Browsing by Author "Valencia, Daniel"

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    Easy, fast and reproducible Stochastic Cellular Automata with chouca
    (2024) Genin, Alexandre; Dupont, Guillaume; Valencia, Daniel; Zucconi, Mauro; Avila-Thiem, M. Isidora; Navarrete, Sergio A.; Wieters, Evie A.
    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.
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    Monitoring the fabric of nature: using allometric trophic network models and observations to assess policy effects on biodiversity
    (2023) Navarrete, Sergio A.; Avila-Thieme, M. Isidora; Valencia, Daniel; Genin, Alexandre; Gelcich, Stefan
    Species diversity underpins all ecosystem services that support life. Despite this recognition and the great advances in detecting biodiversity, exactly how many and which species co-occur and interact, directly or indirectly in any ecosystem is unknown. Biodiversity accounts are incomplete; taxonomically, size, habitat, mobility or rarity biased. In the ocean, the provisioning of fish, invertebrates and algae is a fundamental ecosystem service. This extracted biomass depends on a myriad of microscopic and macroscopic organisms that make up the fabric of nature and which are affected by management actions. Monitoring them all and attributing changes to management policies is daunting. Here we propose that dynamic quantitative models of species interactions can be used to link management policy and compliance with complex ecological networks. This allows managers to qualitatively identify 'interaction-indicator' species, which are highly impacted by management policies through propagation of complex ecological interactions. We ground the approach in intertidal kelp harvesting in Chile and fishers' compliance with policies. Results allow us to identify sets of species that respond to management policy and/or compliance, but which are often not included in standardized monitoring. The proposed approach aids in the design of biodiversity programmes that attempt to connect management with biodiversity change.This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.