Food limitation at species range limits: Impacts of food availability on the density and colony expansion of prairie dog populations at their northern periphery
Bibliographic record
Abstract
Understanding limiting factors affecting population growth for imperilled species is crucial for conservation and management. This research investigates whether black–tailed prairie dog (Cynomys ludovicianus) populations are food limited on their northernmost range extent. We measured background vegetation rates and used food supplementation in a ‘before–after–control–impact’ (BACI) design to test whether increased food positively impacted prairie dog population density and colony expansion. Experimental results did not support food limitation. Overall, density increased from 2008 to 2009 but remained relatively similar between control and treatment plots. Correlations between natural, non-supplemented vegetation biomass and prairie dog density suggest that natural food availability in 2008 may have driven population growth into 2009. Natural food availability was highly variable among years and prairie dog densities may be impacted by food scarcity in some years but not others. Colony spatial expansion was greater in the absence of food supplementation, suggesting food scarcity may drive colony expansion. This research has important implications for the conservation and management of prairie dogs and species that depend on them such as reintroduced black–footed ferrets (Mustela nigripes) in Canada and other populations across their range.
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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 | 0.000 | 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".