Spatial Risk Assessment for Coastal Seagrass Habitats in the Great Barrier Reef World Heritage Area: a case study of the dry and wet tropics

[Extract] Seagrasses are specialised marine flowering plants that grow in the estuary and nearshore environments of most of the world’s continents. There are relatively few species globally (about 60) and these are grouped into just 13 Genera and 5 Families. Most are entirely marine although some species (such as Enhalus acoroides) cannot reproduce unless emergent at low tide. There are 15 species of seagrass in the GBRWHA. The high diversity of seagrass reflects the variety of habitats, the extensive bays, estuaries, coasts, lagoons and reefs that are available for seagrass colonization. More than 5,000 km2 of coastal seagrass meadows are in eastern Queensland waters shallower than 15 m and it is expected that approximately 40,000km2 of the seafloor in the GBRWHA deeper than 15 m has some seagrass (Coles et al. 2007). This represents about 36% of the total recorded area of seagrass in Australia

Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBR-WHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine-and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learn-ing pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future

Subtidal seagrass detector: development of a deep learning seagrass detection and classification model for seagrass presence and density in diverse habitats from underwater photoquadrats

This paper presents the development and evaluation of a Subtidal Seagrass Detector (the Detector). Deep learning models were used to detect most forms of seagrass occurring in a diversity of habitats across the northeast Australian seascape from underwater images and classify them based on how much the cover of seagrass was present. Images were collected by scientists and trained contributors undertaking routine monitoring using drop-cameras mounted over a 50 x 50 cm quadrat. The Detector is composed of three separate models able to perform the specific tasks of: detecting the presence of seagrass (Model #1); classify the seagrass present into three broad cover classes (low, medium, high) (Model #2); and classify the substrate or image complexity (simple of complex) (Model #3). We were able to successfully train the three models to achieve high level accuracies with 97%, 80.7% and 97.9%, respectively. With the ability to further refine and train these models with newly acquired images from different locations and from different sources (e.g. Automated Underwater Vehicles), we are confident that our ability to detect seagrass will improve over time. With this Detector we will be able rapidly assess a large number of images collected by a diversity of contributors, and the data will provide invaluable insights about the extent and condition of subtidal seagrass, particularly in data-poor areas

Developing and refining biological indicators for condition assessments in an integrated monitoring program

[Extract] Indicators representative of ecosystem condition are required for the long-term monitoring of the Great Barrier Reef (GBR) in a Reef Integrated Monitoring and Reporting Program (RIMREP), which tracks progress towards Reef 2050 Plan targets and objectives. Seagrass meadows are highly sensitive to climatic conditions and environmental pressures such as water quality, as seen through recent (past 10 years) changes in abundance in the GBR (McKenzie, et al., 2016). Due to these impacts, GBR seagrass meadows underwent a period of decline from 2009 to 2011. Widespread loss of seagrass occurred, but in 2015 many meadows had started recovering. The storage reserves within seagrass rhizomes were tested for suitability as a complimentary indicator in the MMP/RIMREP because previous studies had suggested that they are good indicators. We set out to test the relationships between total non-structural carbohydrates (TNSC) and seagrass condition (i.e. trend in abundance, either declining pre 2011 or recovering post 2011), seagrass abundance, water temperature and daily light in a temporal analysis using linear models. Samples were collected quarterly from 2008 to 2015 from four locations (8 sites) for three species (917 samples in total) in the Wet Tropics and Burdekin regions. TNSC was significantly (p<0.001) lower pre 2011 during the period of decline (181and 192 mg gDW-1for intertidal sites pooled and subtidal sites pooled, respectively) than post 2011 during recovery (277 and 289 mg gDW-1) for H. uninervis. A similar trend was observed for T. hemprichii, which occurred at intertidal sites only (168 mg gDW-1 in decline and 208 mg gDW-1in recovery), but not for C. serrulata which had the fewest available data points. The differences were even greater when investigating individual sites. TNSC were also correlated (p<0.001) to seagrass abundance during both the decline and recovery phases. TNSC was positively correlated to water temperature, though the period being assessed was relatively mild in terms of temperature extremes. Therefore, light was the main pressure assessed in this project. A direct effect of light limitation (daily light, average of 30 days prior to TNSC collection) on TNSC was not observed, in fact there was a slight negative effect of light in some analyses. This was contrary to our hypothesis, as low light, at least in part, drove declines in seagrass abundance from 2009 –2011. In an additional spatial analysis, differences in TNSC among regions and habitat types were assessed from 39 sites collected in late 2014 across the GBR. This spatial analysis was carried out to explore representativeness of the sites used in the temporal analysis. There was little difference in TNSC among habitats; however, TNSC varied among NRMs and were lowest in the Mackay Whitsunday and Fitzroy NRMs. This exploration of storage reserves, undertaken at a time of dynamic meadow changes, has yielded exciting results on their variation with meadow condition and abundance. However, we did not provide conclusive evidence to support the inclusion of TNSC as an indicator in monitoring programs such as the MMP at this stage, because the link to the main environmental pressure tested –light –was not demonstrated by this analysis. Irrespective of this, TNSC was an indicator of cumulative stress (being correlated to abundance and condition), but the specific pressure(s) could not be identified. This provides justification for further inquiry into the effect of other pressures (e.g. nutrients and flood plume exposure), other biological processes (e.g. reproduction and meadow expansion) and to obtain further data on other species. We also tested the relationship between %cover and biomass, with the aim of developing biomass calibration formulae. Above-ground biomass and %cover was measured in seven mono-specific meadows for four species and four habitat types. Above ground biomass was highly correlated (p<0.001) to % cover, and the correlation was further improved (lower AIC) by factoring canopy height into the calibration. Even after canopy height was included in the calibration, canopy height strongly affected the calibration values and highlighted the importance of habitat/morphology-specific calibration formulae. Further work is required to capture all species and habitat/morphology combinations that are routinely monitored. With further work, these calibration values will enable integration among seagrass monitoring programs including Queensland Ports Seagrass Monitoring Program and GBR historical baseline data

