Chris J. Topping

ALMaSS, an agent-based model for animals in temperate European landscapes

Abstract The animal, landscape and man simulation system (ALMaSS) was designed as a predictive tool for answering policy questions regarding the effect of changing landscape structure or management on key animal species in the Danish landscape. By combining agent-based models of animals with a comprehensive and dynamic landscape simulation, it aims to improve predictive ability. The landscape model comprises detailed mapping, weather, farm management, and vegetation growth. Each vegetated area has its own growth model and in the case of farmed areas, management is modelled in detail. Animal models are agent-based, designed using the state/transition concept, and are rule-based. Each animal may interact with others and directly with its local environment. Field vole ( Microtus agrestis ) is used as an example of the extent to which dynamic landscapes can influence the population dynamics. Simulations of crop diversity and rotation demonstrate significant effects of spatial and temporal heterogeneity on population sizes, population fluctuations and landscape permeability. These two factors interact and thus different responses to temporal factors occur at different levels of spatial heterogeneity. Spatial and temporal heterogeneity in both the model and the real world are often related to changes in land-use and management. Consequently, the impact of landscape changes on any population can be enormous and heavily spatially influenced. Therefore, the use of dynamic landscapes is seen as an important addition to the modeller’s toolkit.

Biodiversity and the mitigation of climate change through bioenergy: impacts of increased maize cultivation on farmland wildlife

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.

Mighty small: Observing and modeling individual microbes becomes big science

Progress in microbiology has always been driven by technological advances, ever since Antonie van Leeuwenhoek discovered bacteria by making an improved compound microscope. However, until very recently we have not been able to identify microbes and record their mostly invisible activities, such as nutrient consumption or toxin production on the level of the single cell, not even in the laboratory. This is now changing with the rapid rise of exciting new technologies for single-cell microbiology (1, 2), which enable microbiologists to do what plant and animal ecologists have been doing for a long time: observe who does what, when, where, and next to whom. Single cells taken from the environment can be identified and even their genomes sequenced. Ex situ, their size, elemental, and biochemical composition, as well as other characteristics can be measured with high-throughput and cells sorted accordingly. Even better, individual microbes can be observed in situ with a range of novel microscopic and spectroscopic methods, enabling localization, identification, or functional characterization of cells in a natural sample, combined with detecting uptake of labeled compounds. Alternatively, they can be placed into fabricated microfluidic environments, where they can be positioned, exposed to stimuli, monitored, and their interactions controlled “in microfluido.” By introducing genetically engineered reporter cells into a fabricated landscape or a microcosm taken from nature, their reproductive success or activity can be followed, or their sensing of their local environment recorded.

A unifying evolutionary theory for the biomass–diversity–fertility relationship

Although a widely accepted ecological theory predicts that more diverse plant communities should be better able to capture resources and turn carbon dioxide into biomass, the most productive communities known are low diversity agricultural ones. This paradox has fuelled a long running controversy in ecology surrounding the nature of the relationship between diversity, productivity and fertility. Here, an evolutionary computer model is used which demonstrates that given the opportunity, species-rich communities may evolve under high fertility conditions. In contrast to low diversity, highly productive agricultural communities are shown to probably be a recent phenomenon. In simulations where fertility was applied to communities that had evolved under lower nutrient conditions, a few species had the ability to become ‘dominant’. These species were responsible for the loss of diversity and for the majority of biomass production. These results are consistent with complementarity theory applying in nature in old co-evolved low nutrient communities, whereas in recently established fertile agricultural communities, dominant species appear to regulate biomass production. Understanding the nature of these ‘dominant’ species throws light on our understanding of phenotypic plasticity and the ecology of invasive species.

How Predation and Landscape Fragmentation Affect Vole Population Dynamics

Background Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics.

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides.

