Identifying restoration priorities for habitat defragmentation: a case study in Alberta’s oil sands
Bibliographic record
Abstract
Context: Anthropogenic habitat loss and fragmentation are major threats to ecosystems and a focus in conservation. However, conservation is often limited to considerations of site-level processes neglecting the effect of the surrounding landscape that might limit the effectiveness of restoration efforts. Objectives: Using seismic lines in Alberta's oil sands as a case study, we demonstrate an approach that integrates spatial configuration of anthropogenic footprints to prioritize habitat defragmentation. Methods: We quantified the effects of seismic line density and configuration on functional footprints for caribou, butterfly diversity, and vascular plant diversity, to predict whether edge effects are more pronounced under different line densities and configurations. We then estimated the portion of the original functional footprint that would persist in the landscape due to the co-occurrence with other anthropogenic activities. Results: We found that functional footprint for caribou grows rapidly as habitat loss increases. In contrast, butterflies and plants exhibited a more gradual and linear growth in functional footprints at more local scales. This effect varies based on configuration of lines, either suppressing or facilitating the effect of habitat loss on functional habitats. Finally, restoration of all seismic lines without considering other footprints would reduce the original functional footprint by only 57% for caribou. Conclusions: Restoration efforts for habitat defragmentation rarely consider the spatial configuration of linear features, particularly as it relates to the co-occurrence of other footprints that are not being restored. Our functional approach to defragmentation of habitat encompasses different spatial concepts related to anthropogenic forest fragmentation and allows up to a 25-fold gain in cost-effectiveness for seismic lines restoration. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-024-01972-3.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".