Open AccessSpecies–accumulation curves and taxonomic surrogates: an integrated approach for estimation of regional species richness
Author(s) -
Terlizzi Antonio,
Anderson Marti J.,
Bevilacqua Stanislao,
Ugland Karl I.
Publication year - 2014
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12168
Subject(s) - species richness , biodiversity , ecology , sampling (signal processing) , taxonomic rank , biology , species diversity , habitat , mediterranean climate , global biodiversity , taxon , filter (signal processing) , computer science , computer vision
Aim A species–accumulation curve may represent a direct expression of β‐diversity, the rate at which diversity increases from local to regional scale. Patterns of variation in β‐diversity tend to be consistent when measured across lower levels of the Linnaean taxonomic hierarchy (i.e. using species, genera or families). Our aim was to assess the relationships between species–accumulation curves and β‐diversity at different taxonomic levels and to combine the logic of species–accumulation curves with taxonomic surrogacy to provide a new approach for cost‐effective and reliable estimates of large‐scale species richness (γ‐diversity). Location Mediterranean, N Atlantic and SW Pacific. Methods We provide here a novel framework to extrapolate quantitative measures of species richness in large areas from accumulation curves based on extensive sampling at the family level coupled with estimation of species‐to‐family ratios from a subset of sampling units where organisms are identified to the species level. We demonstrated the effectiveness of the approach by analysing six datasets of diverse marine molluscan assemblages from different biogeographical regions and habitat types. Results The approach proposed here can be used successfully to gain substantial efficiencies in sampling, potentially reducing the number of sampling units in which organisms have to be identified at species level between 50 and 75%, while still allowing reliable estimates of regional species richness. Main conclusions Our results highlight the potential of this approach to improve the general exploration of biodiversity, especially for large‐scale monitoring programs. The method we propose differs from previously described approaches by taking into account the spatial heterogeneity of species distributions within the sampled area and also by relying on estimates of species‐to‐family ratios obtained directly from the specific area of interest.