Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat

Machine learning algorithms are being increasingly used to process large volumes of wildlife imagery data from unmanned aerial vehicles (UAVs); however, suitable algorithms to monitor multiple species are required to enhance efficiency. Here, we developed a machine learning algorithm using a low-cost computer. We trained a convolutional neural network and tested its performance in: (1) distinguishing focal organisms of three marine taxa (Australian fur seals, loggerhead sea turtles and Australasian gannets; body size ranges: 0.8–2.5 m, 0.6–1.0 m, and 0.8–0.9 m, respectively); and (2) simultaneously delineating the fine-scale movement trajectories of multiple sea turtles at a fish cleaning station. For all species, the algorithm performed best at detecting individuals of similar body length, displaying consistent behaviour or occupying uniform habitat (proportion of individuals detected, or recall of 0.94, 0.79 and 0.75 for gannets, seals and turtles, respectively). For gannets, performance was impacted by spacing (huddling pairs with offspring) and behaviour (resting vs. flying shapes, overall precision: 0.74). For seals, accuracy was impacted by morphology (sexual dimorphism and pups), spacing (huddling and creches) and habitat complexity (seal sized boulders) (overall precision: 0.27). For sea turtles, performance was impacted by habitat complexity, position in water column, spacing, behaviour (interacting individuals) and turbidity (overall precision: 0.24); body size variation had no impact. For sea turtle trajectories, locations were estimated with a relative positioning error of <50 cm. In conclusion, we demonstrate that, while the same machine learning algorithm can be used to survey multiple species, no single algorithm captures all components optimally within a given site. We recommend that, rather than attempting to fully automate detection of UAV imagery data, semi-automation is implemented (i.e. part automated and part manual, as commonly practised for photo-identification). Approaches to enhance the efficiency of manual detection are required in parallel to the development of effective implementation of machine learning algorithms

An evaluation of Sea Search as a citizen science programme in Marine Protected Areas

Citizen science involves collaboration between multi-sector agencies and the public to address a natural resource management issue. The Sea Search citizen science programme involves community groups in monitoring and collecting subtidal rocky reef and intertidal rocky shore data in Victorian Marine Protected Areas (MPAs), Australia. In this study we compared volunteer and scientifically collected data and the volunteer motivation for participation in the Sea Search programme. Intertidal rocky shore volunteer-collected data was found to be typically comparable to data collected by scientists for species richness and diversity measures. For subtidal monitoring there was also no significant difference for species richness recorded by scientists and volunteers. However, low statistical power suggest only large changes could be detected due to reduced data replication. Generally volunteers recorded lower species diversity for biological groups compared to scientists, albeit not significant. Species abundance measures for algae species were significantly different between volunteers and scientists. These results suggest difficulty in identification and abundance measurements by volunteers and the need for additional training requirements necessary for surveying algae assemblages. The subtidal monitoring results also highlight the difficulties of collecting data in exposed rocky reef habitats with weather conditions and volunteer diver availability constraining sampling effort. The prime motivation for volunteer participation in Sea Search was to assist with scientific research followed closely by wanting to work close to nature. This study revealed two important themes for volunteer engagement in Sea Search: 1) volunteer training and participation and, 2) usability of volunteer collected data for MPA managers. Volunteer-collected data through the Sea Search citizen science programme has the potential to provide useable data to assist in informed management practices of Victoria&rsquo;s MPAs, but requires the support and commitment from all partners involved.<br /

Contrasting patterns of population connectivity between regions in a commercially important mollusc Haliotis rubra: integrating population genetics, genomics and marine LiDAR data

Estimating contemporary genetic structure and population connectivity in marine species is challenging, often compromised by genetic markers that lack adequate sensitivity, and unstructured sampling regimes. We show how these limitations can be overcome via the integration of modern genotyping methods and sampling designs guided by LIDAR and SONAR datasets. Here we explore patterns of gene flow and local genetic structure in a commercially harvested abalone species (Haliotis rubra) from South Eastern Australia, where the viability of fishing stocks is believed to be dictated by recruitment from local sources. Using a panel of microsatellite and genome-wide SNP markers we compare allele frequencies across a replicated hierarchical sampling area guided by bathymetric LIDAR imagery. Results indicate high levels of gene flow and no significant genetic structure within or between benthic reef habitats across 1400 km of coastline. These findings differ to those reported for other regions of the fishery indicating that larval supply is likely to be spatially variable, with implications for management and long-term recovery from stock depletion. The study highlights the utility of suitably designed genetic markers and spatially informed sampling strategies for gaining insights into recruitment patterns in benthic marine species, assisting in conservation planning and sustainable management of fisheries

