PROJECTS APPROVED BY THE sPLOT STEERING COMMITTEE:
(#01) sPlot – a global database of fine-grain plant community data
Helge Bruelheide, Jürgen Dengler, Borja Jiménez-Alfaro, Oliver Purschke
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.
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.
We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g. 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.
We present the first maps of global patterns of community richness and community‐weighted means of key traits.
The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
Bruelheide, H., Dengler, J., Jiménez-Alfaro, B., Purschke, O., Hennekens, S.M., Chytrý, M., Pillar, V.D., Jansen, F., Kattge, J., Sandel, B., Aubin, I., Biurrun, I., Field, R., Haider, S., Jandt, U., Lenoir, J., Peet, R.K., Peyre, G., Sabatini, F.M., Schmidt, M., Schrodt, F., Winter, M., Aćić, S., Agrillo, E., Alvarez, M., Ambarlı, D., Angelini, P., Apostolova, I., Arfin Khan, M.A.S., Arnst, E., Attorre, F., Baraloto, C., Beckmann, M., Berg, C., Bergeron, Y., Bergmeier, E., Bjorkman, A.D., Bondareva, V., Borchardt, P., Botta-Dukát, Z., Boyle, B., Breen, A., Brisse, H., Byun, C., Cabido, M.R., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Curran, M., Ćušterevska, R., Stevanović, Z.D., De Bie, E., De Ruffray, P., De Sanctis, M., Dimopoulos, P., Dressler, S., Ejrnæs, R., El-Rouf Mousa El-Sheikh, M.A., Enquist, B., Ewald, J., Fagúndez, J., Finckh, M., Font, X., Forey, E., Fotiadis, G., García-Mijangos, I., de Gasper, A.L., Golub, V., Gutierrez, A.G., Hatim, M.Z., He, T., Higuchi, P., Holubová, D., Hölzel, N., Homeier, J., Indreica, A., Gürsoy, D.I., Jansen, S., Janssen, J., Jedrzejek, B., Jiroušek, M., Jürgens, N., Kącki, Z., Kavgacı, A., Kearsley, E., Kessler, M., Knollová, I., Kolomiychuk, V., Korolyuk, A., Kozhevnikova, M., Kozub, Ł., Krstonošić, D., Kühl, H., Kühn, I., Kuzemko, A., Küzmič, F., Landucci, F., Lee, M.T., Levesley, A., Li, C.-F., Liu, H., Lopez-Gonzalez, G., Lysenko, T., Macanović, A., Mahdavi, P., Manning, P., Marcenò, C., Martynenko, V., Mencuccini, M., Minden, V., Moeslund, J.E., Moretti, M., Müller, J.V., Munzinger, J., Niinemets, Ü., Nobis, M., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Ozinga, W.A., Pauchard, A., Pedashenko, H., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rodwell, J., Ruprecht, E., Rūsiņa, S., Samimi, C., Schaminée, J.H.J., Schmiedel, U., Šibík, J., Šilc, U., Škvorc, Ž., Smyth, A., Sop, T., Sopotlieva, D., Sparrow, B., Stančić, Z., Svenning, J.-C., Swacha, G., Tang, Z., Tsiripidis, I., Turtureanu, P.D., Ugurlu, E., Uogintas, D., Valachovič, M., Vanselow, K.A., Vashenyak, Y., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D.A., Wana, D., Weiher, E., Wesche, K., Whitfeld, T., Willner, W., Wiser, S., Wohlgemuth, T., Yamalov, S., Zizka, G. & Zverev, A. (2019) sPlot – a new tool for global vegetation analyses. Journal of Vegetation Science:30, 161-186.
(#02) A resampling strategy to analyze community assembly rules in a big data world
Jonathan Lenoir, Université de Picardie Jules Verne, France
A methodological paper proposing and describing a resampling strategy to deal with the unbalanced spatial distribution of data from most global databases such as the sPlot vegetation database (cf. cartogram of the number of vegetation plots per country). The basic idea of the resampling strategy is to perform a systematic sampling within the environmental space to capture a balanced sample that is representative of the available environmental variability. To represent the environmental variability that is available on a global scale, we will use a set of meaningful macroclimatic variables (e.g. the 19 BIOCLIM variables of WorldClim) that we will reduce by selecting the 2 to 3 first dimensions of an ordination method such as principal component analysis. Once the reduced environmental space will be delimited (cf. convex hull approach), a multidimensional grid will be laid out inside the convex hull to perform a systematic sampling. The obtained grid will represent the perfect configuration required to adequately sample the environmental space available at a global scale. All vegetation plots from the sPlot database will then be plotted within this environmental space to assess the density of vegetation plots available per environmental unit. The density of vegetation plots for a given environmental unit will likely be proportional to the total terrestrial surface area available for that environmental unit. Finally, a fixed number (n) of vegetation plots will be randomly selected from each environmental unit to get a balanced sample. This step will be repeated several times (k iterations) for each environmental unit. Among the k iterations of n vegetation plots, we will select the iteration that both maximizes the mean dissimilarity in species composition between the n selected vegetation plots and minimizes the standard deviation in the dissimilarity in species composition between the n selected vegetation plots. Such a resampling strategy will allow us to maximize the diversity of vegetation types sampled per environmental unit as well as among environmental units while minimizing the risk of spatial autocorrelation (cf. pseudo-replicates) in the vegetation data. Although developed in the framework of sPlot, this resampling strategy will also be widely applicable to any large ecological dataset.
(#03) Global trait-environment relationships revealed by sPlot
Helge Bruelheide, iDiv - Martin Luther University, Germany
The trait composition of plant communities is determined by climatic and edaphic factors, as well as by successional stage and disturbance regime. However, the relative strengths of macroclimate and local environmental factors in explaining global trait distributions at the scale of communities still remain unclear. Global studies typically focus on either sets of individual geo-located records or coarse-grain species assemblages, while the fine-grain functional composition of plant communities is only available for very limited local spatial extents. However, environmental factors act on the trait composition of communities through biotic interaction namely at this fine scale. So far, we lacked a comprehensive global database with a spatial resolution corresponding to the community scale at which individuals varying in functional traits co-exist.
Meanwhile, the new sPlot 2.0 database holds 1.1 million vegetation plots from more than 100 databases worldwide. More than 60% of the most frequent species are represented by at least one trait from the TRY 3.0 trait database, and gap-filling techniques have been used to estimate trait values for these most frequent species. For all plots, we calculated the community weighted mean (CWM) and functional diversity (FD) for 18 traits. We plan to use resampling strategies to achieve a representative stratification of plots in global climate space. Using linear regression techniques, principal component analysis (PCA) and redundancy analysis (RDA), we have related CWM and FD to bioclimatic variables. Furthermore, we plan to calculate geographically weighted regressions (GWR) and moving window analysis to assess spatial variation in trait-environment relationships. Finally, we plan to explore the influence of gap-filling and compare relationships based on CWMs of non-gap filled TRY data with those based on original trait mean values.
Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S.M., Botta-Dukát, Z., Chytrý, M., Field, R., Jansen, F., Kattge, J., Pillar, V.D., Schrodt, F., Mahecha, M.D., Peet, R.K., Sandel, B., van Bodegom, P., Altman, J., Alvarez-Dávila, E., Arfin Khan, M.A.S., Attorre, F., Aubin, I., Baraloto, C., Barroso, J.G., Bauters, M., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Blonder, B., Čarni, A., Cayuela, L., Černý, T., Cornelissen, J.H.C., Craven, D., Dainese, M., Derroire, G., De Sanctis, M., Díaz, S., Doležal, J., Farfan-Rios, W., Feldpausch, T.R., Fenton, N.J., Garnier, E., Guerin, G.R., Gutiérrez, A.G., Haider, S., Hattab, T., Henry, G., Hérault, B., Higuchi, P., Hölzel, N., Homeier, J., Jentsch, A., Jürgens, N., Kącki, Z., Karger, D.N., Kessler, M., Kleyer, M., Knollová, I., Korolyuk, A.Y., Kühn, I., Laughlin, D.C., Lens, F., Loos, J., Louault, F., Lyubenova, M.I., Malhi, Y., Marcenò, C., Mencuccini, M., Müller, J.V., Munzinger, J., Myers-Smith, I.H., Neill, D.A., Niinemets, Ü., Orwin, K.H., Ozinga, W.A., Penuelas, J., Pérez-Haase, A., Petřík, P., Phillips, O.L., Pärtel, M., Reich, P.B., Römermann, C., Rodrigues, A.V., Sabatini, F.M., Sardans, J., Schmidt, M., Seidler, G., Silva Espejo, J.E., Silveira, M., Smyth, A., Sporbert, M., Svenning, J.-C., Tang, Z., Thomas, R., Tsiripidis, I., Vassilev, K., Violle, C., Virtanen, R., Weiher, E., Welk, E., Wesche, K., Winter, M., Wirth, C. & Jandt, U. (2018) Global trait–environment relationships of plant communities. Nature Ecology & Evolution, 2, 1906-1917.
(#04) The role of climate stability for trait variability across scales and biomes (DISCONTINUED)
Oliver Purschke, iDiv - Martin Luther University, Germany
There are a number of conflicting hypothesis about how temporal environmental variability (EVt) affects the distribution of traits (e.g. functional diversity) within communities. In this study, we aim to quantify the extent to which the effect of EVt on trait variability depends on i) the time scale at which EVt occurs, ii) climate type (i.e. biogeographic region), iii) formation (grassland vs. forest) and iv) spatial grain size. Although effects of spatial gradients of environmental means, e.g. those related to energy, on species and trait diversity are well studied, there has been little emphasis on how temporal variation around environmental mean values affects biodiversity. There is a rich body of theory making predictions about how temporal environmental variability, through changes in the trait distribution within communities, affects coexistence mechanisms that generate biodiversity. For example, at local scales (small grain sizes, i.e. at the vegetation-plot level), where biotic processes such as competition play a major role, temporal environmental variability can promote diversity through temporal niche partitioning: species that differ in their functional characteristics (traits) will be favored at different periods of time, in which case temporal variability will increase trait diversity within communities. In contrast, some forms of environmental variability, such as long-term climatic variability (i.e. climate change velocity), have been demonstrated to decrease trait variability, with long-term unstable and harsh environments having the lowest diversity of trait states ('physiological tolerance hypothesis'), suggesting that the temporal scale at which EVt occurs determines whether EVt promotes or constrains trait variability. Further, because the frequency at which climatic extreme events occur differs among climates, the effect ETv on trait variability is expected to vary among biomes. So far, studies of ETv on trait diversity have focused on single temporal scales and bioclimatic regions, and there is therefore a need for synthetic studies across biomes and scales.
(#05) The role of climate stability for trait variability across scales and biomes: Can Earth observation data be used to measure changes in taxonomic and functional diversity?
Brody Sandel, Santa Clara University, USA
Large amounts of data are being generated by Earth observation satellites, but the information that is extracted falls far short of what is needed to monitor biological diversity from space. Global efforts to measure biodiversity from space have, broadly, focussed on two types of variables: ecosystem variables (e.g. LAI, GPP, NPP, leaf chlorophyll and water content) and vegetation indices (e.g. NDVI, EVI). Technical limitations and potentially invalid assumptions still hamper accurate estimates of ecosystem variables, but perhaps a more fundamental issue that prevents the use of ecosystem variables in assessing (changes in) diversity patterns is that their relationships with diversity are not known. For example, plant communities with different species composition may have identical productivity. Vegetation indices (typically described as ‘greenness’) are problematic for the same reasons and in addition it is difficult to establish a precise ecological meaning for vegetation indices. In short, establishing clear links between Earth observation data and ground data is essential for effective monitoring of biodiversity from space. As part of the Biosphere Atmosphere Change Index (BACI), which is funded under the EU Horizons 2020 programme, this study aims to produce spatially up-scaled indices of change in community composition and ecosystem functioning. These maps will be used within the BACI project to validate and inform change detection from Earth observation data.
(#06) Global patterns of phylogenetic similarity and abundance of plants in their native and exotic ranges
Tiffany Knight, iDiv - Martin Luther University Germany
Masha T. van der Sande - Wageningen University & Research, Wageningen, The Netherlands
It is well recognized that environmental filtering and biotic resistance play a strong role in determining which alien species will successfully naturalize and which communities get invaded. Environmental conditions filter alien species from the species pool based on their functional traits. Resident species with similar functional traits will compete with alien species for resources, providing biotic resistance to invasion. These alternative mechanisms should result in patterns in functional and phylogenetic similarity in co-occurring native and alien species; and the strength and direction of these patterns as compared to null expectations should indicate the relative important of environmental filtering and biotic resistance in structuring invasions. These patterns should depend on the spatial scale of investigation; environmental filtering is expected to be an important mechanism at regional spatial scales whereas biotic resistance determines local co-existence patterns. It is rarely tested whether alien species are limited by different mechanisms in their native and exotic ranges. For example, aliens might have higher abundances in their exotic compared to native ranges due to the availability of resources that are used less by co-occurring species in their exotic range. The sPLOT dataset combined with phylogenetic and/or functional information on species provides a new opportunity for understanding invasion patterns at both regional and local spatial scales. Addressing the relative roles of environmental filtering and biotic resistance of plants in their native and exotic ranges requires coding plant species as alien or native in each community plot within sPLOT. This is best achieved through collaboration with Global Naturalized Alien Flora (GloNAF). Species will be identified that have adequate sampling in sPLOT in both their native and alien ranges. We will compare the phylogenetic and/or functional similarity between each focal alien species and the species that it co-occurs with in its exotic and native ranges using appropriate null models. We will consider whether these patterns correlate with the relative abundance achieved by species in their native and exotic ranges, and whether patterns of phylogenetic/functional similarity change with spatial scale and across environmental gradients.
