title: Combined Influences of Model Choice, Data Quality, and Data Quantity When Estimating Population Trends.
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.
authors.affiliation: Department of Biology, University of California Riverside, Riverside, CA, United States of America.
authors.forename: Pamela
authors.initials: P
authors.lastname: Rueda-Cediel
authors.affiliation: Department of Biology, University of California Riverside, Riverside, CA, United States of America.
authors.forename: Kurt E
authors.initials: KE
authors.lastname: Anderson
authors.affiliation: Arthur Rylah Institute for Environmental Research, Heidelberg, Victoria, Australia; The School of Biosciences, The University of Melbourne, Parkville, Victoria, Australia.
authors.forename: Tracey J
authors.initials: TJ
authors.lastname: Regan
authors.affiliation: School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, United States of America.
authors.forename: Janet
authors.initials: J
authors.lastname: Franklin
authors.affiliation: Department of Biology, University of California Riverside, Riverside, CA, United States of America.
authors.forename: Helen M
authors.initials: HM
authors.lastname: Regan
mesh_terms: D000818:Animals; D003198:Computer Simulation; D000068598:Data Accuracy; D008018:Life Cycle Stages; D008962:Models, Theoretical; D011157:Population Dynamics; D012908:Snails / Q000254:growth & development*