The Best of Two Worlds: Using Stacked Generalisation for Integrating Expert Range Maps in Species Distribution Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023272%3A_____%2F24%3A10136653" target="_blank" >RIV/00023272:_____/24:10136653 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/24:10490467

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1111/geb.13911" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/geb.13911</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/geb.13911" target="_blank" >10.1111/geb.13911</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Best of Two Worlds: Using Stacked Generalisation for Integrating Expert Range Maps in Species Distribution Models

Popis výsledku v původním jazyce

Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species&apos; realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species&apos; range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species&apos; ranges and thereby range limits that are complementary to information offered by SDMs. Innovation: Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our approach with an occurrence dataset for 49 bat species covering four biodiversity hotspots in the Eastern Mediterranean, Western Asia and Central Asia.Our approach offers a flexible method to integrate expert range maps with any combination of SDM modelling algorithms, thus facilitating the use of algorithm ensembles. In addition, it provides a novel, data-driven way to account for uncertainty in expert-defined ranges not requiring prior knowledge about their accuracy, which is often lacking. Integrating expert range maps into SDMs for bats resulted in more realistic predictions of distribution patterns that showed narrower niche breadths and smaller range overlaps between species compared to traditional SDMs. Our approach holds promise to improve assessments of species distributions, while our work highlights the overlooked potential of stacked generalisation as an ensemble method in species distribution modelling.

Název v anglickém jazyce

The Best of Two Worlds: Using Stacked Generalisation for Integrating Expert Range Maps in Species Distribution Models

Popis výsledku anglicky

Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species&apos; realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species&apos; range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species&apos; ranges and thereby range limits that are complementary to information offered by SDMs. Innovation: Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our approach with an occurrence dataset for 49 bat species covering four biodiversity hotspots in the Eastern Mediterranean, Western Asia and Central Asia.Our approach offers a flexible method to integrate expert range maps with any combination of SDM modelling algorithms, thus facilitating the use of algorithm ensembles. In addition, it provides a novel, data-driven way to account for uncertainty in expert-defined ranges not requiring prior knowledge about their accuracy, which is often lacking. Integrating expert range maps into SDMs for bats resulted in more realistic predictions of distribution patterns that showed narrower niche breadths and smaller range overlaps between species compared to traditional SDMs. Our approach holds promise to improve assessments of species distributions, while our work highlights the overlooked potential of stacked generalisation as an ensemble method in species distribution modelling.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10613 - Zoology

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Global Ecology and Biogeography

ISSN

1466-822X

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001309136200001

EID výsledku v databázi Scopus

—