Towards global data products of Essential Biodiversity Variables on species traits

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.Essential Biodiversity Variables (EBVs) are intended to provide standardized measurements for reporting biodiversity change. Here, the authors outline the conceptual and empirical basis for the use of EBVs based on species traits, and highlight tools necessary for creating comprehensive EBV data products.

[1]  E. K. Roberts.  Marine Biological Association of the United Kingdom , 1938, Nature.

[2]  J. Gaillard,et al.  An analysis of demographic tactics in birds and mammals , 1989 .

[3]  Robert W. Pearcy,et al.  Plant Physiological Ecology , 1989, Springer Netherlands.

[4]  D. Pauly,et al.  Fishing down marine food webs , 1998, Science.

[5]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[6]  Sebastiaan A.L.M. Kooijman,et al.  Dynamic Energy and Mass Budgets in Biological Systems , 2000 .

[7]  S. Lavorel,et al.  Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail , 2002 .

[8]  Richard M. Bruskiewich,et al.  Linking genotype to phenotype: the International Rice Information System (IRIS) , 2003, ISMB.

[9]  Pim Martens,et al.  The European Phenology Network , 2003, International journal of biometeorology.

[10]  P. Reich,et al.  A handbook of protocols for standardised and easy measurement of plant functional traits worldwide , 2003 .

[11]  James H. Brown,et al.  Toward a metabolic theory of ecology , 2004 .

[12]  Michael W. Carroll.  Creative Commons and the New Intermediaries , 2005 .

[13]  Stuart Weibel,et al.  The Dublin Core: A Simple Content Description Model for Electronic Resources , 2005 .

[14]  Koji Nomura,et al.  Challenges and Future Directions , 2005 .

[15]  R. Primack,et al.  Photographs and herbarium specimens as tools to document phenological changes in response to global warming. , 2006, American journal of botany.

[16]  Eun-Shik Kim.  Development, potentials, and challenges of the International Long-Term Ecological Research (ILTER) Network , 2006, Ecological Research.

[17]  William W. Hargrove,et al.  NEON: a hierarchically designed national ecological network , 2007 .

[18]  C. Violle,et al.  Let the concept of trait be functional , 2007 .

[19]  Roberta E. Martin,et al.  Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR. , 2008 .

[20]  Vasant G Honavar,et al.  Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species. , 2008, Journal of animal science.

[21]  Shawn Bowers,et al.  Advancing ecological research with ontologies. , 2008, Trends in ecology & evolution.

[22]  Cynthia L. Smith,et al.  Integrating phenotype ontologies across multiple species , 2010, Genome Biology.

[23]  D. Richardson,et al.  Something in the way you move: dispersal pathways affect invasion success. , 2009, Trends in ecology & evolution.

[24]  R. Hilborn,et al.  Rebuilding Global Fisheries , 2009, Science.

[25]  Brian R. Johnson,et al.  NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure , 2010 .

[26]  J. Silvertown.  A new dawn for citizen science. , 2009, Trends in ecology & evolution.

[27]  Roberta E. Martin,et al.  Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests , 2009 .

[28]  Janan T Eppig,et al.  The mammalian phenotype ontology: enabling robust annotation and comparative analysis , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.

[29]  Brian R. Johnson,et al.  NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure , 2010 .

[30]  D. Coates,et al.  Inland capture fisheries , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  V. J. Wearmouth,et al.  Body size‐dependent responses of a marine fish assemblage to climate change and fishing over a century‐long scale , 2010 .

[32]  F. Müller.  Long-Term ecological research : between theory and application , 2010 .

[33]  Onisimo Mutanga,et al.  Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery , 2010 .

[34]  Dick de Zwart,et al.  Toward a knowledge infrastructure for traits‐based ecological risk assessment , 2011, Integrated environmental assessment and management.

[35]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[36]  D. Roberts,et al.  Validation of biological collections as a source of phenological data for use in climate change studies: a case study with the orchid Ophrys sphegodes , 2011 .

[37]  Matthew O. Jones,et al.  Satellite passive microwave remote sensing for monitoring global land surface phenology , 2011 .

[38]  Joshua S Madin,et al.  A generic structure for plant trait databases , 2011 .

