Temporal variability in effective size (N _e ) identifies sampling bias in close kin mark recapture estimate of population abundance (N c(CKMR) )
Bibliographic record
Abstract
Although efforts to estimate Ne, Nc, and their ratio in wild populations are expanding, few empirical studies investigate interannual changes in these parameters. Hence, we do not know how representative many estimates may be. Answering this question requires studies of long-term population dynamics. We non-lethally sampled N=5400 brook trout (Salvelinus fontinalis) from seven populations during 6 consecutive years (2014-2019) and genotyped them at 33 microsatellites to examine variation in Ne, Nc and their ratio. Nc was estimated by Mark-Recapture (Nc(MR)) (2014-2018) as well as by Close-Kin-Mark-Recapture (Nc(CKMR)) (2015-2017). Within populations, annual variation in Ne (max/min Ne) ranged from 1.6-fold to 58-fold. Over all 7 populations, median annual variation in Ne was 5-fold. These results reflect important interannual changes in reproductive success variance. Within population Nc(MR) varied by a median of 2.7. Thus, Ne varied nearly twice as much as did Nc(MR) . Our results suggest that, at least in small populations, any single annual estimate of Ne is unlikely to be representative of long-term dynamics. At least 3-4 annual estimates may be required for an estimate of contemporary Ne to be representative. For five of the seven populations, Nc(MR) was indistinguishable from Nc(CKMR). The two populations with discordant estimates exhibited the largest annual Ne variation (58-fold and 35.4-fold). These results suggest sampling effort in these two streams may have been insufficient to capture the genetic diversity of the entire population. Our study demonstrates how knowledge of temporal variation in Ne can be used to identify potential biases in Nc(CKMR).
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".