This study investigated pollinator assemblage diversity and richness in four forested ecosystems of southern Punjab, Pakistan, with different landscape types. Pirowal is situated in the plains of irrigated Punjab, Lal Suhanra is part of a sandy desert ecosystem, Ghazi Ghat is part of the Indus River delta, and Fort Munro is located in dry hilly mountains. A yearlong survey of pollinator populations was carried out in these four forested ecosystems from January to December of 2010. Fortnightly hand netting was performed for collecting flower-visiting insects whereas, pan traps of three colors (white, blue, and yellow) were deployed for collecting the data. A total of 8,812 individuals from two orders (Lepidoptera and Diptera) were observed, including 22 families and 154 species. Bees were the most abundant, with 4,502 individuals, and the most species-rich taxa, with 70 species in five families, followed by flies having 2,509 individuals and 51species in 10 families. Wasps were the least abundant with 1,801 individuals and 33 species in seven families. The assemblage structure of pollinator communities as visualized through rank abundance curves showed that there were many species with low abundance and only a few species with a much higher abundance. The most abundant species among the bees, in order, were Nomia sp.3, Megachile bicolor, and Colletes sp.3; among flies, Syrphus sp.2, Calliphoridae sp.1, and Empididae sp.4; and among wasps, Tiphiidae sp.1, Myzininae sp.2, and Scelionidae sp.1.

International audienceIn survey sampling, calibration is a popular tool used to make total estimators consistent with known totals of auxiliary variables and to reduce variance. When the number of auxiliary variables is large, calibration on all the variables may lead to estimators of totals whose mean squared error (MSE) is larger than the MSE of the Horvitz-Thompson estimator even if this simple estimator does not take account of the available auxiliary information. We study a new technique based on dimension reduction through principal components that can be useful in this large dimension context. Calibration is performed on the first principal components, which can be viewed as the synthetic variables containing the most important part of the variability of the auxiliary variables. When some auxiliary variables play a more important role than others, the method can be adapted to provide an exact calibration on these variables. Some asymptotic properties are given in which the number of variables is allowed to tend to infinity with the population size. A data driven selection criterion of the number of principal components ensuring that all the sampling weights remain positive is discussed. The methodology of the paper is illustrated, in a multipurpose context, by an application to the estimation of electricity consumption with the help of 336 auxiliary variables

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Spatial distribution of drought plays key role specifically in hydrological research. Drought is a complex stochastic natural hazard caused by prolonging shortage of rainfall and available water resources. The multiscalar drought indices (based on probability distribution) are commonly used for making effective drought mitigation policies. In addition, the multi-scalar drought indices have great extent of the inaccurate determination of drought classes due to its probabilistic nature. However, the interpretation and applicability of various time scales are cumbersome for multi-scalar drought in various meteorological stations at a particular region. In this regards, accurate estimation and continuous monitoring of future drought at regional level requires a more appropriate and important time scale with respect to regions under study. In this study, we aimed to investigate the appropriate time scale for multi-scalar drought indices by using georeference points of meteorological stations. We used Boruta algorithm with two random forest adapters of machine learning algorithms for regionalized optimization of drought monitoring time scale. We explored the appropriate time scale for the Standardized Precipitation Temperature Index (SPTI) of 52 meteorological stations that are located across Pakistan. Results show that the significant importance of SPTI-1 (1-month time scale) for further spatial and temporal studies. That is, being high ranked and prominence, SPTI-1 has the ability to capture the characteristics of all other time scales that are in some circumstances applicable for drought characterization and classification.

The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas.

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