Experimental designs for studying small-mammal responses to fire in North American conifer forests
Bibliographic record
Abstract
Climate change is altering fire regimes. As fire regimes change, it is important to understand how mammals respond to these altered post-fire landscapes. Because fires vary in size, severity and landscape context, it is important to know the experimental designs and response variables used to address post-fire responses of mammals. We analysed 48 papers published from 1988 to 2015 that examined responses of small mammals to natural or prescribed fire in North American conifer forests. These papers used different experimental contrasts (e.g. burned vs unburned sites, time series, within-fire heterogeneity). Most studies (89.6%) presented species richness or index-derived abundances of common species as their response variable(s). Many studies did not fully describe the fires being examined; these omissions make it more difficult to interpret and compare results among studies. The limited scope of inference presented by the papers in this review leads us to recommend a minimal set of information that should be presented about each fire studied. We conclude by outlining how different experimental designs and response variables can be used for effective inference. We highlight major pathways forward for examining responses of small forest mammals to the important changes in fire regime that are occurring.
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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 | 0.086 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".