ADDRESSING MULTIPLE FACETS OF BIAS AND UNCERTAINTY IN CONTINENTAL-SCALE BIODIVERSITY DATABASES

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F24%3AN0000029" target="_blank" >RIV/60460709:41320/24:N0000029 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41330/24:100035

Výsledek na webu

<a href="https://journals.ku.edu/jbi/article/view/21810" target="_blank" >https://journals.ku.edu/jbi/article/view/21810</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.12179384" target="_blank" >10.5281/zenodo.12179384</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ADDRESSING MULTIPLE FACETS OF BIAS AND UNCERTAINTY IN CONTINENTAL-SCALE BIODIVERSITY DATABASES

Popis výsledku v původním jazyce

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias-taxonomic, spatial, and temporal-as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou's index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

Název v anglickém jazyce

ADDRESSING MULTIPLE FACETS OF BIAS AND UNCERTAINTY IN CONTINENTAL-SCALE BIODIVERSITY DATABASES

Popis výsledku anglicky

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias-taxonomic, spatial, and temporal-as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou's index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biodiversity Informatics

ISSN

1546-9735

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

56-77

Kód UT WoS článku

001328396900001

EID výsledku v databázi Scopus

—