Spatial scale modulates the strength of ecological processes driving disease distributions

Significance For four decades, ecologists have hypothesized that biotic interactions predominantly control species’ distributions at local scales, whereas abiotic factors operate more at regional scales. Here, we demonstrate that the drivers of three emerging diseases (amphibian chytridiomycosis, West Nile virus, and Lyme disease) in the United States support the predictions of this fundamental hypothesis. Humans are contributing to biodiversity loss, changes in dispersal patterns, and global climate change at an unprecedented rate. Our results highlight that common single-scale analyses can misestimate the impact that humans are having on biodiversity, disease, and the environment. Humans are altering the distribution of species by changing the climate and disrupting biotic interactions and dispersal. A fundamental hypothesis in spatial ecology suggests that these effects are scale dependent; biotic interactions should shape distributions at local scales, whereas climate should dominate at regional scales. If so, common single-scale analyses might misestimate the impacts of anthropogenic modifications on biodiversity and the environment. However, large-scale datasets necessary to test these hypotheses have not been available until recently. Here we conduct a cross-continental, cross-scale (almost five orders of magnitude) analysis of the influence of biotic and abiotic processes and human population density on the distribution of three emerging pathogens: the amphibian chytrid fungus implicated in worldwide amphibian declines and West Nile virus and the bacterium that causes Lyme disease (Borrelia burgdorferi), which are responsible for ongoing human health crises. In all three systems, we show that biotic factors were significant predictors of pathogen distributions in multiple regression models only at local scales (∼102–103 km2), whereas climate and human population density always were significant only at relatively larger, regional scales (usually >104 km2). Spatial autocorrelation analyses revealed that biotic factors were more variable at smaller scales, whereas climatic factors were more variable at larger scales, as is consistent with the prediction that factors should be important at the scales at which they vary the most. Finally, no single scale could detect the importance of all three categories of processes. These results highlight that common single-scale analyses can misrepresent the true impact of anthropogenic modifications on biodiversity and the environment.

[1]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[2]  Kate E. Jones,et al.  Global trends in emerging infectious diseases , 2008, Nature.

[3]  S. Lazic,et al.  Model based Inference in the Life Sciences: a Primer on Evidence , 2011 .

[4]  Owen L. Petchey,et al.  Unraveling the Interplay of Community Assembly Processes Acting on Multiple Niche Axes across Spatial Scales , 2014, The American Naturalist.

[5]  T. Raffel,et al.  Confronting inconsistencies in the amphibian‐chytridiomycosis system: implications for disease management , 2014, Biological reviews of the Cambridge Philosophical Society.

[6]  L. Heaney Dynamic disequilibrium: a long-term, large-scale perspective on the equilibrium model of island biogeography , 2000 .

[7]  David E. Blockstein,et al.  Landscape Linkages and Biodiversity , 1993 .

[8]  R. Ostfeld,et al.  Spatial epidemiology: an emerging (or re-emerging) discipline. , 2005, Trends in ecology & evolution.

[9]  Patrick J. McIntyre,et al.  Evolution and Ecology of Species Range Limits , 2009 .

[10]  R. Ostfeld,et al.  Climate Change and Infectious Diseases: From Evidence to a Predictive Framework , 2013, Science.

[11]  C. Parmesan Ecological and Evolutionary Responses to Recent Climate Change , 2006 .

[12]  R. Ostfeld,et al.  Biodiversity and Disease Risk: the Case of Lyme Disease , 2000 .

[13]  Gary R. Graves,et al.  Macroecological signals of species interactions in the Danish avifauna , 2010, Proceedings of the National Academy of Sciences.

[14]  A. M. Kilpatrick,et al.  Globalization, Land Use, and the Invasion of West Nile Virus , 2011, Science.

[15]  Brody Sandel,et al.  Scale as a lurking factor: incorporating scale-dependence in experimental ecology , 2009 .

[16]  Jonathan M. Chase,et al.  Spatial scale dictates the productivity–biodiversity relationship , 2002, Nature.

[17]  L. Kramer,et al.  Vector-Virus Interactions and Transmission Dynamics of West Nile Virus , 2013, Viruses.

[18]  Jeremy M. Cohen,et al.  Biodiversity inhibits parasites: Broad evidence for the dilution effect , 2015, Proceedings of the National Academy of Sciences.

[19]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[20]  C. Wood,et al.  Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission. , 2013, Trends in ecology & evolution.

[21]  C. Rahbek The role of spatial scale and the perception of large‐scale species‐richness patterns , 2004 .

[22]  David R. Anderson,et al.  Model Based Inference in the Life Sciences: A Primer on Evidence , 1998 .

[23]  P. Daszak,et al.  The ecology and impact of chytridiomycosis: an emerging disease of amphibians. , 2010, Trends in ecology & evolution.

[24]  Andrew J Tatem,et al.  Global traffic and disease vector dispersal. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[25]  R. Ostfeld,et al.  Frontiers in research on biodiversity and disease. , 2015, Ecology letters.

[26]  J. Chave The problem of pattern and scale in ecology: what have we learned in 20 years? , 2013, Ecology letters.

[27]  Kate E. Jones,et al.  Impacts of biodiversity on the emergence and transmission of infectious diseases , 2010, Nature.

[28]  J. Wiens Spatial Scaling in Ecology , 1989 .

[29]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[30]  J. Lockwood,et al.  Biotic homogenization: a few winners replacing many losers in the next mass extinction. , 1999, Trends in ecology & evolution.

[31]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[32]  Yiming Li,et al.  Climate, vegetation, introduced hosts and trade shape a global wildlife pandemic , 2013, Proceedings of the Royal Society B: Biological Sciences.

[33]  D. Richardson,et al.  Inferring Process from Pattern in Plant Invasions: A Semimechanistic Model Incorporating Propagule Pressure and Environmental Factors , 2003, The American Naturalist.

[34]  B S Schwartz,et al.  Environmental risk factors for Lyme disease identified with geographic information systems. , 1995, American journal of public health.

[35]  B. McGill Matters of Scale , 2010, Science.

[36]  Jonathan M. Chase,et al.  Ecological correlates of risk and incidence of West Nile virus in the United States , 2008, Oecologia.

[37]  B. Menge,et al.  Species Diversity Gradients: Synthesis of the Roles of Predation, Competition, and Temporal Heterogeneity , 1976, The American Naturalist.

[38]  A. Dobson,et al.  Frontiers in climate change–disease research , 2011, Trends in Ecology & Evolution.

[39]  S. Levin The problem of pattern and scale in ecology , 1992 .

[40]  R. Macarthur The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture , 2005 .