Was the Grass Always Greener? Mapping the Historical Extent of Grassland Ecosystems in the San Juan Islands
Bibliographic record
Abstract
The San Juan Islands, an archipelago in the Salish Sea between Vancouver Island and the Washington coast, are one of the few places native temperate grasslands are found in western Washington State. These ecosystems are important sources of biodiversity and support many rare and endemic species. In addition to their ecological importance, native temperate grasslands have profound cultural significance to the Coast Salish peoples who historically stewarded these landscapes using traditional land management practices-particularly fire-for the production of bulb crops such as common camas (Camassia quamash). Unfortunately, these ecologically and culturally valuable ecosystems have become rare, greatly impacted by the combined pressures of changes in land use, invasive species, and the exclusion of fire from the landscape. While land managers and conservation experts are aware of the current threats facing native temperate grasslands, the lack of historical context and baseline knowledge has made it impossible to fully understand the long-term trends in extent and distribution of this ecosystem. To address this knowledge gap, I used historical landcover data and multispectral imagery to create a high-resolution, spatially explicit dataset in ArcGIS Pro, representing grassland landcover on the San Juan Islands at multiple time periods since the early years of European and American colonization. Spatial analysis of the dataset was conducted in ArcGIS Pro to quantify grassland loss between time periods, and identify landcover types replacing grasslands. The results reveal significant decreases in grassland extent between time periods, resulting in a 78% decrease in the extent of non-agricultural grasslands since 1890. These changes are primarily a result of conversion to agriculture, and encroachment or succession to forest. The spatial data and analyses created in this study help to develop the historical baseline of native temperate v grasslands on the San Juan Islands, adding to our understanding of the lingering legacy that changes in land use have had on this ecosystem, with the potential to aid in the development of effective conservation and restoration practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".