Modelling effects of honeybee behaviors on the distribution of pesticide in nectar within a hive and resultant in-hive exposure

This is the author accepted manuscript. The final version is available from the American Chemical Society via the DOI in this record.Recently, the causes of honeybee colony losses have been intensely studied, showing that there are multiple stressors implicated in colony declines, one stressor being the exposure to pesticides. Measuring exposure of individual bees within a hive to pesticide is at least as difficult as assessing the potential exposure of foraging bees to pesticide. We present a model to explore how heterogeneity of pesticide distribution on a comb in the hive can be driven by worker behaviors. The model contains simplified behaviors to capture the extremes of possible heterogeneity of pesticide location/deposition within the hive to compare with exposure levels estimated by averaging values across the comb. When adults feed on nectar containing the average concentration of all pesticide brought into the hive on that particular day it is likely representative of the worst case exposure scenario. However, for larvae, clustering of pesticide in the comb can lead to higher exposure levels than taking an average concentration in some circumstances. The potential for extrapolating the model to risk assessment is discussed.J.R. was funded to do this work on an Industrial CASE PhD studentship funded by the Biology and Biotechnology Sciences Research Council of the UK (BBSRC), and Syngenta. J.O. and M.B. were supported on BBSRC project BB/K014463/1

REVIEW: Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models

Published© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Summary 1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee – varroa mite – virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.Biotechnology and Biological Sciences Research Council (BBSRC

Predicting honeybee colony failure: using the BEEHAVE model to simulate colony responses to pesticides

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tTo simulate effects of pesticides on different honeybee (Apis mellifera L.) life stages, we used the BEEHAVE model to explore how increased mortalities of larvae, in-hive workers, and foragers, as well as reduced egg-laying rate, could impact colony dynamics over multiple years. Stresses were applied for 30 days, both as multiples of the modeled control mortality and as set percentage daily mortalities to assess the sensitivity of the modeled colony both to small fluctuations in mortality and periods of low to very high daily mortality. These stresses simulate stylized exposure of the different life stages to nectar and pollen contaminated with pesticide for 30 days. Increasing adult bee mortality had a much greater impact on colony survival than mortality of bee larvae or reduction in egg laying rate. Importantly, the seasonal timing of the imposed mortality affected the magnitude of the impact at colony level. In line with the LD50, we propose a new index of "lethal imposed stress": the LIS50 which indicates the level of stress on individuals that results in 50% colony mortality. This (or any LISx) is a comparative index for exploring the effects of different stressors at colony level in model simulations. While colony failure is not an acceptable protection goal, this index could be used to inform the setting of future regulatory protection goals.J.R. was funded to do this work on an Industrial CASE PhD studentship funded by the Biology and Biotechnology Sciences Research Council of the UK (BBSRC), and Syngenta. J.O., M.B., and P.K. were supported on BBSRC project BB/K014463/

BEEHAVE: A systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure

Journal Article© 2014 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly citedSummary: A notable increase in failure of managed European honeybee Apis mellifera L. colonies has been reported in various regions in recent years. Although the underlying causes remain unclear, it is likely that a combination of stressors act together, particularly varroa mites and other pathogens, forage availability and potentially pesticides. It is experimentally challenging to address causality at the colony scale when multiple factors interact. In silico experiments offer a fast and cost-effective way to begin to address these challenges and inform experiments. However, none of the published bee models combine colony dynamics with foraging patterns and varroa dynamics. We have developed a honeybee model, BEEHAVE, which integrates colony dynamics, population dynamics of the varroa mite, epidemiology of varroa-transmitted viruses and allows foragers in an agent-based foraging model to collect food from a representation of a spatially explicit landscape. We describe the model, which is freely available online (www.beehave-model.net). Extensive sensitivity analyses and tests illustrate the model's robustness and realism. Simulation experiments with various combinations of stressors demonstrate, in simplified landscape settings, the model's potential: predicting colony dynamics and potential losses with and without varroa mites under different foraging conditions and under pesticide application. We also show how mitigation measures can be tested. Synthesis and applications. BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics. © 2014 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.Biotechnology and Biological Sciences Research Council (BBSRC

Assessing population impacts of toxicant-induced disruption of breeding behaviours using an individual-based model for the three-spined stickleback

