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Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number R 0 , a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R 0 through the individual-based model, including contact tracing, inferring the Kermack–McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R 0 for the ash dieback epidemic in the UK. • Computational individual-based models can simulate the spread of plant disease. • Disease progression can be summarised by the epidemiological parameter R 0. • R 0 can be calculated from an individual-based model in several ways. • Methods include: analytic expressions, parameter inference, and contact-tracing. • Each has (dis)advantages — the best choice will vary depending on requirements. [ABSTRACT FROM AUTHOR]

• Synoptic analysis of two IBM types yielded interesting details on similarities and differences. • New methodology of recombining model components was crucial to analysis. • Models with detailed interaction structure are only beneficial if the data provide the interaction information required. • Substantial progress in estimating the parameters of spatial IBMs was made. Agent/individual-based models (A/IBM) help to explain in a mechanistic way how spatial plant patterns evolve through time. In the past, seemingly different and independent types of A/IBMs were developed for modelling the dynamics of tree populations, e.g. growth interaction (GI) and shot noise (SN) models. In this paper, we present a new, advanced methodology of pattern-oriented modelling (POM) for the comparative, synoptic analysis of the behaviour of different types of A/IBMs by using recombinations of model components, validation and sensitivity analysis. We analysed model behaviour for spatio-temporal data from natural forests of interior Douglas fir (Pseudotsuga menziesii var glauca (Mirb.) Franco) and Scots pine (Pinus sylvestris L.) populations from Canada and the UK, respectively. Our detailed analysis clarified that both models, GI and SN along with their recombinations performed similarly and belong to the same group of A/IBMs. From the application of our new methodology we learnt that SN models were able to describe interactions more accurately than GI models and additionally produce interaction fields that can be used for other modelling purposes. On the other hand the GI model was more robust when using observed data that did not include sufficient information on tree interactions. Maximum-likelihood estimations were more reliable in spatial regression analysis than least-squares methods and should be preferred in spatial A/IBM parametrisation. [ABSTRACT FROM AUTHOR]