Science and community monitoring to inform management of phosphorus in the Canadian Lake Erie watersheds
Notice bibliographique
Résumé
Algal blooms in Lake Erie have worsened in severity, extent, and duration due to agricultural phosphorus (P) loading. Science, policy, and community action are needed to identify and implement feasible solutions for freshwater protections. To improve conservation action for P-reduction in the Lake Erie watershed, we must understand landscape drivers that enhance P runoff across agricultural regions with seasonal and spatial variation. The goals of this thesis were to assess landscape determinants of seasonal P variations in the understudied Canadian Lake Erie watershed, and to develop tools to amplify community stream monitoring and environmental engagement. A seven-year dataset revealed that P loading has significantly increased on an annual basis (2012 – 2019) in the late-winter-early-spring (February, March, April) in three separate Lake Erie tributaries (the East Sydenham River, the Thames River, and the Grand River). I demonstrated that year-over-year increases in tile drainage area on farms explained 23 – 49% of the year-over-year increases in P loading; however, I found the effects of tile drainage were moderated by differences in soil texture, land use/land cover, regional climate, and urban point sources. Using a nested-watershed approach for one year in 13 monitoring stations of the mixed-use Grand River watershed, I determined that row crop area was a strong predictor of P concentration in the Grand River when fields are bare, soils exposed, and nutrient assimilation by biota nonexistent. I recommend the use of year-round field cover by cover crops as a conservation measure to attenuate P-enrichment in the Grand River since sub-watersheds with increased cover crop proportion had reduced P concentrations and turbidity throughout the year. In another one-year study of 13 sub-watersheds across a gradient row crop and tile-drained sub-watersheds of the East Sydenham River and Grand River, I provided quantitative evidence of the negative effects of intensively row crop and tile-drained sub-watersheds on soluble reactive P export during late-winter-early-spring, and particulate loss during the growing season. To engage agricultural communities in monitoring P export from farms, I developed a bioassay that uses stream algae to determine P concentration in low-order streams. This method involving substrate rods (i.e., Peristix) is cost-effective, requires very little training, and yielded data that were significantly related to P concentrations in agricultural streams in the growing season. I recommend that environmental agencies and landowners use this bioassay to identify areas to implement conservation measures to reduce P export from the Lake Erie watershed. For greater application of community-science tools, I created the CommunityFIRST framework, which emphasizes feasible community collaborations, inclusive volunteerism involving a range of abilities and knowledge systems, using tools developed in this thesis that are relevant to the community’s environmental issues and carried out in a supportive and trusting environment between researchers and community members. My research integrates science and community methods to advance our understanding of agricultural land management and seasonal P loading from the Canadian Lake Erie watershed.
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 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 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 ».