Combined influences of model choice, data quality, and data quantity when estimating population trends

Pamela Rueda-Cediel, Kurt E. Anderson, Tracey J. Regan, Janet Franklin, Helen M. Regan

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated.We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context.We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Original languageEnglish (US)
Article numbere0132255
JournalPLoS One
Volume10
Issue number7
DOIs
StatePublished - Jul 15 2015

Fingerprint

Measurement errors
Time series
time series analysis
Population
viability
Conservation
prediction
life history
Aptitude
Population Growth
Snails
Model structures
Parameterization
Data Accuracy
Risk assessment
Decision Making
Research Design
risk assessment
snails
Decision making

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Rueda-Cediel, P., Anderson, K. E., Regan, T. J., Franklin, J., & Regan, H. M. (2015). Combined influences of model choice, data quality, and data quantity when estimating population trends. PLoS One, 10(7), [e0132255]. https://doi.org/10.1371/journal.pone.0132255

Combined influences of model choice, data quality, and data quantity when estimating population trends. / Rueda-Cediel, Pamela; Anderson, Kurt E.; Regan, Tracey J.; Franklin, Janet; Regan, Helen M.

In: PLoS One, Vol. 10, No. 7, e0132255, 15.07.2015.

Research output: Contribution to journalArticle

Rueda-Cediel, P, Anderson, KE, Regan, TJ, Franklin, J & Regan, HM 2015, 'Combined influences of model choice, data quality, and data quantity when estimating population trends', PLoS One, vol. 10, no. 7, e0132255. https://doi.org/10.1371/journal.pone.0132255
Rueda-Cediel, Pamela ; Anderson, Kurt E. ; Regan, Tracey J. ; Franklin, Janet ; Regan, Helen M. / Combined influences of model choice, data quality, and data quantity when estimating population trends. In: PLoS One. 2015 ; Vol. 10, No. 7.
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