Contribution of disturbance to distribution and abundance in a fire‐adapted system
Bibliographic record
Abstract
Species distribution modeling (SDM) is an essential tool in understanding species ranges, but models haven't incorporated disturbance‐related variables. This is true even for regions where long histories of disturbance have resulted in disturbance‐adapted species. Therefore, the degree to which including disturbance‐related variables in SDMs might improve their performance is unclear. We used hierarchical partitioning to determine how fire patterns contribute to variation in species abundance and presence, examining both the total variation disturbance‐related variables explained, and how much of this variation is independent of soil and climate variables. For 27 Proteaceae species in the fire‐adapted Cape Floristic Region of South Africa , we found that fire variability, frequency, and area burned tended to have explanatory power similar in size to that of soil and climate variables. Importantly, for SDMs of abundance, fire‐related variables explained additional variation not captured by climatic variables, resulting in markedly increased model performance. In systems with high disturbance rates, species are less likely to be in equilibrium with their environment, and SDMs including variables describing disturbance regimes may be better able to capture the probability of a species being present at a site. Finally, the differential effect of fire on species abundance and presence suggests functional differences between these responses, which could hamper attempts to make predictions about species abundances using models of presence.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".