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More than half of data deficient species predicted to be threatened by extinction

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  • Cardillo, M. & Meijaard, E. Are comparative studies of extinction risk useful for conservation? Trends Ecol. Evol. 27, 167–171 (2012).

    PubMed 
    Article 

    Google Scholar 

  • Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).

    PubMed 
    Article 

    Google Scholar 

  • Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2, 81–98 (2015).

    Google Scholar 

  • Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Sci. (80-.). 366, eaax3100 (2019).

    Article 
    CAS 

    Google Scholar 

  • Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Sci. (80-.) 353, 288–291 (2016).

    CAS 
    Article 

    Google Scholar 

  • Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Sci. (80-.). 344, 1246752–1246752 (2014).

    CAS 
    Article 

    Google Scholar 

  • IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo (2019) https://doi.org/10.5281/zenodo.3831674.

  • Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Rodrigues, A., Pilgrim, J., Lamoreux, J., Hoffmann, M. & Brooks, T. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).

    PubMed 
    Article 

    Google Scholar 

  • Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).

    PubMed 
    Article 

    Google Scholar 

  • Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the Ocean? PLoS Biol. 9, e1001127 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Bachman, S. P. et al. Progress, challenges and opportunities for Red Listing. Biol. Conserv. 234, 45–55 (2019).

    Article 

    Google Scholar 

  • Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN red list. Conserv. Lett. 7, 126–130 (2014).

    Article 

    Google Scholar 

  • IUCN. The IUCN Red List of Threatened Species. Version 2021-2. https://www.iucnredlist.org (2021).

  • Cazalis, V. et al. Bridging the research-implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).

    PubMed 
    Article 

    Google Scholar 

  • IUCN Standards and Petitions Committee. Guidelines for using the IUCN Red List Categories and Criteria. Prepared by the Standards and Petitions Committee. Downloadable from https://www.iucnredlist.org/documents/RedListGuidelines.pdf vol. 15 (2022).

  • Bland, L. M. et al. Toward reassessing data‐deficient species. Conserv. Biol. 31, 531–539 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Butchart, S. H. M. & Bird, J. P. Data Deficient birds on the IUCN Red List: What don’t we know and why does it matter? Biol. Conserv. 143, 239–247 (2010).

    Article 

    Google Scholar 

  • Zhao, L. et al. Spatial knowledge deficiencies drive taxonomic and geographic selectivity in data deficiency. Biol. Conserv. 231, 174–180 (2019).

    Article 

    Google Scholar 

  • Parsons, E. C. M. Why IUCN should replace “Data Deficient” conservation status with a precautionary “Assume Threatened” Status—A Cetacean Case Study. Front. Mar. Sci. 3, 2015–2017 (2016).

    Google Scholar 

  • Roberts, D. L., Taylor, L. & Joppa, L. N. Threatened or Data Deficient: assessing the conservation status of poorly known species. Divers. Distrib. 22, 558–565 (2016).

    Article 

    Google Scholar 

  • Jetz, W. & Freckleton, R. P. Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140016 (2015).

    Article 

    Google Scholar 

  • Howard, S. D. & Bickford, D. P. Amphibians over the edge: silent extinction risk of Data Deficient species. Divers. Distrib. 20, 837–846 (2014).

    Article 

    Google Scholar 

  • Jarić, I., Courchamp, F., Gessner, J. & Roberts, D. L. Potentially threatened: a Data Deficient flag for conservation management. Biodivers. Conserv. 25, 1995–2000 (2016).

    Article 

    Google Scholar 

  • Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).

    PubMed 
    Article 

    Google Scholar 

  • Butchart, S. H. M. et al. Measuring Global Trends in the status of biodiversity: red list indices for birds. PLoS Biol. 2, e383 (2004).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • United Nations. Transforming our World: the 2030 Agenda for Sustainable Development. A/RES/70/1 (2015).

  • Butchart, S. H. M. et al. Using Red List Indices to measure progress towards the 2010 target and beyond. Philos. Trans. R. Soc. B Biol. Sci. 360, 255–268 (2005).

    CAS 
    Article 

    Google Scholar 

  • Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 0023 (2017).

    Article 

    Google Scholar 

  • Mooers, A. Ø., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for Phylogenetic Conservation Prioritization. PLoS One 3, e3700 (2008).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Runting, R. K., Phinn, S., Xie, Z., Venter, O. & Watson, J. E. M. Opportunities for big data in conservation and sustainability. Nat. Commun. 11, 2003 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).

