Efficient use of Community Monitoring Data: Estimating Patterns in Co–occurrence, and Novel Multi–species Models that Address Two Types of Sampling Error

PATTON, PHILIP THOMAS. Efficient use of Community Monitoring Data: Estimating Patterns in Co–occurrence, and Novel Multi–species Models that Address Two Types of Sampling Error. (Under the direction of Dr. Krishna Pacifici.) One of the most efficient ways to monitor wildlife involves collecting occurrence data for multiple species within an ecological community, which produces a versatile dataset that can be used to answer a wide variety of questions related to wildlife ecology and management. In the two principle chapters of this thesis, I demonstrated the versatility of these data by using them to address two distinct research questions. When evaluating the ecological value of land use within a landscape, investigators typically rely on measures of habitat quality and habitat selection. Traditional measures of habitat selection and habitat quality require data from resource intensive study designs. Often, managers must evaluate ecological value despite only having data from less resource intensive study designs. In the first chapter of this thesis, I used community occurrence data to measure habitat quality and habitat selection for the Puerto Rican Vireo, an endemic songbird whose population growth is depressed by brood parasitism from the Shiny Cowbird. I estimated vireo occupancy probability as a measure of habitat selection, and the probability of cowbird occurrence given vireo presence as a measure of habitat quality. To estimate the latter, I explored different ways of incorporating host information into joint occurrence models. These included the conditional occurrence model (Waddle et al., 2010), a novel two-stage occurrence model, and a commonly used model that includes the naïve occurrence of the host as a covariate. I assessed the predictive performance of each model using posterior predictive checks, a pseudo–R2, and DIC. The two–stage model performed best by our measures of predictive performance. Vireos preferentially selected forested sites and shaded coffee sites over sun coffee sites. By our measure of habitat quality, either type of coffee plantation was of poor quality, and the forested sites were of high quality. This suggested that shade coffee may be an ecological trap for the vireo in our study area. False positive sampling errors, whereby species are detected at sites they do not occupy, are largely unavoidable in occurrence sampling. Studies of single species occurrence models show that false positives severely bias estimates of occurrence. Community occurrence models (Dorazio & Royle, 2005) produce precise and unbiased estimates of occurrence and biodiversity. However, these models could be severely biased by false positives, and the literature lacks any examples of community occurrence models that accommodate false positive sampling errors. In the second chapter of this thesis, I developed novel community models that address false positive and false negative sampling errors. Using Monte Carlo simulation and an example application, I showed how these models perform relative to the Dorazio & Royle (2005) model. One of the new models, a community level extension of the Royle & Link (2006) model, predicted the example data set better than Dorazio & Royle (2005) model. The two new community level false positive models performed equally well in simulation, and better than the foundational community model. False positive detections positively bias estimates of occurrence in community occupancy models, just as they do in single-species models. Practitioners using community level occurrence models should consider fitting the models described here, lest they risk having biased estimates affect decisions in inference or management. Both of these chapters demonstrated ways of efficiently using community occurrence data to answer questions related to ecology, management, and methodology. © Copyright 2016 by Philip Thomas Patton All Rights Reserved Efficient use of Community Monitoring Data: Estimating Patterns in Co–occurrence, and Novel Multi–species Models that Address Two Types of Sampling Error by Philip Thomas Patton A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Master of Science Fisheries, Wildlife, and Conservation Biology Raleigh, North Carolina 2016