Loss of habitat and connectivity erodes species diversity, ecosystem functioning, and stability in metacommunity networks
Bibliographic record
Abstract
Habitat loss fragments metacommunities, altering the movement of species between previously connected habitat patches. The consequences of habitat loss for ecosystem functioning depend, in part, on how these changes in connectivity alter the spatial insurance effects of biodiversity. Spatial insurance is the maintenance of biodiversity and stable ecosystem functioning in changing environments that occurs when species are able to move between local habitat patches in order to track conditions to which they are adapted. Spatial insurance requires a combination of species sorting dynamics, which allow species to disperse to habitats where they are productive, and mass effect dynamics, where dispersal allows species to persist in marginal habitats where environmental conditions do not support growth. Here we use a spatially explicit metacommunity model to show that the relative contribution of species sorting and mass effects to spatial insurance changes with the rate of dispersal. We then simulate different sequences of habitat loss by removing habitat patches based on their betweenness centrality (the degree to which a patch serves as a connection between other patches in the metacommunity). We demonstrate that the sequence of habitat loss has a large, non‐linear impact on diversity, ecosystem functioning and stability. Spatial insurance is lost because habitat fragmentation impedes species sorting, while promoting mass effects and dispersal limitation. We find that species sorting dynamics, and thus spatial insurance, are most robust to the removal of habitat patches with low betweenness centrality. These findings advance our understanding of how habitat connectivity facilitates the maintenance of biodiversity and ecosystem functioning, and may prove useful for the design of habitat networks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".