Realized population change for long‐term monitoring: California spotted owl case study

The annual rate of population change ((t)) is a good metric for evaluating population performance because it summarizes survival and recruitment rates and can be used for open populations. Another measure of population performance, realized population change ((t)) is an encompassing metric of population trend over a period of time; it is the ratio of population size at an end time period relative to the initial population size. Our first goal was to compare mean and (t) as summaries of population change over time. Our second goal was to evaluate different methods for estimating these parameters; specifically we wished to compare the value of estimates from fixed effects models, random effects estimates from mixed effects models, and Bayesian Markov chain Monte Carlo (MCMC) methods. Our final goal was to evaluate the use of the posterior distribution of (t) as a means for estimating the probability of population decline retrospectively. To meet these goals, we used California spotted owl (Strix occidentalis occidentalis) data collected on 3 study areas from 1990 to 2011 as a case study. The estimated MCMC median s for 2 of the study areas were 0.986 and 0.993, indicating declining populations, whereas median was 1.014 for the third study area, indicating an increasing population. For 2 of the study areas, estimated MCMC median (t)s over the 18-year monitoring period were 0.78 and 0.89, suggesting 21% and 11% declines in population size, whereas the third study area was 1.22 suggesting a 22% increase. Results from (t) analyses highlight that small differences in mean from 1.0 (stationary) can result in large differences in population size over a longer time period; these temporal effects are better depicted by (t). Fixed effects, random effects, and MCMC estimates of mean and median and of (t) were similar (9% relative difference). The estimate of temporal process variance was larger for MCMC than the random effects estimates. Results from a Bayesian approach using MCMC simulations indicated that the probabilities of a 15% decline over 18 years were 0.69, 0.40, and 0.04 for the 3 study areas, whereas the probabilities the populations were stationary or increasing were 0.07, 0.22, and 0.82. For retrospective analyses of monitored populations, using Bayesian MCMC methods to generate a posterior distribution of (t) is a valuable conservation and management tool for robustly estimating probabilities of specified declines of interest. (c) 2013 The Wildlife Society.