No abstract

Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation.

A large number of occurrences of the highly pathogenic avian influenza (HPAI) H5N1 virus in wild birds were reported in Europe. The relationship between the occurrence pattern and environmental factors has, however, not yet been explored. This research uses logistic regression to quantify the relationships between anthropogenic or physical environmental factors and HPAI H5N1 occurrences. Our results indicate that HPAI H5N1 occurrences are highly correlated with the following: the increased normalized difference vegetation index (NDVI) in December; intermediate NDVI in March; lower elevations; increased minimum temperatures in January; and reduced precipitation in January. A predictive risk map of HPAI H5N1 occurrences in wild birds in Europe was generated on the basis of five key environmental factors. Independent validation of the risk map showed the predictive model to be of high accuracy (79%). The analysis suggests that HPAI H5N1 occurrences in wild birds are strongly influenced by the availability of food resources and are facilitated by increased temperatures and reduced precipitation. We therefore deduced that HPAI H5N1 occurrences in wild birds in Europe are probably caused by contact with other wild birds and not by contact with domestic poultry. These findings are important considerations for the global surveillance of HPAI H5N1 occurrences in wild birds.

Many migrating herbivores rely on plant biomass to fuel their life cycles and have adapted to following changes in plant quality through time. The green wave hypothesis predicts that herbivorous waterfowl will follow the wave of food availability and quality during their spring migration. However, testing this hypothesis is hampered by the large geographical range these birds cover. The satellite-derived normalized difference vegetation index (NDVI) time series is an ideal proxy indicator for the development of plant biomass and quality across a broad spatial area. A derived index, the green wave index (GWI), has been successfully used to link altitudinal and latitudinal migration of mammals to spatio-temporal variations in food quality and quantity. To date, this index has not been used to test the green wave hypothesis for individual avian herbivores. Here, we use the satellite-derived GWI to examine the green wave hypothesis with respect to GPS-tracked individual barnacle geese from three flyway populations (Russian n = 12, Svalbard n = 8, and Greenland n = 7). Data were collected over three years (2008–2010). Our results showed that the Russian and Svalbard barnacle geese followed the middle stage of the green wave (GWI 40–60%), while the Greenland geese followed an earlier stage (GWI 20–40%). Despite these differences among geese populations, the phase of vegetation greenness encountered by the GPS-tracked geese was close to the 50% GWI (i.e. the assumed date of peak nitrogen concentration), thereby implying that barnacle geese track high quality food during their spring migration. To our knowledge, this is the first time that the migration of individual avian herbivores has been successfully studied with respect to vegetation phenology using the satellite-derived GWI. Our results offer further support for the green wave hypothesis applying to long-distance migrants on a larger scale.