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. 2015 Jun 23;112(25):7761-6.
doi: 10.1073/pnas.1502350112. Epub 2015 Jun 8.

Mass extinction in poorly known taxa

Claire Régnier  1 , Guillaume Achaz  2 , Amaury Lambert  3 , Robert H Cowie  4 , Philippe Bouchet  5 , Benoît Fontaine  6
Affiliations

Affiliations

  • 1 Institut de Systématique, Evolution, Biodiversité, UMR 7205 CNRS Muséum national d'Histoire naturelle (MNHN), Université Pierre et Marie Curie (UPMC), Ecole pratique des hautes études (EPHE), Muséum national d'Histoire naturelle, Sorbonne Universités, 75231 Paris Cedex 05, France; cregnier@mnhn.fr.
  • 2 UMR 7138, CNRS Evolution Paris Seine, Université Pierre et Marie Curie, 75252 Paris Cedex 05, France; Atelier de Bioinformatique, Université Pierre et Marie Curie, 75252 Paris Cedex 05, France; UMR 7241, INSERM U1050, Center for Interdisciplinary Research in Biology, Collège de France, 75005 Paris, France;
  • 3 UMR 7241, INSERM U1050, Center for Interdisciplinary Research in Biology, Collège de France, 75005 Paris, France; UMR 7599 Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, CNRS, 75252 Paris Cedex 05, France; UMR 7599 Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot, CNRS, 75252 Paris Cedex 05, France;
  • 4 Pacific Biosciences Research Center, University of Hawaii, Honolulu, HI 96822;
  • 5 Institut de Systématique, Evolution, Biodiversité, UMR 7205 CNRS Muséum national d'Histoire naturelle (MNHN), Université Pierre et Marie Curie (UPMC), Ecole pratique des hautes études (EPHE), Muséum national d'Histoire naturelle, Sorbonne Universités, 75231 Paris Cedex 05, France;
  • 6 UMR 7204, Département Ecologie et Gestion de la Biodiversité, Muséum National d'Histoire Naturelle, 75231 Paris Cedex 05, France.

Mass extinction in poorly known taxa

Claire Régnier et al. Proc Natl Acad Sci U S A. .
. 2015 Jun 23;112(25):7761-6.
doi: 10.1073/pnas.1502350112. Epub 2015 Jun 8.

Authors

Claire Régnier  1 , Guillaume Achaz  2 , Amaury Lambert  3 , Robert H Cowie  4 , Philippe Bouchet  5 , Benoît Fontaine  6

Affiliations

  • 1 Institut de Systématique, Evolution, Biodiversité, UMR 7205 CNRS Muséum national d'Histoire naturelle (MNHN), Université Pierre et Marie Curie (UPMC), Ecole pratique des hautes études (EPHE), Muséum national d'Histoire naturelle, Sorbonne Universités, 75231 Paris Cedex 05, France; cregnier@mnhn.fr.
  • 2 UMR 7138, CNRS Evolution Paris Seine, Université Pierre et Marie Curie, 75252 Paris Cedex 05, France; Atelier de Bioinformatique, Université Pierre et Marie Curie, 75252 Paris Cedex 05, France; UMR 7241, INSERM U1050, Center for Interdisciplinary Research in Biology, Collège de France, 75005 Paris, France;
  • 3 UMR 7241, INSERM U1050, Center for Interdisciplinary Research in Biology, Collège de France, 75005 Paris, France; UMR 7599 Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, CNRS, 75252 Paris Cedex 05, France; UMR 7599 Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot, CNRS, 75252 Paris Cedex 05, France;
  • 4 Pacific Biosciences Research Center, University of Hawaii, Honolulu, HI 96822;
  • 5 Institut de Systématique, Evolution, Biodiversité, UMR 7205 CNRS Muséum national d'Histoire naturelle (MNHN), Université Pierre et Marie Curie (UPMC), Ecole pratique des hautes études (EPHE), Muséum national d'Histoire naturelle, Sorbonne Universités, 75231 Paris Cedex 05, France;
  • 6 UMR 7204, Département Ecologie et Gestion de la Biodiversité, Muséum National d'Histoire Naturelle, 75231 Paris Cedex 05, France.

