Remote inferences and direct observations provide complementary insights into foraging behavior
Bibliographic record
Abstract
Abstract Behaviorists sometimes view askance studies where researchers indirectly observe animals, consequently challenging whether remotely inferred behavior is true behavioral research. Alternatively, others purport that technological advancements, like Global Position System (GPS) tags or biologgers, have expanded the scope of behavioral research to temporal and spatial scales infeasible for direct observation. To spotlight strengths and shortcomings in approaches to behavioral research, we interrogated the use of techniques and their assumptions in foraging research, a behavior of interest to ecologists and behaviorists. We reviewed 604 foraging behavior studies to synthesize and compare foraging research across disciplines, taxa, and methods. We sorted approaches by the data they collect and their associated assumptions and determined that rather than two categories of direct vs. remote, there were five: direct observation, tracking, biologger, remote audio-visual, and remote spatial. Categories differed in their spatial extents, with remote spatial research having a much larger extent (up to 1.6 million km 2 ) than direct observational or remote audio-visual studies. Remote spatial studies also spanned large temporal extents, but temporal coverage (the proportion of total study duration when data are actively collected) was lower compared to biologger research. Methods were also applied to different stages of the behavioral process of foraging: direct observations and tracking involved searching for resources at finer scales. 23% of studies used > 2/5 categories. A compound approach provided a more nuanced and complete description of foraging behavior. Thus, our understanding of behavior improves when multiple approaches are applied in conjunction to our question of interest. Lay summary How do you study behavior? Your first response might be simply watching animals behave. While there are many ways to indirectly infer behavior without directly observing wildlife, some question these approaches because we cannot be 100% confident in the inferred behavior. We reviewed >600 behavior studies and found that though remote inferences rely on assumptions, so do direct observations. Remote inferences also permit studying temporal and spatial scales infeasible when watching wildlife directly.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".