Time-lag effects of habitat loss, but not fragmentation, on deadwood-dwelling lichens
Bibliographic record
Abstract
Abstract Context Landscape habitat amount is known to increase biodiversity, while the effects of habitat fragmentation are still debated. It has been suggested that negative fragmentation effects may occur with a time lag, which could explain inconsistent results. However, there is so far no empirical support for this idea. Objectives We evaluated whether habitat amount and fragmentation at the landscape scale affect the species density of deadwood-dwelling lichens, and whether these effects occur with a time lag. Methods We surveyed deadwood-dwelling lichens in woodland key habitats in two regions in northern Sweden, and modelled their species density as a function of past (1960s) and present (2010s) habitat amount (old forest area) and fragmentation (edge density) in the surrounding landscapes. Results Present habitat amount generally had weak positive effects on lichen species density. Positive effects of the past habitat amount were stronger, indicating a time lag in habitat amount effects. Habitat fragmentation effects were generally weak and similar whether fragmentation was measured in the past or the present landscapes, indicating no time lag in fragmentation effects. Conclusions We found a time lag effect of habitat amount, but not fragmentation. This result is not consistent with suggestions that time lags explain the mixed observations of fragmentation effects. Time-lag effects of habitat amount suggest that the studied lichen communities face an extinction debt. Conservation should therefore prioritize increasing the amount of old forest, for example by creating forest reserves, to maintain the current lichen diversity. More generally, our results imply that studies examining only the present habitat amount risk under-estimating its importance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".