Decision context as a necessary component of population viability analysis assessments: response to Chaudhary and Oli 2020.

Abstract

Appendix S1. Questions used to prospectively guide the development and documentation of population viability analysis (PVA) models. The question set contains four major categories. The Decision Context (A, questions i–v) category reflects an expansion on the original questions developed by Chaudhary and Oli (2020), which included the Background, Model, and Analysis Quality categories (B–D, questions 1−32). Questions within the Decision Context (A) category evaluate if decision context attributes and model limitations are clearly described, as well as the relevancy of model outputs and potential management actions or scenarios to decision makers. Background Quality (B) comprises information on the study species, data collection, and objectives. Model Quality (C) evaluates parameter estimation procedures, appropriateness of model structure given the species’ life history and available data, and the inclusion of stochasticity (e.g., demographic, environmental), threats, and management actions. Analysis Quality (D) evaluates components of the simulation framework, including the appropriateness of the time horizon and number of iterations, sensitivity analyses, and reporting of PVA metrics, including growth rate (lambda) and extinction probabilities. This list is not exhaustive but represents many common elements across decision-support PVAs specifically tailored to inform a management decision as well as heuristic PVAs in which the primary motivation may be for learning. Finally, we recognize that not all questions may be relevant to all PVA types; for example, Decision Context questions are likely less relevant for heuristic compared to decision support. Therefore, we intend for this list to be used prospectively to guide the development and documentation of PVAs, rather than a post-hoc assessment of quality, given the difficulty in establishing common metrics across PVAs developed for extremely different purposes. Appendix S2. List of the 10 highest- and lowest-ranked population viability analysis (PVA) papers (n = 160 total) appraised by Chaudhary and Oli (2020), as indicated in the C&O Rank column. We reviewed these 20 papers and classified each based on their Model Type, which describes the primary motivation for PVA development. If the PVA was constructed to directly inform a management decision, we classified it as a “Decision Support” model, while PVAs developed primarily for learning purposes (e.g., to better understand population dynamics or relative threat risks) were classified as “Heuristic.” If a distinction could not be made, we classified it as “Unclear.” Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.