This semester we welcome Daniel Suh and Grant Foster to the lab. Daniel is starting his PhD in Ecology/IDEAS with interests in parasites and invasive species. Previously, Daniel earned his BS in Biology from Pepperdine and worked as a research assistant in Mike Levy’s zoonotic disease lab at U Penn. Grant is an Ecology/Biology major and honors student. His primary research in the lab is centered on understanding specificity of complex life cycle parasites

Annakate will attend the 73rd Annual Conference of the Southeast Association of Fish and Wildlife Agencies this Fall, after winning a competitive stipend. The meeting will allow Annakate to hear research from state and federal agencies, citizen’s organizations, universities, and private wildlife research groups, fisheries and wildlife scientists, agency enforcement personnel, and other natural resource related organizations. Congratulations Annakate!

Andrew led a recent study aimed at characterizing the phylogenetic specialism of around 1500 mammal parasites, finding that patterns of specialism vary among parasite types and transmission modes. Some parasites show an aggregation in the host phylogeny consistent with a leaps-and-creeps history; occasionally, a parasite will take a large leap to a new host species and creep through the phylogeny acquiring related hosts to the one it jumped to. The study provides a vocabulary and context to consider case studies, including zoonoses, and the authors hope that it can catalyze more studies on the evolutionary ecology of parasite host range. The paper is published in Proceedings of the Royal Society B and was profiled on the journals blog.

Andrew was involved in a link prediction problem study with recent Odum graduate, Tad Dallas and colleague John Drake, which was recently published in PLoS Computational Biology. They wanted to (i) estimate missing but likely links in a host-parasite network, (ii) test if those missing links changed the network structure and (iii) for a real data set, establish what biological features best predict missing links. They were able to recover randomly removed missing links with high accuracy, and showed that putting these links in to the host-parasite network can dramatically change network structure. For a rodent-parasite dataset, they found that host litter size and diet breadth, along with parasite taxonomy were important features for accurately predicting missing links.