[39]  Barry Smith,et al.  Ontologies as Integrative Tools for Plant Science Nih Public Access Author Manuscript $watermark-text Ontology 101 $watermark-text , 2022 .

[40]  M. D. Schwartz,et al.  From Caprio's lilacs to the USA National Phenology Network , 2012 .

[41]  Guy Woodward,et al.  Biodiversity, species interactions and ecological networks in a fragmented world , 2012 .

[42]  Janan T. Eppig,et al.  The Vertebrate Trait Ontology: a controlled vocabulary for the annotation of trait data across species , 2013, Journal of Biomedical Semantics.

[43]  Damaris Zurell,et al.  Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics , 2013, Movement Ecology.

[44]  Gregory P. Asner,et al.  Observing Changing Ecological Diversity in the Anthropocene , 2013 .

[45]  Mark Gahegan,et al.  Biodiversity data should be published, cited, and peer reviewed. , 2013, Trends in ecology & evolution.

[46]  Peter Carey,et al.  Environmental stratifications as the basis for national, European and global ecological monitoring , 2013 .

[47]  N. Pettorelli,et al.  Essential Biodiversity Variables , 2013, Science.

[48]  P. Brancalion,et al.  Functional Extinction of Birds Drives Rapid Evolutionary Changes in Seed Size , 2013, Science.

[49]  P. Reich,et al.  New handbook for standardised measurement of plant functional traits worldwide , 2013 .

[50]  Brody Sandel,et al.  Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide , 2014, Ecology and evolution.

[51]  X. Le Roux,et al.  Trait-based approaches for understanding microbial biodiversity and ecosystem functioning , 2014, Front. Microbiol..

[52]  Roberta E. Martin,et al.  Amazonian functional diversity from forest canopy chemical assembly , 2014, Proceedings of the National Academy of Sciences.

[53]  P. Backwell,et al.  Temporal patterns of avian body size reflect linear size responses to broadscale environmental change over the last 50 years , 2014 .

[54]  S. Nagai,et al.  Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) , 2015, Ecological Research.

[55]  Barry Smith,et al.  Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies , 2014, PloS one.

[56]  William K. Michener,et al.  Ecological data sharing , 2015, Ecol. Informatics.

[57]  Toke T. Høye,et al.  Ecological specialization matters: long‐term trends in butterfly species richness and assemblage composition depend on multiple functional traits , 2015 .

[58]  C. A. Mücher,et al.  Environmental science: Agree on biodiversity metrics to track from space , 2015, Nature.

[59]  A. Gómez,et al.  La ecología funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones , 2015 .

[60]  Simon Claus,et al.  Biological and ecological traits of marine species , 2015, PeerJ.

[61]  J. Pergl,et al.  Getting the Right Traits: Reproductive and Dispersal Characteristics Predict the Invasiveness of Herbaceous Plant Species , 2015, PloS one.

[62]  Graziano Pesole,et al.  Towards global interoperability for supporting biodiversity research on essential biodiversity variables (EBVs) , 2015 .

[63]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[64]  Andrew K. Skidmore,et al.  Advances in remote sensing of vegetation function and traits , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[65]  Joanna Baker,et al.  The role of life history traits in mammalian invasion success , 2015, Ecology letters.

[66]  A. Dell,et al.  Scaling-up Trait Variation from Individuals to Ecosystems , 2015 .

[67]  Y. Buckley,et al.  Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide , 2015, Proceedings of the National Academy of Sciences.

[68]  Doreen Ware,et al.  The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences , 2016, PLoS biology.

[69]  M. Nachman,et al.  Natural history collections as windows on evolutionary processes , 2016, Molecular ecology.

[70]  Peter Haase,et al.  Bridging the gap between biodiversity data and policy reporting needs: An Essential Biodiversity Variables perspective , 2016 .

[71]  Paul M. Thompson,et al.  Phenological sensitivity to climate across taxa and trophic levels , 2016, Nature.

[72]  John Wieczorek,et al.  The importance of digitized biocollections as a source of trait data and a new VertNet resource , 2016, Database J. Biol. Databases Curation.

[73]  Lauren V. Weatherdon,et al.  Is citizen science an open science in the case of biodiversity observations , 2017 .

[74]  Chris Mungall,et al.  The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation , 2016, Journal of Biomedical Semantics.

