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,Arthur J Lustig

doi:10.1371/journal.pone.0132255

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.

Highlights

Estimating and projecting population trends is essential to ascertaining threat status and developing conservation strategies
We focused on density-independent models throughout for two important reasons: 1) we wished to investigate the effects of process error, measurement error, and data length on modeling declining populations that are less likely to be driven by density dependence, and 2) we wished to compare and contrast our results with those in key papers which do not include density dependence [23, 25]
We found that scalar models can project percent declines quite well relative to matrix models for short-lived organisms with simple life histories, like the ones studied here, given their comparable precision and less biased predicted declines

Summary

Introduction

Estimating and projecting population trends is essential to ascertaining threat status and developing conservation strategies. PVA has been used to inform decisions to list species under the United States Endangered Species Act including the northern spotted owl (Strix occidentalis caurina), and is commonly used to identify conservation management strategies, for recovery planning, and for habitat conservation plans for threatened species [5,6,7]. Despite their long-standing use, the question of how to construct and apply PVAs is still an area of active research and debate, especially under the high environmental variability and data uncertainty that characterize most conservation applications [5,8]

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