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Solitary bees provide an important ecological and agricultural service by pollinating both wild plants and crops, often more effectively than honey bees. In the context of worldwide pollinators' declines, it is important to better understand the functioning of populations under multiple stressors at larger spatial and temporal scales. Here we propose building a detailed, spatially-explicit agent-based model of one of the best-studied species of solitary bees, Osmia bicornis L. In this Formal Model, we review various aspects of O. bicornis biology and ecology in detail and provide descriptions of their planned implementations in the model. We also discuss the model gaps and limitations, as well as inclusions and exclusions, allowing a dialogue with the reviewers about the model's design.The ALMaSS model of O. bicornis aims to provide a realistic and detailed representation of O. bicornis populations in space and time in European agricultural landscapes. The model will be a part of the Animal, Landscape and Man Simulation System (ALMaSS); thus will be able to utilise a highly detailed, dynamic ALMaSS landscape model. It will consider the behaviour of all bee life stages daily and use state transitions to allow each individual to decide their behaviour. The development of egg-to-pupa stages in the nest will be temperature-driven. Adult bees, after they emerge from the nest in spring, will interact with the environment. They will be able to search for suitable nesting locations, provision their brood cells with pollen and reproduce. Modelled females will balance offspring size and number following the optimal allocation theory, but local environmental factors will modify their actual parental investment decisions. The model will include the daily mortality rate for the egg-to-pupa stages, overwintering mortality, and background mortality outside the nest. We will also consider the risk of open-cell parasitism as increasing with the time the brood cell is open.With the level of detail suggested, the model will be able to simulate population-level dynamics in response to multiple factors at the landscape scale over long periods. The European Food Safety Authority (EFSA) has suggested O. bicornis as a model organism for non-Apis solitary bees in the pesticide risk assessment scheme. Therefore, we hope our model will be a first step in building future landscape risk assessments for solitary bees.

A limitação natural de pragas é um serviço de ecossistema que deve ser promovido nos sistemas agrícolas, já que pode ser usada como alternativa, ou complementarmente, à aplicação de pesticidas. Consiste no consumo das pragas pelos seus inimigos naturais e pode ser promovida através do estabelecimento de componentes paisagísticas que forneçam, a estes inimigos das pragas, alimento alternativo e refúgio, para além da aplicação de práticas agrícolas sustentáveis. No entanto, o delineamento de uma estratégia eficaz de limitação natural de pragas está dependente da caracterização detalhada dos comportamentos dos animais que fazem parte dessa cadeia trófica, nomeadamente, das pragas e dos seus predadores, o que pode ser feito através de simulações computacionais. Neste contexto, e no âmbito do projeto OLIVESIM, estamos a construir um sistema de modelos para simular a biologia e o comportamento de duas espécies de artrópodes (1) a mosca-da-azeitona, Bactrocera oleae (Rossi), praga chave da oliveira, e (2) a aranha de solo, Haplodrassus rufipes (Lucas), um dos potenciais predadores da praga, e as suas interações com a paisagem selecionada para realizar este estudo, localizada na região de Trás-os-Montes (Mirandela, Portugal) e caracterizada fundamentalmente por olival tradicional. Para isso, está a ser utilizado o sistema ALMaSS (Animal, Landscape and Man Simulation System), que recebe inputs diários de variáveis climáticas e também de ações de gestão agrícola, e que é calibrado com dados sobre abundância quantitativa e qualitativa de animais. Neste simpósio focamo-nos nestes dois modelos, mostrando também o estado atual deste trabalho. Natural pest control is an ecosystem service that should be promoted in agricultural systems, as it can be used as an alternative or complementary to pesticide application. It consists in increasing the populations of natural pest enemies by establishing landscape components that provide them with alternative food and refuge, and by applying sustainable agricultural practices. However, the design of an effective strategy for natural pest control depends on the detailed characterization of the behaviour of animals that are part of this trophic chain, namely pests and their predators, which can be done through computational simulations. In this context, and in the scope of project OLIVESIM, we are building a system of models to simulate the biology and behaviours of two species (1) the olive fly, Bactrocera oleae (Rossi), key pest of the olive tree, and (2) a ground spider, Haplodrassus rufipes (Lucas), one of the potential predators of the pest, and their interactions with the landscape selected to carry out this study, located in the region of Trás-os-Montes (Mirandela, Portugal) and characterized primarily by traditional olive groves. For this, the ALMaSS (Animal, Landscape and Man Simulation System) system is being used, which receives daily inputs of climatic variables and agricultural management actions, and which is calibrated with data on quantitative and qualitative abundance of animals. In this symposium, we focus on the models for these two animal species, showing the current state of this work. info:eu-repo/semantics/publishedVersion

