Carrying capacity and cumulative effects management: A case study using bighorn sheep
Bibliographic record
Abstract
Abstract Successful cumulative effects management is fundamental for conservation policy and practice. We investigated the application of a carrying capacity (CC) model as a cumulative effects management tool for bighorn sheep ( Ovis canadensis canadensis ) in British Columbia, Canada, where CC is defined as the natural limit of a sustainable population that is set by the availability of resources in the environment. We estimated winter CC using forage availability across winter ranges, weighted by relative selection by sheep and a safe use factor, and divided by overwinter forage requirements to determine how many sheep the landscape can support. We explored application of our model to decision‐making about new industrial projects or conservation activities in a cumulative effects context. Cumulative effects include both positive and negative contributions to animal populations and we simulated the potential positive outcomes of burning to increase bighorn sheep carrying capacity in our study area. Our results show that carefully planned conservation actions could generate a 5% increase in CC (i.e., from 493 to 519 sheep). Robust tools and scientific techniques that are capable of quantifying multiple impacts and conservation actions and that consider spatial processes over long temporal scales, such as the CC model presented, should be applied to help inform decisions about how to better manage cumulative habitat change and achieve conservation objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".