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Abstract Studies of oscillatory populations have a long history in ecology. A first‐principles understanding of these dynamics can provide insights into causes of population regulation and help with selecting detailed predictive models. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing oscillations. We employ statistical physics concepts, including measures of spatial synchrony, that incorporate patterns at all scales and are novel to ecology, to show that spatial patterns can, under broad and well‐defined circumstances, elucidate drivers of population dynamics. We find that when neighbours are coupled (e.g. by dispersal), noisy intrinsic oscillations become distinguishable from noise‐induced oscillations at a transition point related to synchronisation that is distinct from the deterministic bifurcation point. We derive this transition point and show that it diverges from the deterministic bifurcation point as stochasticity increases. The concept of universality suggests that the results are robust and widely applicable. 

During community assembly, abiotic factors can influence species at multiple stages during their life history, for example by affecting early settlement or establishment probabilities and thus initial densities (route 1: abiotic effects on density), or later by affecting the strength of biotic interactions during subsequent life stages (route 2: abiotic effects on interaction strengths). Since real abiotic landscapes are multivariate and complex, how these two distinct routes of abiotic influence affect community patterns has not been quantified. Using an individual-based spatially explicit simulation model, we compared scenarios where abiotic conditions shaped initial densities, interaction strengths, or both, of plant species with unique abiotic niches. We then partitioned the effect of the abiotic landscape on community patterns into components arising from variable density, variable interaction strengths, and their interaction. Even when plants responded to identical landscapes, variable density and variable interaction strengths led to different community patterns, and their combined effects were non-additive. Variable density promoted more spatial structure, while variable interaction strengths promoted higher local species richness. We highlight important implications these findings have in applied plant community ecology. 

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of ‘universal’ properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system’s dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.