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Gold, K. M.

Publications and source records attributed to Gold, K. M..

3 recordsLinked to original sources

Low current and future spotted lanternfly suitability in New York wine-growing regions tempers vineyard risk

O_LIThe spread of the spotted lanternfly (Lycorma delicatula) is facilitated by its ecological flexibility and generalist diet breadth. Among 100 other plants, spotted lanternfly feed on grapevine, so high vineyard density could make suitable habitat even more attractive. This invasive polyphagous insect threatens New York viticulture, a >$6 billion industry, but the local risk remains uncertain. New York state may include boundary conditions defining the edges of environmentally suitable habitat. C_LIO_LIUsing 16,028 spotted lanternfly observations, a species distribution model was developed for the northeastern U.S. using climatic, geographic and human influence variables. Suitability was modeled at 1km under both current (2011-2040, RCP 7.0) and worsening near-future (2041-2070, RCP 8.5) climate scenarios. Suitability models were also developed for four common tree hosts (tree -of-heaven, black walnut, red maple and sugar maple), to evaluate if combined models that include established plant host suitability improve estimates of spotted lanternfly occurrence. Kernel Density estimates of vineyard density were used to add a maximum of 10% risk. C_LIO_LICombining the spotted lanternfly suitability with tree-of-heaven suitability improves estimates of spotted lanternfly occurrence. Human influence and humidity were important for defining suitability. Frost change frequency increased in importance under near-future climate change conditions. On Long Island, 83% vineyards are at very high risk for spotted lanternfly (>75% suitability). Across New York state, 33% of all vineyards are classified as very high risk. Under future climate change conditions, the number of vineyards at very high risk of spotted lanternfly increases by 7% in the Finger Lakes but decreases by 22% on Long Island. C_LIO_LIThis model improves estimates of spotted lanternfly potential distribution by integrating suitability of its established host tree-of-heaven. Estimating potential expansion under near-future climate conditions provides information on variables that define the environmental limits of suitability. Quantifying the local risk of vineyards to spotted lanternfly at the 1km scale creates an important tool for allocating resources to mitigate and manage this pest. C_LI Open research statementMost data are already published and publicly available, with those items properly cited in this submission (GBIF species observations, CHELSA CMIP6 environmental variables, USGS topography, NASA remote sensing). However, observations of spotted lanternfly from the New York State Department of Agriculture and Markets (N=10,144) and specific vineyard locations from the New York Wine & Grape Foundation (N=6,651) are considered sensitive information and are not shared here. Researchers interested in these datasets may reach out to these organizations to request access.

ecology↗

Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. Growers currently rely on frequent fungicide applications for control, but this practice has led to widespread resistance. Rapid remote detection and surveillance of GDM outbreaks would enable precision pesticide applications to target effective but resistance-prone fungicides where and when most needed, while relying on less resistance-prone protectants elsewhere. High resolution commercial satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence in two acres of Chardonnay grapevines in Geneva, NY, USA in June-August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs derived from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.88 (SkySat) and 0.94 (PlanetScope). However, we do not observe significant differences between VIs of high and low damage classes until late July-early August. We identify cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

plant biology↗

Scalable early detection of grapevine virus infection with airborne imaging spectroscopy

Viral diseases, including Grapevine Leafroll-associated Virus Complex 3 (GLRaV-3), cause $3 billion in damages and losses to the United States wine and grape industry annually. GLRaV-3 has a well-studied, year-long latent period in which vines are infectious but do not yet display visible symptoms, making it an ideal model pathosystem to evaluate the scalability of symptomatic and asymptomatic imaging spectroscopy-based disease detection. Plant disease causes physiological and chemical changes to occur locally and systemically throughout a plant, which imaging spectroscopy can detect both directly and indirectly. Reliable and scalable disease detection during the latent period would greatly reduce management costs, as current detection methods are entirely ground-based, labor-intensive, and expensive. Here, we use data collected in September 2020 by the NASA Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA. During September 2020 and 2021, industry collaborators scouted 317 acres of Vitis vinifera winegrapes for visible disease symptoms, and collected a subset for confirmation molecular testing at a commercial facility. Grapevines identified as visibly diseased in 2021 were assumed to have been latently infected (asymptomatic) during the September 2020 AVIRIS-NG data collection. We combined random forest with synthetic minority oversampling technique (SMOTE) to train multiple spectral models able to distinguish between non-infected (NI) and GLRaV-3-infected grapevines. We observed clear spectral differences that allowed for differentiation between NI and GLRaV-3 infected vines both pre- and post-symptomatically at 1m through 5m resolution. Our two best performing models had 87% accuracy (0.73 Kappa) distinguishing between NI and asymptomatic (aSy), and 85% accuracy (0.71 Kappa) distinguishing between NI and (aSy + symptomatic [Sy]) respectively. We hypothesize these spectral differences are linked to changes in overall plant physiology induced by disease, as visible foliar symptoms were restricted to the lower canopy. HighlightsO_LIAirborne imaging spectroscopy allows for scalable early-detection models of grapevine leafroll-associated virus complex 3 (GLRaV-3). C_LIO_LIRandom Forest based models trained with scouting ground data and imaging spectroscopy are accurate up to 5 meter but perform best at 3 meter spatial resolution. C_LIO_LIGLRaV-3 detection via imaging spectroscopy will not replace existing field scouting strategies or molecular testing but supplement by allowing for more strategic resource deployment to improve the overall financial, environmental, and societal sustainability of winegrape production. C_LI

pathology↗