PREDICTING TREE DIVERSITY ACROSS THE UNITED STATES AS A FUNCTION OF MODELED GROSS PRIMARY PRODUCTION
Bibliographic record
Abstract
At the regional and continental scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions (2000-2004) might best be correlated with tree richness if expressed through satellite-derived measures of gross primary production (GPP), rather than the more commonly used, but less consistently derived, net primary production. To evaluate current patterns in tree diversity across the contiguous United States we acquired information on tree composition from the USDA Forest Service's Forest Inventory and Analysis program that represented more than 17,4000 survey plots. We selected 2693 cells of 1000 km2 within which a sufficient number of plots were available to estimate tree richness per hectare. Our estimates of forest productivity varied from simple vegetation indices indicative of the fraction of light intercepted by canopies at 16-d intervals, a product from the MODIS (Moderate Resolution Imaging Spectro-radiometer), to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Systeme Pour l'Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the contiguous United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. When the analyses were concentrated within nine broadly defined ecoregions, predictive relations largely disappeared. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".