Unlocking the forest inventory data: relating individual tree performance to unmeasured environmental factors
Bibliographic record
Abstract
Geographically extensive forest inventories, such as the USDA Forest Service's Forest Inventory and Analysis (FIA) program, contain millions of individual tree growth and mortality records that could be used to develop broad-scale models of forest dynamics. A limitation of inventory data, however, is that individual-level measurements of light (L) and other environmental factors are typically absent. Thus, inventory data alone cannot be used to parameterize mechanistic models of forest dynamics in which individual performance depends on light, water, nutrients, etc. To overcome this limitation, we developed methods to estimate species-specific parameters (thetaG) relating sapling growth (G) to L using data sets in which G, but not L, is observed for each sapling. Our approach involves: (1) using calibration data that we collected in both eastern and western North America to quantify the probability that saplings receive different amounts of light, conditional on covariates x that can be obtained from inventory data (e.g., sapling crown class and neighborhood crowding); and (2) combining these probability distributions with observed G and x to estimate thetaG using Bayesian computational methods. Here, we present a test case using a data set in which G, L, and x were observed for saplings of nine species. This test data set allowed us to compare estimates of thetaG obtained from the standard approach (where G and L are observed for each sapling) to our method (where G and x, but not L, are observed). For all species, estimates of thetaG obtained from analyses with and without observed L were similar. This suggests that our approach should be useful for estimating light-dependent growth functions from inventory data that lack direct measurements of L. Our approach could be extended to estimate parameters relating sapling mortality to L from inventory data, as well as to deal with uncertainty in other resources (e.g., water or nutrients) or environmental factors (e.g., temperature).
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".