Preserving identity in capture–mark–recapture studies: increasing the accuracy of minimum number alive (MNA) estimates by incorporating inter-census trapping efficiency variation
Bibliographic record
Abstract
Abstract Quantifying abundance is often key to understanding ecological and evolutionary processes in wild populations. Despite shortcomings in producing accurate abundance estimates, minimum number alive (MNA) remains a widely used tool, due to its intuitive computation, reliable performance as an abundance indicator, and linkage to individual life-histories. Here, we propose a novel “efficiency-modified” MNA (eMNA) metric, which aims to preserve MNA’s favourable aspects while remedying its flaws, by incorporating (a) growth correlates to back-age individuals first captured as adults, and (b) estimates of undetected persistence beyond last capture based on time-varying capture efficiency. We evaluate eMNA through samplings of a simulated baseline population parameterised using data from a long-term demographic study of European badgers ( Meles meles ), under three different levels of capture efficiency (low; intermediate/“real” based on badger field data; high). We differentiate between eMNA’s performance as an abundance estimator —how well it approximates true abundance (accuracy)—and as an abundance indicator —how tightly it correlates with population abundance and changes thereof (precision). eMNA abundance estimates were negatively biased at all capture efficiencies. However, this bias was negligible at intermediate-to-high capture efficiency, particularly once low-information terminal sampling years (the first year and final three years of simulated studies) were removed. Excluding these years, eMNA under-estimated abundance by only 3.5 badgers (1.5% of population) at intermediate (real) capture efficiencies, and performed as a precise abundance indicator, with half the standard deviation of Cormack–Jolly–Seber probabilistic estimates and proving robust to inter-sampling variation in capture efficiency. Using undetected persistence probabilities to parameterise survival regression, we recreated baseline age-based survival relationships, albeit with some negative bias for under-represented ages. We offer considerations on the continued limitations of using eMNA for abundance estimates, minimum study duration for reliability, the metric’s benefits when individual identity is required, and potential for further improvement.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".