Das Zoologische Forschungsmuseum Alexander Koenig

ist ein Forschungsmuseum der Leibniz Gemeinschaft

Megatransects and species richness estimation

AutorInnen: 
Huber BA, Chao A
Erscheinungsjahr: 
2019
Vollständiger Titel: 
Inferring global species richness from megatransect data and undetected species estimates
Autor/-innen des ZFMK: 
Org. Einordnung: 
Publiziert in: 
Contributions to Zoology
Publikationstyp: 
Zeitschriftenaufsatz
DOI Name: 
https://doi.org/10.1163/18759866-20191347
Bibliographische Angaben: 
Huber BA, Chao A. 2019. Inferring global species richness from megatransect data and undetected species estimates. Contributions to Zoology 88: 42-53.
Abstract: 
Ratio-like approaches for estimating global species richness have been criticised for their unjustified extrapolation from regional to global patterns. Here we explore the use of cumulative percentages of ‘new’ (i.e. not formally described) species over large geographic areas (‘megatransects’) as a means to overcome this problem. In addition, we take into account undetected species and illustrate these combined methods by applying them to a family of spiders (Pholcidae) that currently contains some 1,670 described species. The raw global cumulative percentage of new species (‘new’ as of the end of 2008, when 1,001 species were formally described) is 75.1%, and is relatively constant across large biogeographic regions. Undetected species are estimated using the Chao2 estimator based on species incidence data (date by species and locality by species matrices). The estimated percentage of new species based on the date by species matrices is 76.0% with an estimated standard error (s.e.) of 2.6%. This leads to an estimated global species richness of about 4,200 with a 95% confidence interval of (3,300, 5,000). The corresponding values based on locality by species matrices are 84.2% (s.e. 3.0%) and 6,300 with a 95% confidence interval of (4,000, 8,600). Our results suggest that the currently known 1,670 species of Pholcidae may represent no more than about 25–40% of the total species richness. The impact of further biasing factors like geography, species size and distribution, cryptic species, and model assumptions needs to be explored.