Bridging gaps in demographic analysis with phylogenetic imputation.

Tamora D. James,Owen R. Jones,Dylan Z. Childs,Roberto Salguero‐Gómez,Andrew P. Beckerman

doi:10.1111/cobi.13658

Abstract

Phylogenetically informed imputation methods have rarely been applied to estimate missing values in demographic data but may be a powerful tool for reconstructing vital rates of survival, maturation, and fecundity for species of conservation concern. Imputed vital rates could be used to parameterize demographic models to explore how populations respond when vital rates are perturbed. We used standardized vital rate estimates for 50 bird species to assess the use of phylogenetic imputation to fill gaps in demographic data. We calculated imputation accuracy for vital rates of focal species excluded from the data set either singly or in combination and with and without phylogeny, body mass, and life-history trait data. We used imputed vital rates to calculate demographic metrics, including generation time, to validate the use of imputation in demographic analyses. Covariance among vital rates and other trait data provided a strong basis to guide imputation of missing vital rates in birds, even in the absence of phylogenetic information. Mean NRMSE for null and phylogenetic models differed by <0.01 except when no vital rates were available or for vital rates with high phylogenetic signal (Pagel's λ > 0.8). In these cases, including body mass and life-history trait data compensated for lack of phylogenetic information: mean normalized root mean square error (NRMSE) for null and phylogenetic models differed by <0.01 for adult survival and <0.04 for maturation rate. Estimates of demographic metrics were sensitive to the accuracy of imputed vital rates. For example, mean error in generation time doubled in response to inaccurate estimates of maturation time. Accurate demographic data and metrics, such as generation time, are needed to inform conservation planning processes, for example through International Union for Conservation of Nature Red List assessments and population viability analysis. Imputed vital rates could be useful in this context but, as for any estimated model parameters, awareness of the sensitivities of demographic model outputs to the imputed vital rates is essential.

Highlights

Understanding population responses to human-induced threats, such as habitat loss and degradation, climate change, and overexploitation (Brook et al 2003; Parmesan 2006; Maclean & Wilson 2011; Maxwell et al 2016), is crucial for identifying at-risk species and to guide conservation interventions (e.g., Bruna et al 2009; Dahlgren et al 2016; Lunn et al 2016)
Mean NRMSE for null and phylogenetic models differed by 0.8). In these cases, including body mass and life-history trait data compensated for lack of phylogenetic information: mean normalized root mean square error (NRMSE) for null and phylogenetic models differed by
High phylogenetic signal suggested that adult survival and maturation rate should be successful targets for phylogenetic imputation but high variance in Pagel’s λ for maturation rate suggested greater phylogenetic uncertainty in maturation rate

Summary

Introduction

Understanding population responses to human-induced threats, such as habitat loss and degradation, climate change, and overexploitation (Brook et al 2003; Parmesan 2006; Maclean & Wilson 2011; Maxwell et al 2016), is crucial for identifying at-risk species and to guide conservation interventions (e.g., Bruna et al 2009; Dahlgren et al 2016; Lunn et al 2016). Other approaches include combining estimates from populations to form a representative model for a species (Sæther & Bakke 2000) and parameterization of models based on a range of plausible values (Rodríguez et al 2004) or on data from captive individuals (e.g., Young et al 2012). Such approaches produce bias (Schafer & Graham 2002), and their use raises concerns about the reliability of model outputs and the ability to make robust conclusions (Sæther & Engen 2002; Ellner & Fieberg 2003; McGowan et al 2011). Formal methods for estimating missing vital rates and quantifying uncertainty in such estimates are needed

Methods

Results

Conclusion