Evaluation of invasive and non‐invasive methods to monitor rodent abundance in the Arctic
Bibliographic record
Abstract
Abstract Monitoring rodent abundance is critical to understand direct and indirect trophic interactions in most northern terrestrial ecosystems. However, logistic constraints can prevent researchers from using capture–mark–recapture methods, a robust approach to estimate abundance. Our objective was to determine the correlation between abundance estimates of Arctic lemmings obtained from live‐trapping data with spatially explicit capture–recapture models (SECR; N/ha) and abundance indices obtained from snap‐trapping along trap lines (N/100 trap‐nights), winter nest sampling along transects with distance sampling models (N/ha), burrow counting within quadrats (N/100 m2), and incidental observations (N/100 observer‐hr). We also evaluated the impact of reduced sampling effort on the bias and precision of each abundance estimate. Data from brown (Lemmus trimucronatus) and collared lemmings (Dicrostonyx groenlandicus) were collected each year from 2007 to 2016 on Bylot Island, Nunavut, Canada. Snap‐trapping (r = 0.90) and incidental observations (r = 0.92) yielded the highest correlations with live‐trapping densities for brown lemmings, the most abundant species. When combining abundance of both lemming species, snap‐trapping (r = 0.77) and incidental observations (r = 0.90) also yielded the highest correlations. Indices from winter nests and burrows were also correlated (r > 0.50) with live‐trapping densities, but to a lesser degree. We found that bias generally increased when effort was reduced for methods involving modeling of capture or detection probabilities (i.e., live‐trapping, winter nests), but remained low for the other methods. In contrast, precision of estimates remained high when usingSECRmodels, but decreased substantially for the other methods during years of low lemming abundance. Non‐convergence ofSECRand distance sampling models generally increased when reducing effort and was frequent in years of low lemming abundance. Interestingly, collecting >200 h of incidental observations generated highly reliable estimates of lemming abundance compared to results from live‐trapping, indicating that such non‐invasive method can provide valuable data at low cost. We provide guidelines on other invasive or non‐invasive methods that can be used when small mammals cannot be live‐trapped and suggest the effort required to achieve a given precision.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".