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

Farshid S. Ahrestani; Mark Hebblewhite; Eric Post

doi:10.1038/srep03125

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

Farshid S. Ahrestani
, Mark Hebblewhite
, Eric Post

W. A. Franke College of Forestry and Conservation

Pennsylvania State University

Research output: Contribution to journal › Article › peer-review

56 Scopus citations

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 language	English
Article number	3125
Journal	Scientific Reports
Volume	3
DOIs	https://doi.org/10.1038/srep03125
State	Published - Nov 8 2013

Funding

We thank the following biologists for sharing their data or helping us obtain data: Mark Bradley, Doug Bergeson, Troy Hegel, Anne Hubbs, Tom Hurd, Wlodek Jedzrejewski, Bogumila Jedzrejewska, Dale Miquelle, and Olga Zaumyslova. We thank Bogumila Jedzrejewska for assistance in translating Russian literature, Justine Blanford for the species distributions polar maps, and J. Nowak and A. Flesch for advice on Bayesian state-space modelling. Funding was provided by NASA grant NNX11AO47G and the University of Montana.

Funders	Funder number
National Aeronautics and Space Administration	NNX11AO47G

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10.1038/srep03125

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title = "The importance of observation versus process error in analyses of global ungulate populations",

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.",

author = "Ahrestani, \{Farshid S.\} and Mark Hebblewhite and Eric Post",

note = "Funding Information: We thank the following biologists for sharing their data or helping us obtain data: Mark Bradley, Doug Bergeson, Troy Hegel, Anne Hubbs, Tom Hurd, Wlodek Jedzrejewski, Bogumila Jedzrejewska, Dale Miquelle, and Olga Zaumyslova. We thank Bogumila Jedzrejewska for assistance in translating Russian literature, Justine Blanford for the species distributions polar maps, and J. Nowak and A. Flesch for advice on Bayesian state-space modelling. Funding was provided by NASA grant NNX11AO47G and the University of Montana.",

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N2 - 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.

AB - 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.

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SN - 2045-2322

VL - 3

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ER -

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

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