Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae): Simulation and visualization of current and future distributions along the Eastern Afromontane

Abstract

There is a scarcity of laboratory and field-based results showing the movement of the diamondback moth (DBM) Plutella xylostella (L.) across a spatial scale. We studied the population growth of the diamondback moth (DBM) Plutella xylostella (L.) under six constant temperatures, to understand and predict population changes along altitudinal gradients and under climate change scenarios. Non-linear functions were fitted to continuously model DBM development, mortality, longevity and oviposition. We compiled the best-fitted functions for each life stage to yield a phenology model, which we stochastically simulated to estimate the life table parameters. Three temperature-dependent indices (establishment, generation and activity) were derived from a logistic population growth model and then coupled to collected current (2013) and downscaled temperature data from AFRICLIM (2055) for geospatial mapping. To measure and predict the impacts of temperature change on the pest’s biology, we mapped the indices along the altitudinal gradients of Mt. Kilimanjaro (Tanzania) and Taita Hills (Kenya) and assessed the differences between 2013 and 2055 climate scenarios. The optimal temperatures for development of DBM were 32.5, 33.5 and 33°C for eggs, larvae and pupae, respectively. Mortality rates increased due to extreme temperatures to 53.3, 70.0 and 52.4% for egg, larvae and pupae, respectively. The net reproduction rate reached a peak of 87.4 female offspring/female/generation at 20°C. Spatial simulations indicated that survival and establishment of DBM increased with a decrease in temperature, from low to high altitude. However, we observed a higher number of DBM generations at low altitude. The model predicted DBM population growth reduction in the low and medium altitudes by 2055. At higher altitude, it predicted an increase in the level of suitability for establishment with a decrease in the number of generations per year. If climate change occurs as per the selected scenario, DBM infestation may reduce in the selected region. The study highlights the need to validate these predictions with other interacting factors such as cropping practices, host plants and natural enemies.

Effects of climate variability on insect pests of cabbage: adapting alternative planting dates and cropping pattern as control measures

“…The results of this study are consistent with current trends in climate dynamics, which require adaptation of cropping seasons to the ecology of insect pests, with particular attention on alternative planting dates and sustainable treatments [6,38]. These results reflect the overdue need for adapted integrated pest management approaches that incorporate planting dates with local farm management practices under different climate scenarios [39]. The significantly low pest infestation during late planting compared to normal and early plantings can be attributed to current climate dynamics in the study area with decreasing rainfall and increasing temperature [27,38], and demonstrates the efficacy of alternative planting dates as control measure for cabbage pests [28].…”

A temperature-dependent phenology model for the greenhouse whitefly Trialeurodes vaporariorum (Hemiptera: Aleyrodidae)

“…Assessing the pest’s population growth potential is an important aspect that can be determined through the study of life table parameters and insect phenology. Several phenological models have been developed for insect pests that can predict life parameters under a temperature regime, taking into account the total life cycle of insects ( Sporleder et al, 2004 ; Ngowi et al, 2017 ). Comprehensive research has been conducted on studying the effect of temperature on T.vaporariorum in different crops ( Table 7 ).…”

Influence of elevated temperature on the fitness of Diamondback moth, Plutella xylostella (L.) in cauliflower

“…The intrinsic rate of increase (r ) had increased significantly from 0.22 m to 0.28 when temperature increased from 31°C to 34°C (P< 0.001). Ngowi et al (2017) showed that r value increased from m 0.001 at 10°C to 0.20 at 30°C. It had been reported that the preadult and adult development rate varied at different temperatures (Folguera et al, 2010).…”