Description: The field of ecoinformatics provides concepts, methods and standards to guide management and analysis of ecological data with particular emphasis on exploration of co-occurrences of organisms and their linkage to environmental conditions and taxon attributes. In this editorial, introducing the Special Feature ‘Ecoinformatics and global change’, we reflect on the development of ecoinformatics and explore its importance for future global change research with special focus on vegetation-plot data. We show how papers in this Special Feature illustrate important directions and approaches in this emerging field. We suggest that ecoinformatics has the potential to make profound contributions to pure and applied sciences, and that the analyses, databases, meta-databases, data exchange formats and analytical tools presented in this Special Feature advance this approach to vegetation science and illustrate and address important open questions. We conclude by describing important future directions for the development of the field including incentives for data sharing, creation of tools for more robust statistical analysis, utilities for integration of data that conform to divergent taxonomic standards, and databases that provide detailed plot-specific data so as to allow users to find and access data appropriate to their research needs.

Description: Questions: What are the most likely environmental drivers for compositional herb layer changes as indicated by trait differences between winner and loser species? Location: Weser-Elbe region (NW Germany). Methods: We resurveyed the herb layer communities of ancient forest patches on base-rich sites of 175 semi-permanent plots. Species traits were tested for their ability to discriminate between winner and loser species using logistic regression analyses and deviance partitioning. Results: Of 115 species tested, 31 were identified as winner species and 30 as loser species. Winner species had higher seed longevity, flowered later in the season and more often had an oceanic distribution compared to loser species. Loser species tended to have a higher specific leaf area, were more susceptible to deer browsing and had a performance optimum at higher soil pH compared to winner species. The loser species also represented several ancient forest and threatened species. Deviance partitioning indicated that local drivers (i.e. disturbance due to forest management) were primarily responsible for the species shifts, while regional drivers (i.e. browsing pressure and acidification from atmospheric deposition) and global drivers (i.e. climate warming) had moderate effects. There was no evidence that canopy closure, drainage or eutrophication contributed to herb layer changes. Conclusions: The relative importance of the different drivers as indicated by the winner and loser species differs from that found in previous long-term studies. Relating species traits to species performance is a valuable tool that provides insight into the environmental drivers that are most likely responsible for herb layer changes.

Description: Tropical forests are biologically diverse ecosystems that play important roles in the carbon cycle and maintenance of global biodiversity. Understanding how tropical forests respond to environmental changes is important, as changes in carbon storage can modulate the rate and magnitude of climate change. Applying an ecoinformatics approach for managing long-term forest inventory plot data, where individual trees are tracked over time, facilitates regional and cross-continental forest research to evaluate changes in taxonomic composition, growth, recruitment and mortality rates, and carbon and biomass stocks. We developed ForestPlots.net as a secure, online inventory data repository and to facilitate data management of long-term tropical forest plots to promote scientific collaborations among independent researchers. The key novel features of the database are: (a) a design that efficiently deals with time-series data; (b) data management tools to assess potential errors; and (c) a query library to generate outputs (e.g. biomass and carbon stock changes over time).