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Impact of Climate Change on Two High-Altitude Restricted and Endemic Flycatchers of The Western Ghats, India


Affiliations
1 Department of Wildlife Science, College of Forestry, Kerala Agricultural University, Thrissur 680 656, India
 

Climate change has been influencing bird species in different ways. Some documented changes include reduction in geographic range, decline in abundance and changes in the seasonality of migratory bird species in spring after overwintering in the tropics. We undertook a study on two species of high-elevation dependant, restricted-range flycatchers: Black-and-orange Flycatcher (BOF) Ficedula nigrorufa (Jerdon, 1839) and Nilgiri Flycatcher (NIF) Eumyias albicaudatus (Jerdon, 1840), to determine how they respond to the predicted climate change scenarios. We used 194 and 300 independent occurrence points for BOF and NIF to develop climate models and understand the species responses to climate change scenarios using MaxEnt algorithm. We also used isothermality, mean temperature of coldest quarter and slope for developing the BOF model. For NIF, we used isothermality, mean temperature of coldest quarter, precipitation of driest month, precipitation of warmest quarter, slope and enhanced vegetation index. The mean temperature of coldest quarter (BIO 11) was the most crucial variable influencing climate suitability for both the species. The model predicted the current extent of occurrence of 6532 sq. km as suitable for BOF and 12,707 sq. km for NIF, within their ranges. However, only 27% and 24% of the existing suitable area of BOF and NIF respectively, falls within the protected area network in the Western Ghats. Future predictions suggest suitable area loss to the tune of 20–31% for BOF and 36–46% for NIF by 2050

Keywords

Biodiversity Hotspots, Climate Change, Habitat Loss, Species Distribution Modelling.
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  • IPBES, IPBES Global Assessment Summary for Policymakers (eds Díaz, S. et al.), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Secretariat, Bonn, Germany, 2019.

  • Allen, M. et al., Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Sustain. Develop. Efforts Eradicate Poverty, 2018, p. 616.

  • Thomas, C. D. et al., Extinction risk from climate change. Nature, 2004, 427, 145–148.

  • Malcolm, J. R., Liu, C., Neilson, R. P., Hansen, L. and Hannah, L., Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol., 2006, 20, 538–548.

  • Walther, G. R. et al., Ecological responses to recent climate change. Nature, 2002, 416, 389–395.

  • Walther, G. R., Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. London, Ser. B., 2010, 365, 2019–2024.

  • Nogués-Bravo, D., Araújo, M. B., Errea, M. P. and Martínez-Rica, J. P., Exposure of global mountain systems to climate warming during the 21st century. Global Environ. Change, 2007, 17, 420– 428.

  • Whiteman, C. D., Mountain Meteorology: Fundamentals and Applications, Oxford University Press, Oxford, United Kingdom, 2000.

  • Rogora, M. et al., Assessment of climate change effects on mountain ecosystems through a cross-site analysis in the Alps and Apennines. Sci. Total Environ., 2018, 624, 1429–1442.

  • McCormack, J. E., Huang, H. and Knowles, L. L., Sky islands. Encycl. Islands, 2009, 4, 841–843.

  • Sukumar, R., Suresh, H. S. and Ramesh, R., Climate change and its impact on tropical Montane ecosystems in Southern India. J. Biogeogr., 1995, 22, 533–536.

  • Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. and Kent, J., Biodiversity hotspots for conservation priorities. Nature, 2000, 403, 853–858.

  • UNESCO World Heritage Committee, Decisions Adopted during the 41st Session of the World Heritage Committee, Krakow, Poland, 2017.

  • UNESCO, Nilgiri Biosphere Reserve, India, 2012; https://en.unesco.org/biosphere/aspac/nilgiri (accessed on 29 March 2021).

  • UNESCO, Agasthyamala Biosphere Reserve, India; 2016; https://en.unesco.org/biosphere/aspac/agasthyamala (accessed on 29 March 2021).

  • Ricketts, T. H. et al., Pinpointing and preventing imminent extinctions. Proc. Natl. Acad. Sci. USA, 2005, 102, 18497–18501.

  • Robin, V. V., Sinha, A. and Ramakrishnan, U., Ancient geographical gaps and paleo-climate shape the phylogeography of an endemic bird in the sky Islands of Southern India. PLoS ONE, 2010, 5, e13321.

