Estimating animal density using the <scp>Space‐to‐Event</scp> model and bootstrap resampling with motion‐triggered camera‐trap data
Bibliographic record
Abstract
Abstract Over the past few decades, the use of camera‐traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually‐identifiable animals are well‐established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space‐To‐Event (STE) model—originally designed to be used with time‐lapse images—on motion‐triggered camera‐trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion‐trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1‐s sampling window was important to avoid the positive bias that results from violating the instantaneous‐sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse‐based STE to motion‐trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion‐triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non‐instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".