The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models

Climate change and biodiversity loss have become increasingly prominent in recent years. To evaluate these two issues, prediction models have been developed on the basis of ecological-niche (or climate-envelope) models. However, the spatial scale and extent of the underlying environmental data are k...

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Autores principales:	Ziye Huang, Anmin Huang, Terence P. Dawson, Li Cong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MaxEnt model spatial extent ecological niche <i>Ailuropoda melanoleuca</i> Min Mountain System Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/3c0011b5ebf540df9c0dc27512f0bcae
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id	oai:doaj.org-article:3c0011b5ebf540df9c0dc27512f0bcae
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spelling	oai:doaj.org-article:3c0011b5ebf540df9c0dc27512f0bcae2021-11-11T19:26:01ZThe Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models10.3390/su1321117072071-1050https://doaj.org/article/3c0011b5ebf540df9c0dc27512f0bcae2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11707https://doaj.org/toc/2071-1050Climate change and biodiversity loss have become increasingly prominent in recent years. To evaluate these two issues, prediction models have been developed on the basis of ecological-niche (or climate-envelope) models. However, the spatial scale and extent of the underlying environmental data are known to affect results. To verify whether the difference in the modelled spatial extent will affect model results, this study uses the MaxEnt model to predict the suitability range of giant pandas in the Min Mountain System (MMS) area through modelling performed (1) at a nationwide scale and (2) at a restricted MMS extent. The results show that, firstly, both models performed well in terms of accuracy. Secondly, extending the modelling extent does help improve the modelling results when the distribution data is incomplete. Thirdly, when environmental information is insufficient, the qualitative analysis should be combined with quantitative analysis to ensure the accuracy and practicality of the research. Finally, when predicting a suitability distribution of giant pandas, the modelling results under different spatial extents can provide management agencies at the various administrative levels with more targeted giant panda protective measures.Ziye HuangAnmin HuangTerence P. DawsonLi CongMDPI AGarticleMaxEnt modelspatial extentecological niche<i>Ailuropoda melanoleuca</i>Min Mountain SystemEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11707, p 11707 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MaxEnt model spatial extent ecological niche <i>Ailuropoda melanoleuca</i> Min Mountain System Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	MaxEnt model spatial extent ecological niche <i>Ailuropoda melanoleuca</i> Min Mountain System Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Ziye Huang Anmin Huang Terence P. Dawson Li Cong The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
description	Climate change and biodiversity loss have become increasingly prominent in recent years. To evaluate these two issues, prediction models have been developed on the basis of ecological-niche (or climate-envelope) models. However, the spatial scale and extent of the underlying environmental data are known to affect results. To verify whether the difference in the modelled spatial extent will affect model results, this study uses the MaxEnt model to predict the suitability range of giant pandas in the Min Mountain System (MMS) area through modelling performed (1) at a nationwide scale and (2) at a restricted MMS extent. The results show that, firstly, both models performed well in terms of accuracy. Secondly, extending the modelling extent does help improve the modelling results when the distribution data is incomplete. Thirdly, when environmental information is insufficient, the qualitative analysis should be combined with quantitative analysis to ensure the accuracy and practicality of the research. Finally, when predicting a suitability distribution of giant pandas, the modelling results under different spatial extents can provide management agencies at the various administrative levels with more targeted giant panda protective measures.
format	article
author	Ziye Huang Anmin Huang Terence P. Dawson Li Cong
author_facet	Ziye Huang Anmin Huang Terence P. Dawson Li Cong
author_sort	Ziye Huang
title	The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
title_short	The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
title_full	The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
title_fullStr	The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
title_full_unstemmed	The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models
title_sort	effects of the spatial extent on modelling giant panda distributions using ecological niche models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c0011b5ebf540df9c0dc27512f0bcae
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The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models

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