The role of landscape connectivity in resistance, resilience, and recovery of multi‐trophic microarthropod communities
Bibliographic record
Abstract
There is a need to find generalizable mechanisms supporting ecological resilience, resistance, and recovery. One hypothesized mechanism is landscape connectivity, a habitat configuration that allows movement of biotic and abiotic resources between local patches. Whether connectivity increases all or only one of resistance, resilience, and recovery has not been teased apart, however, and has been difficult to test at large scales and for complex trophic webs. Natural microcosms offer a complex system that can be manipulated to test questions at a landscape-scale relative to the community of study. Here, we test the role of connectivity in altering resistance, resilience, and recovery to a gradient of heating disturbance in moss microcosms. To test across trophic levels, we focused on community composition as our metric of response and applied three connectivity treatments - isolation, connected to an equally disturbed patch, and connected to an undisturbed patch. We found that connectivity between equally disturbed patches boosted resistance of communities to disturbance. Additionally, recovery was linear and rapid in communities connected to undisturbed landscapes, hump shaped when connected to equally disturbed landscapes, and linear but slow in isolated communities. We did not find thresholds on the disturbance gradient at which disturbed communities exhibited zero or increasing dissimilarity to controls through time, so were unable to draw conclusions on the role of connectivity in ecological resilience. Ultimately, isolated communities exhibited increasingly variable composition and slow recovery patterns even in control communities when compared with connected treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".