Precommercial forest thinning alters abundance but not survival of snowshoe hares
Bibliographic record
Abstract
Abstract Anthropogenic landscape modification is a major threat to global biodiversity and ecosystem integrity. Thus, land use practices that reconcile human needs with protection of species and ecological processes are of interest. Precommercial forest thinning (PCT) is a land use practice that is believed to be less ecologically disruptive than other silvicultural approaches (e.g., clear‐cutting). The impacts of PCT on wildlife populations are not well understood, however, and the effects of this practice on individual animals have not been rigorously examined. Accordingly, we investigated short‐term population and individual responses of snowshoe hares (Lepus americanus) to PCT in western Oregon during 2001–2002. We live‐trapped hares (n = 143) seasonally and equipped them with mortality‐sensitive radio‐collars on 3 manipulated sites that were precommercially thinned and on 3 undisturbed controls. We also quantified the activity (movement rates) of all collared hares using an automated data‐logger. As a result, we were able to document the effects of PCT on hare abundance, individual survival, and daily movement. Thinning significantly reduced hare abundance relative to that on control sites, but had no effect on individual mortality or activity. We infer, therefore, that a portion of the hares on thinned sites dispersed to adjacent habitat, where they survived as well as conspecifics on both control and manipulated sites but were unavailable for subsequent capture. We conclude that PCT affects hare density in the short‐term (i.e., at least in the first few years post‐treatment) largely via altered behavior. Thus, if the immediate impacts of this practice on hare populations are to be minimized, thinned stands should be imbedded in a habitat matrix that facilitates the occupancy of individuals dispersing from disturbed patches. © 2012 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 | 0.000 |
| 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) | 0.001 | 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".