Determining Spread Rate of Kudzu Bug (Hemiptera: Plataspidae) and Its Associations With Environmental Factors in a Heterogeneous Landscape

Science paper or big data? Assessing invasion dynamics using observational data

Non-native species are spreading at an unprecedented rate over large spatial scales, with global environmental change and growth in commerce providing novel opportunities for range expansion. Assessing the pattern and rate of spread is key to the development of strategies for safeguarding against future invasions and efficiently managing existing ones. Such assessments often depend on spatial distribution data from online repositories, which can be spatially biased, imprecise, and lacking in quantity. Here, the influence of disparities between occurrence records from online data repositories and what is known of the invasion history from peer-reviewed published literature on non-native species range expansion was evaluated using 6693 records of the Pacific oyster, Magallana gigas (Thunberg, 1793), spanning 56 years of its invasion in Europe. Two measures of spread were calculated: maximum rate of spread (distance from introduction site over time) and accumulated area (spatial expansion). Results suggest that despite discrepancies between online and peer-reviewed data sources, including a paucity of records from the early invasion history in online repositories, the use of either source does not result in significantly different estimates of spread. Our study significantly improves our understanding of the European distribution of M. gigas and suggests that a combination of short- and long-range dispersal drives range expansions. More widely, our approach provides a framework for comparison of online occurrence records and invasion histories as documented in the peer-reviewed literature, allowing critical evaluation of both data sources and improving our understanding of invasion dynamics significantly.

Kudzu Bug (Megacopta cribraria) and Associated Egg Parasitoids Emergence Rates in Alabama are Predicted by Weather Indices

The kudzu bug, Megacopta cribraria (Fabricius) (Hemiptera: Plataspidae), is a major economic pest of soybean in the southeastern United States. With climate warming, this pest is expected to move northward and cause additional crop damage. Parasitoid biocontrol is a potential method of integrated pest management for kudzu bug. Two species of egg parasitoid wasps have been observed emerging from kudzu bug egg masses in the southeastern United States: Paratelenomus saccharalis (Dodd) (Hymenoptera: Platygastridae) and Ooencyrtus nezarae (Ishii) (Hymenoptera: Encyrtidae). This paper used egg mass emergence data collected between 2018 and 2020 in Alabama soybean fields and compared the data to weather indices. Indices included the number of days with minimum temperatures below zero, accumulated rainfall (mm m-2), as well as species specific metrics of accumulated growing degree days, accumulated daily minimum temperature (°C), and accumulated daily maximum temperature (°C). Emergence of the generalist parasitoid, O. nezarae, was highly correlated with kudzu bug nymph abundance, accumulated degree day, accumulated daily temperatures, and precipitation. Ooencyrtus nezarae emergence was predicted in a stepwise regression equation by aggregated degree day and date of collection, which indicates that seasonality may be a predictor of its presence. In contrast, collections of the specialist parasitoid, P. saccharalis, were near-zero throughout the collection period, suggesting that this species may no longer be a usable biocontrol agent in the southeastern United States as a result of external limiting factors.

Susceptibility of Different Life Stages of Kudzu Bug Megacopta cribraria (F.) (Hemiptera: Plataspidae) to Two Different Native Strains of Beauveria bassiana

This is the first study that examined and compared the survival, LC50, and RR50 estimates of Megacopta cribraria F. (Hemiptera: Plataspidae) nymphs and adults that were exposed to two native Beauveria bassiana isolates (Previously codified as NI8 and KUDSC strains) at four concentrations. The greatest reduction in survival and mortality was observed primarily on or after 10 d post-exposure to B. bassiana isolates. Survival of early instars (2nd, 3rd) were not affected by either strains or concentration at 3 d and 5 d post-exposure. Survival of later instars (5th) and adults was significantly reduced when exposed to the KUDSC strain at all concentrations. Comparison of dose−mortality values (LC50) using resistance ratios (RR50) were significantly different between life stages of the kudzu bug for both strains of B. bassiana. The LC50 values showed that kudzu bug adults are more susceptible than any other life stage when exposed to either strain. The KUDSC strain was more pathogenic than NI8 10 d after exposure, but NI8 exhibited significantly higher pathogenicity than KUDSC 20 d after exposure. Our results suggest potential field application of B. bassiana for kudzu bug control and their integration into pest management strategies to suppress them before they cause economic damage to soybean crops.

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

Flight capacity increases then declines from the core to the margins of an invasive species' range

Individuals that disperse farther than other individuals are more likely to be on the frontlines of spreading populations and may be more likely to mate with one another as a consequence of their spatial proximity. Over generations, this process-known as spatial sorting-can produce patterns of increasing dispersal ability from a population's core towards the spreading front. By contrast, when the spread of a population is limited by the availability of suitable habitat, theory predicts that range boundaries can select against more dispersive phenotypes and produce patterns of decreasing dispersal capacity towards population margins. In a common garden study of invasive kudzu bugs (Megacopta cribraria)-which are limited by the availability of hostplants in their southern and western margins-I show that midrange individuals fly 49% farther than individuals in the core and 37% farther than individuals at margins. This result highlights that other processes, such as maternal effects or selection at range boundaries, may create more complicated patterns of dispersal ability across landscapes than predicted by models of spatial sorting alone.