Paths to extinction: mode of habitat loss structures extinction thresholds and debts
Bibliographic record
Abstract
Species are faced with global changes that can lead to decline and extinction through one of two routes: habitat destruction, such as occurs locally with land-use change, and habitat degradation, which often occurs through larger-scale changes such as nitrogen deposition or climate change. We develop theory showing that, even when these two forms of global change have identical impact on a species’ total amount of habitat, they have qualitatively different consequences for species dynamics and extinction. Using metapopulation theory and simulations, we find distinct impacts of these global changes characterized through several responses: the rate of species decline with habitat reduction, extinction thresholds, and the duration of extinction debts. Habitat degradation causes a faster decline in species when habitat reduction is low, making it particularly detrimental for rare species. Habitat destruction has smaller impacts for low habitat reduction but shows clear thresholds beyond which it surpasses degradation’s negative impact; the location and steepness of the threshold depends on species dispersal, with poor dispersers having steeper thresholds. Symptomatic sampling to predict population trends is predicted to fail for both types of global change, due to thresholds (habitat destruction) and lagged dynamics that lead to extinction debts (habitat degradation). Our research clarifies why impacts of one type of global change may poorly predict impacts of the other and suggests general rules for predicting the long-term impacts of global changes based on species traits.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".