Degree day-based model predicts pink bollworm phenology across geographical locations of subtropics and semi-arid tropics of India.

There is a global concern about the effects of climate change driven shifts in species phenology on crop pests. Using geographically and temporally extensive data set of moth trap catches and temperatures across the cotton growing states of India, we predicted the phenology of cotton pink bollworm Pectinophora gossypiella (Saunders). Our approach was centered on growing degree days (GDD), a measure of thermal accumulation that provides a mechanistic link between climate change and species' phenology. The phenology change was predicted by calculating absolute error associated with DD and ordinal date, an alternative predictor of phenology, for peak moth abundance. Our results show that GDD outperformed the ordinal dates in predicting peak moth abundance in 6 out of 10 selected locations. Using established thresholds of 13.0/34.0 °C, mean DD accumulated between the consecutive moth peaks across different years were estimated at 504.05 ± 4.84. Seven generations were determined for pink bollworm in a cropping season, the length of which varied between 35 and 73 days in response to temperature. Pink bollworm population reached its peak during third generation which can be the target for management actions. The study provides essential information for developing pink bollworm management strategies under climate change.

Temperature Impacts the Development and Survival of Common Cutworm (Spodoptera litura): Simulation and Visualization of Potential Population Growth in India under Warmer Temperatures through Life Cycle Modelling and Spatial Mapping.

The common cutworm, Spodoptera litura, has become a major pest of soybean (Glycine max) throughout its Indian range. With a changing climate, there is the potential for this insect to become an increasingly severe pest in certain regions due to increased habitat suitability. To examine this possibility, we developed temperature-based phenology model for S. litura, by constructing thermal reaction norms for cohorts of single life stages, at both constant and fluctuating temperatures within the ecologically relevant range (15-38°C) for its development. Life table parameters were estimated stochastically using cohort updating and rate summation approach. The model was implemented in the geographic information system to examine the potential future pest status of S. litura using temperature change projections from SRES A1B climate change scenario for the year 2050. The changes were visualized by means of three spatial indices demonstrating the risks for establishment, number of generations per year and pest abundance according to the temperature conditions. The results revealed that the development rate as a function of temperature increased linearly for all the immature stages of S. litura until approximately 34-36°C, after which it became non-linear. The extreme temperature of 38°C was found lethal to larval and pupal stages of S. litura wherein no development to the next stage occurred. Females could lay no eggs at the extreme low (15°C) and high (> 35°C) test temperatures, demonstrating the importance of optimum temperature in determining the suitability of climate for the mating and reproduction in S. litura. The risk mapping predicts that due to temperature increase under future climate change, much of the soybean areas in Indian states like Madhya Pradesh, Maharashtra and Rajasthan, will become suitable for S. litura establishment and increased pest activity, indicating the expansion of the suitable and favourable areas over time. This has serious implication in terms of soybean production since these areas produce approximately 95% of the total soybeans in India. As the present model results are based on temperature only, and the effects of other abiotic and biotic factors determining the pest population dynamics were excluded, it presents only the potential population growth parameters for S. litura. However, if combined with the field observations, the model results could certainly contribute to gaining insight into the field dynamics of S. litura.

Predicting the potential geographic distribution of cotton mealybug Phenacoccus solenopsis in India based on MAXENT ecological niche model.

Mealybug, Phenacoccus solenopsis Tinsley has recently emerged as a serious insect pest of cotton in India. This study demonstrates the use of Maxent algorithm for modeling the potential geographic distribution of P. solenopsis in India with presence-only data. Predictions were made based on the analysis of the relationship between 111 occurrence records for P. solenopsis and the corresponding current and future climate data defined on the study area. The climate data from worldclim database for current (1950-2000) and future (SRES A2 emission scenario for 2050) conditions were used. DIVA-GIS, an open source software for conducting spatial analysis was used for mapping the predictions from Maxent. The algorithm provided reasonable estimates of the species range indicating better discrimination of suitable and unsuitable areas for its occurrence in India under both present and future climatic conditions. The fit for the model as measured by AUC was high, with value of 0.930 for the training data and 0.895 for the test data, indicating the high level of discriminatory power for the Maxent. A Jackknife test for variable importance indicated that mean temperature of coldest quarter with highest gain value was the most important environmental variable determining the potential geographic distribution of P. solenopsis. The approaches used for delineating the ecological niche and prediction of potential geographic distribution are described briefly. Possible applications and limitations of the present modeling approach in future research and as a decision making tool in integrated pest management are discussed.