Navigating fragmented landscapes: Canada lynx brave poor quality habitats while traveling
Bibliographic record
Abstract
Anthropogenic and natural habitat fragmentation inhibit movement of animals through landscapes. An important challenge for connectivity conservation is determining which conditions facilitate or limit movements, so that these areas can be prioritized for protection or restoration. We examine Canada lynx Lynx canadensis habitat connectivity in the fragmented North Cascade Mountains of Washington, as an example of a highly mobile species that is specialized both on prey and in habitat needs. We identify lynx Habitat Concentration Areas based on Core Habitat Models, parameterize resistance surfaces from our Matrix Habitat Model, and develop linkages of habitat lynx use to move between patches of high quality habitat. We identify a number of linkages for lynx comprised of habitat conditions that differed from high quality core patches identified from our habitat modeling. Radio-locations from lynx confirm lower-quality habitats of low resistance to movement were used by traveling lynx. Our results thus suggest traveling lynx do indeed use a much broader range of habitats than do lynx moving within core areas. For lynx in the North Cascades, our results show that maintaining connectivity will require preserving habitats and linkages that would previously have been deemed unsuitable for lynx. Maintaining connectivity for lynx is particularly important given the many recent large wildfires in this region that have reduced the number of mature forest stands that form prime habitat for lynx. Policy implications. Our results strongly suggest that habitat connectivity models should be based on empirical information of animal location data and focused on matrix habitat analysis. Traveling predators use a wide suite of habitats, resulting in more and broader linkage zones that should inform conservation efforts. Failure to identify these areas of functional connectivity could result in the oversight of usable linkage zones, leaving them without protection and vulnerable to degradation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".