Climate- and soil-based models of site productivity in eastern US tree species
Bibliographic record
Abstract
As concerns rise over potential effects of greenhouse gas related climate change on terrestrial ecosystems, forest managers require growth and yield modeling capabilities responsive to changing climate conditions. Our goal was to develop prediction models of site index for eastern US forest tree species with climate and soil properties as predictors for use in predicting potential responses of forest productivity to climate change. Species-specific site index data from the USDA Forest Service Forest Inventory and Analysis (FIA) program were linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic (SSURGO) database. Random forest regression tree based ensemble prediction models of site index were constructed based on 37 climate-related and 15 soil attributes. In addition to a species-specific site index, aggregate models were developed for species grouped into two broad categories: conifer (softwood) and hardwood (broadleaved) species groups. Species-specific models based on climate and soil predictors explained the most variation in site index of any models tested (R2 = 62.5%, RMSE = 3.2 m). Comparable results were found when grouping species into conifer and hardwood groups (R2 = 63.9%, RMSE = 4.6 m for conifers; R2 = 35.9%, RMSE = 4.2 m for hardwoods). Model predictions based on multiple global circulation models (GCMs) and Intergovernmental Panel on Climate Change (IPCC) development scenarios were tested for statistical significance using bootstrap resampling methods. Results showed significant increases over the 21st century in mean site index for conifers between +0.5 and +2.4 m. Over the same time period, mean hardwood site index showed decreases of as much as −1.7 m for the scenarios tested. The results demonstrate the utility of using climate and soils data in predicting site index across a large geographic region, and the potential of climate change to alter forest productivity in the eastern US. Additional investigation is needed to interpret spatial patterns and ecological relationships related to predictions from this type of model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".