Browsing by Author "Kothari, Shan"

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    Blinded by the Light: The Functional Ecology of Plant-Light Interactions
    (2020-07) Kothari, Shan
    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.
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    Forests and Biodiversity cleaned biomass survey data 2013-2018
    (2021-02-12) Kothari, Shan; Montgomery, Rebecca A; Cavender-Bares, Jeannine; kotha020@umn.edu; Kothari, Shan; University of Minnesota Cavender-Bares Lab; University of Minnesota Montgomery Lab; Cedar Creek Ecosystem Science Reserve
    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.
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    Leaf carbon fraction data from tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016
    (2020-08-12) Schweiger, Anna K; Lapadat, Cathleen; Kothari, Shan; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine
    This data set contains results from carbon fraction analysis (Fiber Analyzer 200, ANKOM Technology), including non-structural carbohydrates, hemicellulose, cellulose, lignin, neutral detergent fiber, and acid detergent fiber contents in percent (%) from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. The data was collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.
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    Models for: Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves
    (2022-06-23) Kothari, Shan; Beauchamp-Rioux, Rosalie; Laliberté, Etienne; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine; ASCEND BII, Canadian Airborne Biodiversity Observatory
    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. Here, we present the model coefficients and a README that provides examples of how to apply them to other data.