Special Issue "Capturing Adaptive Processes in Computational Models of Ecosystems and the Earth System"

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A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (30 May 2015)

Special Issue Editors

Guest Editor
Dr. James Dyke

Faculty of Physical Sciences and Engineering, University of Southampton, Southampton, SO17 1BJ, UK
E-Mail
Interests: artificial life; complex systems; ecology; evolution; gaia
Guest Editor
Dr. Hywel Williams

College of Life and Environmental Sciences, University of Exeter, Prince of Wales Road, Exeter, EX4 4PS, UK
E-Mail
Phone: +44 1392 723777
Interests: complex biological systems; agent-based & evolutionary models in biology; evolution of nutrient cycling and metabolic cooperation; niche construction and biosphere evolution; marine ecosystem dynamics

Special Issue Information

Dear Colleagues,

Adaptation is an essential feature of the interactions between living organisms and their physical environment. Individual organisms learn or acclimate during their lifetime. Ecological competition shapes populations and communities. Genetic variation and natural selection lead to evolutionary changes in how organisms respond to, and have an impact on, their abiotic environments. Since biotic processes are central to the flows of material and energy in ecosystems at all scales, adaptation has, and will continue to have, a major role in the dynamics of ecosystems and the biosphere. Consequently, computational models that seek to predict the responses of ecosystems and/or the Earth system to environmental changes must adequately represent the effects of adaptation.

Traditionally, ecosystem and Earth system models have used fixed representations of biological processes. Recently, models of marine and terrestrial ecosystems, and of the Earth system, have begun to include a richer representation of biological processes at a variety of levels. Such models often include some form of adaptation, e.g., physiological acclimation, ecological competition, or evolutionary adaptation. These models have ably captured observed patterns and may form a good basis for improving predictions regarding the impacts of global change.

However, models that include complex adaptive processes present a number of computational challenges. Such models may be difficult to construct, parameterize, and validate. Interpretation of the model’s output can also be challenging. Furthermore, scaling up to high spatial or temporal resolutions may be computationally demanding. Nonetheless, increases in computational power, developments in techniques for the simulation and representation of biological processes at all scales, and the increased availability of large datasets (e.g., from genomics and satellite observations) present a huge opportunity for improving predictive models of ecosystems.

In this Special Issue of Computation, we invite submissions on any aspect of the methodology, use, and scientific basis of computational models of ecosystems (and of the Earth system) that account for adaptive processes. Papers may report on original research, discuss methodological aspects, review the current state of the art (or historical origins), or offer perspectives on future prospects.

Topics might include, but are not restricted to, the following:

  • Marine ecosystem models
  • Terrestrial ecosystem models
  • Soil ecosystem models
  • Earth system & biosphere models
  • Empirical basis & model validation
  • Process-based & mechanistic models
  • Impacts of & responses to environmental change
  • Trait-based models
  • Individual-based or agent-based models
  • Adaptive dynamics
  • Food web & community models
  • Alternative formalisms (e.g., cellular automata, genetic algorithms, digital evolution)
  • Systems biology & systems ecology

The aim is to draw together a variety of approaches and application areas, at any spatial and temporal scale, unified around the core concept of representing adaptation (broadly defined) in computational models of ecosystems and of the Earth system. Please contact the editors if you are unsure of your proposed topic’s fit.

Dr James Dyke
Dr. Hywel Williams
Guest Editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


Published Papers (2 papers)

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Research

Open AccessArticle Visual Simulation of Soil-Microbial System Using GPGPU Technology
Computation 2015, 3(1), 58-71; doi:10.3390/computation3010058
Received: 13 August 2014 / Accepted: 21 January 2015 / Published: 27 February 2015
Cited by 1 | PDF Full-text (2190 KB) | HTML Full-text | XML Full-text
Abstract
General Purpose (use of) Graphics Processing Units (GPGPU) is a promising technology for simulation upscaling; in particular for bottom–up modelling approaches seeking to translate micro-scale system processes to macro-scale properties. Many existing simulations of soil ecosystems do not recover the emergent system scale
[...] Read more.
General Purpose (use of) Graphics Processing Units (GPGPU) is a promising technology for simulation upscaling; in particular for bottom–up modelling approaches seeking to translate micro-scale system processes to macro-scale properties. Many existing simulations of soil ecosystems do not recover the emergent system scale properties and this may be a consequence of “missing” information at finer scales. Interpretation of model output can be challenging and we advocate the “built-in” visual simulation afforded by GPGPU implementations. We apply this GPGPU approach to a reaction–diffusion soil ecosystem model with the intent of linking micro (micron) and core (cm) spatial scales to investigate how microbes respond to changing environments and the consequences on soil respiration. The performance is evaluated in terms of computational speed up, spatial upscaling and visual feedback. We conclude that a GPGPU approach can significantly improve computational efficiency and offers the potential added benefit of visual immediacy. For massive spatial domains distribution over GPU devices may still be required. Full article
Open AccessArticle Cultural Collapse and System Survival Due to Environmental Modification
Computation 2014, 2(3), 83-101; doi:10.3390/computation2030083
Received: 3 March 2014 / Revised: 19 June 2014 / Accepted: 25 June 2014 / Published: 29 July 2014
PDF Full-text (1426 KB) | HTML Full-text | XML Full-text
Abstract
We consider a simple mathematical approach to the rise and fall of societies based on population growth and its effects on the environment, both beneficial and detrimental. We find that in any simple model of population dynamics with environmental coupling, stable cultures are
[...] Read more.
We consider a simple mathematical approach to the rise and fall of societies based on population growth and its effects on the environment, both beneficial and detrimental. We find that in any simple model of population dynamics with environmental coupling, stable cultures are impossible. Populations inevitably grow or decline exponentially. Further, if the parameters defining a civilisation are allowed to evolve towards an evolutionarily stable state, the only possible solutions are those where each culture ultimately declines. However, computer simulation with multiple competing cultures show that while each eventually collapses, some are always extant and the system is robust. In this broad class of models, individual death is a requirement for system survival. Full article

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