Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates
Bibliographic record
Abstract
Summary In recent years, the Normalized Difference Vegetation Index (NDVI) has been used to assess the relationships between habitat quality and animal life‐history traits. Since numerous ecological studies now use NDVI rather than perform direct vegetation assessments, field validations are essential to provide confidence in the biological significance of NDVI estimates. While some studies have compared NDVI with plant biomass, very few examined the relationship between NDVI and changes in vegetation quality. Using data from two long‐term studies of alpine ungulates, we assessed the relationship between two NDVI indices and the date of peak in faecal crude protein (FCP), which represents temporal variability in the availability of high‐quality vegetation. We also evaluated if NDVI data could predict annual variation in the timing of spring green‐up. In both populations, integrated NDVI in June was negatively correlated with the date of the peak in FCP, indicating that high integrated NDVI values corresponded to early springs in alpine habitats. Maximum NDVI increase during spring green‐up was positively correlated with the timing of peak FCP, illustrating that rapid increases in NDVI represented delayed springs. Predicted values of date of peak FCP estimated each year from NDVI data satisfactorily fitted observed values, and prediction intervals included all observed values. These results suggest that NDVI can reliably predict variation over years in the timing of spring. Synthesis and applications. Our long‐term studies demonstrate that a multi‐year time series of Normalized Difference Vegetation Index (NDVI) can reliably measure yearly changes in the timing of the availability of high‐quality vegetation for temperate herbivores. This finding therefore supports the use of NDVI as a proxy for vegetation attributes in population ecology and wildlife management studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".