Social Sensors for Wildlife: Ecological Opportunities in the Era of Camera Ubiquity
Bibliographic record
Abstract
Expansive study areas, such as those used by highly-mobile species, provide numerous logistical challenges for researchers. Community science initiatives have been proposed as a means of overcoming some of these challenges but often suffer from low uptake or limited long-term participation rates. Nevertheless, there are many places where the public has a much higher visitation rate than do field researchers. Here we demonstrate a passive means of collecting community science data by sourcing ecological image data from the digital public, who act as “eco-social sensors,” via a public photo-sharing platform—Flickr. To achieve this, we use freely-available Python packages and simple applications of convolutional neural networks. Using the Weddell seal (Leptonychotes weddellii) on the Antarctic Peninsula as an example, we use these data with field survey data to demonstrate the viability of photo-identification for this species, supplement traditional field studies to better understand patterns of habitat use, describe spatial and sex-specific signals in molt phenology, and examine behavioral differences between the Antarctic Peninsula’s Weddell seal population and better-studied populations in the species’ more southerly fast-ice habitat. While our analyses are unavoidably limited by the relatively small volume of imagery currently available, this pilot study demonstrates the utility an eco-social sensors approach, the value of ad hoc wildlife photography, the role of geographic metadata for the incorporation of such imagery into ecological analyses, the remaining challenges of computer vision for ecological applications, and the viability of pelage patterns for use in individual recognition for this species.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".