  • Robin, V. V. and Nandini, R., Shola habitats on sky islands: status of research: on montane forests and grasslands in southern India. Curr. Sci., 2012, 103, 1427–1437.

  • Arasumani, M., Khan, D., Vishnudas, C. K., Muthukumar, M., Bunyan, M. and Robin, V. V., Invasion compounds an ecosystemwide loss to afforestation in the tropical grasslands of the Shola sky islands. Biol. Conserv., 2019, 230, 141–150.

  • Clement, P., Black-and-orange flycatcher (Ficedula nigrorufa), version 1.0. In Birds of the World (eds del Hoyo, J. et al.), Cornell Lab of Ornithology, Ithaca, New York, USA, 2021.

  • Khan, M. A. R., Ecology of the black-and-orange flycatcher Muscicapa nigrorufa (Jerdon) in southern India. J. Bombay Nat. Hist. Soc., 1979, 75, 773–791.

  • Clement, P., Nilgiri flycatcher (Eumyias albicaudatus), version 1.0. In Birds of the World (eds del Hoyo, J. et al.), Cornell Lab of Ornithology, Ithaca, NY, USA, 2021.

  • BirdLife International, Ficedula nigrorufa. IUCN Red List of Threat and Species 2017 e.T22709415A118488735, 2017.

  • BirdLife International, Eumyias albicaudatus. IUCN Red List Threat and Species 2017 e.T22709449A118490523, 2017.

  • SoIB, State of India’s Birds 2020: Range, trends and conservation status. The SoIB Partnership, 2018; https://www.stateofindiasbirds.in/

  • Jones, M. C. et al., Predicting the impact of climate change on threatened species in UK waters. PLoS ONE, 2013, 8, e54216.

  • Root, T., Energy constraints on avian distributions and abundances. Ecology, 1988, 69, 330–339.

  • Root, T. L. and Schneider, S. H., Can large-scale climatic models be linked with multiscale ecological studies? Conserv. Biol., 1993, 7, 256–270.

  • Allouche, O., Tsoar, A. and Kadmon, R., Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol., 2006, 43, 1223–1232.

  • Peterson, A. T., Papeş, M. and Soberón, J., Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell., 2008, 213, 63–72.

  • Cobos, M. E., Peterson, A. T., Barve, N. and Osorio-Olvera, L., kuenm: an R package for detailed development of ecological niche models using Maxent. Peer J., 2019, 7, e6281.

  • Peterson, A. T. and Robins, C. R., Using ecological-niche modeling to predict barred owl invasions with implications for spotted owl conservation. Conserv. Biol., 2003, 17, 1161–1165.

  • Praveen, J. and Nameer, P. O., An Atlas of the Birds of Kerala, Kerala Agricultural University and Bird Count India, 2021, p. 219.

  • Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., and Kelling, S., eBird: a citizen-based bird observation network in the biological sciences. Biol. Conserv., 2009, 142, 2282–2292.

  • Pacifici, K. et al., Integrating multiple data sources in species distribution modeling: a framework for data fusion. Ecology, 2017, 98, 840–850.

  • Sullivan, B. L. et al., Using open access observational data for conservation 37. Robinson, O. J., Ruiz-Gutierrez, V., Fink, D., Meese, R. J., Holyoak, M. and Cooch, E. G., Using citizen science data in integrated population models to inform conservation. Biol. Conserv., 2018, 227, 361–368.

  • Strimas-Mackey, M. et al., Best practices for using eBird data. Cornell Lab of Ornithology, Ithaca, New York, USA, 2020.

  • Kadmon, R., Farber, O. and Danin, A., Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol. Appl., 2004, 14, 401–413.

  • Williams, P. H., Margules, C. R. and Hilbert, D. W., Data requirements and data sources for biodiversity priority area selection. J. Biosci., 2002, 27, 327–338.

  • Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. and Anderson, R. P., spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 2015, 38, 541–545.

  • R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2021.

  • Root, T., Environmental factors associated with avian distributional boundaries. J. Biogeogr., 2006, 15, 489.

