Would future climate warming cause zoonotic diseases to spread over long distances?

Dipus sagitta is a major rodent found in arid environments and desert areas. They feed on plant seeds, young branches and some small insects, and have hibernating habits. Peak Dipus sagitta numbers impact the construction of the plant community in the environment, but also have a human impact as these rodents carry a variety of parasitic fleas capable of spreading serious diseases to humans. Based on 216 present distribution records of Dipus sagitta and seven environmental variables, this article simulates the potential distribution of Dipus sagitta during the Last Glacial Maximum, the mid-Holocene, the present and the future (2070s, RCP4.5, RCP8.5). This study also analyzes the geographic changes of the population distribution and evaluates the importance of climate factors by integrating contribution rate, replacement importance value and the jackknife test using the MaxEnt model. In this study, we opted to assess the predictive capabilities of our model using the receiver operating characteristic (ROC) and partial receiver operating characteristic (pROC) metrics. The findings indicate that the AUC value exceeds 0.9 and the AUC ratio is greater than 1, indicating superior predictive performance by the model. The results showed that the main climatic factors affecting the distribution of the three-toed jerboa were precipitation in the coldest quarter, temperature seasonality (standard deviation), and mean annual temperature. Under the two warming scenarios of the mid-Holocene and the future, there were differences in the changes in the distribution area of the three-toed jerboa. During the mid-Holocene, the suitable distribution area of the three-toed jerboa expanded, with a 93.91% increase in the rate of change compared to the Last Glacial Maximum. The size of the three-toed jerboa’s habitat decreases under both future climate scenarios. Compared to the current period, under the RCP4.5 emission scenario, the change rate is −2.96%, and under the RCP8.5 emission scenario, the change rate is −7.41%. This indicates a trend of contraction in the south and expansion in the north. It is important to assess changes in the geographic population of Dipus sagitta due to climate change to formulate population control strategies of these harmful rodents and to prevent and control the long-distance transmission of zoonotic diseases.

[1]  A. Chidthaisong,et al.  Summary for Policymakers , 2022, The Ocean and Cryosphere in a Changing Climate.

[2]  Colin J. Carlson,et al.  Climate change increases cross-species viral transmission risk , 2022, Nature.

[3]  Guanghua Zhao,et al.  Prediction of the potential distribution pattern of the great gerbil (Rhombomys opimus) under climate change based on ensemble modelling. , 2022, Pest management science.

[4]  Zhibin Zhang,et al.  Factors influencing range contraction of a rodent herbivore in a steppe grassland over the past decades , 2022, Ecology and evolution.

[5]  Dipus sagitta , 2022, CABI Compendium.

[6]  Haoxiang Zhao,et al.  Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models , 2021, Insects.

[7]  Brian O'Connor,et al.  Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies , 2020, Ecol. Informatics.

[8]  E. Ebrahimi,et al.  Modelling current and future potential distributions of two desert jerboas under climate change in Iran , 2019, Ecol. Informatics.

[9]  W. Kong,et al.  [Optimizing MaxEnt model in the prediction of species distribution.] , 2019, Ying yong sheng tai xue bao = The journal of applied ecology.

[10]  Xiaodong Wu,et al.  Effects of grazing on the northern three-toed jerboa pre- and post-hibernation , 2018, The Journal of Wildlife Management.

[11]  A. Surov,et al.  Phylogeographical study reveals high genetic diversity in a widespread desert rodent, Dipus sagitta (Dipodidae: Rodentia) , 2018 .

[12]  J. Lendemer,et al.  Climate change impacts on endemic, high-elevation lichens in a biodiversity hotspot , 2016, Biodiversity and Conservation.

[13]  F. G. Barbosa,et al.  Characteristics of the top-cited papers in species distribution predictive models , 2015 .

[14]  Z. Nan,et al.  Climate Change-Induced Range Expansion of a Subterranean Rodent: Implications for Rangeland Management in Qinghai-Tibetan Plateau , 2015, PloS one.

[15]  M. Wisz,et al.  Forecasted coral reef decline in marine biodiversity hotspots under climate change , 2015, Global change biology.

[16]  Jason L. Brown SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses , 2014 .

[17]  P. Roy,et al.  Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills , 2013 .

[18]  C. Nowak,et al.  The impact of global climate change on genetic diversity within populations and species , 2013, Molecular ecology.

[19]  R. Bertrand,et al.  Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France , 2012 .

[20]  Carrie A. Schloss,et al.  Dispersal will limit ability of mammals to track climate change in the Western Hemisphere , 2012, Proceedings of the National Academy of Sciences.

[21]  Jason L. Brown,et al.  Integrating statistical genetic and geospatial methods brings new power to phylogeography. , 2011, Molecular phylogenetics and evolution.

[22]  Trevor Hastie,et al.  A statistical explanation of MaxEnt for ecologists , 2011 .

[23]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[24]  N. Stenseth,et al.  Modeling the epidemiological history of plague in Central Asia: Palaeoclimatic forcing on a disease system over the past millennium , 2010, BMC Biology.

[25]  G. Henebry,et al.  Climate and environmental change in arid Central Asia: impacts, vulnerability, and adaptations. , 2009 .

[26]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[27]  M. Begon,et al.  Empirical assessment of a threshold model for sylvatic plague , 2007, Journal of The Royal Society Interface.

[28]  A. Townsend Peterson,et al.  Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent , 2007 .

[29]  A. Barnosky,et al.  Late Quaternary Extinctions: State of the Debate , 2006 .

[30]  R. Pearson,et al.  Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar , 2006 .

[31]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[32]  L. Fahrig Effects of Habitat Fragmentation on Biodiversity , 2003 .

[33]  M. Graham CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION , 2003 .

[34]  S. Davis,et al.  Extrinsic and intrinsic factors determine the eruptive dynamics of Brandt's voles Microtus brandti in Inner Mongolia, China , 2003 .

[35]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[36]  John F. McLaughlin,et al.  Climate change hastens population extinctions , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[37]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[38]  王珂 Wang Ke,et al.  Assessment of the ecosystem stability of Shapotou Arid Desert Nature Reserve in Ningxia, China , 2019, Acta Ecologica Sinica.

[39]  李. L. Qing,et al.  Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling , 2017 .

[40]  Paola D’A Lessandro,et al.  Using Maximum Entropy Modeling ( MaxEnt ) to predict future trends in the distribution of high altitude endemic insects in response to climate change , 2017 .

[41]  Chris D Thomas,et al.  Climate change and evolutionary adaptations at species' range margins. , 2011, Annual review of entomology.

[42]  Wei Wu,et al.  Maximum entropy niche-based modeling (Maxent) of potential geographical distributions of fruit flies Dacus bivittatus, D. ciliatus and D. vertebrates (Diptera: Tephritidae). , 2009 .

[43]  Long Hao Review of Research on Correlation of Holocene Environmental Change and Human Activities in China , 2009 .

[44]  Cao Feng-hai POPULATION DYNAMICS OF NORTHERN THREE-TOED JERBOA UNDER DIFFERENT DISTURBANCE IN DESERT REGION , 2009 .

[45]  Yue Le-ping Holocene Climate Change and Desertification in Northern China , 2007 .