van der Sande, M.T., Bruelheide, H., Dawson, W., Dengler, J., Essl, F., Field, R., Haider, S., van Kleunen, M., Kreft, H., Pagel, J., Pergl, J., Purschke, O., Pyšek, P., Weigelt, P., Winter, M., Attorre, F., Aubin, I., Bergmeier, E., Chytrý, M., Dainese, M., De Sanctis, M., Fagundez, J., Golub, V., Guerin, G.R., Gutiérrez, A.G., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Kearsley, E., Klotz, S., Kramer, K., Moretti, M., Niinemets, Ü., Peet, R.K., Penuelas, J., Petřík, P., Reich, P.B., Sandel, B., Schmidt, M., Sibikova, M., Violle, C., Whitfeld, T.J.S., Wohlgemuth, T. & Knight, T.M. Similar factors underlie tree abundance in forests in native and alien ranges. Global Ecology and Biogeography, n/a
(#07) Scaling taxonomical, functional and phylogenetic community diversity (DISCONTINUED)
Oliver Purschke, iDiv - Martin Luther University, Germany
(#08) A global assessment of functional diversity and redundancy effects on ecosystem stability during climatic anomalies
Valério D. Pillar, UFRGS, Brazil
Identifying the relationships between diversity and stability is relevant for the adaptation to climate change. Are plant communities with higher diversity more stable under the impact of climatic anomalies? Here we propose to evaluate the effects of functional diversity and functional redundancy on the stability (resistance and resilience) of primary productivity, estimated by remote sensing proxies (e.g., NDVI), during and immediately after precipitation anomalies worldwide. For this, the sPlot database will be sampled in order to select plots that (1) could be spatially and temporally aggregated locally in order to compose larger units with a size compatible with the resolution and time window of the remote sensing product (e.g. MODIS started on year 2000), (2) for which core plant trait data is available at the TRY database for a reasonable proportion of the species found at the plots, and (3) for which precipitation data obtained from remote sensing (TRMM) is available (0-60º N-S). For each aggregated unit (set of plots in a given site), we will compile precipitation data obtained by satellite (TRMM) and identify periods (growing seasons) with negative and positive precipitation anomalies compared to climatic normals (1981- 2010). Aggregated units whose vegetation was described during an anomalous period, or without any identified precipitation anomaly posterior to the vegetation description will be discarded. For the remaining aggregated units, we will compile remote sensing indexes to be used as proxy for primary productivity (e.g., MODIS NDVI and EVI) and with these indexes we will compute, for each aggregated unit, baseline values considering only neutral periods (outside precipitation anomalies). For each aggregated unit, we will select the closest anomalous precipitation period posterior to the vegetation survey, for which we will compute resistance and resilience, and percentage change (positive or negative) for the primary productivity proxies. Also, for each aggregated unit we will compute community-weighted means (CWM), functional diversity (Rao entropy), and functional redundancy (difference between Gini–Simpson index of species diversity and Rao entropy) using plant traits from TRY (gap filled data). The links between CWM, diversity, redundancy and stability (resistance and resilience) will be examined by means of path analysis and linear models, across all biome types or for each biome type separately.
 Isbell, F. et al. (2015) Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526, 574–577.
 Fischer, F.M. et al. (2016) Plant species richness and functional traits affect community stability after a flood event. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 371.
(#09) Downscaling of species distribution models: towards fine-grain presence-absences for grasses
Brody Sandel, Santa Clara University, USA
The limited accuracy of occurrence data, especially for a large and less well-known group such as Poaceae, is a substantial impediment to understanding the mechanisms shaping species distributions at large spatial extents. Species checklists at the level of “botanical countries” are the only comprehensive dataset that provides reliable coarse-grain presences for all grass species at global scale (WCSP; Royal Botanic Gardens Kew, 2015). At the same time, sPlot made a huge step to gather fine-scale presence-absence data at a global scale. Both coarse-grain and fine-grain presences could be now combined to get fine-scale map of occurrences.
We are using a downscaling method of species distribution model to get fine-scale presence probabilities of grasses based on a hierarchical Bayesian modelling framework. This most recent method of downscaling from coarse-grain resolution and fine-scale environmental data has been developed by Keil et al. (2013). We are adapting it to the case of plant distributions, taking advantage of multi-scale reliable information available for grasses (checklist, vegetation plots, point records). While the method is currently tested at the scale of the USA, our goal is to perform the analysis at global scale.
For Poaceae family, we thus aim to gather for the first time coarse-grain presence data (species checklists) with fine-scale presence-absence data from vegetation plots (sPlot) and point presences (GBIF) to get fine-grain probabilities of grass occurrences worldwide.
Keil, P., Belmaker, J., Wilson, A.M., Unitt, P. & Jetz, W. (2013) Downscaling of species distribution models: a hierarchical approach. Methods in Ecology and Evolution, 4, 82–94.
Royal Botanic Gardens Kew (2015) World Checklist of Selected Plant Families.
(#10) A global model of local fern diversity
Michael Kessler, University of Zurich, Switzerland
We want to use a large compilation of vegetation plots with data on ferns and lycophytes (for brevity: ferns) to produce a global model of fern species richness at the local scale. This model can then be linked to data on, e.g., life forms (terrestrial/epiphytic) or phylogenetic relationships, to address questions about the global distribution and evolution of fern diversity. We are still uncertain about the exact delimitation of the first paper, whether it will “just” be the diversity model or whether we go straight for one of the more derived analyses. The plan thus is to first compile the data and conduct some preliminary analyses and based on this, to then decide how to subdivide and place the publications.
Based on 20 years of field work, we have already compiled a database of some 3500 plots with data on ferns. All plots are 20 m x 20 m in size and at least have a differentiation between epiphytic and terrestrial life forms plus indication of whether a species was sterile or fertile. The newer plots also have estimates of numbers of individuals (partly separate by sterile/fertile plants) and data on different epiphytic life zones (Johansson zones). These data are global but with a very strong focus on tropical regions. The aim of the present proposal is to add methodologically comparable data from especially temperate and boreal regions, to achieve a global coverage.
For the modeling, we will use several environmental predictors: A new global climate model at high resolution (~1 x 1 km) (www.chelsa-climate.org) to apply generalized linear models to the plot data to establish richness-climate relationships and then extend this to a global scale; a global dataset of soil conditions (harmonized world soil database, http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/); a global cloud climatology (http://www. earthenv.org/cloud).
Weigand, A., Abrahamczyk, S., Aubin, I., Bita-Nicolae, C., Bruelheide, H., I. Carvajal-Hernández, C., Cicuzza, D., Nascimento da Costa, L.E., Csiky, J., Dengler, J., Gasper, A.L.d., Guerin, G.R., Haider, S., Hernández-Rojas, A., Jandt, U., Reyes-Chávez, J., Karger, D.N., Khine, P.K., Kluge, J., Krömer, T., Lehnert, M., Lenoir, J., Moulatlet, G.M., Aros-Mualin, D., Noben, S., Olivares, I., G. Quintanilla, L., Reich, P.B., Salazar, L., Silva-Mijangos, L., Tuomisto, H., Weigelt, P., Zuquim, G., Kreft, H. & Kessler, M. Global fern and lycophyte richness explained: How regional and local factors shape plot richness. Journal of Biogeography, n/a
(#11) Probabilistic species pools at the plot level
Helge Bruelheide, iDiv - Martin Luther University, Germany
The definition of a species pool is a crucial step for many types of analysis in community ecology, including community assembly processes, invasability or resistance. Knowledge of the species pool is necessary to estimate the number of non-resident species that potentially can occur in a plot (“dark diversity”). In a heterogeneous database as sPlot with varying plot sizes and samples from a wide range of habitats, the definition of a species pool is also fundamental for calculating species richness at a particular spatial scale.
