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. 2023 Jul 24;13(1):11906.
doi: 10.1038/s41598-023-38966-1.

Climate-driven marmot-plague dynamics in Mongolia and China

Lei Xu  1 , Qian Wang  1 , Ruifu Yang  2 , Dalantai Ganbold  3 , Nyamdorj Tsogbadrakh  3 , Kaixing Dong  1 , Min Liu  4 , Doniddemberel Altantogtokh  3 , Qiyong Liu  5 , Sainbileg Undrakhbold  6   7 , Bazartseren Boldgiv  8 , Wannian Liang  9 , Nils Chr Stenseth  10   11   12
Affiliations

Climate-driven marmot-plague dynamics in Mongolia and China

Lei Xu et al. Sci Rep. .

Erratum in

Abstract

The incidence of plague has rebounded in the Americas, Asia, and Africa alongside rapid globalization and climate change. Previous studies have shown local climate to have significant nonlinear effects on plague dynamics among rodent communities. We analyzed an 18-year database of plague, spanning 1998 to 2015, in the foci of Mongolia and China to trace the associations between marmot plague and climate factors. Our results suggested a density-dependent effect of precipitation and a geographic location-dependent effect of temperature on marmot plague. That is, a significantly positive relationship was evident between risk of plague and precipitation only when the marmot density exceeded a certain threshold. The geographical heterogeneity of the temperature effect and the contrasting slopes of influence for the Qinghai-Tibet Plateau (QTP) and other regions in the study (nQTP) were primarily related to diversity of climate and landscape types.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Marmot plague in China and Mongolia during 1998–2015 CE. Circles show areas of plague surveillance (red indicates plague detection at a site during the study period, and gray no such detection). The green, dark blue, orange, and light blue areas represent plague foci for four species of marmots in Mongolia and China–. Marmota sibirica foci (green areas) and Mamota baibacina foci (light blue areas) are partially overlapping in Mongolian Altai region. Map was generated with ArcGIS Desktop (ESRI, Inc, Version 16.0, https://desktop.arcgis.com). Boundary shapefiles of China and Mongolia from GADM data (https://gadm.org/data.html).
Figure 2
Figure 2
The effect of marmot density on local precipitation-driven plague dynamics. We found partial effects on plague prevalence for marmot population density, flea index, and annual cumulative precipitation. Plots show the relationship between (A) marmot density and plague prevalence, obtained from f1 in Formula 1; (B) flea index and plague prevalence, obtained from f2 in Formula 1; (C) precipitation and plague prevalence when marmot density was < 0.63, obtained from f4 in Formula 1; and (D) precipitation and plague prevalence when marmot density was >  = 0.63, obtained from f5 in Formula 1. The red curves in panels A, B, and D indicate P < 0.05, while the curve in Panel C is black because P > 0.05.
Figure 3
Figure 3
Geographical heterogeneity of the effect of annual average temperature on the risk of prevalence of marmot plague, based on Model (1) f3. We predicted the prevalence of marmot plague cases from the final model. The color gradient represents predicted log-transformed plague prevalence, ranging from 0 (yellow) to 1 (red). Points show the locations of plague surveillance, and the point size indicates the occurrence risk of plague. Blue shading indicates the QTP region, a.k.a., the Third Pole. Map was generated with ArcGIS Desktop (ESRI, Inc, Version 16.0, https://desktop.arcgis.com). Boundary shapefiles of China and Mongolia from GADM data (https://gadm.org/data.html).
Figure 4
Figure 4
The different effects of annual average temperature on the occurrence of marmot plague in the QTP and other regions plotted in Fig. 3.
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