Aquatic bird assemblages of a tropical African man-made lake
Bibliographic record
Abstract
Aquatic birds are important biotic components of Lake Ecosystem serving as indicator of pollution, sources of protein, fishing agent, income, recreation, tourism, sport and pest control. The aim of this study was to provide a baseline data and checklist of the bird assemblages in Asa Lake, Ilorin, Nigeria. Bird sampling was conducted weekly for 24 weeks, by walking 10 mS-1 along a line transect on the bank of the lake. The populations were classified as rare, frequently seen and common based on the number of sightings, and abundance. Diversity indices such as dominance, evenness, Simpson diversity, Shanon-Weiner diversity, Marglef’s richness were estimated. 13682 birds consisting of 17 species in 9 families were recorded in the lake. Species were found in highest number during the rains. Water fowl was the most abundant species 42.21%. African jacana was the least abundant constituting 0.67%. Family Ardeidae was the most abundant in terms of species diversity. Three species were commonly seen, four frequently seen, while nine were rare. Diversity indices of the bird species were; (D) 0.045, (1-D) 0.95; (e^H/S) 0.96, Margalef richness 3.67 and (H) 2.99. The assemblage of bird species in the lake was high due to the availability of food, submerged aquatic macrophytes seeds and foliage, large volume of water, surface area and depth of the lake and absence of large predators. Morphological characteristics and behavioural tactics of the birds were attributed to the high occurrence of some of the birds. For continued residency of the birds in the lake, lake best management practices (LBMP) should be implemented. Continuous monitoring of the birds (bio monitor) in the lake should also be done to give an insight into the ecological conditions of the lake and at the same time serving as aesthetic organisms in the lake. Keywords: Birds; assemblages; lake; foraging; diversity; macrophytes; waterfowl References: Ali, S. (1943). 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In Aquatic birds in the trophic web of lakes. Springer, Dordrecht, pp. 107-119. Hoyer, M. V. (2013). Lake Management and Aquatic Birds. 17, 17-20. Knapton, R. W., & Petrie, S. A. (1999). Changes in distribution and abundance of submerged macrophytes in the Inner Bay at Long Point, Lake Erie: implications for foraging waterfowl. Journal of Great Lakes Research, 25(4), 783-798. MaGuarran, A. (1988). Ecological diversity and its measurement. Princeton University Press. New Jersey, p. 178. Malizia, L. R. (2001). Seasonal fluctuations of birds, fruits, and flowers in a subtropical forest of Argentina. The Condor, 103(1), 45-61. Mustapha, M. K. (2011). Perspectives in the Limnology of Shallow Tropical African Reservoirs in Relation to Their Fish and Fisheries. Journal of Transdisciplinary Environmental Studies, 10(1). Oliveira, D. M. M. D. (2006). Efeitos bioticos e abioticos de ambientes alagaveis nas assembleias de aves aquaticas e piscivoras no Pantanal, Brasil. Paszkowski, C. A., & Tonn, W. M. (2000). Community concordance between the fish and aquatic birds of lakes in northern Alberta, Canada: the relative importance of environmental and biotic factors. Freshwater Biology, 43(3), 421-437. Rajpar, M. N., & Zakaria, M. (2011). Bird species abundance and their correlationship with microclimate and habitat variables at Natural Wetland Reserve, Peninsular Malaysia. International Journal of Zoology, 2011. Serle, W., Morel, G. J., & Hartwig, W. (1992). Birds of West Africa. Collins Publication, London, p. 351. Shirihai, H., & Christie, D. (1996). The Macmillan birder's guide to European and Middle Eastern birds: including North Africa. Macmillan Pub Limited. Suter, W. (1994). Overwintering waterfowl on Swiss lakes: how are abundance and species richness influenced by trophic status and lake morphology?. In Aquatic Birds in the Trophic Web of Lakes. Springer, Dordrecht, pp. 1-14.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".