Over the past several years, the term PFAS (per- and polyfluoroalkyl substances) has grown to be emblematic of environmental contamination, garnering public, scientific, and regulatory concern. PFAS are synthesized by two processes, direct fluorination (e.g., electrochemical fluorination) and oligomerization (e.g., fluorotelomerization). More than a megatonne of PFAS is produced yearly, and thousands of PFAS wind up in end-use products. Atmospheric and aqueous fugitive releases during manufacturing, use, and disposal have resulted in the global distribution of these compounds. Volatile PFAS facilitate long-range transport, commonly followed by complex transformation schemes to recalcitrant terminal PFAS, which do not degrade under environmental conditions and thus migrate through the environment and accumulate in biota through multiple pathways. Efforts to remediate PFAS-contaminated matrices still are in their infancy, with much current research targeting drinking water.

Light-based archival tags are increasingly being used on free-ranging marine vertebrates to study their movements using geolocation estimates. These methods use algorithms that incorporate threshold light techniques to determine longitude and latitude. More recently, researchers have been using sea surface temperature (SST) to determine latitude in temperate regions. The accuracy and application of these algorithms have not been validated on freeranging birds. Errors in both geolocation methods were quantified by double-tagging Laysan (Phoebastria immutabilis Rothschild) and black-footed (P. nigripes Audubon) albatrosses with both leg mounted archival tags that measured SST and ambient light, and satellite transmitters.

The assimilation of population models into ecological risk assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated modeling framework and decision guide to aid the development of population models with respect to ERA objectives and data availability. In the framework, the trade‐offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The decision guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the decision guide and modeling framework into a comprehensive approach, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment (Pop‐GUIDE), for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop‐GUIDE, assessors are guided through the trade‐offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop‐GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. Integr Environ Assess Manag 2021;17:767–784. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERAs) has been demonstrated extensively over the past few decades. There is little debate about the utility of these models to translate multiple organism-level endpoints into a holistic interpretation of effect to the population; however, there continues to be a struggle for actual application of these models as a common practice in ERA. Although general frameworks for developing models for ERA have been proposed, there is limited guidance on when models should be used, in what form, and how to interpret model output to inform the risk manager's decision. We propose a framework for developing and applying population models in regulatory decision making that focuses on trade-offs of generality, realism, and precision for both ERAs and models. We approach the framework development from the perspective of regulators aimed at defining the needs of specific models commensurate with the assessment objective. We explore why models are not widely used by comparing their requirements and limitations with the needs of regulators. Using a series of case studies under specific regulatory frameworks, we classify ERA objectives by trade-offs of generality, realism, and precision and demonstrate how the output of population models developed with these same trade-offs informs the ERA objective. We examine attributes for both assessments and models that aid in the discussion of these trade-offs. The proposed framework will assist risk assessors and managers to identify models of appropriate complexity and to understand the utility and limitations of a model's output and associated uncertainty in the context of their assessment goals. Integr Environ Assess Manag 2018;14:369-380. Published 2017. This article is a US Government work and is in the public domain in the USA.

Species sensitivity distributions (SSD) are probability distributions of chemical toxicity of multiple species and have had limited application in wildlife risk assessment because of relatively small data sets of wildlife toxicity values. Interspecies correlation estimation (ICE) models predict the acute toxicity to untested taxa from known toxicity of a single surrogate species. ICE models were used to predict toxicity values to wildlife species and generate SSDs for 23 chemicals using four avian surrogates. The hazard levels associated with the fifth percentile of the distribution (HD5) were compared for ICE SSDs and independent SSDs created with measured data. SSDs were composed of either avian only or avian and mammalian taxa. ICE HD5s were within 5-fold of 90% of measured HD5s and were generally higher than measured HD5s. The first percentile of the distribution (HD1) and the fifth percentile of the lower confidence limit (HDL) of ICE SSDs produced values that were not significantly different from measured HD5s. Using a bird surrogate to predicttoxicity to birds and the Norway rat to predict toxicity to mammals improved some estimates of ICE HD5s compared with those generated using only bird surrogates. These results indicate that ICE models can be used to generate SSDs comparable to those derived from measured wildlife toxicity data and provide robust estimates of the HD5.