Predicting trajectories of temperate forest understorey vegetation responses to global change

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F24%3A00588230" target="_blank" >RIV/67985939:_____/24:00588230 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41320/24:100451 RIV/61989592:15310/24:73626547

Výsledek na webu

<a href="https://doi.org/10.1016/j.foreco.2024.122091" target="_blank" >https://doi.org/10.1016/j.foreco.2024.122091</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.foreco.2024.122091" target="_blank" >10.1016/j.foreco.2024.122091</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting trajectories of temperate forest understorey vegetation responses to global change

Popis výsledku v původním jazyce

Predicting forest understorey community responses to global change and forest management is vital given the importance of the understorey for biodiversity conservation and forest functioning. Though substantial effort has gone into disentangling the impact of global change on understorey communities, scarcity of information on sitespecific environmental drivers across large temporal-spatial scales has limited our ability to predict global change effects at specific forest sites. In this study, using vegetation resurvey and soil data from 1363 plots across temperate Europe, we applied a machine learning approach (gradient boosting regression, GBR) to model and predict site-specific responses of four understorey properties to global change. We applied our final GBR models at 8 forest sites in Austria to validate the model performance, predict understorey trajectories, and evaluate the effect of alternative scenarios for future nitrogen(N) deposition, climate change and forest management on the projected trajectories. Our results showed that the R2 value of the four final GBR models on the independent testing dataset ranged between 0.611 and 0.723 and the most important environmental drivers in predicting the trajectory of understorey properties at specific forest sites were soil pH, soil total carbon-to-nitrogen ratio, overstorey shade-casting ability and regional-scale mean annual precipitation. The out-of-sample R2 value of the four final GBR models on the Austrian data ranged between 0.224 and 0.561. The forecasted trajectories for the Austrian forest sites showed that site-specific understorey responses to near-future climate warming were expected to be weak. Under N deposition decreases, the proportion of woody species was predicted to increase, while species richness and total vegetation cover were predicted to decrease. Furthermore, under a closed canopy, the understorey community was predicted to shift towards more woody species and more forest specialists, albeit with reduced species richness and vegetation cover. Given expected warming and declining N

Název v anglickém jazyce

Predicting trajectories of temperate forest understorey vegetation responses to global change

Popis výsledku anglicky

Predicting forest understorey community responses to global change and forest management is vital given the importance of the understorey for biodiversity conservation and forest functioning. Though substantial effort has gone into disentangling the impact of global change on understorey communities, scarcity of information on sitespecific environmental drivers across large temporal-spatial scales has limited our ability to predict global change effects at specific forest sites. In this study, using vegetation resurvey and soil data from 1363 plots across temperate Europe, we applied a machine learning approach (gradient boosting regression, GBR) to model and predict site-specific responses of four understorey properties to global change. We applied our final GBR models at 8 forest sites in Austria to validate the model performance, predict understorey trajectories, and evaluate the effect of alternative scenarios for future nitrogen(N) deposition, climate change and forest management on the projected trajectories. Our results showed that the R2 value of the four final GBR models on the independent testing dataset ranged between 0.611 and 0.723 and the most important environmental drivers in predicting the trajectory of understorey properties at specific forest sites were soil pH, soil total carbon-to-nitrogen ratio, overstorey shade-casting ability and regional-scale mean annual precipitation. The out-of-sample R2 value of the four final GBR models on the Austrian data ranged between 0.224 and 0.561. The forecasted trajectories for the Austrian forest sites showed that site-specific understorey responses to near-future climate warming were expected to be weak. Under N deposition decreases, the proportion of woody species was predicted to increase, while species richness and total vegetation cover were predicted to decrease. Furthermore, under a closed canopy, the understorey community was predicted to shift towards more woody species and more forest specialists, albeit with reduced species richness and vegetation cover. Given expected warming and declining N

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

<a href="/cs/project/GA21-11487S" target="_blank" >GA21-11487S: Adaptace, vyhnutí, nebo vyhynutí: propojení ekologie společenstev a ekofyziologie k porozumění vlivu vlhkostního deficitu v temperátních lesích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Forest Ecology and Management

ISSN

0378-1127

e-ISSN

1872-7042

Svazek periodika

566

Číslo periodika v rámci svazku

AUG 15

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

122091

Kód UT WoS článku

001262929500001

EID výsledku v databázi Scopus

2-s2.0-85196302959