Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F24%3A00584878" target="_blank" >RIV/60077344:_____/24:00584878 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12310/24:43908617

Výsledek na webu

<a href="https://taiwania.ntu.edu.tw/abstract/1970" target="_blank" >https://taiwania.ntu.edu.tw/abstract/1970</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.6165/tai.2024.69.1" target="_blank" >10.6165/tai.2024.69.1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Popis výsledku v původním jazyce

Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25-50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests, thus, further supporting sustainable forest management.

Název v anglickém jazyce

Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Popis výsledku anglicky

Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25-50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests, thus, further supporting sustainable forest management.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10611 - Plant sciences, botany

Projekt

<a href="/cs/project/GX19-28126X" target="_blank" >GX19-28126X: Testování mechanismů udržujících vysokou druhovou rozmanitost v potravních sítích experimentální manipulací trofických kaskád v tropickém deštném lese</a><br>

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

Taiwania

ISSN

0372-333X

e-ISSN

—

Svazek periodika

69

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

TW - Čínská republika (Tchaj-wan)

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001182402700001

EID výsledku v databázi Scopus

2-s2.0-85185963981