Modeling habitat potential for Canada lynx in Michigan
Bibliographic record
Abstract
Abstract In the ruling to list Canada lynx ( Lynx canadensis ) as a federally threatened species, the U.S. Fish and Wildlife Service (USFWS) identified the Great Lakes region as an area that historically contained lynx and, hence, could potentially contribute to population recovery. More recent critical habitat designations by the USFWS only recognize Minnesota, USA as important to recovery in the Great Lakes. Although there is no current evidence of a resident lynx population in the Upper Peninsula (UP) of Michigan, USA, trapping and track records over the past century suggest the region was periodically invaded after lynx population irruptions in Canada. In support of state and federal agency efforts in Michigan to provide and conserve lynx habitat, we quantified habitat potential using a spatially explicit, landscape‐level model based on relationships among lynx, their primary prey, snowshoe hare ( Lepus americanus ), and vegetation attributes. Outputs from the model indicated that habitat in the UP supports low hare densities (<0.07–0.75 hares/ha). Corresponding potential lynx densities ranged from 0/100 km 2 in the southern and northeast UP to 5/100 km 2 in the central–eastern UP. Model estimates of potential hare density were correlated with winter track‐surveys ( R 2 = 0.4, P < 0.001). Current absence of a resident lynx population in Michigan is likely attributed to confounding factors (e.g., habitat, competition, status of source population) but our results indicate that current habitat quality, quantity, and spatial configuration are exerting large‐scale negative influences. These results are generally consistent with the USFWS determination that Michigan's UP most likely functions as dispersal habitat. © 2011 The Wildlife Society.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".