Influences of Microhabitat Constraints and Rock‐Climbing Disturbance on Cliff‐Face Vegetation Communities
Bibliographic record
Abstract
Many researchers report that rock climbing has significant negative effects on cliff biota. Most work on climbing disturbance, however has not controlled for variation in microsite characteristics when comparing areas with and without climbing presence. Additionally, some researchers do not identify the style or difficulty level of climbing routes sampled or select climbing routes that do not represent current trends in the sport. We solved these problems by sampling climbing areas used by advanced "sport" climbers and quantifying differences in microtopography between climbed and control cliffs. We determined whether differences in vegetation existed between pristine and sport-climbed cliff faces when microsite factors were not controlled. We then determined the relative influence of the presence of climbing, cliff-face microtopography, local physical factors, and regional geography on the richness, abundance, and community composition of cliff-face vascular plants, bryophytes, and lichens. When we did not control for microsite differences among cliffs, our results were consistent with the majority of prior work on impacts of climbing (i.e., sport-climbed cliff faces supported a lower mean richness of vascular plants and bryophytes and significantly different frequencies of individual species when compared with pristine cliff faces). When we investigated the relative influences of microtopography and climbing disturbance, however the differences in vegetation were not related to climbing disturbance but rather to the selection by sport climbers of cliff faces with microsite characteristics that support less vegetation. Climbed sites had not diverged toward a separate vegetation community; instead, they supported a subset of the species found on pristine cliff faces. Prior management recommendations to restrict development of new climbing routes should be reevaluated based on our results.
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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 | 0.000 |
| 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".