Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A91596" target="_blank" >RIV/60460709:41330/22:91596 - isvavai.cz</a>

Výsledek na webu

<a href="https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13956" target="_blank" >https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13956</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/2041-210X.13956" target="_blank" >10.1111/2041-210X.13956</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Popis výsledku v původním jazyce

The performance of species distribution models is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of finescale environmental data in SDMs, it is important to test this assumption. Models using finescale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the tradeoffs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m finescale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grai

Název v anglickém jazyce

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Popis výsledku anglicky

The performance of species distribution models is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of finescale environmental data in SDMs, it is important to test this assumption. Models using finescale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the tradeoffs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m finescale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grai

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

2022

Kód důvěrnosti údajů

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

Název periodika

Methods in Ecology and Evolution

ISSN

2041-210X

e-ISSN

2041-2096

Svazek periodika

13

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

2289-2302

Kód UT WoS článku

000842324500001

EID výsledku v databázi Scopus

2-s2.0-85136912927