Plant spectra as integrative measures of plant phenotypes

Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and the community- or ecosystem-level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co-occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most

Coupling spectral and resource-use complementarity in experimental grassland and forest communites

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n-dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity

Potential ecological impacts of climate intervention by reflecting sunlight to cool Earth

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth’s surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth’s albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth’s climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth’s surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity

Does the earnings quality matter? Evidence from a quasi-experimental setting

Investor preference for local stocks provides a quasi-experimental setting to investigate whether the market rewards firms that comply with generally accepted accounting principles. We show firms with low earnings quality trade at a premium compared to firms in compliance with accounting principles; the difference in values is greater when the role of local investor over-trading is stronger in stock price-formation, in other words for the more isolated firms. The value of the information not conveyed to the market through accounting disclosure accounts for 30% of the market-to-book. Results are robust to earnings quality definition, and show while non-local investors are sensitive to the quality of accounting information, local and better-informed investors are not. Overall, accounting quality matters. (C) 2016 Published by Elsevier Inc

Characterization of a Family of Cubic Dynamical Systems

Article published in Mathematics Exchange, 8(1), 2011.Motivated by the fact that cubic maps have found potential appli- cations to modeling of biological and physical processes, we examine a family of discrete, non-linear dynamical systems comprising one-parameter real variable cubic polynomials of a certain form. We examine and classify their xed points and 2-cycles over various parametric domains. We also study their bifurcation diagrams and use a variety of techniques to analyze their chaotic behavior

Blinded by the Light: The Functional Ecology of Plant-Light Interactions

University of Minnesota Ph.D. dissertation. July 2020. Major: Plant and Microbial Biology. Advisor: Jeannine Cavender-Bares. 1 computer file (PDF); v, 190 pages.The capture of sunlight by plants and other primary producers is the greatest driver of the world’s carbon cycle. The photosynthetic machinery that plants use to fix carbon dioxide and light energy into storable carbohydrates must be able to handle intense fluxes of energy, and both lack and excess of light put plants at a disadvantage—either from starvation or from damage. Plant leaves evolve in how they absorb, reflect, or avoid light in ways that can be explained as functional adaptations to their environment. Here, I present four studies on the interactions between plant tissue and the light environment—two of which concern the functional role of light capture or avoidance in ecological strategies, and two of which are methodological studies that explain how we can use plants’ interactions with light to understand their strategies more broadly. Chapter 1 reports on a study in the Big Biodiversity (BioDIV) experiment that seeks to characterize the range of strategies that plants have to cope with excess light under stressful conditions. In a survey of prairie plants, we find that species may either primarily use biochemical or structural strategies to protect themselves from excess light. The position along this continuum is phylogenetically conserved. Communities with more species relying on biochemical mechanisms are more resilient aboveground during water-limited periods. Chapter 2 uses growth surveys and physiological measurements in the Forests and Biodiversity (FAB) experiment to show how broadleaf trees respond to shade from faster-growing conifer neighbors. While most species were harmed by shade, growing slower and assimilating less carbon, two species showed the opposite trend. These two species were the most shade-tolerant in the experiment and were exceptionally susceptible to photoinhibition, such that shade from their neighbors facilitated their growth. All species relied on photoprotection more in sunnier environments. Chapters 3 and 4 use reflectance spectroscopy to estimate traits in different kinds of leaf tissue. Chapter 3 focuses on leaf litter, whose chemical traits are often measured to gain insight into components of nutrient cycle such as nutrient resorption and decomposition. We show that we can estimate a fiber content and elemental composition using pressed-leaf spectra and, with somewhat higher accuracy, ground-leaf spectra. Chapter 4 is about pressed leaves, such as herbarium specimens, whose functional traits ecologists increasingly seek to measure in order to fill in trait databases or understand the impacts of global anthropogenic changes. We show that reflectance spectroscopy can provide non-destructive estimates of several leaf functional traits from pressed leaves, which may extend the possibility of using a wider variety of herbarium specimens in functional ecology

Forests and Biodiversity cleaned biomass survey data 2013-2018

This dataset includes annual growth survey measurements from the Forests and Biodiversity 1 (e271) experiment at Cedar Creek Ecosystem Science Reserve in East Bethel, MN. The dataset also includes a script that allows users to reproduce the figures and statistics reported in the cited paper. This version of the dataset is specifically meant to support the inferences in that paper, rather than serving as the version of record. Please consult the Cedar Creek Data Catalog (https://www.cedarcreek.umn.edu/research/data) to find the authoritative version to be used for general purposes.National Science Foundation DEB #1234162 to Cedar Creek LTER.NSF Graduate Research Fellowship (Grant No. 00039202

Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves

Abstract More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non‐destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least‐squares regression to build models linking pressed‐leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh‐ or ground‐leaf spectra of the same samples. The traits our pressed‐leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water‐related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 20). For most chemical traits, pressed‐leaf models performed better than fresh‐leaf models, but worse than ground‐leaf models. Pressed‐leaf models were worse than fresh‐leaf models for estimating LMA and LDMC, but better than ground‐leaf models for LMA. Finally, in a subset of samples, we used partial least‐squares discriminant analysis to classify specimens among 10 species with near‐perfect accuracy (>97%) from pressed‐ and ground‐leaf spectra, and slightly lower accuracy (>93%) from fresh‐leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed‐leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far‐reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections

GF_cover-richness_RM

Growth-form cover and richness in the 117 reference-meadow transects sampled in 2013