Effects of wind on lake plankton patchiness and trophic interactions
Notice bibliographique
Résumé
Over thirty years of research has shown that zooplankton and phytoplankton exist in patches generated by physical (e.g., water movement caused by wind) and biological processes (e.g., vertical migration). These patches constantly change in response to weather conditions, however the trophic effects of this have not been thoroughly evaluated because previous studies have included relatively few transects. In this study, 150 sampling transects were collected from two basins (South Arm and Annie Bay) of Lake Opeongo (ON, Canada) under varying wind conditions. While the basins are biologically similar, South Arm is more exposed to the prevailing westerly winds, due to its size and orientation. On each transect, water temperature, chlorophyll concentration (proxy for phytoplankton) and zooplankton size were simultaneously recorded with a spatial resolution of 1.5m. The spatial patterns of each recorded variable were described at a wide range of scales using wavelet analysis, since spectral analysis was inappropriate due to the presence of large-scale trends and abrupt changes in variability. Statistically significant changes in variability were detected in all of the variables and the locations of these changes were mapped. The results showed that change points were relatively uniformly distributed along the sheltered Annie Bay transects, but were more concentrated in the most exposed region of the South Arm transects. Changes in wind conditions were correlated with the spatial patterns of the recorded variables. The results showed that large-scale downwind accumulations were more frequent in South Arm, where winds were generally stronger and more persistent. Small-scale variability was associated with strong winds in both basins. Carbon-budget simulations were used to investigate the link between wind-driven spatial patchiness and trophic interactions. Spatially-explicit budgets evaluated zooplankton growth potential against uniform conditions, which assumed median values of temperature and chlorophyll concentration. Regardless of basin, a growth advantage was observed more frequently than a disadvantage. However, statistical models show that the basins had different spatial patterns of chlorophyll concentration and wind conditions responsible for generating this growth advantage. The results demonstrate that observed spatial patterns should be incorporated into carbon budget simulations.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».