Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F24%3A00599097" target="_blank" >RIV/67985939:_____/24:00599097 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41330/24:98847

Výsledek na webu

<a href="https://doi.org/10.1111/ecog.07294" target="_blank" >https://doi.org/10.1111/ecog.07294</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/ecog.07294" target="_blank" >10.1111/ecog.07294</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Popis výsledku v původním jazyce

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

Název v anglickém jazyce

Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Popis výsledku anglicky

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Ecography

ISSN

0906-7590

e-ISSN

1600-0587

Svazek periodika

2024

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

e07294

Kód UT WoS článku

001284244200001

EID výsledku v databázi Scopus

2-s2.0-85200141679