Project

sPlot − Plant trait-environment relationships, biodiversity and invasion patterns across the world's biomes

Goal: The sDiv project sPlot (https://www.idiv.de/splot) originally was aimed at analysing the plant trait-environment relationships across the world’s biomes. After two workshops (March 2013, December 2014) and with the recent release of the sPlot 2.0 database in January 2016 we still pursue this original aim but have widened our scope based on the unique data source we created. sPlot 2.0 contains >1.1 million vegetation plots from 130 countries and all seven continents plus a taxonomic backbone that has been successfully matched with a gap-filled version of the global plant trait database TRY for 18 major functional traits. The small-grained, geo-referenced co-occurrence data at the global extent, in combination with the available trait data, opens new avenues for addressing fundamental questions of functional biogeography, community assembly, diversity patterns, invasion biology and macroecology.

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Francesco Maria Sabatini
added 3 research items
Understanding the variation in community composition and species abundances, i.e., β-diversity, is at the heart of community ecology. A common approach to examine β-diversity is to evaluate directional turnover in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distances. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 149 datasets comprising different types of organisms and environments. We modelled an exponential distance decay for each dataset using generalized linear models and extracted r ² and slope to analyse the strength and the rate of the decay. We studied whether taxonomic or functional similarity has stronger decay across the spatial and environmental distances. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm, and organismal features. Taxonomic distance decay was stronger along spatial and environmental distances compared with functional distance decay. The rate of taxonomic spatial distance decay was the fastest in the datasets from mid-latitudes while the rate of functional decay increased with latitude. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distances but a higher rate of decay along environmental distances. Marine ecosystems had the slowest rate of decay. This synthesis is an important step towards a more holistic understanding of patterns and drivers of taxonomic and functional β-diversity.
Aim Alpine ecosystems differ in area, macroenvironment and biogeographical history across the Earth, but the relationship between these factors and plant species richness is still unexplored. Here, we assess the global patterns of plant species richness in alpine ecosystems and their association with environmental, geographical and historical factors at regional and community scales. Location Global. Time period Data collected between 1923 and 2019. Major taxa studied Vascular plants. Methods We used a dataset representative of global alpine vegetation, consisting of 8,928 plots sampled within 26 ecoregions and six biogeographical realms, to estimate regional richness using sample‐based rarefaction and extrapolation. Then, we evaluated latitudinal patterns of regional and community richness with generalized additive models. Using environmental, geographical and historical predictors from global raster layers, we modelled regional and community richness in a mixed‐effect modelling framework. Results The latitudinal pattern of regional richness peaked around the equator and at mid‐latitudes, in response to current and past alpine area, isolation and the variation in soil pH among regions. At the community level, species richness peaked at mid‐latitudes of the Northern Hemisphere, despite a considerable within‐region variation. Community richness was related to macroclimate and historical predictors, with strong effects of other spatially structured factors. Main conclusions In contrast to the well‐known latitudinal diversity gradient, the alpine plant species richness of some temperate regions in Eurasia was comparable to that of hyperdiverse tropical ecosystems, such as the páramo. The species richness of these putative hotspot regions is explained mainly by the extent of alpine area and their glacial history, whereas community richness depends on local environmental factors. Our results highlight hotspots of species richness at mid‐latitudes, indicating that the diversity of alpine plants is linked to regional idiosyncrasies and to the historical prevalence of alpine ecosystems, rather than current macroclimatic gradients.
