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Abstract Natural systems exhibit high spatial variability across multiple scales. Models that can capture ecosystem dynamics across space and time by explicitly incorporating major biological mechanisms are crucial, both for management and for ecological insight. In the case of coral reef systems, much focus has been on modelling variability between reefs, despite substantial variability also existing within reefs. We developed C~scape, a coral metacommunity modelling framework that integrates the demography of corals with population-level responses to physical and environmental spatial layers, to facilitate spatiotemporal predictions of coral dynamics across reefs at fine (100s of metres to kilometres) scales. We used satellite-derived habitat maps to modulate community growth spatially, as a proxy for the many interacting physical and environmental factors—e.g., depth, light, wave exposure, temperature, and substrate type—that drive within-reef variability in coral demography. With a case study from the Great Barrier Reef, we demonstrate the model’s capability for producing hindcasts of coral cover dynamics and show that overlooking within-reef variability may lead to misleading conclusions about metacommunity dynamics. C~scape provides a valuable framework for exploring a range of management and restoration scenarios at relevant spatial scales.

Globally marine-terrestrial interfaces are highly impacted due to a range of human pressures. Seagrass habitats exist in the shallow marine waters of this interface, have significant values and are impacted by a range of pressures. Cumulative risk analysis is widely used to identify risk from multiple threats and assist in prioritizing management actions. This study conducted a cumulative risk analysis of seagrass habitat associated with the Australian continent to support management actions. We developed a spatially explicit risk model based on a database of threats to coastal aquatic habitat in Australia, spanning 35,000 km of coastline. Risk hotspots were identified using the model and reducing the risk of nutrient and sediment pollution for seagrass habitat was assessed. Incorporating future threats greatly altered the spatial-distribution of risk. High risk from multiple current threats was identified throughout all bioregions, but high risk from climate change alone manifested in only two. Improving management of nutrient and sediment loads, a common approach to conserve seagrass habitat did reduce risk, but only in temperate regions, highlighting the danger of focusing management on a single strategy. Monitoring, management and conservation actions from a national and regional perspective can be guided by these outputs.

Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

Background:. Skin cancer incidence has been rapidly increasing over the past 2 decades, and the resulting defects from excision have significant aesthetic and functional implications. In particular, wound coverage for large scalp and forehead defects with calvarial exposure can lead to hairline distortion, contour irregularities, and alopecia. We describe a 2-stage technique for scalp reconstruction, which preserves the normal hairline, covers exposed bone with vascularized tissue, and restores an aesthetic soft-tissue contour. Methods:. This is a retrospective case series of 13 adults with ages ranging from 50 to 89 years. All patients underwent Mohs surgery on the forehead or scalp between July 2014 and April 2017. Patients underwent a 2-staged reconstruction with an initial pericranial flap and dermal substitute placement followed by the placement of a split-thickness skin graft within 4–6 weeks. Results:. Over a 3-year period, 13 patients had successful reconstruction of the scalp defect without alteration of the hairline or contour irregularity. Two patients had minor complications after the first-stage procedure with successful aesthetic reconstruction. Conclusions:. Full-thickness defects of the scalp and forehead with bone exposure provide a reconstructive challenge for plastic surgeons. Reconstructive algorithms continue to evolve and should be tailored to best suit patients’ needs and medial comorbidities. Two-staged reconstruction with local pericranial flap provides a safe and efficacious reconstruction that minimizes hairline distortion, contour irregularity, and donor site morbidity.

Improved development of remote sensing approaches to deliver timely and accurate measurements for environmental monitoring, particularly with respect to marine and estuarine environments is a priority. We describe a machine learning, cloud processing protocol for simultaneous mapping seagrass meadows in waters of variable quality across Moreton Bay, Australia. This method was adapted from a protocol developed for mapping coral reef areas. Georeferenced spot check field-survey data were obtained across Moreton Bay, covering areas of differing water quality, and categorized into either substrate or ≥25% seagrass cover. These point data with coincident Landsat 8 OLI satellite imagery (30 m resolution; pulled directly from Google Earth Engine’s public archive) and a bathymetric layer (30 m resolution) were incorporated to train a random forest classifier. The semiautomated machine learning algorithm was applied to map seagrass in shallow areas of variable water quality simultaneously, and a bay-wide map was created for Moreton Bay. The output benthic habitat map representing seagrass presence/absence was accurate (63%) as determined by validation with an independent data set.

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).

The use of drones to study marine animals shows promise for the examination of numerous aspects of their ecology, behaviour, health and movement patterns. However, the responses of some marine phyla to the presence of drones varies broadly, as do the general operational protocols used to study them. Inconsistent methodological approaches could lead to difficulties comparing studies and can call into question the repeatability of research. This review draws on current literature and researchers with a wealth of practical experience to outline the idiosyncrasies of studying various marine taxa with drones. We also outline current best practice for drone operation in marine environments based on the literature and our practical experience in the field. The protocols outlined herein will be of use to researchers interested in incorporating drones as a tool into their research on marine animals and will help form consistent approaches for drone-based studies in the future.

In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority. One strategy, including resilient areas in marine protected area (MPA) networks, relies on information on the spatial distribution of resilience. We assess the ability to model and map six indicators of coral reef resilience—stress-tolerant coral taxa, coral generic diversity, fish herbivore biomass, fish herbivore functional group richness, density of juvenile corals and the cover of live coral and crustose coralline algae. We use high spatial resolution satellite data to derive environmental predictors and use these in random forest models, with field observations, to predict resilience indicator values at unsampled locations. Predictions are compared with those obtained from universal kriging and from a baseline model. Prediction errors are estimated using cross-validation, and the ability to map each resilience indicator is quantified as the percentage reduction in prediction error compared to the baseline model. Results are most promising (percentage reduction = 18.3%) for mapping the cover of live coral and crustose coralline algae and least promising (percentage reduction = 0%) for coral diversity. Our study has demonstrated one approach to map indicators of coral reef resilience. In the context of MPA network planning, the potential to consider reef resilience in addition to habitat and feature representation in decision-support software now exists, allowing planners to integrate aspects of reef resilience in MPA network development.

The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine ecosystem processes. The capacity to accurately and consistently map and monitor these benthic habitats is critical to developing and implementing management applications. This paper presents a method for integrating field survey data and high spatial resolution, multi-spectral satellite image data to map bathymetry and seagrass in shallow coastal waters. Using Quickbird 2 satellite images from 2004 and 2007, acoustic field survey data were used to map bathymetry using a linear and ratio algorithm method; benthic survey field data were used to calibrate and validate classifications of seagrass percentage cover and seagrass species composition; and a change detection analysis of seagrass cover was performed. The bathymetry mapping showed that only the linear algorithm could effectively and accurately predict water depth; overall benthic map accuracies ranged from 57–95%; and the change detection produced a reliable change map and showed a net decrease in seagrass cover levels, but the majority of the study area showed no change in seagrass cover level. This study demonstrates that multiple spatial products (bathymetry, seagrass and change maps) can be produced from single satellite images and a concurrent field survey dataset. Moreover, the products were produced at higher spatial resolution and accuracy levels than previous studies in Moreton Bay. The methods are developed from previous work in the study area and are continuing to be implemented, as well as being developed to be repeatable in similar shallow coastal water environments.