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Context In temperate hardwood forests, increased intensity of soil and canopy disturbances tends to increase species richness due to the establishment of numerous early-successional plant species. However, while competitive pioneer species from early stages of succession can become recalcitrant and alter patterns of natural regeneration, very few studies have examined longer-term effects of these treatments on plant biodiversity. Aims In this study, we investigated mid-term (ca. 20 years) effects of different regeneration treatments with varying soil and canopy disturbance intensities. We compared understory plant communities in temperate hardwood forests from all the South of Quebec (Canada). Methods Using circular experimental plots of 1962.5 m2 (radius = 25 m), we measured taxonomic and functional diversity indices and soil properties using four levels of disturbance intensity in six temperate hardwood forests of Quebec distributed along a longitudinal gradient. Reference forests, i.e. control forests with no silvicultural treatment known for ≥ 80 years, were compared to 20-year-old single-tree selection cuts, group-selection cuts and group-selection cuts with soil scarification. Results Species richness in both group-selection treatments was higher than that in reference forests. Plant equitability and beta diversity among sites in both group-selection treatments were lower than in single-tree selection cuts and control forests. More intense treatments contributed to the mid-term persistence of recalcitrant competitor species (e.g. Rubus idaeus L., Prunus pensylvanica L.f.) whereas soil scarification appears to have negative sustained effects on species known to be sensitive to regeneration treatments (e.g. Monotropa uniflora L., Dryopteris spinulosa Kuhn). Conclusions In temperate hardwood forests of Southern Quebec, silvicultural treatments of higher intensities resulted in detrimental effects on soil properties, especially in the surface horizon, 20 years after disturbance. This legacy, in turn, affected the composition and diversity of understory plant communities. The more intense silvicultural treatments contributed to the persistence of pioneer species better adapted to a wider range of environmental conditions and resulted in a decrease in understory plant community heterogeneity among sites. Conversely, single-tree selection cutting appeared to be the most appropriate silvicultural treatment for maintaining soil functions and heterogeneity of understory plant communities after 20 years; composition and structure being similar to long-undisturbed forests.

Motivation: Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n=95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c.50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01–40,000m². Time period and grain: 1888–2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records. Software format: Three main matrices (.csv), relationally linked.

Additional file 1. Completed SPIRIT guidelines checklist for this DECODE trial protocol article.

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.

To disentangle the influence of environmental factors at different spatial grains (regional and local) on fern and lycophyte species richness and ask how regional and plot-level richness are related to each other. Location: Global. Time period: Present. Major Taxa studied: Ferns and lycophytes. Methods: We explored fern and lycophyte species richness at two spatial grains, regional (hexagonal grid cells of 7,666 km2) and plot level (300–500 m2), in relation to environmental data at regional and local grains (the 7,666 km2 hexagonal grid cells and 4 km2 square grid cells, respectively). For the regional grain, we obtained species richness data for 1,243 spatial units and used them together with climatic and topographical predictors to model global fern richness. For the plot-level grain, we collated a global dataset of nearly 83,000 vegetation plots with a surface area in the range 300–500 m2 in which all fern and lycophyte species had been counted. We used structural equation modelling to identify which regional and local factors have the biggest effect on plot-level fern and lycophyte species richness worldwide. We investigate how plot-level richness is related to modelled regional richness at the plot's location. Results: Plot-level fern and lycophyte species richness were best explained by models allowing a link between regional environment and plot-level richness. A link between regional richness and plot-level richness was essential, as models without it were rejected, while models without the regional environment-plot-level richness link were still valid but had a worse goodness-of-fit value. Plot-level richness showed a hump-shaped relationship with regional richness. Main conclusions: Regional environment and regional fern and lycophyte species richness each are important determinants of plot-level richness, and the inclusion of one does not substitute the inclusion of the other. Plot-level richness increases with regional richness until a saturation point is reached, after which plot-level richness decreases despite increasing regional richness, possibly reflecting species interactions.