Habitat-Distribution Modeling of a Recolonizing Black Bear, <em>Ursus americanus</em>, Population in the Trans-Pecos Region of Texas
Bibliographic record
Abstract
Black Bears (Ursus americanus) were once widespread across Texas, but their numbers were reduced in the early 1900s. Recolonization of the Trans-Pecos region of Texas has occurred via bears migrating northward from Mexico. Recent bear sightings have increased in this area. This could be an indication that the population in Texas is beginning to recover, but the population will continue to expand only if there is suitable habitat to occupy. To help identify suitable habitat and restoration areas, we developed a predictive habitat-distribution model by using records of Black Bear sightings from 1996 to 2003 to map the species' distribution. Using Bayesian statistics, we modeled the probability of occurrence of Black Bears in the Trans-Pecos region based on sighting locations. We used GIS layers for land use/landcover, elevation, water sources, and road networks to obtain covariates in our modeling. We used a 10-fold cross-validation to test the effectiveness of using sighting data. Our results indicated a negative association with bare areas, agriculture, and grassland landcovers. In addition, southern aspect, elevation, distance to water, slope, and western aspect also influenced suitable habitat. Both the original and validation datasets correctly classified bear sightings 93.9% and 93.7% of the time, respectively. Our model can be used to target restoration efforts to enhance the ability of the Black Bear to expand in the Trans-Pecos region. It can also identify private landowners most likely to be affected by the expansion of Black Bears for education and cooperative efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".