Habitat selection by Common Gartersnakes (<i>Thamnophis sirtalis</i>) is affected by vegetation structure but not by location of Northern Leopard Frog (<i>Lithobates pipiens</i>) prey
Bibliographic record
Abstract
Understanding the factors affecting habitat selection of species is important for effective management and for conservation because habitat selection affects fitness. We tested the competing, but not mutually exclusive, hypotheses that habitat selection of Common Gartersnakes (Thamnophis sirtalis) at a fine spatial scale is driven by vegetation structure or by Northern Leopard Frog (Lithobates pipiens) prey abundance. We conducted surveys for snakes and frogs in six, 1-ha study grids in eastern Ontario in 2014 and 2015. Common Gartersnakes used areas dominated by forbs more than expected based on availability, and used grassy areas less than expected based on availability. Gartersnakes showed no preference for sites with more frogs. Thus, vegetation structure is important in habitat selection of Common Gartersnakes, but Northern Leopard Frog abundance is not. Common Gartersnakes and Northern Leopard Frogs did have a preference for forbs, but gartersnakes do not appear to be using habitat specifically based on frog abundance at a fine scale. Future work should study habitat use by snakes over a longer period to account for high variability in frog abundance and for temporal changes in habitat structure. Future work should also examine the distribution of other prey items in relation to the distribution of snakes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".