Water depth, vegetation height, and offshore distance are critical factors in nest-site selection for Grey Crowned Crane at Lake Ol' Bolossat, Kenya
Bibliographic record
Abstract
Grey Crowned Crane Balearica regulorum is described as an icon of Africa’s wetlands and grasslands and is listed as Endangered on the IUCN Red List of Threatened species. Conservation efforts are partially hindered by lack of information on factors influencing breeding productivity, such as nest-site selection. Factors influencing nest-site selection were investigated at Lake Ol’ Bolossat, a 43.3 km2 wetland located in the central Kenya from 30 paired nests. Generalized Linear Mixed-Effects Models were used to analyse the relationship between factors influencing nest-site selection by cranes and variables that were predicted to have a compelling influence on nest-site selection besides i) food and nesting materials availability i.e. the offshore distance of the nest and water depth, and ii) nest concealment and susceptibility to predation i.e. vegetation height and grazing intensity. Results show that variables which had a significant influence on nest-site selection were: water depth (p=0.005), the offshore distance from the nest (p=0.037), and vegetation height (p=0.035). Cranes located their nests in water points above 50 cm deep, vegetation height of 60-90 cm, and preferably 100 m offshore. A minimum of 103 territorial pairs, both breeding and non-breeding cranes, were recorded. The middle section of the lake had the highest number (52), while north and south had 32 and 19 pairs respectively. The mean distance between any two pairs was 302.53±17.02 (SE) meters. This study sheds some light on the understanding of characteristics of Grey Crowned Crane’s nesting sites that will facilitate manipulation and management of breeding sites. Lake Ol’ Bolossat is consequently a critical breeding site with a substantial role in the species’ population recovery and survival. A wetland management option that aims at achieving sustainable use of lake’s resources by local communities without compromising needs of wildlife is highly commended.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".