Grass type, vegetation cover, and predation affect abundance of Microtus californicus and Thomomys bottae in a coastal Mediterranean ecosystem
Bibliographic record
Abstract
Invasive grasses negatively impact ecosystems and have become dominant over native grasses in central California. This study measured vegetation cover, the extent of ungulate grazing, vegetation height and predation in invasive and native grasses and examined the response of Pocket gophers (Thomomys bottae) and California meadow voles ( Microtus californicus) to invasive and native grasses. It was predicted that both T. bottae and M. californicus are more abundant in native grasses compared to invasive grasses. This study also predicted that M. californicus would prefer less grazed grasses, but T. bottae would prefer grazed grass. Finally, it was predicted that there would be a negative relationship between predatory bird activity and small mammal abundance. Results suggested that while T. bottae preferred mixed grasses, M. californicus preferred Harding grass ( Phlalaris aquatica ), which is an invasive grass type. Furthermore, vegetation cover, but not tallest grass height, affected small mammal abundance. Results indicated a negative relationship between the presence of T. bottae and M. californicus . However, there was a positive relationship between predatory bird activity and small mammal abundance. It was proposed that other factors such as location of the native grass affected small mammal abundance. The negative relationship between T. bottae and M. californicus was thought to result from T. bottae reduction of vegetation cover, which caused a reduction in food supply and shelter from predatory birds. Studying the negative impact of invasive grasses on ecological systems in central California is a critical step towards developing invasive species management techniques.
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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 | 0.000 |
| 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".