Decadal study of Avifaunal Diversity of Banni Grass land, Katchchh, Gujarat, India
Bibliographic record
Abstract
ABSTRACT Banni Grass land was one of the largest grasslands of arid systems in India. The region shows extreme climatic conditions with summer temperature hovering around 45° C to 50° C temperature and winter temperature dropping to 2 to 3 ° C and annual rainfall is only 2-3 cm. This type of climatic regime supports specific grasses and other vegetation that in turn sustains avifauna. Over the years, the area has undergone changes in vegetation characteristics and human pressures which has an impact on regional climate affecting the bird diversity. Since, birds are the important indicators of health of ecosystem the present study was undertaken to bring out the decadal changes in the faunal diversity. Study was undertaken in seven villages located within the Banni grasslands and the results show change in avifaunal composition as well as diversity between 2004 and 2014. The paper discusses how the vegetation change coupled with human interference have affected the bird composition and is important finding for long term conservation strategy of this unique grasslands. Keywords: Birds, Grassland, plants, Desert ecosystem. REFERENCES Butcher, G.S. 2007. Common birds in decline, a state of the birds report. Audubon 109, 58–62. Butler R. W. and Taylor W. 2005. A Review of Climate Change Impacts on Birds. USDA Forest Service Gen. Tech. Rep. PSW-GTR-191. pp. 1107-1109. Donald, P.F., Pisano, G., Rayment, M.D. & Pain, D.J. 2002. The Common Agricultural Policy, EU enlargement and the conservation of Europe’s farmland birds. Agr. Ecosyst. Environ. 89: pp- 167–182. GEC (Gujarat Ecology Commission) 1998. Ecorestoration of Banni Grassland. First Annual Technical Report, Gujarat Ecology Commission, Vadodara. Pp- 59. Henderson, I.G., Fuller, R.J., Conway, G.J. & Gough, S.J. 2004. Evidence for declines in populations of grassland-associated birds in marginal upland areas of Britain. Bird Study 51: pp- 12–19. Hoshino 2010. Soil organic carbon, temperature and water as affected by landuse and climate change. Global Environmental Research. 14: pp-23-28. Koladiya M. H., Gajera N. B., Mahato A. K. Roy, Kumar V. V. and Asari R. V. 2016. Birds of Banni Grassland. Gujarat Institute of Desert Ecology (GUIDE). Published by Ravi Sankaran Foundation. Koladiya M. H., Gajera N. B., Roy Mahato A. K., Vijay Kumar V., Asari R.V., 2016. Birds of Banni Grassland. Gujarat Institute of Desert Ecology (GUIDE). Published by The Ravi Sankaran Foundation. Krebs, C. J. 1985. Ecology: the experimental analysis of distribution and abundance. Third edition, Harper and Row Publishers, New York. Lima, S. L. 1990. Protective cover and the use of space: different strategies in finches. Oikos, 58: pp-151–158. Miller-Rushing, A. J., Primack, R. B., & Sekercioglu, C. H. 2010. Conservation consequences of climate change for birds. Effects of climate change on birds. Oxford University Press, Oxford, pp-295-310. Monroe A. P. and O’Connell T. J. 2014. Winter Bird Habitat Use in a Heterogeneous Tall grass Prairie the American Midland Naturalist 171(1) (2014) 171: pp-97–115. North American Bird Conservation Initiative, U.S. Committee, 2010. The State of the Birds 2010 Report on Climate Change, United States of America. U.S. Department of the Interior: Washington, DC. NRCS 1999. Grassland Birds. Fish and Wildlife Habitat Management leaflet. United State Department of Agriculture. NRCS, Natural Resource Conservation Service. Wild life Management Institute. Perrins, C. M. and Birkhead, T. R. (1983). Avian Ecology. Blackie. Glasgow and London. New York. Rahmani, A. R. 1998. The Banni grassland: Natural resource under siege. Sanctuary Asia. XVIII (3). pp- 40-49. Rotenberry, J. T. and J. A. Wiens. 1980. Habitat structure, patchiness, and avian communities in North American steppe vegetation: a multivariate analysis. Ecology, 61: pp-1228–1250. Singh, A., Singh Laura J. 2012. Avian and Plant Species Diversity and their Inter-relationship in Tilyar Lake, Rohtak (Haryana). Bulletin of Environment, Pharmacology and Life Sciences. 1808 Bull. Environ. Pharmacol. Life Sci.; Volume 1 [9]. Pp- 65 – 68. Sateesh Pujari and Estari Mamidala (2015). Anti-diabetic activity of Physagulin-F isolated from Physalis angulata fruits. The Ame J Sci & Med Res, 2015,1(1):53-60. Tiwari, J. K and A. R. Rahmani 1998. The Banni grassland. In A study on the ecology of grasslands of the Indian plains with particular reference to their endangered fauna. Bombay Natural History Society and Center of Wildlife and Ornithology. Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C. T., Beebee, J. C., Fromentin, J.-M., Hoegh-Guldberg, O. and F. Barlein. 2002. Ecological responses to recent climate change. Nature 416: pp-389-395. Ware, D. M. and R. E. Thomson. 2000. Interannual to multi decadal climate variations in the Northeast Pacific. Journal of Climate 13: pp- 3209-3220. WATTS, B. D. 1996. Social strategy and cover in Savannah Sparrows. Auk, 113: pp- 960–963. Welty, J. C. and Baptista L. 1988. In: The life of birds, fourth edition. Saunders College Publishing. New York, Chicago, San Francisco, Philadelphia, Montreal, Toronto, London, Sydney, Tokyo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".