Dissecting NDVI–species richness relationships in Hawaiian dry forests
Bibliographic record
Abstract
Abstract Aim A growing body of research has used the normalized difference vegetation index (NDVI) as a proxy for productivity to predict species richness. Yet the mechanisms that produce the relationship between NDVI and species richness remain unclear because of correlated biotic and abiotic factors that influence NDVI. In this study we investigated different biotic and abiotic effects that potentially drive plant species richness–productivity relationships. Location Hawaiian Islands, USA. Methods We quantified woody plant species richness, structure (density, basal area and canopy height), and species composition along a precipitation gradient of 14 Hawaiian dry forest plots. We then used structural equation models combined with 10 years of satellite data to disentangle the effects of precipitation, structure and NDVI‐estimated productivity on species richness. Results Underlying the simple correlation between NDVI and species richness was the indirect effect of precipitation and direct effect of forest structure. The best‐fit model showed there was no direct effect of NDVI on species richness. Main conclusions Our results demonstrate that complex relationships drive simple correlations between species richness and productivity. Considering the mechanisms and underlying factors driving NDVI–species richness relationships could improve predictions of species diversity as satellite measures of productivity have an increasingly important role in habitat mapping, species distribution modelling and predictions for global change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".