TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Improving knowledge of urban vegetation by applying GIS technology to existing databases.

Question: Can we improve the knowledge of urban vegetation using data from ongoing floristic and management projects with a data mining approach? We have two questions: 1. How strong is the relationship between land cover pattern and the species composition of vegetation? 2. What is the relationship between land cover pattern and species richness? Location: Trieste, northeastern Italy. Methods: Using land cover maps and GIS we characterized the cells of a floristic project grid by percentage cover of land cover types. We applied Canonical Correlation Analysis to test the correlation between floristic composition of the cells and land cover. We classified the cells by clustering methods, based on land cover description. With these clusters, we analysed the variation of species composition of urban vegetation along a gradient of urban density. We used Jaccard\u2bcs similarity index to compare floristic composition of the clusters with the floristic composition of the homogeneous cells with respect to the land cover types. To answer question 2, we calculated land cover heterogeneity with the Shannon index and correlated the number of species in clusters with land cover heterogeneity and urban density. Results: Each land cover type contributes to species richness and species composition of the clusters. Species richness decreases significantly and linearly as urban density increases and land cover heterogeneity decreases in the clusters. Conclusions: A data mining approach can combine different existing projects to improve knowledge of the urban vegetation system. The methods we have applied offer tools to answer the specific questions mentioned above

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?: Linking microbes to ecosystem processes

International audienceMicroorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology