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The quantity of plastic fishing gear lost to the ocean remains largely undocumented despite its importance for understanding the magnitude of fishery contributions to plastic marine debris and mitigating contamination at the source. This study estimated plastic gear loss from a major Australian pot fishery by interviewing 50 commercial fishers from the West Coast Rock Lobster Managed Fishery in 2021 to document the plastic gear components used, the number of pots lost, and the causes for gear loss. The estimated annual percentage of gear lost (10.8%) and rate of “active” gear loss by fishing effort (0.2%) were lower than global estimates for other pot fisheries indicating that plastic gear loss from this commercial fishery is relatively low. Plastic gear loss was attributed to rope (47.0%), pot components (30.7%), and floats (22.3%), of which 78.0% were polypropylene and polyethylene. The composition of pots, measures used to prevent gear loss, and changes to management could influence plastic gear loss from this fishery. This is the first study to examine plastic gear loss from an Australian pot fishery with a dynamic approach that provides robust estimates for better understanding plastic gear loss from fisheries and informing plastic mitigation efforts.

Ecklonia radiata is the main foundation species in Australian temperate reefs, yet little has been published on its reproduction and how this may change across its depth range (1-50+ m). In this study, we examined differences in sporophyte morphology and zoospore production during a reproductive season and across four depths (7, 15, 25, and 40 m). Additionally, we examined differences in germination rate, survival, and morphological traits of gametophytes obtained from these four depths, cultured under the same light and temperature conditions. Multivariate morphology of sporophytes differed significantly between deep (~40 m) and shallow sites (7 and 15 m), but individual morphological traits were not significantly different across depths. Total spore production was similar across depths but the peak of zoospore release was observed in February at 15 m of depth (6,154 zoospores · mm-2 of tissue) and the minimum observed in January at 7, 25, and 40 m (1,141, 987, and 214 zoospores · mm-2 of tissue, respectively). The source depth of zoospores did not have an influence in the germination rate or the survival of gametophytes, and only gametophytes sourced from 40 m sites presented significantly less surface area and number of branches. Overall, these results indicate that E. radiata's reproductive performance does not change across its depth range and that kelp beds reproducing in deeper areas may contribute to the replenishment of their shallow counterparts. We propose that deep kelps may constitute a mechanism of resilience against climate change and anthropogenic disturbances.

Plastic pollution is a global environmental and human health issue, with plastics now ubiquitous in the environment and biota. Despite extensive international research, key knowledge gaps ("known unknowns") remain around ecosystem-scale and human health impacts of plastics in the environment, particularly in limnetic, coastal and marine systems. Here we review aquatic plastics research in three contrasting geographic and cultural settings, selected to present a gradient of heavily urbanised (and high population density) to less urbanised (and low population density) areas: China, the United Kingdom (UK), and Australia. Research from each country has varying environmental focus (for example, biota-focussed studies in Australia target various bird, fish, turtle and seal species, while UK and China-based studies focus on commercially important organisms such as bivalves, fish and decapods), and uses varying methods and reporting units (e.g. mean, median or range). This has resulted in aquatic plastics datasets that are hard to compare directly, supporting the need to converge on standardised sampling methods, and bioindicator species. While all the study nations show plastics contamination, often at high levels, datasets are variable and do not clearly demonstrate pollution gradients.

Underwater imaging is being extensively used for monitoring the abundance of lobster species and their biodiversity in their local habitats. However, manual assessment of these images requires a huge amount of human effort. In this article, we propose to automate the process of lobster detection using a deep learning technique. A major obstacle in deploying such an automatic framework for the localization of lobsters in diverse environments is the lack of large annotated training datasets. Generating synthetic datasets to train these object detection models has become a popular approach. However, the current synthetic data generation frameworks rely on automatic segmentation of objects of interest, which becomes difficult when the objects have a complex shape, such as lobster. To overcome this limitation, we propose an approach to synthetically generate parts of the lobster. To handle the variability of real-world images, these parts were inserted into a set of diverse background marine images to generate a large synthetic dataset. A state-of-the-art object detector was trained using this synthetic parts dataset and tested on the challenging task of Western rock lobster detection in West Australian seas. To the best of our knowledge, this is the first automatic lobster detection technique for partially visible and occluded lobsters.

