Species-observer link and kernel density estimation of background points allow for sampling bias correction in bird species distribution models

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Species-observer link and kernel density estimation of background points allow for sampling bias correction in bird species distribution models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Species-observer link and kernel density estimation of background points allow for sampling bias correction in bird species distribution models

Popis výsledku anglicky

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.

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

<a href="/cs/project/SS02030018" target="_blank" >SS02030018: Centrum pro krajinu a biodiverzitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ecography

ISSN

0906-7590

e-ISSN

0906-7590

Svazek periodika

2025

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

0-13

Kód UT WoS článku

001611052800001

EID výsledku v databázi Scopus

—