Seagrass mapping synthesis: a resource for coastal management in the Great Barrier Reef World Heritage Area

This project provides an up to date synthesis of the available information on seagrass in the Great Barrier Reef World Heritage Area (GBRWHA). It brings together more than 30 years of spatial information and data collection into easy to use spatial GIS layers that provide key information on species, meadow type and age and reliability of the data. The project provides: Seagrass site and meadow-specific data in Geographic Information System (GIS) layers to provide seagrass data to inform research analysis and management advice. A site layer that includes >66,000 individual survey sites with information including latitude/longitude, Natural Resource Management region, site depth, seagrass presence/absence, dominant seagrass species, presence/absence of individual species, survey date, survey method, and data custodian. A meadow layer that includes 1169 individual and/or composite seagrass meadows with information including individual meadow persistence, meadow location (intertidal/subtidal), meadow density based on mean biomass and/or mean percent cover, meadow area, dominant seagrass species, seagrass species present, range of survey dates, survey method, and data custodian. Metadata to enable interpretation of the information and to identify the original data custodians for assistance with interpretation. Outcomes: This study consolidates all available seagrass spatial data for the GBRWHA collected from 1984 to December 2014 by the TropWATER Seagrass Group and CSIRO in a GIS database. It assembles and documents the state of spatial knowledge of seagrass in the GBRWHA. The spatial data is based on methods developed by TropWATER and CSIRO for seagrass habitat surveys of subtidal meadows, and TropWATER methods for intertidal surveys. Methods include sampling by boat (free divers, underwater video camera, grabs, sled with net sampling), helicopter and walking. 447,530 hectares of seagrasses were mapped (modelled deep water seagrass areas are not included in area figures in this report) within the GBRWHA; much of which provides habitat for commercial and traditional fishery species, and an important food resource for dugong and green turtle populations. Data is included for twelve seagrass species from three families. Seagrass was present at 39% of all sites visited. The study identifies areas where much of the data available for management is more than 20 years old or where there are specific habitats unsurveyed. Large areas of central and northern Queensland require updating. Several key habitat types such as reef platform seagrass meadows are poorly represented in the data

Seagrass meadows globally as a coupled social–ecological system: Implications for human wellbeing

Seagrass ecosystems are diminishing worldwide and repeated studies confirm a lack of appreciation for the value of these systems. In order to highlight their value we provide the first discussion of seagrass meadows as a coupled social–ecological system on a global scale. We consider the impact of a declining resource on people, including those for whom seagrass meadows are utilised for income generation and a source of food security through fisheries support. Case studies from across the globe are used to demonstrate the intricate relationship between seagrass meadows and people that highlight the multi-functional role of seagrasses in human wellbeing. While each case underscores unique issues, these examples simultaneously reveal social–ecological coupling that transcends cultural and geographical boundaries. We conclude that understanding seagrass meadows as a coupled social–ecological system is crucial in carving pathways for social and ecological resilience in light of current patterns of local to global environmental change

A framework for the resilience of seagrass ecosystems

Seagrass ecosystems represent a global marine resource that is declining across its range. To halt degradation and promote recovery over large scales, management requires a radical change in emphasis and application that seeks to enhance seagrass ecosystem resilience. In this review we examine how the resilience of seagrass ecosystems is becoming compromised by a range of local to global stressors, resulting in ecological regime shifts that undermine the long-term viability of these productive ecosystems. To examine regime shifts and the management actions that can influence this phenomenon we present a conceptual model of resilience in seagrass ecosystems. The model is founded on a series of features and modifiers that act as interacting influences upon seagrass ecosystem resilience. Improved understanding and appreciation of the factors and modifiers that govern resilience in seagrass ecosystems can be utilised to support much needed evidence based management of a vital natural resource

2017 Scientific Consensus Statement: land use impacts on the Great Barrier Reef water quality and ecosystem condition, Chapter 1: the condition of coastal and marine ecosystems of the Great Barrioer Reef and their responses to water quality and disturbances

The condition of coastal and marine ecosystems on the Great Barrier Reef and their responses to water quality and disturbances. The Great Barrier Reef marine ecosystems and their associated catchments are part of a dynamic, interconnected system. This chapter provides an up-to-date review of the state of knowledge relating to the conditions and trends of key Great Barrier Reef coastal and marine ecosystems, including current knowledge on key drivers of change and activities leading to pressures and impacts on these ecosystems. Drivers include the impacts of land run-off, coastal development activities and other disturbances such as extreme weather events that influence Great Barrier Reef water quality and the health of marine and coastal ecosystems

Synthesizing 35 years of seagrass spatial data from the Great Barrier Reef World Heritage Area, Queensland, Australia

The Great Barrier Reef World Heritage Area in Queensland, Australia contains globally significant seagrasses supporting key ecosystem services, including habitat and food for threatened populations of dugong and turtle. We compiled 35 years of data in a spatial database, including 81,387 data points with georeferenced seagrass and species presence/absence, depth, dominant sediment type, and collection date. We include data collected under commercial contract that have not been publicly available. Twelve seagrass species were recorded. The deepest seagrass was found at 76 m. Seagrass meadows are at risk from anthropogenic, climate and weather processes. Our database is a valuable resource that provides coastal managers and the global marine community with a long-term spatial resource describing seagrass populations from the mid-1980s against which to benchmark change. We address the data issues involved in hindcasting over 30 years to ensure confidence in the accuracy and reliability of data included