Building a Global Ecosystem Research Infrastructure to Address Global Grand Challenges for Macrosystem Ecology

The development of several large-, "continental"-scale ecosystem research infrastructures over recent decades has provided a unique opportunity in the history of ecological science. The Global Ecosystem Research Infrastructure (GERI) is an integrated network of analogous, but independent, site-based ecosystem research infrastructures (ERI) dedicated to better understand the function and change of indicator ecosystems across global biomes. Bringing together these ERIs, harmonizing their respective data and reducing uncertainties enables broader cross-continental ecological research. It will also enhance the research community capabilities to address current and anticipate future global scale ecological challenges. Moreover, increasing the international capabilities of these ERIs goes beyond their original design intent, and is an unexpected added value of these large national investments. Here, we identify specific global grand challenge areas and research trends to advance the ecological frontiers across continents that can be addressed through the federation of these cross-continental-scale ERIs.Peer reviewe

Enabling FAIR research in Earth Science through research objects

Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific information and resources in a way compliant with the FAIR principles and sustain the development of key infrastructure and tools. This paper provides an account of the challenges, experiences and solutions involved in the adoption of FAIR around research objects over several Earth Science disciplines. During this journey, our work has been comprehensive, with outcomes including: an extended research object model adapted to the needs of earth scientists; the provisioning of digital object identifiers (DOI) to enable persistent identification and to give due credit to authors; the generation of content-based, semantically rich, research object metadata through natural language processing, enhancing visibility and reuse through recommendation systems and third-party search engines; and various types of checklists that provide a compact representation of research object quality as a key enabler of scientific reuse. All these results have been integrated in ROHub, a platform that provides research object management functionality to a wealth of applications and interfaces across different scientific communities. To monitor and quantify the community uptake of research objects, we have defined indicators and obtained measures via ROHub that are also discussed herein.Published550-5645IT. Osservazioni satellitariJCR Journa

Success in Competition for Space in Two Invasive Coral Species in the western Atlantic - Tubastraea micranthus and T. coccinea.

Invasion success by an alien species is dependent upon rate of reproduction, growth, mortality, physical characteristics of the environment, and successful competition for resources with native species. For sessile, epibenthic marine species, one critical resource is space. We examined competitive success in two invasive Indo-Pacific corals involved in competition for space in the northern Gulf of Mexico-Tubastraea coccinea and T. micranthus-on up to 13 offshore oil/gas platforms south of the Mississippi River. Still-capture photos of thousands of overgrowth interactions between the target corals and other sessile epibenthic fauna were analyzed from ROV videos collected at 8-183 m depth. T. micranthus was observed overgrowing >90% of all sessile epibenthic species which it encountered. Frequencies of competitive success varied significantly between platforms. T. coccinea was competitively superior to all competitors pooled, at the 60% level. There was little variability between T. coccinea populations. T. coccinea encountered the following species most frequently-the encrusting sponges Xestospongia sp. (with the commensal Parazoanthus catenularis), X. carbonaria, Dictyonella funicularis, Mycale carmigropila, Phorbas amaranthus, and Haliclona vansoesti-and was found to be, on average, competitively superior to them. Both T. micranthus and T. coccinea appear to be good competitors for space against these species in the northern Gulf of Mexico. Competitive success in T. micranthus was highest in the NE part of the study area, and lowest in the SW area near the Mississippi River plume. T. coccinea's competitive success peaked in the SW study area. This suggests that variation in competitive success both within and between populations of these species may be due to differences in local environmental factors

Towards interoperable research site documentation – Recommendations for information models and data provision

Information related to research sites is essential when describing the context of observations. It is a key element of site-based research infrastructures (RIs) and their catalogues. This paper is dedicated to the comparison of (meta)data models describing research sites in the ecosystem domain. A special focus is on sites in terrestrial and freshwater ecosystems. This should provide the basis for a common site-based data model to exchange data from different site-based catalogue services. This should substantially improve interoperability of site-level information between different RIs. For this purpose, we selected well-established site catalogues that feature web-accessible documentation of the sites and means to export this information. Using either dedicated descriptions of the data models, or, if these were not available, by deriving the data model from the records extracted from the catalogues, we identified the commonalities, differences and gaps of the underlying data models. Based on the findings, we define a set of mandatory core fields to be used in every site catalogue in the ecosystem domain. A set of additional fields are recommended to be implemented. In addition, we formulate general recommendations on how to best serve site data, considering the technical interface, the data format and the data license. The aim of these recommendations is to increase interoperability between site catalogues in general and the selected catalogues in particular. This fosters analyses of the existing ecosystem research infrastructures on a national, regional and global scale and increase the ability of these infrastructures to answer large-scale environmental questions

A High-throughput Data Ingest Pipeline for Semantic Data-stores

Ontologies offer multiple benefits for biodiversity data processing and analysis, including precisely defined vocabularies, robust pathways for data integration, and support for automated machine reasoning.  However, ontologies have yet to be widely deployed for biodiversity data processing and analysis.  Reasons for this include: specialized skills and coordination are needed for mapping terms to source data, data processing and machine reasoning are computationally expensive, and there is a scarcity of tools for working with ontologies and RDF triples.  In this presentation we will discuss a data processing pipeline (available at https://github.com/biocodellc/ppo-data-pipeline) which simplifies complex implementation tasks, offers tools for data ingest, triplifying, and reasoning, and makes datasets available for indexing

Percent wins in competition for space between <i>Tubastraea micranthus</i> and all other sessile epifauna pooled, by platform.

<p>Percent competitive success plus 95% confidence limits shown. The competition variable is defined as the number of competitive successes over the total number of interactions within a taxonomic group. An overgrowth frequency of significantly >50% was considered to be an indicator of competitive success. A total of 15 platforms were surveyed. Those platforms possessing a number of interactions sufficient for quantitative analysis were included in the study. Data tested for significant variation from 1:1 ratio of competitive successes to losses. Range of number of interactions per platform (n): 18–361. Overall competitive success was significantly higher than the expected 50% level over all platforms (p < 0.01, Fisher’s Exact Test). Highly variable win frequencies between platforms (p < 0.001, Goodness of Fit Test, G-statistic). No significant sub-sets of platforms.</p

Percent of success in competition for space between <i>Tubastraea coccinea</i> and <i>Xestospongia carbonaria</i>, by platform.

<p>Mean plus 95% confidence limits shown. Range of number of interactions per platform (n): 20–58. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144581#pone.0144581.g002" target="_blank">Fig 2</a> legend for additional details. Overall competitive success significantly higher than 50% (p < 0.001, Fisher’s Exact Test). Highly variable success frequencies between platforms. <i>T</i>. <i>coccinea</i> on GI-93C, GI-90A, MC-311A, GI-115A, GI-116A, ST-206A, and ST-185B all exhibited significantly high competitive success (p < 0.01–001, Goodness of Fit Test, G-statistic). The remainder exhibited competitive success frequencies which did notvary significantly from 50% (p> 0.05).</p

A map of all platforms surveyed by ROV during this study.

<p>Platform code and position shown. Point of first sighting (2007) shown as a triangle (GI-93C).</p

Percent of competitive success in competition for space between <i>Tubastraea coccinea</i> and <i>Dictyonella funicularis</i>, by platform.

<p>Mean plus 95% confidence limits shown. Range of number of interactions per platform (n): 13–83. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144581#pone.0144581.g002" target="_blank">Fig 2</a> legend for additional details. Overall competitive successes significantly higher than 50% (p < 0.001, Fisher’s Exact Test). Highly variable win frequencies between platforms. Platforms exhibiting significant competitive success were GI-90A, MC-311A, GI-116A, MC-109A, SP-87D, and ST-185A (p < 0.01–0.001, Goodness of Fit Test, G-statistic). The remainder exhibited no significant difference with a 50% competitive success level (p > 0.05).</p