A conceptual framework was developed by a working group of the Scientific Committee of the European Food Safety Authority (EFSA) to guide risk assessors and risk managers on when and how to integrate ecological recovery and resilience assessments into environmental risk assessments (ERA). In this commentary we advocate that a systems approach is required to integrate the diversity of ecosystem services (ES) providing units, environmental factors, scales, and stressor-related responses necessary to address the context dependency of recovery and resilience in agricultural landscapes. A future challenge in the resilience assessment remains to identify the relevant bundles of ecosystem services provided by different types of agroecosystem that need to be assessed in concert. Integr Environ Assess Manag 2018;14:586-591. (c) 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

A conceptual framework was developed by a working group of the Scientific Committee of the European Food Safety Authority (EFSA) to guide risk assessors and risk managers on when and how to integrate ecological recovery and resilience assessments into environmental risk assessments (ERA). In this commentary we advocate that a systems approach is required to integrate the diversity of ecosystem services (ES) providing units, environmental factors, scales, and stressor-related responses necessary to address the context dependency of recovery and resilience in agricultural landscapes. A future challenge in the resilience assessment remains to identify the relevant bundles of ecosystem services provided by different types of agroecosystem that need to be assessed in concert. Integr Environ Assess Manag 2018;14:586-591. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental ToxicologyChemistry (SETAC).

EFSA performs environmental risk assessments (ERAs) for single potential stressors such as plant protection products, genetically modified organisms and feed additives and for invasive alien species that are harmful for plant health. In this risk assessment domain, the EFSA Scientific Committee recognises the importance of more integrated ERAs considering both the local and landscape scales, as well as the possible co-occurrence of multiple potential stressors that fall under the remit of EFSA, which are important when addressing ecological recovery. In this scientific opinion, the Scientific Committee gathered scientific knowledge on the potential for the recovery of non-target organisms for the further development of ERA. Current EFSA guidance documents and opinions were reviewed on how ecological recovery is addressed in ERA schemes. In addition, this scientific opinion is based on expert knowledge and data retrieved from the literature. Finally, the information presented in this opinion was reviewed by experts from the relevant EFSA Panels, European risk assessment bodies and through an open consultation requesting input from stakeholders. A conceptual framework was developed to address ecological recovery for any assessed products, and invasive alien species that are harmful for plant health. This framework proposes an integrative approach based on well-defined specific protection goals, scientific knowledge derived by means of experimentation, modelling and monitoring, and the selection of focal taxa, communities, processes and landscapes to develop environmental scenarios to allow the assessment of recovery of organisms and ecological processes at relevant spatial and temporal scales.

Current EU regulatory risk assessment allows application of pesticides provided that recovery of non-target arthropods in-crop occurs within a year. Despite long-established theory of source sink dynamics, risk assessment ignores depletion of surrounding populations and typical field trials are restricted to plot-scale experiments. Here, we use agent-based modelling of 2 contrasting invertebrates, a spider and a beetle, to assess how area of pesticide application and environmental half-life (DT50) affect the assessment of recovery at the plot scale and impact the population at the landscape scale. Small-scale plot experiments were simulated for pesticides with different application rates and DT50s. The same pesticides were then evaluated at the landscape scale (10 x 10 km) assuming continuous year-on-year usage. Our results show that recovery time estimated from plot experiments is a poor indicator of long-term population impact at the landscape level and that the spatial scale of pesticide application strongly determines population-level impact. This raises serious doubts as to the utility of plot-recovery experiments in pesticide regulatory risk assessment for population-level protection. Predictions from the model are supported by empirical evidence from a series of studies carried out in the decade starting in 1988. The issues raised then can now be addressed using simulation. Prediction of impacts at landscape scales should be more widely used in assessing the risks posed by environmental stressors.