AIM: To investigate how changing grid size can alter model predictions of the distribution of mesophotic taxa and how it affects different modelling methods. LOCATION: Ningaloo Marine Park, Western Australia. TAXON: Benthic mesophotic taxa: corals, macroalgae and sponges. METHODS: We determined the distributions of the major benthic taxonomic groups: corals, macroalgae and sponges, using a number of modelling techniques and an ensemble using the ‘sdm’ R package. A range of grid sizes were used (10, 50, 100 and 250 m) to identify how model predictions were altered. Models were evaluated using the area under the curve of a receiver operator characteristic plot (AUC) and the true skill statistic (TSS) using a spatially independent dataset. RESULTS: Grid size had a large effect on model performance across the taxonomic groups. Model outputs were compared to null surfaces and 88.8% of models performed significantly better than null. Distribution of corals was best predicted using the finest grid size (10 m) regardless of modelling method, although a model ensemble produced the best results (AUC = 0.80, TSS = 0.52). Macroalgae and sponges were better predicted at coaster grids sizes (250 m). Again, ensembles performed well for both macroalgae (AUC = 0.83, TSS = 0.63) and sponges (AUC = 0.88, TSS = 0.66). Model ensembles maintained high accuracy across grid sizes and were consistently the best, or second‐best, performing method. MAIN CONCLUSIONS: This study has shown how grid size should be considered when producing distribution models. Identifying the most relevant grid size and being aware of the influence it may have will provide more accurate predictions of the distributions of taxa. Ensemble methods maintained good performance across scenarios and thus provide a useful tool for conservation and management especially where single modelling methods showed high levels of variability.

Marine heatwaves (MHWs) have been documented around the world, causing widespread mortality of numerous benthic species on shallow reefs (less than 15 m depth). Deeper habitats are hypothesized to be a potential refuge from environmental extremes, though we have little understanding of the response of deeper benthic communities to MHWs. Here, we show how increasing depth moderates the response of seaweed- and coral-dominated benthic communities to an extreme MHW across a subtropical–temperate biogeographical transition zone. Benthic community composition and key habitat-building species were characterized across three depths (15, 25 and 40 m) before and several times after the 2011 Western Australian MHW to assess resistance during and recovery after the heatwave. We found high natural variability in benthic community composition along the biogeographic transition zone and across depths with a clear shift in the composition after the MHW in shallow (15 m) sites but a lot less in deeper communities (40 m). Most importantly, key habitat-building seaweeds such as Ecklonia radiata and Syctothalia dorycarpa which had catastrophic losses on shallow reefs, remained and were less affected in deeper communities. Evidently, deep reefs have the potential to act as a refuge during MHWs for the foundation species of shallow reefs in this region.

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, like kelps. This paper presents an automatic hierarchical classification method (local binary classification as opposed to the conventional flat classification) to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs 57.6% and 77.2% vs 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys., MDPI Sensors

Despite a growing understanding of the importance of mesophotic ecosystems, they remain relatively unexplored globally, and particularly in the Indian Ocean. The composition of benthic communities of the Ningaloo Marine Park in deeper water (> 20 m) was determined using autonomous underwater vehicles (AUVs). Environmental variables collected by the AUVs as well as geomorphological variables derived from high-resolution multibeam bathymetry were used to explain the distribution of these communities. Imagery was classified for benthic biota, and non-hierarchical k-means clustering was used to identify community groups. Overall, 12 community groups were identified which fell into three broad categories: (1) coral-dominated, (2) CCA-dominated; and (3) macroalgae- and filter-feeder-dominated. Specific deeper water communities (> 50 m) were characterised by the presence of sponges or other non-photosynthetic filter feeders. Corals were confined to depths

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naive Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8–1). Total agreement between classifiers was high at the broadest level of classification (75–80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19–45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Mesophotic coral ecosystems (MCEs) occur at depths beyond those typically associated with coral reefs. Significant logistical challenges associated with data collection in deep water have resulted in a limited understanding of the ecological relevance of these deeper coral ecosystems. We review the trends in this research, covering the geographic spread of MCE research, the focus of these studies, the methods used, how MCEs differ in terms of species diversity and begin to assess connectivity of coral populations. Clear locational biases were observed, with studies concentrated in a few discrete areas mainly around the Atlantic region. The focus of MCE studies has diversified in recent years and more detailed aspects of MCE ecology are now being investigated in particular areas of research. Advances in technology are also reflected in the current range of research, with a wider variety of methods now employed. However, large information gaps are present in entire regions and particularly in relation to the threats, impacts and subsequent management of MCEs. Analysis of species diversity shows that initial definitions based on depth alone may not be appropriate globally, while further taxonomic resolution may also be required to deduce the full biodiversity of major groups in certain regions. Genetic studies to date show species-specific results, although distinct deeper populations do appear to exist, which raises questions regarding the potential of MCEs to act as refugia.

