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Enregistrement W2605597166

Methods for restoring sedge/grass meadow community in a Typha-invaded Lake Ontario drowned-river-mouth wetland

2017· dissertation· en· W2605597166 sur OpenAlexaboutno aff
Kathleen Buckler

Notice bibliographique

RevueSUNY Digital Repository Support (State University of New York System) · 2017
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueCoastal wetland ecosystem dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTyphaWetlandTypha angustifoliaGeographyAgroforestryEcologyEnvironmental scienceBiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Water-level regulation has resulted in vegetation changes in Lake Ontario coastal wetlands. The vegetation has shifted from structurally complex sedge/grass meadow communities to communities dominated by invasive Typha, specifically the hybrid cattail Typha x glauca. This study aims to identify control techniques for Typha x glauca to be used in wetlands hydrologically connected to Lake Ontario. The tested control techniques were implemented in a Lake Ontario drowned river-mouth wetland in 2010 and 2011 and were administered along the active invasion zone between a dense Typha stand and remaining sedge/grass meadow. Multiple physical and chemical treatment techniques were implemented over a two-year period at Kents Creek, in northern New York. Treatments included cutting (C), spraying (S) glyphosate (Rodeo) onto cut stalks, and wicking (W) cattail re-sprouts with glyphosate later in the growing season (August). Each treatment method had the following year options: the cut, spray, and wick treatments were applied in year 1 or in both years 1 and 2 (C1S1W1 or C12S12W12). All possible treatments yielded 12 treatment combinations, plus two control plots. Each treatment option was randomly assigned within each of five treatment replicates. All five treatment replicates were located in the invasion zone that had ~25% cover invading Typha and ~75% remaining sedge/grass meadow community. Vegetation sampling occurred in early summer (late June) and again in late summer (August) before treatment in both years. Cattail stem counts and species percent cover data were collected to analyze the effects of each treatment combination. Environmental variables (soil moisture, sediment depth, water-table elevation, soil organic matter, and bulk density) were measured to assist in explaining treatment success or failure and to assess differences among replicates. In addition to looking at the effects that the treatments had on reducing Typha stem density and percent cover, I assessed whether the treatments had an effect on the growth and expansion of existing sedge/grass meadow species, specifically Calamagrostis canadensis and Carex lacustris. Vegetation was sampled again in August 2012 following one full growing season after the second year treatments were applied. Seven treatment combinations: C12, C12W12, C12S, C12SW1, C1W1, C1W12, and C1SW12 significantly reduced cattail stem counts from June 2010 to August 2012. The wick (W) treatment, which was applied to the re-sprouted cattail stems in late August, was the most important treatment, when combined with other treatments (cutting and spraying). Five treatment combinations: C1SW12, C12W12, C12S, C1W12, and C12W1significantly reduced cattail percent cover from June 2010 to August 2012. Although application of the wick treatment in August was the most successful treatment method, the addition of other treatments earlier in the growing season increased Typha stress and led to increased reductions in Typha stems and percent cover. The success of cattails is strongly correlated with stable, high water levels that increase soil moisture. Four of the five replicates were statistically similar in terms of soil moisture throughout the study. Replicate 5 had significantly drier oils than the remaining replicates and had substantially fewer initial Typha stems and lower percent cover, which led to slightly different results in this replicate. For management practices, I suggest using the early summer cutting and late summer wicking treatments, as these two treatments (in combination) were the most effective at reducing Typha stems and percent cover.

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 candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,710
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,024
Tête enseignante GPT0,247
Écart entre enseignants0,223 · 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.

Devis d'étudeObservationnel
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

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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