Competition and evolution in virtual plant communities: a new modeling approach

Bornhofen, Stefan; Lattaud, Claude

doi:10.1007/s11047-008-9089-5

Cited by 30 publications

(22 citation statements)

References 71 publications

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“…In contrast, Ulam considered trees as self-organizing structures, in which branching patterns emerge from the competition of individual units for space. This idea, originally expressed in terms of 2D cellular automata, was extended to 3D voxel spaces and augmented with constructs needed for realistic image synthesis by Arvo and Kirk [1988], Greene [1989] and, more recently, Beneš and Millan [2002], Pałubicki [2007], and Bornhofen and Lattaud [2008]. All of these authors emphasized the ability of their models to adapt to the surrounding space, be it in the form of obstacles or support for growth.…”

Section: Previous Workmentioning

confidence: 99%

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Self-organizing tree models for image synthesis

et al. 2009

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E PRESENT a method for generating realistic models of temperate-climate trees and shrubs. This method is based on the biological hypothesis that the form of a developing tree emerges from a self-organizing process dominated by the competition of buds and branches for light or space, and regulated by internal signaling mechanisms. Simulations of this process robustly generate a wide range of realistic trees and bushes. The generated forms can be controlled with a variety of interactive techniques, including procedural brushes, sketching, and editing operations such as pruning and bending of branches. We illustrate the usefulness and versatility of the proposed method with diverse tree models, forest scenes, animations of tree development, and examples of combined interactive-procedural tree modeling.

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Section: Previous Workmentioning

confidence: 99%

“…As an alternative to space colonization, we use a method proposed by Pałubicki [2007] (see also [Bornhofen and Lattaud 2008]) to compute a coarse estimate of the exposure of each bud to light. The space is divided into a grid of voxels.…”

Section: Shadowmentioning

confidence: 99%

Self-organizing tree models for image synthesis

et al. 2009

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“…In the last 20 years, the field has moved from studying the evolution and population dynamics of simplified individuals [12,38] to multi-species interactions and diversification [8,19]. A great number of evolutionary computational models have been recently proposed, whether at the abstract level of large interacting populations [10,48] or at the fine-grained level of multicellular growth [16].…”

Section: The Rise Of Virtual Biologymentioning

confidence: 99%

“…Their simulations ended in ecosystems of tall plants with no phenotypic diversification. In [8], they concluded that the genetic search space of the D0L system was too limited, constraining evolutionary dynamics too severely and preventing it from evolving a wide variety of plant morphologies.…”

Section: A Generative Approach To Diversitymentioning

confidence: 99%

Emergent Diversity in an Open-Ended Evolving Virtual Community

et al. 2012

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A version of this paper with color figures is available online at http://dx.doi.org/10.1162/ artl_a_00059. Subscription required.Abstract Understanding the dynamics of biodiversity has become an important line of research in theoretical ecology and, in particular, conservation biology. However, studying the evolution of ecological communities under traditional modeling approaches based on differential calculus requires speciesʼ characteristics to be predefined, which limits the generality of the results. An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. We study here the formation, evolution, and diversity dynamics of a community of virtual plants with a novel individual-centered model involving three different scales: the genetic, the developmental, and the physiological scales. It constitutes an original attempt at combining development, evolution, and population dynamics (based on multi-agent interactions) into one comprehensive, yet simple model. In this world, we observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants. Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions.

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“…The principle of introducing stochastic variation to the parameter values q when the growth of a model is simulated can be applied to virtually any model: recursive (Honda 1971;Prusinkiewicz et al 2001) or self-organizing (Ulam 1962;Bornhofen and Lattaud 2009); functional (Mäkelä and Hari 1986) or structural (Prusinkiewicz and Lindenmayer 1990;Fisher 1992), or any other.…”

Section: Introductionmentioning

confidence: 99%

Data-based stochastic modeling of tree growth and structure formation

et al. 2016

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Data-based stochastic modeling of tree growth and structure formation Potapov I., Järvenpää M., Åkerblom M., Raumonen P., Kaasalainen M. (2016). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica vol. 50 no. 1 article id 1413. 11 p. Highlights• We propose a stochastic version of the tree growth model LIGNUM for producing tree structures consistent with detailed terrestrial laser scanning data, and we provide the proofof-concept by using model-based simulations and real laser scanning data.• Trees produced with the data-based model resemble the trees of the dataset, and are statistically similar but not copies of each other; the number of such synthetic trees is not limited. AbstractWe introduce a general procedure to match a stochastic functional-structural tree model (here LIGNUM augmented with stochastic rules) with real tree structures depicted by quantitative structure models (QSMs) based on terrestrial laser scanning. The matching is done by iteratively finding the maximum correspondence between the measured tree structure and the stochastic choices of the algorithm. First, we analyze the match to synthetic data (generated by the model itself), where the target values of the parameters to be estimated are known in advance, and show that the algorithm converges properly. We then carry out the procedure on real data obtaining a realistic model. We thus conclude that the proposed stochastic structure model (SSM) approach is a viable solution for formulating realistic plant models based on data and accounting for the stochastic influences.

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Competition and evolution in virtual plant communities: a new modeling approach

Cited by 30 publications

References 71 publications

Self-organizing tree models for image synthesis

Self-organizing tree models for image synthesis

Emergent Diversity in an Open-Ended Evolving Virtual Community

Data-based stochastic modeling of tree growth and structure formation

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