Browsing by Author "Montgomery, Rebecca"
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Item 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, JeannineThe 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.Item Data and Code for Mechanistic links between physiology and spectral reflectance enable pre-visual detection of oak wilt and drought stress(2024-01-04) Sapes, Gerard; Schroeder, Lucy; Scott, Allison; Clark, Isaiah; Juzwik, Jennifer; Montgomery, Rebecca; Guzman Q., J. Antonio; Cavender-Bares, Jeannine; gsapes@ufl.edu; Sapes, Gerard; University of Minnesota; University of Florida; Northern Research Station, USDA Forest ServiceTree mortality due to global change-including range expansion of invasive pests and pathogens- is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen to detect and distinguish both types of stressors. We also performed a field experiment where we validated the capacity of spectral reflectance models to predict physiological status and distinguish oak wilt from healthy trees. The data and code provided here address these goals.Item Driven to Discover Citizen Science Curriculum Guide: Phenology and Nature's Notebook(University of Minnesota Extension, 2018) Thompson, Ami; Strauss, Andrea L.; Oberhauser, Karen S.; Kooman, Michele H.; Montgomery, Rebecca; Andicoechea, Jonathan; Blair, Robert B.Item Looking for a few good citizen scientists: Phenology brings climate change to your own backyard!(University of Minnesota Extension, 2015) Carlson, Stephan; Montgomery, Rebecca; Buyarski, ChrisPhenology is the timing of seasonal biological events such as budburst, flowering, bird migration and leaf coloring. It has provided the most compelling evidence that plants and animals are responding to changes in climate across the globe. Minnesota temperatures have risen by ~2 degrees F over the last 50 years and are projected to rise by ~7-9 degrees F by the end of the century. There is a critical need to understand how our natural resources are responding to climate change. Phenology provides an excellent indicator of climate change and can be collected locally. But, how is phenology changing our plants, birds, insect pests, pollinators, or fish across the State. This lack of knowledge hinders our ability to predict species and interactions that might be vulnerable to climate change. Historical observations of phenology made in Minnesota over the past 100 years coupled with new data from trained observers across the state is helping to identify species and species interactions that may be vulnerable to climate change. Phenology provides a means to open a dialogue about climate change using phenomena people can observe in their own backyards. This personal connection to place can be more powerful than stories of melting ice caps or disappearing islands. Four historical data sets of the last 50 years will be shared along with opportunities for a network of citizen observers to provide input for local monitoring of phenology sites across the State. The National and State Phenology data and protocols will also be shared.Item Mechanistic links between physiology and spectral reflectance enable pre-visual detection of oak wilt and drought stress(Proceedings of the National Academy of Sciences, 2024-02) Sapes, Gerard; Schroeder, Lucy; Scott, Allison; Clark, Isaiah; Juzwik, Jennifer; Montgomery, Rebecca; Guzmán Q., J. Antonio; Cavender-Bares, JeannineTree mortality due to global change—including range expansion of invasive pests and pathogens—is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content and cellular damage detected oak wilt and drought 13 days before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.