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Alpine burrow-sharing mammals and birds show similar population-level climate change risks

Nature Climate Change volume 13, pages 990–996 (2023)Cite this article

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Abstract

Climate adaptation and dispersal can determine a species’ response to climate change. However, quantifying how they can mitigate climate change risks remains a challenge. Here we combine ecological genomic, niche modelling and landscape genetic approaches to reveal similar population-level vulnerability for a keystone species and its two beneficiary species in an alpine grassland ecosystem in the Qinghai–Tibetan Plateau. We use climate-associated genotypes to identify population-level adaptation and model maladaptation with and without dispersal and find that contemporary populations in southwestern ranges are the most vulnerable to climate change. This vulnerability cannot be mitigated by dispersal to more suitable niches because of climate maladaptation and landscape barriers. Overall, combined multiple climate change risk estimates in coevolving species can be used to improve climate change vulnerability assessments beyond what can be learned from a single species or modelling.

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Fig. 1: Schematic framework illustrating speciesʼ responses to climate change.
Fig. 2: Sampling localities and climate adaptation of the three species studied.
Fig. 3: Estimates of vulnerability to climate change in the three species.
Fig. 4: The three species show concordance in their genetic offsets and niche changes under the future climate.

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Data availability

The resequencing data of 11 WR snowfinch and RN snowfinch individuals from ref. 22 can be found in Short Read Archive under the project number PRJNA417520 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA417520). The resequencing data of the plateau pika from ref. 21 can be found in the National Genomics Data Centre (https://db.cngb.org/) under the accession number CNP0003365 (https://db.cngb.org/search/project/CNP0003365/). Sequencing data generated in this study have been deposited in the National Genomics Data Centre (https://db.cngb.org/) under the accession number CNP0004029 (https://db.cngb.org/search/project/CNP0004029/).

Code availability

Datasets and analysis scripts can be found at GitHub: https://github.com/willright28/Tibet-mammals-and-birds (ref. 95).

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Acknowledgements

We sincerely thank H. Qiao and X. Liu for their facilitating ecological niche modelling. This research was funded by the Second Tibetan Plateau Scientific Expedition and Research (2019QZKK0501 to Y.Q., 2019QZKK0402 to D.G. and 2019QZKK0304-02 to F.L.), the National Natural Science Foundation of China (NSFC32020103005 to Y.Q., NSFC 32070434 to G.S. and NSFC 32170426 to D.G.), the Third Xinjiang Scientific Expedition and Research (2022xjkk0205 to Y.Q.) and the Swedish Research Council (621-2017-3693 to P.G.P.E.).

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Author notes

  1. These authors contributed equally: Yilin Chen, Deyan Ge.

Authors and Affiliations

  1. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Yilin Chen, Deyan Ge, Gang Song, Zhixin Wen, Qisen Yang, Fumin Lei & Yanhua Qu

  2. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Yilin Chen, Deyan Ge, Fumin Lei & Yanhua Qu

  3. Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden

    Per G. P. Ericson & Yanhua Qu

  4. Faculty of Biodiversity and Conservation, Southwest Forestry University, Kunming, China

    Xu Luo

  5. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Fumin Lei

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Contributions

Y.Q., D.G., P.G.P.E., F.L. and Q.Y. conceived, designed and refined the study; Y.C., D.G., P.G.P.E. and Y.Q. performed data analysis with assistance from G.S., Z.W. and X.L.; Y.Q., Y.C. and P.G.P.E. wrote the paper with edits from all authors.

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Correspondence to Qisen Yang, Fumin Lei or Yanhua Qu.

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Extended data

Extended Data Fig. 1 Population genetic structure of the three species.

Uniform manifold approximation and projection (a) and Admixture (b) do not detect clearly genetic divergence and the optimal number of clusters (k) explaining variation among populations is k = 1. Dark colored circles in (a) show individuals collected from north part of ranges, while light colored circles are those from south part of range.

Extended Data Fig. 2 The climatically adapted phenotypes (calculating as genome-estimated breeding values, GEBVs) are not uniformed distributed and vary with the gradients of the three climatic variables.

Solid lines and shadows show loess fitting curve and 95% confidence interval. These curves reflect that the GEBVs change across the maximum temperatures in the warmest month (a, bio5), the minimum temperature in the coldest month (b, bio6) and seasonal precipitation (c, bio15), suggesting different reactions to the given climatic variable. Large and small GEBVs values can be considered warm-wet adapted phenotypes (that is, sampling sites in southwestern parts) and cold-dry adapted phenotypes (that is, sampling sites in northeastern parts).

Extended Data Fig. 3 The heterozygosity of randomly extracted SNPs does not significantly correlate with the three climatic variables (grey dots and lines), while that of climate associated SNPs shows significant correlation with the three climatic variables.

Solid lines and shadows showed linear regression fitting curve and 95% confidence interval. (a) maximum temperatures in the warmest month (bio5), (b) the minimum temperature in the coldest month (bio6), (c) seasonal precipitation (bio15). These results indicate that the potential role of these candidate SNPs on local climate adaptation is not confounded by population genetic structure.

Extended Data Fig. 4 GradientForest predicted local genetic offsets (GO) of the three species in response to future climate changes.