A central question in contemporary ecology is how climate change will alter ecosystem structure and function across scales of space and time. Climate change has been shown to alter ecological patterns from individuals to ecosystems, often with negative implications for ecosystem functions and services. Furthermore, as climate change fuels more frequent and severe extreme climate events (ECEs) like marine heatwaves (MHWs), such acute events become increasingly important drivers of rapid ecosystem change. However, our understanding of ECE impacts is hampered by limited collection of broad scale in situ data where such events occur. In 2011, a MHW known as the Ningaloo Niño bathed the west coast of Australia in waters up to 4°C warmer than normal summer temperatures for almost 2 months over 1000s of kilometers of coastline. We revisit published and unpublished data on the effects of the Ningaloo Niño in the seagrass ecosystem of Shark Bay, Western Australia (24.6–26.6° S), at the transition zone between temperate and tropical seagrasses. Therein we focus on resilience, including resistance to and recovery from disturbance across local, regional and ecosystem-wide spatial scales and over the past 8 years. Thermal effects on temperate seagrass health were severe and exacerbated by simultaneous reduced light conditions associated with sediment inputs from record floods in the south-eastern embayment and from increased detrital loads and sediment destabilization. Initial extensive defoliation of Amphibolis antarctica, the dominant seagrass, was followed by rhizome death that occurred in 60–80% of the bay's meadows, equating to decline of over 1,000 km2 of meadows. This loss, driven by direct abiotic forcing, has persisted, while indirect biotic effects (e.g., dominant seagrass loss) have allowed colonization of some areas by small fast-growing tropical species (e.g., Halodule uninervis). Those biotic effects also impacted multiple consumer populations including turtles and dugongs, with implications for species dynamics, food web structure, and ecosystem recovery. We show multiple stressors can combine to evoke extreme ecological responses by pushing ecosystems beyond their tolerance. Finally, both direct abiotic and indirect biotic effects need to be explicitly considered when attempting to understand and predict how ECEs will alter marine ecosystem dynamics.

Healthy coral reefs play a vital role in maintaining biodiversity in tropical marine ecosystems. Remote imaging techniques have facilitated the scientific investigations of these intricate ecosystems, particularly at depths beyond 10 meters where SCUBA diving techniques are not time or cost efficient. With millions of digital images of the sea floor collected using Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs), manual annotation of this data by marine experts is a tedious, repetitive and time consuming task. It takes 10-30 minutes for a marine expert to meticulously annotate a single image. Automated technology to monitor the health of the oceans would allow for transformational ecological outcomes by standardizing methods to detect and identify species. This paper aims to automate the analysis of large available AUV imagery by developing advanced deep learning tools for rapid and large-scale automatic annotation of marine coral species. Such an automated technology would greatly benefit marine ecological studies in terms of cost, speed and accuracy. To this end, we propose a deep learning based classification method for coral reefs and report the application of the proposed technique to the automatic annotation of unlabelled mosaics of the coral reef in the Abrolhos Islands, Western Australia. Our proposed method automatically quantified the coral coverage in this region and detected a decreasing trend in coral population, which is in line with conclusions drawn by marine ecologists.

A feedback between seagrass presence, suspended sediment and benthic light can induce bistability between two ecosystem states: one where the presence of seagrass reduces suspended sediment concentrations to increase benthic light availability thereby favoring growth, and another where seagrass absence increases turbidity thereby reducing growth. This literature review identifies (1) how the environmental and seagrass meadow characteristics influence the strength and direction (stabilizing or destabilizing) of the seagrass-sediment-light feedback, and (2) how this feedback has been incorporated in ecosystem models proposed to support environmental decision making. Large, dense seagrass meadows in shallow subtidal, non-eutrophic systems, growing in sediments of mixed grain size and subject to higher velocity flows, have the greatest potential to generate bistability via the seagrass-sediment-light feedback. Conversely, seagrass meadows of low density, area and height can enhance turbulent flows that interact with the seabed, causing water clarity to decline. Using a published field experiment as a case study, we show that the seagrass-sediment-light feedback can induce bistability only if the suspended sediment has sufficient light attenuation properties. The seagrass-sediment-light feedback has been considered in very few ecosystem models. These models have the potential to identify areas where bistability occurs, which is information that can assist in spatial prioritization of conservation and restoration efforts. In areas where seagrass is present and bistability is predicted, recovery may be difficult once this seagrass is lost. Conversely, bare areas where seagrass presence is predicted (without bistability) may be better targets for seagrass restoration than bare areas where bistability is predicted.

