Cryptic dispersal networks shape biodiversity in an invaded landscape
Bibliographic record
Abstract
Species interact with the physical world in complex ways, and life-history strategies could cause species to differ in how they experience the 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 live on discrete patches of serpentine habitat embedded within an invaded nonserpentine habitat matrix. Species in these communities differ in dispersal mode (gravity, animal, or wind); thus we used satellite imagery to quantify landscape features that might differentially influence connectivity for some dispersal- mode groups over others (surface streams, animal paths). 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 most important to each dispersal mode were generally consistent with our predictions based on species' putative dispersal vectors, but also included unexpected interactive effects. For example, the richness of animal-dispersed species was positively associated with animal connectivity when patches were close in space, but when patches were isolated, animals had a strong negative effect. This finding alludes to the reduced ability of animals to disperse seeds between suitable patches in invaded landscapes because of increased inter-patch distances. Real landscapes include complex spatial flows of energy and matter, which, as our work demonstrates, sets up ecological opportunity for organisms to differ in how they disperse in a common landscape.
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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 | 0.002 |
| 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) | 0.001 | 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".