There are many attempts of defining species pools, both using internal criteria (e.g. sum of all species that occur in a set of plots) or external criteria (e.g. a geographic region or a certain climate space). However, most attempts result in a pool of species that simply reflects the overall g diversity of this set. Similarly, attempts to define the species pool by floristic similarity among plots or by environmental similarity may unnecessarily constrain the potential species pool. Here, I propose to define a species pool as the set of species that are potentially able to occur in a given community. An implication is that a species-pool is community-specific. If a plot vegetation record is considered a community, which in this proposal I do, each plot can be characterized by its own potential species pool.
(#12) Temperate deciduous forests of the northern hemisphere
Javier Loidi (Basque Country University, Spain) and Robert K Peet (University of North Carolina, USA)
Deciduous broadleaved summer-green forests are dominant in three regions of the Northern Hemisphere: Eastern Asia, Western and Central Europe, and Eastern North America. The dominant climate of these three regions is temperate, and these forests seem to be a clear adaptive response to cold winters and warm and wet summers. These three regions also share a common ancient history until they were separated in the Tertiary. After they split, each region experienced a common pattern of climatic changes, but with unique tectonic episodes and orographic circumstances. These specific histories were responsible for some known biodiversity patterns that exist today such as the difference in tree species richness among the three regions. Our primary aim is to do a global synthesis of the biodiversity of the herbaceous floras of the three regions. Most papers dealing with the global diversity of temperate forests have focused on tree floras, while fewer articles have examined the herbaceous understory. The distribution and diversity patterns of the understory are, nonetheless, strongly related to the glacial refugia that existed during the cold periods in Europe and North America. By doing a comparative analysis of the floristic and functional diversity of the understories of the three regions, we expect to gain insight into the historical and macroclimatic drivers of biodiversity. Our secondary aim is to incorporate into the study plant traits related to the seed regeneration niche. Seed traits are of high importance in the processes of plant community assembly and movement, but they have been rarely incorporated into vegetation studies because they are under-represented in database compilations. The advantage of focusing studies on temperate forests is that there is a relatively high number of seed biology studies at the species level, at least for European and North American forest species. There is also a potential number of references to be found in Russian and Japanese sources. Therefore, temperate woodlands offer a good opportunity to conduct a first global synthesis of seed traits at the community level, and possibly link these to community change during periods of climate change.
(#13) Global patterns of aquatic macrophyte diversity – environment relationships with focus on native versus invasive species
Franziska Schrodt, University of Nottingham, UK
Fresh waters are especially important ecosystems due to them hosting relatively larger proportion of biodiversity compared to terrestrial systems and being a source for essential but threatened ecosystem services, such as provision of drinking water and climate change mitigation (Vörösmarty et al. 2010). However, aquatic macrophytes have shown inconsistent patterns in relation to many climate-derived biogeographical and ecological gradients (e.g. Kindleman et al. 2007, Alahuhta 2015) and to date no large scale (“global”) database of aquatic macrophyte distributions exists.
This project aims to combine the extensive database assembled by Alahuhta et al. (globally 19 regions with data from 29 to 2000 lakes) with data on aquatic macrophytes from sPlot and traits from TRY in order to analyse global patterns in taxonomic, phylogenetic and functional diversity and their interrelationships with environmental variables (including climatic data as well as soils and remotely sensed vegetation indices (using Sentinel 2 data where available), indices of water quality e.g. from the USGS) and spatial variables (based on Moran’s eigenvector maps). Our purpose is to compare patterns in different diversity measures and related ecological gradients found among different study regions.
We further aim to assess potential differences between native and invasive aquatic macrophyte – ecological relationships making use of the GloNAF database on global naturalized alien floras. Our object is to study whether native macrophyte species respond differently than invasive species to ecological gradients. Specifically, we hope to find out if dispersal contributes more strongly to distribution of invasive than native species. Our study will combine community and species level data to address these fundamental macroecological study questions in a notoriously understudied biological group, namely aquatic macrophytes.
(#14) Functional convergence of terrestrial ecosystems within world biomes
Borja Jiménez-Alfaro, iDiv - Martin Luther University, Germany
Convergence is a classical ecological concept that explains the similarity in structure or function between organisms in different areas subject to similar evolutionary or environmental forces. At the community level, functional convergence or trait convergence generally refers to the similarities or dissimilarities between co-occurring species in response to community assembly processes. The interplay of species evolution and community assembly result in plant communities with different species´ relative abundances that sustain ecosystems. Functional convergence between ecosystems exists when vegetation types from distant regions show similarity in their dominant functional attributes. Functional convergence has been traditionally shown in Mediterranean ecosystems, and more recently also in temperate rainforests of Chile and New Zealand. The causes of this convergence can be primarily attributed to similar environmental constrains, but also to similarities in evolutionary history, assembly drivers, or just chance. However, despite a few well-known examples of functional convergence in plant communities, the real extent of this pattern across world biomes is unknown.
This project will use the sPlot vegetation database to test whether ecosystem convergence exists across different continents. We will use the world biomes defined by Schlultz as operational units with a consistent climatic similarity worldwide. We will look at functional similarities across continents within the same biome, assuming climatic conditions as a major driver of ecosystem convergence. We will analyze vegetation plots representing forests and grasslands as the most dominant terrestrial ecosystems. For each one of the world biomes, we will compare the Community Weighted Means calculated for each plot for different traits. Only those biomes and continents with a minimum number of samples (for example 300 plots) will be compared. Analyses will include single-trait and multi-trait variation using univariate and multivariate statistics, respectively. Convergence or divergence will be estimated as a surrogate of the similarities detected in the statistical tests across different continents. Results will show a general overview of the cases in which ecosystem convergence can be expected across different continents within each biome, for single traits and for the full trait combination.
(#15) Cross-scale transferability of species niche breadth estimates
Stephan Kambach and Helge Bruelheide, Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg (MLU) and Department of Community Ecology, Helmholtz-Centre for Environmental Research Leipzig (UFZ)
In the literature there are several different definitions and methods to infer the position of a species niche and, likewise, the corresponding niche breadth (i.e. the range of tolerated abiotic and biotic conditions). Geo-referenced vegetation surveys are a valuable tool to simultaneously infer the realized niche breadth for a large set of species across habitat types and ecoregions. In an ongoing project we found a high correlation between climate-envelope and species co-occurrence based niche breadth estimates for species present in the Alps Vegetation Database (Kambach, Lenoir, Bruelheide et al., under revision). We also found a considerable mismatch between niche breadth estimates derived from the Alps region and estimates that were obtained at the global extent leading us to the question “Can niche breadth estimates that are calculated from large, but restricted datasets accurately predict species global niche breadth?”We plan to introduce this study with a short review of published niche breadth estimation methods which we will then group based on their considered niche dimension (e.g. via climate envelopes, on-site measurements, species co-occurrence, etc.) and behaviour in simulated vegetation datasets.The emerging non-redundant niche breadth estimation methods will then be applied to derive niche breadth estimates for the species that comprehensively represented in the sPlot database. We plan to calculate and compare niche breadth estimates that are derived at the global but also at a regionally and ecologically restricted datasets (e.g. based on habitat types). Our aim is to explore under which circumstances niche breadth estimates that are derived from a restricted subset of the species global occurrence can yield valid estimates of species’ globally achieved niche breath.
