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Enregistrement W3006519214 · doi:10.1002/aps3.11324

Novel methodologies to disentangle plant–environment interactions

2020· article· en· W3006519214 sur OpenAlexfundaboutno aff
Sally M. Chambers

Notice bibliographique

RevueApplications in Plant Sciences · 2020
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant and animal studies
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of VermontUniversity of GuelphUniversity of CincinnatiUniversity of Michigan
Mots-clésBiologyTrophic levelEcologyPlant communityPlant reproductionAbiotic componentPlant biologyVariety (cybernetics)PollinationBotanySpecies richnessComputer science

Résumé

récupéré en direct d'OpenAlex

Due to their sessile nature, plant species are forced to interact with the biotic and abiotic factors of their surrounding environment, forming complex webs that either promote or inhibit growth and reproduction. These intricate interactions may be expressed at different trophic, spatial, or temporal scales. Consequently, the evolutionary history, community structure, and geographical distribution of plant species are shaped by their interactions with the surrounding environment. Therefore, plant–environment interactions are integral components of a wide array of plant-based disciplines, and the researchers trying to untangle these complex webs may employ a variety of methodologies to address their questions of interest. Different facets of these plant–environment interactions take the stage in this unique collaboration between three academic journals: the International Journal of Plant Sciences (IJPS), the American Journal of Botany (AJB), and Applications in Plant Sciences (APPS). Manuscripts contributed by IJPS focus specifically on the paleobotanical (edited by Dr. Selena Smith, University of Michigan) and morphological (edited by Dr. Dan Chitwood, Michigan State University) aspects of the plant–environment spectrum. Contributions from AJB focus on three subjects pertaining to plant–environment interactions: plant responses to stressful environments (edited by Dr. Regina Baucom, University of Michigan), environmental impacts on plant–microbe mutualisms (edited by Dr. Katy Heath, University of Illinois), and changes in plant reproduction driven by environmental conditions (coedited by Drs. Regina Baucom, Jannice Friedman [Queens University], Katy Heath, and Sharon Kessler [Purdue University]). Given that APPS is a journal dedicated to newly developed methodologies and protocols, the articles featured in this issue focus on new ways that researchers are investigating plant–environment interactions. In general, a variety of approaches are used to understand the degree to which environmental factors impact various aspects of plant biology. Techniques can be used in the field, in a laboratory setting, or may even be computational in nature. Collectively, these methodologies are useful for the investigation of plant–environment interactions under a variety of temporal and spatial scales. The three articles summarized below feature novel approaches used to examine plant–environment interactions under contemporary, paleobotanical, and future scenarios. Methodologies used in the field to examine plant–environment interactions may involve the manipulation of a single environmental factor. These approaches are especially common when the goal is to examine plant responses to global climate change. For instance, manipulations in the field may be undertaken to simulate elevated drought conditions in areas where this is predicted to occur. However, these approaches are often expensive, labor intensive, and only suitable for certain types of plant species. Cranston et al. (2020) specifically discuss the difficulties in simulating drought conditions in a dense mature forest composed of large trees. To study the impacts of drought on the charismatic conifer Agathis australis (D. Don) Loudon, the authors developed a novel, inexpensive methodology that may be used in a variety of forest systems where the goal is to limit natural precipitation. Composed of a waterproof tarpaulin, this novel throughfall exclusion design is collapsible and portable, making it relatively easy to transport, which is critical when working in complicated terrain. The authors installed a variety of data loggers to determine if the design does in fact simulate drought conditions and if the trees exhibit any responses that differ from control trees. Roughly one year after installation, Cranston et al. found differences in soil moisture patterns between control and drought-induced trees, as well as differences in physiological responses between these two treatments. Members of the family Apocynaceae are notorious for their complex floral morphologies and intricate pollinator interactions. Insects of different guilds are known to visit the flowers of different species in this family; however, these visitors differ in their probability of effectively pollinating a particular species. After documenting plant–pollinator interactions in the field, Koptur et al. (2020) set out to determine which visitors may be the most effective pollinators for three Apocynaceae species by simulating pollination events in a common garden setting. Four different diameters of fishing line were used to mimic the proboscis widths of different insect visitors originally observed in the field. Pollen capture was estimated by inserting 12 cm of the four different fishing lines and counting the number of pollen grains obtained by each, while pollen deposition was determined by the amount of area stained by each of the four different fishing lines when preloaded with pollen and subsequently dyed. The authors found a significant relationship among plant species and the number of pollen grains captured by each line. They also uncovered differences in pollen deposition among plant species and line diameters. These results suggest that fishing line may serve as a useful and inexpensive proxy when trying to disentangle plant–insect interactions in search of effective pollinators. Computational modeling has become a widely used tool for examining plant–environment interactions across a variety of temporal scales. Most readers will likely be familiar with species distribution models, which are commonly used to understand how plants interact with environmental factors, such as temperature and precipitation, and how their distributions will shift in the future as these factors change. By utilizing contemporary community records, Harbert and Baryiames (2020) have demonstrated that their new R package, cRacle, reliably estimates a variety of temperature and precipitation conditions. The authors especially encourage those working in the paleobotanical community to utilize this package to estimate climatic conditions of the past based on the fossil record. Plant–environment interactions are complex and multifaceted, spanning trophic levels as well as spatial and temporal scales. Novel methodologies are critical to advance our understanding of these interactions and how plants respond when shifts occur. The articles featured here highlight some of the new ways that researchers are investigating these relationships, which will help address a broad array of plant biology questions. Many thanks to Drs. Regina Baucom, Robert Grese, Cora McAllister, Jillian Meyers, David Michener, and Selena Smith (University of Michigan) for initiating this cross-journal special issue; Dr. Theresa Culley (University of Cincinnati, editor-in-chief of Applications in Plant Sciences) and Beth Parada (managing editor of Applications in Plant Sciences) for their editorial assistance and expertise; and Dr. Chris Caruso (University of Guelph, editor-in-chief of the International Journal of Plant Sciences) and Dr. Pamela Diggle (University of Vermont, editor-in-chief of the American Journal of Botany) for their contributions to this special issue. Finally, many thanks to all of the individuals who assisted with the review process.

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)0,000
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: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,897
Score d'incertitude au seuil0,250

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,268
Tête enseignante GPT0,305
Écart entre enseignants0,037 · 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'étudeExpérimental (laboratoire)
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

Citations1
Publié2020
Routes d'admission2
Résumé présentoui

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