Tracking subtle seasonal shifts in pigment composition with hyperspectral reflectance in a temperate evergreen forest
Bibliographic record
Abstract
Pigment dynamics in temperate evergreen forests remain poorly characterized, despite their year-round photosynthetic activity and importance for carbon cycling. Developing rapid, nondestructive methods to estimate pigment composition enables high-throughput assessment of plant acclimation states. In this study, we investigate the seasonality of eight chlorophyll and carotenoid pigments and hyperspectral reflectance data collected at both the needle (400-2400 nm) and canopy (420-850 nm) scales in longleaf pine (Pinus palustris Mill.) at the Ordway Swisher Biological Station in north-central Florida, USA. Needle spectra were obtained at three distinct times throughout the year, while tower-based spectra were collected continuously over a 9-month period. Seasonal trends in photoprotective pigments (e.g. lutein and xanthophylls) and photosynthetic pigments (e.g. chlorophylls) aligned closely with seasonal changes in photosynthetically active radiation and gross primary productivity. To track inter-tree and seasonal variability in pigment pools with hyperspectral reflectance data, we used correlation analyses and ridge regression models. Ridge regression models using the full hyperspectral range outperformed predictions using standard linear regression with specific wavelengths in a normalized difference index fashion. Ridge regression successfully predicted all pigment pools (R2 > 0.5) with comparable accuracy at both the needle and canopy scales. The models performed best for lutein, neoxanthin, antheraxanthin, and chlorophyll a and b-which had greater inter-tree and seasonal variation-and achieved moderate accuracy for violaxanthin, alpha-carotene and beta-carotene. These results provide a foundation for scaling biochemical traits from ground-based sensors to airborne and satellite platforms, particularly in ecosystems with subtle changes in pigment dynamics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".