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Spatial models of giant pandas under current and future conditions reveal extinction risks

Nature Ecology & Evolution volume 5, pages 1309–1316 (2021)Cite this article

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Abstract

In addition to habitat loss and fragmentation, demographic processes—the vagaries of births, deaths and sex ratio fluctuations—pose substantial threats to wild giant panda populations. Additionally, climate change and plans for the Giant Panda National Park may influence (in opposing directions) the extinction risk for wild giant pandas. The Fourth National Giant Panda Census showed pandas living in 33 isolated populations. An estimated 259 animals live in 25 of these groups, ~14% of the total population. We used individual-based models to simulate time series of these small populations for 100 years. We analysed the spatial pattern of their risk of extinction under current conditions and multiple climate change models. Furthermore, we consider the impact of the proposed Giant Panda National Park. Results showed that 15 populations face a risk >90%, and for 3 other populations the risk is >50%. Of the 15 most at-risk populations, national parks can protect only 3. Under the Representative Concentration Pathway 8.5 climate change scenario, the 33 populations will probably further divide into 56 populations. Some 41 of them will face a risk >50% and 35 face a risk >90%. Although national parks will probably connect some fragmented habitats, 26 populations will be outside national park planning. Our study gives practical advice for conservation policies and management and has implications for the conservation of other species in the world that live in isolated, fragmented habitats.

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Fig. 1: The 33 isolated populations of giant pandas.
Fig. 2: Relationship between population size and the fraction of simulations going extinct.
Fig. 3: Radar charts of fraction of simulations of giant panda populations going extinct in 100 yr.
Fig. 4: The spatial pattern of extinction risks under different scenarios of climate change and Giant Panda National Park.
Fig. 5: Branch diagrams of populations and their extinction possibilities under current conditions and RCP 8.5 scenario, and the corresponding national park scenarios.

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Data availability

Data that support the findings of this study are available in the main text or the Supplementary Information.

Code availability

The main text describes the procedure flow of the individual-based algorithm. Code for running the model is available at https://github.com/konglingqiao/eco.

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Acknowledgements

Z.O. and L.K. received support from the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (grant no. 2019QZKK0308). W.X. received support from the National Natural Science Foundation of China (grant no. 31971542). Z.O. received support from Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19050504). We thank the National Forestry and Grassland Administration (National Park Administration) for data support.

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Authors and Affiliations

  1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Lingqiao Kong, Weihua Xu, Yi Xiao, Hao Shi & Zhiyun Ouyang

  2. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Stuart L. Pimm

  3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

    Zhiyun Ouyang

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  1. Lingqiao Kong
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Contributions

L.K., Z.O., W.X. and S.L.P. designed the research. L.K., W.X., Y.X. and H.S. performed the research. L.K., W.X., Y.X. and H.S. analysed the data. L.K., Z.O., S.L.P., W.X. and Y.X. wrote the paper.

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Correspondence to Zhiyun Ouyang.

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The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Zhi Lu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Population dynamics of giant panda under optimal conditions.

Different initial age structures (youth majority structure, adult majority structure, old majority structure and the stable age structure) are used to simulate population trends for 100 years. There is a dramatic decline for the old majority structure. Regardless of the initial age structure, the population will grow slowly and steadily when the age structure becomes stable in the long run.

Extended Data Fig. 2 Uncertainty in Survival Probabilities (SP).

(1): Uncertainty in the SP of the individuals under one year old. (2)-(27): Uncertainty in the SP of the individuals aged 1 to 26.

Extended Data Fig. 3 Uncertainty analysis of Reproductive Probabilities (RP).

(1)-(17): Uncertainty in the RP of the individuals aged 5 to 21.

Extended Data Fig. 4 Uncertainty Analysis of initial age structure.

Extinction probabilities of populations with different numbers of individuals under different initial age structures.

Extended Data Fig. 5 Extinction possibility (%) of each population.

Cells filled with colour show the extinction possibilities under different scenarios. S1: Business as usual; S2: RCP2.6; S3: RCP4.5; S4: RCP 8.5; S5: Giant Panda National Park; S6~S8: (RCP2.6, RCP4.5, RCP 8.5) & Giant Panda National Park. The darker the red, the higher the extinction possibility, and the darker the blue, the lower the extinction possibility. The second column corresponds to the current giant panda populations in Fig. 1 in the main text.

Extended Data Fig. 6 Procedure flow of extinction risk simulation of giant panda populations.

(1.1) Individual assignment; (1.2) Reproduction simulation; (1.3) Judgement of survival.

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Kong, L., Xu, W., Xiao, Y. et al. Spatial models of giant pandas under current and future conditions reveal extinction risks. Nat Ecol Evol 5, 1309–1316 (2021). https://doi.org/10.1038/s41559-021-01520-1

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