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

Document Type

Article

Journal/Book Title/Conference

Journal of Wildlife Management

Volume

77

Issue

7

Publisher

Wiley

Publication Date

8-1-2013

First Page

1449

Last Page

1458

Abstract

The annual rate of population change (lt) is a good metric for evaluating populationperformance because it summarizes survival and recruitment rates and can be used for open populations.Another measure of population performance, realized population change (Dt) is an encompassing metric ofpopulation trend over a period of time; it is the ratio of population size at an end time period relative to theinitial population size. Our first goal was to compare mean l and Dtas summaries of population change overtime. Our second goal was to evaluate different methods for estimating these parameters; specifically wewished to compare the value of estimates from fixed effects models, random effects estimates from mixedeffects models, and Bayesian Markov chain Monte Carlo (MCMC) methods. Our final goal was to evaluatethe us e of th e posterior distribution of Dtas a means for estimating the probability of population declineretrospectively. To meet these goals, we used California spotted owl (Strix occidentalis occidentalis) datacollected on 3 study areas from 1990 to 2011 as a case study. The estimated MCMC median ls for 2 of thestudy areas were 0.986 and 0.993, indicating dec lining populations, whereas median l was 1.014 for the thirdstudy area, indicating an increasing popu lation. For 2 of the study areas, estimated MCMC median Dts overthe 18-year monitorin g 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 Dtanalyses highlight that smalldifferences in mean l from 1.0 (stationary) can result in large differences in population size over a longer timeperiod; these temporal effects are better depicted by Dt. Fixed effects, random effects, and MCMC esti matesof mean and median l and of Dtwere similar (9% relative difference). The estimate of temporal processvariance was larger for MCMC than the random effects estimates. Results from a Bayesian approach usingMCMC simulations indicated that the probabilities of a 15% decline over 18 years were 0.69, 0.40, and0.04 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, us ing Bayesian MCMC methods togenerate a posterior distribution of Dtis a valuable conservation and management tool for robustly estimatingprobabilities of specified declines of in terest.

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