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Thinning strategies are a prime factor in generating spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term spatial-temporal experiments in forest stands is clearly difficult. This means that forest modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Särkkä algorithm for modelling the space-time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard-and soft-core interaction functions. The results obtained strongly suggest that combining the immigration-growth-spatial interaction model with spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.

Point process theory plays a fundamental role in the analysis and modelling of spatial forest patterns. For instance, the Ripley's K function and its density with respect to the area, i.e. the pair correlation function, have been extensively used to analyse and characterise stationary forest configurations. However, the stationarity condition is not often met in practice when analysing real data. Thus, the development and application of new statistics to measure the degree of inhomogeneity suggests the use of inhomogeneous statistics to describe forest stands. In this paper, we restrict our attention to the inhomogeneous pair correlation function in the context of replicated spatial data. We then analyse the spatial configuration of pure and mixed conifer stands in a case study in Central Catalonia, North-East of Spain. Our results suggest that whilst P. sylvestris tend to be aggregated for short inter-tree distances, P. nigra and P. halepensis keep a minimum interevent distance between trees. Regarding the mixed stands, trees of distinct species tend to be segregated from each other. Tentative explanations for these results are related with site properties, competition effects, shade tolerance and silviculture practices applied in this forest region.

This paper reviews the main applications of (marked) point process theory in forestry including functions to analyse spatial variability and the main (marked) point process models. Although correlation functions do describe spatial variability at distinct range of scale, they are clearly restricted to the analysis of few dominant species since they are based on pairwise analysis. This has over-simplified the spatial analysis of complex forest dynamics involving "large" number of species. Moreover, although process models can reproduce, to some extent, real forest spatial patterns of trees, the biological forest-ecological interpretation of the resulting spatial structures is difficult since these models usually lack of biological realism. This problem gains in strength as usually most of these point process models are defined in terms of purely spatial relationships, though in real life, forest develop through time. We thus aim to discuss the applicability of such formulations to analyse and simulate "real" forest dynamics and unwrap their shortcomes. We present a unified approach of modern spatially explicit forest growth models. Finally, we focus on a continuous space-time stochastic process as an alternative approach to generate marked point patterns evolving through space and time.