A review of computer tools for prediction of ecosystems and populations: We need more open-source software. (December 2020)

• Record Type: Journal Article
• Authors: Paradis, Emmanuel
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Biodiversity -- Ecosystem -- Forecasting -- Modelling -- Software; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104872
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Computer programs have taken an important role in the prediction of future ecological trajectories. This study reviewed the existing computer programs that aim to predict future trajectories of ecosystems, biological populations, or ecological systems. Overall, 106 programs were examined. A rich set of programs exist and most of them are free. A substantial proportion (43%) are distributed open source. A majority (57%) of these programs took some form of uncertainty into account, although not always in a rigorous way. Programs considering uncertainty were preferentially implemented in a standard compiled language (C/Fortran) or a statistical language (R). The vast majority of the programs were delivered with recent, sometimes extensive, documentation. None of the reviewed programs used an open-source collaborative framework. Two points require future attention from developers of software for ecological prediction: greater care in the implementation of uncertainty, particularly with appropriate statistical methods, and adoption of an open-source, collaborative framework. Highlights: More than 100 computer programs for ecosystem prediction were reviewed. The availability of software and documentation are overall good. Many programs are free but open collaborative platfroms are rarely used. Consideration of prediction uncertainty varied a lot. There is need to better use the open source framework for ecosystem prediction.