William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico, is the DataONE Principal Investigator.
Ecological Informatics is an evolving discipline that takes into account the data intensive nature of ecology, the valuable information content of ecological data, and the need to communicate results and inform decisions, including those related to research, conservation and resource management. I review major events in the development of ecological informatics, and then examine the state-of-the-art, and identify emerging trends. An historical look at the field reveals that many milestone events over the past six decades have enabled ecologists to address “big” research and research digital data challenges. These milestones can be categorized among: (1) funding of major research and associated infrastructure initiatives; (2) new information technology initiatives; (3) sociocultural organization initiatives; and (4) policy implementation. Furthermore, some of the milestones are motivated by factors intrinsic to the biodiversity and ecological sciences (e.g., need for specific data standards), whereas others are motivated by extrinsic factors (e.g. legislation, publisher mandates for data availability).
The current scope of ecological informatics focuses on novel concepts and techniques for ecological monitoring, data management, data analysis, synthesis and forecasting. Ecological monitoring relies still on classical field sampling, metagenomics, taxonomic classification and ad hoc laboratory experiments. However, novel sensor-based technology has revolutionised our capacity to not only increase data frequency but more importantly data quality as best exemplified by recent developments in bioacoustics, thermal imaging, phenology imagery, metagenomics and remote sensing. Data management takes into account the growing quantity and quality of ecological data by means of appropriate ontologies, structured metadata, and scientific workflows to facilitate archiving, access, querying, and sharing of data. Data analysis unravels the complexity of ecological data e.g. by multivariate statistics, self-organising maps, wavelets and population distribution models. Synthesis and forecasting of distinct nonlinear ecological data requires powerful inferential models by machine learning, as well as process-based and hybrid models.
The talk concludes with a review of emerging trends affecting ecological informatics. These trends include increased adoption of in situ sensors, the funding of ecological observatories, and the recognition of the need to access and interpret high quality data to understand and mitigate grand environmental challenges.