A spatial point process model to estimate individual centres of activity from passive acoustic telemetry data
Bibliographic record
Abstract
Abstract Failure to account for time‐varying detection ranges when inferring space use of marine species from passive acoustic telemetry data can bias estimates and result in erroneous biological conclusions. This potential source of bias is widely acknowledged but often ignored in practice due to a lack of available statistical methods. Here, we describe and apply a spatial point process model for estimating individual centres of activity ( COA s) from acoustic telemetry data that can be modified to account for both receiver‐ and time‐specific detection probabilities. We use simulation testing to evaluate the suitability of the proposed models for estimating COA s and compare their performance to that of the popular mean‐weighted COA method for a variety of scenarios. We illustrate how the approach can be applied to correct for variable detection ranges by integrating data from moored test tags and demonstrate how accounting for time‐varying detection probabilities can impact space use estimates by fitting the model to data collected from a black sea bass ( C entropristis striata ) on a receiver array off the east coast of the United States. The proposed model reduced bias in COA estimates, particularly when tagged individuals occurred along the periphery of the receiver array. The test tag‐integrated model largely corrected the bias associated with receiver‐ and time‐specific detection probabilities. When applied to the black sea bass detection data, the model revealed fine‐scale movements not apparent when detection ranges were assumed constant. Spatial management practices for coastal marine species are often based on trends in space use inferred from passive acoustic telemetry data, which can be misinterpreted when factors influencing detection ranges are not accounted for. Our approach provides a general framework for estimating individual COA s that can be modified on a study‐specific basis to ensure resulting patterns of space use reflect a species’ movements and behaviour, rather than variation in receiver detection ranges.
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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 | 0.000 |
| 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".