APPLICATION OF A MOOSE HABITAT SUITABILITY INDEX MODEL TO VERMONT WILDLIFE MANAGEMENT UNITS
Bibliographic record
Abstract
Habitat Suitability Index (HSI) models translate existing knowledge of a species' habitat requirements into quantitative measures of habitat quality. The HSI is a numerical index that represents the ability of a given habitat to provide life requisites for a species on a scale from 0 (unsuitable habitat) to 1 (optimal habitat). Habitat Suitability Index models are useful in natural resource planning for predicting the impacts of resource management practices on wildlife habitat. Many moose (Alces alces) HSI models require the labor-intensive collection of ground-level browse density data, which limits their applications for analyzing large landscapes required by moose. Some, however, have been developed utilizing remotely sensed data to analyze large study areas. I tested the usefulness of one of these models, created for the Lake Superior region, to 2 Wildlife Management Units (WMUs) in Vermont. Areas of study WMUs, and I, were 680 km 2 and 729 km 2 , respectively. The model quantified 4 landscape-scale habitat variables representing annual cover types required by moose: percent area of regenerating forest, non-forested wetland, spruce/ fir forest, and deciduous/mixed forest. Model analyses were performed using a Geographic Information System (GIS). The model was useful in estimating relative habitat suitability of both WMUs, identifying within-WMU habitat variation, quantifying change in habitat suitability following a natural habitat-altering event, and predicting temporal change in moose habitat due to changes in forest management practices. The model revealed significant differences in habitat suitability of 0.64 for WMU E1 and 0.34 for WMU I. To determine within-WMU habitat variation, both WMUs were divided into 25-km 2 evaluation units, which approximated the annual home range of moose in New England, and a HSI was calculated for each unit. Habitat suitability of 81 km 2 of WMU I increased from 0.30 to 0.53 due to an increase in regenerating forest following heavy canopy damage from an ice storm in January 1998. A reduction in habitat suitability from 0.81 to 0.35 of Silvio O. Conte National Fish and Wildlife Refuge lands within WMU E1 was observed following a simulation in which all timber harvesting as a forest management practice was eliminated. Initial validation of this model for analyzing moose habitat at the WMU-scale is supported by correlation of HSI output to moose harvest data for WMU E1 25-km 2 evaluation units and by comparison of HSI to estimated moose densities for both WMUs. ALCES VOL. 38: 89-107 (2002)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".