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.
- MeSH
- biodiverzita MeSH
- ekologie MeSH
- ekosystém * MeSH
- přístup k informacím * MeSH
- rostliny MeSH
- Publikační typ
- časopisecké články MeSH
Despite their importance, how plant communities and soil microorganisms interact to determine the capacity of ecosystems to provide multiple functions simultaneously (multifunctionality) under climate change is poorly known. We conducted a common garden experiment using grassland species to evaluate how plant functional structure and soil microbial (bacteria and protists) diversity and abundance regulate soil multifunctionality responses to joint changes in plant species richness (one, three and six species) and simulated climate change (3°C warming and 35% rainfall reduction). The effects of species richness and climate on soil multifunctionality were indirectly driven via changes in plant functional structure and their relationships with the abundance and diversity of soil bacteria and protists. More specifically, warming selected for the larger and most productive plant species, increasing the average size within communities and leading to reductions in functional plant diversity. These changes increased the total abundance of bacteria that, in turn, increased that of protists, ultimately promoting soil multifunctionality. Our work suggests that cascading effects between plant functional traits and the abundance of multitrophic soil organisms largely regulate the response of soil multifunctionality to simulated climate change, and ultimately provides novel experimental insights into the mechanisms underlying the effects of biodiversity and climate change on ecosystem functioning.
A central challenge in ecology is to understand the relative importance of processes that shape diversity patterns. Compared with aboveground biota, little is known about spatial patterns and processes in soil organisms. Here we examine the spatial structure of communities of small soil eukaryotes to elucidate the underlying stochastic and deterministic processes in the absence of environmental gradients at a local scale. Specifically, we focus on the fine-scale spatial autocorrelation of prominent taxonomic and functional groups of eukaryotic microbes. We collected 123 soil samples in a nested design at distances ranging from 0.01 to 64 m from three boreal forest sites and used 454 pyrosequencing analysis of Internal Transcribed Spacer for detecting Operational Taxonomic Units of major eukaryotic groups simultaneously. Among the main taxonomic groups, we found significant but weak spatial variability only in the communities of Fungi and Rhizaria. Within Fungi, ectomycorrhizas and pathogens exhibited stronger spatial structure compared with saprotrophs and corresponded to vegetation. For the groups with significant spatial structure, autocorrelation occurred at a very fine scale (<2 m). Both dispersal limitation and environmental selection had a weak effect on communities as reflected in negative or null deviation of communities, which was also supported by multivariate analysis, that is, environment, spatial processes and their shared effects explained on average <10% of variance. Taken together, these results indicate a random distribution of soil eukaryotes with respect to space and environment in the absence of environmental gradients at the local scale, reflecting the dominant role of drift and homogenizing dispersal.
- MeSH
- ekologie MeSH
- Eukaryota klasifikace MeSH
- houby klasifikace genetika izolace a purifikace MeSH
- lesy MeSH
- půdní mikrobiologie * MeSH
- společenstvo * MeSH
- stromy mikrobiologie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Estonsko MeSH
Plant species richness and the presence of certain influential species (sampling effect) drive the stability and functionality of ecosystems as well as primary production and biomass of consumers. However, little is known about these floristic effects on richness and community composition of soil biota in forest habitats owing to methodological constraints. We developed a DNA metabarcoding approach to identify the major eukaryote groups directly from soil with roughly species-level resolution. Using this method, we examined the effects of tree diversity and individual tree species on soil microbial biomass and taxonomic richness of soil biota in two experimental study systems in Finland and Estonia and accounted for edaphic variables and spatial autocorrelation. Our analyses revealed that the effects of tree diversity and individual species on soil biota are largely context dependent. Multiple regression and structural equation modelling suggested that biomass, soil pH, nutrients and tree species directly affect richness of different taxonomic groups. The community composition of most soil organisms was strongly correlated due to similar response to environmental predictors rather than causal relationships. On a local scale, soil resources and tree species have stronger effect on diversity of soil biota than tree species richness per se.
- MeSH
- biodiverzita * MeSH
- biomasa MeSH
- Eukaryota klasifikace genetika izolace a purifikace MeSH
- houby klasifikace genetika izolace a purifikace MeSH
- půda chemie parazitologie MeSH
- půdní mikrobiologie * MeSH
- společenstvo MeSH
- stromy mikrobiologie MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Finsko MeSH
Functional diversity (FD) is an important component of biodiversity that quantifies the difference in functional traits between organisms. However, FD studies are often limited by the availability of trait data and FD indices are sensitive to data gaps. The distribution of species abundance and trait data, and its transformation, may further affect the accuracy of indices when data is incomplete. Using an existing approach, we simulated the effects of missing trait data by gradually removing data from a plant, an ant and a bird community dataset (12, 59, and 8 plots containing 62, 297 and 238 species respectively). We ranked plots by FD values calculated from full datasets and then from our increasingly incomplete datasets and compared the ranking between the original and virtually reduced datasets to assess the accuracy of FD indices when used on datasets with increasingly missing data. Finally, we tested the accuracy of FD indices with and without data transformation, and the effect of missing trait data per plot or per the whole pool of species. FD indices became less accurate as the amount of missing data increased, with the loss of accuracy depending on the index. But, where transformation improved the normality of the trait data, FD values from incomplete datasets were more accurate than before transformation. The distribution of data and its transformation are therefore as important as data completeness and can even mitigate the effect of missing data. Since the effect of missing trait values pool-wise or plot-wise depends on the data distribution, the method should be decided case by case. Data distribution and data transformation should be given more careful consideration when designing, analysing and interpreting FD studies, especially where trait data are missing. To this end, we provide the R package "traitor" to facilitate assessments of missing trait data.
