Methods and models for identifying thresholds of habitat loss
Bibliographic record
Abstract
There is mounting evidence that many taxa respond in non‐linear ways to perturbation (i.e. deviations from a natural trajectory brought on by an external agent), and many statistical, physical and ecological methods have been developed to detect the critical points or thresholds of perturbation. The majority of these methods define thresholds as the perturbation points causing abrupt ecological response, but in reality most species or ecosystems do not show a break point response but more gradual transitional change to perturbation. We develop a new method which delineates thresholds as a region in which the slope of the relationship between ecological response ( y ) and perturbation ( x ; e.g. habitat loss) is larger than 1: | dy / dx |≥ 1, where both x ‐ and y ‐axes are scaled to (0, 1) range. The lower end of threshold zones so defined is of particular ecological interest because it is the smallest x that may trigger impending catastrophic response to a small change in x . We derived two landscape models (edge length and the number of patches of species distribution) and two biodiversity models (endemics–area relationship and half‐population curve) to test this method. We applied our zonal thresholding method to these four models fit to empirical data of two forest plots to detect thresholds of species distribution to habitat loss. The two landscape metric models predict that no species could tolerate more than 40% of habitat loss and these thresholds can be much lower for relatively rare species with occupancy < 0.4 and for aggregated habitat loss compared to random habitat loss. The half‐population model leads to a similar threshold level of 40% habitat loss. Overall, we suggest the maximum permissible habitat loss threshold to be between 0–40%, depending on the pre‐disturbed occupancy (or abundance) of a species. This habitat loss threshold falls within the otherwise wide range of thresholds calculated from conventional methods. Our study contributes novel methods and models to quantify the effect of habitat loss on species distribution and diversity in landscapes with potential for conservation applications.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".