 This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThe effects of toxicant exposure on individuals captured in standard environmental risk assessments (ERA) do not necessarily translate proportionally into effects at the population-level. Population models can incorporate population resilience, physiological susceptibility, and likelihood of exposure, and can therefore be employed to extrapolate from individual- to population-level effects in ERA. Here, we present the development of an individual-based model (IBM) for the three-spined stickleback (Gasterosteus aculeatus) and its application in assessing population-level effects of disrupted male breeding behaviour after exposure to the anti-androgenic pesticide, fenitrothion. The stickleback is abundant in marine, brackish, and freshwater systems throughout Europe and their complex breeding strategy makes wild populations potentially vulnerable to the effects of endocrine disrupting chemicals (EDCs). Modelled population dynamics matched those of a UK field population and the IBM is therefore considered to be representative of a natural population. Literature derived dose-response relationships of fenitrothion-induced disruption of male breeding behaviours were applied in the IBM to assess population-level impacts. The modelled population was exposed to fenitrothion under both continuous (worst-case) and intermittent (realistic) exposure patterns and population recovery was assessed. The results suggest that disruption of male breeding behaviours at the individual-level cause impacts on population abundance under both fenitrothion exposure regimes; however, density-dependent processes can compensate for some of these effects, particularly for an intermittent exposure scenario. Our findings further demonstrate the importance of understanding life-history traits, including reproductive strategies and behaviours, and their density-dependence, when assessing the potential population-level risks of EDCs.Syngenta LtdBiotechnology and Biological Sciences Research Council (BBSRC

Earthworm distribution and abundance predicted by a process-based model

Earthworms are significant ecosystem engineers and are an important component of the diet of many vertebrates and invertebrates, so the ability to predict their distribution and abundance would have wide application in ecology, conservation and land management. Earthworm viability is known to be affected by the availability and quality of food resources, soil water conditions and temperature, but has not yet been modelled mechanistically to link effects on individuals to field population responses. Here we present a novel model capable of predicting the effects of land management and environmental conditions on the distribution and abundance of Aporrectodea caliginosa, the dominant earthworm species in agroecosystems. Our process-based approach uses individual based modelling (IBM), in which each individual has its own energy budget. Individual earthworm energy budgets follow established principles of physiological ecology and are parameterised for A. caliginosa from experimental measurements under optimal conditions. Under suboptimal conditions (e.g. food limitation, low soil temperatures and water contents) reproduction is prioritised over growth. Good model agreement to independent laboratory data on individual cocoon production and growth of body mass, under variable feeding and temperature conditions support our representation of A. caliginosa physiology through energy budgets. Our mechanistic model is able to accurately predict A. caliginosa distribution and abundance in spatially heterogeneous soil profiles representative of field study conditions. Essential here is the explicit modelling of earthworm behaviour in the soil profile. Local earthworm movement responds to a trade-off between food availability and soil water conditions, and this determines the spatiotemporal distribution of the population in the soil profile. Importantly, multiple environmental variables can be manipulated simultaneously in the model to explore earthworm population exposure and effects to combinations of stressors. Potential applications include prediction of the population-level effects of pesticides and changes in soil management e.g. conservation tillage and climate change

Heterogeneity in biological assemblages and exposure in chemical risk assessment: exploring capabilities and challenges in methodology with two landscape-scale case studies

