REVIEW: Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters
Bibliographic record
Abstract
Summary Research addressing the effects of habitat fragmentation on species, assemblages or ecosystems has been fraught with difficulties, from its conceptual foundation to statistical analyses and interpretation. Yet, it is critical to address such challenges as ecosystems are rapidly being altered across the world. Many studies have concluded that effects of habitat loss exceed those of fragmentation per se, that is, the degree to which a given amount of habitat is broken apart. There is also evidence from different biomes and taxa that habitat configuration, that is, the spatial arrangement of habitat at a given time, may influence several landscape processes such as functional connectivity, edge and matrix effects, and thus population viability. Instead of focusing attention on the relative influence of either habitat loss or fragmentation, we must identify portions of the gradient in habitat amount where configuration effects are most likely to be observed. Here, we suggest that all species are, to a certain degree, sensitive to landscape change and that, assuming a homogeneous matrix, habitat configuration will have a higher influence on species at intermediate values of habitat amount, where configuration has potentially the greatest variability. On the basis of empirical studies and simulations, we expect that species that are relatively tolerant to fragmentation of their habitat will exhibit a wider band where amount and configuration interact compared to species less tolerant to fragmentation. Synthesis and applications. Reducing habitat loss should be a top priority for conservation planners. However, researchers should also investigate the indirect impacts of habitat loss on biodiversity through fragmentation effects. This research aims to identify windows of opportunity where habitat configuration can mitigate to some extent the effects of habitat loss, particularly through the maintenance of functional connectivity.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".