Territoriality of Giant Otter Groups in an Area with Seasonal Flooding

Territoriality carries costs and benefits, which are commonly affected by the spatial and temporal abundance and predictability of food, and by intruder pressure. Giant otters (Pteronura brasiliensis) live in groups that defend territories along river channels during the dry season using chemical signals, loud vocalizations and agonistic encounters. However, little is known about the territoriality of giant otters during the rainy season, when groups leave their dry season territories and follow fish dispersing into flooded areas. The objective of this study was to analyze long-term territoriality of giant otter groups in a seasonal environment. The linear extensions of the territories of 10 giant otter groups were determined based on locations of active dens, latrines and scent marks in each season. Some groups overlapped the limits of neighboring territories. The total territory extent of giant otters was correlated with group size in both seasons. The extent of exclusive territories of giant otter groups was negatively related to the number of adults present in adjacent groups. Territory fidelity ranged from 0 to 100% between seasons. Some groups maintained their territory for long periods, which demanded constant effort in marking and re-establishing their territories during the wet season. These results indicate that the defense capacity of groups had an important role in the maintenance of giant otter territories across seasons, which may also affect the reproductive success of alpha pairs.     

Social and River Networks for the Trees: Wounaan's Riverine Rhizomic Cosmos and Arboreal Conservation

ABSTRACT   The effects of environmental conservation and development are of significant anthropological interest. Recent focus on the politics of knowledge and translation has shown the importance of cosmology in conservation encounters. I examine how Wounaan indigenous peoples and extralocal conservation practitioners "translate" eastern Panama based on their own cosmologies. ¹ Specifically, I explore how Wounaan's social and river-networked rhizomic cosmos is overlooked in the practice of forest-focused conservation. This results from Panama's environmental history, in which actors simplified early representations of a complex landscape to one characterized by forests, as well as a Western bias toward forests with scant attention paid to cosmology. Finally, I note how Wounaan negotiate this cultural disconnect by emphasizing their ties to forests. In so doing, they buttress the arboreal bias, in turn reinforcing power relations, but also giving themselves political leverage in conservation activities. These results inform recent discussion about politics and scientific praxis in conservation.     

Network Theory and the Formation of Groups Without Evolutionary Forces

This paper presents a modified random network model to illustrate how groups can form in the absence of evolutionary forces, assuming groups are collections of entities at any level of organization. This model is inspired by the Zero Force Evolutionary Law, which states that there is always a tendency for diversity and complexity to increase in any evolutionary system containing variation and heredity. That is, in the absence of evolutionary forces, the expectation is a continual increase in diversity and complexity at any level of biological hierarchy. I show that, when modeled, this expectation of increasing variation results not only in the formation of groups, but also in a higher probability of group formation than is found in a model that is purely random.     

Diversity of mtDNA in Southern River Otter (Lontra provocax) from Argentinean Patagonia

Lontra provocax is an endemic species from Patagonia that has been categorized as "endangered" by the International Union for Conservation of Nature. In this study, noninvasive molecular methods were used to investigate the genetic diversity and haplotype distribution of L. provocax in Argentinean Patagonia. We analyzed 150 scat samples collected from 1995 to 2006 and obtained 13 sequences of control region with 1 haplotype and 34 sequences of cytochrome b with 4 haplotypes. The population of the south of Patagonia (Tierra del Fuego and De los Estados Island) showed a relatively high haplotype diversity (h = 0.71) and was statistically different to the population of the north (analysis of molecular variance, F(ST) = 0.15, P = 0.018). We concluded that there are 2 different subpopulations of L. provocax that deserve conservation attention and that the southern population appears not to have suffered a human-induced population bottleneck of the sort typically experienced by various otter species around the world.     

Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans’ physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks), we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods.     

Biology and conservation of the Giant Otter Pteronura brasiliensis

The Giant Otter Pteronura brasiliensis (Zimmermann, 1780) is endemic to South America, distributed throughout the Orinoco, Amazon and La Plata River basins and numerous localities in the Guyanas. This otter is the largest member of the family Mustelidae, clearly distinguished from other South American otter species by morphological and behavioural characteristics. Large-scale pelt hunting in the 1950s and 1960s led to the classification of the species as 'vulnerable' by the World Conservation Union (IUCN, 1990). Habitat degradation and destruction now present the largest threat to remaining populations. The current distribution of the Giant Otter is presented, emphasizing recent information collected in the Amazon Basin. Morphology, systematics, habitat, behaviour, diet, predation, competition, reproduction, development and physiology are discussed. New data on habitat use and preference, behaviour and diet in northern Amazônia are included, and recommendations for future conservation measures are made. Because of the decimation of the Giant Otter in its southern distribution and the habitat destruction occurring in the remainder of its range, we recommend that this species be elevated to 'endangered' status in the IUCN Red Data Book.     

