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Using remotely sensed and climate data to predict the current and potential future geographic distribution of a bird at multiple scales: the case of Agelastes meleagrides, a western African forest endemic
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Understanding geographic distributions of species is a crucial step in spatial planning for biodiversity conservation, particularly as regards changes in response to global climate change. This information is especially important for species of global conservation concern that are susceptible to the effects of habitat loss and climate change. In this study, we used ecological niche modeling to assess the current and future geographic distributional potential of White-breasted Guineafowl (Agelastes meleagrides) (Vulnerable) across West Africa.
We used primary occurrence data obtained from the Global Biodiversity Information Facility and national parks in Liberia and Sierra Leone, and two independent environmental datasets (Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index at 250 m spatial resolution, and Worldclim climate data at 2.5′ spatial resolution for two representative concentration pathway emissions scenarios and 27 general circulation models for 2050) to build ecological niche models in Maxent.
From the projections, White-breasted Guineafowl showed a broader potential distribution across the region compared to the current IUCN range estimate for the species. Suitable areas were concentrated in the Gola rainforests in northwestern Liberia and southeastern Sierra Leone, the Tai-Sapo corridor in southeastern Liberia and southwestern Côte d’Ivoire, and the Nimba Mountains in northern Liberia, southeastern Guinea, and northwestern Côte d’Ivoire. Future climate-driven projections anticipated minimal range shifts in response to climate change.
By combining remotely sensed data and climatic data, our results suggest that forest cover, rather than climate is the major driver of the species’ current distribution. Thus, conservation efforts should prioritize forest protection and mitigation of other anthropogenic threats (e.g. hunting pressure) affecting the species.
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Using remotely sensed and climate data to predict the current and potential future geographic distribution of a bird at multiple scales: the case of Agelastes meleagrides, a western African forest endemic
(
),
Abstract
Understanding geographic distributions of species is a crucial step in spatial planning for biodiversity conservation, particularly as regards changes in response to global climate change. This information is especially important for species of global conservation concern that are susceptible to the effects of habitat loss and climate change. In this study, we used ecological niche modeling to assess the current and future geographic distributional potential of White-breasted Guineafowl (Agelastes meleagrides) (Vulnerable) across West Africa.
We used primary occurrence data obtained from the Global Biodiversity Information Facility and national parks in Liberia and Sierra Leone, and two independent environmental datasets (Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index at 250 m spatial resolution, and Worldclim climate data at 2.5′ spatial resolution for two representative concentration pathway emissions scenarios and 27 general circulation models for 2050) to build ecological niche models in Maxent.
From the projections, White-breasted Guineafowl showed a broader potential distribution across the region compared to the current IUCN range estimate for the species. Suitable areas were concentrated in the Gola rainforests in northwestern Liberia and southeastern Sierra Leone, the Tai-Sapo corridor in southeastern Liberia and southwestern Côte d’Ivoire, and the Nimba Mountains in northern Liberia, southeastern Guinea, and northwestern Côte d’Ivoire. Future climate-driven projections anticipated minimal range shifts in response to climate change.
By combining remotely sensed data and climatic data, our results suggest that forest cover, rather than climate is the major driver of the species’ current distribution. Thus, conservation efforts should prioritize forest protection and mitigation of other anthropogenic threats (e.g. hunting pressure) affecting the species.
References(50)
Allport G. The status and conservation of threatened birds in the Upper Guinea forest. Bird Conserv Int. 1991;1:53-74.
Baker DJ, Hartley AJ, Burgess ND, Butchart SHM, Carr JA, Smith RJ, Belle E, Willis SG. Assessing climate change impacts for vertebrate fauna across the West African protected area network using regionally appropriate climate projections. Divers Distrib. 2015;21:991-1003.
Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model. 2011;222:1810-9.
Brown JL. SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol. 2014;5:694-700.
Campbell LP, Luther C, Moo-Llanes D, Ramsey JM, Danis-Lozano R, Peterson AT. Climate change influences on global distributions of dengue and chikungunya virus vectors. Philos Trans R Soc B. 2015;370:20140135.
Cobos ME, Peterson AT, Barve N, Osorio-Olvera L. kuenm: an R package for detailed development of ecological niche models using Maxent. PeerJ. 2019;7:e6281.
Díaz S, Pascual U, Stenseke M, Martín-López B, Watson RT, Molnár Z, Hill R, Chan KMA, Baste IA, Brauman KA, et al. Assessing nature's contributions to people. Science. 2018;359:270-2.
