David Lusseau

The emergent properties of a dolphin social network

Many complex networks, including human societies, the Internet, the World Wide Web and power grids, have surprising properties that allow vertices (individuals, nodes, Web pages, etc.) to be in close contact and information to be transferred quickly between them. Nothing is known of the emerging properties of animal societies, but it would be expected that similar trends would emerge from the topology of animal social networks. Despite its small size (64 individuals), the Doubtful Sound community of bottlenose dolphins has the same characteristics. The connectivity of individuals follows a complex distribution that has a scale-free power-law distribution for large k. In addition, the ability for two individuals to be in contact is unaffected by the random removal of individuals. The removal of individuals with many links to others does affect the length of the information path between two individuals, but, unlike other scale-free networks, it does not fragment the cohesion of the social network. These self-organizing phenomena allow the network to remain united, even in the case of catastrophic death events.

Animal social networks: an introduction

Network analysis has a long history in the mathematical and social sciences and the aim of this introduction is to provide a brief overview of the potential that it holds for the study of animal behaviour. One of the most attractive features of the network paradigm is that it provides a single conceptual framework with which we can study the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.). Graphical tools allow a visual inspection of networks which often helps inspire ideas for testable hypotheses. Network analysis itself provides a multitude of novel statistical tools that can be used to characterise social patterns in animal populations. Among the important insights that networks have facilitated is that indirect social connections matter. Interactions between individuals generate a social environment at the population level which in turn selects for behavioural strategies at the individual level. A social network is often a perfect means by which to represent heterogeneous relationships in a population. Probing the biological drivers for these heterogeneities, often as a function of time, forms the basis of many of the current uses of network analysis in the behavioural sciences. This special issue on social networks brings together a diverse group of practitioners whose study systems range from social insects over reptiles to birds, cetaceans, ungulates and primates in order to illustrate the wide-ranging applications of network analysis.

Cetaceans have complex brains for complex cognition.

A group of eminent cetacean researchers respond to headlines charging that dolphins might be flippin idiots. They examine behavioural, anatomical and evolutionary data to conclude that the large brain of cetaceans evolved to support complex cognitive abilities.

Incorporating uncertainty into the study of animal social networks

Network analysis is rapidly establishing itself as a powerful tool for studying the structure and dynamics of complex systems (Albert & Barabasi, 2002; Newman, 2003). It has proven useful in understanding social interactions among humans and non-humans and how global properties emerge from them (Girvan & Newman, 2002; Watts et al., 2002; Dodds et al., 2003; Lusseau, 2003; Lusseau & Newman, 2004; Croft et al., 2005; Flack et al., 2006; Lusseau, 2007b). It has also been helpful in describing and predicting the behavior of technological networks and some biological systems for which all interactions can be described as known absolute values. However, the application of network analysis to social systems involving non-human organisms has been slower, because it has been difficult to infer the statistical and biological significance of observed network statistics and structures (Croft et al., 2005; Lusseau et al., 2006). Two key aspects have presented difficulties. Firstly, in contrast to some human studies, analysts estimate social relationships among individuals, they do not know them, and often they estimate those based on quite limited data. Researchers estimate relationships by observing interactions or associations between individuals, ranging from behavioral events (such as grooming) to co-occurrence. They can then build relationship measures using interaction rates or association indices (Whitehead & Dufault, 1999). Yet these observations do not represent all the interactions occurring between individuals, they are a sample. Studies in animal network analyses have never discussed sampling uncertainty even though its consequences can greatly affect the results of such analyses when sample size, i.e. the number of times individuals are observed, is small. For example if two individuals are together 50% of the time and so have a true association index (Cairns & Schwager, 1987) of 0.5, if they were identified together 10 times the 95% confidence interval for the estimated association index is about 0.3-0.7 (Whitehead, 2008). A second problem is that most network analyses of non-humans have focused on binary networks, in which relationships are defined as being either present or absent. The matrix that represents the network contains only ones (when two individuals are defined as associated) and zeros (when they are not). Researchers have used binary transformations of continuous matrices of interaction rates or association indices to describe animal social networks. These transformations require certain arbitrary manipulations which can be justified to varying degrees (Lusseau, 2003; Croft et al., 2005). For example, one might decide that association indices smaller than an arbitrary value (say 0.5) should indicate the lack of a relationship (assigned a value of zero in the binary matrix) and those greater than 0.5 as a relationship (assigned a value of one in the binary matrix). Another example is to define pairs of which the association index is greater than expected if interactions occurred by chance as relationships (ones) and others not possessing relationships (zeros). Authors largely ignore these manipulations

Cyclicity in the structure of female baboon social networks

There is an established and very influential view that primate societies have identifiable, persistent social organizations. It assumes that association patterns reflect long-term strategic interests that are not qualitatively perturbed by short-term environmental variability. We used data from two baboon troops in markedly different habitats over three consecutive seasons to test this assumption. Our results demonstrate pronounced cyclicity in the extent to which females maintained differentiated relationships. When food was plentiful, the companionships identified by social network analysis in the food-scarce season disappeared and were replaced by casual acquaintanceships more representative of mere gregariousness. Data from the fourth, food-scarce, season at one site indicated that few companions were re-united. It is likely that this reflected stochastic variation in individual circumstances. These results suggest that attention could profitably be paid to the effects of short-term local contingencies on social dynamics, and has implications for current theories of primate cognitive evolution.

