fisheries worldwide. Identifying illegal behaviour, specifically fishing and at-sea transhipment, continues to be a fundamental hurdle in combating IUU fishing. Here, we explore the use of spatial statistical methods to identify vessels behaving anomalously, in particular with regard to loitering, using the Indonesian Exclusive Economic Zone (EEZ) and surrounding waters as a case-study. Using Automatic Identification System (AIS) for vessel tracking, we applied Generalized Additive Models to capture both the temporal and spatial nature of loitering behaviour. We identified three statistically anomalous loitering behaviours (based on time, speed and distance) and applied the models to 2700 vessels in the region. We were able to rank vessels for individual and joint probability of atypical behaviour, providing a hierarchical list of vessels engaging in anomalous behaviour. While identification of irregular behaviour does not mean vessels are definitely engaging in illegal activities, this statistical modelling approach can be used to prioritise the allocation of enforcement resources and assist authorities under the United Nations Food and Agricultural Organization Port State Measures Agreement for management and enforcement of IUU fishing associated activities.

Collecting enough data to obtain reasonable abundance estimates of whales is often difficult, particularly when studying rare species. Passive acoustics can be used to detect whale sounds and are increasingly used to estimate whale abundance. Much of the existing effort centres on the use of acoustics to estimate abundance directly, e.g. analysing detections in a distance sampling framework. Here, we focus on acoustics as a tool incorporated within mark-recapture surveys. In this context, acoustic tools are used to detect and track whales, which are then photographed or biopsied to provide data for mark-recapture analyses. The purpose of incorporating acoustics is to increase the encounter rate beyond using visual searching only. While this general approach is not new, its utility is rarely quantified. This paper predicts the "acoustically-assisted" encounter rate using a discrete-time individual-based simulation of whales and survey vessel. We validate the simulation framework using existing data from studies of sperm whales. We then use the framework to predict potential encounter rates in a study of Antarctic blue whales. We also investigate the effects of a number of the key parameters on encounter rate. Mean encounter rates from the simulation of sperm whales matched well with empirical data. Variance of encounter rate, however, was underestimated. The simulation of Antarctic blue whales found that passive acoustics should provide a 1.7-3.0 fold increase in encounter rate over visual-only methods. Encounter rate was most sensitive to acoustic detection range, followed by vocalisation rate. During survey planning and design, some indication of the relationship between expected sample size and effort is paramount; this simulation framework can be used to predict encounter rates and establish this relationship. For a case in point, the simulation framework indicates unequivocally that real-time acoustic tracking should be considered for quantifying the abundance of Antarctic blue whales via mark-recapture methods.

Aerial surveys of marine mammals are routinely conducted to assess and monitor species' habitat use and population status. In Australia, dugongs (Dugong dugon) are regularly surveyed and long-term datasets have formed the basis for defining habitat of high conservation value and risk assessments of human impacts. Unmanned aerial vehicles (UAVs) may facilitate more accurate, human-risk free, and cheaper aerial surveys. We undertook the first Australian UAV survey trial in Shark Bay, western Australia. We conducted seven flights of the ScanEagle UAV, mounted with a digital SLR camera payload. During each flight, ten transects covering a 1.3 km(2) area frequently used by dugongs, were flown at 500, 750 and 1000 ft. Image (photograph) capture was controlled via the Ground Control Station and the capture rate was scheduled to achieve a prescribed 10% overlap between images along transect lines. Images were manually reviewed post hoc for animals and scored according to sun glitter, Beaufort Sea state and turbidity. We captured 6243 images, 627 containing dugongs. We also identified whales, dolphins, turtles and a range of other fauna. Of all possible dugong sightings, 95% (CI = 90%, 98%) were subjectively classed as 'certain' (unmistakably dugongs). Neither our dugong sighting rate, nor our ability to identify dugongs with certainty, were affected by UAV altitude. Turbidity was the only environmental variable significantly affecting the dugong sighting rate. Our results suggest that UAV systems may not be limited by sea state conditions in the same manner as sightings from manned surveys. The overlap between images proved valuable for detecting animals that were masked by sun glitter in the corners of images, and identifying animals initially captured at awkward body angles. This initial trial of a basic camera system has successfully demonstrated that the ScanEagle UAV has great potential as a tool for marine mammal aerial surveys.