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Enregistrement W4403658932 · doi:10.3389/fevo.2024.1488465

Editorial: The adaptation, plasticity and extinction of forest plants to climate change: mechanisms behind the morphological, physiological, phenological and ecological traits

2024· editorial· en· W4403658932 sur OpenAlexaboutno aff
Weiwei Huang, Johan Gielis, Peijian Shi

Notice bibliographique

RevueFrontiers in Ecology and Evolution · 2024
Typeeditorial
Langueen
DomaineEnvironmental Science
ThématiquePlant Water Relations and Carbon Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
Mots-clésPhenologyClimate changeEcologyAdaptation (eye)Extinction (optical mineralogy)Phenotypic plasticityBiologyEnvironmental changePaleontology

Résumé

récupéré en direct d'OpenAlex

Climate change is one of the greatest threats to humankind’s current and future survival. Forests are one of the most essential solutions to addressing the effects of climate change by absorbing huge amounts of carbon dioxide (Pugh et al., 2019; Jiang et al., 2020; Wang et al., 2020). Furthermore, forest loss and degradation are both a cause and an effect of our changing climate (Sasaki and Putz, 2009; Griscom et al., 2017). The expected future climate with increasing drought episodes, seasonally warm temperatures, and severe storms, poses a challenge for forest management since it will affect the growth, mortality, species composition, and distribution of future forests (Figure 1; Huang et al., 2017; Shi et al., 2020). However, how the growth of different species responds differently to long-term drought and high temperatures has been poorly understood. Climate-growth relationships are an important tool for investigating tree growth responses under changing climate and thus provide a scientific basis for future forest management (Huang et al., 2022; Leifsson et al., 2024).The papers collected in this Research Topic cover some important topics associated with climate change, including phenology, morphology, tree-ring growth, and wood anatomical traits of trees’ response to long-term climate change in Australia’s cool temperate rainforest, Amazon flooding forest, and Canada’s temperate forest, and also includes drought and heavy metal control experiments, as well as an article investigating the mathematical intricacies of bamboo internode elongation.Australian cold temperate moist forests are known to have the highest aboveground biomass carbon stocks of all forest types (Keith et al., 2009). However, little is known about the effects of Australia’s cool temperate rainforest climate on long-term phenology. Vogado et al. provided 20 years of defoliation data from cool-temperature Nothofagus rainforests in New South Wales, Australia. They found that defoliation at the community level was mainly affected by Nothofagus moorei, driven by temperature and wind speed, and Ceratopetalum apetalum, driven by temperature, rainfall, and solar radiation. In addition, the average dates of community defoliation were increased by advanced solar radiation. All species presented seasonality in phenological behaviors, but seasonality peaked in different months and is influenced by different climate variables. The Amazon floodplain forest is one of the largest flood-pulsed environments in the world (Junk et al., 2011), whose phenology and diameter increment are mainly triggered by flood pulses. de Sá et al. found that the tree growth and xylem anatomical characteristics of Hydrochorea corymbosa in várzea flooded forest in Central Amazon responded diversely to flooding and non-flooding periods. High flood levels during the end of the flood negatively affected vessel diameter in June and positively influenced parenchyma quantity in September and October. During the non-flooded period in December, the annual tree growth negatively correlated with the vapor pressure deficit. The vessel diameter was negatively affected by the September maximum temperature. The authors suggest that intensification of the hydrological regime and the severe droughts during the non-flooded periods can be a risk for H. corymbosa in the Central Amazonian floodplains. Different tree species growing under the same conditions showed different plasticity and adaptation to climate change (Huang et al., 2017). Populations of a species descending from different origins may also exhibit different climatic adaptations. Zhou et al. observed that Acer saccharum seedlings originating from inland areas showed higher plasticity of bud burst than those from coastal areas at the beginning of leaf development in Quebec, Canada. Trees experiencing a wider climatic fluctuation may exhibit higher plasticity.In addition to field experiments, control experiments are a method to study the effects of climate on trees by precisely controlling a single factor or multiple factors to examine the impacts of factor changes on tree growth. Xiao et al. set a pot experiment to investigate the effects of droughts and re-watering on the dynamics of non-structural carbohydrates (NSCs) in the different organs of two-year-old Pinus yunnanensis seedlings. Under drought, when the carbohydrates produced by photosynthesis could not satisfy the energy required for respiration, plants began to consume stored NSC. “Carbon starvation” occurs when stored NSCs cannot meet the energy required for cellular metabolism (McDowell et al., 2008, 2011). The author found that during the early stage of drought, the drought resistance of P. yunnanensis seedlings was enhanced by increasing soluble sugar concentration; in the later stages of drought, stored starch in organs, stems, and coarse roots were consumed. Growth under moderate drought was promoted after re-watering, suggesting that moderate drought stress can enhance drought tolerance and compensatory growth. Different drought treatments may lead to discrepancies in results. Zhao et al. found a diminishing relationship between trait patterns versus soil water content (SWC) from N-fixing non-legumes, N-fixing legumes to non-N-fixing plants. Whereas, several other studies have shown that drought intensification is more detrimental to non-N-fixing plants compared to N-fixing plants (Hofer et al., 2016, 2017). Climate change may increase runoff and accelerate leaching, thereby increasing heavy metal concentrations in soils. Alotaibi et al. found that the increasing concentration of Pb and Cd delayed the Calligonum comosum seeds’ germination rate and speed. The authors suggest that the enhanced redox proteins and proteins involved in ATP synthesis may be a possible mechanism for seed tolerance to heavy metals. Bamboo is one of the fastest-growing plants on earth, which can grow almost a meter in a single day and grow to its full height of 2 m to above 30 m within a few months (Shi et al., 2017), and sequester large amounts of atmospheric carbon to mitigate climate change. However, the relationship between internode length and serial number changes with culm height growth is not well understood. Tan et al. investigated the mathematical intricacies of the internode elongation pattern of Phyllostachys edulis, Phyllostachys iridescens, and Pseudosasa amabilis involved in the rapid culm growth.In conclusion, the papers published in this research topic expand the current understanding of the adaptation and plasticity of different species growing in diverse continents with diverse climate conditions to climate change, as well as the mechanisms behind the morphological, physiological, phenological, and ecological traits.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,919
Score d'incertitude au seuil0,835

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,209
Écart entre enseignants0,200 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2024
Routes d'admission1
Résumé présentoui

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