Lead author:

Oliver Purschke, iDiv – Martin Luther University, Germany

Project outline:

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.

Lead author:

Brody Sandel, Santa Clara University, USA

Project outline:

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.

Lead author:

Oliver Purschke, iDiv – Martin Luther University, Germany

Lead author:

Brody Sandel, Santa Clara University, USA

Project outline:

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.

References

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.

Lead author:

Franziska Schrodt, University of Nottingham, UK

Project outline:

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.

Lead authors:

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)

Project outline:

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.

Lead authors:

Jose M Serra-Diaz (Aarhus University, Denmark), Franziska Schrodt (University of Nottingham, UK), Jens-C. Svenning (Aarhus University, Denmark)

Project outline:

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.

Lead authors:

Gwendolyn Peyre (University of the Andes, Colombia)

Project outline:

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.

Lead authors:

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)

Project outline:

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.

Lead authors:

Renske E. Onstein, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig

Project outline:

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.

Lead authors:

Alexander Zizka, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig

Project outline:

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.

Lead authors:

Gabriel Walther, Max Planck Institute for Biogeochemistry Jena, Germany.

Project outline:

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.

Lead authors:

Zhiyao Tang, sPlot Consortium Member, Institute of Ecology, Peking University

Project outline:

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).

Lead authors:

Zhiyao Tang, Yanpei Guo, Hongtu Zhang – Institute of Ecology, Peking University

Project outline:

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.

Lead authors:

Keiichi Fukaya & Yasuhiro Kubota – Lab. Biodiversity & Conservation Biogeography, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan.

Project outline:

Understanding numbers of organisms and their taxonomic diversity on the planet is a fundamental issue in evolutionary biology ever since Darwin. Nonetheless, species pool properties of biodiversity remain unclear yet.  A solution to these problems is to obtain data on species abundance distribution (SAD) over a global extent that directly informs size and biodiversity of the metacommunity (i.e., species pool). However, such data is unrealistic beyond our observation ability. In this view, Fukaya et al. (2020) developed a novel hierarchical model that estimates SADs over a large geographic extent, which we call as “macroscale SADs”. The model integrates spatially replicated multispecies detection-nondetection observations and information on the geographical distribution of plant species. Fukaya et al. (2020) applied the model to the vegetation dataset in the East Asian islands, Japan, and successfully estimated absolute size of communities and species abundance for 1,248 woody plants at a 10 km grid square resolution. Notably, the estimated macroscale SADs enabled us to identify fundamental macroevolutionary properties of metacommunity, such as the rate of speciation and the average lifespan of species (Ricklefs 2003), based on the unified neutral theory (UNTB; Hubbell 2001) with protracted speciation (Rosindell et al. 2010, Haegeman & Etienne 2017). Their accurate estimates are critically informative for both basic and applied field of ecology and biogeography; the proposed approach improves the identification of the species pool (γ diversity) along geographical gradients, facilitating our understanding of the origin and maintenance of biodiversity from an evolutionary perspective, the evaluation of the role of macroevolutionary processes in community assembly, and the design of the protected areas network to capture metacommunity/metapopulation structures across a large geographic extent. In this project, we apply the macro-scale SAD model to vegetation data across countries and regions in the sPlot and estimate global-scale abundance for plant species. Then, we reveal macroevolutionary parameters (speciation rate and species lifespan) related to the origin and maintenance of plant biodiversity on the planet. This analysis of global-scale SADs highlights region-specific historical diversification processes which shaped large-scale plant diversity patterns relevant to abiotic characteristics, such as paleogeography and paleoclimate.

Lead author:

Benton Taylor, Assistant Professor, Harvard University

Project outline:

