Do rivermouths alter nutrient and seston delivery to the nearshore?
Notice bibliographique
Résumé
Summary Tributary inputs to lakes and seas are often measured at riverine gages, upstream of lentic influence. Between these riverine gages and the nearshore zones of large waterbodies lie rivermouths, which may retain, transform and contribute materials to the nearshore zone. However, the magnitude and timing of these rivermouth effects have rarely been measured. During the summer of 2011, 23 tributary systems of the Laurentian Great Lakes were sampled from river to nearshore for dissolved and particulate carbon (C), nitrogen (N) and phosphorus (P) concentrations, as well as bulk seston and chlorophyll a concentrations. Three locations per system were sampled: in the upstream river, in the nearshore zone and at the outflow from the rivermouth to the lake. Using stable oxygen isotopes, a water‐mixing model was developed to estimate the nutrient concentration that would occur at the rivermouth if mixing was strictly conservative (i.e. if no processing occurred within the rivermouth). Deviations between these conservative mixing estimates and measured nutrient concentrations were identified as rivermouth effects on nutrient concentrations. Rivermouths had higher concentration of C and P than nearshore areas and more chlorophyll a than upstream river waters. Compared to the conservative mixing model, rivermouths as a class appeared to be summer‐time sources of N, P and chlorophyll a . Substantial among rivermouth variation occurred both in the effect size and direction for all constituents. Using principal component analysis, two groups of rivermouths were identified: rivermouths that had a large effect on most constituents and those that had very little effect on any of the measured constituents. ‘High‐effect’ rivermouths had more abundant upstream croplands, which were presumably the sources of inorganic nutrients. Cross‐validated models built using characteristics of the rivermouth were not good predictors of variation in rivermouth effects on most constituents. For consumers feeding on seston and microbes and vascular autotrophs directly taking up dissolved nutrients, rivermouths are more resource‐rich than upstream riverine or nearby Great Lakes waters. Given declines over time in open‐lake productivity within the Great Lakes, rivermouths may contribute more productivity than their size would suggest to the Great Lakes food web.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 tête enseignante, 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 ».