Genin, A. | bioRxiv Science

bioRxiv2024

Fine-scale co-ocurrence patterns in grasslands reflect competition for space rather than broad plant strategies

O_LIEstimating the sign and strength of interactions among plants is central to understand the dynamics and functioning of communities, but is challenging to do for species-rich communities. Instead, spatial relationships between plants (clustering or spatial segregation) are sometimes used as a surrogate for the net effect of interactions ocurring between plants (positive or negative, respectively). However, this approach remains poorly tested outside of arid and alpine ecosystems, the ecological settings it originated from. C_LIO_LIIn experimental rangelands, we explored how management intensification, sheep exclusion and a natural soil depth gradient control the level of plant spatial segregation, or negative co-occurrence, usually considered as a measurement of competition intensity. We link these spatial patterns to classical broad plant strategies defined by 11 locally measured functional traits, and to the realized vegetation height and cover. C_LIO_LIPlant segregation was highest when both grazing and fertilization were applied. Unexpectedly, general plant strategies (competitive, and acquisitive strategies) had little relationship with plant spatial patterns. Instead, spatial constraints increased segregation wherever cover was high and free bare ground was limited, or where plant growth is restricted by grazing to a few centimeters above ground. C_LIO_LIThese results show that fine-scale spatial patterns appear to capture competition for space, rather than for light or resources, as suggested by broad plant strategies. This may explain discrepancies in conclusions drawn from spatial patterns in grasslands, and clarifies the way towards a mechanistic understanding of spatial patterns. C_LIO_LISynthesis. The fine scale spatial organization of plant communities has been thought to reflect the intensity of competition among plants, but this approach has struggled to provide consistent results in grasslands. We show here that spatial patterns reflect competition for space rather than broad plant strategies captured by plant functional traits, helping us read observed plant spatial patterns to map interactions among plants in the field. C_LI

ecology↗

bioRxiv2023

Easy, fast and reproducible Stochastic Cellular Automata with 'chouca'

Stochastic cellular automata (SCA) are models that describe spatial dynamics using a grid of cells that switch between discrete states over time. They are widely used to understand how small-scale processes scale up to affect ecological dynamics at larger spatial scales, and have been applied to a wide diversity of theoretical and applied problems in all systems, such as arid ecosystems, coral reefs, forests, bacteria, or urban growth. Despite their wide applications, SCA implementations are often ad-hoc, lacking performance, guarantees of correctness and poorly reproducible. De novo implementation of SCA for each specific system and application also represents a major barrier for many practitioners. To provide a unifying, well-tested technical basis to this class of models and facilitate their implementation, we built chouca, an R package that translates definitions of SCA models into compiled code, and runs simulations in an efficient way. chouca supports SCA based on rectangular grids where transition probabilities are defined for each cell, with performance typically two to three orders of magnitude above typical implementations in interpreted languages (e.g. R, Python), all while maintaining an intuitive interface in the R environment. Exact and mean-field simulations can be run, and both numerical and graphical results can be easily exported. Besides providing better reproducibility and accessibility, a fast engine for SCA unlocks novel, computationally intensive statistical approaches, such as simulation-based inference of ecological interactions from field data, which represents by itself an important avenue for research. By providing an easy and efficient entry point to SCAs, chouca lowers the bar to the use of this class of models for ecologists, managers and general practitioners, providing a leveled-off reproducible platform while opening novel methodological approaches.

ecology↗