Accurate estimation of connectivity among populations is fundamental for determining the drivers of population resilience, genetic diversity, adaptation and speciation. However the separation and quantification of contemporary versus historical connectivity remains a major challenge. This review focuses on marine angiosperms, seagrasses, that are fundamental to the health and productivity of temperate and tropical coastal marine environments globally. Our objective is to understand better the role of sexual reproduction and recruitment in influencing demographic and genetic connectivity among seagrass populations through an integrated multidisciplinary assessment of our present ecological, genetic, and demographic understanding, with hydrodynamic modelling of transport. We investigate (i) the demographic consequences of sexual reproduction, dispersal and recruitment in seagrasses, (ii) contemporary transport of seagrass pollen, fruits and seed, and vegetative fragments with a focus on hydrodynamic and particle transport models, and (iii) contemporary genetic connectivity among seagrass meadows as inferred through the application of genetic markers. New approaches are reviewed, followed by a summary outlining future directions for research: integrating seascape genetic approaches; incorporating hydrodynamic modelling for dispersal of pollen, seeds and vegetative fragments; integrating studies across broader geographic ranges; and incorporating non-equilibrium modelling. These approaches will lead to a more integrated understanding of the role of contemporary dispersal and recruitment in the persistence and evolution of seagrasses.

Artificial reefs are frequently installed to provide additional habitat that enhances recreational activities such as diving and fishing. This study assessed changes to habitats, macroinvertebrates and fish after installation of an artificial reef, deployed to influence the marine community and create a more appealing dive trial at Port Coogee, Western Australia. This was achieved by comparing the communities at three control sites to the impact site (artificial reef) before installation and at increased time intervals after installation (3, 11 and 20 months). Across the sampling period and at the impacted site the cover of brown turfing algae increased significantly by 87%. Relative to previous monitoring 18 new species of macroinvertebrate and on average 138 more individuals were detected during post reef surveys. Both the species richness and abundance of fish tended to increase at all sites over the duration of the survey. Although this result was not significant, the final sampling period detected 14 more species and on average 31 more individuals than the previous sampling period. The colonisation of these structures and the progression of the marine community at this site provides valuable insight into the potential function of engineered artificial reefs.

The coastal waters of southern and south-western Australia are home to almost 30,000 km2 of seagrass, dominated by temperate endemic species of the genera Posidonia and Amphibolis. In this region, seagrasses are common in estuaries and sheltered coastal areas including bays, lees of islands, headlands, and fringing coastal reefs. Additionally, extensive meadows exist in the inverse estuaries of the Gulfs in South Australia, and in Shark Bay in Western Australia. This chapter explores (i) how geological time has shaped the coastline and influenced seagrasses, (ii) present day habitats and drivers, (iii) how biogeography patterns previously reported have been altered due to anthropogenic and climate impacts, and (iv) emerging threats and management issues for this region. Species diversity in this region rivals those of tropical environments, and many species have been found more than 30 km offshore and at depths greater than 40 m. Seagrasses in this region face a future of risk from multiple stressors at the ecosystem scale with coastal development, eutrophication, extreme climate events and global warming. However, our recent improved understanding of seagrass recruitment, restoration and resilience provides hope for the future management of these extraordinary underwater habitats.

Seagrass seascapes are 100s m2 to 1000s of km2 coastal regions in nearshore, sandy to muddy benthic environments that are characterized by the presence of seagrasses. Here we explore the development of seagrass seascape research in Australia. Determining the distribution of seagrasses started with mapping their extent, but improvements in remote sensing and statistical modelling has allowed us assess the large scale spatial distribution and temporal dynamics of seagrass seascapes. We use a case study from Moreton Bay, near Brisbane, Queensland to demonstrate changes in seagrass meadows over time. Terrestrial landscape indices and their use in seagrass studies is reviewed. Some indices perform better to summarize patch to meadow scale changes in the distribution and structure of seagrasses. A case-study is then presented, comparing landscape indices calculated from observed changes in seagrass patches and meadows to a spatially-explicit model simulation, to explore the drivers for changes in the seagrass seascape’s demographic processes, clonal growth and recruitment from seeds. The role of landscape structure in the movement and abundance of associated fauna in seagrass seascapes using landscape approaches is then reviewed. This is followed by a summary outlining directions for future research that combine landscape ecology and remote sensing techniques with population and community biology.

Movement is fundamental to the ecology and evolutionary dynamics within species. Understanding movement through seed dispersal in the marine environment can be difficult due to the high spatial and temporal variability of ocean currents. We employed a mutually enriching approach of population genetic assignment procedures and dispersal predictions from a hydrodynamic model to overcome this difficulty and quantify the movement of dispersing floating fruit of the temperate seagrass Posidonia australis Hook.f. across coastal waters in south-western Australia. Dispersing fruit cohorts were collected from the water surface over two consecutive years, and seeds were genotyped using microsatellite DNA markers. Likelihood-based genetic assignment tests were used to infer the meadow of origin for seed cohorts and individuals. A three-dimensional hydrodynamic model was coupled with a particle transport model to simulate the movement of fruit at the water surface. Floating fruit cohorts were mainly assigned genetically to the nearest meadow, but significant genetic differentiation between cohort and most likely meadow of origin suggested a mixed origin. This was confirmed by genetic assignment of individual seeds from the same cohort to multiple meadows. The hydrodynamic model predicted 60% of fruit dispersed within 20 km, but that fruit was physically capable of dispersing beyond the study region. Concordance between these two independent measures of dispersal provides insight into the role of physical transport for long distance dispersal of fruit and the consequences for spatial genetic structuring of seagrass meadows.