    PubMed 
    Article 

    Google Scholar 

  • Hino, M., Benami, E. & Brooks, N. Machine learning for environmental monitoring. Nat. Sustain 1, 583–588 (2018).

    Article 

    Google Scholar 

  • Wearn, O. R., Freeman, R. & Jacoby, D. M. P. Responsible AI for conservation. Nat. Mach. Intell. 1, 72–73 (2019).

    Article 

    Google Scholar 

  • Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).

    Article 

    Google Scholar 

  • Bland, L. M. & Böhm, M. Overcoming data deficiency in reptiles. Biol. Conserv. 204, 16–22 (2016).

    Article 

    Google Scholar 

  • Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).

    PubMed 
    Article 

    Google Scholar 

  • Luiz, O. J., Woods, R. M., Madin, E. M. P. & Madin, J. S. Predicting IUCN extinction risk categories for the World’s Data Deficient Groupers (Teleostei: Epinephelidae). Conserv. Lett. 9, 342–350 (2016).

    Article 

    Google Scholar 

  • Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).

    Article 

    Google Scholar 

  • Walls, R. H. L. & Dulvy, N. K. Tracking the rising extinction risk of sharks and rays in the Northeast Atlantic Ocean and Mediterranean Sea. Sci. Rep. 11, 15397 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Walls, R. H. L. & Dulvy, N. K. Eliminating the dark matter of data deficiency by predicting the conservation status of Northeast Atlantic and Mediterranean Sea sharks and rays. Biol. Conserv. 246, 108459 (2020).

    Article 

    Google Scholar 

  • IUCN. Species Information Service. Version 2020-3. https://www.iucnredlist.org/resources/spatial-data-download (2021).

  • IUCN. The IUCN Red List of Threatened Species. Version 2020-3. https://www.iucnredlist.org (2020).

  • Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).

    Article 

    Google Scholar 

  • Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–34 (2014).

    Article 

    Google Scholar 

  • Selig, E. R. et al. Global priorities for Marine biodiversity conservation. PLoS One 9, e82898 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • O’Hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Mittermeier, R. A., Goetsch Mittermeier, C., Gil, P. R. & Wilson, E. O. Megadiversity: Earth’s Biologically Wealthiest Nations. CEMEX (2005).

  • Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0. (2020).

  • GBIF. The Global Biodiversity Information Facility: What is GBIF? https://www.gbif.org/what-is-gbif (2021).

  • OBIS. Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. (2021).

  • Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0. https://cran.r-project.org/package=rgbif (2021).

  • Provoost, P. & Bosch, S. robis: Ocean Biodiversity Information System (OBIS) Client. R package version 2.3.9. https://CRAN.R-project.org/package=robis. (2020).

  • Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).

    Article 

    Google Scholar 

  • Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad, Dataset https://doi.org/10.5061/dryad.kd1d4 (2018).

  • ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. http://maps.elie.ucl.ac.be/CCI/viewer/download.php (2017).

  • Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Chang. Biol. 25, 811–826 (2019).

    PubMed 
    Article 

    Google Scholar 

  • Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. 109, 16083–16088 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). Cambridge, UK: UNEP-WCMC and IUCN www.protectedplanet.net (2021).

  • Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Sci. (80-.) 342, 850–853 (2013).

    CAS 
    Article 

    Google Scholar 

  • Tuanmu, M. N. & Jetz, W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 24, 1329–1339 (2015).

    Article 

    Google Scholar 

  • Maggi, F., Tang, F. H. M., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Byers, L. et al. A Global Database of Power Plants. World Resour. Inst. 1–18 (2019).

  • Mulligan, M., van Soesbergen, A. & Sáenz, L. GOODD, a global dataset of more than 38,000 georeferenced dams. Sci. Data 7, 31 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Boulay, A.-M. et al. The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). Int. J. Life Cycle Assess. 23, 368–378 (2018).

    Article 

    Google Scholar 

  • Barbarossa, V. et al. Erratum: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015. Sci. Data 5, 180078 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. 117, 3648–3655 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Domisch, S., Amatulli, G. & Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci. Data 2, 150073 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).

    PubMed 
    Article 

    Google Scholar 

  • Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163 (2006).