Chemical exposure concentrations and the composition of ecological receptors (e.g., species) vary in space and time, resulting in landscape-scale (e.g. catchment) heterogeneity. Current regulatory, prospective chemical risk assessment frameworks do not directly address this heterogeneity because they assume that reasonably worst-case chemical exposure concentrations co-occur (spatially and temporally) with biological species that are the most sensitive to the chemical’s toxicity. Whilst current approaches may parameterise fate models with site-specific data and aim to be protective, a more precise understanding of when and where chemical exposure and species sensitivity co-occur enables risk assessments to be better tailored and applied mitigation more efficient. We use two aquatic case studies covering different spatial and temporal resolution to explore how geo-referenced data and spatial tools might be used to account for landscape heterogeneity of chemical exposure and ecological assemblages in prospective risk assessment. Each case study followed a stepwise approach: i) estimate and establish spatial chemical exposure distributions using local environmental information and environmental fate models; ii) derive toxicity thresholds for different taxonomic groups and determine geo-referenced distributions of exposure-toxicity ratios (i.e., potential risk); iii) overlay risk data with the ecological status of biomonitoring sites to determine if relationships exist. We focus on demonstrating whether the integration of relevant data and potential approaches is feasible rather than making comprehensive and refined risk assessments of specific chemicals. The case studies indicate that geo-referenced predicted environmental concentration estimations can be achieved with available data, models and tools but establishing the distribution of species assemblages is reliant on the availability of a few sources of biomonitoring data and tools. Linking large sets of geo-referenced exposure and biomonitoring data is feasible but assessment of risk will often be limited by the availability of ecotoxicity data. The studies highlight the important influence that choices for aggregating data and for the selection of statistical metrics have on assessing and interpreting risk at different spatial scales and patterns of distribution within the landscape. Finally, we discuss approaches and development needs that could help to address environmental heterogeneity in chemical risk assessment

Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine‐Active Chemicals

Data Availability Statement: The stickleback and trout population model code and a log of changes from the original versions are available in the Supporting Information. The FOCUS output files from Toxic Substances in Surface Waters (TOXSWA) are also available, with details of how these were used in the population models.Supporting Information is available online at https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5640#support-information-section .Copyright © 2023 The Authors. The presence of endocrine-active chemicals (EACs) in the environment continues to cause concern for wildlife given their potential for adverse effects on organisms. However, there is a significant lack of understanding about the potential effects of EACs on populations. This has real-world limitations for EAC management and regulation, where the aim in environmental risk assessment is to protect populations. We propose a methodological approach for the application of modeling in addressing the population relevance of EAC exposure in fish. We provide a case study with the fungicide prochloraz to illustrate how this approach could be applied. We used two population models, one for brown trout (Salmo trutta; inSTREAM) and the other for three-spined stickleback (Gasterosteus aculeatus) that met regulatory requirements for development and validation. Effects data extracted from the literature were combined with environmentally realistic exposure profiles generated with the FOCUS SW software. Population-level effects for prochloraz were observed in some modeling scenarios (hazard-threshold [HT]) but not others (dose–response), demonstrating the repercussions of making different decisions on implementation of exposure and effects. The population responses, defined through changes in abundance and biomass, of both trout and stickleback exposed to prochloraz were similar, indicating that the use of conservative effects/exposure decisions in model parameterization may be of greater significance in determining population-level adverse effects to EAC exposure than life-history characteristics. Our study supports the use of models as an effective approach to evaluate the adverse effects of EACs on fish populations. In particular, our HT parameterization is proposed for the use of population modeling in a regulatory context in accordance with Commission Regulation (EU) 2018/605.BASF SE; UK Research and Innovation. Grant Number: MR/V025570/1

Impact of enhanced Osmia bicornis (Hymenoptera: Megachilidae) populations on pollination and fruit quality in commercial sweet cherry (Prunus avium L.) orchards

The impact on pollination of supplementing wild pollinators with commercially reared Osmia bicornis in commercial orchards growing the self-fertile sweet cherry variety “Stella” was investigated in each of two years. The quality characteristics used by retailers to determine market value of fruit were compared when insect pollination was by wild pollinators only, or wild pollinators supplemented with O. bicornis released at recommended commercial rates. No effect of treatment on the number of fruit set or subsequent rate of growth was recorded. However, supplemented pollination resulted in earlier fruit set when compared to pollination by wild pollinators alone and offered the potential benefit of a larger proportion of the crop reaching optimum quality within a narrower time range, resulting in more consistent produce. Retailers use five key quality criteria in assessment of market value of cherries (the weight of individual fruit, width at the widest point, fruit colour, sugar content and firmness). Price paid to growers depends both on meeting the criteria and consistency between fruit in these characteristics. In both years, the commercial criteria were met in full in both treatments, but harvested fruit following supplemented pollination were consistently larger and heavier compared to those from the wild pollinator treatment. In the year where supplemented pollination had the greatest impact on the timing of fruit set, fruit size and sugar content were also less variable than when pollination was by wild species only. The implications for the commercial use of O. bicornis in cherry orchards are considered

Three questions to ask before using model outputs for decision support

Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs