Browsing by Author "Lapadat, Cathleen"

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    Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination
    (2022-04-26) Sapes, Gerard; Lapadat, Cathleen; Schweiger, Anna K.; Juzwik, Jennifer; Montgomery, Rebecca; Gholizadeh, Hamed; Townsend, Philip A.; Gamon, John A.; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine
    The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high ac- curacy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non- infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance pro- vides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread.
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    Content of leaf pigments of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016
    (2020-09-01) Schweiger, Anna K; Fredericksen, Brett; Lapadat, Cathleen; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine
    This data set contains the content of chlorophyll a, chlorophyll b, β-carotene, lutein, neoxanthin, violaxanthin, antheraxanthin and zeaxanthin pigments from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Mass- and area-based pigment contents were determined using high-performance liquid chromatography (HPLC). Data were 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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    Leaf carbon and nitrogen content of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016
    (2020-09-01) Schweiger, Anna K; Lapadat, Cathleen; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, Jeannine
    This data set contains carbon and nitrogen content from combustion–reduction elemental analysis (TruSpec CN Analyzer, LECO) from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Data were 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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    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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    Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments
    (2021-06-08) Cavender-Bares, Jeannine; Schweiger, Anna K.; Gamon, John; Gholizadeh, Hamed; Kimberly, Helzer; Lapadat, Cathleen; Madritch, Michael; Townsend, Philip A.; Wang, Zhihui; Hobbie, Sarah E.; cavender@umn.edu; Cavender-Bares, Jeannine
    Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling, providing a potential link between remote sensing and belowground processes. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments—the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. Specifically, we tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass and green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates. Aboveground biomass (or cover) did not predict these belowground attributes. The strong, contrasting associations between the quantity and chemistry of aboveground inputs with belowground soil processes and properties provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.