Metacommunity diversity depends on connectivity and patch arrangement in heterogeneous habitat networks
Bibliographic record
Abstract
Connectivity is critical to the maintenance of biodiversity in fragmented landscapes, but its effects differ depending on the arrangement of linkages within a habitat network. Additionally, heterogeneity in habitat quality within the habitat network can alter patterns of diversity at local and regional scales in the metacommunity. Using a controlled experiment we examined the interactive effects of habitat connectivity, network form (linear vs square), and habitat patch quality on a moss‐inhabiting microarthropod community. We fragmented moss habitat while controlling for habitat loss, and altered habitat patch quality by regulating moisture conditions in landscapes differing in patch arrangement. Habitat patch quality had a significant effect on patterns of species richness, extinction, abundance and biomass. The effects of network form on diversity were strongest in heterogeneous landscapes. Gamma and beta diversity were greatest in continuous and linear landscapes. However, linear habitat networks showed marked patch specific edge effects that were detrimental to diversity under heterogeneous conditions. We provide direct evidence that habitat network structure impacts species community properties through mass effects, that are most evident when heterogeneity in habitat patch quality is present within the network. We conclude that habitat quality at the individual patch level and the distribution of high‐quality habitat within the network are important factors affecting biodiversity in metacommunities.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".