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Understanding the spatial dynamics of insect distributions provides useful information about their ecological requirements and can also be used in site-specific pest management programs. Interactions between prey and predator are spatially and temporally dynamic and can be affected by several factors. In this study, geostatistics was used to characterize the spatial variability of spotted alfalfa aphid, Therioaphis maculata Buckton and coccinellid lady beetles in alfalfa fields. Global positioning and geographic information systems were used for spatial sampling and mapping the distribution pattern of these insects. This study was conducted in three alfalfa fields with areas of 7.3, 3.1 and 0.5 ha and two growing seasons, 2013 and 2014. The 0.5 ha field was divided into 10 × 10m grids and 3.1 and 7.3 ha fields were divided into 30 × 30m grids. Weekly sampling began when height of alfalfa plants reached about 15cm and was continued until the cuttings of alfalfa hay. For sampling, 40 and 10 stems were chosen randomly in 30 × 30m and 10 × 10m grids, respectively and shaken into a white pan three times. Aphids and coccinellids fallen in the pan were counted and recorded. Semivariance analysis indicated that distribution of T. maculata and coccinellids was aggregated in the fields. Comparison of the distribution maps of aphid and lady beetles indicated that there was an overlap between the maps, but they did not coincide completely. This study revealed that relationship between spotted alfalfa aphid and lady beetles was spatially dynamic. These results can be used in biological control and site-specific management programs of T. maculata.
 

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Aims The purpose of this study was to evaluate the competency of logistic regression (LR) and maximum entropy (MaxEnt) models to predict the distribution of Dorema ammoniacum D. Don. in rangeland habitats in the central region of Iran, Yazd province.
Materials & Methods The potential distribution map of Dorema ammoniacum D. Don. was prepared. The homogenous habitats were identified, and vegetation sampling was conducted using a systematic random method. The data including: soil (physical and chemical properties), physiographic (slope, aspect and altitude), and vegetation data (presence and absence) were used. Soil sampling was performed at two depths of 0-30, and 30-60 cm. The required maps were prepared using interpolation method. Statistics were taken from 90 plots along 9 transect both in the presence and absence area. Response curve and Jackknife test (for MaxEnt method) were employed to identify the most important environmental predictive factors. The kappa index was used to determine the agreement between the actual and predicted maps.
Findings The accuracy of predicted map was weak in LR Model (AUC= 0.65), but it was considerably high in the MaxEnt model (AUC=0.87). The agreement between the predicted map of MaxEnt model, and ground truths was very good (kappa=0.74), and the agreement between predicted map generated by LR with the ground-truths was medium (kappa=0.5).
Conclusion This plant has a limited ecological niche; therefore, the MaxEnt model could take precedence over the LR model because the only data it employs is the presence of the species.

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A new species of jumping spiders (Araneae, Salticidae), Afraflacilla goaensis Gawas & Tripathi sp. nov., is described, diagnosed and illustrated based on a single male specimen collected from a mangrove in Goa, India. Considering this finding, the genus Afraflacilla previously known from the Indian states of Tamil Nadu, Kerala, Gujarat, and Rajasthan, is now documented for the first time in Goa. Finally, the Indian records of the genus Afraflacilla are mapped.