On the Use of Published Demographic Data for Population-Level Risk Assessment in Birds

ABSTRACT

As the practice of using population models for wildlife risk assessment has become more common, so has the practice of using surrogate data, typically taken from the published scientific literature, as inputs for demographic models. This practice clearly exposes the user to inferential errors. However, it is likely to continue because demographic data are expensive to gather. We review potential errors associated with the use of previously published demographic data and how those errors propagate into the endpoints of demographic projection models. We suggest methods for inferring bias in model endpoints when multiple and opposing biases are present in the demographic input data. We provide an example using Eastern Meadowlarks (Sturnella magna), a common songbird in Midwestern grasslands and agro-ecosystems. We conclude with a brief review of methods that could improve inference made using published demographic data, including methods from life-history theory, meta-analysis, and Bayesian statistics.

Key words:

• population-level risk assessment
• demographic models
• surrogate data

ACKNOWLEDGMENTS

J. Grear, T. Gleason, and two anonymous reviewers provided valuable comments on an earlier draft of this manuscript. The information in this document has been funded wholly by the U.S. Environmental Protection Agency. It has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

This work is a product of the U.S. Government and is not copyrighted.

Notes

¹f/bf/y = female offspring per breeding female per year; ϕ = apparent survival.

¹ s = 0.594 ± 0.172 (Michel et al. 2005).

²Note that the sensitivity to survival depends only on fecundity, whereas the sensitivity to fecundity depends only on survival. Thus for all models,d /d = 0.3. See Appendix for formulae.

³Minimum coefficient of variation required (assuming equal CV for all parameters) to bound confidence interval away from 1 (see Appendix for details).