Simulating the dynamics of linear forests in Great Plains agroecosystems under changing climates
Bibliographic record
Abstract
Most forest growth models are not suitable for the highly fragmented, linear (or linearly shaped) forests in the Great Plains agroecosystems (e.g., windbreaks, riparian forest buffers), where such forests are a minor but ecologically important component of the land mosaics. This study used SEEDSCAPE, a recently modified gap model designed for cultivated land mosaics in the Great Plains, to simulate the effects of climate change on the dynamics of such linear forests. We simulated the dynamics of windbreaks with different initial planting species richness and widths (light changes as the selected resulting factor) using current climate data and nested regional circulation models (RegCMs). Results indicated that (i) it took 7080 simulation years for the linear forests to reach a steady state under both normal (present-day) and warming climates; (ii) warming climates would reduce total aboveground tree biomass and the spatial variation in biomass, but increase dominance in the linear forests, especially in the upland forests; (iii) linear forests with higher planting species richness and smaller width produced higher aboveground tree biomass per unit area; and (iv) the same species performed very differently with different climate scenarios, initial planting diversity, and forest widths. Although the model still needs further improvements (e.g., the effects of understory species should be included), the model can serve as a useful tool in modeling the succession of linear forests in human-dominated land mosaics under changing climates and may also have significant practical implications in other systems.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".