Drivers of Change in Haida Gwaii Kelp Forests: Combining Satellite Imagery with Historical Data to Understand Spatial and Temporal Variability
Notice bibliographique
Résumé
Globally, kelp forests provide the foundation of temperate coastal ecosystems through the creation of three-dimensional habitat which supports many significant economic, cultural, and ecological species. With the increasing threat of local and global anthropogenic stressors including climate change, the long-term, large-scale monitoring of kelp forests is crucial in understanding the response of these foundation ecosystems to spatio-temporal drivers in a time of rapid global change. On the coast of British Columbia, Canada, kelp forests of Macrocystis pyrifera and Nereocystis luetkeana show highly variable patterns of change, however, lack sufficient time series data to truly understand trends, threats, and drivers. In particular, Haida Gwaii, an archipelago off the main coast of British Columbia, supports some of the most expansive kelp forests in the province. However, local Haida people have observed drastic declines at traditional harvesting sites leading to the prioritization of the management, monitoring and protection of these crucial ecosystems throughout their territory. Remote sensing technology now provides an effective way to track changes and trends in remote kelp forest ecosystems at large scales like that of the Haida Gwaii coastline. Specifically, Earth Observation satellite imagery of a variety of spatial resolutions exists back to the 1970s which can be leveraged for mapping kelp forest canopies through time. This research aims to quantify the distribution, variability, and drivers of change of Haida Gwaii kelp forests with the use of satellite imagery and historical data. Specifically, to address this goal, (i) we develop a methodological framework that enables the creation of a long-term dataset of kelp canopy area using archived multispectral satellite imagery from multiple satellite sensors that vary in their spatial resolution (0.5 m – 60 m) and temporal coverage (1973-2021). To do this, we combine a workflow of standardized remote sensing practices and an adaptable image-to-image object-based classification approach to create the multi-satellite kelp mapping (MSKM) framework including an analysis of the impact spatial resolution has on the detectability of kelp forests and highlight ocean floor slope as a metric to understand uncertainties associated with using products from a range of spatial resolutions. In particular, we find that ocean floor slopes higher than 11.4 % led to high uncertainties when using medium-resolution imagery and as such, these areas were removed from further analyses. Next, (ii) we define changes in Haida Gwaii kelp forest canopy in association with drivers of change over the last 100 years using historical data (1867-1945) and medium- to high-resolution archived satellite imagery (1973-2021) at regional to local scales of analysis. Overall, kelp forest canopy area varied with low- and high-frequency climate indices where lower kelp forest canopy area occurred during warmer conditions. Additionally, patterns of kelp forests change varied across subregions where areas in the North showed considerable losses associated with a strong local gradient of sea surface temperature coupled with the cool to warm regime shift that occurred within the Pacific Decadal Oscillation in the late 1970s. In comparison, kelp forests in the cooler areas in the South showed long term resilience persisting for over a century throughout multiple heatwaves and regime shifts.
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 | 0,001 | 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».