The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non‐equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state‐space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state‐of‐the‐art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process‐oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.

A novel, yet generic, Bayesian approach to parameter inference in a stochastic, spatio‐temporal model of dispersal and colonisation is developed and applied to the invasion of a region by an alien plant species. The method requires species distribution data from multiple time points, and accounts for temporal uncertainty in colonisation times inherent in such data. Covariates, such as climate parameters, altitude and land use, which capture variation in the suitability of sites for plant colonisation, are easily incorporated into the model. The method assumes no local extinction of occupied sites and thus is primarily applicable to modelling distribution data at relatively coarse spatial resolutions of plant species whose range is expanding over time. The implementation of the model and inference algorithm are illustrated through application to British floristic atlas data for the widespread alien Heracleum mantegazzianum (giant hogweed) assessed at a 10 × 10 km resolution in 1970 and 2000. We infer key characteristics of this species, predict its future spread, and use the resulting fitted model to inform a simulation‐based assessment of the methodology. Simulated distribution data are used to validate the inference algorithm. Our results suggest that the accuracy of inference is not sensitive to the number of distribution time points, requiring only that there are at least two points in time when distributions are mapped. We demonstrate the utility of the modelling approach by making future forecasts and historic hindcasts of the distribution of giant hogweed in Great Britain. Giant hogweed is one of the worst alien plants in Britain and has rapidly increased its range since 1970, yet we highlight that a further 20% of land area remains susceptible to colonisation by this species. We use the robustness of this case study to discuss the potential for modelling distribution data for other species and at different spatial scales.

Epidemiological models are frequently used by EFSA as a support tool to quantify the risk of introduction, establishment, spread and transmission of diseases of importance to livestock and wildlife in the EU. Modelling approaches from fields outside veterinary epidemiology might also be applicable, and may provide new ideas that could be leveraged to improve the utility of models used by EFSA. This project was set up to identify models that are currently used within all relevant fields, and to build an inventory describing their characteristics. A series of expert workshops were held to obtain insights into unfamiliar research fields, and to build a dictionary of terms for a literature search within each identified field. A subsequent search of the literature identified 3,625 potentially applicable papers matching the search criteria, of which 468 passed a second screening phase and went on to be extracted into the final inventory. A secondary aim of the project was to demonstrate the potential use of this inventory in developing new models to be applied to three case studies: the introduction of peste des petits ruminants (PPR), the establishment of African swine fever (ASF), and the spread of bluetongue (BT). The novel PPR model was developed to propagate uncertainty associated with a data-sparse environment, and incorporates ideas from graph theory and operations research to obtain an efficient representation of disease spread. Two novel models were developed for ASF, based on spatial K-functions and using INLA to model the complex spatial structure in the absence of denominator data. A novel modelling framework was developed for bluetongue to implement the highly complex mechanistic model incorporating a high level of data availability with a flexible network representation of between-farm disease spread. For each case study, the novel models compared favourably to the existing models that represented the previous state-of-the-art modelling approaches.

This article discusses the context in which 'study support' has emerged in higher education in the UK. Within this context the article documents the establishment of a 'devolved model' of academic skills at the University of Huddersfield. Whilst acknowledging that this model is not unique, its formation allows for the exploration of pedagogical and practical issues. It highlights the complexity of providing support which is effective and viable, recognising that the increasing diversity of the student body calls for multiple strategies.  An examination of the evolution of the provision at Huddersfield illustrates the journey from a focus on student deficit and retention towards one clearly associated with learning development.  This model assumes an integrated, flexible and student centred approach within the subject discipline, rather than one which is extra-curricular and may be perceived as 'remedial'. Originally predicated on the individual student tutorial and standalone workshop, the provision is now focusing on working within the disciplines to embed academic development within the curriculum.

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