Papers & ReportsAgriculture and Food, Crop Protection2018
Honey bee colony-level exposure and effects in realistic landscapes: An application of BEEHAVE simulating clothianidin residues in corn pollen
Discerning potential effects of insecticides on honey bee colonies in field studies conducted under realistic conditions can be challenging because of concurrent interactions with other environmental conditions. Honey bee colony models can control exposures and other environmental factors, as well as assess links among pollen and nectar residues in the landscape, their influx into the colony, and the resulting exposures and effects on bees at different developmental stages. We extended the colony model BEEHAVE to represent exposure to the insecticide clothianidin via residues in pollen from treated cornfields set in real agricultural landscapes in the US Midwest. We assessed their potential risks to honey bee colonies over a 1-yr cycle. Clothianidin effects on colony strength were only observed if unrealistically high residue levels in the pollen were simulated. The landscape composition significantly impacted the collection of pollen (residue exposure) from the cornfields, resulting in higher colony-level effects in landscapes with lower proportions of semi-natural land. The application of the extended BEEHAVE model with a pollen exposure-effects module provides a case study for the application of a mechanistic honey bee colony model in pesticide risk assessment integrating the impact of a range of landscape compositions.
Schmolke, A., Abi-Akar, F., Hinarejos, S. (2018), Honey bee colony-level exposure and effects in realistic landscapes: An application of BEEHAVE simulating clothianidin residues in corn pollen. Environ Toxicol Chem. DOI: 10.1002/etc.4314
Papers & ReportsCrop Protection2018
A plea for consistency, transparency, and reproducibility in risk assessment effect models
Ecological risk assessments (ERAs) are moving toward using populations and ecosystem services as explicit protection goals, and impacts on these are difficult, if not impossible, to measure empirically. Mechanistic effect models are recognized as necessary tools for ERA that complement empirical data. But we need a strategy to make them consistent, transparent and reproducible following similar principles as those used to develop standardized experimental designs for empirical tests. Despite some progress, the use of mechanistic effect models in ERA remains rare. Although some general guidance exists, the ERA community lacks a coherent strategy for model design and implementation. The strategy needs to be compatible with different legislative needs, recognize limitations in data and resources, and involve all stakeholder groups to ensure buy-in. Benefits would include increased cost-effectiveness of model development, implementation and interpretation; minimization of effort needed by risk assessors and managers to evaluate models; and more effective communication of model outputs to a broader stakeholder community. More importantly, it would increase mechanistic understanding of the impacts of chemicals and other stressors across levels of biological organization – from the things that we measure to the things that we care about.
Forbes, V.E., Schmolke, A., Accolla, C., Grimm, V. (2018), A plea for consistency, transparency, and reproducibility in risk assessment effect models. Environ Toxicol Chem. 38: 9-11. doi.org/10.1002/etc.4291
Papers & ReportsCrop Protection2018
Plant guttation water as a potential route for pesticide exposure in honey bees: a review of recent literature
Because honey bees periodically collect water, guttation water from treated crops has been suggested as a potential exposure route to systemic pesticides. We reviewed studies that were published in the scientific literature since a previous review of the topic. We identified several studies that reported residue levels of pesticides in guttation water. However, few studies addressed guttation water as a potential exposure route to honey bees. In these studies, no significant effects on honey bee colony health or overwintering survival were observed when colonies were located within fields of treated crops during guttation periods. The previous and current review suggests that exposure to pesticides via guttation water alone is unlikely to negatively affect honey bee colonies. A better understanding of water foraging by honey bees would be needed to address whether guttation water could represent a relevant exposure route of honey bees to systemic pesticides.
