Evol Ecol Res 18: 477-502 (2017) Full PDF if your library subscribes.
A survey of computational methods for fossil data analysis
Indrė Žliobaitė1,2, Kai Puolamäki3, Jussi T. Eronen1,4 and Mikael Fortelius1,5
1Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland, 2Department of Computer Science, University of Helsinki, Helsinki, Finland, 3Finnish Institute of Occupational Health, Helsinki, Finland, 4BIOS Research Unit, Helsinki, Finland and 5Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo, Norway
Correspondence: I. Žliobaitė, Department of Geosciences and Geography, University of Helsinki, Helsinki 00014, Finland.
Aim: (1) Survey and organize computational approaches to fossil data analysis into a methodological framework. (2) Highlight the kinds of research questions about evolutionary and environmental change that can be answered by applying computational algorithms to mammal fossil data to better understand past ecosystems and climates.
Questions: What models have been used for what research questions? What is their scope of application? What are their potential limitations?
Search methods: Our search of the literature was based on personal knowledge in combination with keyword-based searches. Papers were considered relevant if data-driven computational methods were used to analyse relationships between organisms and their environments at evolutionary time scales.
Conclusions: We demonstrate that different research questions may be answered with the same computational algorithm, and different algorithms may be needed to answer the same question in different contexts. We believe that in order to move forward, we need to match knowledge of methods with knowledge of the fossil record in a research question-driven way. Figure 2 presents a proposed workflow. Following this framework, we survey existing work and highlight what research questions can potentially be answered with which methods, some of which may not have been reported in the evolutionary palaeontology literature to date. The outcome of this survey is a proposal for a research agenda in computational fossil data analysis.
Keywords: big data, computational fossil data analysis, data mining, ecometrics, evolutionary palaeontology, machine learning, mammals.
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