Cumulative and component impacts of the human footprint on remotely sensed biodiversity indicators using dissimilarity to high integrity reference states
Bibliographic record
Abstract
• Quantifies ecological dissimilarity using remote-sensing derived biodiversity indicators as a proxy for forest integrity. • Demonstrates that anthropogenic pressures significantly influence forest structural integrity, but not functional integrity. • Employs a robust matching technique to ensure comparisons are made against suitable high-integrity forest analogs. • Provides a scalable methodology for identifying high-integrity forest ecosystems for conservation and restoration. Forests with high ecological integrity are fundamental for biodiversity conservation and provide integral ecosystem services. These forests have natural or near-natural ecosystem structure, function, and composition. Anthropogenic pressures such as habitat loss, overexploitation of natural resources, and land use changes are leading to the degradation or loss of high-integrity forests. As a result, assessing forest integrity over large areas is increasingly important for a range of conservation initiatives. In this study, we used remote sensing-derived forest structural and functioning metrics alongside a high-quality reference state to calculate ecological dissimilarity as a proxy for ecological integrity. We examined stand-level integrity and focused on forest structural attributes such as canopy height, cover, complexity, and biomass, as well as the Dynamic Habitat Indices, which summarize annual energy availability relevant for biodiversity. We further refined our reference states by using coarsened exact matching to ensure our comparisons were drawn from suitable protected analogs. We applied these methods to Vancouver Island, Canada, where we assessed the distance, in structural and functional space, to matched high-integrity forests found in the island’s oldest and largest protected area. We also assessed how individual and cumulative anthropogenic pressure affect the ecological integrity of forests on the island. We found that mean forest structural dissimilarity increased from 0.79 to 1.61 under high levels of anthropogenic pressure (ANOVA; p < 0.001), while functional dissimilarity was not impacted by any anthropogenic pressure (ANOVA; p > 0.05). This indicates that anthropogenic pressures were observed to directly influence forest canopy characteristics, and less so energy availability. For individual pressures, we found that built environments, harvesting, and population density influenced structural dissimilarity (ANOVA; p < 0.05), while roads did not influence structural dissimilarity (ANOVA; p > 0.05). These methods for identifying high-integrity forests can be used to identify areas to be prioritized for protection or restoration, which in turn progresses towards the Kunming-Montreal Global Biodiversity Framework’s goal of 30 % of all ecosystems protected, while focusing on high-integrity ecosystems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".