Nitrogen is an essential limiting nutrient crucial for plant growth. Biological nitrogen fixation (BNF) is the primary process through which atmospheric nitrogen is converted to its biologically-available form. Some nitrogen-fixing bacteria are free-living in soil, while others can form symbiosis with plants and lichen of various growth forms (i.e., lichen, moss, herbaceous plants, shrubs, and trees). Biological nitrogen fixation can be a crucial, if not dominant, input of nitrogen wherever nitrogen-fixers are abundant. However, despite the recognized importance of BNF, we lack a comprehensive understanding of the global patterns of nitrogen-fixer abundances and the mechanisms underlying these abundance patterns. Why do nitrogen fixers not become sufficiently abundant to relieve nitrogen limitation at high latitudes? What drives the general shift from tree to shrub to moss/lichen nitrogen fixation as one moves from the equator toward the poles? We aim to utilize sPlot’s global vegetation database to establish the global distribution patterns of multiple growth forms of nitrogen-fixers and how these abundances change across varied biomes. We are interested in both the absolute abundance of nitrogen fixers, critical for understanding ecosystem-level nitrogen inputs, and the relative abundance of nitrogen fixers, critical for answering fundamental questions on the competition between nitrogen fixers and comparable non-fixing organisms. We then seek to use state-of-the-art statistical modeling to evaluate how climate factors and functional traits together drive nitrogen-fixer distribution patterns. Previous efforts to understand broad patterns of nitrogen-fixer abundances have focused either on specific regions (e.g. North America) or specific nitrogen-fixer growth forms (e.g. nitrogen-fixing trees) and have failed to fully uncover the mechanisms driving these abundance patterns. Our proposed research will, for the first time, provide a global map of nitrogen-fixer distributions of various growth forms (lichen, moss, herbs, shrubs, and trees) and elucidate the ecological drivers of these patterns. This novel understanding will make important contributions to the development of mechanistic theory on the regulation of nitrogen inputs and global patterns of nitrogen limitation.

Lead author:

Eric Garnier, CNRS, Centre d’Ecologie Fonctionnelle et Evolutive (UMR 5175), Montpellier Cedex 5, France

Project outline:

Pollen is a powerful proxy to reconstruct both vegetation and climate variation throughout time. Among other micro-fossil studies, pollen is one of the most accurate proxy due to the stronger modelling method used to reconstruct paleoclimate and paleoenvironment: transfer function for climate (Chevalier et al. 2020) and biomization (Prentice et al. 1996; Harrison et al. 2010) for biome reconstructions. In both cases, the relation between pollen and environmental forcing relies on uniformitarianism assumption: the pollen response to vegetation/climate is the same today as in the past (Jackson et Williams 2004). That is why the studies on modern analogues is necessary. Especially, the main problem is due to the difference between pollen and plant taxa, as pollen is not  always identifiable at the species level.
Arid biomes such as desert, steppes and shrublands need a special focus to better understand the mechanism controlling their distribution through space and time (Lu et al. 2018), in particular because a spreading of these biomes is expected in the future (Huang et al. 2016; Park et al. 2018). In the Arid Central Asia (ACA, area covering the dry continental Asia from Iran to Mongolia), the pollen calibration work is necessary (Tian et al. 2014). Indeed, a wide diversity of open lands exist in ACA and is controlled by very low annual precipitation. A number of other, still unknown, bioclimatic parameters control this biome and ecotone diversity. Especially in the past, modelling the spatial variation between cold steppes and warm steppes is a complex task(Tarasov et al. 1998).
The paleo pollen signal recorded in peat or lake sediment is commonly used as predictor for past vegetation reconstruction. In the present, a calibration study between surface pollen and surrounding vegetation is necessary to quantify the biases recorded by the pollen signal. The sPlot database will allow us to test whether surface pollen samples can be used as a proxy of extant vegetation by comparing vegetation plots and pollen fractional abundances at the ACA scale. The blue dots on Figure 1 shows the pollen surface samples available in ACA. We require vegetation plot from the same area to estimate the average biases in the pollen rain in ACA. Especially, the aim of this project is to better constraint the link between pollen rain and vegetation/climate in arid area using the powerful tool of trait-based ecology. We will calculate the community-weighted means (CWM) of traits (Garnier et al. 2004) applied on vegetation plot and on modern pollen rain and compare them to assess the reliability of pollen as past trait records. After that, we will try to improve the plant functional type (PFT) classification used in vegetation reconstruction (Harrison et al. 2010) and apply it on past pollen records to understand which trait variation drove the vegetation shift throughout time in Lake Ayrag (Mongolia).

