Climate effects on honey bees can be mitigated by beekeeping management in Kenya
Bibliographic record
Abstract
In recent decades, worldwide concerns about the health of honey bees motivated the development of surveys to monitor the colony losses, of which Sub-Saharan Africa has had limited representation. In the context of climate change, understanding how climate affects colony losses has become fundamental, yet literature on this subject is scarce. For the first time, we conducted a survey to estimate the livestock decrease of honey bee colonies in Kenya for the year 2021-2022 to explore the effects of environmental conditions, such as temperature and precipitation, on livestock decrease. We define "livestock decrease" from the beekeeper's perspective, including dead colonies but also, in the specific context of the tropics, the colonies that absconded from the apiary. A total of 589 beekeepers from a variety of areas participated in the survey. Kenyan beekeepers had an average of 36.6% livestock decrease in 2021-2022, with higher decreases during the dry and hot (31.9%) than during the wet and cold season (20.2%). We found that livestock decreases were more important with temperature for both dry and hot and wet and cold seasons. Interestingly, we found that precipitation mitigated temperature effects on livestock decrease for both seasons. Finally, we found that beekeepers practicing water supplementation had up to 10% less livestock decrease during the dry and hot season than those that did not, suggesting it to be a relevant adaptive strategy to mitigate livestock decrease. It is worth noting that beekeepers can renew their stock by trapping swarms, yet this represents a cost in time and baiting materials. Based on climate change projections, we predicted that annual and seasonal livestock decrease would remain in the same range at horizon 2050 and horizon 2100. These results pinpoint difficulties in maintaining livestock for beekeepers in Kenya and provide clues for strategies to pursue in the context of climate change.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".