Empathy Emerges Spontaneously in the Ultimatum Game: Small Groups and Networks

The Ultimatum game, in which one subject proposes how to share a pot and the other has veto power on the proposal, in which case both lose everything, is a paradigmatic scenario to probe the degree of cooperation and altruism in human subjects. It has been shown that if individuals are empathic, i.e., they play the game having in mind how their opponent will react by offering an amount that they themselves would accept, then non-rational large offers well above the smallest possible ones are evolutionarily selected. We here show that empathy itself may be selected and need not be exogenously imposed provided that interactions take place only with a fraction of the total population, and that the role of proposer or responder is randomly changed from round to round. These empathic agents, that displace agents with independent (uncorrelated) offers and proposals, behave far from what is expected rationally, offering and accepting sizable fractions of the amount to be shared. Specific values for the typical offer depend on the details of the interacion network and on the existence of hubs, but they are almost always significantly larger than zero, indicating that the mechanism at work here is quite general and could explain the emergence of empathy in very many different contexts.     

Raccoon Social Networks and the Potential for Disease Transmission

Raccoons are an important vector of rabies and other pathogens. The degree to which these pathogens can spread through a raccoon population should be closely linked to association rates between individual raccoons. Most studies of raccoon sociality have found patterns consistent with low levels of social connectivity within populations, thus the likelihood of direct pathogen transmission between raccoons is theoretically low. We used proximity detecting collars and social network metrics to calculate the degree of social connectivity in an urban raccoon population for purposes of estimating potential pathogen spread. In contrast to previous assumptions, raccoon social association networks were highly connected, and all individuals were connected to one large social network during 15 out of 18 months of study. However, these metrics may overestimate the potential for a pathogen to spread through a population, as many of the social connections were based on relatively short contact periods. To more closely reflect varying probabilities of pathogen spread, we censored the raccoon social networks based on the total amount of time spent in close proximity between two individuals per month. As this time criteria for censoring the social networks increased from one to thirty minutes, corresponding measures of network connectivity declined. These findings demonstrate that raccoon populations are much more tightly connected than would have been predicted based on previous studies, but also point out that additional research is needed to calculate more precise transmission probabilities by infected individuals, and determine how disease infection changes normal social behaviors.     

Social Influence and the Collective Dynamics of Opinion Formation

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.     

Dimensionality of Social Networks Using Motifs and Eigenvalues

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.     

The Role of Tactile and Chemical Stimuli in the Formation and Maintenance of the Processions of the Social Caterpillar Hylesia lineata (Lepidoptera: Saturniidae)

Colonies of the social caterpillar Hylesia lineata (Lepidoptera: Satumiidae) form long, single-file, head-to-tail processions as they move between their shelters and distant feeding sites. Although investigations of other processionary species have implicated a silk trail in the processionary process, silk plays little or no role in initiating or maintaining processions in H. lineata. Studies we report here implicate both tactile stimuli and a trail pheromone in the establishment and maintenance of processions. Processionaries elicit locomotion in the individual preceding them in line by brushing their heads against prominent sulci that project from the tips of their abdomens. Caterpillars mark their pathways with a pheromone deposited by brushing the ventral surfaces of their last abdominal segments against the substrate. The persistent pheromone is soluble in hexanes and appears to be secreted from glandular setae found on the proximal regions of the anal prolegs and the venter. In Y-choice tests, caterpillars selected newer trails over older trails and stronger trails over weaker trails. They did not distinguish between trials deposited by newly fed caterpillars and those deposited by starved caterpillars. Despite the unidirectional nature of processions, there is no indication that caterpillars can determine from the trail alone the direction in which the procession advanced. The significance of these findings to the foraging ecology of the caterpillars is discussed.     

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background

Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.

Methods/Principal Findings

To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.

Conclusion/Significance

Our results indicate that the network prediction system thus established is quite promising and encouraging.     

Matrilineal Cohesion and Social Networks in Macaca fuscata

In a provisioned troop of Japanese macaques (Macaca fuscata) in Arashiyama, Japan, greater adherence to Kawamura's rules of matrilineal rank inheritance and youngest ascendancy occurred among high-ranking females versus low-ranking females. Accordingly, high-ranking females formed more clustered hierarchies and low-ranking females had more dispersed hierarchies. A proximate explanation for this finding may be related to differences in how females maintain their social networks. To determine whether the clustering in the hierarchy was reflected in patterns of social cohesiveness, I compared network sizes of coalition and grooming partners for females in each third of the hierarchy. I calculated the proportion of available partners that were coalition and grooming partners within each category of relatedness (0.5 r 0.004 and r = 0). High-ranking females formed coalitions with a large proportion of their close relatives and a small proportion of their distant relatives; middle-ranking females supported an intermediate proportion of their close relatives and a small proportion of their distant relatives; and, low-ranking females formed coalitions with very few available close and distant relatives. High-ranking females groomed nearly all available close relatives and an intermediate proportion of distant relatives, whilst middle- and low-ranking females groomed a large proportion of available close relatives and a very small proportion of distant relatives. Thus, levels of clustering within the hierarchy appeared to reflect levels of social cohesion, in terms of grooming and coalition formation.     

Influence of Reciprocal Links in Social Networks

How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven''t displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks.     

Emergence,Evolution and Scaling of Online Social Networks

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.     

Contagion of Cooperation in Static and Fluid Social Networks

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects’ desire to attract new cooperative partners: even if many of one’s current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.     

Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals'' preferences and their experiences (the outcomes of decisions). Instead, because an individual''s learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members'' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.     

Political Ecology across Spaces, Scales, and Social Groups

Political Ecology across Spaces, Scales, and Social Groups. Susan Paulson and Lisa Gezon, eds. New Brunswick, NJ: Rutgers University Press, 2005. 289 pp.