References:

Chevalier, Manuel, Basil A.S. Davis, Oliver Heiri, Heikki Seppä, Brian M. Chase, Konrad Gajewski, Terri Lacourse, et al. 2020. « Pollen-Based Climate Reconstruction Techniques for Late Quaternary Studies ». Earth-Science Reviews 210 (novembre): 103384. doi.org/10.1016/j.earscirev.2020.103384.
Garnier, Eric, Jacques Cortez, Georges Billès, Marie-Laure Navas, Catherine Roumet, Max Debussche, Gérard Laurent, et al. 2004. « PLANT FUNCTIONAL MARKERS CAPTURE ECOSYSTEM PROPERTIES DURING SECONDARY SUCCESSION ». Ecology 85 (9): 2630-37. doi.org/10.1890/03-0799.
Harrison, Sandy P., I. Colin Prentice, Doris Barboni, Karen E. Kohfeld, Jian Ni, et Jean-Pierre Sutra. 2010. « Ecophysiological and bioclimatic foundations for a global plant functional classification ». Journal of vegetation Science 21 (2): 300-317. doi.org/10.1111/j.1654-1103.2009.01144.x.
Huang, Jianping, Haipeng Yu, Xiaodan Guan, Guoyin Wang, et Ruixia Guo. 2016. « Accelerated dryland expansion under climate change ». Nature Climate Change 6 (2): 166-71. doi.org/10/gbn62d.
Jackson, Stephen T., et John W. Williams. 2004. « Modern analogs in Quaternary paleoecology: here today, gone yesterday, gone tomorrow? » Annual Review of Earth and Planetary Sciences 32. doi.org/10/fwjqs3.
Lu, Kai-Qing, Gan Xie, Min Li, Jin-Feng Li, Anjali Trivedi, David K. Ferguson, Yi-Feng Yao, et Yu-Fei Wang. 2018. « Pollen spectrum, a cornerstone for tracing the evolution of the eastern central Asian desert ». Quaternary Science Reviews 186: 111-22. doi.org/10/gdjvqw.
Park, Chang-Eui, Su-Jong Jeong, Manoj Joshi, Timothy J. Osborn, Chang-Hoi Ho, Shilong Piao, Deliang Chen, Junguo Liu, Hong Yang, et Hoonyoung Park. 2018. « Keeping global warming within 1.5° C constrains emergence of aridification ». Nature Climate Change 8 (1): 70.

Lead author:

Qiong Cai  & Zhiyao Tang – Institute of Ecology, College of Urban and Environmental Sciences, Peking University (PKU)

Project outline:

Global patterns of local species diversity are believed to be affected by both regional (e.g., diversification, extinction and dispersal) and local (e.g., adaptions to local environments and biotic interactions) processes. Nonetheless, their relative importance remains elusive. In addition, the unique historical and biogeographic features of different regions could influence regional processes, which in turn affect local species diversity. Fagus-dominated forests, one of the most representative temperate forests in the Northern Hemisphere, are disjointedly distributed in three continents, providing an ideal system to study the relative importance of regional and local processes on local species diversity patterns.

There are different methods to test the relative roles of regional and local effects on local species richness. One widely accepted method is to examine the relationship between regional and local species richness. Another method is to directly disentangle the relative importance of biogeographic region, contemporary climate and other regional or local factors, by methods such as generalized linear models, stepwise regression and random forest analyses.

Combining a database of Fagus plots in China by extensive field surveys with that in sPlot, it is possible to get one of the largest datasets of beech forests ever assembled. Furtherly, with the regional species pool information and climate data accordingly, we could explore the global pattern of local species diversity of beech forests as well as the relative roles of regional and local effects on it.

Lead author:

Dario Ciaramella, Italy

Project outline:

Coniferous forests are distributed all over the world within several ecoregions. Despite being considered as a monotonous formation of homogeneous physiognomy and floristic composition, they are complex mosaics of different plant communities with a wide range of climatic and topographic conditions, as well as with an old biogeographic history. It has been hypothesized that all the present-day coniferous forests of the Northern Hemisphere are derived from the Arcto-Tertiary Geoflora and were later modified and splitted in many isolated occurrences by continental cooling and drying trends, mountain uplifting, and glaciation during and after the LGM. In contrast, the coniferous forests of the Southern Hemisphere are characterised by substantially different floristic affinities with those of Paleartic and Nearctic latitudes, and are derived from a Gondwana flora. The fact that most of the genera and closely related species are common to a number of fragmented areas suggests a common history of evolution of these forests. For instance, the floristic core of temperate high-montane coniferous forests shows strong similarities to boreal forests, at least in the lower strata with mosses and lichens almost all cosmopolitan. Recent studies pointed out the importance of the evolutionary and glacial-postglacial history of the known spatial patterns of forest species richness at the global scale, beyond climatic, edaphic and topographic drivers. At the same time, both ecology and history might be imprinted on the distribution of life forms in different regions, but relevant information is still unsatisfactory.

