Lead author:

Helge Bruelheide, iDiv – Martin Luther University, Germany

Project outline:

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.

Publications:

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.

doi: 10.1038/s41559-018-0699-8

Lead author:

Jonathan Lenoir, Université de Picardie Jules Verne, France

Project outline:

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.

Publications:

Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E.A., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Alvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., de Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El-Sheikh, M.A.E.-R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán-de-Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J.-A., Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas-Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J.-C., Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H.-F., Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M. & Bruelheide, H. (2021) sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots. Global Ecology and Biogeography, 30, 1740-1764.

Lead author:

Tiffany Knight, iDiv – Martin Luther University Germany

Masha T. van der Sande – Wageningen University & Research, Wageningen, The Netherlands

Project outline:

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.

Publications:

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

doi:10.1111/geb.13027

Lead author:

Valério D. Pillar, UFRGS, Brazil

Project outline:

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[1], and percentage change (positive or negative)[2] 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.

[1] Isbell, F. et al. (2015) Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526, 574–577.

[2] 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.

Publication: Engel, T., Bruelheide, H., Hoss, D., Sabatini, F. M., Altman, J., Arfin-Khan, M. A. S., Bergmeier, E., Černý, T., Chytrý, M., Dainese, M., Dengler, J., Dolezal, J., Field, R., Fischer, F. M., Huygens, D., Jandt, U., Jansen, F., Jentsch, A., Karger, D. N., Kattge, J., Lenoir, J., Lens, F., Loos, J., Niinemets, Ü., Overbeck, G. E., Ozinga, W. A., Penuelas, J., Peyre, G., Phillips, O., Reich, P. B., Römermann, C., Sandel, B., Schmidt, M., Schrodt, F., Velez-Martin, E., Violle, C., Pillar, V. (2023). Traits of dominant plant species drive normalized difference vegetation index in grasslands globally. Global Ecology and Biogeography. 32(5):695–706.

https://doi.org/10.1111/geb.13644

Lead author:

Michael Kessler, University of Zurich, Switzerland

Project outline:

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).

Publications

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

https://doi.org/10.1111/jbi.13782

Lead author:

Francesco Sabatini, University of Bologna, Italy

Project outline:

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.

Publication:

Sabatini, F.M., Jiménez-Alfaro, B., Jandt, U., Chytrý, M., Field, R., Kessler, M., Lenoir, J., Schrodt, F., Wiser, S.K., Arfin Khan, M.A.S., Attorre, F., Cayuela, L., De Sanctis, M., Dengler, J., Haider, S., Hatim, M.Z., Indreica, A., Jansen, F., Pauchard, A., Peet, R.K., Petřík, P., Pillar, V.D., Sandel, B., Schmidt, M., Tang, Z., van Bodegom, P., Vassilev, K., Violle, C., Alvarez-Davila, E., Davidar, P., Dolezal, J., Hérault, B., Galán-de-Mera, A., Jiménez, J., Kambach, S., Kepfer-Rojas, S., Kreft, H., Lezama, F., Linares-Palomino, R., Monteagudo Mendoza, A., N’Dja, J.K., Phillips, O.L., Rivas-Torres, G., Sklenář, P., Speziale, K., Strohbach, B.J., Vásquez Martínez, R., Wang, H.-F., Wesche, K. & Bruelheide, H. (2022) Global patterns of vascular plant alpha diversity. Nature Communications, 13, 4683. (article online)