In agricultural landscapes, the composition and spatial configuration of
cultivated and semi-natural elements strongly impact species dynamics, their
interactions and habitat connectivity. To allow for landscape structural
analysis and scenario generation, we here develop statistical tools for real
landscapes composed of geometric elements including 2D patches but also 1D
linear elements such as hedges. We design generative stochastic models that
combine a multiplex network representation and Gibbs energy terms to
characterize the distributional behavior of landscape descriptors for land-use
categories. We implement Metropolis-Hastings for this new class of models to
sample agricultural scenarios featuring parameter-controlled spatial and
temporal patterns (e.g., geometry, connectivity, crop-rotation).
Pseudolikelihood-based inference allows studying the relevance of model
components in real landscapes through statistical and functional validation,
the latter achieved by comparing commonly used landscape metrics between
observed and simulated landscapes. Models fitted to subregions of the Lower
Durance Valley (France) indicate strong deviation from random allocation, and
they realistically capture small-scale landscape patterns. In summary, our
approach of statistical modeling improves the understanding of structural and
functional aspects of agro-ecosystems, and it enables simulation-based
theoretical analysis of how landscape patterns shape biological and ecological
processes