Fluid hunter motivation in Central Africa: Effects on behaviour, bushmeat and income
Bibliographic record
Abstract
Abstract Individual motivation for the rural use of common‐pool resources (CPRs) can be fluid, with the line between subsistence and commercial often unclear and in flux. Implications of fluid motivation are understudied yet important for social–ecological systems (SESs), such as bushmeat hunting throughout Central Africa that is essential to local protein/nutrition, income and culture. Making locally informative predictions of multiple SESs nested within a landscape‐scale SES has been historically difficult, but community‐driven participatory approaches provide new kinds and quantities of data, opening previously inaccessible doors for research and governance. We apply hierarchical Bayesian structural equation modelling to a novel dataset of 910 hunts from 111 gun and trap hunters across nine villages in Gabon, generated in a participatory process whereby hunters conducted GPS self‐follows in conjunction with paraecologist surveys of their motivation, behaviour and offtake. We (i) establish the human behaviour driving gun‐hunting and trapping success and predict its effect on offtake across villages and (ii) link fluid motivation of gun hunters to their behaviour, number of animals hunted, biomass yielded and income earned. Gun hunts across villages yielded more animals during the night than the day, and when hunters brought high amounts of ammunition and walked far distances from villages. Gun hunts were less successful when coupled with trapping while per‐hunt success of trapping itself was generally low and difficult to predict. Fluid gun hunters hunted fewer animals when motivated strictly by subsistence, despite no reduction in ammunition brought or distance walked, while offtake from strictly commercial versus mixed motivation was the same. Numbers of animals hunted, biomass and income were tightly linked. We discuss the implications of these results for the ecological sustainability of hunting and participatory forecasting in bushmeat research and policy. Read the free Plain Language Summary for this article on the Journal blog.
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.
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 | 0.000 | 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".