Seedling establishment is a crucial life history stage in seagrasses, yet factors that affect seedling health are poorly characterized. We investigated if organic matter (OM) additions to sediments provided nutritional benefits for seagrass seedlings through microbial degradation. We tested the effects of sedimentary OM additions on Posidonia australis seedlings growing in tank cultures. We focussed on sediment biogeochemical processes and microbial communities that may impact seedling growth and physiology. Enrichment of sediments with OM changed microbial community composition (DNA-ARISA) and a significant increase in hydrolytic enzyme expression. Total seedling biomass did not differ between OM treatments, but above:belowground biomass increased with OM enrichment. Nitrogen and phosphorus concentration of seagrass leaves was lower with increasing OM. Seagrass derived OM has been considered a refractory store of carbon, yet here we show its deposition into sediments significantly alters belowground conditions. Remineralization of the OM changes both physical and chemical nature of sediments that leads to greater biochemical activity, change in microbial communities and greater investment into above ground photosynthetic biomass. The presence of OM may assist seagrass seedling survival during early development by enhancing root branching and stability in sediments, but is unlikely to provide nutritional benefits.

Summary Extreme climatic events will dictate the response of ecosystems to climate change, yet are understudied in marine ecosystems. The interaction of stressors from such events has the potential to amplify negative impacts and drive ecosystems into alternate states. Here, we show a drastic response of a temperate seagrass species (Amphibolis antarctica) in Shark Bay – a World Heritage Site in Western Australia at a temperate–tropical transition zone – to two stressors driven by concurrent extreme climatic events: a marine heatwave (Ningaloo Nina) and the Gascoyne floods that impacted the west coast of Australia in the austral summer of 2010–2011. Widespread defoliation (leaf loss) of A. antarctica was observed in the months following the extreme events and was highest at sites affected by flooding (Wooramel River floods). We propose that the negative impact was magnified by the synergistic interactions both stressors had on the carbon balance of the plant. The elevated temperatures increased plant demand for carbon, which could not be met through photosynthesis due to turbid floodwaters reducing light availability, resulting in the plant having a negative carbon balance. Two years following the extreme events, recovery of leaf biomass was evident, though still 7–20% of historical averages. In contrast, below-ground biomass decreased by an order of magnitude in the two years following the events. As below-ground reserves underpin the tolerance of large seagrass species like A. antarctica to disturbances, the declining trajectory of below-ground biomass will likely manifest as a loss of resilience in A. antarctica to future disturbances. Synthesis. Given the ecological importance of Amphibolis antarctica in Shark Bay as a foundation species – accounting for 85% (˜3700 km2) of the cover of seagrasses in Shark Bay – predicted increases in the frequency and magnitude of similar climatic events could have catastrophic implications for the future of this World Heritage embayment. Where extreme climatic events overlap and cause multiple, synergistic stressors to plant communities, ecological responses are likely to be more extreme, particularly in ecosystems where foundation species exist near upper thermal tolerance limits.

Understanding the extent and impact of factors influencing the levels and structur- ing of genetic diversity within natural populations is a key objective of ecological genetics. For marine angiosperms, variation in abiotic environmental factors at the local scale can have a major influence on levels of clonality and spatial genetic structure, and thus influence mating systems, sexual reproduction, and recruitment. Identifying the key drivers of genetic structuring is critical for genetic management of ecological restoration success, especially in systems where the nature and extent of clonality is highly variable. Here, we quantify clonality and patterns of genetic structure in the temperate Australian seagrass Posidonia australis. We examine the loca- tion of meadows in relation to water movement and prevailing winds to assess their relative influence on local spatial genetic structuring. Measures of genetic diversity, assessed with 7 polymorphic microsatellite loci, were highly variable across 13 meadows sampled within and around a natural embayment on the west coast of Australia. The overall structure of P. australis meadows across this region is best explained as one of 'chaotic' genetic patchiness, with signifi- cant differentiation among most meadows (pairwise FST values), high levels of genetic diversity in meadows that are in more open waters, and lower genetic diversity at inshore sites facing strong prevailing winds at the time of seed dispersal or that have little water movement. A strong isolation by distance relationship within the embayment is consistent with prevailing winds (which create surface currents) at the time of peak pollen and seed release, strongly influencing dispersal direction.

