Landscape geometry determines community response to disturbance
Bibliographic record
Abstract
Ecological communities are impacted by anthropogenic changes in both habitat geometry (i.e. amount, shape, fragmentation and connectivity of habitat) and disturbance regime. Although the effect of each of these drivers on diversity is well‐documented, few studies have considered how habitat geometry and disturbance interact to affect diversity. We used a miniature landscape of moss patches to experimentally manipulate both habitat geometry and disturbance frequency on microarthropod communities. Species richness and abundance in local patches declined linearly with disturbance rate in all experimental landscapes, but the speed of this decline (a measure of ecological resilience) depended on the size and connectivity of the surrounding region. Reductions in region size had little effect on community resilience to disturbance until habitat loss resulted in complete loss of connectivity between patches, suggesting a threshold in community response to habitat loss. Beyond this threshold, repeated disturbance resulted in rapid declines in patch species richness and abundance and substantial changes in community composition. These effects of habitat geometry and disturbance on diversity were scale‐dependent. Gamma (regional‐scale) diversity was unaffected by habitat geometry, suggesting experimental reductions in alpha (local‐scale) diversity were offset by increases in beta diversity. There was no effect of body size, abundance, or trophic position in determining species response to disturbance. Taxonomic grouping had a weak effect, with oribatids less affected by drought. We conclude that, in this system, dispersal from the surrounding metacommunity is vital in allowing recovery of local communities from disturbance. When habitat loss and fragmentation disrupt this process, extinctions result. Studies that examine separately the effects of habitat alterations and disturbance on diversity may therefore underestimate their combined effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".