Social networks and occupational choice: the endogenous formation of attitudes and beliefs about tax compliance

The paper analyses the emergence of group-specific attitudes and beliefs about tax compliance when individuals interact in a social network. It develops a model in which taxpayers possess a range of individual characteristics – including attitude to risk, potential for success in self-employment, and the weight attached to the social custom for honesty – and make an occupational choice based on these characteristics. Occupations differ in the possibility for evading tax. The social network determines which taxpayers are linked, and information about auditing and compliance is transmitted at meetings between linked taxpayers. Using agent-based simulations, the analysis demonstrates how attitudes and beliefs endogenously emerge that differ across sub-groups of the population. Compliance behaviour is different across occupational groups, and this is reinforced by the development of group-specific attitudes and beliefs. Taxpayers self-select into occupations according to the degree of risk aversion, the subjective probability of audit is sustained above the objective probability, and the weight attached to the social custom differs across occupations. These factors combine to lead to compliance levels that differ across occupations.

Stream water quality and nutrient export in the Slapton Catchments

A weekly programme of water quality monitoring has been conducted by Slapton Ley Field Centre since 1970. Samples have been collected for the four main streams draining into Slapton Ley, from the Ley itself and from other sites within the catchment. On occasions, more frequent sampling has been undertaken during short-term research projects, usually in relation to nutrient export from the catchment. These water quality data, unparalleled in length for a series of small drainage basins in the British Isles, provide a unique resource for analysis of spatial and temporal variations in stream water quality within an agricultural area. Not surprisingly, given the eutrophic status of the Ley, most attention has focused on the nutrients nitrate and phosphate. A number of approaches to modelling nutrient loss have been attempted, including time series analysis and the application of nutrient export and physically-based models.

Phosphorus loss from agricultural catchments: pathways and implications for management

The contribution non-point P sources make to the total P loading on water bodies in agricultural catchments has not been fully appreciated. Using data derived from plot scale experimental studies, and modelling approaches developed to simulate system behaviour under differing management scenarios, a fuller understanding of the processes controlling P export and transformations along non-point transport pathways can be achieved. One modelling approach which has been successfully applied to large UK catchments (50-350km2 in area) is applied here to a small, 1.5 km2 experimental catchment. The importance of scaling is discussed in the context of how such approaches can extrapolate the results from plot-scale experimental studies to full catchment scale. However, the scope of such models is limited, since they do not at present directly simulate the processes controlling P transport and transformation dynamics. As such, they can only simulate total P export on an annual basis, and are not capable of prediction over shorter time scales. The need for development of process-based models to help answer these questions, and for more comprehensive UK experimental studies is highlighted as a pre-requisite for the development of suitable and sustainable management strategies to reduce non-point P loading on water bodies in agricultural catchments.

Deriving global flood hazard maps of fluvial floods through a physical model cascade

Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25×25 km grid, which is then reprojected onto a 1×1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25×25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

Mathematics applied to the climate system: outstanding challenges and recent progress

The societal need for reliable climate predictions and a proper assessment of their uncertainties is pressing. Uncertainties arise not only from initial conditions and forcing scenarios, but also from model formulation. Here, we identify and document three broad classes of problems, each representing what we regard to be an outstanding challenge in the area of mathematics applied to the climate system. First, there is the problem of the development and evaluation of simple physically based models of the global climate. Second, there is the problem of the development and evaluation of the components of complex models such as general circulation models. Third, there is the problem of the development and evaluation of appropriate statistical frameworks. We discuss these problems in turn, emphasizing the recent progress made by the papers presented in this Theme Issue. Many pressing challenges in climate science require closer collaboration between climate scientists, mathematicians and statisticians. We hope the papers contained in this Theme Issue will act as inspiration for such collaborations and for setting future research directions.

The AVOID programme’s new simulations of the global benefits of stringent climate change mitigation

Quantitative simulations of the global-scale benefits of climate change mitigation are presented, using a harmonised, self-consistent approach based on a single set of climate change scenarios. The approach draws on a synthesis of output from both physically-based and economics-based models, and incorporates uncertainty analyses. Previous studies have projected global and regional climate change and its impacts over the 21st century but have generally focused on analysis of business-as-usual scenarios, with no explicit mitigation policy included. This study finds that both the economics-based and physically-based models indicate that early, stringent mitigation would avoid a large proportion of the impacts of climate change projected for the 2080s. However, it also shows that not all the impacts can now be avoided, so that adaptation would also therefore be needed to avoid some of the potential damage. Delay in mitigation substantially reduces the percentage of impacts that can be avoided, providing strong new quantitative evidence for the need for stringent and prompt global mitigation action on greenhouse gas emissions, combined with effective adaptation, if large, widespread climate change impacts are to be avoided. Energy technology models suggest that such stringent and prompt mitigation action is technologically feasible, although the estimated costs vary depending on the specific modelling approach and assumptions.

Modelling human visual navigation using multi-view scene reconstruction

It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the observer’s prediction of landmark location based on standard photogrammetric methods and then combine location predictions to compute likelihood maps of navigation behaviour. In one model, each scene point is treated independently in the reconstruction; in the other, the pertinent variable is the spatial relationship between pairs of points. Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back. Error distributions varied substantially with changes in scene layout; we compared these directly with the likelihood maps to quantify the success of the models. We also measured error distributions when participants manipulated the location of a landmark to match the preceding interval, providing a direct test of the landmark-location stage of the navigation models. Models such as this, which start with scenes and end with a probabilistic prediction of behaviour, are likely to be increasingly useful for understanding 3D vision.