Scientific Data 2:150057 doi:10.1038/sdata.2015.57 (2015); Published 27 Oct 2015; Updated 20 Dec 2016 The authors regret that Ezequiel Marzinelli was omitted in error from the author list of the original version of this Data Descriptor. This omission has now been corrected in the HTML and PDF versions of this Data Descriptor.

Healthy coral reefs play a vital role in maintaining biodiversity in tropical marine ecosystems. Deep sea exploration and imaging have provided us with a great opportunity to look into the vast and complex marine ecosystems. Data acquisition from the coral reefs has facilitated the scientific investigation of these intricate ecosystems. Millions of digital images of the sea floor have been collected with the help of Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). Automated technology to monitor the health of the oceans allows for transformational ecological outcomes by standardizing methods for detecting and identifying species. Manual annotation is a tediously repetitive and a time consuming task for marine experts. It takes 10-30 minutes for a marine expert to meticulously annotate a single image. This paper aims to automate the analysis of large available AUV imagery by developing advanced deep learning tools for rapid and large-scale automatic annotation of marine coral species. Such an automated technology would greatly benefit marine ecological studies in terms of cost, speed, accuracy and thus in better quantifying the level of environmental change marine ecosystems can tolerate. We propose a deep learning based classification method for coral reefs. We also report the application of the proposed technique towards the automatic annotation of unlabelled mosaics of the coral reef in the Abrolhos Islands, Western Australia. Our proposed method automatically quantifies the coral coverage in this region and detects a decreasing trend in coral population which is in line with conclusions by marine ecologists.

Ecosystem reconfigurations arising from climate-driven changes in species distributions are expected to have profound ecological, social, and economic implications. Here we reveal a rapid climate-driven regime shift of Australian temperate reef communities, which lost their defining kelp forests and became dominated by persistent seaweed turfs. After decades of ocean warming, extreme marine heat waves forced a 100-kilometer range contraction of extensive kelp forests and saw temperate species replaced by seaweeds, invertebrates, corals, and fishes characteristic of subtropical and tropical waters.This community-wide tropicalization fundamentally altered key ecological processes, suppressing the recovery of kelp forests., This work was funded by the Australian Research Council (T.W., G.A.K.), the Hermon Slade Foundation (T.W., S.B.), a U.K. Natural Environment Research Council Independent Research Fellowship (D.A.S.), the Australian Institute of Marine Science (T.W., M.D., B.R.), the Australian National University (C.J.F.), the Western Australian Museum (J.F.), the Department of Parks and Wildlife (T.H.H., S.W.), CSIRO Oceans and Atmosphere (R.C.B., F.D., M.A.V.), Fisheries Research and Development Corporation project no. 2008/013 (R.K.H., G.A.K.), The Marsden Fund of The Royal Society of New Zealand (M.S.T.), and the WA Strategic Research Fund for the Marine Environment (R.B., M.A.V., J.F.).

Smale, D. A., Kendrick, G. A., Harvey, E. S., Langlois, T. J., Hovey, R. K., Van Niel, K. P., Waddington, K. I., Bellchambers, L. M., Pember, M. B., Babcock, R. C., Vanderklift, M. A., Thomson, D. P., Jakuba, M. V., Pizarro, O., and Williams, S. B. 2012. Regional-scale benthic monitoring for ecosystem-based fisheries management (EBFM) using an autonomous underwater vehicle (AUV). – ICES Journal of Marine Science, 69: 1108–1118. Monitoring marine habitats and biodiversity is critical for understanding ecological processes, conserving natural resources, and achieving ecosystem-based fisheries management (EBFM). Here, we describe the application of autonomous underwater vehicle (AUV) technology to conduct ongoing monitoring of benthic habitats at two key locations in Western Australia. Benthic assemblages on rocky reefs were sampled with an AUV, which captured >200 000 geo-referenced images. Surveys were designed to obtain 100% coverage of 25 × 25 m patches of benthic habitat. In 2010, multiple patches were surveyed at 15–40-m depths at three reference sites at the Houtman Abrolhos Islands and at six reference sites at Rottnest Island. The following year, repeat surveys of the same geo-referenced patches were conducted. Benthic assemblages at the Houtman Abrolhos Islands were varied in that one reference site was dominated by hard corals, whereas the other two were macroalgae dominated. Conversely, assemblages at Rottnest Island were dominated by the kelp Ecklonia radiata. The AUV resurveyed each patch with high precision and demonstrated adequate power to detect change. Repeated observations at the reference sites will track natural variability in benthic habitat structure, which in turn will facilitate the detection of ecological change and ultimately feed back into EBFM processes.