Schmolke, A., Kearns, B., O’Neil, B. (2018), Plant guttation water as a potential route for pesticide exposure in honey bees: a review of recent literature. Apidologie. Volume 49, 637-646. doi.org/10.1007/s13592-018-0591-1
Papers & ReportsWater/Wastewater Assessments2018
Simplifying environmental mixtures-An aquatic exposure-based approach via land use scenarios
Posthuma, L., Brown, C., de Zwart, D., Diamond, J., Dyer, S.D., Hamer, M., Holmes, C.M., Marshal, S., Burton Jr., G.A. (2018), Simplifying environmental mixtures-an aquatic exposure-based approach via land use scenarios. Environ Toxicol Chem. 37: 671-673. doi.org/10.1002/etc.4063
Papers & ReportsCrop Protection2018
Adapting population models for application in pesticide risk assessment: a case study with Mead’s milkweed
Population models can facilitate assessment of potential impacts of pesticides on populations or species rather than individuals and have been identified as important tools for pesticide risk assessment of nontarget species including those listed under the Endangered Species Act. Few examples of population models developed for this specific purpose are available; however, population models are commonly used in conservation science as a tool to project the viability of populations and the long‐term outcomes of management actions. We present a population model for Mead’s milkweed (Asclepias meadii), a species listed as threatened under the Endangered Species Act throughout its range across the Midwestern United States. We adapted a published population model based on demographic field data for application in pesticide risk assessment. Exposure and effects were modeled as reductions of sets of vital rates in the transition matrices, simulating both lethal and sublethal effects of herbicides. Two herbicides, atrazine and mesotrione, were used as case study examples to evaluate a range of assumptions about potential exposure-effects relationships. In addition, we assessed buffers (i.e., setback distances of herbicide spray applications from the simulated habitat) as hypothetical mitigation scenarios and evaluated their influence on population‐level effects in the model. The model results suggest that buffers can be effective at reducing risk from herbicide drift to plant populations. These case studies demonstrate that existing population models can be adopted and integrated with exposure and effects information for use in pesticide risk assessment.
Schmolke, A., Roy, C., Brain, R., Forbes, V. (2018), Adapting population models for application in pesticide risk assessment: A case study with Mead’s milkweed. Environ Toxicol Chem. 37: 2235-2245. doi.org/10.1002/etc.4172
PresentationsHome and Personal Care Products2018
Estimating environmental emissions and aquatic fate of sludge-bound CECs using spatial modeling and US datasets
In the US, 50% of the sludge produced during wastewater treatment is recycled to land (www.epa.gov/biosolids). Some chemicals in consumer products may be highly removed during the wastewater treatment process due to sorption and binding to organic matter, ending up in sludge solids where it has the potential to be applied to land surfaces, subject to erosion or runoff processes potentially entering nearby surface waters. However, biosolids mass applied to land is not evenly distributed across the US landscape due to variable population density, local sludge management practices, and availability of land application sites. We have developed a proof-of-concept model to aide in the prospective assessment of CECs contained in WWTP sludge applied to land. This spatially-explicit, national model is based on publicly available datasets, combined with a spatial-hydrologic framework containing geographically variable emissions linked to a river network allowing for environmental transport via surface water. The hydrologic framework is based on a set of basins and rivers (www.hydrosheds.org) linked to emission characteristics for over 77,000 sub-basins. Emission characteristics are derived from facility data in the USEPA Clean Watersheds Needs Survey (www.epa.gov/cwns) to estimate consumer product usage linked to wastewater treatment, and spatially-variable data on biosolid applications. The USDA Cropland Data Layer (www.nass.usda.gov) provides potential land application sites, from which proximity to surface water plays a role in the potential for CECs to transport from land to freshwater (using a meta-model estimated from pesticide assessment models). Concentrations of CECs are routed through the river network based on local river attributes (e.g., flow) combined with assumptions about chemical fate in the aquatic environment. Results of various simulations show the spatial patterns of biosolids applications, potential to enter surface water, and estimated freshwater concentrations of an ingredient in a hypothetical consumer product. Implications of altering model assumptions are discussed. While the presented material is a simulated example of the environmental emission and fate of a consumer product ingredient, it represents a viable approach to assessing whether this pathway via land applied biosolids may be of concern for consumer product chemicals, and ultimately helping to inform environmental policy on this subject.
Christopher Holmes, Joshua Amos, Amy Ritter, and Marty Williams (Waterborne Environmental). Estimating environmental emissions and aquatic fate of sludge-bound CECs using spatial modeling and US datasets. Platform SETAC 2018. Sacramento, CA.
