Loss of only the smallest patches will reduce species diversity in most discrete habitat networks
Bibliographic record
Abstract
Abstract Under many global‐change scenarios, small habitat patches are the most vulnerable to destruction. For example, smaller ponds are at greater risk in a drying climate and their loss would remove any obligate aquatic individuals present. We asked what proportional loss of species diversity from metacommunities comprised of discrete habitat patches should be expected from attrition (complete loss) of only the smallest patches under such a premise. We analyzed 175 published datasets for different taxonomic groups (vertebrates, invertebrates, and plants) and habitat types (islands, habitat islands, and fragments). We simulated the destruction of only the smallest patches to an approximate 20% of total area (range: 15.2%–24.2%) and analyzed species loss. Mean [± 95% CI] species loss was 12.7% [10.8, 14.6], although 18.3% of datasets lost no species. Four broad patterns of species loss were evident, reflecting underlying differences in minimum area requirements and the degree of species turnover among patches. Regression modeling showed species loss increased with greater species turnover among patches (βSIM) and decreased with greater area scaling of diversity (i.e., larger power‐law island species–area relationship exponents). Losses also increased with greater numbers of single‐patch endemics and with increasing proportions of patches destroyed. After accounting for these predictors, neither taxonomic group nor habitat type increased explained variation in species loss. Attrition of the smallest patches removed species in >80% of metacommunities, despite all larger patches and >75% of total area remaining intact. At both 10% and 20% area reduction, median species loss across all datasets was around 50% higher than predicted from methods based on the species–area relationship. We conclude that any mechanism of global change that selectively destroys small habitat patches will lead to imminent extinctions in most discrete 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".