Manganese is a magnetic resonance imaging (MRI) contrast agent for small animals that can provide a blood-flow-independent measure of neuronal activation. Established Mn MRI methods have limited ability to measure concentrations of Mn or details of its distribution in vivo, which limits their experimental power. Positron emission tomography (PET) can measure quantitative distributions in vivo in small animals of positron-emitting radionuclides such as Mn-52, although has poorer spatial resolution than MRI. Autoradiography (AR) can also measure quantitative distributions of Mn-52, in ex vivo brain tissue, with spatial resolution similar to MRI. This work has three primary goals: to develop and characterize Mn-52 as a radionuclide for PET in phantoms and in small animals, to develop a quantitative MRI method for measuring Mn concentration in the brain of small animals, and to validate the MRI results by comparisons with AR and PET. Mn-52 was produced by proton irradiation of natural Cr foil, isolated by column chromatography, and used as a PET tracer for the first time in phantoms and in vivo in rats. Mn-52 phantom image quality metrics were similar to F-18, an established PET radionuclide. After systemic administration in rats, Mn-52 accumulation was seen in bones, but little was seen in the brain, due to the blood-brain barrier. Direct injection into the lateral ventricle effectively delivers Mn-52 throughout the rat brain. Mn-52 AR images were acquired and used for comparison with MRI. MRI R1 relaxation rate maps of rat brain were acquired using a radiofrequency field strength independent inversion recovery Look-Locker sequence, and used to generate relaxation rate change and Mn concentration maps after Mn administration. These maps showed excellent quantitative agreement with PET and AR images of the same animal, confirming that MR R1 change accurately measures Mn concentration in rat brain in the range 0 to 0.1 mM, in the absence of other sources of R1 change. However, at some point above this concentration, measured R1 becomes inaccurate. Accordingly, Mn concentration mapping with MRI is a potentially useful tool to improve the experimental power of Mn-uptake imaging to assess neuronal activation.

The primary focus of environmental risk assessment (ERA) for non-target organisms is on the direct effect of the toxicant. In some cases what are often termed large-scale sources of variation are also considered (e.g. expected crop distribution). However, these describe only part of the variation that occurs in the real world. Landscapes vary in structure, meaning that the field size and proximity to primary NTO habitats will vary. There is climatic variation driving changes in phenology and behaviour, and management changes in the proportion of crops grown, but also changes in how they are cultivated in time and space. All of these factors can affect the risk assessment. There is also another, difficult to observe, property of real systems, and that is the spatio-temporal dynamics associated with populations, climate, management, and ecology and behaviour, and the potential for feedback loops. These interactions can exacerbate or ameliorate impacts either via local feedback mechanisms e.g. multiple stressors, or by virtue of the spatial population dynamics. This uncertainty is normally considered as stochasticity in ERAs and the factors are often incorporated into a single general term, utilizing a safety factor to account for uncertainty. This, however, robs us of both understanding and predictive power, since probability distribution can only be based on statistical expectations of past events, which do not necessarily account for interactions in the future. An alternative approach capable of dealing with these system properties is agentbased modelling (ABM).ABMs are capable of integrating a range of drivers and actors in space and time and can represent detailed farming operations on a large scale, integrating these with realistic models of animal populations, and expressing each animal as an individual agent. As such they are capable of representing very complex dynamics in time and space. Another key model attribute is the ability of agents to respond to the information they gather from their local environment. This automatically integrates many of the dynamics difficult to capture in traditional models, e.g. source-sink dynamics are emergent properties and do not need to be imposed.ALMaSS is one such model system and is used here to exemplify aspects of complexity in population-level risk assessment for terrestrial mammals, birds, and arthropods exposed to pesticides. Results are used to argue for greater realism and population-level ERA.

Agent-baserede modeller blev benyttet til at beskrive den potentielle kumulative effekt af tilstedeværelse af forskellige scenarier af havvindmølleparker i Østersøen.

Rapporten beskriver effekterne på økonomi, landbrugspraksis og udvalgte faunaarter i marklandskaber af at indføre henholdsvis en omsættelig pesticidkvote, der ville reducere pesticidanvendelsen med 25 procent, og en obligatorisk sprøjtefri randzone i markkanterne. Rapporten sammenfatter resultaterne af et forskningsprojekt, der undersøger mulighederne for at understøtte målene i den danske pesticidpolitik gennem brug af EU's landbrugsordninger. Analyserne viser, at kvotescenariet nok reducerer pesticidforbruget betydeligt, men at det ikke forbedrer levebetingelserne for de seks dyrearter. Randzonescenariet har stort set ingen effekter. Analyserne bygger på de senere års økonomiske og biologiske konsekvensmodeller, der er videreudviklet i et samlet modelkompleks til kvantitative integrerede konsekvensvurderinger af politik-scenarier. Analysens konklusioner skal derfor også tages med det forbehold, at de ikke giver et fuldt realistisk billede af virkeligheden, men viser potentialet i at modellere effekter i hele kæden fra politik til natur.

Ecologists have had limited success in understanding which introduced species may become invasive. An evolutionary model is used to investigate which traits are associated with invasiveness. Translocation experiments were simulated in which species were moved into similar but evolutionary younger communities. The main findings were that species that had previously been the most abundant in their original communities have significantly higher rates of establishment than did species that had previously occurred at low abundance in their original community. However if establishment did occur, previously abundant and previously low abundance species were equally likely to become dominant and were equally likely to exclude other species from their new community. There was a suggestion that the species that were most likely to establish and exclude others were “genetically” different. When species that had evolved in different simulations (but with identical environmental conditions), were transplanted into communities that had also evolved in different simulations of the same conditions, the outcomes were difficult to predict. Observed rates of establishment and subsequent competitive dominance were observed to be species and community combination specific. This evolutionary study represents a novel in silico attempt to tackle invasiveness in an experimental framework, and may provide a new methodology for tackling these issues.