(#16) Projecting tree diversity and distributions in a changing world
Jose M Serra-Diaz (Aarhus University, Denmark), Franziska Schrodt (University of Nottingham, UK), Jens-C. Svenning (Aarhus University, Denmark)
The big question that we will address is: “How will tree species diversity react to future global climate change?” Forests are among the most important ecosystems on Earth, harboring a substantial proportion of biodiversity and providing vital ecosystem services such as carbon sequestration, climate regulation, erosion protection, and timber and non-timber forest products. The diversity of tree species plays a central role in forest ecosystems and for the subsistence of millions of people in rural communities world-wide. Part of the challenge in understanding drivers of tree diversity is that we do not have a complete picture of the current tree distribution and diversity of tree species worldwide.
To improve our understanding of global tree distributions, we will apply advanced SDM-based approaches with thorough handling of spatial autocorrelation, pseudo-absences and model complexity to the ca. 65,000 identified tree species globally. For those species with very few records, we will complement the use of SDMs with functional traits and phylogeny to provide insights on species range responses to climate. Specifically, we will use gap filled trait data to understand climatic responses for rare species – where there are too few records to implement the SDM approach with confidence. In an additional step, this will potentially allow estimating climatic-response functions which will then be projected under different climate change scenarios and evaluate shifts in tree diversity under climate change.
(#17) Spatial patterns of plant assemblages in mountains. A global analysis
Gwendolyn Peyre (University of the Andes, Colombia)
This project is part of the “mountain diversity” studies that are based on sPlot data and focus on different dimensions of plant diversity in the alpine regions of the world. We focus on beta diversity and specifically in understanding the spatial patterns of plant assemblages and differentiation along spatial and environmental gradients in mountain ranges across the globe. We aim at conducting a bioregionalization analysis based on vascular plants in the world’s main mountain ranges to identify bioregions and characterize them within each range, and compare the level of division between ranges. Our study area will include the northern Andes, Rocky Mountains, Alps and Himalayas, which together encompass North-South vs. East-West orientations and temperate vs. tropical features. We plan using vegetation plot data corresponding to the Alpine biome and contained in sPlot, and work with presence/absence data. We will employ three different and complementary approaches: generalized dissimilarity modelling (GDM), partitioning around medoids (PAM) and Kohonen self-organising map (SOM), to carry out the bioregionalization analysis so we can test their efficiency on different datasets, and select the most significant results. All techniques rely on the concept of relationship between increasing ecological distance and observed compositional dissimilarity between vegetation plots, and all are considered adequate for vegetation data spread over large areas.
(#18) Global patterns of taxonomical and functional diversity above the treeline
Riccardo Testolin and Fabio Attorre (Sapienza University of Rome, Italy), Borja Jiménez-Alfaro (iDiv - Martin Luther University, Germany)
This project is, together with sPlot #17, part of the “mountain diversity” sPlot studies focusing on different dimensions of plant diversity in the alpine regions of the world. In particular, this study is the first part of a PhD project by Riccardo Testolini in the University La Sapienza, co-supervised by sPlot members Fabio Attorre and Borja Jiménez-Alfaro. We want to use sPlot to compare plant community diversity of alpine areas using vegetation plot data from different continents. Our main aim is to investigate major patterns and drivers of taxonomical and functional diversity in alpine regions worldwide, focusing on alpha- and gamma-diversity. Pre-selected regions with available data are: Alaskan arctic tundra, Rocky alpine mountains, Colombian Páramo, Scandinavian boreal tundra, European Alpine vegetation, oromediterranean Atlas Mountains, Semi-arid Iranian alpine mountains, Pamir plateau and Altai alpine tundra. First, we will explore data quality by filtering vegetation data to a representative number of plots per region, ensuring a relatively equal sampling effort in similar habitats. We will model local plot species richness, functional trait values aggregated in CWMs and functional diversity indices, in response to environmental predictors (mostly climate and soil data) within regions and across regions, using correlative regression analyses (e.g. GLMs). In addition, we will compare regional richness with rarefaction curves, and will use interpolation and extrapolation techniques (e.g. those implemented in iNEXT R package) to estimate species pool sizes. To understand better the observed patterns, we will conduct a climatic and historical characterization of the studied regions. The results will provide a first overview of the global patterns of plant diversity in alpine vegetation, in order to interpret the role of environmental drivers versus regional (historical) differences in shaping alpine communities.
(#19) Toward a mechanistic description of land uses for ecological studies: Building a Vegetation <> Land-use converter for Europe
Anne Mimet (Helmholtz-Zentrum für Umweltforschung (UFZ), Leipzig, Germany; German Institute of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany)
Julia Joswig (German Institute of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany)
Land-uses constitute one of the main threat to biodiversity and ecosystem functions and services in an increasingly anthropized world. By changing the biotic and abiotic conditions, land-uses very strongly change the composition and structure of all plant and animal communities. For plants and low mobile animal species, land-use can have large direct impacts, killing the individuals and/or their offsprings. In addition, land-use very often has strong indirect impacts on species, by changing resource and habitat quality and availability. The filtering processes induce by land-uses on plant communities have been explored during the last 20 years. These studies provide a reliable mechanistic knowledge and understanding of the set of response traits by which land-use filter species and change the functional composition of communities. Meanwhile, our knowledge of how land-use modifies animal communities mainly relies on statistical relationships relating distribution data to land-use descriptions. Compared to plants, the development of a mechanistic understanding of animals’ responses to land-use is complicated by the mostly indirect effects of land-use on animals, via changes in resource and habitat. Building this mechanistic understanding depends on our ability to describe the changes in resources and habitats induce by land-use.
In this project, we aim to build a Vegetation <> Land-use converter for Europe which will offer a translation between land-uses and functional vegetation groups. The converter will use an ecologically meaningful description of land-uses, where land-use is described into three dimensions: the frequency/intensity and the type of land-use and the potential productivity and an appropriate and extensive set of traits. The sPlot data will be used to calibrate the sections of the model related to the responses to natural gradients and to provide the traits data. Land-use / Vegetation data from numerous European data bases and publications will be used to calibrate the sections of the model related to the responses to the land-uses. By providing the needed basis for establishing mechanistic links between land-use and structure of communities, the converter will be useful to project scenarios of biodiversity (plants, animals and ecosystem services). It will also be used in a current Flexpool project to extract more information about past land-use from Holocene pollen data.
