51 research outputs found

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

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    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

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

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    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

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

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    [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

    A framework for the resilience of seagrass ecosystems

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    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

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

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    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

    Crowdsourcing conservation: The role of citizen science in securing a future for seagrass

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    Seagrass meadows are complex social-ecological systems. Understanding seagrass meadows demands a fresh approach integrating “the human dimension”. Citizen science is widely acknowledged for providing significant contributions to science, education, society and policy. Although the take up of citizen science in the marine environment has been slow, the need for such methods to fill vast information gaps is arguably great. Seagrass meadows are easy to access and provide an example of where citizen science is expanding. Technological developments have been pivotal to this, providing new opportunities for citizens to engage with seagrass. The increasing use of online tools has created opportunities to collect and submit as well as help process and analyse data. Citizen science has helped researchers integrate scientific and local knowledge and engage communities to implement conservation measures. Here we use a selection of examples to demonstrate how citizen science can secure a future for seagrass

    Seagrass ecosystem contributions to people's quality of life in the Pacific Island Countries and Territories

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    Seagrass ecosystems provide critical contributions (goods and perceived benefits or detriments) for the livelihoods and wellbeing of Pacific Islander peoples. Through in-depth examination of the contributions provided by seagrass ecosystems across the Pacific Island Countries and Territories (PICTs), we find a greater quantity in the Near Oceania (New Guinea, the Bismarck Archipelago and the Solomon Islands) and western Micronesian (Palau and Northern Marianas) regions; indicating a stronger coupling between human society and seagrass ecosystems. We also find many non-material contributions historically have been overlooked and under-appreciated by decision-makers. Closer cultural connections likely motivate guardianship of seagrass ecosystems by Pacific communities to mitigate local anthropogenic pressures. Regional comparisons also shed light on general and specific aspects of the importance of seagrass ecosystems to Pacific Islanders, which are critical for forming evidence-based policy and management to ensure the long-term resilience of seagrass ecosystems and the contributions they provide

    Global challenges for seagrass conservation

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    Seagrasses, flowering marine plants that form underwater meadows, play a significant global role in supporting food security, mitigating climate change and supporting biodiversity. Although progress is being made to conserve seagrass meadows in select areas, most meadows remain under significant pressure resulting in a decline in meadow condition and loss of function. Effective management strategies need to be implemented to reverse seagrass loss and enhance their fundamental role in coastal ocean habitats. Here we propose that seagrass meadows globally face a series of significant common challenges that must be addressed from a multifaceted and interdisciplinary perspective in order to achieve global conservation of seagrass meadows. The six main global challenges to seagrass conservation are (1) a lack of awareness of what seagrasses are and a limited societal recognition of the importance of seagrasses in coastal systems; (2) the status of many seagrass meadows are unknown, and up-to-date information on status and condition is essential; (3) understanding threatening activities at local scales is required to target management actions accordingly; (4) expanding our understanding of interactions between the socio-economic and ecological elements of seagrass systems is essential to balance the needs of people and the planet; (5) seagrass research should be expanded to generate scientific inquiries that support conservation actions; (6) increased understanding of the linkages between seagrass and climate change is required to adapt conservation accordingly. We also explicitly outline a series of proposed policy actions that will enable the scientific and conservation community to rise to these challenges. We urge the seagrass conservation community to engage stakeholders from local resource users to international policy-makers to address the challenges outlined here, in order to secure the future of the world’s seagrass ecosystems and maintain the vital services which they supply

    Seagrass meadows of Hervey Bay and the Great Sandy Strait, Queensland, derived from field surveys conducted 6-14 December, 1998

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    Approximately 2,362 ±289 km**2 of seagrass meadows were mapped in the waters of Hervey Bay and Great Sandy Strait between 6 and 14 December 1998. This was the first comprehensive survey of the Great Sandy region. The survey involved examination of 1,104 field validation points from Elliot Heads and Sandy Cape in the north to Tin Can Bay in the south, and identified 174 individual meadows. Seagrass extended from the intertidal and shallow subtidal waters to a depth of 32m. Seven species of seagrass were identified (Cymodocea serrulata, Halodule uninervis, Syringodium isoetifolium, Halophila decipiens, Halophila ovalis, Halophila spinulosa and Zostera muelleri) within 22 seagrass meadow/community types. Mapping survey methodologies followed standardised global seagrass research methods (McKenzie et al. 2001a, bit.ly/2t7U4M4) using both in situ and remote assessments. Within intertidal and shallow subtidal waters (2-10m depth), observers walked or free-dived to assess survey points. Seagrass habitat characteristics including visual estimates of above-ground percentage cover or biomass (3 replicates of a 50cm x 50cm quadrat) and species composition were recorded at each point according to standard methodologies (McKenzie et al. 2001b, bit.ly/2t5SWsK; McKenzie et al. 2014, bit.ly/2rN4EUN). Water depth and visual/tactile description of sediment were also recorded at each survey point. At each deep water point (>10m) a real time video slaved to monitor on-board a vessel was used to record an image of bottom habitat for 4 to 6 minutes of time at drift speed (minimum 100m tow). Data on seagrass species presence and biomass, macro-algae, and sediment description was obtained from post processed video images as per Coles et al. (2009, bit.ly/2s28gXc). In conjunction with each camera tow a sled net and grab sample of the sediment were collected to confirm the taxonomy and sediment characterisation inferred from the video. A differential handheld global positioning system (GPS) was used to locate each survey point (accuracy ±5m). Seagrass meadow boundaries were determined based on the survey point positions and the presence of seagrass, coupled with depth contours and remote sensing (e.g., aerial photography) where available. The meadow boundary accuracy varied from 5m to 1,200m. The resulting polygon data of each seagrass meadow was saved as an ArcMap shapefile and projected to AGD94. Of the estimated 2,307 ±279 km**2 of seagrass in Hervey Bay, nearly half were large continuous deep-waters meadows of medium-high biomass Halophila spinulosa with Halophila ovalis/ Halophila decipiens in the southern section of the bay. The eastern section of the bay was generally barren substrate with isolated patches of H. spinulosa/ H. ovalis/ H. decipiens. The shallow subtidal banks were covered with low biomass H. spinulosa/ H. decipiens. On the intertidal sand banks, meadows were generally low biomass Zostera muelleri or Halodule uninervis, with H. ovalis. In the Great Sandy Strait, most of the 5,554 ±1,446 ha of seagrass were intertidal on large mud- and sand-banks, predominantly in the northern and central sections. Dense Z. muelleri with H. ovalis meadows dominated the intertidal banks, with the exception of Kauri Creek bank in the south, which was dominated by Cymodocea serrulata. Shallow subtidal meadows contributed only 5% to the total Great Sandy Strait seagrass. Subtidal meadows were dominated by Halophila species (H. spinulosa, H decipiens, H. ovalis) or Z. muelleri and occurred mostly in the northern and southern sections of the Strait in narrow bands along the edge of intertidal banks, or extending across the large subtidal banks. The meadows in Hervey Bay and the Great Sandy Strait are one of the largest single areas of seagrass resources on the eastern Australian seaboard. The meadows form part of significant internationally listed Great Sandy Strait Ramsar site and are within the Great Sandy Marine Park, providing critical habitat for dugong and turtle populations, shorebirds and productive fisheries
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