A systematic survey of the integration of animal behavior into conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it