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Development of global ocean observing capacity for the biological EOVs is on the cusp of a step-change. Current capacity to automate data collection and processing and to integrate the resulting data streams with complementary data, openly available as FAIR data, is certain to dramatically increase the amount and quality of information and knowledge available to scientists and decision makers into the future. There is little doubt that scientists will continue to expand their understanding of what lives in the ocean, where it lives and how it is changing. However, whether this expanding information stream will inform policy and management or be incorporated into indicators for national reporting is more uncertain. Coordinated data collection including open sharing of data will help produce the consistent evidence-based messages that are valued by managers. The GODS Biology and Ecosystems Panel is working with other global initiatives to assist this coordination by defining and implementing Essential Ocean Variables. The biological EOVs have been defined, are being updated following community feedback, and their implementation is underway. In 2019, the coverage and precision of a global ocean observing system capable of addressing key questions for the next decade will be quantified, and its potential to support the goals of the UN Decade of Ocean Science for Sustainable Development identified. Developing a global ocean observing system for biology and ecosystems requires parallel efforts in improving evidence-based monitoring of progress against international agreements and the open data, reporting and governance structures that would facilitate the uptake of improved information by decision makers.

Using imagery at 30 m spatial resolution from the most recent Landsat satellite, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), we scale up reef metabolic productivity and calcification from local habitat-scale (10(-1) to 10(0) km(2)) measurements to regional scales (10(3) to 10(4) km(2)). Distribution and spatial extent of the North Florida Reef Tract (NFRT) habitats come from supervised classification of the Landsat imagery within independent Landsat-derived Millennium Coral Reef Map geomorphologic classes. This system minimizes the depth range and variability of benthic habitat characteristics found in the area of supervised classification and limits misclassification. Classification of Landsat imagery into 5 biotopes (sand, dense live cover, sparse live cover, seagrass, and sparse seagrass) by geomorphologic class is >73 % accurate at regional scales. Based on recently published habitat-scale in situ metabolic measurements, gross production (P=3.01 x 10(9) kg C yr(-1)), excess production (E = -5.70 x 10(8) kg C yr(-1)), and calcification (G = -1.68 x 10(6) kg CaCO3 yr(-1)) are estimated over 2711 km(2) of the NFRT. Simple models suggest sensitivity of these values to ocean acidification, which will increase local dissolution of carbonate sediments. Similar approaches could be applied over large areas with poorly constrained bathymetry or water column properties and minimal metabolic sampling. This tool has potential applications for modeling and monitoring large-scale environmental impacts on reef productivity, such as the influence of ocean acidification on coral reef environments.

The loss of coral reef habitats has been witnessed at a global scale including in the Florida Keys and the Caribbean. In addition to field surveys that can be spatially limited, remote sensing can provide a synoptic view of the changes occurring on coral reef habitats. Here, we utilize an 18-year time series of Landsat 5/TM and 7/ETM+ images to assess changes in eight coral reef sites in the Florida Keys National Marine Sanctuary, namely Carysfort Reef, Grecian Rocks, Molasses Reef, Conch Reef, Sombrero Reef, Looe Key Reef, Western Sambo and Sand Key Reef. Twenty-eight Landsat images (1984-2002) were used, with imagery gathered every 2 years during spring, and every 6 years during fall. The image dataset was georectified, calibrated to remote sensing reflectance and corrected for atmospheric and water-column effects. A Mahalanobis distance classification was trained for four habitat classes ('coral', 'sand', 'bare hardbottom' and 'covered hardbottom') using in situ ground-truthing data collected in 2003-2004 and using the spectral statistics from a 2002 image. The red band was considered useful only for benthic habitats in depths less than 6 m. Overall mean coral habitat loss for all sites classified by Landsat was 61% (3.4%/year), from a percentage habitat cover of 19% (1984) down to 7.6% (2002). The classification results for the eight different sites were critically reviewed. A detailed pixel by pixel examination of the spatial patterns across time suggests that the results range from ecologically plausible to unreliable due to spatial inconsistencies and/or improbable ecological successions. In situ monitoring data acquired by the Coral Reef Evaluation and Monitoring Project (CREMP) for the eight reef sites between 1996 and 2002 showed a loss in coral cover of 52% (8.7%/year), whereas the Landsat-derived coral habitat areas decreased by 37% (6.2%/year). A direct trend comparison between the entire CREMP percent coral cover data set (1996-2004) and the entire Landsat-derived coral habitat areas showed no significant difference between the two time series (ANCOVA; F-test, p = 0.303, n = 32), despite the different scales of measurements.