Jürgen Dengler
added a research item
Aim: This work explores whether the commonly observed positive range size–niche breadth relationship exists for Fagus, one of the most dominant and widespread broad-leaved deciduous tree genera in temperate forests of the Northern Hemisphere. Additionally, we ask whether the 10 extant Fagus species’ niche breadths and climatic tolerances are under phylogenetic control. Location: Northern Hemisphere temperate forests. Taxon: Fagus L. Methods: Combining the global vegetation database sPlot with Chinese vegetation data, we extracted 107,758 relevés containing Fagus species. We estimated biotic and climatic niche breadths per species using plot-based co-occurrence data and a resource-based approach, respectively. We examined the relationships of these estimates with range size and tested for their phylogenetic signal, prior to which a Random Forest (RF) analysis was applied to test which climatic properties are most conserved across the Fagus species. Results: Neither biotic niche breadth nor climatic niche breadth was correlated with range size, and the two niche breadths were incongruent as well. Notably, the widespread North American F. grandifolia had a distinctly smaller biotic niche breadth than the Chinese Fagus species (F. engleriana, F. hayatae, F. longipetiolata and F. lucida) with restricted distributions in isolated mountains. The RF analysis revealed that cold tolerance did not differ among the 10 species, and thus may represent an ancestral, fixed trait. In addition, neither biotic nor climatic niche breadths are under phylogenetic control. Main Conclusions: We interpret the lack of a general positive range size–niche breadth relationship within the genus Fagus a s a r esult of the widespread d istribution, high among-region variation in available niche space, landscape heterogeneity and Quaternary history. The results hold when estimating niche sizes either by fine-scale co-occurrence data or coarse-scale climate data, suggesting a mechanistic link between factors operating across spatial scales. Besides, there was no evidence for diverging ecological specialization within the genus Fagus.
Marten Winter
added a research item
Based on plant occurrence data covering all parts of Germany, we investigated changes in the distribution of 2136 plant species between 1960 and 2017. We analyzed 29 million occurrence records over an area of ~350,000 km2 on a 5 × 5 km grid using temporal and spatiotemporal models and accounting for sampling bias. Since the 1960s, more than 70% of investigated plant species showed declines in nationwide occurrence. Archaeophytes (species introduced before 1492) most strongly declined but also native plant species experienced severe declines. In contrast, neophytes (species introduced after 1492) increased in their nationwide occurrence but not homogeneously throughout the country. Our analysis suggests that the strongest declines in native species already happened in the 1960s–1980s, a time frame in which often few data exist. Increases in neophytic species were strongest in the 1990s and 2010s. Overall, the increase in neophytes did not compensate for the loss of other species, resulting in a decrease in mean grid cell species richness of −1.9% per decade. The decline in plant biodiversity is a widespread phenomenon occurring in different habitats and geographic regions. It is likely that this decline has major repercussions on ecosystem functioning and overall biodiversity, potentially with cascading effects across trophic levels. The approach used in this study is transferable to other large‐scale trend analyses using heterogeneous occurrence data. Full Text available as OpenAccess here: https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.15447
Francesco Maria Sabatini
added a research item
Aim To identify functional traits that best predict community assembly without knowing the driving environmental factors. Methods We propose a new method that is based on the correlation r(XY) between two matrices of potential community composition: matrix X is fuzzy-weighted by trait similarities of species, and matrix Y is derived by Beals smoothing using the probabilities of species co-occurrences. Since matrix X is based on one or more traits, r(XY) measures how well the traits used for fuzzy-weighting reflect the observed co-occurrence patterns. We developed an optimization algorithm that identifies those traits that maximize this correlation, together with an appropriate permutational test for significance. Using metacommunity data generated by a stochastic, individual-based, spatially explicit model, we assessed the type I error and the power of our method across different simulation scenarios, varying environmental filtering parameters, number of traits and trait correlation structures. We then applied the method to real-world community and trait data of dry calcareous grassland communities across Germany to identify, out of 49 traits, the combination of traits that maximizes r(XY). Results The method correctly identified the relevant traits involved in the community assembly mechanisms specified in simulations. It had high power and accurate type I error and was robust against confounding aspects related to interactions between environmental factors, strength of limiting factors, and correlation among traits. In the grassland dataset, the method identified five traits that best explained community assembly. These traits reflected the size and the leaf economics spectrum, which are related to succession and resource supply, factors that may not be always measured in real-world situations. Conclusions Our method successfully identified the relevant traits mediating community assembly driven by environmental factors which may be hidden for not being measured or accessible at the spatial or temporal scale of the study.
Jürgen Dengler
added a research item
Aim: 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.
