Influence of spatial and temporal variation on establishing stable isotope baselines of <scp>δ<sup>15</sup>N</scp>, <scp>δ<sup>13</sup>C</scp>, and <scp>δ<sup>34</sup>S</scp> in a large freshwater lake
Notice bibliographique
Résumé
Abstract It is essential to establish a baseline in studies using stable isotopes to interpret trophic relationships across ecosystems and through time. Studies in freshwater ecosystems struggle to quantify baseline stable isotopes due to difficulties collecting representative samples, particularly from pelagic habitats. We assessed temporal and spatial variation in δ13C, δ15N, and δ34S in a commonly used pelagic baseline, seston (n = 156), in Lake Erie to understand mechanisms that correlate with baseline stable isotope dynamics in large lakes. Seston contains a wide range of material which can confound stable isotope interpretation, and we examined the utility of element content and ratios to account for variation in sample source. Seston was collected in each of the three basins of Lake Erie from May to October in 2017–2019 at nearshore (<10 m depth) and offshore (>10 m depth) sites. General linear models were conducted on each stable isotope (δ15N, δ13C, and δ34S) and sample composition (variables: %N, %C, %S, C:N, C:S, and N:S) to assess how basin, month, and collection year influenced seston stable isotopes and composition. Sample composition (variables: %N, %C, %S, C:N, C:S, and N:S), which is rarely reported for organisms in stable isotope studies, was constant throughout the sample period with no temporal or spatial trends except for small variations in %C, C:N, C:S, and N:S. This indicated that the temporal and spatial trends observed within the stable isotopes were related to seasonal changes in system processes and plankton community dynamics, with few or minimal changes in the amount of detrital and inorganic material within seston. Values and trends of δ15N, δ13C, and δ34S in seston were comparable to those measured previously in Lake Erie and other Laurentian Great Lakes. All three isotopes increased from May to October of each sample year and varied spatially, δ15N was higher, δ34S was lower, and δ13C was the same in the west basin compared to the central and eastern basins of Lake Erie, which did not differ. These trends probably reflect seasonal changes in plankton community composition and nutrient cycling throughout the lake and are potentially linked to the presence of Microcystis blooms in the western basin during the late summer and autumn. Seston turns over quickly, as shown by the rapid changes in stable isotope values throughout the study, which confounds the investigation of stable isotopes in upper trophic levels, and especially in organisms that have slower tissue turnover and move throughout the lake seasonally. Additionally, the variable composition of seston (e.g., % C, % N, % S, C:N, C:S, N:S) necessitates analysing sample composition to determine the degree of abiotic (e.g., detritus, sediment, particulate organic matter) and biotic (e.g., phytoplankton, zooplankton) content in it.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».