Modern rhodolith-dominated carbonates at Punta Chivato, Mexico

Rhodolith-dominated carbonate environments, characterized by high abundances of free-living coralline algae, have been described globally from a wide range of Recent and fossil shallow marine settings. In the present-day warm-temperate Gulf of California, Mexico, rhodolith-dominated systems are important contributors to carbonate production. One of the most prolific rhodolith factories is located on the Punta Chivato shelf, in the central Gulf of California, where due to a lack of input of terrigenous material from the arid hinterland, carbonate content averages 79%. Punta Chivato rhodoliths thrive above the shallow euphotic zone under normal saline, warm-temperate and meso- to eutrophic conditions. A detailed sedimentologic study combined with acoustic seafloor mapping indicates the presence of extensive rhodolith-dominated facies at subtidal water depth covering an area of \u3e17 km2. Additional facies, surrounding the rhodolith-dominated facies include a fine-grained molluscan, a transitional bivalve-rhodolith and a bivalve facies. While the Punta Chivato shelf yields average abundances of 38% rhodolith-derived coralline algal components in the gravel-sized sediment fraction, the rhodolith facies itself is characterized by more than 60% coralline algal components. Other important carbonate producers at Punta Chivato include bivalves (35%), bryozoa (11%) and gastropods (8%). The present study shows that acoustic sediment mapping yields highly resolved continuous coverage of the seafloor and can distinguish modern rhodolith facies from surrounding sediment. This has important implications for quantifying rhodolith-dominated settings globally, as well as for ecological and conservation studies. © Publications Scientifiques du Muséum national d\u27Histoire naturelle, Paris

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions

A field and video-annotation guide for baited remote underwater stereo-video surveys of demersal fish assemblages

Researchers TL, BG, JW, NB and JM were supported by the Marine Biodiversity Hub through funding from the Australian Government's National Environmental Science Program. Data validation scripts and GlobalArchive.org were supported by the Australian Research Data Commons, the Gorgon-Barrow Island Gorgon Barrow Island Net Conservation Benefits Fund, administered by the Government of Western Australia and the BHP/UWA Biodiversity and Societal Benefits of Restricted Access Areas collaboration.1. Baited remote underwater stereo-video systems (stereo-BRUVs) are a popular tool to sample demersal fish assemblages and gather data on their relative abundance and body-size structure in a robust, cost-effective, and non-invasive manner. Given the rapid uptake of the method, subtle differences have emerged in the way stereo-BRUVs are deployed and how the resulting imagery are annotated. These disparities limit the interoperability of datasets obtained across studies, preventing broad-scale insights into the dynamics of ecological systems. 2. We provide the first globally accepted guide for using stereo-BRUVs to survey demersal fish assemblages and associated benthic habitats. 3. Information on stereo-BRUV design, camera settings, field operations, and image annotation are outlined. Additionally, we provide links to protocols for data validation, archiving, and sharing. 4. Globally, the use of stereo-BRUVs is spreading rapidly. We provide a standardised protocol that will reduce methodological variation among researchers and encourage the use of Findable, Accessible, Interoperable, and Reproducible (FAIR) workflows to increase the ability to synthesise global datasets and answer a broad suite of ecological questions.Publisher PDFPeer reviewe

Granitic coastal geomorphology: Applying integrated terrestrial and bathymetric LiDAR with multibeam sonar to examine coastal landscape evolution

Coasts composed of resistant lithologies such as granite are generally highly resistant to erosion. They tend to evolve over multiple sea level cycles with highstands acting to remove subaerially weathered material. This often results in a landscape dominated by plunging cliffs with shore platforms rarely occurring. The long-term evolution of these landforms means that throughout the Quaternary these coasts have been variably exposed to different sea level elevations which means erosion may have been concentrated at different elevations from today. Investigations of the submarine landscape of granitic coasts have however been hindered by an inability to accurately image the nearshore morphology. Only with the advent of multibeam sonar and aerial laser surveying can topographic data now be seamlessly collected from above and below sea level. This study tests the utility of these techniques and finds that very accurate measurements can be made of the nearshore thereby allowing researchers to study the submarine profile with the same accuracy as the subaerial profile. From a combination of terrestrial and marine LiDAR data with multibeam sonar data, it is found that the morphology of granite domes is virtually unaffected by erosion at sea level. It appears that evolution of these landscapes on the coast is a very slow process with modern sea level acting only to remove subaerially weathered debris. The size and orientation of the joints determines the erosional potential of the granite. Where joints are densely spaced (&lt;2 m apart) or the bedrock is highly weathered can semi-horizontal surfaces form