James Burridge

Plant-plant interactions shape community productivity and resilience, yet how genetic variation influences these interactions remains poorly understood. We conducted multi-environment field phenotyping of 200 common bean (Phaseolus vulgaris) lines grown in association with three maize (Zea mays ssp. mays) landraces to characterize the determinants of interspecific interactions. Our results reveal that maize landraces constitute distinct biotic environments, and interactions were strongly asymmetric, with maize acting as the dominant competitor. Bean responses to the identity of the maize partner involved phenology, vegetative development, productivity, and root architecture, and varied across field environments, with no evidence of bean local adaptation. We used genome-wide association to link variation across the bean genome to variation in bean and maize traits, thereby capturing both direct and indirect genetic effects, and tested for maize bean interactions. We identified six genomic regions with indirect effects: three with stable effects across maize partners and three whose effects varied with maize partner identity, including candidate genes involved in neighbor perception and facilitative interactions. Our findings demonstrate that the phenotypic effects of genetic variation extend beyond individual plants, with competitive tolerance modulated by the genetic identity of neighboring plants and the environment.

Is it possible to develop a "physics of language" which can explain the spatial, temporal, and social patterns we see and which can predict future change like we forecast the weather? Such a theory is likely to involve ideas from statistical physics. A substantial literature already applies these ideas to language. However, we lack a model which can match the spatial-temporal detail of historical changes at the level of individual linguistic features and which offers a principled mechanism to predict the future. Here we present a statistical field theory for the evolution of linguistic variables which takes steps to fill this gap. Linguistic variant frequencies are represented as a stochastic state field with spatial interaction and social conformity, coupled to a latent bias field with Onsager-Machlup action that reduces overfitting to data. We derive parameter inference procedures and demonstrate them using examples of large-scale dialect survey data from the 20th-century United States. The bias field has a characteristic half-life, which determines the horizon over which linguistic change can be predicted. Inferred model parameters provide evidence for surface-tension-driven coarsening of dialect regions, with population-density gradients exerting systematic forces on interfaces.

A method is presented for combining the feature extraction power of neural networks with model based dimensionality reduction to produce linguistically motivated low dimensional measurements of sounds. This method works by first training a convolutional neural network (CNN) to predict linguistically relevant category labels from the spectrograms of sounds. Then, idealized models of these categories are defined as probability distributions in a low dimensional measurement space with locations chosen to reproduce, as far as possible, the perceptual characteristics of the CNN. To measure a sound, the point is found in the measurement space for which the posterior probability distribution over categories in the idealized model most closely matches the category probabilities output by the CNN for that sound. In this way, the feature learning power of the CNN is used to produce low dimensional measurements. This method is demonstrated using monophthongal vowel categories to train this CNN and produce measurements in two dimensions. It is also shown that the perceptual characteristics of this CNN are similar to those of human listeners.

One approach to understand people's efforts to reduce disease transmission, is to consider the effect of behaviour on case rates. In this paper we present a spatial infection-reducing game model of public behaviour, formally equivalent to a Hopfield neural network coupled to SIRS disease dynamics. Behavioural game parameters can be precisely calibrated to geographical time series of Covid-19 active case numbers, giving an implied spatial history of behaviour. This is used to investigate the effects of government intervention, quantify behaviour area by area, and measure the effect of wealth on behaviour. We also demonstrate how a delay in people's perception of risk levels can induce behavioural instability, and oscillations in infection rates.

Discovering and quantifying the drivers of language change is a major challenge. Hypotheses about causal factors proliferate, but are difficult to rigorously test. Here we ask a simple question: can 20th century changes in English be explained as a consequence of spatial diffusion, or have other processes created bias in favour of certain linguistic forms? Using two of the most comprehensive spatial datasets available, which measure the state of English at the beginning and end of the 20th century, we calibrate a simple spatial model so that, initialised with the early state, it evolves into the later. Our calibrations reveal that while some changes can be explained by diffusion alone, others are clearly the result of substantial asymmetries between variants. We discuss the origins of these asymmetries and, as a by-product, we generate a full spatio–temporal prediction for the spatial evolution of English features over the 20th century, and a prediction of the future.

Both linguistic and genetic evolution involve copying and mutation of variants. The simplest copying process assumes that variants are reproduced at a rate equal to their current frequency, exemplified by Kimura's stepping stone model of neutral evolution, and the voter model. In this case, spatial patterns are driven by noise. In the linguistic context, an alternative possibility is that speakers preferentially select variants which are already popular, yielding patterns driven by surface tension, exemplified by the Ising model. In this paper, we model language change using a spatial network of speakers, inspired by the Hopfield neural network. The model's universality class—Voter or Ising—is determined by speakers' learning function. We view maps generated by the Survey of English Dialects as samples from our network. Maximum likelihood analysis, and comparison of spatial auto-correlations between real and simulated maps, indicates that the underlying copying processes is more likely to belong to the conformity-driven Ising class.

We investigate the typical sizes and shapes of sets of points obtained by irregularly tracking two-dimensional Brownian bridges. The tracking process consists of observing the path location at the arrival times of a non-homogeneous Poisson process on a finite time interval. The time varying intensity of this observation process is the tracking strategy. By analysing the gyration tensor of tracked points we prove two theorems which relate the tracking strategy to the average gyration radius, and to the asphericity—a measure of how non-spherical the point set is. The act of tracking may be interpreted either as a process of observation, or as process of depositing time decaying ‘evidence’ such as scent, environmental disturbance, or disease particles. We present examples of different strategies, and explore by simulation the effects of varying the total number of tracking points.

The neutral theory of genetic and linguistic evolution holds that the relative frequencies of variants evolve by random drift. Neutral evolution remains a plausible null model of language change. In this paper we provide evidence against the neutral hypothesis by considering the geographical patterns observed in language surveys. We model speakers as neurons in a Hopfield network embedded in space, analogous to one of the classical two dimensional lattice models of statistical physics. The universality class of the model depends on the form of the activation function of the neurons, which encodes learning behaviour of speakers. We view maps generated by the Survey of English Dialects as samples from our network. Maximum likelihood analysis, and comparison of spatial auto-correlations between real and simulated maps, indicates that the maps are more likely to belong to the conformity-driven Ising class, where interfaces are driven by surface tension, rather than the neutral Voter class, where they are driven by noise.

We investigate the typical sizes and shapes of sets of points obtained by irregularly tracking two-dimensional Brownian bridges. The tracking process consists of observing the path location at the arrival times of a non-homogeneous Poisson process on a finite time interval. The time varying intensity of this observation process is the tracking strategy. By analysing the gyration tensor of tracked points we prove two theorems which relate the tracking strategy to the average gyration radius, and to the asphericity -- a measure of how non-spherical the point set is. The act of tracking may be interpreted either as a process of observation, or as process of depositing time decaying "evidence" such as scent, environmental disturbance, or disease particles. We present examples of different strategies, and explore by simulation the effects of varying the total number of tracking points.