Fragmentation effects on an endangered species across a gradient from the interior to edge of its range
Bibliographic record
Abstract
Understanding how habitat fragmentation affects individual species is complicated by challenges associated with quantifying species-specific habitat and spatial variability in fragmentation effects within a species' range. We aggregated a 29-year breeding survey data set for the endangered marbled murrelet (Brachyramphus marmoratus) from >42,000 forest sites throughout the Pacific Northwest (Oregon, Washington, and northern California) of the United States. We built a species distribution model (SDM) in which occupied sites were linked with Landsat imagery to quantify murrelet-specific habitat and then used occupancy models to test the hypotheses that fragmentation negatively affects murrelet breeding distribution and that these effects are amplified with distance from the marine foraging habitat toward the edge of the species' nesting range. Murrelet habitat declined in the Pacific Northwest by 20% since 1988, whereas the proportion of habitat comprising edges increased by 17%, indicating increased fragmentation. Furthermore, fragmentation of murrelet habitat at landscape scales (within 2 km of survey stations) negatively affected occupancy of potential breeding sites, and these effects were amplified near the range edge. On the coast, the odds of occupancy decreased by 37% (95% confidence interval [CI] -54 to 12) for each 10% increase in edge habitat (i.e., fragmentation), but at the range edge (88 km inland) these odds decreased by 99% (95% CI 98 to 99). Conversely, odds of murrelet occupancy increased by 31% (95% CI 14 to 52) for each 10% increase in local edge habitat (within 100 m of survey stations). Avoidance of fragmentation at broad scales but use of locally fragmented habitat with reduced quality may help explain the lack of murrelet population recovery. Further, our results emphasize that fragmentation effects can be nuanced, scale dependent, and geographically variable. Awareness of these nuances is critical for developing landscape-level conservation strategies for species experiencing broad-scale habitat loss and fragmentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".