<?xml version="1.0" encoding="UTF-8" ?>
<xml>
<records>
<record>
<title>Modeling at the interface of ecology and epidemiology</title>
<authors>
<author>Min Su</author>
<author>Hong Wang</author>
</authors>
<affiliations>
<affiliation>
School of Mathematics, Hefei University of Technology, Hefei 230009, China
</affiliation>
</affiliations>
<journal>Computational Ecology and Software</journal>
<issn>ISSN 2220-721X</issn>
<homepage>http://www.iaees.org/publications/journals/ces/online-version.asp</homepage>
<year>2015</year>
<volume>5</volume>
<issue>4</issue>
<startpage>367</startpage>
<endpage>379</endpage>
<publisher>International Academy of Ecology and Environmental Sciences</publisher>
<location>Hong Kong</location>
<date>
<received>25 July 2015</received>
<accepted>3 August 2015</accepted>
<published>1 December 2015</published>
</date>
<keywords>
<keyword>eco-epidemiology</keyword>
<keyword>community structure</keyword>
<keyword>spatial structure</keyword>
<keyword>predation pressure</keyword>
<keyword>species coexistence</keyword>
</keywords>
<abstract>
We briefly present a synthesis of theoretical models in eco-epidemiology which merges the fields of ecology and epidemics. In particular we discuss the role of parasites/pathogens in community assembly formation and species coexistence, as well as the potential of biological control. Recent works have revealed that the complexity in parasite-mediated interactions can alter the dynamic behavior of eco-epidemiological systems, exhibiting oscillations, switching stability regimes. Both community structure and interaction strength also can affect the role of parasites in the host-parasite dynamics. The emerging research area focuses on the spatial structure and distribution pattern in eco-epidemiology. Compared with the well mixed system, spatial structure in eco-epidemiology can lead to different dynamic behavior. We therefore highlight the need to address the impact of parasites/pathogens on real community structures and combine the evolutionary potential to predict the complex dynamics during the biological control in eco-epidemiological systems.
</abstract>
<doi>DOI 10.0000/issn-2220-721x-compuecol-2015-v5-0027</doi>
<url>http://www.iaees.org/publications/journals/ces/articles/2015-5(4)/modeling-ecology-and-epidemiology.pdf</url>
</record>
</records>
</xml>
