Rove beetle (Staphylinidae) assemblages following the cumulative effect of wildfire and linear footprint in Boreal treed peatlands of northeastern Alberta (Canada)
Bibliographic record
Abstract
Cumulative effects of anthropogenic and natural disturbances have become increasingly relevant in the context of biodiversity conservation. Oil and gas (OG) exploration and extraction activities have created thousands of kilometers of linear footprints in boreal ecosystems of Alberta, Canada. Among these disturbances, seismic lines (narrow corridors cut through the forest) are one of the most common footprints and have become a significant landscape feature influencing the maintenance of forest interior habitats and biodiversity. Wildfire is a common stand-replacing natural disturbance in the boreal forest, and as such, it is hypothesized that its effects can mitigate the linear footprint associated with OG exploration, but only a few studies have examined its effectiveness. We studied the short-term (1 year post-fire) response of rove beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire (Fort McMurray). While rove beetle species richness was higher in seismic lines in both burned and unburned habitats compared to the adjacent peatland, diversity was greater only in seismic lines of burned areas. Abundance was lower in the burned adjacent peatland but similarly higher in the remaining habitats. Assemblage composition on seismic lines was significantly different from that in the adjacent forest and edge habitats within both burned and unburned sites. Moreover, species composition in burned seismic lines was different to either unburned lines or burned forest and edge. Euaesthethus laeviusculus and Gabrius picipennis were indicator species of burned line habitats, are sensitive to post-fire landscape and can occupy wet habitats with moss cover more efficiently than when these habitats are surrounded by unburned forest. Although these results are based on short-term responses, they suggest that wildfire did not reduce the linear footprint, and instead, the cumulative effect of these two disturbances had a more complex influence on rove beetle recovery at the landscape level than for other invertebrates. Therefore, continued monitoring of these sites can become useful to evaluate changes over time and to better understand longer-term biodiversity responses to the cumulative effects of wildfire and linear disturbances in boreal treed peatlands, given the long-lasting effect of such disturbances.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".