I will describe a generalisation of the Seiberg-Witten equations to a Spin-c manifold of any dimension. The equations are for a U(1) connection A and spinor \phi and also an odd-degree differential form b (of inhomogeneous degree). Clifford action of the form is used to perturb the Dirac operator D_A. The first equation says that (D_A+b)(\phi)=0. The second equation involves the Weitzenböck remainder for D_A+b, setting it equal to q(\phi), where q(\phi) is the same quadratic term which appears in the usual Seiberg-Witten equations. This system is elliptic modulo gauge in dimensions congruent to 0,1 or 3 mod 4. In dimensions congruent to 2 mod 4 one needs to take two copies of the system, coupled via b. I will also describe a variant of these equations which make sense on manifolds with a Spin(7) structure. The most important difference with the familiar 3 and 4 dimensional stories is that compactness of the space of solutions is, for now at least, unclear. This is joint work with Partha Ghosh and, in the Spin(7) setting, Ragini Singhal.

Consumer-resource interactions are central to ecology, as all organisms rely on consuming resources to survive. Functional responses describe how a consumer's feeding rate changes with resource availability, influenced by processes like searching for, capturing, and handling resources. To study functional responses, experiments typically measure the amount of food consumed—often in discrete units like prey—over a set time. These experiments systematically vary prey availability to observe how it affects the consumer's feeding behaviour. The data generated by such experiments are often analysed using differential equation-based models. Here, we argue that such models do not represent a realistic data-generating process for many such experiments and propose an alternative stochastic individual-based model. This class of models, however, is expensive for inference, and we use machine learning methods to expedite fitting these models to data. We then use our method to do generalised linear model-based inference for a series of experiments conducted on a stickleback fish. Our methodology is made available to others in a Python package for Bayesian hierarchical inference for stochastic, individual-based models of functional responses.

How do mobile organisms situate themselves in space?  This is a fundamental question in both ecology and cell biology but, since space use is an emergent feature of movement processes operating on small spatio-temporal scales, it requires a mathematical approach to answer.  In recent years, increasing empirical research has shown that non-locality is a key aspect of movement processes, whilst mathematical models have demonstrated its importance for understanding emergent space use patterns.  In this talk, I will describe a broad class of models for modelling the space use of interacting populations, whereby directed movement is in the form of non-local advection.  I will detail various methods for ascertaining pattern formation properties of these models, fundamental for answering the question of how organisms situate themselves in space, and describe some of the rich variety of patterns that emerge. I will also explain how to connect these models to data on animal and cellular movement.