Sean F. Hanser

Social network theory: new insights and issues for behavioral ecologists

Until recently, few studies have used social network theory (SNT) and metrics to examine how social network structure (SNS) might influence social behavior and social dynamics in non-human animals. Here, we present an overview of why and how the social network approach might be useful for behavioral ecology. We first note four important aspects of SNS that are commonly observed, but relatively rarely quantified: (1) that within a social group, differences among individuals in their social experiences and connections affect individual and group outcomes; (2) that indirect connections can be important (e.g., partners of your partners matter); (3) that individuals differ in their importance in the social network (some can be considered keystone individuals); and (4) that social network traits often carry over across contexts (e.g., SN position in male–male competition can influence later male mating success). We then discuss how these four points, and the social network approach in general, can yield new insights and questions for a broad range of issues in behavioral ecology including: mate choice, alternative mating tactics, male–male competition, cooperation, reciprocal altruism, eavesdropping, kin selection, dominance hierarchies, social learning, information flow, social foraging, and cooperative antipredator behavior. Finally, we suggest future directions including: (1) integrating behavioral syndromes and SNT; (2) comparing space use and SNS; (3) adaptive partner choice and SNS; (4) the dynamics and stability (or instability) of social networks, and (5) group selection shaping SNS.

Applicability of Information Theory to the Quantification of Responses to Anthropogenic Noise by Southeast Alaskan Humpback Whales

We assess the effectiveness of applying information theory to the characterization and quantification of the affects of anthropogenic vessel noise on humpback whale (Megaptera novaeangliae) vocal behavior in and around Glacier Bay, Alaska. Vessel noise has the potential to interfere with the complex vocal behavior of these humpback whales which could have direct consequences on their feeding behavior and thus ultimately on their health and reproduction. Humpback whale feeding calls recorded during conditions of high vessel-generated noise and lower levels of background noise are compared for differences in acoustic structure, use, and organization using information theoretic measures. We apply information theory in a self-referential manner (i.e., orders of entropy) to quantify the changes in signaling behavior. We then compare this with the reduction in channel capacity due to noise in Glacier Bay itself treating it as a (Gaussian) noisy channel. We find that high vessel noise is associated with an increase in the rate and repetitiveness of sequential use of feeding call types in our averaged sample of humpback whale vocalizations, indicating that vessel noise may be modifying the patterns of use of feeding calls by the endangered humpback whales in Southeast Alaska. The information theoretic approach suggested herein can make a reliable quantitative measure of such relationships and may also be adapted for wider application to many species where environmental noise is thought to be a problem.

Modeling the exposure of greater sage‐grouse to noise from industrial gas drilling rigs.

Natural gas and methane extraction is a growing industry in Wyoming, and some greater sage‐grouse leks appear to be declining in areas near industrial sites. The goal of this project is to develop a model for understanding whether industrial noise has played a significant role in these reductions in lek attendance. A software package called NMSIM, previously developed by Wylie Laboratories to measure noise exposure from aircraft for the National Park Service, is being used. NMSIM utilizes amplitude measurements, recorded a set distance from the noise source, topographic map data, and measurements of other factors affecting sound propagation such as temperature and humidity, to build a spatially explicit model simulating how noise from the industrial sites propagates over the surrounding terrain. Simulation results are then verified using a set of noise exposure measurements taken from known locations around the gas drilling rigs. In addition to explaining historic lek attendance patterns, this model will ...

Processing acoustic features of animal vocalizations using classification procedures.

Remote methods for classifying age, sex, group membership, or individual identification of animals that live in visually obscured environments are extremely valuable tools for field biologists, but reliable identification of individual callers still presents important challenges. Acoustic features of animal vocalizations can be processed to extract caller identification using a variety of sophisticated classification techniques, but the exact classification process can be difficult to justify rigorously and challenging to repeat on novel data. A feature extraction and classification process that is clear, simple, and repeatable would be a major benefit to wildlife studies. Classification and regression trees (CART) generate intuitive and clear processes for handling multidimensional acoustic information. Examples of CART applied to Mexican spotted owl (Strix occidentalus lucida) and humpback whale (Megaptera novaeangliae) vocalizations will be provided. These CART results will be compared to other classif...

A comparison of two information theory approaches to animal communication systems

Information theory has been used to analyze and explore animal communication systems using two different approaches. The first approach—which is the original application outlined by Shannon and Weaver (1949)—considers the temporal distribution (i.e., probabilities of occurrence) of the components of a communication system to quantify the diversity and complexity of a system’s (repertoires) organization. This quantification of the communication complexity is based on the information content, not just at the repertoire level, but also at the sequential (i.e., Markovian) organizational levels of the signals constituting the communication system. The second approach, which has been much more widely applied, with mixed results over several decades, considers a communication system’s ability to essentially ‘‘encode’’ signaling between individuals in order to measure how much information has been ‘‘transmitted.’’ We will outline and contrast these two approaches, pointing out that until the communication complex...