We examined the root systems of 2 temperate seagrasses, Posidonia australis and P. sinuosa, testing the effects of nutrients and season on root architecture. Transplants of each spe- cies were grown in pots containing a standardised sand medium and a localised supply of nitrogen (N), phosphorus (P), or N and P combined. P. australis and P. sinuosa showed similar root architec- ture (primary roots with numerous second-order laterals and few higher-order laterals), but P. aus- tralis produced a larger root system with longer laterals making up the bulk of root length. Sea- grass with combined N and P produced a higher lateral root density (330 ± 18 (SE) branches m �1 root compared to controls with 250 ± 22), and a lower topological index (TI: 0.72 ± 0.03 compared to controls with 0.84 ± 0.02). Despite this response in root architecture, there was no significant change in traits associated with absorptive capacity. Separate nutrient additions had little effect on roots. Root architecture was more complex in summer for both species, with more higher-order laterals (TI: 0.73 ± 0.02) resulting in a more dichotomous pattern compared to winter (TI: 0.86 ± 0.01). P. australis had twice the root length and higher specific root length in summer than winter, mostly due to increases in lateral root number and length. This study demonstrated that P. aus- tralis and P. sinuosa display architectural and morphological plasticity with season and to a lesser extent nutrient addition. The complex root architecture in the slow-growing temperate Posidonia species may reflect hydrodynamic exposure and the importance for anchorage in wave-swept environments.

Global seagrass losses parallel significant declines observed in corals and mangroves over the past 50 years. These combined declines have resulted in accelerated global losses to ecosystem services in coastal waters. Seagrass meadows can be extensive (hundreds of square kilometers) and long-lived (thousands of years), with the meadows persisting predominantly through vegetative (clonal) growth. They also invest a large amount of energy in sexual reproduction. In this article, we explore the role that sexual reproduction, pollen, and seed dispersal play in maintaining species distributions, genetic diversity, and connectivity among seagrass populations. We also address the relationship between long-distance dispersal, genetic connectivity, and the maintenance of genetic diversity that may enhance resilience to stresses associated with seagrass loss. Our reevaluation of seagrass dispersal and recruitment has altered our perception of the importance of long-distance dispersal and has revealed extensive dispersal at scales much larger than was previously thought possible.

The effects of nutrients and planting season on root growth were investigated in transplants of the seagrasses Posidonia australis and P. sinuosa. Difficulties with sampling and estimating root growth of these submerged plants were overcome by growing transplants, with all roots removed initially, in pots containing a standardized sand medium. Nitrogen (N), phosphorus (P), and nitrogen and phosphorus combined (N + P) were added to pots. Root elongation and productivity were measured after harvesting at 1 month, 4 months, and 6 months in consecutive experiments beginning in autumn and spring. Roots formed within a month, growing into extensive root systems. Posidonia australis root growth was three-fold less over winter (10 mm d21 per plant) than summer (33 mm d21 per plant), with some plants producing up to 9 m of roots in 6 months. Posidonia sinuosa root-growth rates were significantly lower than those of P. australis and did not vary between winter and summer (6 mm d21 per plant). Root productivity was lower with N and N + P addition for P. sinuosa and with N addition for P. australis in summer. In contrast, P addition had little effect on root growth, reducing only primary root growth in P. australis. Addition of nutrients did not result in the expected increases in fine roots in the zone around the nutrient source. This technique for measuring root growth demonstrated the potential of these Posidonia transplants to produce extensive root systems within months of planting, in contrast to the much slower growth of

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

Accurate estimation of connectivity among populations is fundamental for determining the drivers of population resilience, genetic diversity, adaptation and speciation. However the separation and quantification of contemporary versus historical connectivity remains a major challenge. This review focuses on marine angiosperms, seagrasses, that are fundamental to the health and productivity of temperate and tropical coastal marine environments globally. Our objective is to understand better the role of sexual reproduction and recruitment in influencing demographic and genetic connectivity among seagrass populations through an integrated multidisciplinary assessment of our present ecological, genetic, and demographic understanding, with hydrodynamic modelling of transport. We investigate (i) the demographic consequences of sexual reproduction, dispersal and recruitment in seagrasses, (ii) contemporary transport of seagrass pollen, fruits and seed, and vegetative fragments with a focus on hydrodynamic and particle transport models, and (iii) contemporary genetic connectivity among seagrass meadows as inferred through the application of genetic markers. New approaches are reviewed, followed by a summary outlining future directions for research: integrating seascape genetic approaches; incorporating hydrodynamic modelling for dispersal of pollen, seeds and vegetative fragments; integrating studies across broader geographic ranges; and incorporating non-equilibrium modelling. These approaches will lead to a more integrated understanding of the role of contemporary dispersal and recruitment in the persistence and evolution of seagrasses.

