Bridging the gap between knowledge and action: Collaborative science for managing the impacts of eutrophication and harmful algal blooms on lake ecosystem services
Notice bibliographique
Résumé
Lakes around the world are experiencing increasing degradation due to climate change and human activity. In particular, cultural eutrophication—defined as the overfertilization of waterbodies with nutrients such as nitrogen and phosphorus due to human activities—and associated harmful algal blooms pose significant environmental, social, and economic challenges. These challenges include declines in biodiversity, disruptions to drinking water treatment, and loss of cultural ecosystem services such as recreation. Despite extensive scientific understanding of eutrophication, a gap persists between understanding and effective management. This thesis aims to bridge that gap by examining the long-term drivers and societal impacts of water quality degradation in two regionally important, diverse, temperate lake ecosystems in Canada: Buffalo Pound Lake in Saskatchewan and Elk/Beaver Lake in British Columbia. By collaborating with end-users and community members and applying biophysical and economic modelling, this thesis advances the understanding of how eutrophication and broader water quality degradation affect key lake ecosystem services—such as drinking water provision and cultural services—and offers recommendations to close the gap between knowledge and action. To examine impacts on drinking water provision in Buffalo Pound Lake—a shallow drinking water reservoir in a highly agricultural, dryland region—I use generalized additive modeling and long-term data (30+ years) to identify key drivers impacting source water quality. In Elk/Beaver Lake, another lake valued for its cultural ecosystem services, I used an economic tool known as a discrete choice experiment to understand community preferences and willingness to pay for lake restoration. Key findings from these two case studies include: 1) climate variability—including both wet, cool and warm, dry cycles—is a key driver of phytoplankton biomass (measured as chlorophyll a and overall water treatability, as indicated by concentrations of dissolved organic carbon, total dissolved solids, turbidity, and odour; 2) interbasin transfers from mesotrophic supply reservoirs may have dilution benefits for nutrients, phytoplankton biomass, and dissolved organic carbon, although these benefits are constrained by physical and social limitations and may involve trade-offs such as increased turbidity; 3) lake degradation and harmful algal blooms substantially reduce the use and enjoyment of lake ecosystem services in Elk/Beaver Lake, with the community expressing strong preferences for protecting and restoring non-use ecosystem services (e.g., biodiversity, lake aesthetics) and recreational ecosystem services; and 4) willingness to pay for lake restoration was high—collectively estimated at $27–$55 million per year—exceeding projected restoration costs in Elk/Beaver Lake and contributing to growing evidence of substantial societal benefits of lake restoration. Working with end-users and community members in both case studies provided insights into the impact of water quality degradation on key ecosystem services and enhanced the relevance of outcomes. By integrating ecological and human dimensions of water quality, this thesis offers valuable guidance on how to anticipate, manage, and respond to worsening water quality under future environmental change. Although the case studies differ in geography and emphasis, both illustrate how climate change and human activity threaten multiple lake ecosystem services, emphasizing the need for integrated, interdisciplinary, and transdisciplinary research to bridge the gap between knowledge and action on eutrophication and its impacts. The costs of managing and adapting to the impacts of eutrophication are high, but the costs of inaction may be greater. Protecting lake ecosystem services in the future will require collaborative, problem-driven, and action-oriented approaches that balance understanding the environmental, social, and economic dimensions with identifying strategies for achieving desired outcomes for lake ecosystems. As climate variability and anthropogenic pressures on lake ecosystem services intensify, collaborative efforts to protect and restore them will become increasingly critical.
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 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 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 ».