Differences in trait–environment relationships: Implications for community weighted means tests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F23%3A00574017" target="_blank" >RIV/60077344:_____/23:00574017 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12310/23:43906670

Výsledek na webu

<a href="https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2745.14172" target="_blank" >https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2745.14172</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/1365-2745.14172" target="_blank" >10.1111/1365-2745.14172</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Differences in trait–environment relationships: Implications for community weighted means tests

Popis výsledku v původním jazyce

1. One of J.P. Grime's greatest achievements was demonstrating the importance of the relationship between the environment and plant functional traits for understanding community assembly processes and the effects of biodiversity on ecosystem functioning. A popular approach assessing trait–environment relationships is the community weighted means (CWMs) method, which evaluates changes in communities' average trait values along gradients, with Grime being among its first practitioners.n2. Today the CWM method is well-established but some scholars have criticized it for inflated Type I errors. That is, in some scenarios of compositional turnover along a gradient, CWM tests can provide significant results even for randomly generated traits. Null models have been proposed to correct for such effects by randomizing trait values across species (CWM-sp). We review different approaches relating traits to the environment within the framework of the accepted dichotomy between species-level (observations are species) versus community-level (observations are community parameters) analyses. Between these families of analyses and their combinations, a great variety of methods exist that test different trait–environment relationships, each with different null hypotheses and ecological questions.n3. In classic CWM tests, the null hypothesis focuses on characteristics of trait distributions at the community level along gradients. The Type I error rate should not be a priori considered inflated when this test is used to identify changes in community trait structure affecting the functioning of communities. Trait changes observed with CWM tests may be accurate, but the interpretation that a specific trait drives turnover may be fallacious. Approaches like CWM-sp may be more appropriate for testing other ecological hypotheses, such as whether trait–environment relationships are widespread across species. In effect, this moves the ecological focus towards species-level analyses, that is on the adaptive value of traits and their relation to species niches.n4. Synthesis. There is no single trait–environment relationship. Species-level and community-level analyses, including variants within them, test different relationships with different null hypotheses, such that the potential for inflated error rates can be misleading. Using a spectrum of methods provides a comprehensive picture of the diversity of trait–environment relationships.

Název v anglickém jazyce

Differences in trait–environment relationships: Implications for community weighted means tests

Popis výsledku anglicky

1. One of J.P. Grime's greatest achievements was demonstrating the importance of the relationship between the environment and plant functional traits for understanding community assembly processes and the effects of biodiversity on ecosystem functioning. A popular approach assessing trait–environment relationships is the community weighted means (CWMs) method, which evaluates changes in communities' average trait values along gradients, with Grime being among its first practitioners.n2. Today the CWM method is well-established but some scholars have criticized it for inflated Type I errors. That is, in some scenarios of compositional turnover along a gradient, CWM tests can provide significant results even for randomly generated traits. Null models have been proposed to correct for such effects by randomizing trait values across species (CWM-sp). We review different approaches relating traits to the environment within the framework of the accepted dichotomy between species-level (observations are species) versus community-level (observations are community parameters) analyses. Between these families of analyses and their combinations, a great variety of methods exist that test different trait–environment relationships, each with different null hypotheses and ecological questions.n3. In classic CWM tests, the null hypothesis focuses on characteristics of trait distributions at the community level along gradients. The Type I error rate should not be a priori considered inflated when this test is used to identify changes in community trait structure affecting the functioning of communities. Trait changes observed with CWM tests may be accurate, but the interpretation that a specific trait drives turnover may be fallacious. Approaches like CWM-sp may be more appropriate for testing other ecological hypotheses, such as whether trait–environment relationships are widespread across species. In effect, this moves the ecological focus towards species-level analyses, that is on the adaptive value of traits and their relation to species niches.n4. Synthesis. There is no single trait–environment relationship. Species-level and community-level analyses, including variants within them, test different relationships with different null hypotheses, such that the potential for inflated error rates can be misleading. Using a spectrum of methods provides a comprehensive picture of the diversity of trait–environment relationships.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

<a href="/cs/project/GA20-13637S" target="_blank" >GA20-13637S: Diverzifikace na několika úrovních: zkoumání vlivu inter- a intra-specifické diferenciace rostlin na koexistenci a fungování</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Ecology

ISSN

0022-0477

e-ISSN

1365-2745

Svazek periodika

111

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

2328-2341

Kód UT WoS článku

001037922500001

EID výsledku v databázi Scopus

2-s2.0-85166229522