Using remotely sensed and climate data to predict the current and potential future geographic distribution of a bird at multiple scales:
dc.contributor.author | Freeman, Benedictus | |
dc.contributor.author | Jiménez-Garcia, Daniel | |
dc.contributor.author | Barca, Benjamin | |
dc.contributor.author | Grainger, Matthew | |
dc.date.accessioned | 2019-09-06T18:17:53Z | |
dc.date.available | 2019-09-06T18:17:53Z | |
dc.date.issued | 2019-06-10 | |
dc.identifier.citation | TY - JOUR AU - Freeman, Benedictus AU - Jiménez-García, Daniel AU - Barca, Benjamin AU - Grainger, Matthew PY - 2019 DA - 2019/06/10 TI - 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 JO - Avian Research SP - 22 VL - 10 IS - 1 AB - 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. SN - 2053-7166 UR - https://doi.org/10.1186/s40657-019-0160-y DO - 10.1186/s40657-019-0160-y ID - Freeman2019 ER - | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/29544 | |
dc.description | The data analyzed during the current study are available in the following databases: the Global Biodiversity Information Facility (GBIF; https ://www.gbif.org). Data from surveys in Sapo National Park in Liberia and Gola Rainforest National Park in Sierra Leone are available from the corresponding author on reasonable request. Environmental datasets are available at https ://terra .nasa.gov/data and http://www.worldclim.org. | en_US |
dc.description.abstract | Background: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.Methods: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.Results: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 southwest‑ern 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.Conclusions: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 protec‑tion and mitigation of other anthropogenic threats (e.g. hunting pressure) affecting the species. | en_US |
dc.description.sponsorship | Conservation International | en_US |
dc.description.sponsorship | Global Environment Facility-funded Grant # GEF-5810 | en_US |
dc.publisher | BMC | en_US |
dc.relation.isversionof | https://avianres.biomedcentral.com/articles/10.1186/s40657-019-0160-y | en_US |
dc.rights | Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Climate change | en_US |
dc.subject | Conservation | en_US |
dc.subject | Conservation planning | en_US |
dc.subject | Ecological niche modeling | en_US |
dc.subject | Species distribution | en_US |
dc.subject | Upper Guinea Forest | en_US |
dc.subject | White-breasted Guineafowl | en_US |
dc.title | Using remotely sensed and climate data to predict the current and potential future geographic distribution of a bird at multiple scales: | en_US |
dc.title.alternative | the case of Agelastes meleagrides, a western African forest endemic | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Freeman, Benedictus | |
kusw.kudepartment | Ecology and Evolutionary Biology | en_US |
dc.identifier.doi | 10.1186/s40657-019-0160-y | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-0895-4952 | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.rights.accessrights | openAccess | en_US |
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Except where otherwise noted, this item's license is described as: Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.