Pesticides are regulated in Europe and this process includes an environmental risk assessment (ERA) for non-target arthropods (NTA). Traditionally a non-spatial or field trial assessment is used. In this study we exemplify the introduction of a spatial context to the ERA as well as suggest a way in which the results of complex models, necessary for proper inclusion of spatial aspects in the ERA, can be presented and evaluated easily using abundance and occupancy ratios (AOR). We used an agent-based simulation system and an existing model for a widespread carabid beetle (Bembidion lampros), to evaluate the impact of a fictitious highly-toxic pesticide on population density and the distribution of beetles in time and space. Landscape structure and field margin management were evaluated by comparing scenario-based ERAs for the beetle. Source-sink dynamics led to an off-crop impact even when no pesticide was present off-crop. In addition, the impacts increased with multi-year application of the pesticide whereas current ERA considers only maximally one year. These results further indicated a complex interaction between landscape structure and pesticide effect in time, both in-crop and off-crop, indicating the need for NTA ERA to be conducted at landscape- and multi-season temporal-scales. Use of AOR indices to compare ERA outputs facilitated easy comparison of scenarios, allowing simultaneous evaluation of impacts and planning of mitigation measures. The landscape and population ERA approach also demonstrates that there is a potential to change from regulation of a pesticide in isolation, towards the consideration of pesticide management at landscape scales and provision of biodiversity benefits via inclusion and testing of mitigation measures in authorisation procedures.

Spatial Dynamic Factors Affecting Population-Level Risk Assessment for a Terrestrial Arthropod: An Agent-Based Modeling Approach

ABSTRACT Agent-based models (ABMs) explicit handling of space and time and integration of nonlinear interactions between system components to create a system response could facilitate realistic risk assessment modeling. We used this approach to evaluate the impact of spatial dynamic factors on a theoretical risk assessment of an insecticide on a carabid beetle population (based on Bembidion lampros). Results indicated that both impacts and recovery were dependent on period of application, area treated, spatial distribution of stressor, beetle dispersal, and underlying habitat suitability. The impact of the stressor was detected far outside the area to which it was applied, and the extent of this impact was affected by beetle dispersal and landscape structure. The results call into question the validity of the recovery endpoint as assessed by field trials because model recovery was primarily influenced by reinvasion, depleting surrounding areas. Despite the simplicity of the acute stressor/Bembidion system modeled, the results obtained were highly variable dependent on the precise inputs used. Therefore, we suggest that application of modeling to risk assessment in the future will require a much more complete description of the risk assessment problem than has hitherto been the case.

Landscape structure and management alter the outcome of a pesticide ERA: Evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data. Ten different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably. The study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Trait evolution in an individual-based model of herbaceous vegetation

Many theoretical studies of evolution are based upon the concepts of the evolutionary stable strategy and optimal life-history solutions. An individual based model of vegetation is used to simulate life-history evolution under two different sets of environmental conditions. At one level the results suggest that optimal life-history solutions do appear to evolve. At the end of the simulations the vegetation that evolved in a fertile and uncut environment was taller, thinner and germinated later than that which developed in a less fertile and cut habitat. However, between simulation variation was observed to be high, particularly for the parameter regulating the timing of reproduction, and it showed no indication of reaching fixation. When this trait was prevented from mutating, the variances of other traits were seen to increase. Although at the population level between simulation variation was high, some traits achieved a degree of stability within simulations, suggesting that multiple adaptive peaks may be being approached. However, there was little evidence of trait fixation occurring within the most abundant ‘genotype’. It is considered that frequency dependent selection/Red Queen dynamics may be acting to prevent the most abundant ‘genotype’ from reaching fixation. It is argued that if such processes prevent optimal genetic solutions from being achieved then the search for evolutionary stable strategies within the evolution of life-histories may be over simplistic.

Landscape structure mediates the effects of a stressor on field vole populations

Spatio-temporal landscape heterogeneity has rarely been considered in population-level impact assessments. Here we test whether landscape heterogeneity is important by examining the case of a pesticide applied seasonally to orchards which may affect non-target vole populations, using a validated ecologically realistic and spatially explicit agent-based model. Voles thrive in unmanaged grasslands and untreated orchards but are particularly exposed to applied pesticide treatments during dispersal between optimal habitats. We therefore hypothesised that vole populations do better (1) in landscapes containing more grassland and (2) where areas of grassland are closer to orchards, but (3) do worse if larger areas of orchards are treated with pesticide. To test these hyposeses we made appropriate manipulations to a model landscape occupied by field voles. Pesticide application reduced model population sizes in all three experiments, but populations subsequently wholly or partly recovered. Population depressions were, as predicted, lower in landscapes containing more unmanaged grassland, in landscapes with reduced distance between grassland and orchards, and in landscapes with fewer treated orchards. Population recovery followed a similar pattern except for an unexpected improvement in recovery when the area of treated orchards was increased. Outside the period of pesticide application, orchards increase landscape connectivity and facilitate vole dispersal and so speed population recovery. Overall our results show that accurate prediction of population impact cannot be achieved without taking account of landscape structure. The specifics of landscape structure and habitat connectivity are likely always important in mediating the effects of stressors.