Abstract

Since the 1980s, many have suggested we are in the midst of a massive extinction crisis, yet only 799 (0.04%) of the 1.9 million known recent species are recorded as extinct, questioning the reality of the crisis. This low figure is due to the fact that the status of very few invertebrates, which represent the bulk of biodiversity, have been evaluated. Here we show, based on extrapolation from a random sample of land snail species via two independent approaches, that we may already have lost 7% (130,000 extinctions) of the species on Earth. However, this loss is masked by the emphasis on terrestrial vertebrates, the target of most conservation actions. Projections of species extinction rates are controversial because invertebrates are essentially excluded from these scenarios. Invertebrates can and must be assessed if we are to obtain a more realistic picture of the sixth extinction crisis.

Keywords: IUCN Red List; biodiversity crisis; invertebrates.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Collection dates for 200 randomly…

Fig. 1.

Collection dates for 200 randomly selected land snail species. Collection dates, recorded from…

Fig. 1.
Collection dates for 200 randomly selected land snail species. Collection dates, recorded from the literature, museum collections, and consultation with experts for 200 randomly selected land snail species, ordered by date of last collection. Each horizontal line represents all of the collection dates for a single species from 1786 until 2012. Open circles (○; top line) represent all collection dates of mollusks (i.e., proxy for sampling effort). Note the large proportion of species known only from the original collection (e.g., most species between lines 150 and 200).
Fig. S1.

Fig. S1.

Number of mollusk species in…

Fig. S1.

Number of mollusk species in our sample assigned to the different extinction risk…

Fig. S1.
Number of mollusk species in our sample assigned to the different extinction risk categories: (A) following IUCN criteria; (B) following our assessment; and (C) following the model prediction. The IUCN categories are simplified in this figure such that endangered includes the categories critically endangered, endangered, and vulnerable, and extant includes the categories least concern and near threatened. Color coding of the model assessments are graded to reflect the probabilistic nature of the assessments.
Fig. 2.

Fig. 2.

Schematic representation of data used…

Fig. 2.

Schematic representation of data used in the probabilistic model for a single hypothetical…

Fig. 2.
Schematic representation of data used in the probabilistic model for a single hypothetical species. Black circles (●), years in which the species was found; open circles (○), years when searches were undertaken but the species was not found; dashes, years in which there were no searches. The year in which the species was last seen is designated t*, and the year of the last survey is T. Extinction can happen only after year S (year of extinction rate shift), and after the last successful search. The extinction year (E) is unknown. Given the sampling effort before and after the last collection (number of searches, successful or not), the model assesses whether not finding a species is due to extinction or to insufficient sampling.
Fig. S2.

Fig. S2.

Number of land snail species…

Fig. S2.

Number of land snail species assessed in the 14 geographic areas recognized.

Fig. S2.
Number of land snail species assessed in the 14 geographic areas recognized.
Fig. S3.

Fig. S3.

Posterior distribution of S. Using…

Fig. S3.

Posterior distribution of S. Using a uniform prior between 1800 and 1950, we…

Fig. S3.
Posterior distribution of S. Using a uniform prior between 1800 and 1950, we retrieved the marginal posterior distribution of S using MCMC on the whole data set. The distribution is clearly unimodal and centered around 1900 and gives an estimate of the date when the extinction rate rose suddenly on a worldwide scale.
Fig. 3.

Fig. 3.

Number of mollusk species in…

Fig. 3.

Number of mollusk species in our sample assigned to the different extinction risk…

Fig. 3.
Number of mollusk species in our sample assigned to the different extinction risk categories. Assessments are given according to the model prediction, the expert assessment, and the IUCN criteria. The IUCN categories are simplified in this figure such that endangered includes the categories critically endangered, endangered, and vulnerable, and extant includes the categories least concern and near threatened. Color coding of the model assessments are graded to reflect the probabilistic nature of the assessments.
Fig. S4.

Fig. S4.

Posterior distributions of λ and …

Fig. S4.

Posterior distributions of λ and µ for the 14 geographic areas. Using uniform…

Fig. S4.
Posterior distributions of λ and µ for the 14 geographic areas. Using uniform priors in [0, 1], we retrieved the marginal posterior distributions of λ (success probability of a field survey) and µ (yearly extinction rate after S) using MCMC on data from each geographic area independently. Solid circles and triangles give the mean estimates of λ and µ, respectively, and vertical bars report the associated 95% credibility intervals. Areas were sorted in increasing order of µ estimates. To assess the performance of the MCMC algorithm, we built, for each geographic area, 10 artificial datasets by simulation assuming the estimated λ and µ. Based on the artificial datasets, we computed estimates of λ and µ using the same MCMC algorithm. Estimates from simulated datasets are indicated as open circles and triangles.
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