Plant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystem-level processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties. We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.

/pdf/new-handbook-for-standardised-measurement-of-plant-5aljxhhg5y.pdf

New handbook for standardised measurement of plant functional traits worldwide

Human production of food and energy is the dominant continental process that breaks the triple bond in molecular nitrogen (N2) and creates reactive nitrogen (Nr) species. Circulation of anthropogenic Nr in Earth’s atmosphere, hydrosphere, and biosphere has a wide variety of consequences, which are magnified with time as Nr moves along its biogeochemical pathway. The same atom of Nr can cause multiple effects in the atmosphere, in terrestrial ecosystems, in freshwater and marine systems, and on human health. We call this sequence of effects the nitrogen cascade. As the cascade progresses, the origin of Nr becomes unimportant. Reactive nitrogen does not cascade at the same rate through all environmental systems; some systems have the ability to accumulate Nr, which leads to lag times in the continuation of the cascade. These lags slow the cascade and result in Nr accumulation in certain reservoirs, which in turn can enhance the effects of Nr on that environment. The only way to eliminate Nr accumul...

http://bioeeos660-f12-bowen.wikispaces.umb.edu/file/view/Galloway+et+al.+2003.pdf

The Nitrogen Cascade

Summary 1. The leaf economics spectrum (LES) provides a useful framework for examining species strategies as shaped by their evolutionary history. However, that spectrum, as originally described, involved only two key resources (carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether the economics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entire plants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selection along trait trade-off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uniformly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and all resources. 2. Evidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related to water as well as carbon and nutrients. These apply generally within and across scales (within and across communities, climate zones, biomes and lineages). 3. There are linkages across organs and coupling among resources, resulting in an integrated whole-plant economics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span and high rates of resource acquisition and flux at organ and individual scales. The reverse is true for species with the slow strategy. Different traits may be important in different conditions, but as being fast in one respect generally requires being fast in others, being fast or slow is a general feature of species. 4. Economic traits influence performance and fitness consistent with trait-based theory about underlying adaptive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus help explain the distribution of species and the assembly of communities across light, water and nutrient gradients. Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or primary productivity, and slow traits with slow process rates. 5. Synthesis. Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stems and roots is a key feature of the plant universe and helps to explain individual ecological strategies, community assembly processes and the functioning of ecosystems.

/pdf/the-world-wide-fast-slow-plant-economics-spectrum-a-traits-2qqc6fx0m7.pdf

The world‐wide ‘fast–slow’ plant economics spectrum: a traits manifesto

Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-2486.2011.02451.x

TRY - a global database of plant traits

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

/pdf/deep-learning-and-process-understanding-for-data-driven-3so9cx99my.pdf

Deep learning and process understanding for data-driven Earth system science

Abstract Concern is resurfacing in the United States over the long-term effects of excess nitrogen (N) deposition and mobility in the environment. We present here a new synthesis of existing data sets for the northeastern United States, intended to answer a single question: Is N deposition altering the N status of forest ecosystems in this region? Surface water data suggest a significant increase in nitrate losses with N deposition. Soil data show an increase in nitrification with decreasing ratio of soil carbon to nitrogen (C:N) but weaker relationships between N deposition and soil C:N ratio or nitrification. Relationships between foliar chemistry and N deposition are no stronger than with gradients of climate and elevation. The differences in patterns for these three groups of indicators are explained by the degree of spatial and temporal integration represented by each sample type. The surface water data integrate more effectively over space than the foliar or soil data and therefore allow a more comprehensive view of N saturation. We conclude from these data that N deposition is altering N status in northeastern forests.

Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests

Concern is resurfacing in the United States over the long-term effects of excess nitrogen (N) deposition and mobility in the environment We present here a new synthesis of existing data sets for the northeastern United States, intended to answer a single question: Is N deposition altering the N status of forest ecosystems in this region? Surface water data suggest a significant increase in nitrate losses with N deposition Soil data show an increase in nitrification with decreasing ratio of soil carbon to nitrogen (C:N) but weaker relationships between N deposition and soil C:N ratio or nitrification Relationships between foliar chemistry and N deposition are no stronger than with gradients of climate and elevation The differences in patterns for these three groups of indicators are explained by the degree of spatial and temporal integration represented by each sample type The surface water data integrate more effectively over space than the foliar or soil data and therefore allow a more comprehensive view of N saturation We conclude from these data that N deposition is altering N status in northeastern forests

https://udel.edu/~inamdar/nps2007/n_discussion.pdf

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

Remote sensing of foliar chemistry has been recognized as an important element in producing large-scale, spatially explicit estimates of forest ecosystem function. This study was designed to determine whether data from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) could be used to determine forest canopy chemistry at a spatial resolution of 20 m, and if so, to use that information to drive an ecosystem pro- ductivity model. Foliage and leaf litter were sampled on 40 plots at Blackhawk Island, Wisconsin, and Harvard Forest, Massachusetts, to determine canopy-level nitrogen and lignin concentrations. At the time of the field sampling, AVIRIS data were acquired for both study areas. Calibration equations were developed, relating nitrogen and lignin to selected first-difference spectral bands (R2 = 0.87 and 0.77, respectively). Calibration equa- tions were evaluated on the basis of inter- and intrasite statistics. These equations were applied to all image pixels to make spatially explicit estimates of canopy nitrogen and lignin for both study sites. These estimates of nitrogen and lignin concentrations were then used with existing models to predict net ecosystem productivity at Harvard Forest and nitrogen mineralization rates at Blackhawk Island.

https://www.jstor.org/stable/pdfplus/2269510.pdf

High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes

https://harvardforest.fas.harvard.edu/sites/harvardforest.fas.harvard.edu/files/publications/pdfs/Magill_ForestEco&Manag_2004.pdf

Mary E. Martin

Papers

Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests

Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes

Ecosystem response to 15 years of chronic nitrogen additions at the Harvard Forest LTER, Massachusetts, USA