The Test of Time: Using Historical Methods to Assess Models of Ecological Change on California’s Hardwood Rangelands
Bibliographic record
Abstract
Geographers and environmental scientists use conceptual models to understand ecological processes and support management decisions. Most of these models are based on short-term experiments and field observations, which might not account for longer term forces that shape ecosystems over decades to centuries. How can scholars use historical sources and methods to improve conceptual models of ecological change? In this article, we present the results of a study that employed methods from environmental history and historical geography to assess three conceptual models that researchers have used to study ecological changes on California’s hardwood rangelands: the succession and climax, state and transition, and cyclical replacement models. The succession and climax model fared poorly at all spatial scales. The historical record contained substantial evidence to support the predictions of the state and transition model at the small spatial scale of the plot or field (0.1–100 ha) and the very large spatial scale of the hardwood rangeland bioregion (4 million ha). The cyclical replacement model performed well at the intermediate scale of the landscape or typical cattle ranch (100–10,000 ha). Historical data and methods hold considerable untapped potential for assessing, building on, and improving conceptual models of ecological change in geography and the environmental sciences.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".