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.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.