Cryptic Dispersal Networks Connect Habitat Patches in ways not Captured by Distance
Bibliographic record
Abstract
Abstract Species interact with the physical world in complex ways, and life history strategies might cause species to differ in how they experience connectedness of the same landscape. As a consequence, dispersal limitation might be present but not captured by distance-based measures of connectivity. To test these ideas, we surveyed plant communities that associate with serpentine soils but differ in dispersal mode (gravity, animal, or wind), and used satellite imagery to quantify forms of landscape connectivity associated with each dispersal mode. Our data yielded two key insights: First, dispersal limitation appeared to be absent using a conventional distance-based measure of connectivity, but emerged after considering forms of landscape connectivity relevant to each dispersal mode. Second, the landscape variables that emerged as important to each dispersal mode were generally consistent with our predictions based on putative dispersal vectors, and included interactive effects that allude to the altered efficacy of animal dispersal in invaded landscapes. Our results have broad implications for understanding how ecological communities reorganize as landscapes are fragmented, patches are lost, and the function of dispersal life histories is altered.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".