Dry stress decreases areas suitable for Neoleucinodes elegantalis (Lepidoptera: Crambidae) and affects its survival under climate predictions in South America

Horizon scanning to assess the bioclimatic potential for the alien species Spodoptera eridania and its parasitoids after pest detection in West and Central Africa

BACKGROUND

The southern armyworm (SAW) Spodoptera eridania (Stoll) (Lepidoptera: Noctuidae) is native to the tropical Americas where the pest can feed on more than 100 plant species. SAW was recently detected in West and Central Africa, feeding on various crops including cassava, cotton, amaranth and tomato. The current work was carried out to predict the potential spatial distribution of SAW and four of its co-evolved parasitoids at a global scale using the maximum entropy (Maxent) algorithm.

RESULTS

SAW may not be a huge problem outside its native range (the Americas) for the time being, but may compromise crop yields in specific hotspots in coming years. The analysis of its potential distribution anticipates that the pest might easily migrate east and south from Cameroon and Gabon.

CONCLUSION

The models used generally demonstrate that all the parasitoids considered are good candidates for the biological control of SAW globally, except they will not be able to establish in specific climates. The current paper discusses the potential role of biological control using parasitoids as a crucial component of a durable climate-smart integrated management of SAW to support decision making in Africa and in other regions of bioclimatic suitability. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Wet and warm conditions contribute to the occurrence of the neotropical butterfly Ascia monuste orseis Godart (Lepidoptera: Pieridae) on Brassica crops

Ascia monuste orseis Godart (Lepidoptera: Pieridae) is a neotropical butterfly distributed in South America. During the larval stage, this insect causes economic losses on Brassica crops. Wet and warm conditions are known to increase subspecies occurrence, but it remains unclear why these conditions are more suitable. In this study, we have shown that both conditions are highly favourable for A. monuste orseis. We determined the thermal requirements for immature development and then created models for A. monuste orseis occurrence using Climex algorithm. Two models were built: one for the year-round presence and other for seasonal suitability. We validated the models using subspecies occurrence records and monitoring in two Brazilian regions (Northeast and Southeast). The minimum, optimum and maximum temperature for immature development were estimated at 16.37, 29.16 and 34.95 °C, respectively. The model for year-round presence indicated tropical areas as highly suitable for A. monuste orseis occurrence (with 88% of accuracy) and the seasonal models showed unsuitable areas in some parts of South America during cold and dry periods. Such predictions were observed in the monitored areas where A. monuste orseis was not found. These results can be associated with the mortality caused by low temperature to immature stages and drought conditions that may induce adult migration to moist habitats. Thus, we suggest that A. monuste orseis occurs mainly during wet and warm seasons on Brassica crops due to deleterious effects caused by cold and dry conditions. This information can be used to improve A. monuste orseis management in Brassica crops.

DDRP: Real-time phenology and climatic suitability modeling of invasive insects

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Conventional Sampling Plan for Scouting Neoleucinodes elegantalis (Lepidoptera: Crambidae) Eggs on Tomato Fruits

The small tomato borer, Neoleucinodes elegantalis (Guenée), is an important pest of tomato (Solanum lycopersicum L.) in South and Central America. This pest is a potential threat for many tomato-producing areas and was listed in 2014 as an A1 quarantine pest by the European and Mediterranean Plant Protection Organization. Soon after hatching, the neonate N. elegantalis larvae penetrate the fruits where they feed until pupation. Therefore, effective N. elegantalis management relies on the timely scouting of egg densities to allow decision-making prior to penetration of neonates into fruits. This study aimed to develop a conventional sampling plan for scouting N. elegantalis eggs on tomato plants. The most representative and precise sampling unit was the most basal fruit cluster for plants bearing up to three clusters (S1 plants). For plants with more than three fruit clusters (S2 plants), the most representative and precise sampling unit was the combination of the second and third fruit clusters. Among the four variables evaluated (eggs/fruit, egg masses/fruit, percentage fruit with eggs, and percentage clusters with eggs), the percentage of clusters with eggs was the most economical for N. elegantalis sampling based on the number of samples and cost required. For this variable, the number of samples determined at the 25% error level was 42 and 36 samples for S1 and S2 plants, respectively. The sampling plan developed for scouting N. elegantalis is fast, reflects pest infestation in tomato fields, and costs less than US$1.50 per field scouted.