Dowsett-Lemaire F, Dowsett RJ. The avifauna of the proposed Kyabobo National Park in eastern Ghana. Malimbus. 2007;29:61-88.
Francis IS, Penford N, Gartshore ME, Jaramillo A. The White-breasted Guineafowl Agelastes meleagrides in Taï National Park, Côte d'Ivoire. Bird Conserv Int. 1992;2:25-60.
Freeman B, Roehrdanz PR, Peterson AT. Modeling endangered mammal species distributions and forest connectivity across the humid Upper Guinea lowland rainforest of West Africa. Biodivers Conserv. 2019a;28:671-85.
Freeman B, Danjuma DF, Molokwu-Odozi M. Status of globally threatened birds of Sapo National Park, Liberia. Ostrich. 2019b;90:19-24.
Gocic M, Trajkovic S. Spatiotemporal characteristics of drought in Serbia. J Hydrol. 2014;510:110-23.
Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, Regan TJ, Brotons L, McDonald-Madden E, Mantyka-Pringle C, et al. Predicting species distributions for conservation decisions. Ecol Lett. 2013;16:1424-35.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25:1965-78.
Huete A, Justice C, Van Leeuwen W. MODIS vegetation index (MOD13). Algorithm Theor Basis Doc. 1999;3:213.
Hurvich CM, Tsai C-L. Regression and time series model selection in small samples. Biometrika. 1989;76:297-307.
Klop E, Lindsell JA, Siaka AM. The birds of Gola Forest and Tiwai Island, Sierra Leone. Malimbus. 2010;32:33-58.
Li R, Tian H, Li X. Climate change induced range shifts of Galliformes in China. Integr Zool. 2010;5:154-63.
Ludwig GX, Alatalo RV, Helle P, Lindén H, Lindström J, Siitari H. Short-and long-term population dynamical consequences of asymmetric climate change in black grouse. Phil Trans R Soc B. 2006;273:2009-16.
Malcolm JR, Liu C, Neilson RP, Hansen L, Hannah LEE. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv Biol. 2006;20:538-48.
Manjunath Aradhya VN, Niranjan SK, Hemantha Kumar G. Probabilistic neural network based approach for handwritten character recognition. Int J Comput Commun. 2010;1:9-13.
Martins DS, Raziei T, Paulo AA, Pereira LS. Spatial and temporal variability of precipitation and drought in Portugal. Nat Hazard Earth Syst. 2012;12:1493-501.
Mittermeier RA, Myers N, Thomsen JB, Da Fonseca GA, Olivieri S. Biodiversity hotspots and major tropical wilderness areas: approaches to setting conservation priorities. Conserv Biol. 1998;12:516-20.
Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403:853-8.
Olson DM, Dinerstein E. The Global 200: priority ecoregions for global conservation. Ann Mo Bot Gard. 2002;89:199-224.
Pearson RG, Dawson TP. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr. 2003;12:361-71.
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr. 2007;34:102-17.
Peterson AT, Papeş M, Soberón J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model. 2008;213:63-72.
Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB. Ecological niches and geographic distributions. Princeton: Princeton University Press; 2011.
Şekercioǧlu ÇH, Primack RB, Wormworth J. The effects of climate change on tropical birds. Biol Conserv. 2012;148:1-18.
Shcheglovitova M, Anderson RP. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecol Model. 2013;269:9-17.
Stattersfield A, Crosby M, Long A, Wege D. Endemic bird areas of the world: priorities for biodiversity conservation. Cambridge: BirdLife International; 1998.
Stephens PA, Mason LR, Green RE, Gregory RD, Sauer JR, Alison J, Aunins A, Brotons L, Butchart SHM, Campedelli T, et al. Consistent response of bird populations to climate change on two continents. Science. 2016;352:84-7.
Waltert M, Seifert C, Radl G, Hoppe-Dominik B. Population size and habitat of the White-breasted Guineafowl Agelastes meleagrides in the Taï region, Côte d'Ivoire. Bird Conserv Int. 2010;20:74-83.
Zhang JP, Zhu T, Zhang QH, Li CC, Shu HL, Ying Y, Dai ZP, Wang X, Liu XY, Liang AM, Shen HX, Yi BQ. The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings. Atmos Chem Phys. 2012;12:5031-53.
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Acknowledgements
We are thankful to A. Townsend Peterson for his helpful review and comments during the preparation of this manuscript and to members of the KUENM group at the University of Kansas for their useful feedback on methods used in this study.
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