Response Time of Wetland Biodiversity to Road Construction on Adjacent Lands
Bibliographic record
Abstract
Abstract: Road construction may result in significant loss of biodiversity at both local and regional scales due to restricted movement between populations, increased mortality, habitat fragmentation and edge effects, invasion by exotic species, or increased human access to wildlife habitats, all of which are expected to increase local extinction rates or decrease local recolonization rates. Species loss is unlikely to occur immediately, however. Rather, populations of susceptible species are expected to decline gradually after road construction, with local extinction occurring sometime later. We document lags in wetland biodiversity loss in response to road construction by fitting regression models that express species richness of different taxa ( birds, mammals, plants, and herptiles) as a function of both current and historical road densities on adjacent lands. The proportion of variation in herptile and bird richness explained by road densities increased significantly when past densities were substituted for more current densities in multiple regression models. Moreover, for vascular plants, birds, and herptiles, there were significant negative effects of historical road densities when the most current densities were controlled statistically. Our results provide evidence that the full effects of road construction on wetland biodiversity may be undetectable in some taxa for decades. Such lags in response to changes in anthropogenic stress have important implications for land‐use planning and environmental impact assessment.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".