Boll weevil, Anthonomous grandis grandis Boheman first invaded U.S. cotton in Texas in the late 1800s, and spread throughout U.S. cotton-growing regions by the 1920s. Boll weevil eradication efforts initiated in the eastern United States in the 1980s resulted in its elimination except in the southernmost region of Texas and adjoining areas of Mexico. We focused on geographic information system (GIS)-based mapping and spatial analyses of boll weevil trap data to consider whether landscape features were associated with spatially variable detections of boll weevils. Seven years of trap data were overlaid with data layers of vector-based classifications of cropland, transportation, and hydrological features. New boll weevil detections in 2018 were 108 km north of cotton fields in the Lower Rio Grande Valley (LRGV) where more persistent detections occurred. Focusing on LRGV data, 14 of 24 correlations of number of boll weevil captures to nearest distances to selected landscape features were negative. In follow-up analyses, best data fit was seen using stepwise regression. In 2010, waterbodies and watermelon fields were influential linear terms (partial R² = 0.14 and 0.064, respectively; model R² = 0.32). In 2014, the Rio Grande River as a linear term was influential (partial R² = 0.15; model R² = 0.24). Boll weevil captures tended to increase in closer proximity to these landscape features. Results of 2010 were consistent with expectations of remnant populations in the LRGV spreading locally, while 2014 results may reflect remnant populations or re-introductions from boll weevil moving longer distances into the LRGV.

Invasive airborne insects pose major challenges in natural resource and agriculture management, as they can rapidly spread over large distances and cross physical boundaries. Field monitoring and local management are important tools to prevent and control infestations but require additional coordination to be operative region-wide. Computational modeling techniques have been effective in simulating local population dynamics and in capturing spread of invasive species on a regional scale. We use an integrated ecological model to simulate local and regional infestation dynamics of sugarcane aphids, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae), on sorghum, Sorghum bicolor (L.) Moench (family Poaceae), in the southern to central Great Plains of the United States. Local dynamics of aphid populations on sorghum are simulated by a spatially explicit, individual-based model, whereas regional aphid migration is simulated by an atmospheric model that computes inert air particle (aphid) transport, dispersion, and deposition. Simulation results indicate timing of initial infestations in the south affects spatiotemporal patterns of infestation throughout the region. Probability of local infestations is a function of both percentage of land occupied by growing sorghum and prevailing winds. Thus, due to availability of sorghum, relatively later dates of initial infestation in the south will probably lead to infestations farther north that become established more quickly following the first appearance of aphids in the south. The model we present, in coordination with field monitoring schemes, could be applied as a forecasting tool in region-wide pest management systems.

Solid wood packaging material (WPM) is widely recognized as a high-risk pathway for transport and potential introduction of wood-boring insects, including longhorned beetles in the family Cerambycidae. These beetles also are occasionally imported in finished wood products, such as furniture and decorative items. A targeted effort to identify wood borers intercepted as larvae in WPM at U.S. ports between 2012 and 2018 revealed that one of the most frequently intercepted species was Trichoferus campestris (Faldermann), a cerambycid native to Asia. Trichoferus campestris is a pest of quarantine concern in the United States, Canada, and Europe. The establishment risk of this beetle in the United States is high because of its frequent introduction through multiple pathways and its potential to inhabit natural and urban forests as well as agricultural systems. In this study, we compiled port interception and detection data to examine risk based on historical introductions and pathways. We tested whether the intended destination of cargo intercepted with T. campestris-infested WPM can be used as a predictor of inland introductions, assuming that individuals of T. campestris are likely to be moved through established trade routes between export–import partners. We also developed maps to predict likely areas of introduction and establishment in the United States based on pathway analysis and climate suitability data. The maps will enable informed prioritization of resources in pest surveillance, and may serve as models for other wood borers identified in the WPM and wood products pathway.

