The effects of fragmentation per se on patch occupancy are stronger and more positive in a landscape with a higher quality and more homogeneous matrix
Bibliographic record
Abstract
Habitat fragmentation per se – independent of habitat amount – often increases patch occupancy, possibly because patches are closer together in landscapes with higher fragmentation per se, which should increase dispersal success. Here, we ask whether this effect is influenced by the quality and/or heterogeneity of the landscape matrix, i.e. the non‐habitat portion of the landscape. Specifically, we expect the positive effect of fragmentation per seshould be accentuated when matrix quality is high, reducing dispersal mortality. In contrast, when matrix quality is low, high dispersal mortality should lead to fewer colonisations, and accumulation of extinctions across the smaller patches in a more‐fragmented landscape could lead to negative effects of fragmentation per se. Additionally, matrix heterogeneity could obscure fragmentation effects, as the link between habitat spatial distribution and between‐patch dispersal becomes less predictable. We test these ideas using Glanville fritillary butterfly Melitaea cinxia occupancy data for 4291 habitat patches in the Åland Islands, Finland. Habitat patches for the study species are discrete and well‐defined areas where at least one of the two host species occurs. Adult individuals disperse from habitat patches, spending time in the landscape matrix while searching for new habitat patches. Our predictions were mostly supported. Fragmentation effects were more strongly positive when matrix quality was high; however, we did not see the predicted negative effect of fragmentation per se in landscapes with low matrix quality. As predicted, fragmentation effects on patch occupancy were weaker in landscapes with a more heterogeneous matrix. Our findings may explain why fragmentation effects are often weak. They also suggest that the moderating effects of matrix quality and heterogeneity should be explicitly considered when interpreting effects of habitat fragmentation per se on species distributions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".