The effects of the spatial extent on modelling giant panda distributions using ecological niche models

TY - JOUR

T1 - The effects of the spatial extent on modelling giant panda distributions using ecological niche models

AU - Huang, Ziye

AU - Huang, Anmin

AU - Dawson, Terence P.

AU - Cong, Li

N1 - Funding Information: Thanks to Harriet Dawson (University of Edinburgh) for her review and suggestions for improvement on earlier drafts of this manuscript. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/11/1

Y1 - 2021/11/1

N2 - 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.

AB - 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.

KW - Ailuropoda melanoleuca

KW - Ecological niche

KW - MaxEnt model

KW - Min mountain system

KW - Spatial extent

UR - http://www.scopus.com/inward/record.url?scp=85117691555&partnerID=8YFLogxK

U2 - 10.3390/su132111707

DO - 10.3390/su132111707

M3 - Article

AN - SCOPUS:85117691555

SN - 2071-1050

VL - 13

JO - Sustainability (Switzerland)

JF - Sustainability (Switzerland)

IS - 21

M1 - 11707

ER -