The public promotion of renewable energies is expected to increase the number of biogas plants and stimulate energy crops cultivation (e. g. maize) in Germany. In order to assess the indirect effects of the resulting land-use changes on biodiversity, we developed six land-use scenarios and simulated the responses of six farmland wildlife species with the spatially explicit agent-based model system ALMaSS. The scenarios differed in composition and spatial configuration of arable crops. We implemented scenarios where maize for energy production replaced 15% and 30% of the area covered by other cash crops. Biogas maize farms were either randomly distributed or located within small or large aggregation clusters. The animal species investigated were skylark (Alauda arvensis), grey partridge (Perdix perdix), European brown hare (Lepus europaeus), field vole (Microtus agrestis), a linyphiid spider (Erigone atra) and a carabid beetle (Bembidion lampros). The changes in crop composition had a negative effect on the population sizes of skylark, partridge and hare and a positive effect on the population sizes of spider and beetle and no effect on the population size of vole. An aggregated cultivation of maize amplified these effects for skylark. Species responses to changes in the crop composition were consistent across three differently structured landscapes. Our work suggests that with the compliance to some recommendations, negative effects of biogas-related land-use change on the populations of the six representative farmland species can largely be avoided.

The Evolve model was used to simulate the relationship between soil fertility and species diversity, and between diversity and yield. In the first set of simulations, the effect of soil nutrient status (N=15-225 in arbitrary units of soil fertility) on the ability of diversity to evolve was investigated. Mutations in life history traits were allowed to occur in 1 of 500 propagules. In simulation 1, diversity was allowed to evolve under intense grazing. In simulation 2, the community of species that had evolved at the end of each of the first set of simulations were transplanted into a fertile environment (N=225). Each of these communities was then allowed to interact for a further 100 years, but this time with no mutations occurring. In the final year of the simulation, the aboveground biomass of the grassland was recorded on a monthly basis. At the end of the first set of simulations, the evolution of species diversity was more pronounced in grasslands with the most fertile soil. These highly fertile and diverse grasslands produced the highest levels of biomass, while grasslands characterized by low levels of species diversity and soil fertility produced almost no biomass. After 100 years of plant competition in the second set of simulations, June biomass production was found to be weakly and negatively correlated with species richness. However, when total annual biomass production was considered, the highest yields were observed to be associated with the most diverse grasslands. These diverse communities were consistently more productive across the entire year than the less diverse grasslands.

Small animal positron emission tomography (PET) image analysis can be particularly challenging with heavily-lesioned animal disease models with limited tracer uptake such as the 6-hydroxydopamine (OHDA) lesioned rat model of Parkinson's disease. Methodology-related variations in measured values of 10% or 15% can obscure meaningful biological differences, so accurate analysis methods are essential. However, placing regions of interest (ROIs) on these images without additional guidance is unreliable, and can lead to significant errors in results. To address this problem, this work develops a partly atlas-guided method place ROIs on structures that lack specific binding with presynaptic dopaminergic tracers. The method is tested by correlation of PET binding potential (BP) with autoradiographic binding measurements, and with repeated PET scans of the same subjects, both with the presynaptic tracer ¹¹C-dihydrotetrabenazine (DTBZ). The method is found to produce reliable results. When directly comparing PET images of the same subject to detect changes, it is essential to minimize variations due to analysis method. To this end, a masking method for automated image registration (AIR) of PET images with dopaminergic tracer rat images is developed. Coregistration with AIR and separate ROI placement are compared and tested with repeated scans of the same rat with DTBZ, and are found to be equivalent. Kinetic modelling algorithms may also introduce bias or scatter to binding potentials (BP) calculated from TACs or in parametric images. To determine the optimal method for this step, algorithms for dopaminergic tracers are compared for small animal DTBZ, ¹¹C-methylphenidate (MP), and ¹¹C-raclopride (Rac) data. Among the tested methods is a new variant of the Logan graphical kinetic modelling method, developed in this work, that issignificantly less biased by target tissue TAC noise than the standard Logan approach. The modified graphical method is further compared with the Logan graphical algorithms with added-noise simulations. The simplified reference tissue model (SRTM) is found to have the best method for ROI TAC data, while the modified graphical algorithm may be preferred when generating parametric images.