The role of competition and habitat in structuring small mammal communities in a tropical montane ecosystem in southern India

Small mammals were sampled in two natural habitats (montane stunted evergreen forests and montane grassland) and four anthropogenic habitats (tea, wattle, bluegum and pine plantation) in the Upper Nilgiris in southern India. Of the species trapped, eight were in montane evergreen forests and three were in other habitats. Habitat discrimination was studied in the rodents Rattus rattus and Mus famulus and the shrew Suncus montanus in the montane forest habitat. Multivariate tests on five variables (canopy cover, midstorey density, ground cover, tree density, canopy height) showed that R. rattus uses areas of higher tree density and lower canopy cover. Suncus montanus and M. famulus use habitat with higher tree density and ground cover and lower canopy height. Multivariate tests did not discriminate habitat use between the species. Univariate tests, however, showed that M. famulus uses areas of higher tree density than R. rattus and S. montanus. Rattus rattus was the dominant species in the montane forest, comprising 60.9% of total density, while the rodent Millardia meltada was the dominant species in the grassland. Studies of spatial interaction between these two species in habitats where they coexisted showed neither overlap nor avoidance between the species. Rattus rattus, however, did use areas of lower ground cover than did M. meltada. The analysis of spatial interactions between the species, habitat discrimination and use, and the removal experiments suggest that interspecific competition may not be a strong force in structuring these small mammal communities. There are distinct patterns in the use of different habitats by some species, but microhabitat selection and segregation is weak. Other factors such as intraspecific competition may play a more important role in these communities.

Gamma-ray luminosity function of gamma-ray bright AGNs

Detection of gamma-ray emissions from a class of active galactic nuclei (viz blazars), has been one of the important findings from the Compton Gamma-Ray Observatory (CGRO). However, their gamma-ray luminosity function has not-been well determined. Few attempts have been made in earlier works, where BL Lacs and Flat Spectrum Radio Quasars (FSRQs) have been considered as a single source class. In this paper, we investigated the evolution and gamma-ray luminosity function of FSRQs and BL Lacs separately. Our investigation indicates no evolution for BL Lacs, however FSRQs show significant evolution. Pure luminosity evolution is assumed for FSRQs and exponential and power law evolution models are examined. Due to the small number of sources, the low luminosity end index of the luminosity function for FSRQs is constrained with an upper limit. BL Lac luminosity function shows no signature of break. As a consistency check, the model source distributions derived from these luminosity functions show no significant departure from the observed source distributions.

Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated be fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. Thes parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.

Swam Intelligent Based Inverse Model for Laboratory Double Reservoir Diffusion Experiments

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time