You shall know a species by the company it keeps: Leveraging co-occurrence data to improve ecological prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F24%3A101460" target="_blank" >RIV/60460709:41320/24:101460 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1111/jvs.13314" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/jvs.13314</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/jvs.13314" target="_blank" >10.1111/jvs.13314</a>

Jazyk výsledku

angličtina

Název v původním jazyce

You shall know a species by the company it keeps: Leveraging co-occurrence data to improve ecological prediction

Popis výsledku v původním jazyce

AimMaking predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Because of the huge number of species, ecologists seek generalizations based on species' traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species' ecological attributes not captured by traits or phylogenies. We propose using a novel ordination technique to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction.MethodWe present an efficient method to derive species vectors from co-occurrence data using Global Vectors for Word Representation (GloVe), an unsupervised learning algorithm originally designed for language modelling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from sPlotOpen, an open-access global vegetation plot database, and tested their ability to predict elevational range shifts in European montane plant species.ResultsCo-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information.ConclusionsGiven the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities and ecosystems.

Název v anglickém jazyce

You shall know a species by the company it keeps: Leveraging co-occurrence data to improve ecological prediction

Popis výsledku anglicky

AimMaking predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Because of the huge number of species, ecologists seek generalizations based on species' traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species' ecological attributes not captured by traits or phylogenies. We propose using a novel ordination technique to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction.MethodWe present an efficient method to derive species vectors from co-occurrence data using Global Vectors for Word Representation (GloVe), an unsupervised learning algorithm originally designed for language modelling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from sPlotOpen, an open-access global vegetation plot database, and tested their ability to predict elevational range shifts in European montane plant species.ResultsCo-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information.ConclusionsGiven the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities and ecosystems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Vegetation Science

ISSN

1100-9233

e-ISSN

1100-9233

Svazek periodika

35

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

SE - Švédské království

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001368756200001

EID výsledku v databázi Scopus

2-s2.0-85208637479