Exploring the role of fire, succession, climate, and weather on landscape dynamics using comparative modeling
Why this work is in the frame
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Bibliographic record
Abstract
An assessment of the relative importance of vegetation change and disturbance as agents of landscape change under current and future climates would (1) provide insight into the controls of landscape dynamics, (2) help inform the design and development of coarse scale spatially explicit ecosystem models such as Dynamic Global Vegetation Models (DGVMs), and (3) guide future land management and planning. However, quantification of landscape change from vegetation development and disturbance effects is difficult because of the large space and long time scales involved. Comparative simulation modeling experiments, using a suite of models to simulate a set of scenarios, can provide a platform for investigating landscape change over more ecologically appropriate time and space scales that control vegetation and disturbance. We implemented a multifactorial simulation experiment using five landscape fire succession models to explore the role of fire and vegetation development under various climates on a neutral landscape. The simulation experiment had four factors with two or three treatments each: (1) fire (fire and no fire), (2) succession (dynamic and static succession), (3) climate (historical, warm-wet, warm-dry), and (4) weather (constant, variable). We found that, under historical climates, succession changed more area annually than fire by factors of 1.2 to 34, but one model simulated more landscape change from fire (factor of 0.1). However, we also found that fire becomes more important in warmer future climates with factors decreasing to below zero for most models. We also found that there were few differences in simulation results between weather scenarios with low or high variability. Results from this study show that there will be a shift from vegetation processes that control today's landscape dynamics to fire processes under future warmer and drier climates, and this shift means that detailed representations of both succession and fire should be incorporated into models to realistically simulate interactions between disturbance and vegetation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 it