Seagrass meadows occupy a large proportion of the world's coastal oceans and are some of the most productive systems on Earth. Direct and indirect human-derived impacts have led to significant seagrass declines worldwide and the alteration of services linked to their biodiversity. Effective conservation and the provision of sustainable recovery goals for ecologically significant species are limited by the absence of reliable information on seagrass extent. This is especially true for the Wider Caribbean region (WCR) where many conservation initiatives are under way, but are impaired by the lack Of accurate baseline habitat maps. To assist with such a fundamental conservation need using high-resolution remote sensing data, both environmental and methodological challenges need to be tackled. First, the diversity of environments, the heterogeneity of habitats, and the vast extent of the targeted region mean that local expertise and field data of adequate quality and resolution are seldom available. Second, large-scale high-resolution mapping requires several hundred Landsat 5 and 7 images, which poses substantial processing problems. The main goal of this study was to test the feasibility of achieving Landsat-based large-scale seagrass mapping with limited ground-truth data and acceptable accuracies. We used the following combination of methods to map seagrass throughout the WCR: geomorphological segmentation, contextual editing, and supervised classifications. A total of 40 Landsat scenes (path-row) were processed. Three major classes were derived ('dense seagrass', 'medium-sparse seagrass', and a generic 'other' class). Products' accuracies were assessed against (i) selected in situ data; (ii) patterns detectable with very high-resolution IKONOS images; and (iii) published habitat maps with documented accuracies. Despite variable overall classification accuracies (46-88%), following their critical evaluation, the resulting thematic maps were deemed acceptable to (i) regionally Provide an adequate baseline for further large-scale conservation programs and research actions; and (ii) regionally re-assess carrying capacity estimates for green turtles. They certainly represent a drastic improvement relative to current regional databases.

Seamounts are habitats of considerable interest in terms of conservation and biodiversity, and in terms of fisheries for bentho-pelagic and pelagic species. Twenty previously compiled datasets including seamount/underwater feature lists, bathymetric maps and emerged feature maps from different sources (ship-derived and satellite altimetry-derived) at different spatial scales (from individual cruise to worldwide satellite data) were gathered in order to compile an enhanced list of underwater features for parts of the western and central Pacific Ocean (WCPO). The KL04 dataset [Kitchingman, A., and Lai, S., 2004. Inferences on potential seamount locations from mid-resolution bathymetric data. Fisheries Centre Research Reports 12 (5), 7-12], listing seamount positions and depths as calculated from satellite altimetry-derived bathymetry, provided the baseline data for this study as it covered the entire region of interest and included summit depth information. All KL04 potential seamounts were cross-checked with other datasets to remove any atolls and islands that had been incorrectly classified as seamounts, to add seamounts undetected by KL04, to update the overall database (geolocation, depth, elevation, and name) and to compile a 12-class typology of the different types of underwater features. of the 4626 potential seamounts identified in KL04, 719 were multiple identifications of the same large underwater features and 373 (10%) were actually emerged banks, atolls and islands, leaving 3534 actual underwater features. Conversely, 487 underwater features were documented in other datasets but not registered by KL04. The screening of all the potential WCPO seamounts produced a final list of 4021 underwater features with agreed upon position and information. This enhanced list should have many applications in oceanography, biodiversity conservation and studies of the influence of seamounts on pelagic ecosystems and fisheries.