Evidence for social role in a dolphin social network

Social animals have to take into consideration the behaviour of conspecifics when making decisions to go by their daily lives. These decisions affect their fitness and there is therefore an evolutionary pressure to try making the right choices. In many instances individuals will make their own choices and the behaviour of the group will be a democratic integration of everyone’s decision. However, in some instances it can be advantageous to follow the choice of a few individuals in the group if they have more information regarding the situation that has arisen. Here I provide early evidence that decisions about shifts in activity states in a population of bottlenose dolphin follow such a decision-making process. This unshared consensus is mediated by a non-vocal signal, which can be communicated globally within the dolphin school. These signals are emitted by individuals that tend to have more information about the behaviour of potential competitors because of their position in the social network. I hypothesise that this decision-making process emerged from the social structure of the population and the need to maintain mixed-sex schools.

Unsustainable dolphin-watching tourism in Fiordland, New Zealand.

Bottlenose dolphins are a key resource of the tourism industry in Fiordland and are used on a daily basis by the tour operators offering cruises on the fiords. Recent studies have shown that the current levels of dolphin-boat interactions in this region cannot be sustained by bottlenose dolphins. Interactions have both shortand longterm effects on both individuals and their populations. Population models indicate that these effects may be affecting the viability of the three bottlenose dolphin populations living in Fiordland. We are currently observing drastic changes in the bottlenose dolphin population living in Doubtful Sound which can be linked to the level of boat interactions to which they are currently exposed. The creation of a multi-level marine mammal sanctuary would help minimise dolphin-boat interactions and still allow for some further growth in the tourism sector in Fiordland.

Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population

Background Access to oestrus females tends to be the main driver of male sociality. This factor can lead to complex behavioural interactions between males and groups of males. Male bottlenose dolphins may form alliances to consort females and to compete with other males. In some populations these alliances may form temporary coalitions when competing for females. I examined the role of dyadic and group interactions in the association patterns of male bottlenose dolphins in Doubtful Sound, New Zealand. There is no apparent mating competition in this population and no consortship has been observed, yet agonistic interactions between males occur regularly. Methodology/Principal Findings By comparing the network of male interactions in several social dimensions (affiliative, agonistic, and associative) I show that while agonistic interactions relate to dyadic association patterns, affiliative interactions seem to relate to group association patterns. Some evidence suggests that groups of males also formed temporary coalitions during agonistic interactions. While different groups of males had similar relationships with non-oestrus females, the time they spent with oestrus females and mothers of newborns differed greatly. Conclusions/Significance After considering several hypotheses, I propose that the evolution of these complex relationships was driven by sexual competition probably to out-compete other males for female choice.

A claim in search of evidence: reply to Manger's thermogenesis hypothesis of cetacean brain structure

In a recent publication in Biological Reviews, Manger (2006) made the controversial claim that the large brains of cetaceans evolved to generate heat during oceanic cooling in the Oligocene epoch and not, as is the currently accepted view, as a basis for an increase in cognitive or information‐processing capabilities in response to ecological or social pressures. Manger further argued that dolphins and other cetaceans are considerably less intelligent than generally thought. In this review we challenge Manger’s arguments and provide abundant evidence that modern cetacean brains are large in order to support complex cognitive abilities driven by social and ecological forces.

Efficient coding in dolphin surface behavioral patterns

W ords that are more frequently used tend to be shorter in human language [1, 2] [Figure 1(a)]. The length of a word can be measured in letters or phonemes. The shortening of words with frequency can be regarded as evidence of efficient coding: by employing shorter codes for more frequent words, it is possible to increase the rate of information transmitted [3]. This tendency is the rationale behind file compression techniques such as Huffman codes [4]. Efficient coding has not been reported in other species to our knowledge. Here, we will provide the first evidence of a negative correlation between frequency and code size in the surface behavioral patterns of bottlenose dolphins (Tursiops sp.). Surface behavioral patterns represent a series of body movement and behavioral units, which can be clearly distinguished as a bout at the water surface. Our standard classification of behavioral patterns has been used to define the ethogram of dolphin populations in many previous studies (see Ref. 5 for a review). Patterns, described in Ref. 5, were defined to be mutually exclusive and cumulatively inclusive. As a whole, they describe the entire behavioral repertoire of the bottlenose dolphin population living in Doubtful Sound, New Zealand, that can be observed at the surface. Here, we defined behavioral units that were unambiguously representing distinct parts in the body movement, which represented behavioral patterns. They were also defined to be mutually exclusive. We defined these units and pattern composition independently from this study and prior to it. Thus, this analysis did not influence the definition of units and patterns. These behavioral units can be used in different combinations to produce different behavioral patterns, which in many cases involve more than one behavioral unit (Table *Present address: Institute of Biological and Environmental Sciences, University of Aberdeen, Tillydrone Avenue, Aberdeen, AB24 2TZ, UK Correspondence to: Ramon Ferrer-i-Cancho, Departament de Llenguatges i Sistemes Informàtics, TALP Research Center, Universitat Politècnica de Catalunya, Campus Nord, Edifici B6 C/Jordi Girona Salgado 1-3, 08034 Barcelona, Spain. (e-mail: rferrericancho@lsi.upc.edu)