Login / Signup

Mollusc species from the Pontocaspian region - an expert opinion list.

Frank P WesselinghThomas A NeubauerVitaliy V Anistratenkonull Maxim V VinarskiTamara YaninaJan Johan Ter PoortenPavel KijashkoChristian AlbrechtOlga Yu AnistratenkoAnouk D'HontPavel FrolovAlberto Martínez ÁndaraArjan GittenbergerAleksandre Gogaladzenull Mikhail KarpinskyMatteo LattuadaLuis PopaArthur F SandsSabrina van de V LdeJustine VandendorpeThomas Wilke
Published in: ZooKeys (2019)
Defining and recording the loss of species diversity is a daunting task, especially if identities of species under threat are not fully resolved. An example is the Pontocaspian biota. The mostly endemic invertebrate faunas that evolved in the Black Sea - Caspian Sea - Aral Sea region and live under variable salinity conditions are undergoing strong change, yet within several groups species boundaries are not well established. Collection efforts in the past decade have failed to produce living material of various species groups whose taxonomic status is unclear. This lack of data precludes an integrated taxonomic assessment to clarify species identities and estimate species richness of Pontocaspian biota combining morphological, ecological, genetic, and distribution data. In this paper, we present an expert-working list of Pontocaspian and invasive mollusc species associated to Pontocaspian habitats. This list is based on published and unpublished data on morphology, ecology, anatomy, and molecular biology. It allows us to (1) document Pontocaspian mollusc species, (2) make species richness estimates, and (3) identify and discuss taxonomic uncertainties. The endemic Pontocaspian mollusc species richness is estimated between 55 and 99 species, but there are several groups that may harbour cryptic species. Even though the conservation status of most of the species is not assessed or data deficient, our observations point to deterioration for many of the Pontocaspian species.
Keyphrases
  • randomized controlled trial
  • gene expression
  • machine learning
  • clinical practice
  • single molecule
  • artificial intelligence
  • quality improvement
  • data analysis