Species-observer link and kernel density estimation of background points allow for sampling bias correction in bird species distribution models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F25%3A102786" target="_blank" >RIV/60460709:41330/25:102786 - isvavai.cz</a>
Result on the web
<a href="https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08202" target="_blank" >https://nsojournals.onlinelibrary.wiley.com/doi/10.1002/ecog.08202</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/ecog.08202" target="_blank" >10.1002/ecog.08202</a>
Alternative languages
Result language
angličtina
Original language name
Species-observer link and kernel density estimation of background points allow for sampling bias correction in bird species distribution models
Original language description
Species distribution models (SDMs), broadly referring to both species distribution and ecological niche modelling frameworks, are widely used to predict habitat suitability. However, their performance can be biased by uneven sampling effort in occurrence data. Building on two existing approaches, we propose a novel method for sampling bias correction, consisting of the estimation of observer kernel densities for individual species and their subsequent weighting according to the relative contribution of individual observers to the total number of focus species presences. This approach, the ‘presence-weighted observer-oriented approach' (PW-OOA), aimed to provide a better estimation of sampling effort, thus further improving SDM prediction performance. Using bird occurrence data from the Czech Republic, we modelled the distributions of 109 species using four approaches to bias correction: spatial thinning of species presences (STSP), target group occurrences background (TGOB), TGOB+ (tuned up by adjusting kernel smoothing bandwidths) and the new PW-OOA method. We compared the results with simple random background sampling. Models were evaluated using independent reference (presence–absence) data. The PW-OOA method outperformed the other approaches, with the greatest improvement detected for species with higher prevalence. However, as internal validation can be misleading with biased occurrences, we recommend TGOB+ as the most robust approach without independent data; with such data, PW-OOA is superior. While no single optimal combination of bandwidth and observers' weights was identified across species, the PW-OOA method provides a flexible framework to account for observer-specific sampling biases. This study demonstrates the crucial importance of considering the behavior of individual observers and sampling intensity smoothing when correcting for sampling bias in SDMs based on unstructured opportunistic occurrence data.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
<a href="/en/project/SS02030018" target="_blank" >SS02030018: Center for Landscape and Biodiversity</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Ecography
ISSN
0906-7590
e-ISSN
0906-7590
Volume of the periodical
2025
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
0-13
UT code for WoS article
001611052800001
EID of the result in the Scopus database
—