Estimating Detection Probability for Burmese Pythons with Few Detections and Zero Recaptures

Abstract

Detection has been a long-standing challenge to monitoring populations of cryptic herpetofauna, which often have detection probabilities that are closer to zero than to one. Burmese Pythons (Python bivittatus [=Python molurus bivittatus]), a recent invader in the Greater Everglades Ecosystem of Florida, are cryptic snakes that have long periods of inactivity. In addition, management actions such as removal of every python encountered create challenges for estimating population size and quantifying effects of management using traditional statistical approaches. We used Bayesian analysis of data collected from 59 visual surveys (144 person-surveys) covering a total distance of 485.6 km (1,185.1 person-km) and radiotelemetry to estimate detection probability for Burmese Pythons. These estimates can improve interpretation of encounter and removal data. We found that detection probability ranged from 0.0001 to 0.0146 depending on whether effort units accounted for total human effort across multiple surveyors and statistical method used. On the basis of our surveys, detection probabilities for Burmese Pythons are therefore likely <0.05, but factors such as the number of searchers or time of day may improve detection probability. Traditional capture-recapture or visual surveys are, however, unlikely to yield accurate information on Burmese Python population size or trends across time without cost-prohibitive effort. Consequently, novel methods development to monitor or measure Burmese Python populations, including techniques better equipped to handle very low detection, are critically needed for informative and reliable inferences about population size or the management effects of python removal.