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This study sought to develop models for predicting near-term (1-3 day) fecal contamination events in coastal shellfish growing waters. Using Random Forest regression, we (1) developed fecal coliform (FC) concentration models for shellfish growing areas using watershed characteristics and antecedent hydrologic and meteorologic observations as predictors, (2) tested the change in model performance associated when forecasted, as opposed to measured, rainfall variables were used as predictors, and (3) evaluated model predictor importance in relation to shellfish sanitation management criteria. Models were trained to 10 years of coastal FC measurements (n = 1285) for 5 major shellfish management areas along the Florida (USA) coast. Model performance varied between the 5 management areas with R 2 ranging from 0.36 to 0.72. Antecedent precipitation variables were among the most important predictors in the day-of forecast models in all management areas. When forecasted rainfall was included in the models, wind components became increasingly important., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Competing Interests: The authors have declared that no competing interests exist.

Background: Thrips (order Thysanoptera) infestations of cotton seedlings result in plant injury, increasing the detrimental consequences of other challenges to production agriculture, such as abiotic stress or infestation by other pests. Using Frankliniella fusca as a thrips species of focus, we empirically developed a composite model of thrips phenology and cotton seedling susceptibility to predict site-specific infestation risk so that monitoring and other resources can be allocated efficiently, to optimize the timing of thrips control measures to maximize effectiveness, and to inform stakeholders about the dynamics of thrips infestation and cotton seedling injury at a time when thrips are evolving resistance to commonly-used pesticides., Results: A mixture distribution model of thrips infestation potential, fit to data describing F. fusca adult dispersal in time, proved best for predicting infestations of F. fusca on cotton seedlings. Thrips generations occurring each year as a function of weather are represented as a probability distribution. A model of cotton seedling growth was also developed to predict susceptibility as a function of weather. Combining these two models resulted in a model of seedling injury, which was validated and developed for implementation as a software tool., Conclusions: Experimental validation of the implemented model demonstrated the utility of its output in predicting infestation risk. Successful implementation and use of the software tool derived from this model was enabled by close cooperation with university extension personnel, agricultural consultants, and growers, underscoring the importance of stakeholder and expert input to the success of applied analytical research. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

Trends in average annual or seasonal precipitation are insufficient for detecting changes in the climatic fire season, especially in regions where the fire season is defined by wet-dry seasonal cycles and lightning activity. Using an extensive dataset (1897-2017) in the Coastal Plain of the southeastern United States, we examined changes in annual dry season length, total precipitation, and (since 1945) the seasonal distribution of thunder-days as a correlate of lightning activity. We found that across the entire region, the dry season has lengthened by as much as 156 days (130% over 120 years), both starting earlier and ending later with less total precipitation. Less rainfall over a longer dry season, with no change in seasonal thunderstorm patterns, likely increases both the potential for lightning-ignited wildfires and fire severity. Global climate change could be having a hitherto undetected influence on fire regimes by altering the synchrony of climatic seasonal parameters., (© 2019 John Wiley & Sons Ltd.)

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.

Climate projections from 20 downscaled global climate models (GCMs) were used with the 3-PG model to predict the future productivity and water use of planted loblolly pine (Pinus taeda) growing across the southeastern United States. Predictions were made using Representative Concentration Pathways (RCP) 4.5 and 8.5. These represent scenarios in which total radiative forcing stabilizes before 2100 (RCP 4.5) or continues increasing throughout the century (RCP 8.5). Thirty-six sites evenly distributed across the native range of the species were used in the analysis. These sites represent a range in current mean annual temperature (14.9-21.6°C) and precipitation (1,120-1,680 mm/year). The site index of each site, which is a measure of growth potential, was varied to represent different levels of management. The 3-PG model predicted that aboveground biomass growth and net primary productivity will increase by 10%-40% in many parts of the region in the future. At cooler sites, the relative growth increase was greater than at warmer sites. By running the model with the baseline [CO 2 ] or the anticipated elevated [CO 2 ], the effect of CO 2 on growth was separated from that of other climate factors. The growth increase at warmer sites was due almost entirely to elevated [CO 2 ]. The growth increase at cooler sites was due to a combination of elevated [CO 2 ] and increased air temperature. Low site index stands had a greater relative increase in growth under the climate change scenarios than those with a high site index. Water use increased in proportion to increases in leaf area and productivity but precipitation was still adequate, based on the downscaled GCM climate projections. We conclude that an increase in productivity can be expected for a large majority of the planted loblolly pine stands in the southeastern United States during this century., (© 2017 John Wiley & Sons Ltd.)

