ٍEfficiency of mating disruption for controlling the leopard moth, Zeuzera pyrina L. (Lep.: Cossidae), in walnut orchards

Leopard moth, Zeuzera pyrina L. (Lep.: Cossidae), is a key pest of walnut in Iran. The efficiency of mating disruption by two commercial products including Isonet® Z and Zeumat Universe® was evaluated in 2011 in East Azerbaijan and Kerman provinces. In each hectar, 300-600 pheromone dispensers for mating disruption were attached to the trees before the emergence of adults. Three pheromone traps were also installed in each plot to evaluate the orientation disruption before the adults' emergence. The pheromone traps did not capture any adult in the plots where mating disruption was performed during the experiment in both provinces while the traps caught the adults in the control plots. This result confirmed the efficiency of the used number of products for orientation disruption of the males. The averages of the number of larval entrances on the branches of plots where mating disruption was conducted by Isonet® Z, Zeumat Universe® and control were respectively 0.18 ± 0.03, 0.13 ± 0.02 and 0.45 ± 0.04 in Kerman province, 0.18 ± 0.03, 0.33 ± 0.04 and 0.63 ± 0.07 in East Azarbaiejan province and 0.18 ± 0.02, 0.2 ± 0.02 and 0.52 ± 0.04 (average ± S. E.) for the pooled data of both regions. There was a significant difference among the averages (P < 0.01).The results showed the efficiency of mating disruption technique by both products for reducing the damage of the pest even after one year. The number of larval entrances on the branches of the trees in mating disruption plots was 60-70% less than control plots. The results confirmed that the mating disruption with 300 pheromone dispensers of each product is efficient for reducing the damage of the pest in the walnut orchards

Study on daily and reproduction activity of melon weevil, Acythopeus curvirostris persicus (Col.: Curculionidae), in Birjand, Iran

Melon weevil, Acytopeus curvirostris persicus Thompson, is one of the most important pests of melons that is spread in the Middle East countries. In this study, diurnal and seasonal locomotor and flight activity of melon weevil were investigated under field condition using bucket traps (baited with conspecifics male and cucumber fruit) and also mating behavior pattern and its effect on fecundity (under semifield conditions) and fertility (under laboratory conditions). The results showed that both males and females of melon weevil have distinct daily activity. Two activity peaks were observed at 7:00-9:00 am and 17:00-19:00 pm. Maximum flight activity was observed in the afternoon (14:00 hours) in field condition. Both sexes emerged in early cultivation season (late June) simultaneously and the number of captured weevil was the highest in early October (6.1 ± 2.6 weevils per week). Emerged adults from pupal cocoon mated after about eight days. Peak of mating occurred during 14:30-16:30 pm. Number of mating during lifetime of males (18.3 ± 3.4) was nearly two times greater than females (9.6 ± 2.2). The mean number of eggs laid during reproduction period in single and multiple mating were 29.2 ± 3.5 and 52.4 ± 6.2 eggs per female, respectively. Oviposition was maximum on 12 and 36 days after mating. Hatching rates of eggs in single and multiple mating were 85 ± 7% and 82.5 ± 9%, respectively. Multiple mating had no significant effect on fecundity and fertility. There was no relationship between mating frequency and temperature

Gis-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision-recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran

Mitochondrial genetic variation and invasion history of red palm weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae), in Middle-East and Mediterranean Basin

The Red Palm Weevil (RPW), Rhynchophorus ferrugineus (Olivier), (Coleoptera, Curculionidae, Rhynchophorinae), is an invasive pest of palm trees. RPW has invaded Middle East and several countries of the Mediterranean Basin during the last three decades. The mitochondrial genetic variation of RPW was investigated in the Middle-East and the Mediterranean basin areas using partial sequences of the Cytochrome c oxidase sub-unit 1 (CO1) gene. A 546-base pair portion of COI gene was sequenced for 310 individuals of RPW sampled from 14 different invaded countries resulting in eight different haplotypes. Eight haplotypes were subdivided into two phylogenetic groups according to their geographic positions. The obtained genetic diversity suggested that RPW population subdivided genetically into different sub-populations under the influence of genetic drift favored by founder events. RPW followed three different routes of invasion during the last 30 years. Likely, Middle-east populations and the Mediterranean ones are originating from different geographic populations of RPW. The data reported in this paper present an interesting and useful step toward the understanding of the genetic variation and invasion history of RPW

Origin and taxonomic status of the Palearctic population of the stem borer Sesamia nonagrioides (Lefebvre) (Lepidoptera: Noctuidae)

The major pest of maize in Mediterranean Europe, the stem borer Sesamia nonagrioides (Lefebvre) (Lepidoptera: Noctuidae), has a fragmented distribution, north and south of the Sahara. The present study aimed: (1) to clarify the uncertain taxonomic status of the Palearctic and sub-Saharan populations which were first considered as different species and later on as subspecies (Sesamia nonagrioides nonagrioides and Sesamia nonagrioides botanephaga) and (2) to investigate the origin of the Palearctic population which extends from Spain to Iran, outside what is considered typical for this mainly tropical genus. We reconstructed the evolutionary history of both populations using one nuclear and two mitochondrial genes. The sub-Saharan taxon was fragmented in two isolated populations (West and East) whose mitochondrial genes were distant by 2.3%. The Palearctic population was included in the East African clade and its genes were close or identical to those of a population from Central Ethiopia, where the species was discovered for the first time. Similarly, in Africa, the alleles of the nuclear gene were distributed mainly in two West and East clades, whereas some Palearctic alleles belonged to the West clade. The Palearctic population originated therefore from East and West Africa and is the progeny of the cross between these two African populations. The main species concepts were in agreement, leading to the conclusion that the three populations are still conspecific. In the surveyed regions, the species therefore does not include two subspecies but three isolated populations. The Palearctic population suffered from severe bottlenecks that resulted in the fixation of one East African mitochondrial genome and the large reduction in its genetic diversity compared to the African populations. The data suggest that natural colonization of the Palearctic region was more plausible than human introduction. The allelic distribution of the Palearctic population was similar to that of species that survived the last glaciation. It is concluded that the African populations expanded during the last interglacial, crossed the Sahara and mixed in North Africa where fixation of the East mitochondrial genome occurred. The species then colonized Europe westward through only one eastern entrance. The coalescent-based estimate of the time to the ancestor of the Palearctic population was 108 000 years, which is consistent with this scenario

Flood susceptibility mapping using an improved analytic network process with statistical models

Abstract Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency

Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of iran

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping