Designing and implementing a data model for describing environmental monitoring and research sites.

With new technological advancements and increasing demands of open data in environmental sciences, the requirements for data are increasing in a variety of ways. Having machine and human readable documentation about environmental research and monitoring sites available online is one of them. The Dynamic Ecological Information Management System - Site and Dataset Registry (DEIMS-SDR, https://www.deims.org/) is a research and monitoring site registry that allows the description of in-situ observation or experimental sites, generating persistent, unique and resolvable identifiers for each site. The aim of DEIMS-SDR is to collect site information in a catalogue describing a wide range of sites across the globe, providing information including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available. This article describes the outcomes of the revision of its data model, the conceptual considerations behind it and how it is implemented. These conceptual considerations also encompass the definition of what we call the "onion model of site data interoperability" - a fundamental concept for the design of site data models against the backdrop of data interoperability. Furthermore, we illustrate the capabilities of the revised data model and provide an overview of common data formats for the description of sites, current initiatives driving the harmonisation of descriptions and the outlook of future developments.

Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.

The challenges posed by climate and land use change are increasingly complex, with rising and accelerating impacts on the global environmental system. Novel environmental and ecosystem research needs to properly interpret system changes and derive management recommendations across scales. This largely depends on advances in the establishment of an internationally harmonised, long-term operating and representative infrastructure for environmental observation. This paper presents an analysis evaluating 743 formally accredited sites of the International Long-Term Ecological Research (ILTER) network in 47 countries with regard to their spatial distribution and related biogeographical and socio-ecological representativeness. "Representedness" values were computed from six global datasets. The analysis revealed a dense coverage of Northern temperate regions and anthropogenic zones most notably in the US, Europe and East Asia. Significant gaps are present in economically less developed and anthropogenically less impacted hot and barren regions like Northern and Central Africa and inner-continental parts of South America. These findings provide the arguments for our recommendations regarding the geographic expansion for the further development of the ILTER network.

On the effects of scale for ecosystem services mapping.

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.