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Towards Global Data Products of Essential Biodiversity Variables on Species Traits

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Towards Global Data Products of Essential Biodiversity Variables on Species Traits PERSPECTIVE https://doi.org/10.1038/s41559-018-0667-3 Towards global data products of Essential Biodiversity Variables on species traits W. Daniel Kissling 1*, Ramona Walls2, Anne Bowser3, Matthew O. Jones4, Jens Kattge 5,6, Donat Agosti7, Josep Amengual8, Alberto Basset9, Peter M. van Bodegom10, Johannes H. C. Cornelissen11, Ellen G. Denny12, Salud Deudero13, Willi Egloff7, Sarah C. Elmendorf14,15, Enrique Alonso García16, Katherine D. Jones14, Owen R. Jones17, Sandra Lavorel18, Dan Lear19, Laetitia M. Navarro6,20, Samraat Pawar 21, Rebecca Pirzl22, Nadja Rüger6,23, Sofia Sal21, Roberto Salguero-Gómez24,25,26,27, Dmitry Schigel 28, Katja-Sabine Schulz 29, Andrew Skidmore 30,31 and Robert P. Guralnick32 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 demon- strate 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 develop- ment goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation. n 2013, the Group on Earth Observations Biodiversity Observation and the 17 Sustainable Development Goals (SDGs) identified by the Network (GEO BON) introduced the framework of Essential UN 2030 Agenda for Sustainable Development2. EBVs are concep- Biodiversity Variables (EBVs) to derive coordinated measure- tually located on a continuum between primary data observations I 1 ments critical for detecting and reporting biodiversity change . (‘raw data’) and synthetic or derived metrics (‘indicators’), and can Through this process, 22 candidate EBVs were proposed and orga- be represented as ‘data cubes’ with several basic dimensions (for nized within six classes (‘genetic composition’, ‘species populations’, example, time, space, taxonomy or Earth observation data types)3–5. ‘species traits’, ‘community composition’, ‘ecosystem structure’ and Hence, EBVs allow derivation of biodiversity indicators (for exam- ‘ecosystem function’)1. These EBVs provide a foundation for assess- ple, trends of biodiversity change) such as those developed for the ing progress towards national and international policy goals, includ- Aichi Biodiversity Targets, with several EBVs (for example, spe- ing the 20 Aichi Biodiversity Targets developed by the Parties to the cies population abundance) informing multiple targets1,6. Specific United Nations (UN) Convention on Biological Diversity (CBD) EBVs in the classes species populations, ecosystem structure and 1Department of Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The Netherlands. 2CyVerse, University of Arizona, Tucson, AZ, USA. 3Woodrow Wilson International Center for Scholars, Washington DC, USA. 4University of Montana, W. A. Franke Department of Forestry and Conservation, Missoula, MT, USA. 5Max Planck Institute for Biogeochemistry, Jena, Germany. 6German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. 7Plazi, Bern, Switzerland. 8Area de Conservacion, Seguimiento y Programas de la Red, Organismo Autonomo Parques Nacionales, Ministerio de Agricultura y Pesca, Madrid, Spain. 9Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy. 10Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands. 11Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam, The Netherlands. 12USA National Phenology Network, University of Arizona, Tucson, AZ, USA. 13Instituto Español de Oceanografía, Centro Oceanográfico de Baleares, Palma de Mallorca, Spain. 14National Ecological Observatory Network, Battelle Ecology, Boulder, CO, USA. 15Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA. 16Franklin Institute, University of Alcala, Madrid, Spain. 17Department of Biology, University of Southern Denmark, Odense M, Denmark. 18Laboratoire d’Ecologie Alpine, CNRS - Université Grenoble Alpes, Grenoble, France. 19Marine Biological Association of the United Kingdom, Plymouth, Devon, UK. 20Institute of Biology, Martin Luther University Halle Wittenberg, Halle (Saale), Germany. 21Department of Life Sciences, Imperial College London, Ascot, Berkshire, UK. 22CSIRO and Atlas of Living Australia, Canberra, Australian Capital Territory, Australia. 23Smithsonian Tropical Research Institute, Ancon, Panama. 24Department of Zoology, Oxford University, Oxford, UK. 25Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK. 26Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Queensland, Australia. 27Evolutionary Demography Laboratory, Max Plank Institute for Demographic Research, Rostock, Germany. 28Global Biodiversity Information Facility (GBIF), Secretariat, Copenhagen, Denmark. 29Smithsonian Institution, National Museum of Natural History, Washington DC, USA. 30Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands. 31Department of Environmental Science, Macquarie University, New South Wales, Australia. 32Florida Museum of Natural History, University of Florida, Gainesville, FL, USA. *e-mail: [email protected] NatURE EcologY & EvolUTION | VOL 2 | OCTOBER 2018 | 1531–1540 | www.nature.com/natecolevol 1531 PERSPECTIVE NATURE ECOLOGY & EVOLUTION Box 1 | Defnition and societal relevance of species traits EBVs A species trait can be defned as any phenological, morphological, time and location of trait data collection, is key for monitoring physiological, reproductive or behavioural characteristic of a spe- intra-specifc trait changes. cies that can be measured at an individual level11,91. Hence, species Te societal relevance of EBVs becomes crucial when assessing traits can be quantifed by measuring characteristics of individuals progress towards biodiversity targets and policy goals1,2. Species (for example, timing of fowering, body lengths of fsh individuals, traits EBVs can be important for such targets, including the 20 stem heights and diameters of tree individuals, leaf nitrogen and Aichi Biodiversity Targets developed by Parties to the UN CBD chlorophyll content) or parts of individuals (for example, area of and the 17 SDGs identifed by the UN 2030 Agenda for Sustainable an individual leaf). Development. For instance, the impact of harvesting large fsh Individual variation in trait measurements can be summarized individuals for commercial fsheries could be monitored by trait at diferent hierarchical levels, for instance at the population level measurements that quantify changes in mean or maximum body (for example, mean body length of a fsh species population), at the size (for example, body length at frst maturity) in economically species level (for example, intra-specifc variability of body lengths important fsh populations15,79. Tis would allow deriving size- of a fsh species across its entire geographic range), or across based indicators (for example, trends of maximal fsh body multiple species (for example, as community-weighted means91 lengths over time) and hence measuring overexploitation and or as spectral trait variation when using airborne or spaceborne unsustainable harvesting as specifed in Aichi Target 6 (sustainable remote sensing43,92). Quantifying trait variation across multiple harvesting of fsh and invertebrate stocks and aquatic plants) or species (that is, within a community, ecosystem or landscape) is SDG 2 (sustainable food production). highly relevant for mapping and monitoring ecosystem processes Species traits are also important for understanding the and functional diversity43,51. However, such community- and response of organisms to their environment (‘response traits’)8. ecosystem-level trait variation is mainly relevant for the EBV For instance, phenological trait information (for example, related classes ‘community composition’, ‘ecosystem structure’ and to changes in timing of bird egg laying, phytoplankton population ‘ecosystem function’1, but not for ‘species traits’ because it does not peaks, or plant leafng, fowering and fruiting) can be an early allow attribution of trait variation to the species level1. indicator of climate change impacts21 and has relevance for SDG A key aspect of EBV development is to standardize, 13 (combating climate change and its impacts). Other examples aggregate and harmonize data across time (for example, include trait measurements related to movement behaviour (for temporal resolution), space (for example, spatial resolution example, dispersal distances and pathways, animal home range and geographic extent) and biological organization (for size) and reproduction
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