Forest bird diversity increases with fragmentation per se in landscapes following forest gains
Bibliographic record
Abstract
Abstract Habitat restoration is needed to increase total habitat area, and halt and reverse global biodiversity loss. While scientists and conservation practitioners generally agree that more habitat is better for biodiversity, the preferred spatial arrangement of that habitat in a landscape is debated. The weight of evidence from empirical studies indicates that we can expect biodiversity to be weakly but positively related to fragmentation per se (the spatial arrangement of habitat, independent of its amount) across landscapes that have undergone habitat loss. Can we expect the same in a restoration context? Spatial variation in forest gain and loss provides an opportunity for a ‘natural experiment’ to address the question posed above, allowing assessment of the interactive effects of fragmentation per se and the history of habitat change on biodiversity. Here we used a two‐decade time series of forest cover plus data on forest breeding birds from 1660 locations across the conterminous United States to test whether bird diversity (richness, Shannon diversity, Simpson diversity) responses to forest fragmentation per se depend on the history of forest gain/loss. The effects of forest fragmentation per se on forest bird diversity depended on whether forest had been previously gained or lost. There were stronger, more positive effects of fragmentation on bird diversity in landscapes where forest had been gained than in landscapes where forest had been lost. Critically, effects of forest amount on bird diversity were much stronger than fragmentation, regardless of the history of forest area changes. Synthesis and applications . Our results suggest that, at least for forest breeding birds, the benefits of habitat restoration to increase total forest area could be enhanced by distributing a given amount of restored habitat across multiple patches. Our study also highlights the overarching importance of habitat amount for biodiversity conservation. Taken together, these findings imply that, if we want to maximize the benefits of restoration for biodiversity, the focus should be on maximizing total habitat area and increasing the number of habitat patches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".