EXTINCTION DEBT OF FOREST PLANTS PERSISTS FOR MORE THAN A CENTURY FOLLOWING HABITAT FRAGMENTATION
Bibliographic record
Abstract
Following habitat fragmentation individual habitat patches may lose species over time as they pay off their "extinction debt." Species with relatively low rates of population extinction and colonization ("slow" species) may maintain extinction debts for particularly prolonged periods, but few data are available to test this prediction. We analyzed two unusually detailed data sets on forest plant distributions and land-use history from Lincolnshire, United Kingdom, and Vlaams-Brabant, Belgium, to test for an extinction debt in relation to species-specific extinction and colonization rates. Logistic regression models predicting the presence-absence of 36 plant species were first parameterized using data from Lincolnshire, where forest cover has been relatively low (approximately 5-8%) for the past 1000 years. Consistent with extinction debt theory, for relatively slow species (but not fast species) these models systematically underpredicted levels of patch occupancy in Vlaams-Brabant, where forest cover was reduced from approximately 25% to <10% between 1775 and 1900 (it is presently 6.5%). As a consequence, the ability of the Lincolnshire models to predict patch occupancy in Vlaams-Brabant was worse for slow than for fast species. Thus, more than a century after forest fragmentation reached its current level an extinction debt persists for species with low rates of population turnover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".