Biodiversity and Conservation

, Volume 25, Issue 10, pp 1995–2000 | Cite as

Potentially threatened: a Data Deficient flag for conservation management

  • Ivan Jarić
  • Franck Courchamp
  • Jörn Gessner
  • David L. Roberts


Data Deficient species (DD) comprise a significant portion of the total number of species listed within the IUCN Red List. Although they are not classified within one of the threat categories, they may still face high extinction risks. However, due to limited data available to infer their extinction risk reliably, it is unlikely that the assessment of the true status of Data Deficient species would be possible before many species decline to extinction. An appropriate measure to resolve these problems would be to introduce a flag of potentially threatened species within the Data Deficient category [i.e., DD(PT)]. Such a flag would represent a temporary Red List status for listed Data Deficient species that are, based on the available direct evidence and/or indirect indices, likely to be assigned to one of the threat categories, but where current data remains insufficient for a complete classification. The use of such a flag could increase the focus of the scientific community and conservation decision-makers on such species, thus avoiding the risk that necessary conservation measures are implemented too late. As such, establishment of the DD(PT) category as a kind of alarm for priority species could be beneficial.


Data Deficient Endangered species Extinction risk IUCN Red List Threatened species 



The authors acknowledge the sponsorship provided by the Alexander von Humboldt Foundation and the Federal German Ministry for Education and Research, as well as the support by the Invacost research program, and by the Project No. 173045, funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.


  1. Bland LM, Collen B, Orme DL, Bielby J (2015) Predicting the conservation status of data-deficient species. Conserv Biol 29(1):250–259CrossRefPubMedGoogle Scholar
  2. Burgman MA, Grimson RC, Ferson S (1995) Inferring threat from scientific collections. Conserv Biol 9:923–928CrossRefGoogle Scholar
  3. Burgman MA, Maslin BR, Andrewartha D, Keatley MR, Boek C, McCarthy MA (2000) Inferring threat from scientific collections: power tests and an application to Western Australian Acacia species. In: Ferson S, Burgman MA (eds) Quantitative methods for conservation biology. Springer, New York, pp 7–26CrossRefGoogle Scholar
  4. Butchart SHM, Stattersfield AJ, Brooks TM (2006) Going or gone: defining ‘Possibly Extinct’ species to give a truer picture of recent extinctions. Bull BOC 126:7–24Google Scholar
  5. Davidson AD, Hamilton MJ, Boyer AG, Brown JH, Ceballos G (2009) Multiple ecological pathways to extinction in mammals. Proc Natl Acad Sci USA 106(26):10702–10705CrossRefPubMedPubMedCentralGoogle Scholar
  6. de Lima RF, Bird JP, Barlow J (2011) Research effort allocation and the conservation of restricted-range island bird species. Biol Conserv 144:627–632CrossRefGoogle Scholar
  7. Howard SD, Bickford DP (2014) Amphibians over the edge: silent extinction risk of Data deficient species. Divers Distrib 20(7):837–846CrossRefGoogle Scholar
  8. IUCN (2001) IUCN Red List Categories and Criteria. Version 3.1. Accessed 10 May 2016
  9. IUCN (2015) The IUCN Red List of Threatened Species. Version 2015-4. Accessed 10 May 2016
  10. Jetz W, Freckleton RP (2015) Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Philos Trans R Soc Lond B Biol Sci 370(1662):20140016CrossRefPubMedPubMedCentralGoogle Scholar
  11. Jones KE, Safi K (2011) Ecology and evolution of mammalian biodiversity. Philos Trans R Soc Lond B Biol Sci 366(1577):2451–2461CrossRefPubMedPubMedCentralGoogle Scholar
  12. Luiz OJ, Woods RM, Madin EMP, Madin JS (2016) Predicting IUCN extinction risk categories for the world’s Data deficient groupers (Teleostei: Epinephelidae). Conserv Lett. doi: 10.1111/conl.12230 Google Scholar
  13. McCarthy MA (1998) Identifying declining and threatened species with museum data. Biol Conserv 83:9–17CrossRefGoogle Scholar
  14. McInerny GJ, Roberts DL, Davy AJ, Cribb PJ (2006) Significance of sighting rate in inferring extinction and threat. Conserv Biol 20:562–567CrossRefPubMedGoogle Scholar
  15. Morais AR, Siqueira MN, Lemes P, Maciel NM, De Marco P, Brito D (2013) Unraveling the conservation status of Data deficient species. Biol Conserv 166:98–102CrossRefGoogle Scholar
  16. Quintero E, Thessen AE, Arias-Caballero P, Ayala-Orozco B (2014) A statistical assessment of population trends for data deficient Mexican amphibians. PeerJ 2:e703CrossRefPubMedPubMedCentralGoogle Scholar
  17. Regan HM, Colyvan M, Burgman MA (2000) A proposal for fuzzy International Union for the Conservation of Nature (IUCN) categories and criteria. Biol Conserv 92:101–108CrossRefGoogle Scholar
  18. Robbirt KM, Roberts DL, Hawkins JA (2006) Comparing IUCN and probabilistic assessments of threat: do IUCN Red List criteria conflate rarity and threat? Biodivers Conserv 15:1903–1912CrossRefGoogle Scholar
  19. Roberts DL, Taylor L, Joppa LN (2016) Threatened or data deficient: assessing the conservation status of poorly known species. Divers Distrib 22(5):558–565CrossRefGoogle Scholar
  20. Rodrigues ASL, Pilgrim JD, Lamoreux JF, Hoffmann M, Brooks TM (2006) The value of the IUCN Red List for conservation. Trends Ecol Evol 21(2):71–76CrossRefPubMedGoogle Scholar
  21. Schipper J et al (2008) The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322(5899):225–230CrossRefPubMedGoogle Scholar
  22. Sitas N, Baillie JEM, Isaac NJB (2009) What are we saving? Developing a standardized approach for conservation action. Anim Conserv 12:231–237CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
  2. 2.Institute for Multidisciplinary ResearchUniversity of BelgradeBelgradeSerbia
  3. 3.Ecologie, Systématique and EvolutionUniv. Paris-Sud, CNRS, AgroParisTech, Université Paris-SaclayOrsayFrance
  4. 4.Durrell Institute of Conservation and Ecology, School of Anthropology and ConservationUniversity of KentCanterburyUK

Personalised recommendations