Agent-based modelling of clonal plant propagation across space: Recapturing fairy rings, power laws and other phenomena

An overview of agent-based models in plant biology and ecology

Agent-based modelling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modelling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modelling, by functional-structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition and reproduction across the scales from the plant organ to populations and communities on a range of spatial scales to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.

The plant body as a network of semi-autonomous agents: a review

Plants can solve amazingly difficult tasks while adjusting their growth and development to the environment. They can explore and exploit several resources simultaneously, even when the distributions of these vary in space and time. The systematic study of plant behaviour goes back to Darwin's book The power of movement in plants. Current research has highlighted that modularity is a key to understanding plant behaviour, as the production, functional specialization and death of modules enable the plant to adjust its movement to the environment. The adjustment is assisted by a flow of information and resources among the modules. Experiments have yielded many results about these processes in various plant species. Theoretical research, however, has lagged behind the empirical studies, possibly owing to the lack of a proper modelling framework that could encompass the high number of components and interactions. In this paper, I propose such a framework on the basis of network theory, viewing the plant as a group of connected, semi-autonomous agents. I review some characteristic plant responses to the environment through changing the states of agents and/or links. I also point out some unexplored areas, in which a dialogue between plant science and network theory could be mutually inspiring. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

The effect of ramet mortality on clonal plant growth

Clonal plants grow horizontally by producing multiple physiological individuals (ramets). We studied clonal growth in a homogeneous environment using a dynamic spatial model based on a stochastic cellular automaton. We investigated different growth forms from the aspect of ramet mortality. Non-steady-state and quasi-steady-state cases were defined, and we determined the number of steps suitable for making a reliable difference between these two types of cases. This given number of steps was used when testing for the proportion of quasi-steady-state cases in 1000 repetitions. We also tested the efficiency of occupation in these cases. Our expectation was that higher occupation would be associated with lower ramet mortality. The results only partially verified this hypothesis. Though with increasing ramet mortality, the average number of ramets tended to decrease, it was not the lowest ramet mortality that resulted in the highest occupation. Our results showed that very low ramet mortality was unfavourable for the plant, as the spreading front and the area behind this front were so packed that the plant was not able to return and recolonize the vacated sites in the central area. This resulted in a lower proportion of quasi-steady-state cases and lower occupation in these cases. Our results may contribute to a deeper understanding of clonal plant growth and its limiting factors.

Sexual Recruitment in Zostera marina: Progress toward a Predictive Model

Ecophysiological stress and physical disturbance are capable of structuring meadows through a combination of direct biomass removal and recruitment limitation; however, predicting these effects at landscape scales has rarely been successful. To model environmental influence on sexual recruitment in perennial Zostera marina, we selected a sub-tidal, light-replete study site with seasonal extremes in temperature and wave energy. During an 8-year observation period, areal coverage increased from 4.8 to 42.7%. Gains were stepwise in pattern, attributable to annual recruitment of patches followed by centrifugal growth and coalescence. Recruitment varied from 13 to 4,894 patches per year. Using a multiple linear regression approach, we examined the association between patch appearance and relative wave energy, atmospheric condition and water temperature. Two models were developed, one appropriate for the dispersal of naked seeds, and another for rafted flowers. Results indicated that both modes of sexual recruitment varied as functions of wind, temperature, rainfall and wave energy, with a regime shift in wind-wave energy corresponding to periods of rapid colonization within our site. Temporal correlations between sexual recruitment and time-lagged climatic summaries highlighted floral induction, seed bank and small patch development as periods of vulnerability. Given global losses in seagrass coverage, regions of recovery and re-colonization will become increasingly important. Lacking landscape-scale process models for seagrass recruitment, temporally explicit statistical approaches presented here could be used to forecast colonization trajectories and to provide managers with real-time estimates of future meadow performance; i.e., when to expect a good year in terms of seagrass expansion. To facilitate use as forecasting tools, we did not use statistical composites or normalized variables as our predictors. This study, therefore, represents a first step toward linking remotely acquired environmental data to sexual recruitment, an important measure of seagrass performance that translates directly into landscape-scale coverage change.