A review of computer tools for prediction of ecosystems and populations: We need more open-source software
Environmental Modelling & Software, ISSN: 1364-8152, Vol: 134, Page: 104872
2020
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Review Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1364815220309294; http://dx.doi.org/10.1016/j.envsoft.2020.104872; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85091679087&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1364815220309294; https://dx.doi.org/10.1016/j.envsoft.2020.104872
Elsevier BV
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