    PubMed 
    Article 

    Google Scholar 

  • Schlossberg, S., Chase, M. J., Gobush, K. S., Wasser, S. K. & Lindsay, K. State-space models reveal a continuing elephant poaching problem in most of Africa. Sci. Rep. 10, 10166 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Burn, R. W., Underwood, F. M. & Blanc, J. Global trends and factors associated with the illegal killing of Elephants: a hierarchical Bayesian Analysis of Carcass Encounter Data. PLoS One 6, e24165 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Hauenstein, S., Kshatriya, M., Blanc, J., Dormann, C. F. & Beale, C. M. African elephant poaching rates correlate with local poverty, national corruption and global ivory price. Nat. Commun. 10, 2242 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • UNDP. Human Development Report 2020. The Next Frontier: Human Development and the Anthropocene. New York. http://hdr.undp.org/en/content/human-development-report-2020. (2020).

  • Transparency International. Corruption Perceptions Index 2020. (2020).

  • Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 12485 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 7615 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Halpern, B. S. et al. A global map of human impact on marine ecosystems. Sci. (80-.) 319, 948–952 (2008).

    CAS 
    Article 

    Google Scholar 

  • Assis, J. et al. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).

    Article 

    Google Scholar 

  • Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).

    Article 

    Google Scholar 

  • Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).

    PubMed 
    Article 

    Google Scholar 

  • Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. The Elements of Statistical Learning vol. 27 (Springer New York, 2001).

  • Kampichler, C., Wieland, R., Calmé, S., Weissenberger, H. & Arriaga-Weiss, S. Classification in conservation biology: a comparison of five machine-learning methods. Ecol. Inform. 5, 441–450 (2010).

    Article 

    Google Scholar 

  • LeDell, E. et al. h2o: R Interface for the ‘H2O’ Scalable Machine Learning Platform. R package version 3.36.0.4. https://github.com/h2oai/h2o-3 (2022).

  • H2O.ai. H2O AutoML. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html (2022).

  • Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).

    PubMed 
    Article 

    Google Scholar 

  • Kuhn, M. Building Predictive Models in R using the caret Package. J. Stat. Softw. 28, 1–26 (2008).

    Article 

    Google Scholar 

  • Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Article 

    Google Scholar 

  • Harrell Jr, F. E. Hmisc: Harrell miscellaneous. R package version 4.5-0. (2021).

  • van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super Learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).

  • R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://www.r-project.org/ (2021).

  • RStudio Team. RStudio: integrated development environment for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).

  • Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://cran.r-project.org/package=raster (2019).

  • Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/package=rgdal (2019).

  • Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R package version 0.9-5. https://cran.r-project.org/package=maptools/ (2019).

  • Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). R package version 0.5-1. https://cran.r-project.org/package=rgeos (2019).

  • Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R. (Springer New York, 2013).

  • Pebesma, E. Simple features for R: standardized support for Spatial Vector Data. R. J. 10, 439 (2018).

    Article 

    Google Scholar 

  • Ross, N. Fasterize: Fast Polygon to Raster Conversion. R package version 1.0.3. https://CRAN.R-project.org/package=fasterize (2020).

  • Microsoft Corporation & Weston, S. doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.16. https://CRAN.R-project.org/package=doParallel (2020).

  • Wickham, H. stringr: simple, consistent wrappers for common string operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr (2019).

  • Tuszynski, J. caTools: tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc. R package version 1.18.1. https://CRAN.R-project.org/package=caTools (2021).

  • Wickham, H. et al. Welcome to the tidyverse. Journal of Open Source Software, 4, 1686. https://doi.org/10.21105/joss.01686 (2019).

  • Dragulescu, A. & Arendt, C. xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.5. (2020).

  • Wickham, H. & Bryan, J. readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl (2019).

  • ESRI. ArcGIS Pro version 2.9.0. https://www.esri.com/en-us/home (2022).

  • Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).

  • Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, NY (2016).

  • Wilke, C. O. ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.3. https://CRAN.R-project.org/package=ggridges (2021).

  • South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. https://CRAN.R-project.org/package=rnaturalearth (2017).

  • Garnier, S. viridis: Default Color Maps from ‘matplotlib’. R package version 0.5.1. https://CRAN.R-project.org/package=viridis (2018).

  • Borgelt, J. jannebor/dd_forecast: Code for study ‘More than half of Data Deficient species predicted to be threatened by extinction’ (v1.0.1). https://doi.org/10.5281/zenodo.6627688.Zenodo (2022).


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