  • Fick, S. E. and Hijmans, R. J., WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. Int. J. Climatol., 2017, 37, 4302–4315.

  • Karger, D. N. et al., Climatologies at high resolution for the earth’s land surface areas. Sci. Data, 2017, 4, 1–20.

  • Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E. and Yates, C. J., A statistical explanation of MaxEnt for ecologists. Divers. Distrib., 2011, 17, 43–57.

  • Merow, C., Smith, M. J. and Silander, J. A., A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 2013, 36, 1058–1069.

  • Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. and Blair, M. E., Opening the black box: an open-source release of Maxent. Ecography, 2017, 40, 887–893.

  • Phillips, S. J., Anderson, R. P. and Schapire, R. E., Maximum entropy modeling of species geographic distributions. Ecol. Modell., 2006, 190, 231–259.

  • Muscarella, R. et al., ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol., 2014, 5, 1198–1205.

  • Sanderson, B. M., Knutti, R. and Caldwell, P., A representative democracy to reduce interdependency in a multimodel ensemble. J. Climate, 2015, 28, 5171–5194.

  • Knutti, R., Masson, D. and Gettelman, A., Climate model genealogy: generation CMIP5 and how we got there. Geophys. Res. Lett., 2013, 40, 1194–1199.

  • Liu, C., White, M. and Newell, G., Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr., 2013, 40, 778–789.

  • eBird, eBird: an online database of bird distribution and abundance. Cornell Lab of Ornithology, Ithaca, New York, USA, 2021.

  • Wilcox, B. A. and Murphy, D. D., Conservation strategy: the effects of fragmentation on extinction. Am. Nat., 1985, 125, 879–887.

  • Parmesan, C., Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst., 2006, 37, 637–669.

  • Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. and Courchamp, F., Impacts of climate change on the future of biodiversity. Ecol. Lett., 2012, 15, 365–377.

  • Stuhldreher, G. and Fartmann, T., Threatened grassland butterflies as indicators of microclimatic niches along an elevational gradient – implications for conservation in times of climate change. Ecol. Indic., 2018, 94, 83–98.

  • Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T. and Donald, P. F., Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib., 2016, 22, 1024–1035.

  • Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. and Roy, D. B., Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol., 2014, 5, 1052–1060.

  • Araújo, M. B. and Guisan, A., Five (or so) challenges for species distribution modelling. J. Biogeogr., 2006, 33, 1677–1688.


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  • Impact of Climate Change on Two High-Altitude Restricted and Endemic Flycatchers of The Western Ghats, India

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Authors

E. R. Sreekumar
Department of Wildlife Science, College of Forestry, Kerala Agricultural University, Thrissur 680 656, India
P. O. Nameer
Department of Wildlife Science, College of Forestry, Kerala Agricultural University, Thrissur 680 656, India

Abstract


Climate change has been influencing bird species in different ways. Some documented changes include reduction in geographic range, decline in abundance and changes in the seasonality of migratory bird species in spring after overwintering in the tropics. We undertook a study on two species of high-elevation dependant, restricted-range flycatchers: Black-and-orange Flycatcher (BOF) Ficedula nigrorufa (Jerdon, 1839) and Nilgiri Flycatcher (NIF) Eumyias albicaudatus (Jerdon, 1840), to determine how they respond to the predicted climate change scenarios. We used 194 and 300 independent occurrence points for BOF and NIF to develop climate models and understand the species responses to climate change scenarios using MaxEnt algorithm. We also used isothermality, mean temperature of coldest quarter and slope for developing the BOF model. For NIF, we used isothermality, mean temperature of coldest quarter, precipitation of driest month, precipitation of warmest quarter, slope and enhanced vegetation index. The mean temperature of coldest quarter (BIO 11) was the most crucial variable influencing climate suitability for both the species. The model predicted the current extent of occurrence of 6532 sq. km as suitable for BOF and 12,707 sq. km for NIF, within their ranges. However, only 27% and 24% of the existing suitable area of BOF and NIF respectively, falls within the protected area network in the Western Ghats. Future predictions suggest suitable area loss to the tune of 20–31% for BOF and 36–46% for NIF by 2050

Keywords


Biodiversity Hotspots, Climate Change, Habitat Loss, Species Distribution Modelling.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi10%2F1335-1342