Optimising fire and predator management for conservation
Bibliographic record
Abstract
Interactions between threatening processes compound and accelerate biodiversity decline. Conservation managers need to understand how co-occurring threats interact and account for such interactions when prioritising when, where, and how to manage landscapes to recover declining species. Using the Upper Warren region in south-western Australia as a case study, we develop a framework for identifying optimal fire age classes while also considering predation by introduced red foxes (Vulpes vulpes) – two co-occurring processes affecting the recovery of a threatened faunal community (woylie Bettongia penicillata, chuditch Dasyurus geoffroii, quenda Isoodon fusciventor, and numbat Myrmecobius fasciatus). We fitted a multi-species relative abundance model to a dataset from 548 camera trap sites and tested for associations between each species’ relative abundance and an interaction between fox baiting intensity and time since fire. We then used linear programming optimization to identify the optimal distribution of time since fire values across the study region that maximizes the abundance of four focal species under alternative fox baiting intensity and fire severity scenarios. Fire and baiting both influenced the relative abundance of the four species in our study, with baiting intensity having a positive association with woylie relative abundance. The optimal distribution of time since fire values across the study region varied with the intensity of fox baiting. The importance of older fire ages increased in some locations when fox baiting intensity was high, but these results were highly uncertain and varied spatially. High fox baiting intensities combined with optimal fire age distributions also led to a higher relative abundance of three focal species overall, namely woylie, quenda, and numbat. Our study demonstrates an end-to-end framework for using field data to derive optimal fire regimes for biodiversity in a way that explicitly acknowledges uncertainty and remains useful for conservation decisions. Approaches such as these are essential for managing ecosystems with compounding threats to biodiversity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".