The importance of observation versus process error in analyses of global ungulate populations

Farshid S. Ahrestani, Mark Hebblewhite, Eric Post

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Population abundance data vary widely in quality and are rarely accurate. The two main components of error in such data are observation and process error. We used Bayesian state space models to estimate the observation and process error in time-series of 55 globally distributed populations of two species, Cervus elaphus (elk/red deer) and Rangifer tarandus (caribou/reindeer). We examined variation among populations and species in the magnitude of estimates of error components and density dependence using generalized linear models. Process error exceeded observation error in 75% of all populations, and on average, both components of error were greater in Rangifer than in Cervus populations. Observation error differed significantly across the different observation methods, and predation and time-series length differentially affected the error components. Comparing the Bayesian model results to traditional models that do not separate error components revealed the potential for misleading inferences about sources of variation in population dynamics.

    Original languageEnglish
    Article number3125
    JournalScientific Reports
    Volume3
    DOIs
    StatePublished - Nov 8 2013

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