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Climate change will induce extended heat waves to parts of the vegetation more frequently. High temperatures may act as stress (thermal stress) on plants changing emissions of biogenic volatile organic compounds (BVOCs). As BVOCs impact the atmospheric oxidation cycle and aerosol formation, it is important to explore possible alterations of BVOC emissions under high temperature conditions. Applying heat to European beech, Palestine oak, Scots pine, and Norway spruce in a laboratory setup either caused the well-known exponential increases of BVOC emissions or induced irreversible changes of BVOC emissions. Considering only irreversible changes of BVOC emissions as stress impacts, we found that high temperatures decreased the de novo emissions of monoterpenes, sesquiterpenes and phenolic BVOC. This behaviour was independent of the tree species and whether the de novo emissions were constitutive or induced by biotic stress. In contrast, application of thermal stress to conifers amplified the release of monoterpenes stored in resin ducts of conifers and induced emissions of green leaf volatiles. In particular during insect attack on conifers, the plants showed de novo emissions of sesquiterpenes and phenolic BVOCs, which exceeded constitutive monoterpene emissions from pools. The heat-induced decrease of de novo emissions was larger than the increased monoterpene release caused by damage of resin ducts. For insect-infested conifers the net effect of thermal stress on BVOC emissions could be an overall decrease. Global change-induced heat waves may put hard thermal stress on plants. If so, we project that BVOC emissions increase is more than predicted by models only in areas predominantly covered with conifers that do not emit high amounts of sesquiterpenes and phenolic BVOCs. Otherwise overall effects of high temperature stress will be lower increases of BVOC emissions than predicted by algorithms that do not consider stress impacts.

In June 2009, 22 spectrometers from 14 institutes measured tropospheric and stratospheric NO2 from the ground for more than 11 days during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments (CINDI), at Cabauw, NL (51.97° N, 4.93° E). All visible instruments used a common wavelength range and set of cross sections for the spectral analysis. Most of the instruments were of the multi-axis design with analysis by differential spectroscopy software (MAX-DOAS), whose non-zenith slant columns were compared by examining slopes of their least-squares straight line fits to mean values of a selection of instruments, after taking 30-min averages. Zenith slant columns near twilight were compared by fits to interpolated values of a reference instrument, then normalised by the mean of the slopes of the best instruments. For visible MAX-DOAS instruments, the means of the fitted slopes for NO2 and O4 of all except one instrument were within 10% of unity at almost all non-zenith elevations, and most were within 5%. Values for UV MAX-DOAS instruments were almost as good, being 12% and 7%, respectively. For visible instruments at zenith near twilight, the means of the fitted slopes of all instruments were within 5% of unity. This level of agreement is as good as that of previous intercomparisons, despite the site not being ideal for zenith twilight measurements. It bodes well for the future of measurements of tropospheric NO2, as previous intercomparisons were only for zenith instruments focussing on stratospheric NO2, with their longer heritage.

Measurements of the status and trends of key indicators for the ocean and marine life are required to inform policy and management in the context of growing human uses of marine resources, coastal development, and climate change. Two synergistic efforts identify specific priority variables for monitoring: Essential Ocean Variables (EOVs) through the Global Ocean Observing System (GOOS), and Essential Biodiversity Variables (EBVs) from the Group on Earth Observations Biodiversity Observation Network (GEO BON) (see Data Sheet 1 in Supplementary Materials for a glossary of acronyms). Both systems support reporting against internationally agreed conventions and treaties. GOOS, established under the auspices of the Intergovernmental Oceanographic Commission (IOC), plays a leading role in coordinating global monitoring of the ocean and in the definition of EOVs. GEO BON is a global biodiversity observation network that coordinates observations to enhance management of the world’s biodiversity and promote both the awareness and accounting of ecosystem services. Convergence and agreement between these two efforts are required to streamline existing and new marine observation programs to advance scientific knowledge effectively and to support the sustainable use and management of ocean spaces and resources. In this context, the Marine Biodiversity Observation Network (MBON), a thematic component of GEO BON, is collaborating with GOOS, the Ocean Biogeographic Information System (OBIS), and the Integrated Marine Biosphere Research (IMBeR) project to ensure that EBVs and EOVs are complementary, representing alternative uses of a common set of scientific measurements. This work is informed by the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM), an intergovernmental body of technical experts that helps international coordination on best practices for observing, data management and services, combined with capacity development expertise. Characterizing biodiversity and understanding its drivers will require incorporation of observations fromtraditional andmolecular taxonomy, animal tagging and tracking efforts, ocean biogeochemistry, and ocean observatory initiatives including the deep ocean and seafloor. The partnership between large-scale ocean observing and product distribution initiatives (MBON, OBIS, JCOMM, and GOOS) is an expedited, effective way to support international policy-level assessments (e.g., the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services or IPBES), along with the implementation of international development goals (e.g., the United Nations Sustainable Development Goals). Refereed 14 Manual (incl. handbook, guide, cookbook etc) 2018-06-27

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations.