A ghost fence-gap: surprising wildlife usage of an obsolete fence crossing
Bibliographic record
Abstract
Wildlife fencing has become more prevalent throughout Africa, although it has come with a price of increased habitat fragmentation and loss of habitat connectivity. In an effort to increase connectivity, managers of fenced conservancies can place strategic gaps along the fences to allow wildlife access to outside habitat, permitting exploration, dispersal and seasonal migration. Wildlife can become accustomed to certain movement pathways and can show fidelity to these routes over many years, even at the path level. Our study site has three dedicated wildlife crossings (fence-gaps) in its 142 km perimeter fence, and we continuously monitor these fence-gaps with camera-traps. We monitored one fence-gap before and after a 1.49 km fence section was completely removed and 6.8 km was reconfigured to leave only a two-strand electric fence meant to exclude elephant and giraffe, all other species being able to cross under the exclusionary fence. The removal and reconfiguration of the fence effectively rendered this fence-gap (which was left in place structurally) as a "ghost" fence-gap, as wildlife now had many options along the 8.29 km shared border to cross into the neighboring habitat. Although we documented some decline in the number of crossing events at the ghost-gap, surprisingly, 19 months after the total removal of the fence, we continued to document the usage of this crossing location by wildlife including by species that had not been previously detected at this location. We discuss potential drivers of this persistent and counterintuitive behavior as well as management implications.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".