Using camera traps and N‐mixture models to estimate population abundance: Model selection really matters
Bibliographic record
Abstract
Abstract Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose ( Alces americanus ) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony. The two most parsimonious N‐mixture models (i.e. AIC c ) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".