Drivers of Change in Haida Gwaii Kelp Forests: Combining Satellite Imagery with Historical Data to Understand Spatial and Temporal Variability
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".