COS 140-4 - Disease structured N-mixture models: A tool for inference of disease dynamics without mark-recapture methods

Thursday, August 10, 2017: 9:00 AM
B113, Oregon Convention Center
Graziella V. DiRenzo1, Elise Zipkin2, Evan H. Campbell Grant3, J. Andrew Royle4, Ana V. Longo5, Kelly R. Zamudio5 and Karen R. Lips6, (1)Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, (2)Department of Integrative Biology, Michigan State University, East Lansing, MI, (3)Patuxent Wildlife Research Center, US Geological Survey, (4)USGS Patuxent Wildlife Research Center, Laurel, MD, (5)Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, (6)Department of Biology, University of Maryland, College Park, MD

Emerging infectious diseases threaten human health, food security, and biodiversity. Following an outbreak, persisting hosts must cope with pathogen presence as it continues to infect individuals, leading to host-pathogen coexistence. Despite decades of research, testing host-pathogen coexistence hypotheses with empirical data has been limited because low host abundances and secretive habits make tracking individuals over time difficult. As a result, mark-recapture analyses tend to produce estimates of host demographic rates with low precision and accuracy. Recently developed N-mixture models provide a mechanistic framework to estimate demographic rates using unmarked count data (e.g., abundance indices over time and space). We developed a class of disease-structured N-mixture models to estimate demographic rates and disease dynamics, while accounting for imperfect detection of both hosts and pathogens. Our model structure is analogous to that of multistate mark-recapture models with which one can estimate state-specific parameters, but ours uses unmarked count data to estimate the (1) number of hosts that survive annually in infected and uninfected disease states, (2) transition probability between disease states, and (3) number of individuals recruited via immigration and reproduction.We use our modeling framework to assess support for three host-pathogen coexistence hypotheses − source-sink, eco-evolutionary rescue, and pathogen hotspots − in a Neotropical amphibian community decimated by Batrachochytrium dendrobatidis. During 2010 – 2014, we surveyed amphibians along four 200-m stream and three 400-m trail transects in Parque Nacional G. D. Omar Torríjos Herrera, Coclé Province, El Copé, Panama. We subdivided each transect into 20-m adjacent sites and surveyed each site one to eight times each season.


We found that the primary driver of host-pathogen coexistence was eco-evolutionary rescue, as evidenced by the absence of negative infection effects on amphibian survival or recruitment. Average apparent monthly survival rates of uninfected and infected hosts were both close to 96% (95% CI: Φuninfected = 94.36−98.91%; Φinfected = 94.86−98.01%), and the expected number of uninfected and infected hosts recruited (via immigration/reproduction) was less than one host per disease state per 20-m site every 8.5 months (95% CI; γuninfected = 0.30−0.61, γinfected = 0.28−0.59). The secondary driver of host-pathogen coexistence was pathogen hotspots (i.e., transmission was highest in areas of low abundance); we found no support for the source-sink hypothesis. Our disease-structured N-mixture model represents a valuable advancement for estimating population dynamics using multistate data from unmarked individuals, and provides new opportunities to study disease dynamics in remnant host populations decimated by virulent pathogens.