Likelihood-Based Population Viability Analysis in the Presence of Observation Error

• Activity Number: 471
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Khurram Nadeem*+ and Subhash Lele
• Companies: University of Alberta and University of Alberta
• Keywords: data cloning ; population viability analysis ; observations error ; state-space models ; theta-logistic growth ; Akaike information criterion

Abstract

Population viability analysis (PVA) entails calculation of extinction risk, as defined by various extinction metrics, for a study population. In this paper, we present a likelihood based PVA in the presence of observation error. We illustrate the importance of incorporation of observation error in PVA by reanalyzing a population time series of song sparrow (Melospiza melodia). We incorporated observation error using state-space models fitted via the data cloning method. We show that model with observation error fits better than the one without observation error. The extinction risks predicted by with and without observation error models are quite different. Further analysis of possible causes for observation error revealed that some component of the observation error might be due to unreported dispersal. A complete analysis of such data, thus, would require explicit spatial models and data on dispersal along with observation error. Our conclusions are, therefore, two-fold: 1) observation errors in PVA matter and 2) integrating these errors in PVA is not always enough and can still lead to important biases in parameter estimates if other processes such as dispersal are ignored.