Exposure and effects of clothianidin residues in corn pollen: Honey bee colony simulation in a field setting
As managed pollinator species, honey bees provide major pollination services to a wide variety of crops across the globe. At the same time, they are potentially vulnerable to the effects of systemic neonicotinoids because residues can occur in pollen and nectar collected by the bees. However, the assessment of potential effects of neonicotinoids on colonies in field studies is challenging because multiple environmental conditions interact with the colonies’ health. Honey bee colony models such as BEEHAVE provide the opportunity to assess potential influx of residues into a colony via different routes, and their effects on bees in the hive can be dependent on their stage-dependent consumption rates and sensitivities. We extended BEEHAVE to represent exposure to clothianidin via residues in pollen from treated corn fields. Landscapes around the colonies were simulated using land cover data from sites across the Midwest of the United States. Simulated foragers collect pollen from flower resources across the landscape including corn pollen during the corn blooming period. Clothianidin residues are consumed by larvae and worker bees. Different residue levels in corn pollen were applied to assess impacts on honey bee colonies over a one-year cycle. Clothianidin effects on colony strength were only observed if unrealistically high residue levels in the pollen were simulated. The landscape composition significantly impacted the collection of pollen (residue exposure) from the corn fields, resulting in higher colony-level effects in landscapes with low proportions of semi-natural land. The case study with the mechanistic honey bee colony model presents a path to the application of such models in the context of pesticide risk assessment.
Amelie Schmolke (Waterborne Environmental), Farah Abi-Akar (Waterborne Environmental), Silvia Hinarejos (Sumitomo). Exposure and effects of clothianidin residues in corn pollen: Honey bee colony simulation in a field setting. Platform SETAC 2018. Sacramento, CA.
Applying a mechanistic honey bee colony model to assess multiple factors impacting colony overwintering survival
Honey bee colony feeding studies are one type of Tier II semi-field studies designed to determine potential effects of pesticides on free-foraging whole colonies during and after dietary intake of a known pesticide concentration. These studies represent a progressively more realistic level of refinement for pollinator studies compared to individual laboratory-based studies since they are intended to reflect a worst-case exposure scenario in the field. Colony feeding studies are designed to test toxicity over a foraging season and following overwintering period. However, such studies are very cost- and time-intensive to conduct, and high overwintering losses of control hives have been observed in some studies. Loss of control colonies indicates that stressors other than pesticides, e.g. resource availability, weather, diseases and beekeeping activities, likely influence colony overwintering survival, confounding the assessment of impacts caused by pesticides. Honey bee colony models have been gaining interest as tools in pesticide risk assessment to inform study design and ultimately, colony-level risks to honey bees. In the current study commissioned by the Pollinator Research Task Force, we apply the honey bee colony model BEEHAVE to simulate colony dynamics observed in negative control colonies from multiple colony feeding studies. Detailed landscape-level data inform the resource availability for the simulated foragers in the model. In addition, weather data, initial colony condition and feeding patterns were analyzed across studies and translated to model inputs. In a calibration step, we adjusted parameters in BEEHAVE to achieve simulated dynamics corresponding to colony conditions reported in the studies. Study data collected in summer and fall were analyzed for predictors of overwintering success of individual colonies. BEEHAVE simulations with different combinations of external factors were used to assess their importance for colony condition. Colony conditions at study initialization and feeding patterns both influenced the colony condition in the fall, and thus, the probability of overwintering survival. Model simulations can be used to estimate colony-level outcomes under conditions deviating from those in the studies to inform study design and extend the use of the available data. Pesticide effects can be included in future model analyses, and analyzed in the context of multiple factors that impact colony health and overwintering success.
Amelie Schmolke (Waterborne Environmental), Farah Abi-Akar (Waterborne Environmental), Nika Galic (Syngenta), Silvia Hinarejos (Sumitomo). Applying a mechanistic honey bee colony model to assess multiple factors impacting colony overwintering survival. Platform SETAC 2018. Sacramento, CA.
