Collaborative Research: Developing novel methods for estimating coevolutionary processes using tapeworms and their shark and ray hosts

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

Parasites are everywhere and many species have been with their hosts for a long evolutionary time and are extremely particular about the kinds of hosts they parasitize. Somewhat unexpectedly, recent research suggests that many parasite species do not necessarily share a similar evolutionary history with their hosts; rather other factors may be at play in shaping these parasitic associations. Yet, little is understood about which other factors, such as diet or geographic distribution, influence these relationships. This is largely because a method for assessing the importance of other factors is not currently available. This research will develop a method and will test it using a well-known and species-rich system consisted of the tapeworms of sharks and stingrays. This method will be widely applicable to other coevolutionary systems, helping to further our understanding not only of host-parasite systems in general, but also of other biological systems involving intimate associations. Close species associations are ubiquitous and better knowledge on the role of the environment in shaping species associations will be critical to forecasting biodiversity's response to climate change. The research will train postdoctoral researchers and graduate students in quantitative techniques in evolutionary biology. This project moves towards a more mechanistic modeling of the biological influences involved in the coevolution between parasites and hosts. Phylogenies will be generated for a large number of cestode genera, time-calibrated phylogenies will be generated for their corresponding shark and ray (elasmobranch) hosts and robust parasite-host association data will be compiled. New methods and open-source software tools using approximate Bayesian computation will be developed to allow the estimation and testing of models for the evolution of parasite-host systems. The models for parasite evolution will be geographically explicit and will include factors such as cospeciation, environmentally driven extinction, geographic constraints on dispersal and host-switching, and some effects of intermediate hosts. These rich empirical data sets will allow the use of robust, cross-validation methods for hypothesis testing. The end result will be a methodological framework for assessing the contributions of multiple factors, in addition to cophylogeny, in structuring host associations and parasite evolution. The new software will be used to address three main research questions regarding factors that underlie parasite-host interactions including the relative roles various factors play in systems with different properties.