Keeping track of conceptual and methodological developments is a critical skill for research scientists, but this task is becoming increasingly difficult due to the high rate of academic publication. As a crisis discipline, conservation science is particularly in need of tools that facilitate rapid yet insightful synthesis.
Martin Westgate and colleagues at the Australian National University have recently demonstrated how a commonly-used method for text mining – latent Dirichlet allocation or ‘topic modeling’ – can be used in conjunction with statistical tools already familiar to ecologists (cluster analysis, regression, and network analysis) to investigate trends and identify potential research gaps in the scientific literature. They then demonstrated these properties using the literature on ecological surrogates and indicators as a case study.
Their analysis of topic popularity in their case study showed a strong emphasis on the monitoring and management of fragmented ecosystems, while gap analysis suggested a greater role for genetic surrogates and indicators.
Their results showed that automated text analysis methods need to be used with care, but can provide information that is complementary to that given by systematic reviews and meta-analyses. Text analysis has strong potential for increasing scientists’ capacity for rapid and detailed synthesis of conservation science.
Westgate MJ, PS Barton, JC Pierson & DB Lindenmayer (2015). Text analysis tools for identification of emerging topics and research gaps in conservation science. Conservation Biology, doi:10/111/ cobi.12605. http://onlinelibrary.wiley.com/doi/10.1111/cobi.12605/abstract