Combining an individual-based model and an aquatic food web-ecosystem model to assess ecological risks: A case study with the endangered Topeka shiner
The Comprehensive Aquatic System Model (CASM) is a process-based integrated bioenergetics and habitat quality model that simulates population, community, and ecosystem-level effects of chemical stressors based on an aggregated population structure defined by an average-sized individual. An individual-based bioenergetics and population model (IBM) was developed for the endangered Topeka shiner (Notropis topeka) to incorporate detailed life-history and age-specific and size-specific attributes of population dynamics not represented in the CASM. The models were executed in tandem with daily IBM population growth dynamics transferred to CASM, which in turn computed the corresponding daily modifications to the food web. The CASM food web results were transferred back to the IBM in the form of adjusted Topeka shiner prey biomass values. This uniquely integrated model combination was implemented to simulate potential ecological risks for Topeka shiner in a generalized Iowa headwater pool, representative of known Midwestern habitat and range for this species. Ecological risks were computed using time-integrated differences between the population biomass values of 365-day baseline and exposure simulations. Risks were estimated for example daily pesticide exposures of varying magnitude, timing, and duration. Potential direct toxic effects to Topeka shiners were modelled within the IBM. The resulting modelled impacts on population biomass were used by the CASM to compute corresponding food web-ecosystem effects. The IBM provided the capability to examine the potential population-level risks based on detailed sensitivities of early life stages and adults to pesticide exposures. The CASM extended the IBM assessment capability by extrapolating potential direct effects on Topeka shiners to associated indirect changes in headwater pool community structure and ecosystem function. The presentation highlights the advantages afforded by the integrated IBM-CASM modeling approach to ecological risk assessment.
Steven Bartell (Cardno), Amelie Schmolke (Waterborne Environmental), Colleen Roy (Waterborne Environmental), Nicholas Ralston (University of North Dakota), Daniel Perkins (Waterborne Environmental), Nika Galic (Syngenta), Richard Brain (Syngenta). Combining an individual-based model and an aquatic food web-ecosystem model to assess ecological risks: A case study with the endangered Topeka shiner. Platform SETAC 2018. Sacramento, CA.
A US, field-scale, herbicide spray drift deposition and biological evaluation study: Methods and implications for risk assessment
Spray drift from pesticide applications is considered as a potential route of exposure in environmental risk assessment. Typically, spray drift deposition is modeled using terrestrial plant effects endpoint derived from worst-case, full rate direct spray studies, and combined in the risk assessment framework to represent extreme worst-case risk to non-target organisms. The objective of this work was to merge observed plant effects with spray drift exposure in a single study. A 40-acre field-scale, spray drift study was developed to simultaneously measure spray drift deposition, airborne interception, and potential biological effects of an herbicide under conservative drift conditions and a relatively low-drift nozzle. This study was conducted in four replications, each with a two-swath spray pattern (90 ft per swath) upwind of a deposition zone (perpendicular to wind direction), generally following the generic U.S. Environmental Protection Agency verification protocol, Testing Pesticide Application Spray Drift Reduction Technologies for Row and Field Crops. Within each replicate application, an array of three perpendicular sampling lines were used to measure drift deposition out to 400 ft, airborne interception out to 75 ft, and potential direct plant effects at set distances (5, 15, 25, 23, and 45 ft) from the edge of the downwind spray application . At each distance and sampling line, further replication of spray drift deposition, airborne interception, and biological effects were assessed in replicated fashion in a nested, replicated design. The timing of the herbicide application for each of the four replications targeted steady wind speeds between 8 to 12 mph. Wind direction was measured within a 30-degree angle of the downwind field orientation to ensure that spray drift would travel toward the collection area and across the furthest sampling points. Results from this study design refine effects determined in laboratory studies under worst-case exposure scenarios (i.e. direct over the top application) by addressing how terrestrial non-target plants actually experience exposure under natural conditions in the field, which can better inform risk assessment and risk management decisions.
Daniel Perkins (Waterborne Environmental), Greg Goodwin (Waterborne Environmental), Greg Kruger (University of Nebraska-Lincoln), Abby Lynn (All Aspects Consulting), Farah Abi-Akar (Waterborne Environmental), Richard Brain (Syngenta). A US, field-scale, herbicide spray drift deposition and biological evaluation study: Methods and implications for risk assessment. Platform SETAC 2018. Sacramento, CA.