Wildlife Biology, Big Data, and Reproducible Research

Changes in technology have made it possible to gather vast amounts of data, often of high quality, that in turn can improve the quality of wildlife biology. However, with this growth in data, practices such as data management, exploratory data analysis, data-sharing, and reproducibility of an analysis have become increasingly complex. These practices often depend heavily on computer scripting languages, and are often hidden from the peer-review process despite their influence on the final results. Although these issues have been discussed in the literature, they are generally dealt with in a piecemeal fashion, preventing synthesis, and thereby slowing progress. We offer a conceptual framework to illustrate relationships among these practices, and show where wildlife biology as a field has embraced these changes, where awareness is growing, and where it lags behind other fields. We then present several case studies to emphasize the importance of adopting these practices. Any of these case studies could have been conducted with little attention to these practices or employing scripting languages, but there are many disadvantages to this approach including increased chance of errors, inefficiency, and lack of reproducibility. We suggest that a change in the culture of how wildlife biology is conducted is required and that this change will be fostered by integrating these practices into wildlife biology education, implementation, and embracing the idea of open data and open computer code. (c) 2018 The Wildlife Society.