The time-course of morphological constraints: a study of plurals inside derived words

The avoidance of regular but not irregular plurals inside compounds (e.g. *rats eater vs. mice eater) has been one of the most widely studied morphological phenomena in the psycholinguistics literature. To examine whether the constraints that are responsible for this contrast have any general significance beyond compounding, we investigated derived word forms containing regular and irregular plurals in two experiments. Experiment 1 was an offline acceptability judgment task, and experiment 2 measured eye movements during reading derived words containing regular and irregular plurals and uninflected base nouns. The results from both experiments show that the constraint against regular plurals inside compounds generalizes to derived words. We argue that this constraint cannot be reduced to phonological properties, but is instead morphological in nature. The eye-movement data provide detailed information on the time-course of processing derived word forms indicating that early stages of processing are affected by a general constraint that disallows inflected words from feeding derivational processes, and that the more specific constraint against regular plurals comes in at a subsequent later stage of processing. We argue that these results are consistent with stage-based models of language processing.

Ecosystem effects of CO2 concentration: evidence from past climates

Atmospheric CO2 concentration has varied from minima of 170-200 ppm in glacials to maxima of 280-300 ppm in the recent interglacials. Photosynthesis by C-3 plants is highly sensitive to CO2 concentration variations in this range. Physiological consequences of the CO2 changes should therefore be discernible in palaeodata. Several lines of evidence support this expectation. Reduced terrestrial carbon storage during glacials, indicated by the shift in stable isotope composition of dissolved inorganic carbon in the ocean, cannot be explained by climate or sea-level changes. It is however consistent with predictions of current process-based models that propagate known physiological CO2 effects into net primary production at the ecosystem scale. Restricted forest cover during glacial periods, indicated by pollen assemblages dominated by non-arboreal taxa, cannot be reproduced accurately by palaeoclimate models unless CO2 effects on C-3-C-4 plant competition are also modelled. It follows that methods to reconstruct climate from palaeodata should account for CO2 concentration changes. When they do so, they yield results more consistent with palaeoclimate models. In conclusion, the palaeorecord of the Late Quaternary, interpreted with the help of climate and ecosystem models, provides evidence that CO2 effects at the ecosystem scale are neither trivial nor transient.

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.

Effects of noise and by-catch on a Danish harbour porpoise population

Ships and wind turbines generate noise, which can have a negative impact on marine mammal populations by scaring animals away. Effective modelling of how this affects the populations has to take account of the location and timing of disturbances. Here we construct an individual-based model of harbour porpoises in the Inner Danish Waters. Individuals have their own energy budgets constructed using established principles of physiological ecology. Data are lacking on the spatial distribution of food which is instead inferred from knowledge of time-varying porpoise distributions. The model produces plausible patterns of population dynamics and matches well the age distribution of porpoises caught in by-catch. It estimates the effect of existing wind farms as a 10% reduction in population size when food recovers fast (after two days). Proposed new wind farms and ships do not result in further population declines. The population is however sensitive to variations in mortality resulting from by-catch and to the speed at which food recovers after being depleted. If food recovers slowly the effect of wind turbines becomes negligible, whereas ships are estimated to have a significant negative impact on the population. Annual by-catch rates ≥10% lead to monotonously decreasing populations and to extinction, and even the estimated by-catch rate from the adjacent area (approximately 4.1%) has a strong impact on the population. This suggests that conservation efforts should be more focused on reducing by-catch in commercial gillnet fisheries than on limiting the amount of anthropogenic noise. Individual-based models are unique in their ability to take account of the location and timing of disturbances and to show their likely effects on populations. The models also identify deficiencies in the existing database and can be used to set priorities for future field research.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Understanding the link between single cell and population scale responses of Escherichia coli in differing ligand gradients

We formulate an agent-based population model of Escherichia coli cells which incorporates a description of the chemotaxis signalling cascade at the single cell scale. The model is used to gain insight into the link between the signalling cascade dynamics and the overall population response to differing chemoattractant gradients. Firstly, we consider how the observed variation in total (phosphorylated and unphosphorylated) signalling protein concentration affects the ability of cells to accumulate in differing chemoattractant gradients. Results reveal that a variation in total cell protein concentration between cells may be a mechanism for the survival of cell colonies across a wide range of differing environments. We then study the response of cells in the presence of two different chemoattractants.In doing so we demonstrate that the population scale response depends not on the absolute concentration of each chemoattractant but on the sensitivity of the chemoreceptors to their respective concentrations. Our results show the clear link between single cell features and the overall environment in which cells reside.

Is behavioral ecology important for understanding and predicting population dynamics?

Population ecology is a discipline that studies changes in the number and composition (age, sex) of the individuals that form a population. Many of the mechanisms that generate these changes are associated with individual behavior, for example how individuals defend their territories, find mates or disperse. Therefore, it is important to model population dynamics considering the potential influence of behavior on the modeled dynamics. This study illustrates the diversity of behaviors that influence population dynamics describing several methods that allow integrating behavior into population models and range from simpler models that only consider the number of individuals to complex individual-based models that capture great levels of detail. A series of examples shows the importance of explicitly considering behavior in population modeling to avoid reaching erroneous conclusions. This integration is particularly relevant for conservation, as incorrect predictions regarding the dynamics of populations of conservation interest can lead to inadequate assessment and management. Improved predictions can favor effective protection of species and better use of the limited financial and human conservation resources.

The status and challenge of global fire modelling

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what essons may be learned from FireMIP.