Continental declines in North American small mammal populations
Bibliographic record
Abstract
We initially speculated that non-tropical North American (USA and Canada) small mammal abundances might have increased over the past several decades due to declines in mammalian predators. To test this idea we assembled from small mammal researchers 818 time series of small mammal abundances, containing a total of 5317 individual abundance data points, for 66 species in 21 genera. The resulting database is the largest collection of multi-year abundance data for North American small mammals. We then used a hierarchical Bayesian modelling approach to estimate an overall abundance trend. Contrary to our initial speculation, we found strong support for an overall decline in North American small mammal abundance, with an estimated annual decrease of 3.6 %. Sixty species trends were negative while only six were positive. Given this decline and given that small mammals are important for ecosystem function as prey items, as predators, and for seed dispersal, we suggest conservation efforts should be directed to this generally neglected group. In particular, we need further work to uncover the causes and consequences of small mammal declines, and to develop mitigation strategies to avoid further declines in North American small mammals. • We conduct the first estimate of the overall trend in small mammal abundance in NA. • We find strong evidence for a continent-wide decline in small mammal abundance. • Conservation efforts to understand and mitigate small mammal declines are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".