Measuring habitat fragmentation: An evaluation of landscape pattern metrics
Bibliographic record
Abstract
Summary Landscape patterns influence a range of ecological processes at multiple spatial scales. Landscape pattern metrics are often used to study the patterns that result from the linear and nonlinear interactions between spatial aggregation and abundance of habitat. However, many class‐level pattern metrics are highly correlated with habitat abundance, making their use as a measure of habitat fragmentation problematic. We argue that a class‐level pattern metric should be (1) able to differentiate landscapes across a range of spatial aggregations, and (2) independent of habitat abundance, if it is to be used to distinguish between effects of habitat amount and fragmentation. Based on these criteria and using both simulated and actual landscapes, we evaluated 64 class‐level pattern metrics. These metrics were reclassified into four groups based on their correlation with aggregation and abundance. Among all these metrics, nine were considered robust for fragmentation measurements, which cover most of the characteristics that define pattern, including core area, shape, proximity / isolation, contrast, and contagion / interspersion. Optimal metrics for individual studies will depend on both biological rationales and statistically robust metrics that are appropriate for achieving each study objectives.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".