Social Life of Captive Asian Elephants (Elephas maximus) in Southern India: Implications for Elephant Welfare

Asian elephants in the wild live in complex social societies; in captivity, however, management often occurs in solitary conditions, especially at the temples and private places of India. To investigate the effect of social isolation, this study assessed the social group sizes and the presence of stereotypies among 140 captive Asian elephants managed in 3 captive systems (private, temple, and forest department) in Tamil Nadu, India, between 2003 and 2005. The majority of the facilities in the private (82%) and temple (95%) systems held a single elephant without opportunity for social interaction. The forest department managed the elephants in significantly larger groups than the private and temple systems. Among the 3 systems, the proportion of elephants with stereotypies was the highest in temple (49%) followed by private system (26%) and the forest department facility (6%); this correlates with the social isolation trend observed in the 3 systems and suggests a possible link between social isolation and abnormal elephant behavior separate from other environmental factors. The results of this study indicate it would be of greater benefit to elephant well being to keep the patchily distributed solitary temple and private elephants who are socially compatible and free from contagious diseases in small social groups at ocommon elephant houseso for socialization.

Social selection and the evolution of cooperative groups: The example of the cellular slime moulds

In social selection the phenotype of an individual depends on its own genotype as well as on the phenotypes, and so genotypes, of other individuals. This makes it impossible to associate an invariant phenotype with a genotype: the social context is crucial. Descriptions of metazoan development, which often is viewed as the acme of cooperative social behaviour, ignore or downplay this fact. The implicit justification for doing so is based on a group-selectionist point of view. Namely, embryos are clones, therefore all cells have the same evolutionary interest, and the visible differences between cells result from a common strategy. The reasoning is flawed, because phenotypic heterogeneity within groups can result from contingent choices made by cells from a flexible repertoire as in multicellular development. What makes that possible is phenotypic plasticity, namely the ability of a genotype to exhibit different phenotypes. However, co-operative social behaviour with division of labour requires that different phenotypes interact appropriately, not that they belong to the same genotype, or have overlapping genetic interests. We sketch a possible route to the evolution of social groups that involves many steps: (a) individuals that happen to be in spatial proximity benefit simply by virtue of their number; (b) traits that are already present act as preadaptations and improve the efficiency of the group; and (c) new adaptations evolve under selection in the social context-that is, via interactions between individuals-and further strengthen group behaviour. The Dictyostelid or cellular slime mould amoebae (CSMs) become multicellular in an unusual way, by the aggregation of free-living cells. In nature the resulting group can be genetically homogeneous (clonal) or heterogeneous (polyclonal); in either case its development, which displays strong cooperation between cells (to the extent of so-called altruism) is not affected. This makes the CSMs exemplars for the study of social behaviour.

Science as a hobby: how and why I came to study the social life of an Indian primitively eusocial wasp

For the most part, my research career has involved prying into the life of the locally abundant primitively eusocial paper wasp, Ropalidia marginata, with the aim of understanding the origin and evolution of social life in insects. My interest in this wasp species began as a hobby, but I was privileged to soon convert my hobby into my profession. Here I describe how this conversion came about, what it meant to pursue my hobby as a full-time activity, describe some examples from my research and end with some reflections about the process of doing modern science, especially in India.

Barriers to plantation activities in different agro-ecological zones of Southern India

This paper analyses environmental and socio-economic barriers for plantation activities on local and regional level and investigates the potential for carbon finance to stimulate the increased rates of forest plantation on wasteland, i.e., degraded lands, in southern India. Building on multidisciplinary field work and results from the model GCOMAP, the aim is to (1) identify and characterize the barriers to plantation activities in four agro-ecological zones in the state of Karnataka and (2) investigate what would be required to overcome these barriers and enhance the plantation rate and productivity. The results show that a rehabilitation of the wasteland based on plantation activities is not only possible but also anticipated by the local population and would lead to positive environmental and socio-economic effects at a local level. However, in many cases, the establishment of plantation activities is hindered by a lack of financial resources, low land productivity and water scarcity. Based on the model used and the results from the field work, it can be concluded that certified emission reductions such as carbon credits or other compensatory systems may help to overcome the financial barrier; however, the price needs to be significantly increased if these measures are to have any large-scale impact.

Autonomous and non-autonomous traits mediate social cooperation in Dictyostelium discoideum

In the trishanku (triA(-)) mutant of the social amoeba Dictyostelium discoideum, aggregates are smaller than usual and the spore mass is located mid-way up the stalk, not at the apex. We have monitored aggregate territory size, spore allocation and fruiting body morphology in chimaeric groups of (quasi-wild-type) Ax2 and triA(-) cells. Developmental canalisation breaks down in chimaeras and leads to an increase in phenotypic variation. A minority of triA(-) cells causes largely Ax2 aggregation streams to break up; the effect is not due to the counting factor. Most chimaeric fruiting bodies resemble those of Ax2 or triA(-). Others are double-deckers with a single stalk and two spore masses, one each at the terminus and midway along the stalk. The relative number of spores belonging to the two genotypes depends both on the mixing ratio and on the fruiting body morphology. In double-deckers formed from 1:1 chimaeras, the upper spore mass has more Ax2 spores, and the lower spore mass more triA(-) spores, than expected. Thus, the traits under study depend partly on the cells' own genotype and partly on the phenotypes, and so genotypes, of other cells: they are both autonomous and non-autonomous. These findings strengthen the parallels between multicellular development and behaviour in social groups. Besides that, they reinforce the point that a trait can be associated with a genotype only in a specified context.

