Human–Ape Interactions in a Zoo Setting: Gorillas and Orangutans Modify Their Behavior Depending upon Human Familiarity
Bibliographic record
Abstract
Understanding how human presence influences animal behavior, specifically the behavior of nonhuman primates, has important implications for methodology in cognitive and behavioral studies, for our understanding of human–animal relationships and for animal welfare in captive settings. Conducted in the San Diego Zoo, this study partially replicates an existing study on human–animal relationships, which focused on familiarity effects on the human-directed behavior of great apes residing in the Toronto Zoo. Such a replication in a new location and with different individuals is necessary to expand sample size and to better understand how ontogeny and living environment influence human-directed ape behavior. All-occurrence sampling of human-directed behavior was used to gather data on two gorilla groups and one orangutan group, all housed at the San Diego Zoo. The aim was to compare ape-initiated behaviors toward zoo staff with apeinitiated visitor-directed behavior, in accordance with predictions of the Human–Animal Relationship (HAR) model. Overall, human-directed behaviors were more frequent toward visitors than toward staff, mainly caused by increased visitor-directed visual behavior, specifically brief glances. Given that visual monitoring has been seen as fearful behavior, potentially related to predator monitoring, this would confirm the expectation that lack of familiarity will lead to increased visual vigilance. However, the visitor-directed visual behaviors may not be antagonistic or fearful. Considering the specific behaviors and exhibit designs, curiosity may be an equally likely explanation in this context. Aiming to also understand whether these groups exhibited species differences in human-directed behavior, it was found that orangutans engaged in more affiliative behavior overall than gorillas. Comparing our results with the previous study, we conclude that while effects of familiarity are present, those effects are modulated by both individual ontogeny and exhibit design. While the HAR model is partially supported, additional factors may need to be included to fully interpret the observed behavior. Additional research is needed to further understand the effects of species, sex, and ontogeny on the predictions of the HAR model.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".