Jürgen Dengler
added a research item
Aim: Alien plant species can cause severe ecological and economic problems, and therefore attract a lot of research interest in biogeography and related fields. To identify potential future invasive species, we need to better understand the mechanisms underlying the abundances of invasive tree species in their new ranges, and whether these mechanisms differ between their native and alien ranges. Here, we test two hypotheses: that greater relative abundance is promoted by (1) functional difference from locally co-occurring trees, and (2) higher values than locally co-occurring trees for traits linked to competitive ability. Location: Global. Time period: Present day. Major taxa studied: Trees. Methods: We combined three global plant databases: sPlot vegetation-plot database, TRY plant trait database, and GloNAF naturalized alien flora database. We used a hierarchical Bayesian linear regression model to assess the factors associated with variation in local abundance, and how these relationships vary between native and alien ranges and depend on species’ traits. Results: In both ranges, species reach highest abundance if they are functionally similar to co-occurring species, yet are taller and have higher seed mass and wood density than co-occurring species. Main conclusions: Our results suggest that light limitation leads to strong environmental and biotic filtering, and that it is advantageous to be taller and have denser wood. The striking similarities in abundance between native and alien ranges imply that information from tree species’ native ranges can be used to predict new habitats where introduced species may become dominant.
Miguel D Mahecha
added a research item
Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.
Marco Schmidt
added a research item
Questions: Vegetation-plot records provide information on presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers, and thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. - Location: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected between 1885 and 2015. - Methods: We complemented the information for each plot by retrieving environmental conditions (i.e. climate and soil) and the biogeographic context (i.e. biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. - Results: We present the first maps of global patterns of community richness and community-weighted means of key traits. - Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
Jürgen Dengler
added a research item
Questions: Vegetation-plot records provide information on presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers, and thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. - Location: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected between 1885 and 2015. - Methods: We complemented the information for each plot by retrieving environmental conditions (i.e. climate and soil) and the biogeographic context (i.e. biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. - Results: We present the first maps of global patterns of community richness and community-weighted means of key traits. - Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
Jürgen Dengler
added a research item
Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key remaining question is to what extent community-level trait composition is globally filtered and how well it is related to global vs. local environmental drivers. Here, we perform a global, plot-level analysis of trait-environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes which capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale.Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning or biotic interactions.
Jürgen Dengler
added a research item
The poster describes the current content of the release 2.1 of the global vegetation-plot database ”sPlot” and invites vegetation ecologists with suitable data to contribute them for the forthcoming release 3.0 scheduled for early 2018 and thus become members of the sPlot Consortium.
Oliver Purschke
added 2 research items
The trait composition of plant communities is determined by abiotic, biotic and historical factors, but the importance of macro-climatic factors in explaining trait-environment relationships at the local scale remains unclear. Such knowledge is crucial for biogeographical and ecological theory but also relevant to devise management measures to mitigate the negative effects of climate change. To address these questions, an iDiv Working Group has established the first global vegetation-plot database (sPlot). sPlot currently contains 700,000 plots from over 50 countries and all biomes, and is steadily growing. Approx. 70% of the most frequent species are represented by at least one trait in the global trait database TRY and gap-filled data will become available for the most common traits. We will give an overview about the structure and present content of sPlot in terms of spatial distribution, data properties and trait coverage. We will explain next steps and perspectives, present first cross-biome analyses of community-weighted mean traits and trait variability, and highlight some ecological questions that can be addressed with sPlot.