[75]  Mark D. Schwartz,et al.  The Plant Phenology Monitoring Design for the National Ecological Observatory Network , 2016 .

[76]  Kristin N. Marshall,et al.  Ecological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystems , 2016 .

[77]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing -Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics , 2016, Remote. Sens..

[78]  Craig Moritz,et al.  Biodiversity analysis in the digital era , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[79]  M. Schildhauer,et al.  Monitoring plant functional diversity from space , 2016, Nature Plants.

[80]  Robert Hoehndorf,et al.  The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants , 2016, J. Biomed. Semant..

[81]  S. Wright,et al.  The global spectrum of plant form and function , 2015, Nature.

[82]  Anna F. Cord,et al.  Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives , 2016 .

[83]  N. Pettorelli,et al.  Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions , 2016 .

[84]  A. Marshall,et al.  Realising the potential of herbarium records for conservation biology , 2016 .

[85]  Nathan Wilson,et al.  TraitBank: Practical semantics for organism attribute data , 2016, Semantic Web.

[86]  John Sidney,et al.  An ontology for major histocompatibility restriction , 2016, Journal of Biomedical Semantics.

[87]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[88]  L. Jaulin,et al.  Quantitative Monitoring of the Underwater Environment , 2016 .

[89]  Antonio Novellino,et al.  The European Marine Data and Observation Network (EMODnet): Your Gateway to European Marine and Coastal Data , 2016 .

[90]  Isabelle Mougenot,et al.  Towards a thesaurus of plant characteristics: an ecological contribution , 2017 .

[91]  Denisa Rodila,et al.  Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD) , 2017 .

[92]  M. Schaepman,et al.  Mapping functional diversity from remotely sensed morphological and physiological forest traits , 2017, Nature Communications.

[93]  R. Salguero‐Gómez.  Applications of the fast-slow continuum and reproductive strategy framework of plant life histories. , 2017, The New phytologist.

[94]  Nadejda A. Soudzilovskaia,et al.  Mapping local and global variability in plant trait distributions , 2017, Proceedings of the National Academy of Sciences.

[95]  Andrew K. Skidmore,et al.  Canopy leaf water content estimated using terrestrial LiDAR , 2017 .

[96]  K. Meissner,et al.  How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring , 2017 .

[97]  Pamela S Soltis,et al.  Old Plants, New Tricks: Phenological Research Using Herbarium Specimens. , 2017, Trends in ecology & evolution.

[98]  Michael Drielsma,et al.  Using the essential biodiversity variables framework to measure biodiversity change at national scale , 2017 .

[99]  G. Asner,et al.  Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation , 2017, Science.

[100]  F. Arnaud,et al.  From core referencing to data re-use: two French national initiatives to reinforce paleodata stewardship (National Cyber Core Repository and LTER France Retro-Observatory) , 2017 .

[101]  Ning Jiang,et al.  Our path to better science in less time using open data science tools , 2017, Nature Ecology &Evolution.

[102]  Heiko Balzter,et al.  Connecting Earth observation to high-throughput biodiversity data , 2017, Nature Ecology &Evolution.

[103]  Walter Jetz,et al.  Monitoring biodiversity change through effective global coordination , 2017 .

[104]  Anne Bowser,et al.  An operational definition of essential biodiversity variables , 2017, Biodiversity and Conservation.

[105]  Anne Bowser,et al.  Building essential biodiversity variables(EBVs) of species distribution and abundanceat a global scale , 2017 .

[106]  Vincent S. Smith,et al.  Strategies and guidelines for scholarly publishing of biodiversity data , 2017 .

[107]  José Paulo Sousa,et al.  Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits , 2017 .

[108]  E. N. Stavros,et al.  ISS observations offer insights into plant function , 2017, Nature Ecology &Evolution.

[109]  Ramona Walls,et al.  The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data , 2018, Front. Plant Sci..

[110]  Birgitta König-Ries,et al.  Towards an Ecological Trait-data Standard , 2018, bioRxiv.

[111]  D. Tilman,et al.  Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function , 2018, Nature Ecology & Evolution.

[112]  Anne Bowser,et al.  Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale , 2018, Biological reviews of the Cambridge Philosophical Society.

[113]  M. Tseng,et al.  Decreases in beetle body size linked to climate change and warming temperatures. , 2018, The Journal of animal ecology.