Ülo niinemets’ lab mechanisms of environmental adaptation in plants from molecular to global scales o gosh corpus christi

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Which plant species grow where, alongside which others – and why? The diversity of global vegetation can be described based on only a few traits from each species. This has been revealed by a research team led by Martin Luther University Halle-Wittenberg (MLU) and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. In a new study published in the scientific journal Nature Ecology & Evolution, they present the world’s first global vegetation database which contains over 1.1 million complete lists of plant species sampled across all Earth’s ecosystems. The database could help better predict the consequences of global climate change.

All plants face the same challenges, whether they are small grasses, shrubs or trees. “For example, they have to find an efficient way to conduct photosynthesis in order to obtain the energy they need. gas in texas At the same time, they compete with neighbouring plants for limited resources in the soil, like water and nutrients,” explains Professor Helge Bruelheide from the Institute of Biology / Geobotany at MLU and co-director of iDiv.

Currently around 390,000 plant species are known to science. Over time, each species has developed very different traits in reaction to external factors at their location. These include the plant’s size, the thickness and the chemical constituents of its leaves. These properties are also referred to as functional plant traits. “These functional traits directly influence a plant’s ecosystem function, such as how much biomass it produces or how much carbon dioxide it absorbs from the air,” says Bruelheide.

Until now, researchers have primarily investigated different combinations of these functional traits from the perspective of individual plant species. “In reality, however, plant species rarely occur alone; plants live in communities,” says Bruelheide. Therefore, so-called vegetation databases are needed that contain data on all of the plants growing at a specific location. The German Vegetation Reference Database is an example. electricity was invented in what year It is managed at MLU by Dr. Ute Jandt, a member of Helge Bruelheide’s research group. It contains about data on about 200,000 vegetation plots from published and unpublished vegetation studies. Similar databases exist, or are being compiled, in many other countries.

Up until now there has been no database of databases, to compile and harmonize all these different datasets. As a result, the “sPlot” initiative was launched at the iDiv research centre to develop and set up the first global vegetation database, unifying and merging the existing datasets. “sPlot” currently contains more than 1.1 million vegetation lists from every continent, collected over the past decades by hundreds of researchers from all over the world. “Each point in our database is a real place with precise coordinates and information about all the plant species that co-exist there,” explains Bruelheide.

The research group combined this massive dataset with the world’s largest database for plant traits called “TRY” which is also an iDiv database platform. “It has enabled us to answer questions that nobody has been able to tackle before,” Bruelheide continues. The research tested, for instance, to what extent global factors influence the functional traits of plant communities. Contrary to current opinion, they found that temperature and precipitation play a relatively limited role. “Surprisingly, these two macro-factors are not so important. Our analysis shows, for example, that plant communities are not consistently characterised by thinner leaves as the temperature increases – from the Arctic to the tropical rainforest,” says Bruelheide. Instead the researchers found a close tie between climate variables and the phosphorus supply in the leaves, reflected in the ratio between nitrogen and phosphorus content in the leaf, which is an indicator of plants’ nutritional status. For example, the longer the vegetation period, the lower the phosphorus supply – which also affects leaf thickness. Local land use and the interaction of different plants at a specific location have a much greater impact on the functional traits of plant communities. According to Bruelheide, these findings show that future calculations of plant production in a region cannot only be determined on the basis of simplistic temperature-precipitation models.

The study published in Nature Ecology & Evolution is the first of a series of upcoming papers by the “sPlot” consortium. electricity questions for class 10 Being available on request to other scientists, the “sPlot” database is disclosing unprecedented opportunities to tackle numerous biodiversity questions at the global scale, including the issues pertaining to the distribution of non-native plant species and the similarities and differences of plant communities across world regions.

Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. gas 87 89 91 Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. gas stoichiometry lab The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson’s correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.

You know the saying ‘it takes a whole village to raise one child…’, the same could be true of writing a scientific paper. Sometimes the most exciting scientific questions can only be answered with huge amounts of data, and putting together those data requires a very big team of collaborators. Our study of functional trait change across the tundra biome, published this week in Nature, is a perfect example of the kinds of big-scale questions we can explore when 130 scientists from around the world work together.

The Arctic and alpine tundra are the most rapidly warming parts of the planet, but until recently plant trait data across this vast temperature-limited biome were lacking from global analyses. arkansas gas and oil commission As a result, we had very little understanding of how climate shapes patterns in plant functional traits over space nor the speed with which traits are changing over time at the tundra biome scale. Plant traits are a critical link between vegetation change and ecosystem functions such as carbon storage and surface energy exchange. To quantify climate feedbacks, we need to understand climate-trait relationships.

Taller species like this Salix arctica (Arctic willow) are increasing in the rapidly warming tundra biome, leading to an increase in the height of tundra plant communities. Other key plant functional traits have not responded to warming despite strong relationships between climate and traits across spatial temperature gradients. Photo credit: Anne D. Bjorkman

Our study found that although many traits of the vegetation vary with climate over space, only community-level plant height has been increasing rapidly over time. Much of the community-level change we observed was due to new species entering long-term monitoring plots, rather than just changes in the abundance of resident species. For other plant traits, such as specific leaf area and leaf nitrogen content, soil moisture played a strong role moderating the strength and direction of temperature-trait relationships. Our results help inform projections of the rate at which tundra ecosystems will responding to warming, and thus contribute to global climate feedbacks.

On Qikiqtaruk – Herschel Island, we have observed a localized expansion of the grass species Alopecurus alpinus from the wider species pool (pictured here). o goshi technique This species was originally absent from the long-term plots and has increased the canopy height of the plant community more than four-fold over the past 20 years. Photo credit: Gergana N. Daskalova

Answering biome-scale questions about how tundra plant traits are responding to climate change requires information on changes in community composition at sites across the tundra biome combined with multiple trait observations for nearly every tundra species. Building such massive datasets requires many, many collaborators. This is what we set out to do by forming what we affectionately call the Tundra Trait Team through the sTundra working group of the German Centre for Integrative Biodiversity Research (iDiv). electricity outage chicago The Tundra Trait Team is a collaboration of over 100 scientists from dozens of countries all around the world, all of whom have collected data on the traits of tundra plant species. Combined with data contributed by members of the already-established TRY trait database our study includes exactly 56,048 trait records and community composition observations from 1,520 plots at 117 sites recorded across three decades as a part of the International Tundra Experiment and related efforts.

The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature–trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.