Landscape effects on anuran pond occupancy in an agricultural countryside: barrier-based buffers predict distributions better than circular buffers
Bibliographic record
Abstract
Species movement and occupancy of habitat patches are dependent on landscape permeability. Some land-use types (e.g., roads) may be barriers to animal movement. Analyses of the effect of landscape structure on patch occupancy usually use circular buffers around focal patches. The main assumption of this methodological approach is that species are affected by a particular landscape element equally in every direction from a given patch. This assumption is likely not to hold if animal movement is restricted by barriers because barriers reduce movement patterns and reshape the ideal circular buffer into a noncircular buffer. We developed a method to determine the effect of landscape variables on the distribution of two amphibian species that explicitly takes dispersal barriers into account. We extracted landscape variables within (i) circular buffers (CB) and (ii) barrier-based buffers (BBB). BBB were produced by reducing the boundaries of CB according to major impassable barriers. The BBB approach almost doubled the explained deviance of multiple regression models in comparison with the CB approach. Moreover, CB and BBB models included different predictor variables. We suggest that the BBB approach is more useful than the traditional CB analyses of species–habitat relationships because ecological barriers are explicitly taken into account.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".