Computational Ecology and Software

Aims & Scope

The COMPUTATIONAL ECOLOGY AND SOFTWARE (ISSN 2220-721X) is an open access (BOAI definition) , open peer reviewed online journal (users are free to read, download, copy, distribute, print, search, or link to the full texts of the articles) that considers scientific articles in all different areas of computational ecology. It is the transactions of the International Society of Computational Ecology. The journal aims to disseminate ecological researches, constructions and applications of theories and methods of computational sciences including computational mathematics, computational statistics and computer science. It focuses on modeling, simulation, approximation, prediction, recognition, and classification of ecological issues. The topics to be covered by CES include, but are not limited to: Computation intensive methods, numerical and optimization methods, differential and difference equation modeling and simulation, mathematical models and algorithms, prediction, recognition, classification, statistical computation (Bayesian computing, randomization, bootstrapping, Monte Carlo techniques, stochastic process, etc.), agent-based modeling, individual-based modeling, artificial neural networks, knowledge based systems, machine learning, genetic algorithms, data exploration, network analysis and computation, databases, ecological modeling and computation using Geographical Information Systems, satellite imagery, and other computation intensive theories and methods, etc. Artificial ecosystems, artificial life, complexity of ecosystems and virtual reality. The development, evaluation and validation of software and algorithms for computational ecology. The development and evaluation of apparatus, instruments and machines for ecological and environmental analysis, investigation and monitoring based on the software of computational ecology. Methodological applications of computational ecology in the researches of ecology and environmental sciences. Big data analytics of ecological and environmental data, meta-analysis, casual inference, Bayesian inference, etc.

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