Selecting Surrogate Endpoints for Estimating Pesticide Effects on Avian Reproductive Success

A Markov chain nest productivity model (MCnest) has been developed for projecting the effects of a specific pesticide‐use scenario on the annual reproductive success of avian species of concern. A critical element in MCnest is the use of surrogate endpoints, defined as measured endpoints from avian toxicity tests that represent specific types of effects possible in field populations at specific phases of a nesting attempt. In this article, we discuss the attributes of surrogate endpoints and provide guidance for selecting surrogates from existing avian laboratory tests as well as other possible sources.We also discuss some of the assumptions and uncertainties related to using surrogate endpoints to represent field effects. The process of explicitly considering how toxicity test results can be used to assess effects in the field helps identify uncertainties and data gaps that could be targeted in higher‐tier risk assessments

Incorporating Results of Avian Toxicity Tests into a Model of Annual Reproductive Success

Modeling the effects of pesticide exposure on avian populations requires knowledge of how the pesticide changes survival and fecundity rates for the population. Although avian reproduction tests are the primary source of information on reproductive effects in the pesticide risk assessment process, current tests cannot provide a direct estimate of the effects of a pesticide on fecundity rates. We present a mathematical model that integrates information on specific types of effects from reproduction tests with information on avian life history parameters, the timing of pesticide applications, and the temporal pattern of pesticide exposure levels to estimate pesticide effects on annual reproductive success. The model demonstration follows nesting success of females in no-pesticide or pesticide-exposed populations through a breeding season to estimate the mean number of successful broods per female. We demonstrate the model by simulating populations of a songbird exposed to 1 of 2 hypothetical pesticides during a breeding season. Finally, we discuss several issues for improving the quantitative estimation of annual reproductive success

Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts

Point counts are a common method for sampling avian distribution and abundance. Although methods for estimating detection probabilities are available, many analyses use raw counts and do not correct for detectability. We use a removal model of detection within an N-mixture approach to estimate abundance trends corrected for imperfect detection. We compare the corrected trend estimates to those estimated from raw counts for 16 species using 15 years of monitoring data on three national forests in the western Great Lakes, USA. We also tested the effects of overdispersion by modeling both counts and removal mixtures under three statistical distributions: Poisson, zero-inflated Poisson, and negative binomial. For most species, the removal model produced estimates of detection probability that conformed to expectations. For many species, but not all, estimates of trends were similar regardless of statistical distribution or method of analysis. Within a given combination of likelihood (counts vs. mixtures) and statistical distribution, trends usually differed by both stand type and national forest, with species showing declines in some stand types and increases in others. For three species, Brown Creeper, Yellow-rumped Warbler, and Black-throated Green Warbler, temporal patterns in detectability resulted in substantial differences in estimated trends under the removal mixtures compared to the analysis of raw counts. Overall, we found that the zero-inflated Poisson was the best distribution for our data, although the Poisson or negative binomial performed better for a few species. The similarity in estimated trends that we observed among counts and removal mixtures was probably a result of both experimental design and sampling effort. First, the study was originally designed to avoid confounding observer effects with habitats or time. Second, our time series is relatively long and our sample sizes within years are large

Using Pop-GUIDE to Assess the Applicability of MCnest for Relative Risk of Pesticides to Hummingbirds

Hummingbirds are charismatic fauna that provide important pollination services, including in the continental US, where 15 species regularly breed. Compared to other birds in North America, hummingbirds (family Trochilidae) have a unique exposure route to pesticides because they forage on nectar. Therefore, hummingbirds may be exposed to systemic pesticides borne in nectar. They also may be particularly vulnerable to pesticide exposure due to their small size and extreme metabolic demands. We review relevant factors including hummingbird life history, nectar residue uptake, and avian bioenergetic considerations with the goal of clearly identifying and articulating the specific modeling challenges that must be overcome to develop and/or adapt existing modeling approaches. To help evaluate these factors, we developed a dataset for ruby-throated hummingbirds (Archilochus colubris) and other avian species potentially exposed to pesticides. We used the systemic neonicotinoid pesticide imidacloprid as an illustration and compared results to five other common current use pesticides. We use the structure of Pop-GUIDE to provide a conceptual modeling framework for implementation of MCnest and to compile parameter values and relevant algorithms to predict the effects of pesticide exposure on avian pollinators. Conservative screening assessments suggest the potential for adverse effects from imidacloprid, as do more refined assessments, though many important limitations and uncertainties remain. Our review found many areas in which current USEPA avian models must be improved in order to conduct a full higher-tier risk assessment for avian pollinators exposed to neonicotinoid insecticides, including addition of models suitable for soil and seed treatments within the MCnest environment, ability to include empirical residue data in both nectar and invertebrates rather than relying on existing nomograms, expansion of MCnest to a full annual cycle, and increased representation of spatial heterogeneity. Although this work focuses on hummingbirds, the methods and recommendations may apply more widely to other vertebrate pollinators

Realism, Conservatism, and Tiered Ecological Risk Assessment

Recent research has provided valuable momentum for the development and use of population models for ecological risk assessment (ERA). In general, ERA proceeds along a tiered strategy, with conservative assumptions deployed at lower tiers that are relaxed at higher tiers with ever more realistic models. As the tier increases, so do the levels of time and effort required by the assessor. When faced with many stressors, species, and habitats, risk assessors need to find efficiencies. Conservative lower-tier approaches are well established, but higher-tier models often prioritize accuracy, and conservative approaches are relatively unexplored at higher tiers. A principle of efficiency for ecological modeling for population-level ecological risk assessment is articulated and evaluated against a conceptual model and an existing set of avian models for chemical risk assessment. Here, four published avian models are reviewed in increasing order of realism (risk quotient → Markov chain nest productivity model → endogenous lifecycle model → spatially explicit population model). Models are compared in a pairwise fashion according to increasing realism and evaluated as to whether conservatism increases or decreases with each step. The principle of efficiency is shown to be a challenging ideal, though some cause for optimism is identified. Strategies are suggested for studying efficiency in tiered ecological model deployment

Supplement 1. R and Matlab code for mortality estimators.

<h2>File List</h2><div> <a href="MortalityEstimators.zip">MortalityEstimators.zip</a> (MD5: 09eaef6f667f615cc046e09947a96fa2)</div><h2>Description</h2><div> <p>The associated file “MortalityEstimators.zip” contains Matlab (Mathworks 2012) and R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>) files for the mortality extrapolations described in the manuscript Etterson, M. Hidden Markov models for estimating animal mortality from anthropogenic hazards. Matlab files have suffix “.m” and R files have suffix “.r”. Some of the Matlab functions require the Matlab Statistics Toolbox. Some of the R functions require the R package expm. Table S1 gives a basic description of each function, and each file contains a header giving function syntax. The Matlab and R functions may differ slightly due to differences in programming environment.</p> <p>Table S1. Algorithms provided for estimating animal mortality.</p> -- TABLE: Please see in attached file. -- </div

Appendix A. Derivation of the estimators for ongoing mortality presented in Table 1 from the matrix forms presented in Eqs. 8 and 9.

Derivation of the estimators for ongoing mortality presented in Table 1 from the matrix forms presented in Eqs. 8 and 9

Appendix D. Figures showing bias, asymptotic standard error, and standard deviation for at all levels of sample size, monitoring interval, and discovery distribution.

Figures showing bias, asymptotic standard error, and standard deviation for at all levels of sample size, monitoring interval, and discovery distribution