Disentangling the drivers of the sampling bias of freshwater fish across Europe
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/63211DOI: 10.3390/fishes7060383
ISSN: 2410-3888
Publisher
MDPI
Date
2022-12-10Academic Departments
Universidad de Alcalá. Departamento de Ciencias de la Vida
Bibliographic citation
Rodríguez Rey Gómez, M. & Grenouillet G. 2022, "Disentangling the drivers of the sampling bias of freshwater fish across Europe", Fishes, vol. 7, no. 6, art. no. 383, pp. 1-12.
Keywords
Survey effort
Wallacean shortfall
Completeness
SDM
Accessibility
Stream fish
Reliability
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/fishes7060383Rights
Attribution 4.0 International (CC BY 4.0)
© 2022 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
The Wallacean shortfall refers to the knowledge gap in biodiversity distributions. There is still limited knowledge for freshwater fish species despite the importance of focusing conservation efforts towards this group due to their alarming extinction risk and the increasing human pressure on freshwater ecosystems. Here, we addressed the Wallacean shortfall for freshwater fish faunas across Europe by using the completeness indicator derived from species accumulation curves to quantify the fish sampling efforts. The multiple potential drivers of completeness that were previously related to the sampling efforts for other species (i.e., population density, nature reserves, or distance to cities) were tested using a 10 × 10 km2 grid resolution, as well as environmental (e.g., climatic) factors. Our results suggested that although there was an overall spatial pattern at the European level, the completeness was highly country-dependent. Accessibility parameters explained the sampling efforts, as for other taxa. Likewise, climate factors were related to survey completeness, possibly pointing to the river conditions required for fish sampling. The survey effort map we provide can be used to optimize future sampling, aiming at filling the data gaps in undersampled regions like the eastern European countries, as well as to account for the current bias in any ecological modeling using such data, with important implications for conservation and management.
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