Peering Into the Past Century of Mountain Diversity Change by Uniting Two Modes of Remote Sensing
Bibliographic record
Abstract
Mountain ecosystems are particularly susceptible to climate change and biodiversity loss as altitudinal diversity generates rare habitats and adapted specialist species, both sensitive to change. Mountain songbird diversity can be especially telling of land cover changes given breeding songbirds' strong patterns of habitat preference. However, most records of bird populations go back only a few decades, affecting baselines. Our aim was to examine changes in mountain diversity using a novel approach to analyze historical data that reaches nearly a century back in time. We repeated 46 historical survey photographs and used image analysis tools to quantify landscape change. In parallel, we generated species distribution models for 15 breeding songbird species in the study area. Based on the paired photographs, we modeled changes in bird occurrence. We then analyzed changes in Shannon diversity in terms of both land cover and bird occurrence. Forest cover increased over the past century at the expense of rarer alpine and riparian land covers, leading to decreased landscape diversity. This landscape homogenization resulted in declines in 5 species of songbirds (including 4 that breed in rare habitats), while 9 abundant forest-breeding species were positively impacted, without substantial changes to the diversity of species in the community. We highlight shifts in species occurrence over a time interval not often captured by other methods. Historical photographs linked with species distribution modeling have potential for inferring global change for conservation and landscape management in mountain environments-some of the most challenging places to monitor.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".