The Purr‐fect Catch: Using accelerometers and audio recorders to document kill rates and hunting behaviour of a small prey specialist
Bibliographic record
Abstract
Abstract Characterizing variation in predator behaviour and, specifically, quantifying kill rates is fundamental for parameterizing predator–prey and food web models. Yet, current methods for recording kill rates of free‐ranging predators, particularly those that consume small‐bodied (<2 kg) prey, present a number of associated challenges. In this paper, we deployed custom‐adapted acoustic recorders and tri‐axial accelerometers on free‐ranging Canada lynx Lynx canadensis to assess the capacity of biologging devices to continuously document individual hunting behaviour, including prey selection and kill rates, on a predator that specializes on prey weighing <2 kg. Automated classification of acoustic recordings captured 87% of snowshoe hare kills that were identified through snow‐tracking (26 of 31 kills). Classification of detailed acceleration recordings summarized over minutes, instead of seconds, captured consumption of snowshoe hare Lepus americanus , but not smaller species, at high accuracy (F1 = 0.96). By summarizing acoustic and accelerometer data from free‐ranging lynx, we demonstrate the capacity of these devices to document within‐ and between‐individual variation in diet composition (ranging from 40% to 80% snowshoe hares) and daily feeding bouts (ranging from 0 to 3.5 bouts per day). We suggest that acoustic recorders provide a promising method for characterizing several aspects of predator hunting behaviour including prey selection and chase outcomes, while broad‐scale accelerometer‐based behavioural classifications provide hare kill rates and fine‐scale non‐hunting behavioural information. Combined, the two technologies provide a means to remotely document both kills and feeding events of small‐bodied prey, allowing for individual‐based exploration of functional responses, predator–prey interactions and food web dynamics at temporal scales relevant to environmental change.
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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 | 0.001 | 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 | 0.000 | 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".