Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves
Bibliographic record
Abstract
Abstract More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non‐destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least‐squares regression to build models linking pressed‐leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh‐ or ground‐leaf spectra of the same samples. The traits our pressed‐leaf models could estimate best were LMA ( R 2 = 0.932; %RMSE = 6.56), C ( R 2 = 0.855; %RMSE = 9.03), and cellulose ( R 2 = 0.803; %RMSE = 12.2), followed by water‐related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R 2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly ( R 2 < 0.5, %RMSE > 20). For most chemical traits, pressed‐leaf models performed better than fresh‐leaf models, but worse than ground‐leaf models. Pressed‐leaf models were worse than fresh‐leaf models for estimating LMA and LDMC, but better than ground‐leaf models for LMA. Finally, in a subset of samples, we used partial least‐squares discriminant analysis to classify specimens among 10 species with near‐perfect accuracy (>97%) from pressed‐ and ground‐leaf spectra, and slightly lower accuracy (>93%) from fresh‐leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed‐leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far‐reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".