Forecasting the spread and potential impacts of invasive, alien species is vital to relevant management and policy decisions. Models that estimate areas of potential suitability are useful to guide early detection and eradication, inform effective budget allocations, and justify quarantine regulations. Machine-learning is a rapidly emerging technology with myriad applications, including the analysis of factors that govern species' distributions. However, forecasts for invasive species often require extrapolation into novel spaces, which may severely erode model reliability. Using the popular machine-learning platform, MaxEnt, we integrate numerous tools and recommendations to demonstrate a method of rigorous model development that emphasizes assessment of model transferability. Our models use Lymantria dispar dispar (L.) (Lepidoptera: Erebidae), an insect brought to the United States in the late 1860s from Europe and subsequently well monitored in spread. Recent genetic analyses provide evidence that the eastern North American population originated in Germany, France, and northern Italy. We demonstrate that models built and assessed using typical methodology for invasive species (e.g., using records from the full native geographic range) showed the smallest extent of extrapolation, but the worst transferability when validated with independent data. Conversely, models based on the purported genetic source of the eastern North American populations (i.e., a subset of the native range) showed the greatest transferability, but the largest extent of extrapolation. Overall, the model that yielded high transferability to North America and low extrapolation was built following current recommendations of spatial thinning and parameter optimization with records from both the genetic source in Europe and early North American invasion.

CLIMEX and MED-FOES models integrate climate and data on Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann), biology and use it to define the environmental suitability for the pest at specific geographical locations. CLIMEX calculates growth indices as indicators of conditions that are suitable for medfly population growth. MED-FOES incorporates additional information on pest management interventions to simulate the process and timing of medfly eradication. CLIMEX simulations of climatic suitability in California and Florida indicated that the most favorable periods for medfly population growth are March through May and October through November, whereas the environment would be especially stressful during the summer months, except when irrigation is applied. With irrigation, California is highly suitable for medfly population growth during the summer months. Due to cool temperatures, medfly populations are likely to decline significantly in January through February in Los Angeles, Tampa, and Miami, and probably not survive in San Francisco. According to MED-FOES simulations, it possibly would take longer to eradicate medfly from California than Florida, particularly if the incursions are initiated in the summer months. Medfly annual growth indices for the ENSO La Niña years are relatively low for San Francisco and Los Angeles but above neutral for Tampa and very high for Miami. During the El Niño phase, the growth index remains unchanged for San Francisco, increases for Los Angeles, and decreases for Tampa and Miami. CLIMEX and MED-FOES models are useful for informing plans to manage invasion threats from medfly and other invasive insects.

Management of the European gypsy moth [Lymantria dispar dispar (Linnaeus)] in North America has benefited from more than a century of research. The East Asian strains of the gypsy moth, however, bring new challenges including multiple subspecies (Lymantria dispar asiatica Vnukovskij and Lymantria dispar japonica Motschulsky), broad distributions across heterogeneous habitats, and a lack of data on the variation in the phenology of source populations, which may affect risk.To address these issues, published phenology parameters for eight populations of Asian gypsy moth were used to develop eight strain-specific agent-based phenological models. These models were applied to 47 ports in East Asia where the Asian gypsy moth is native, and output was compared with available trap data to assess the role of interpopulation variation in phenological parameters in predicting moth flight among varied locations, assess variation in the performance of models among years, and assess the importance of modeling phenology using parameters from a ‘local' moth population. Variation in phenological parameters among the eight populations yielded variation in predicted flight times among the 47 ports analyzed, and the use of ‘local’ populations did not generally improve model fit. Model accuracy varied substantially among ports and among years within some ports. The larva-to-adult agent-based models described here have utility in estimating flight periods for some ports in their current form, but variation in model quality across the landscape suggests that there is potential for unsampled and unparameterized moth populations and factors that remain to be quantified.

Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure, and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. In 2018, the U.S. National Phenology Network (USA-NPN) released a suite of ‘Pheno Forecast’ map products relevant to science and management. The Pheno Forecasts include real-time maps and short-term forecasts of insect pest activity at management-relevant spatial and temporal resolutions and are based on accumulated temperature thresholds associated with critical life-cycle stages of economically important pests. Pheno Forecasts indicate, for a specified day, the status of the insect's target life-cycle stage in real time across the contiguous United States. The maps are available for 12 pest species including the invasive emerald ash borer (Agrilus planipennis Fairmaire [Coleoptera: Buprestidae]), hemlock woolly adelgid (Adelges tsugae Annand), and gypsy moth (Lymantria dispar Linnaeus [Lepidoptera: Erebidae]). Preliminary validation based on in-situ observations for hemlock woolly adelgid egg and nymph stages in 2018 indicated the maps to be ≥93% accurate depending on phenophase. Since their release in early 2018, these maps have been adopted by tree care specialists and foresters across the United States. Using a consultative mode of engagement, USA-NPN staff have continuously sought input and critique of the maps and delivery from end users. Based on feedback received, maps have been expanded and modified to include additional species, improved descriptions of the phenophase event of interest, and e-mail-based notifications to support management decisions.