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

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

A model investigation of vegetation-atmosphere interactions on a millennial timescale

A terrestrial biosphere model with dynamic vegetation capability, Integrated Biosphere Simulator (IBIS2), coupled to the NCAR Community Atmosphere Model (CAM2) is used to investigate the multiple climate-forest equilibrium states of the climate system. A 1000-year control simulation and another 1000-year land cover change simulation that consisted of global deforestation for 100 years followed by re-growth of forests for the subsequent 900 years were performed. After several centuries of interactive climate-vegetation dynamics, the land cover change simulation converged to essentially the same climate state as the control simulation. However, the climate system takes about a millennium to reach the control forest state. In the absence of deep ocean feedbacks in our model, the millennial time scale for converging to the original climate state is dictated by long time scales of the vegetation dynamics in the northern high latitudes. Our idealized modeling study suggests that the equilibrium state reached after complete global deforestation followed by re-growth of forests is unlikely to be distinguishable from the control climate. The real world, however, could have multiple climate-forest states since our modeling study is unlikely to have represented all the essential ecological processes (e. g. altered fire regimes, seed sources and seedling establishment dynamics) for the reestablishment of major biomes.

Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Context aware M-commerce services: C-IOB model approach

Context-aware computing is useful in providing individualized services focusing mainly on acquiring surrounding context of user. By comparison, only very little research has been completed in integrating context from different environments, despite of its usefulness in diverse applications such as healthcare, M-commerce and tourist guide applications. In particular, one of the most important criteria in providing personalized service in a highly dynamic environment and constantly changing user environment, is to develop a context model which aggregates context from different domains to infer context of an entity at the more abstract level. Hence, the purpose of this paper is to propose a context model based on cognitive aspects to relate contextual information that better captures the observation of certain worlds of interest for a more sophisticated context-aware service. We developed a C-IOB (Context-Information, Observation, Belief) conceptual model to analyze the context data from physical, system, application, and social domains to infer context at the more abstract level. The beliefs developed about an entity (person, place, things) are primitive in most theories of decision making so that applications can use these beliefs in addition to history of transaction for providing intelligent service. We enhance our proposed context model by further classifying context information into three categories: a well-defined, a qualitative and credible context information to make the system more realistic towards real world implementation. The proposed model is deployed to assist a M-commerce application. The simulation results show that the service selection and service delivery of the system are high compared to traditional system.

Method of designing an access mechanism for social networks

The rapid development of communication and networking has lessened geographical boundaries among actors in social networks. In social networks, actors often want to access databases depending upon their access rights, privacy, context, privileges, etc. Managing and handling knowledge based access of actors is complex and hard for which broad range of technologies need to be called. Access based on dynamic access rights and circumstances of actors impose major tasks on access systems. In this paper, we present an Access Mechanism for Social Networks (AMSN) to render access to actors over databases taking privacy and status of actors into consideration. The designed AMSN model is tested over an Agriculture Social Network (ASN) which utilises distinct access rights and privileges of actors related to the agriculture occupation, and provides access to actors over databases.

Scalable Preference Aggregation in Social Networks

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.

Delineating Ecological Boundaries of Hanuman Langur Species Complex in Peninsular India Using MaxEnt Modeling Approach

Hanuman langur is one of the widely distributed and extensively studied non-human diurnal primates in India. Until recently it was believed to be a single species - Semnopithecus entellus. Recent molecular and morphological studies suggest that the Hanuman langurs consists of at least three species S. entellus, S. hypoleucos and S. priam. Furthermore, morphological studies suggested that both S. hypoleucos and S. priam have at least three subspecies in each. We explored the use of ecological niche modeling (ENM) to confirm the validity of these seven taxa and an additional taxon S. johnii belonging to the same genus. MaxEnt modeling tool was used with 19 bioclimatic, 12 vegetation and 6 hydrological environmental layers. We reduced total environmental variables to 14 layers after testing for collinearity and an independent test for model prediction was done using ENMTools. A total of 196 non-overlapping data points from primary and secondary sources were used as inputs for ENM. Results showed eight distinct ecological boundaries, corroborating the eight taxa mentioned above thereby confirming validity of these eight taxa. The study, for the first time provided ecological variables that determined the ecological requirements and distribution of members of the Hanuman langur species complex in the Indian peninsula.