Two mesoscale circulations, the Sandhills circulation and the sea breeze, influence the initiation of deep convection over the Sandhills and the coast in the Carolinas during the summer months. The interaction of these two circulations causes additional convection in this coastal region. Accurate representation of mesoscale convection is difficult as numerical models have problems with the prediction of the timing, amount, and location of precipitation. To address this issue, the authors have incorporated modifications to the Kain-Fritsch (KF) convective parameterization scheme and evaluated these mesoscale interactions using a high-resolution numerical model. The modifications include changes to the subgrid-scale cloud formulation, the convective turnover time scale, and the formulation of the updraft entrainment rates. The use of a grid-scaling adjustment parameter modulates the impact of the KF scheme as a function of the horizontal grid spacing used in a simulation. Results indicate that the impact of this modified cumulus parameterization scheme is more effective on domains with coarser grid sizes. Other results include a decrease in surface and near-surface temperatures in areas of deep convection (due to the inclusion of the effects of subgrid-scale clouds on the radiation), improvement in the timing of convection, and an increase in the strength of deep convection.

The 170 National Forests and Grasslands (NFs) in the conterminous United States are public lands that provide important ecosystem services such as clean water and timber supply to the American people. This study investigates the potential impacts of climate change on two key ecosystem functions (i.e., water yield and ecosystem productivity) using the most recent climate projections derived from 20 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 5 (CMIP5). We find that future climate change may result in a significant reduction in water yield but an increase in ecosystem productivity in NFs. On average, gross ecosystem productivity is projected to increase by 76 ~ 229 g C m(-2) yr(-1) (8% ~ 24%) while water yield is projected to decrease by 18 ~ 31 mm yr(-1) (4% ~ 7%) by 2100 as a result of the combination of increased air temperature (+1.8 ~ +5.2 °C) and precipitation (+17 ~ +51 mm yr(-1)). The notable divergence in ecosystem services of water supply and carbon sequestration is expected to intensify under higher greenhouse gas emission and associated climate change in the future, posing greater challenges to managing NFs for both ecosystem services.

North Carolina has one of the most complex climates in the United States (U.S.). Analysis of the climate in this state is critical for agricultural and planning purposes. Climate patterns and trends in North Carolina are analyzed for the period 1949-1998. Precipitation, minimum temperature, and maximum temperature are analyzed on seasonal and annual time scales using data collected from the National Weather Service Cooperative Observer Network. Additionally, changes in patterns of occurrence of the last spring freeze and first fall freeze are investigated. Linear time series slopes are analyzed to investigate the spatial and temporal trends of climate variability in North Carolina. Spatial analysis of climate variability across North Carolina is performed using a geographic information system. While most trends are local in nature, there are general statewide patterns. Precipitation in North Carolina has increased over the past 50 years during the fall and winter seasons, but decreased during the summer. Temperatures during the last 10 years are warmer than average, but are not warmer than those experienced during the 1950s. The warm season has become longer, as measured by the dates of the last spring freeze and first fall freeze. Generally, the last 10 years were the wettest of the study period. These conclusions are consistent with earlier studies that show that the difference between the maximum and minimum temperatures is decreasing, possibly due to increased cloud cover and precipitation. Similarly, these results show that temperature patterns are in phase with the North Atlantic Oscillation and precipitation patterns appear to be correlated with the Pacific Decadal Oscillation.