(a)-(d) Local genetic offset modelling shows the high risk for the populations in southwestern parts and low risk for the populations in the northeastern parts of ranges. Left, plateau pika (n = 66); mid, WR-snowfinch (n = 68); right, RN-snowfinch (n = 55). (a) 2070 SSP1-2.6, (b) 2070 SSP5-8.5, (c) 2100 SSP1-2.6, (d) 2100 SSP5-8.5. (e) In all three species, local genetic offset revealed similar but magnitude-dependent spatial patterns under the different climate scenarios (n = 50,000 2.5-min grids). All modelling results consistently suggest that the local populations of the three species vary in their vulnerability to climate change, and that the populations in the southwestern parts of the ranges are most maladapted to future climate. The box plots show the median (center line) and 25th-75th percentiles (box limits). The whiskers extend to the top/bottom to the maxima and minima.

Extended Data Fig. 5 Generalized dissimilarity modelling (GDM) predicted local genetic offsets (GO) of the three species in response to future climate changes.

(a)-(d) Local genetic offset modelling shows the high climate risk for the populations in southwestern parts and low risk for the populations in the northeastern parts of ranges. Left, plateau pika (n = 66); mid, WR-snowfinch (n = 68); right, RN-snowfinch (n = 55). (a) 2070 SSP1-2.6, (b) 2070 SSP5-8.5, (c) 2100 SSP1-2.6, (d) 2100 SSP5-8.5. (e) In all three species, local genetic offset revealed similar but magnitude-dependent spatial patterns under the different climate scenarios (n = 50,000 2.5-min grids). All modelling results consistently suggest that the local populations of the three species vary in their vulnerability to climate change, and that the populations in the southwestern parts of the ranges are most maladapted to future climate. The box plots show the median (center line) and 25th-75th percentiles (box limits). The whiskers extend to the top/bottom to the maxima and minima.

Extended Data Fig. 6 Ecological niche modelling predicted suitable niches of the three species under current and future climate conditions. The ecological niche modelling took biointeraction into accounted.

(a)-(e) Projections of niches suitable for the three species. Left, plateau pika; mid, WR-snowfinch; right, RN-snowfinch. (a) Current time, (b) 2070 SSP1-2.6, (c) 2070 SSP5-8.5, (d) 2100 SSP1-2.6, (e) 2100 SSP5-8.5. Much of the areas in the southwestern parts of ranges will reduce or lose the suitable niches, but those in the northeastern parts will remain climatically suitable for all three species.

Extended Data Fig. 7 GradientForest predicted forward genetic offsets (FGO) of the three species in response to future climate changes.

(a)-(d) Forward genetic offset modelling shows the high risk for the populations in southwestern parts and low risk for the populations in the northeastern parts of ranges. Left, plateau pika (n = 66); mid, WR-snowfinch (n = 68); right, RN-snowfinch (n = 55). (a) 2070 SSP1-2.6, (b) 2070 SSP5-8.5, (c) 2100 SSP1-2.6, (d) 2100 SSP5-8.5. These results show the lowest forward genetic offsets are found for the populations in the northeastern parts of the ranges and the highest genetic offsets for the populations in the southwestern parts of the ranges.

Extended Data Fig. 8 GradientForest predicted reverse genetic offsets (RGO) of the three species in response to future climate changes.

(a)-(d) Reverse genetic offset modelling shows the high risk for the populations in southwestern parts and low risk for the populations in the northeastern parts of ranges. Left, plateau pika (n = 66); mid, WR-snowfinch (n = 68); right, RN-snowfinch (n = 55). (a) 2070 SSP1-2.6, (b) 2070 SSP5-8.5, (c) 2100 SSP1-2.6, (d) 2100 SSP5-8.5. These results show the lowest reverse genetic offsets are found for the populations in the northeastern parts of the ranges and the highest genetic offsets for the populations in the southwestern parts of the ranges.

Extended Data Fig. 9 Landscape genetic analysis predicted the density of dispersals between populations based on the effect of topology for plateau pika, land cover for WR-snowfinch and climatic conditions for RN-snowfinch.

Landscape barriers limit dispersal between southwestern and northeastern populations, and the habitat barriers would be further exacerbated for the RN-snowfinch but remain the same for plateau pika and WR-snowfinch under future climate. Left, plateau pika; mid, WR-snowfinch; right, RN-snowfinch. From top to bottom, current, 2070 SSP1-2.6, 2070 SSP5-8.5, 2100 SSP1-2.6, 2100 SSP5-8.5.

Extended Data Fig. 10 Projection of niche suitability using ecological niche modelling without considering biointeraction.

Three species show high similarity in their current and future suitable niches (left), supporting by high Pearson’s correlation coefficients (middle, rs = 0.81-0.96, P < 2.66e-22, two-tailed Pearson’s correlation) and Schoener’s D niche similarity scores (right, Schoener’s Ds = 0.81-0.93). These results suggest that the three species show similar population-level climate change vulnerability.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Tables 1–5.

Reporting Summary

Supplementary Data 1

Sampling information of the three species used in this study. We sampled and sequenced 66, 68 and 55 individual plateau pikas, WR snowfinches and RN snowfinches across their distribution ranges.

Supplementary Data 2

Statistics of the sequencing data of the three species used in this study. We generated the high-density genomic datasets (mean sequencing depth of 18×–21×) comprising 189 individuals representing the three species across their distribution ranges.

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Chen, Y., Ge, D., Ericson, P.G.P. et al. Alpine burrow-sharing mammals and birds show similar population-level climate change risks. Nat. Clim. Chang. 13, 990–996 (2023). https://doi.org/10.1038/s41558-023-01772-8

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