Toward Implementation of the Global Earth Observation System of Systems Data Sharing Principles

This White Paper reviews the background issues for implementing the GEOSS Data Sharing Principles and recommends Implementation Guidelines to ensure the strongest possible framework for data sharing, consistent with both the spirit and the “letter” of the Principles. As recognized by the 10-Year Implementation Plan, “ensuring that such information is available to those who need it is a function of governments and institutions at all levels.” It is therefore incumbent on governments and institutions participating in GEOSS to continue to develop and implement appropriate policies and procedures that enable and support the GEOSS Data Sharing Principles in fair and effective ways. The implementation approaches discussed here are intended to facilitate this process

Using 3D photogrammetry from ROV video to quantify cold-water coral reef structural complexity and investigate its influence on biodiversity and community assemblage

Fine-scale structural complexity created by reef-building coral in shallow-water environments is influential on biodiversity, species assemblage and functional trait expression. Cold-water coral reefs are also hotspots of biodiversity, often attributed to the hard surface and structural complexity provided by the coral. However, that complexity has seldom been quantified on a centimetric scale in cold-water coral reefs, unlike their shallow-water counterparts, and has therefore never been linked in a similar way to the reef inhabitant community. Structure from motion techniques which create high-resolution 3D models of habitats from sequences of photographs is being increasingly utilised, in tandem with 3D spatial analysis to create useful 3D metrics, such as rugosity. Here, we demonstrate the use of ROV video transect data for 3D reconstructions of cold-water coral reefs at depths of nearly 1000 m in the Explorer Canyon, a tributary of Whittard Canyon, NE Atlantic. We constructed 40 3D models of approximately 25-m-length video transects using Agisoft Photoscan software, resulting in sub-centimetre resolution reconstructions. Digital elevation models were utilised to derive rugosity metrics, and orthomosaics were used for coral coverage assessment. We found rugosity values comparable to shallow-water tropical coral reef rugosity. Reef and nearby non-reef communities differed in assemblage composition, which was driven by depth and rugosity. Species richness, epifauna abundance and fish abundance increased with structural complexity, being attributed to an increase in niches, food, shelter and alteration of physical water movement. Biodiversity plateaued at higher rugosity, illustrating the establishment of a specific reef community supported by more than 30% coral cover. The proportion of dead coral to live coral had limited influence on the community structure; instead, within-reef patterns were explained by depth and rugosity, though our results were confounded to a certain extent by multi-collinearity. Fine-scale structural complexity appeared to be integral to local-scale ecological patterns in cold-water coral reef communities

Temporal and spatial variation in temperature experienced by macrofauna at main endeavour Hydrothermal vent field

A significant focus of hydrothermal vent ecological studies has been to understand how species cope with various stressors through physiological tolerance and biochemical resistance. Yet, the environmental conditions experienced by vent species have not been well characterized. This objective requires continuous observations over time intervals that can capture environmental variability at scales that are relevant to animals. We used autonomous temperature logger arrays (four roughly parallel linear arrays of 12 loggers spaced every 10–12 cm) to study spatial and temporal variations in the thermal regime experienced by hydrothermal vent macrofauna at a diffuse flow vent. Hourly temperatures were recorded over eight months from 2010 to 2011 at Grotto vent in the Main Endeavour vent field on the Juan de Fuca Ridge, a focus area of the Ocean Networks Canada cabled observatory. The conspicuous animal assemblages in video footage contained Ridgeia piscesae tubeworms, gastropods (primarily Lepetodrilus fucensis), and polychaetes (polynoid scaleworms and the palm worm Paralvinella palmiformis). Two dimensional spatial gradients in temperature were generally stable over the deployment period. The average temperature recorded by all arrays, and in some individual loggers, revealed distinctive fluctuations in temperature that often corresponded with the tidal cycle. We postulate that this may be related to changes in bottom currents or fluctuations in vent discharge. A marked transient temperature increase lasting over a period of days was observed in April 2011. While the distributions and behavior of Juan de Fuca Ridge vent invertebrates may be partially constrained by environmental temperature and temperature tolerance, except for the one transient high-temperature event, observed fluid temperatures were generally similar to the thermal preferences for some species, and typically well below lethal temperatures for all species. Average temperatures of the four arrays ranged from 4.1 to 11.0 °C during the deployment, indicating that on an hourly timescale the temperature conditions in this tubeworm community were fairly moderate and stable. The generality of these findings and behavioural responses of vent organisms to predictable rhythmicity and non-periodic temperature shifts are areas for further investigation

Predictive Ensemble Maps for cold-water coral distributions in the Cap de Creus Canyon (NW Mediterranean)

Predictive habitat mapping has shown great promise to improve the understanding of the spatial distribution of benthic habitats. However, although they surely represent an important step forward in process-based ecosystem management, their predictive efficiency is not always tested by independent groundtruthing data. This is particularly true for the deep-sea environment, where sample data are always limited compared to the large extent of the areas to be mapped. The aim of this study is to apply and test different spatial models to statistically predict the distribution of three Cold-Water Coral (CWC) species (Madrepora oculata, Lophelia pertusa and Dendrophyllia cornigera) in the Cap de Creus Canyon (NW Mediterranean), based on high-resolution swath-bathymetry data and video observations from the submersible JAGO (IFM-GEOMAR). Submarine canyons act as specific hosting areas for CWCs, owing to their favourable environmental conditions, which provide habitat and shelter for a wide range of species, including commercially viable fish. Maximum Entropy (MaxEnt), General Additive Model (GAM) and decision tree model (Random Forest) were independently applied to represent non-linear species-environment relationships using terrain variables derived from multibeam bathymetry (slope, geomorphologic category, rugosity, aspect, backscatter). Relevant differences between the three models were observed. Nonetheless, the predicted areas where CWCs should be found with higher probabilities coincided for the three methods when a lower spatial scale was considered. According to the models, CWCs are most likely to be found on the medium to steeply sloping, rough walls of the southern flank of the canyon, aligning with the known CWC ecology acquired from previous studies in the area. As a final step, a probabilistic predictive ensemble has been produced merging the outcomes of the three models considered, providing a more robust prediction for the three species. The main insight is that important discrepancies can arise in using different species distribution models, especially when high spatial resolutions are considered. This could in part be the result of the different statistical assumptions behind each of the models. We suggest that a more reliable prediction could be obtained by merging models into spatial ensembles, able to reduce differences and associated uncertainties, showing hence a strong potential as an objective approach in the planning and management of natural resources

