Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population's fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson's correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana's outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana's landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest's population.
Pieris rapae is a serious pest of brassicas worldwide. We performed de novo assembly of P. rapae transcriptome by next-generation sequencing and assembled approximately 65,727,422 clean paired-end reads into 32,118 unigenes, of which 13,585 were mapped to 255 pathways in the KEGG database. A total of 6173 novel transcripts were identified from reads directly mapped to P. rapae genome. Additionally, 1490 SSRs, 301,377 SNPs, and 29,284 InDels were identified as potential molecular markers to explore polymorphism within P. rapae populations. We screened and mapped 36 transcripts related to OBP, CSP, SNMP, PBAN, and OR. We analyzed the expression profiles of 7 selected genes involved in pheromone transport and degradation by quantitative real-time PCR; these genes are sex-specific and differentially expressed in the developmental stages. Overall, the comprehensive transcriptome resources described in this study could help understand and identify molecular targets particularly reproduction-related genes for developing effective P. rapae management tools.
Matched MeSH terms: Lepidoptera/growth & development