Effect of drought on wildlife activity at artificial waterholes
Bibliographic record
Abstract
Across southern Africa artificial waterholes have been introduced into many national parks to reduce the pressure of water scarcity on animals during drought. However, their introduction can shift ecological dynamics, and many artificial waterholes are now being removed. As global temperatures rise, droughts are predicted to become more frequent and more severe. Whether to retain or remove artificial waterholes thus presents a management dilemma. Here, we examine the effect of an extreme drought on artificial waterhole use in the Kruger National Park, South Africa. Comparing camera trap data collected during a one-in-twenty year drought with observation from a non-drought year, we quantify shifts in waterhole visitation patterns between years. The majority of the species show differences in waterhole use between drought and non-drought years. Species showing the largest temporal shifts include kudu and white rhinoceros, whereas elephants and warthogs show little change between years. Temporal overlaps between species pairs were also highly shifted, with the majority of species overlapping more in drought years, although some (e.g. buffalo and impala) show the opposite trend. Asynchronous shifts in species daily activity cycle may have cascading impacts on interspecific competition, predator-prey interactions, and multi-host disease dynamics. Our study illustrates how the interaction between drought and management choices to mitigate impacts of climate change may have complex and unforeseen ecological consequences. We show that, during drought, artificial waterholes are visited more frequently, likely increasing the frequency of interspecific interactions, including between ungulate herbivores and their predators, and elevating risk of disease spillover.
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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".