(#20) Trait-dependent extinctions across flowering plants in biodiversity hotspots
Renske E. Onstein, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
It is widely recognised that we are entering an extinction event on a scale approaching the past mass extinctions seen in the fossil record. This dramatic loss of biodiversity significantly affects ecosystem services valuable to human well-being. Some places on Earth are most threatened by extinctions due to their exceptionally rapid loss of habitat. These places make up the 36 biodiversity hotspots. Although flowering plants (angiosperms) with a standing diversity of ~250.000 species dominate the vegetation of biodiversity hotspots, our knowledge of angiosperm vulnerability to extinction is lacking. For example, it is well known that extinctions may be selective on functional traits - such as body size in animals, in which larger animals have higher threat of extinction. However, for angiosperms these functional predictors of extinction, or conversely, traits that promote resilience to global change, are unknown. Here, we present a framework to identify traits predictive of extinction threat in angiosperms across space (biodiversity hotspots) and time (the present as well as the geological past) to prioritise functional groups for targeted conservation. The sPlot database will provide the global phylogenetic, distributional, functional trait, that will be coupled with Red List conservation status data for all angiosperms present in the sPlot database. We will use quantitative analytical tools (e.g. structural equation modelling) and phylogenetic comparative methods to analyse these data. First, we will test for trait-driven extinctions across large temporal, spatial and taxonomic scales (i.e. which traits across the sPlots in biodiversity hotspots are associated with IUCN Red List extinction threat status? And have these traits influenced extinction rates in these angiosperms in the geological past?). Second, we aim to identify when, where and why functional traits evolved, and whether patterns of convergence are the result of similar selection pressures across biodiversity hotspots (i.e. does the distribution of functional traits across biodiversity hotspots deviate from a pattern resulting from random evolution of traits on the phylogeny? And if so, is this the result of convergence or in-situ radiations?). This approach maximises our ability to make generalisations. Unravelling the generality of the traits and processes that lead to extinctions is imperative for targeted conservation of vulnerable functional groups across biodiversity hotspots, and across ecosystems more generally.
The research objectives are to:
O1. Identify which functional traits relate to extinction threat across the 36 biodiversity hotspots, and verify whether this is congruent with predicted extinction threat based on evolutionary history;
O2. Quantify the spatial distribution of vulnerable traits in hotspots to identify where (continent), when (time) and why (evolutionary selection pressure) these traits have originated during angiosperm evolution;
O3. Classify the functional groups in need of conservation priority, given their trait-driven extinction threat in hotspots
This is expected to result in at least two publications:
1. Trait-dependent extinctions across flowering plants in biodiversity hotspots;
2. Broad-scale evolutionary convergence of flowering plant traits in biodiversity hotspots.
(#21) Reaching for the sky: Unravelling global patterns and processes to explain convergent evolution of woodiness in angiosperms
Alexander Zizka, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
Phylogenetically derived woodiness, the repeated convergent evolution of woody growth form from herbaceous ancestors, happened hundreds of times during the history of flowering plants, but little is known about its recent geographic prevalence and its impact on evolutionary and ecological success. In this project we will combine occurrences and abundances from sPlot with a novel database on derived woody species and their phylogenetic relationships; and use Bayesian statistical modelling to (1) identify global diversity patterns of derived woody species and determine their correlation with current day climatic factors, (2) compare the environmental niche of derived woody species with their evolutionary sister lineages, and (3) test if the transition towards woodiness is coupled with other characteristic traits into a characteristic syndrome or functional type, in particular traits related to the leaf economics spectrum such as specific leaf area.
(#22) A macroecological survey of intraspecific plant trait variation
Gabriel Walther, Max Planck Institute for Biogeochemistry Jena, Germany.
We conduct a global analysis of intraspecific trait variation (ITV) of plants to investigate their adaptive potential to changing environmental conditions. We assume that species with a higher ITV have a higher adaptive potential to environmental changes. Thus, we want to investigate (i) which traits are especially variable, (ii) which species groups (functional, phylogenetic) show a higher ITV than others and (iii) which variables (e.g. categorical traits, geographical range, abundancy, bioclimatic variables) are most suitable to explain and predict ITV.
We analyse data provided by the TRY database on 14 traits (plant height, specific leaf area, leaf dry matter content, leaf area, leaf thickness, leaf dry mass, leaf fresh mass, leaf density, leaf carbon content (dry mass), leaf phosphorus content (dry mass), leaf nitrogen content (dry mass), leaf chlorophyll content (area), stem diameter, stem specific density). We selected species with at least 25 measurements per trait. From that we calculate a robust version of the coefficient of variation (the CVrobust), which facilitates comparisons of ITV within and across species and traits, for each selected species-trait-combination.
We then compare the CVrobust across all traits to identify traits that show a higher ITV than others. To identify species groups with a higher ITV than others, we analyse the CVrobust of species grouped by categorical traits obtained via TRY (growth form, woodiness, leaf compoundness, leaf phenology, leaf type, photosynthetic pathway, phylogeny). For this, we follow a machine learning based regression approach (boosted regression trees) where the categorical traits are used as predictors to model the ITV (expressed as CVrobust) per trait.
Furthermore, we hypothesize that species with a large distribution (i.e. larger range in geographical and environmental space) show a higher ITV compared to species with a small distribution. We further assume that rare species might have a higher ITV than frequent species. Therefore we want to complement our data by information on species distribution (geographical and environmental space) and species abundance as provided by sPlot.
(#23) Global patterns of plant beta diversity in tree assemblages
Zhiyao Tang, sPlot Consortium Member, Institute of Ecology, Peking University
Biodiversity patterns and their underlying mechanisms have long been focal topics of study for ecologists and biogeographers. However, compared with spatial variation in species richness, ß-diversity, or the dissimilarity of species composition between two or more sites has until recently received limited attention. In a previous, Kraft et al. (2011, Science) found that the global plant ß-diversity is derived from the size of species pool (or γ-diversity in their papers); however, we found that environments per se were important for regulating the elevational and latitudinal gradients of plant patterns ß-diversity in China (Tang et al., 2012, Ecography) . Both papers are limited by the data limitation, with the Kraft paper used only 197 sites across the world, while the Tang paper used only data from China (although contained much more sites). The sPlot dataset provides a unique opportunity to disentangle the influence of species pool and environment per se in regulating the elevational and latitudinal gradients of plant ß-diversity in forests.
There are many methods to calculate ß-diversity. Here we mainly will use two approaches: 1. dissimilarity of species composition for the closely paired plots (to do this, we need to find large number of plot-pairs within limited distance); 2. If the first approach is not applicable, we can use the slope between dissimilarity of species composition and distance of any plot-pairs for each ecoregion. Both have been described in Tuomisto et al. (2010, Ecography).
Tuomisto, H. A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33, 2-22, doi:10.1111/j.1600-0587.2009.05880.x (2010).
(#24) Worldwide niche breadth estimates of beech (Fagus) species
Zhiyao Tang, sPlot Consortium Member, Institute of Ecology, Peking University
Qiong Cai, Department of Ecology, College of Urban and Environmental Sciences, Peking University (PKU) and Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg (MLU)
The genus Fagus is one of the most representative broadleaved deciduous trees in the Northern Hemisphere. Although phylogenetic relationships of different Fagus species have been broadly studied, much less attention has been given to the comparison of their niche breadth, which is expected to differ strongly across species. It is also interesting to see whether two species far in the phylogenetic tree have small overlaps of the niche.
