Phenospectral similarity as an index of ecological integrity
Bibliographic record
Abstract
In collaboration with the International Union for the Conservation of Nature (IUCN) Taskforce on Biodiversity and Protected Areas, countries worldwide are working to develop a new systematic approach to inform the Key Biodiversity Areas (KBAs) initiative. The goal is to map KBAs from the national to global scales with a baseline international standard in support of biodiversity conservation efforts. According to the IUCN standard, one of the five criteria used to identify potential KBAs, is the Ecological Integrity (EI) of the ecosystem. Sites identified with respect to EI must have an intact ecological community and be characterized by minimal anthropogenic disturbance. In this study, a new EI metric, phenospectral similarity (PSpecM), has been developed and implemented in Google Earth Engine to identify potential forest stands of high EI from a large set of candidate stands. The implementation of PSpecM requires a network of known reference sites of high EI and target ecological units of the same land cover type for comparison to help identify potential sites of high EI. Here, we tested PSpecM on a ∼12,000 km 2 study area in the Laurentian region, Quebec, Canada, using Sentinel-2 and PlanetScope (Dove) satellite imagery. Considering the phenological effect on reflectance, we found a 2,700 km 2 spatial extent, equivalent to approximately 22% of the study area, commonly delineated as potential areas of high EI by both PlanetScope (Dove) and Sentinel-2. Without consideration of phenology, the total area delineated as potential areas of high EI increased to 5,505 km 2 , equivalent to around 45% of the study area. Our results show that PSpecM can be computed for rapid assessments of forest stands to identify potential areas of high EI on a large geographic scale and serve as an additional conservation tool that can be applied to the ongoing global and national identification of KBAs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".