The Influence of Landscape Factors on Black-Tailed Prairie Dog (Cynomys ludovicianus) Colony Persistence in Northwest Kansas
Bibliographic record
Abstract
The black-tailed prairie dog (Cynomys ludovicianus) is a colonial and fossorial rodent species that serves as an ecosystem engineer and keystone species in North America’s grasslands. Black-tailed prairie dogs historically ranged from northern Mexico to southern Canada, and from eastern Nebraska to the foothills of the Rocky Mountains. However, with the loss and fragmentation of grasslands, introduction of Sylvatic plague (Yersinia pestis), and control measures such as poisoning and shooting, black-tailed prairie dogs are limited to less than 5 percent of their historical range.\r In this study, I examined how colony area, location, isolation, and surrounding land cover affected the persistence of black-tailed prairie dog colonies in northwest Kansas from 2005-2015. Using aerial imagery from the National Agricultural Imagery Program (NAIP), I attempted to map every black-tailed prairie dog colony in northwest Kansas from 2005-2015. I used generalized linear models and Akaike’s information criterion (AIC) to determine which factors influenced colony persistence.\r I found that the number of black-tailed prairie dog colonies and total area occupied by colonies varied from 2005-2015, with both experiencing a sharp decline from 2014-2015. While the number of colony extinctions per year also varied, the number of new colonies established steadily decreased over the study period. The logarithmic transformation of colony area was the most important variable to colony persistence, occurring in all of the best 25 models. The longitude of the colony was the second most important factor, occurring in 24 of the best 25 models. Determining which factors have the greatest impact on black-tailed prairie dog colony persistence is crucial for the development of conservation management plans for this declining species.
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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 | 0.000 | 0.000 |
| 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".