Jürgen Dengler
added 2 research items
sPlot: the first global vegetation-plot database and opportunities to contribute Jürgen Dengler & the sPlot Core Team Background Vegetation-plot data become increasingly available in regional and national vegetation-plot databases, particularly in Europe (Schaminée et al. 2009), but also on all other continents (Dengler et al. 2011). Access to such data has been facilitated through the global metadatabase GIVD (www.givd.info), set up in collaboration with the IAVS Working Group on Ecoinformatics, but until recently analyses of plot data across several continents were impeded by the fact that it is tedious and time-consuming to retrieve plot data from various different databases and to prepare them for common analyses, which requires standardisation of database format and structure, header data and species taxonomies. To fill this gap, the European Vegetation Survey (EVS), a working group of IAVS, has initiated the first comprehensive continental plot database for Europe, called European Vegetation Archive (EVA), in 2012, which became live in spring 2014 (http://euroveg.org/eva-database; see Jiménez-Alfaro et al. 2013). Parallel to this European effort and in close collaboration with the European partners, an initiative for a global plot database, called " sPlot " , was initiated by an international Working Group at the Synthesis Centre (sDiv) of the German Centre for Integrative Biodiversity Research (iDiv) in Halle-Jena-Leipzig (www.idiv.de) with the first sPlot Workshop in Leipzig in March 2013. Since then, the sPlot Team and Consortium, including many well-known IAVS members, was working on making this idea come true. Finally, in April 2014, a prototype of sPlot could be created by joining major parts of EVA (those whose owners had agreed to make their data available in both supra-national databases) with the first extra-European databases. This process was strongly facilitated through the use of the prototype of Turboveg 3, a software programmed by Stephan Hennekens, that is able to manage different Turboveg 2 database with different taxonomies and header data structures on a common platform, including the rights management, which becomes increasingly important when combining plots from many different sources. Finally, in November 2014, we could release the version 1.0 of sPlot that now contains already data from various continents and all ecozones. From 2–5 December 2014 the second sPlot Workshop took place in Leipzig, in which 28 scientists from nine countries and four continents participated. They were an exciting mixture of representatives of big plot databases (Czech National Database, GVRD and VegMV/Germany, AEKOS/Australia, BIOTA-Western Africa, BIOTA-Southern Africa), trait databases (TRY), theoretical ecologists and specialists for elaborate statistical analyses that combine plot data, trait data, phylogenies, climate data and remote sensing products. The aim of this workshop was to screen the already available data and to plan papers for high-rank journals that make use of the unique data resource that became available with sPlot. Many of the planned papers focus on trait-environment relationships at community level across the world's biomes and do so through close collaboration with the global trait database TRY (Kattge et al. 2011). However, there are also other paper plans that focus on the plot data alone or intend to combine them with phylogenetic, environmental and remote sensing data, e.g. analyses concerning global patterns of plot-scale alpha diversity or plant invasions. sPlot Rules, sPlot Consortium and access to the data sPlot is a truly collaborative project. The sPlot Consortium has currently 88 members from all continents and is governed by an elected Steering Committee (currently: Helge Bruelheide [chair], Milan Chytrý, Valério Pillar, Brody Sandel & Jens Kattge). The Governance and Data Property Rules of the sPlot Working Group (http://www. idiv-biodiversity.de/sdiv/workshops/workshops-2013/splot/ materials/content_56450/sPlot-Rules_approved.pdf) ensure a fair balance of the interests of researchers to do global scale analyses and the rights of data contributors. Most important elements are that (a) data contributors with their data contribution become members of the sPlot Consortium and (b) data in sPlot are not public but restricted to use by sPlot Consortium members. Whenever a Consortium member has proposed a paper using the sPlot data, all Consortium members will be informed and can declare their interest of becoming co-authors (opt-in papers). Further, sPlot will ensure proper attribution and citation whenever data from a contributing database are used and explicitly excludes any analyses below continental level. Thus no data contributor needs to be concerned that data retrieved from sPlot could be used for national or regional analyses that might interfere with publications projects have planned themselves with their single database. Finally, contributed data to sPlot remain the property of the data contributor and can be withdrawn at any time.
We are happy to celebrate with you the release of sPlot 2.0, the most comprehensive vegetation-plot database ever in terms of geographic coverage and the first sPlot version available for scientific studies. sPlot 2.0 (released 21 January 2016) contains 1,115,705 plots from 110 source databases. They originate from all seven continents and a total of 130 countries. Nearly all sPlot data (99.9%) are geo-referenced, albeit sometimes with low precision (but each plot contains a field for spatial precision).
Jürgen Dengler
added a project goal
The sDiv project sPlot (https://www.idiv.de/splot) originally was aimed at analysing the plant trait-environment relationships across the world’s biomes. After two workshops (March 2013, December 2014) and with the recent release of the sPlot 2.0 database in January 2016 we still pursue this original aim but have widened our scope based on the unique data source we created. sPlot 2.0 contains >1.1 million vegetation plots from 130 countries and all seven continents plus a taxonomic backbone that has been successfully matched with a gap-filled version of the global plant trait database TRY for 18 major functional traits. The small-grained, geo-referenced co-occurrence data at the global extent, in combination with the available trait data, opens new avenues for addressing fundamental questions of functional biogeography, community assembly, diversity patterns, invasion biology and macroecology.