State-space models to estimated and understand trends in sparse populations from sparse data

Authors: David Christianson, Michael Bogan, Tyler Coleman, Peter Holm

Year: 2017 (xlix)

Abstract

Monitoring of Quitobaquito pupfish and Sonoyta mud turtle in Organ Pipe Cactus National Monument has been ongoing for several decades, but monitoring methods have changed throughout the years through turnover in personnel and equipment Sampling noise inherent to survey methods, and subtle changes in implementation of survey methods have largely precluded any interpretation of trend from the current monitoring data and there is has not been any estimate of population growth rates in either of the two species This has hampered not only the development and implementation of resource management plans in OPCNM but also the design of resource-question driven research into what factors may influence long-term persistence of these species We present a summary of these survey methods and preliminary analysis into trend We use an Exponential Growth State Space Models to examine existing counts considering the contributions of sampling error and process error contributing to variation in the counts We also considered the potential for density dependent growth using the Ornstein-Uhlenbeck State Space Model (Dennis and Ponciano 2014) The estimate growth rates Quitobaquito pupfish are positive (30% annual growth) but significant uncertainty in these estimates precludes high confidence in these estimates Increased precision in estimating population growth rates may be possible by allocating effort to repeat surveys every other year rather than single surveys each year Linkages between year over year population growth and climatic factors are explored