A systematic survey of the integration of animal behavior into conservation
Bibliographic record
Abstract
The role of behavioral ecology in improving wildlife conservation and management has been the subject of much recent debate. We sought to answer 2 foundational questions about the current use of behavioral knowledge in conservation: To what extent is behavioral knowledge used in wildlife conservation and management, and how does the use of animal behavior differ among conservation fields in both frequency and types of use? We searched the literature for intersections between key fields of animal behavior and conservation and created a systematic heat map (i.e., graphical representation of data where values are represented as colors) to visualize relative efforts. Some behaviors, such as dispersal and foraging, were commonly considered (mean [SE] of 1147.38 [353.11] and 439.44 [108.85] papers per cell, respectively). In contrast, other behaviors, such as learning, social, and antipredatory behaviors were rarely considered (mean [SE] of 33.88 [7.62], 44.81 [10.65], and 22.69 [6.37] papers per cell, respectively). In many cases, awareness of the importance of behavior did not translate into applicable management tools. Our results challenge previous suggestions that there is little association between the fields of behavioral ecology and conservation and reveals tremendous variation in the use of different behaviors in conservation. We recommend that researchers focus on examining underutilized intersections of behavior and conservation themes for which preliminary work shows a potential for improving conservation and management, translating behavioral theory into applicable and testable predictions, and creating systematic reviews to summarize the behavioral evidence within the behavior-conservation intersections for which many studies exist.
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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 | 0.031 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.054 | 0.059 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".