Mapping and assessing the impact of small‐scale ephemeral water sources on wildlife in an African seasonal savannah
Bibliographic record
Abstract
In many savannah regions of Africa, pronounced seasonal variability in rainfall results in wildlife being restricted to floodplains and other habitats adjacent to permanent surface water in the dry season. During the wet season, rainfall fills small-scale, ephemeral water sources that allow wildlife to exploit forage and other resources far from permanent surface water. These water sources remain difficult to quantify, however, due to their small and ephemeral nature, and as a result are rarely included in quantitative studies of wildlife distribution, abundance, and movement. Our goal was to map ephemeral water in Bwabwata National Park in Namibia using two different approaches and to relate measures of ephemeral water to the abundance, distribution, and movement of two large wildlife species. We used high-resolution Google Earth and Esri World imagery to visually identify waterholes. Additionally, we used Sentinel-2 satellite imagery to map ephemeral water across the study area using the Normalized Difference Water Index. With these mapped waterhole layers and data from GPS-collared individuals of African elephant (Loxodonta africana) and African buffalo (Syncerus caffer), we evaluated the importance of ephemeral water in conditioning abundance and movement of these two species. The two approaches to mapping ephemeral water resulted in the visual identification of nearly 10,000 waterholes, and a predicted ephemeral water layer of ~76% accuracy. The inclusion of ephemeral water into models of abundance and movement resulted in improved goodness of fit relative to those without water, and water impacts on abundance and movement were among the strongest of all variables considered. The potential importance of ephemeral water in conditioning the movements and distributions of large herbivores in African savannahs has been difficult to quantify relative to vegetation drivers. Our results suggest research into ephemeral water impacts deserves more attention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".