With the data provided by sPlot, we intend to examine the complete vascular plant communities of these forests, which will be deconstructed in different layers and life-forms, to shed light on the past events which affected the extant forest composition. We will use variables characterising the current climate, LGM climate, elevation and terrain ruggedness. After delimiting discrete geographic regions encompassing the variability of these forests, we will try to find informative (dis-)similarities between these areas. Specifically, we will ask the following questions:
i) Do life-forms and forest layers respond similarly to environmental factors at the global scale? What are the relative effects of historical and current climate on the life-form composition of these forests?
ii) Is distribution of the tree, shrub and floor layers species richness similar in the defined discrete regions? For instance, the low richness of tree species in the European nemoral areas is attributed to their severe elimination during Quaternary glacial periods. Do the other forest components show similar patterns?
iii) How are the affinities between the understory and tree floras in terms of taxonomy and phylogeny? Can we expect these similarities to be high to detect a common history along the Tertiary for both groups of plants?

To answer these questions, after data filtering, stratification and resampling, we will employ generalized linear mixed models (GLMMs) and redundancy analysis (RDA) with variance partitioning to predict the effects of environmental variables on species richness and the variation in life forms along abiotic gradients, respectively. We will calculate the turnover component of spatial taxonomic (TBDturn) and phylogenetic (PBDturn) beta-diversity following Simpson’s index of dissimilarity to explore the relationships between the biotic dissimilarities and the environmental drivers at each taxonomic rank of each layer group, and compare them among regions.

Lead author:

Agathe Toumoulin, Masaryk University, Czech Republic

Project outline:

Fossil leaf traits are commonly used to reconstruct paleoclimate and compare fossil localities. Several uni- and multivariate methods exist, based on classic functional traits (e.g. leaf area and leaf mass per area) but that also consider other characters for some of which an empirical relationship with climate/environment was sometimes evidenced but the function for the individual remains debated or unknown (e.g., type of margins, length to width ratio, leaf base and apex forms). The most known method is the Climate Analysis Multivariate Program (CLAMP) which considers 36 leaf characters and provides 11 paleoclimate estimates (e.g., mean annual temperatures, precipitations of the three driest months, length of the growing season), using canonical correspondence analysis and its own modern trait-climate datasets. However, few of the response traits used in today’s vegetation studies are visible on fossil leaves and it is not certain that all visible characters are functional traits. Recently, Traiser et al. (2018) created a database dedicated to fossil leaf traits, Morphyll. It currently provides measurements for 22 paleofloras of different European (mostly German) localities and Paleocene to Miocene in age (i.e., spanning the last 60 to 20 million years) with trait measurements for ~ 6000 leaves. Some of the traits present in this database are also present in TRY (i.e., leaf area, SLA (LMA may be calculated from fossils), leaf margin type, leaf shape, lamina length and width), which make comparison to extant vegetation possible.
The combination of TRY, sPlot and Morphyll data provides us with the opportunity to describe the distribution of some of the traits visible on fossils within extant bioclimatic zones, to investigate their possible sensitivity to environmental parameters and create a new framework for analysis of deep-time environments.
The project will follow these steps: (1) modern and fossil trait data will be cleaned and homogenized to be comparable. Within each fossil assemblage, trait mean values will be calculated per taxa (nb: we may not use Community Weighted Mean because the abundance of taxa in the fossil assemblage is not the same as that of the community at the time). (2) sPlot will be used to create a functional classification of vegetation units based on similar distributions of the considered traits (i.e., those visible on the fossils, cluster analysis). (3) fossil assemblages will be associated with modern functional analogue classes (using similarity techniques), from which environmental and climate parameters (e.g., temperatures, precipitations, soil characteristics) will be extracted. Finally, (4) the results obtained with this new approach will be compared to previous paleoecological analysis made on these floras (e.g., including CLAMP results and analyses based on the taxonomic composition of the assemblages).