Improving predictive mapping of deep-water habitats

In the deep sea, biological data are often sparse; hence models capturing relationships between observed fauna and environmental variables (acquired via acoustic mapping techniques) are often used to produce full coverage species assemblage maps. Many statistical modelling techniques are being developed, but there remains a need to determine the most appropriate mapping techniques. Predictive habitat modelling approaches (redundancy analysis, maximum entropy and random forest) were applied to a heterogeneous section of seabed on Rockall Bank, NE Atlantic, for which landscape indices describing the spatial arrangement of habitat patches were calculated. The predictive maps were based on remotely operated vehicle (ROV) imagery transects, high-resolution autonomous underwater vehicle (AUV) sidescan backscatter maps and ship-based multibeam bathymetry. Area under the curve (AUC) and accuracy indicated similar performances for the three models tested, but performance varied by species assemblage, with the transitional species assemblage showing the weakest predictive performances. Spatial predictions of habitat suitability differed between statistical approaches, but niche similarity metrics showed redundancy analysis and random forest predictions to be most similar. As one statistical technique could not be found to outperform the others when all assemblages were considered, ensemble mapping techniques, where the outputs of many models are combined, were applied. They showed higher accuracy than any single model. Different statistical approaches for predictive habitat modelling possess varied strengths and weaknesses and by examining the outputs of a range of modelling techniques and their differences, more robust predictions, with better described variation and areas of uncertainties, can be achieved. As improvements to prediction outputs can be achieved without additional costly data collection, ensemble mapping approaches have clear value for spatial management

Linkages between sediment thickness, geomorphology and Mn nodule occurrence: New evidence from AUV geophysical mapping in the Clarion-Clipperton Zone

The relationship between polymetallic nodules (Mn nodules) and deep-sea stratigraphy is relatively poorly studied and the role of sediment thickness in determining nodule occurrence is an active field of research. This study utilizes geophysical observations from three types of autonomous underwater vehicle (AUV) data (multi-beam bathymetry, sub-bottom profiles and underwater photography) in order to assess this relationship. Multi-beam bathymetry was processed with a pattern recognition approach for producing objective geomorphometric classes of the seafloor for examining their relation to sediment thickness and nodule occurrence. Sub-bottom profiles were used for extracting sediment thickness along a dense network of tracklines. Close-range AUV-photography data was used for automated counting of polymetallic nodules and their geometric features and it served as ground truth data. It was observed that higher nodule occurrence were related to layers with increased sediment thickness. This evidence reveals the role of local seafloor heterogeneity in nodule formation and suggests that unique patterns of local stratigraphy may affect geochemical processes that promote polymetallic nodule development at local scales

Author Correction: Species replacement dominates megabenthos beta diversity in a remote seamount setting

The original version of this Article contained errors. Affiliations 1 and 3 were incorrectly given as ‘National Oceanography Centre, Southampton, SO14 3ZH, United Kingdom’ and ‘University of Oxford, Department of Zoology, Oxford, OX2 6GG, United Kingdom’ respectively. In addition, an affiliation was omitted, which is now listed as Affiliation 5. Furthermore, the authors of this manuscript were incorrectly affiliated. Lissette Victorero was incorrectly affiliated with ‘National Oceanography Centre, Southampton, SO14 3ZH, United Kingdom’. Katleen Robert was incorrectly affiliated with ‘National Oceanography Centre, Southampton, SO14 3ZH, United Kingdom’. Laura F. Robinson was incorrectly affiliated with ‘University of Oxford, Department of Zoology, Oxford, OX2 6GG, United Kingdom’. Michelle L. Taylor was incorrectly affiliated with ‘University of Bristol, School of Earth Sciences, Bristol, BS8 1RJ, United Kingdom’. Veerle A.I. Huvenne was incorrectly affiliated with ‘University of Southampton, Ocean and Earth Science, Southampton, SO14 3ZH, United Kingdom’. The correct affiliations list and affiliated authors are given below. Affiliation 1: National Oceanography Centre, University of Southampton Waterfront Campus, Southampton, SO14 3ZH, United Kingdom Lissette Victorero Katleen Robert Veerle A.I. Huvenne Affiliation 2: University of Southampton, Ocean and Earth Science, Southampton, SO14 3ZH, United Kingdom Lissette Victorero Affiliation 3: Fisheries and Marine Institute of Memorial University, St. John’s, NL A1C 5R3, Canada Katleen Robert Affiliation 4: University of Bristol, School of Earth Sciences, Bristol, BS8 1RJ, United Kingdom Laura F. Robinson Affiliation 5: University of Essex, School of Biological Sciences, Colchester, CO4 3SQ, United Kingdom Michelle L. Taylor These errors have now been corrected in the PDF and HTML versions of the paper, and in the accompanying Supplementary information file