There have been different methods developed to estimate niche width, such as climate-envelope or species co-occurrence-based estimates. We plan to compare the niche widths of Fagus species by two methods: 1) estimates based on species co-occurrence data, using the Fridley approach, and 2) by calculating the climate space occupied by a species, using environmental data such as climate and soil properties and the ‘hypervolume’ and ‘dynamic range boxes’ approaches. The basic idea is that co-occurrence niche and climatic niche are related to each other.
So far, we have obtained a database of Fagus plots in China by extensive field surveys. Combined with that in sPlot, it is possible to estimate the realized niche breadth of different Fagus species worldwide using the methods mentioned above. We aim as well to get a more comprehensive understanding of the relationships among species in the genus Fagus.
(#25) Global variation in fine root traits along climate and soil gradients
Daniel Laughlin, Alexandra Weigelt, Liesje Mommer, Helge Bruelheide (sPlot member) + sRoot consortium
Environmental filtering is a key process in community assembly that sorts species along environmental gradients according to variation in ecophysiological tolerance. However, our current understanding of how traits explain turnover in community composition along environmental gradients at a global scale is limited to aboveground traits of leaves, stems, and seeds. Establishment and survival in plants requires the ability to acquire water and nutrients from the soil, a process that is driven by belowground root traits. Our understanding of the key drivers of community assembly is biased toward aboveground traits and therefore woefully incomplete.
Recently, the sDiv working group sRoot has assembled the largest global database of root traits, including measurements of 4 key functional traits (specific root length, root diameter, root tissue density, and root nitrogen concentration) on >1,000 species. We propose to determine the global environmental drivers of root trait variation by integrating the sRoot database of root traits with the sPlot database of vegetation plots. Climate and soil variables for each plot will be extracted from other available databases. This unprecedented integration of the two largest databases of their kind would facilitate the most powerful statistical analysis to test how root traits vary along climate and soil gradients globally.
We will compute community-weighted means for each root trait on each vegetation plot and regress these on climate and soil factors to test whether these traits vary along these gradients at a global scale. We will also test the adaptive value of root traits by fitting GLMMs to determine whether species occurrence is a function of interactions between root traits and environmental gradients. In other words, we will test whether species occurrence in a given environment depends on their root traits. GLMM’s allow us to incorporate random effects to account for how species occurrence varies along the gradients to isolate the trait-by-environment interaction in the fixed effects, as well as other random factors such as the study from which the root trait data originated. This global scale analysis will substantively advance our understanding of how root traits vary along global environmental gradients.
(#26) Global patterns of leaf carbon, nitrogen and phosphorus stoichiometry in plant communities
Zhiyao Tang, Yanpei Guo, Hongtu Zhang - Institute of Ecology, Peking University
Nitrogen (N) and phosphorus (P) are two elements that limit the funcitoning of ecosystems, e.g., carbon (C) sequestration, especially under future conditions of rising atmospheric CO2, N and P deposition, and global climate change. C constitutes the basic structure of plants and accounts for ca. 50% of plant biomass; N is an essential component of enzymes; and P is an essential element of nucleic acids and membrane lipids. The concentrations of N ([N]) and P ([P]) in plant tissues are also critical in controlling other ecological processes, such as grazing, parasitism, and decomposition. Estimates of N and P concentrations and the relationships between the metabolically active N and P concentrations and photosynthetic capacity are often used to predict the future C sequestration of ecosystems under global changes.
At the global scale, Wright et al. (2004) analyzed patterns of leaf C, N, and P stoichiometry at species level (the Wright paper). However, the species level cannot reflect the patterns and processes at the community level, which depend on the abundance (or biomass) of different species within a community. Within a site, leaf nutrient concentrations could vary by an order of magnitude among species. Over- or under-representation of any species may cause errors when estimating ecosystemlevel features. Therefore, it is necessary to integrate abundance (or relative abundance) across a collection of species to better understand ecosystem processes. Recently, the Tang et al. (2018) (the applicant, Zhiyao Tang) has explored the large scale patterns of N and P concentrations and contents of Chinese biomes based on the community weighted mean (CWM) of all the species occurring in the communities (Tang et al., 2018, PNAS). However, both papers are restricted by the data limitation, as the Wright paper did not integrate the relative abundance (or coverage, or biomass), while the Tang paper only used data from China. The sPlot, together with TRY, provides a unique dataset to explore the community weighted mean (CWM) of the plant stoichiometry of the world.
The CWM and abundance based variance can be easily estimated based on data from the sPlot and the TRY. We will compare the CWM, variance of the N and P among biomes, ecoregions and also correlate the relationship between N and P across the world, and along the environmental gradients.
(#27) Relationship between herbaceous plant species large-scale distribution, small-scale dominance and plant functional traits
Maria Sporbert - Martin-Luther University Halle-Wittenberg, Germany
In this study we investigate which traits are important for species to become widespread at largescale and/or dominant at small-scale. Here, we expect species large-scale distribution to be mainly driven by dispersal traits, while species small-scale dominance to be mainly influenced by growth and performance traits. From previous analysis we know that in some species small-scale dominance seems to be unrelated to both, the position of the occurrence in species geographic range and the predicted climatic suitability of the occurrence. Therefore, we expect that whether a species is generally common or rare is mainly driven by functional traits related to competitive ability (e.g. plant height, SLA).
Our species set consists of 517 vascular plant species, for which species dominance has been measured as plant cover. Species cover values were retrieved from 744,513 vegetation plots from the European Vegetation Archive. Range maps from the Chorological Database Halle were used to capture species’ geographic ranges and to derive species’ climatic niches. The distance of each plot to the range centre was calculated per species for each occurrence in the geographic and the climatic space. Species large-scale climatic suitability was predicted from species distribution models (SDMs).
We will apply regression tree analysis to test for the relationship between species’ range size, niche size, species’ dominance in vegetation plots, the position and climatic suitability of the plot occurrence with function traits. Furthermore, we will use principal component analysis to illustrate the relationships.
(#28) Functional composition of a species’ native vs invaded range: A global analysis using plant functional traits
Hamada E. Ali, Botany Department, Faculty of Science, Suez Canal University,
Christine Römermann, Institute of Ecology and Evolution, Friedrich-Schiller-Universität Jena,
Invasive species influence the economic or environmental functioning in many ecosystems worldwide, these effects maximized due to the current changes in climate and land-use. Many studies have shown that, among others, invasive species are especially successful in their new range because they are free of competition. However, whether the functional composition of the new, invaded habitat also plays a role for invasion success is less clear. To find reasons why species become successful invaders, it is important to analyse the difference between a species’ native habitat and the new habitat which it successfully invaded in terms of functional compositions. Asking such a question will help to assess the role the habitat composition of the invaded areas play in successful invasion.
