Assessing pesticide risks to threatened and endangered species using population models: Findings and recommendations from a CropLife America Science Forum.

This brief communication reports on the main findings and recommendations from the 2014 Science Forum organized by CropLife America. The aim of the Forum was to gain a better understanding of the current status of population models and how they could be used in ecological risk assessments for threatened and endangered species potentially exposed to pesticides in the United States. The Forum panelists' recommendations are intended to assist the relevant government agencies with implementation of population modeling in future endangered species risk assessments for pesticides. The Forum included keynote presentations that provided an overview of current practices, highlighted the findings of a recent National Academy of Sciences report and its implications, reviewed the main categories of existing population models and the types of risk expressions that can be produced as model outputs, and provided examples of how population models are currently being used in different legislative contexts. The panel concluded that models developed for listed species assessments should provide quantitative risk estimates, incorporate realistic variability in environmental and demographic factors, integrate complex patterns of exposure and effects, and use baseline conditions that include present factors that have caused the species to be listed (e.g., habitat loss, invasive species) or have resulted in positive management action. Furthermore, the panel advocates for the formation of a multipartite advisory committee to provide best available knowledge and guidance related to model implementation and use, to address such needs as more systematic collection, digitization, and dissemination of data for listed species; consideration of the newest developments in good modeling practice; comprehensive review of existing population models and their applicability for listed species assessments; and development of case studies using a few well-tested models for particular species to demonstrate proof of concept. To advance our common goals, the panel recommends the following as important areas for further research and development: quantitative analysis of the causes of species listings to guide model development; systematic assessment of the relative role of toxicity versus other factors in driving pesticide risk; additional study of how interactions between density dependence and pesticides influence risk; and development of pragmatic approaches to assessing indirect effects of pesticides on listed species.

The minimum detectable difference (MDD) and the interpretation of treatment-related effects of pesticides in experimental ecosystems.

In the European registration procedure for pesticides, microcosm and mesocosm studies are the highest aquatic experimental tier to assess their environmental effects. Evaluations of microcosm/mesocosm studies rely heavily on no observed effect concentrations (NOECs) calculated for different population-level endpoints. Ideally, a power analysis should be reported for the concentration-response relationships underlying these NOECs, as well as for measurement endpoints for which significant effects cannot be demonstrated. An indication of this statistical power can be provided a posteriori by calculated minimum detectable differences (MDDs). The MDD defines the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. The aim of this paper is to expand on the Aquatic Guidance Document recently published by the European Food Safety Authority (EFSA) and to propose a procedure to report and evaluate NOECs and related MDDs in a harmonised way. In addition, decision schemes are provided on how MDDs can be used to assess the reliability of microcosm/mesocosm studies and for the derivation of effect classes used to derive regulatory acceptable concentrations. Furthermore, examples are presented to show how MDDs can be reduced by optimising experimental design and sampling techniques.

Mechanistic modelling of toxicokinetic processes within Myriophyllum spicatum.

Effects of chemicals are, in most cases, caused by internal concentrations within organisms which rely on uptake and elimination kinetics. These processes might be key components for assessing the effects of time-variable exposure of chemicals which regularly occur in aquatic systems. However, the knowledge of toxicokinetic patterns caused by time-variable exposure is limited, and gaining such information is complex. In this work, a previously developed mechanistic growth model of Myriophyllum spicatum is coupled with a newly developed toxicokinetic part, providing a model that is able to predict uptake and elimination of chemicals, as well as distribution processes between plant compartments (leaves, stems, roots) of M. spicatum. It is shown, that toxicokinetic patterns, at least for most of the investigated chemicals, can be calculated in agreement with experimental observations, by only calibrating two chemical- specific parameters, the cuticular permeability and a plant/water partition coefficient. Through the model-based determination of the cuticular permeabilities of Isoproturon, Iofensulfuron, Fluridone, Imazamox and Penoxsulam, their toxicokinetic pattern can be described with the model approach. For the use of the model for predicting toxicokinetics of other chemicals, where experimental data is not available, equations are presented that are based on the log (P oct/wat) of a chemical and estimate parameters that are necessary to run the model. In general, a method is presented to analyze time-variable exposure of chemicals more in detail without conducting time and labour intensive experiments.

Effects of light and temperature fluctuations on the growth of Myriophyllum spicatum in toxicity tests--a model-based analysis.

Laboratory toxicity tests are a key component of the aquatic risk assessments of chemicals. Toxicity tests with Myriophyllum spicatum are conducted based on working procedures that provide detailed instructions on how to set up the experiment, e.g., which experimental design is necessary to get reproducible and thus comparable results. Approved working procedures are established by analyzing numerous toxicity tests to find a compromise between practical reasons (e.g., acceptable ranges of ambient conditions as they cannot be kept completely constant) and the ability for detecting growth alterations. However, the benefit of each step of a working procedure, e.g., the random repositioning of test beakers, cannot be exactly quantified, although this information might be useful to evaluate working procedures. In this paper, a growth model of M. spicatum was developed and used to assess the impact of temperature and light fluctuations within the standardized setup. It was analyzed how important it is to randomly reassign the location of each plant during laboratory tests to keep differences between the relative growth rates of individual plants low. Moreover, two examples are presented on how modeling can give insight into toxicity testing. Results showed that randomly repositioning of individual plants during an experiment can compensate for fluctuations of light and temperature. A method is presented on how models can be used to improve experimental designs and to quantify their benefits by predicting growth responses.