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

被引:6
作者
Chen, Yilin [1 ,2 ]
Ge, Deyan [1 ,2 ]
Ericson, Per G. P. [3 ]
Song, Gang [1 ]
Wen, Zhixin [1 ]
Luo, Xu [4 ]
Yang, Qisen [1 ]
Lei, Fumin [1 ,2 ,5 ]
Qu, Yanhua [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Inst Zool, Key Lab Zool Systemat & Evolut, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Coll Life Sci, Beijing, Peoples R China
[3] Swedish Museum Nat Hist, Dept Bioinformat & Genet, Stockholm, Sweden
[4] Southwest Forestry Univ, Fac Biodivers & Conservat, Kunming, Peoples R China
[5] Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming, Peoples R China
基金
瑞典研究理事会; 中国国家自然科学基金;
关键词
DISTRIBUTION MODELS IMPLICATIONS; QINGHAI-TIBET PLATEAU; LOCAL ADAPTATION; AMERICAN PIKA; R PACKAGE; BIODIVERSITY; DIVERSITY; SELECTION; PATTERNS; BIAS;
D O I
10.1038/s41558-023-01772-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The authors use niche modelling and landscape genetic approaches to understand population-level climate change vulnerability for three alpine species. Their approach reveals similar population-level vulnerability for the studied keystone species and its two beneficiary species. 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.
引用
收藏
页码:990 / +
页数:24
相关论文
共 93 条
[1]   NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].
AKAIKE, H .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723
[2]   Fast model-based estimation of ancestry in unrelated individuals [J].
Alexander, David H. ;
Novembre, John ;
Lange, Kenneth .
GENOME RESEARCH, 2009, 19 (09) :1655-1664
[3]   Avoiding pitfalls when using information-theoretic methods [J].
Anderson, DR ;
Burnham, KP .
JOURNAL OF WILDLIFE MANAGEMENT, 2002, 66 (03) :912-918
[4]   Standards for distribution models in biodiversity assessments [J].
Araujo, Miguel B. ;
Anderson, Robert P. ;
Marcia Barbosa, A. ;
Beale, Colin M. ;
Dormann, Carsten F. ;
Early, Regan ;
Garcia, Raquel A. ;
Guisan, Antoine ;
Maiorano, Luigi ;
Naimi, Babak ;
O'Hara, Robert B. ;
Zimmermann, Niklaus E. ;
Rahbek, Carsten .
SCIENCE ADVANCES, 2019, 5 (01)
[5]   Good neighbours? Determinants of aggregation and segregation among alpine herbivores [J].
Barrio, Isabel C. ;
Hik, David S. .
ECOSCIENCE, 2013, 20 (03) :276-282
[6]   Fitting Linear Mixed-Effects Models Using lme4 [J].
Bates, Douglas ;
Maechler, Martin ;
Bolker, Benjamin M. ;
Walker, Steven C. .
JOURNAL OF STATISTICAL SOFTWARE, 2015, 67 (01) :1-48
[7]   Genomic signals of selection predict climate-driven population declines in a migratory bird [J].
Bay, Rachael A. ;
Harrigan, Ryan J. ;
Le Underwood, Vinh ;
Gibbs, H. Lisle ;
Smith, Thomas B. ;
Ruegg, Kristen .
SCIENCE, 2018, 359 (6371) :83-+
[8]   Spatial bias in the GBIF database and its effect on modeling species' geographic distributions [J].
Beck, Jan ;
Boeller, Marianne ;
Erhardt, Andreas ;
Schwanghart, Wolfgang .
ECOLOGICAL INFORMATICS, 2014, 19 :10-15
[9]   Will climate change promote future invasions? [J].
Bellard, Celine ;
Thuiller, Wilfried ;
Leroy, Boris ;
Genovesi, Piero ;
Bakkenes, Michel ;
Courchamp, Franck .
GLOBAL CHANGE BIOLOGY, 2013, 19 (12) :3740-3748
[10]   Spatial filtering to reduce sampling bias can improve the performance of ecological niche models [J].
Boria, Robert A. ;
Olson, Link E. ;
Goodman, Steven M. ;
Anderson, Robert P. .
ECOLOGICAL MODELLING, 2014, 275 :73-77