The project aims at comparing the functional composition of the invaded vs. the native habitat using both above and belowground plant functional traits and how the functional composition of the invasive vs. native communities will differ based on the functional composition of the invasive species itself. We aim to combine the distribution data from sPlot database, plant functional traits data from TRY database, and climate data (temperature and precipitation) from WorldClim Global Climate Data (http://www.worldclim.org/bioclim) in order to detect global patterns of functional composition of native vs. invasive habitat. Global Invasive Species Database (http://issg.org/database/welcome/) will be used to define which species are invasive in each habitat. Moreover, we will use these data to forecast the future distribution of invasive species under future global change scenarios.
(#29) Do dominant and non-dominant species follow the same assembly rules?
Carlos Alberto Arnillas. University of Toronto – Scarborough
Risto Virtanen. University of Oulu. Ecology and Genetics
Marta Carboni. Department of Sciences Università Roma
Community assembly mechanisms are often considered to work on the whole community in similar ways: either a community is strongly affected by environmental filters (causing coexisting species to be similar) or it is affected by interspecific interactions (e.g., competition, facilitation, which might lead to greater differentiation among species) [1–4]. However, recent studies suggest that dominant and non-dominant species do not only differ in their success (or not) under local conditions, but also in the traits that allow them to survive as dominants or non-dominant species[5–7].
In addition, recent studies have shown evidence for an asymmetry in community assembly mechanisms[8–13]. For instance, dominant plants were found to be phylogenetically under-dispersed, while non-dominants were either randomly dispersed or over-dispersed. Assuming that phylogenetic relatedness is a proxy of species trait similarity [see 14 for detailed assumptions], underdispersion of dominant species suggests that environmental filtering is more important than interspecific interactions in organizing the dominant species, while non-dominant phylogenetic patterns indicate that non-dominants are either equally affected by both mechanisms or more strongly affected by interspecific interactions. The evidence for such dominance asymmetry is however local or regional [8–12], or global but constrained to herbaceous systems.
We aim to assess the generality of dominance asymmetry at a global scale and across multiple plant community types. Following Arnillas et al., we will divide the community in at least three dominance groups using height and cover, or proxies thereof. Then, we will compare phylogenetic and trait dispersal patterns between the different groups. If the dominance asymmetry holds true across multiple community types, we expect to find the dominant group of species less dispersed than the non-dominant plants at each site, regardless of the local conditions. We will identify trait syndromes characteristic of dominant and non-dominant species and assess their phylogenetic signature. We expect to be able to link differences in assembly mechanisms to differences in previously identified reproductive and non-reproductive[15,16] syndromes characteristic of dominant and nondominant species.
1. Data cleaning and filtering: identification of the plots that have all the data needed to perform the analysis (cover, phylogeny, traits; ideally traits locally measured, height, and coordinates)
2. Definition of a set of operative definitions of dominance (e.g., symmetric partition by number of species, symmetric partition by cover, inflection point, inclusion or not of multiple growth forms)
3. Measure phylogenetic and trait dispersal in each dominance group and estimate the difference between them. Compare results using different definitions of dominance.
4. Test global trends in dominance asymmetry: phylogenetic signal of dominance/nondominance; existence or not of trait syndromes; are trends related to climatic or other environmental gradients.
1 Webb, C.O. (2000) Exploring the phylogenetic structure of ecological communities: an example for rain forest trees. Am. Nat. 156, 145–155
2 Cadotte, M.W. and Davies, T.J. (2016) Phylogenies in ecology: a guide to concepts and methods, Princeton University Press.
3 Mayfield, M.M. and Levine, J.M. (2010) Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093
4 Kraft, N.J. et al. (2008) Functional traits and niche-based tree community assembly in an Amazonian forest. Science 322, 580–582
5 Avolio, M.L. et al. (2019) Demystifying dominant species. New Phytol. 223, 1106–1126
6 Mariotte, P. (2014) Do subordinate species punch above their weight? Evidence from above- and below-ground. New Phytol. 203, 16–21
7 Vermeij, G.J. and Grosberg, R.K. (2018) Rarity and persistence. Ecol. Lett. 21, 3–8
8 Arnillas, C.A. and Cadotte, M.W. (2019) Experimental dominant plant removal results in contrasting assembly for dominant and non-dominant plants. Ecol. Lett. 22, 1233–1242
9 Lennon, J.J. et al. (2011) Are richness patterns of common and rare species equally well explained by environmental variables? Ecography 34, 529–539
10 Chai, Y. et al. (2016) Patterns of taxonomic, phylogenetic diversity during a long-term succession of forest on the Loess Plateau, China: insights into assembly process. Sci. Rep. 6, 27087
11 Norden, N. et al. (2017) Opposing mechanisms affect taxonomic convergence between tree assemblages during tropical forest succession. Ecol. Lett. 20, 1448–1458
12 Ricotta, C. et al. (2008) Common species have lower taxonomic diversity Evidence from the urban floras of Brussels and Rome. Divers. Distrib. 14, 530–537
13 Arnillas, C.A. et al. (submitted) Opposing community assembly patterns for dominant and nondominant plant species in herbaceous ecosystems globally. J. Ecol.
14 Gerhold, P. et al. (2015) Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better). Funct. Ecol. 29, 600–614
15 Grime, J.P. (1974) Vegetation classification by reference to strategies. Nature 250, 26–31
16 Grubb, P. (1998) A reassessment of the strategies of plants which cope with shortages of resources. Perspect. Plant Ecol. Evol. Syst. 1, 3–31
(#30) Estimating dark diversity by using species co-occurrences: refining methods for large vegetation plot databases
Meelis Pärtel, University of Tartu, Estonia
Biodiversity at any given spatial scale depends on diversity at larger scales. However, not all regional diversity is relevant for a local site; many organisms in the region simply have different habitat preferences than those available in the local site. An operational link from locally observed (alpha) diversity to larger spatial scales can be established through dark diversity: the set of species that are locally absent at the time of sampling while being both present in the region and ecologically suitable for the local study site. With dark diversity we are better equipped to understand and protect ecosystems.
Dark diversity cannot be measured but it can be estimated. A promising dark diversity estimation method is based on species co-occurrences: if we know which species typically co-occur, we can be confident that they have similar habitat requirements. As a result, we can use observed taxa to estimate occurrence likelihood for absent taxa. During the big data era, information on species co-occurrences is growing rapidly and sPlot is the largest plant co-occurrence data source. To use sPlot for dark diversity estimations globally requires algorithms to distinguish signal from noise by filtering initial data-sets according to study region, sampled scale or other parameters. Comparison with standardized sampling can help to work out ecoinformatic routines to exploit such big data sources.
In September 2018 we launched DarkDivNet (www.bit.do/DarkDivNet), a global standardized sampling network to specifically study the dark diversity of plant communities. Interested scientists all over the world can join the network. As of September 2019 we have >150 potential study areas and many of them have already been sampled.
With this project we aim to refine the methods for estimating plant dark diversity based on recorded species and co-occurrences in sPlot. We will compare the dark diversity estimated from local sampling in DarkDivNet to the ecoinformatic co-occurrence approach in sPlot. We will explore which filters are needed to get comparable results with local sampling in DarkDivNet (e.g. subsetting sPlot according to geographical and climatic distance, community similarity etc.). We will examine how consistent are dark diversity estimations across various regions, ecosystem types and spatial scales.