BACKGROUND AND AIMS Organisms occupying the edges of natural geographical ranges usually survive at the extreme limits of their innate physiological tolerances. Extreme and prolonged fluctuations in environmental conditions, often associated with climate change and exacerbated at species' geographical range edges, are known to trigger alternative responses in reproduction. This study reports the first observations of adventitious inflorescence-derived plantlet formation in the marine angiosperm Posidonia australis, growing at the northern range edge (upper thermal and salinity tolerance) in Shark Bay, Western Australia. These novel plantlets are described and a combination of microsatellite DNA markers and flow cytometry is used to determine their origin. METHODS Polymorphic microsatellite DNA markers were used to generate multilocus genotypes to determine the origin of the adventitious inflorescence-derived plantlets. Ploidy and genome size were estimated using flow cytometry. KEY RESULTS All adventitious plantlets were genetically identical to the maternal plant and were therefore the product of a novel pseudoviviparous reproductive event. It was found that 87 % of the multilocus genotypes contained three alleles in at least one locus. Ploidy was identical in all sampled plants. The genome size (2 C value) for samples from Shark Bay and from a separate site much further south was not significantly different, implying they are the same ploidy level and ruling out a complete genome duplication (polyploidy). CONCLUSIONS Survival at range edges often sees the development of novel responses in the struggle for survival and reproduction. This study documents a physiological response at the trailing edge, whereby reproductive strategy can adapt to fluctuating conditions and suggests that the lower-than-usual water temperature triggered unfertilized inflorescences to 'switch' to growing plantlets that were adventitious clones of their maternal parent. This may have important long-term implications as both genetic and ecological constraints may limit the ability to adapt or range-shift; this seagrass meadow in Shark Bay already has low genetic diversity, no sexual reproduction and no seedling recruitment.

Background The western rock lobster, Panulirus cygnus, is endemic to Western Australia and supports substantial commercial and recreational fisheries. Due to and its wide distribution and the commercial and recreational importance of the species a key component of managing western rock lobster is understanding the ecological processes and interactions that may influence lobster abundance and distribution. Using terrain analyses and distribution models of substrate and benthic biota, we assess the physical drivers that influence the distribution of lobsters at a key fishery site. Methods and Findings Using data collected from hydroacoustic and towed video surveys, 20 variables (including geophysical, substrate and biota variables) were developed to predict the distributions of substrate type (three classes of reef, rhodoliths and sand) and dominant biota (kelp, sessile invertebrates and macroalgae) within a 40 km2 area about 30 km off the west Australian coast. Lobster presence/absence data were collected within this area using georeferenced pots. These datasets were used to develop a classification tree model for predicting the distribution of the western rock lobster. Interestingly, kelp and reef were not selected as predictors. Instead, the model selected geophysical and geomorphic scalar variables, which emphasise a mix of terrain within limited distances. The model of lobster presence had an adjusted D2 of 64 and an 80% correct classification. Conclusions Species distribution models indicate that juxtaposition in fine scale terrain is most important to the western rock lobster. While key features like kelp and reef may be important to lobster distribution at a broad scale, it is the fine scale features in terrain that are likely to define its ecological niche. Determining the most appropriate landscape configuration and scale will be essential to refining niche habitats and will aid in selecting appropriate sites for protecting critical lobster habitats.

Here, we review the literature to evaluate seagrass revegetation projects focussed on Posidonia australis and Amphibolis antarctica, the main affected species in Shark Bay in the World Heritage Area in Western Australia, together with projects from Florida Bay, an analogous system with a long history of seagrass revegetation. We assessed the effectiveness of anchoring planting units, plant-unit density and size on planting-unit survival. We found no positive trends in our assessment, suggesting that there is no discrete technique, approach or technology that could be used with confidence to deliver cost-effective, scalable revegetation. Of concern was that revegetation success was evaluated over comparatively short time frames (1–3 years), driven by the strict time frames or deadlines of governing grant funding and commercial activities, leading to concerns that long-term revegetation outcomes may be difficult to assess with confidence. Several factors influenced revegetation outcomes which were grouped into three ‘filter’ categories; abiotic, biotic and socioeconomic. We recommend that future revegetation programs involving seagrass have greater emphasis on understanding how these filters act independently or collectively to drive successful revegetation as well as developing cost-effective, proven and scalable technology supported by longer-term monitoring to ensure revegetation programs do achieve the desired ecological outcomes.

This chapter presents a summary of the use of deep learning for underwater image analysis, in particular for coral species classification. Deep learning techniques have achieved the state-of-the-art results in various computer vision tasks such as image classification, object detection, and scene understanding. Marine ecosystems are complex scenes and hence difficult to tackle from a computer vision perspective. Automated technology to monitor the health of our oceans can facilitate in detecting and identifying marine species while freeing up experts from the repetitive task of manual annotation. Classification of coral species is a challenging task in itself and deep learning has a potential of solving this problem efficiently.