VARIABILITY OF FISH PRODUCTION: NUTRIENTS AS CHEMICAL DRIVERS ACROSS A DIVERSE GEOGRAPHIC RANGE
Notice bibliographique
Résumé
Total phosphorus (TP) is an essential nutrient established as a driver of primary productivity that we predict plays a large role in the fish biomass in rivers, lakes and reservoirs.Watershed catchment geology and anthropogenic modification to river flow including hydropower influence nutrient variability in rivers.The relationship between nutrient variation and fish biomass was compared on a local-scale between two geological distinct mountain watersheds in southeastern BC, providing an opportunity to examine smaller-scale ecosystem response to nutrient availability.Nutrient regimes were also characterised in rivers regulated by hydropower and reference rivers across Canada to assess water quality trends and expected variability in fish biomass across a broad geographic scale.Local scale aquatic assessment is useful for identifying trends in ecosystem response within a watershed, whereas regional assessments are at a scale that is more applicable for management.Using TP and fish biomass relationships from a meta-analysis of literature, we developed regionally specific nutrient-based fish models.Establishing baseline nutrient regimes and developing models to estimate expected fish biomass specific to regional fish diversity, provides a useful predictive tool for initiating mitigation and compensation for rivers affected by hydropower.Hontela for their support and feedback on my project over the last two years, and to Dr. Mike Bradford my external examiner.This project would not have been possible without funding provided by NSERC HydroNet, University of Lethbridge, and the Industrial support from Lotic Environmental.I would like to thank Jesse Malkin for his enthusiastic attitude and strong back, helping me haul nets and electrofishers through mountain rivers in the East Kootenays.Thank you also to all the members of the Young Researchers Committee across Canada who not only assisted in my data collection but also introduced me to and interconnected so many interesting research topics.Thank you to Atle and CEDREN (Center for Environmental Design and Renewable Energy) in Norway, for the opportunity to gain international perspective on approaches to ecosystem management, and to taste some very expensive Norse delicacies.I truly appreciated being part of HydroNet, a research collaboration that provided great perspective to understanding the process and knowledge transfer from field research to applied management.Thank you to the members of my lab; I could not have had a more supportive group of friends to bounce ideas off of.I look forward to watching our experiences and professional lives intertwine in the future.I am also very grateful to have such supportive friends and family and I intend to spend more time with all of you!Finally, thanks to the best angler I know and my partner Trevor, for helping me keep my experiences in perspective.You kept the stove burning and the garden growing, I could not have done this without you.
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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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) | 0,001 | 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 ».