Influence of disturbance on the connectivity of riparian forest habitat for terrestrial vertebrates
Bibliographic record
Abstract
Natural and anthropogenic disturbance can influence the connectivity of riparian forest ecosystems affecting the movement of terrestrial vertebrates. We examined changes in connectivity of riparian forest habitat for terrestrial vertebrates following natural and anthropogenic disturbance between 2000 and 2021. We quantified changes in patch aggregation and used a circuit-theory model (Omniscape) to quantify changes in connectivity of riparian forest habitat within three hypothetical search distances representing the movement capabilities of terrestrial vertebrates (0–500, 0–1000, 0–2000 m). We replicated the analysis for six (> 10,000 km 2 ) landscapes (i.e., provincial ecosections) in British Columbia, Canada. We used random forest regression to quantify hypothesized relationships between change in habitat connectivity and explanatory variables known to influence disturbance: topography, types of aquatic features (streams, lakes and wetlands), and management regime (industrial forest or protected area). Loss of forest across landscapes ranged from 3.1% to 12.3%. Changes in forest aggregation were consistent with clustered patterns of disturbances such as wildfire and large-scale outbreaks of bark beetles. Topography (elevation, slope, aspect) influenced changes in connectivity of riparian forest habitat. Loss of connectivity was greatest around small streams (< 5 m wide) within the finest search distance (500 m). The maintenance of forest immediately adjacent to water features, and in particular small streams, is important for ensuring the connectivity of riparian forest habitat for terrestrial vertebrates.
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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".