The role of reservoir species in mediating plague's dynamic response to climate

The distribution and transmission of Yersinia pestis, the bacterial agent of plague, responds dynamically to climate, both within wildlife reservoirs and human populations. The exact mechanisms mediating plague's response to climate are still poorly understood, particularly across large environmentally heterogeneous regions encompassing several reservoir species. A heterogeneous response to precipitation was observed in plague intensity across northern and southern China during the Third Pandemic. This has been attributed to the response of reservoir species in each region. We use environmental niche modelling and hindcasting methods to test the response of a broad range of reservoir species to precipitation. We find little support for the hypothesis that the response of reservoir species to precipitation mediated the impact of precipitation on plague intensity. We instead observed that precipitation variables were of limited importance in defining species niches and rarely showed the expected response to precipitation across northern and southern China. These findings do not suggest that precipitation–reservoir species dynamics never influence plague intensity but that instead, the response of reservoir species to precipitation across a single biome cannot be assumed and that limited numbers of reservoir species may have a disproportional impact upon plague intensity.

[1]  S. Atkinson,et al.  Biotic factors limit the invasion of the plague pathogen ( Yersinia pestis ) in novel geographical settings , 2022, Global Ecology and Biogeography.

[2]  Bob O’Hara.  Review for "Species distribution models rarely predict the biology of real populations" , 2021 .

[3]  D. Raoult,et al.  Yersinia pestis: the Natural History of Plague , 2020, Clinical Microbiology Reviews.

[4]  Boris V. Schmid,et al.  Plague reservoir species throughout the world. , 2020, Integrative zoology.

[5]  Poliana Mendes,et al.  Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy , 2020 .

[6]  Neftalí Sillero,et al.  Common mistakes in ecological niche models , 2020, Int. J. Geogr. Inf. Sci..

[7]  Boris V. Schmid,et al.  Evolutionary selection of biofilm-mediated extended phenotypes in Yersinia pestis in response to a fluctuating environment , 2020, Nature Communications.

[8]  C. Golden,et al.  The fate of Madagascar’s rainforest habitat , 2019, Nature Climate Change.

[9]  R. Robinson,et al.  Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models , 2019, Ecology and evolution.

[10]  Daniel S. Park,et al.  A checklist for maximizing reproducibility of ecological niche models , 2019, Nature Ecology & Evolution.

[11]  Stephen E. Fick,et al.  WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas , 2017 .

[12]  Carl Boettiger,et al.  R Python, and Ruby clients for GBIF species occurrence data , 2017 .

[13]  J. Diniz‐Filho,et al.  Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis , 2017 .

[14]  Tyler D. Eddy,et al.  Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) , 2016 .

[15]  C. Matthee,et al.  The influence of life history characteristics on flea (Siphonaptera) species distribution models , 2016, Parasites & Vectors.

[16]  M. Baylis,et al.  Effect of temperature and relative humidity on the development times and survival of Synopsyllus fonquerniei and Xenopsylla cheopis, the flea vectors of plague in Madagascar , 2016, Parasites & Vectors.

[17]  R. Dirzo,et al.  Effects of land use on plague (Yersinia pestis) activity in rodents in Tanzania. , 2015, The American journal of tropical medicine and hygiene.

[18]  Boris V. Schmid,et al.  Climate-driven introduction of the Black Death and successive plague reintroductions into Europe , 2015, Proceedings of the National Academy of Sciences.

[19]  Boris V. Schmid,et al.  The trophic responses of two different rodent–vector–plague systems to climate change , 2015, Proceedings of the Royal Society B: Biological Sciences.

[20]  Robert A. Boria,et al.  ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models , 2014 .

[21]  M. Begon,et al.  Plague epizootic cycles in Central Asia , 2014, Biology Letters.

[22]  Boris V. Schmid,et al.  Wet climate and transportation routes accelerate spread of human plague , 2014, Proceedings of the Royal Society B: Biological Sciences.

[23]  F. Piontek,et al.  The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework , 2013, Proceedings of the National Academy of Sciences.

[24]  Ruifu Yang,et al.  Historical variations in mutation rate in an epidemic pathogen, Yersinia pestis , 2012, Proceedings of the National Academy of Sciences.

[25]  N. Stenseth,et al.  Identification of Chinese plague foci from long-term epidemiological data , 2012, Proceedings of the National Academy of Sciences.

[26]  N. Stenseth,et al.  Plague and Climate: Scales Matter , 2011, PLoS pathogens.

[27]  Kung-Sik Chan,et al.  Dynamics of the plague–wildlife–human system in Central Asia are controlled by two epidemiological thresholds , 2011, Proceedings of the National Academy of Sciences.

[28]  Kung-Sik Chan,et al.  Nonlinear effect of climate on plague during the third pandemic in China , 2011, Proceedings of the National Academy of Sciences.

[29]  Dan L Warren,et al.  Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. , 2011, Ecological applications : a publication of the Ecological Society of America.

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

[31]  N. Stenseth,et al.  Interannual variability of human plague occurrence in the Western United States explained by tropical and North Pacific Ocean climate variability. , 2010, The American journal of tropical medicine and hygiene.

[32]  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.

[33]  Zhibin Zhang,et al.  Effect of ENSO-driven precipitation on population irruptions of the Yangtze vole Microtus fortis calamorum in the Dongting Lake region of China. , 2010, Integrative zoology.

[34]  D. Nogues‐Bravo,et al.  Predicting the past distribution of species climatic niches. , 2009 .

[35]  L. Ning,et al.  Interdecadal change in extreme precipitation over South China and its mechanism , 2009 .

[36]  Steven J. Phillips,et al.  Aligning Conservation Priorities Across Taxa in Madagascar with High-Resolution Planning Tools , 2008, Science.

[37]  N. Stenseth,et al.  Plague: Past, Present, and Future , 2008, PLoS medicine.

[38]  Arnoldo Frigessi,et al.  Climatically driven synchrony of gerbil populations allows large-scale plague outbreaks , 2007, Proceedings of the Royal Society B: Biological Sciences.

[39]  S. B. Pole,et al.  Plague dynamics are driven by climate variation , 2006, Proceedings of the National Academy of Sciences.

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

[41]  Andrew P. Martin,et al.  Testing the Generality of a Trophic-cascade Model for Plague , 2005, EcoHealth.

[42]  S. B. Pole,et al.  Predictive Thresholds for Plague in Kazakhstan , 2004, Science.

[43]  Shang-jen Li.  Bubonic plague in nineteenth-century China , 1999, Medical History.

[44]  D. Yan,et al.  Historical changes and future projection of extreme precipitation in China , 2015, Theoretical and Applied Climatology.