Movement of Social Animals

Animals spend their lives on the move, creating networks of global connectivity that shape and increasingly are being shaped by ecosystem health. From the local quest to find food, shelter and mates, to the long-distance traverses of global migrations, movement provides a biological framework link- ing individual processes to population dynamics in organisms ranging from bacteria to humans. Yet, animal movement remains a black box, limiting our ability to model and predict important processes, including potential impacts of human-induced rapid environmental change, spread of diseases, and sustaining biodiversity. Resolving these questions is computationally challenging because of the complex interplay between individual, social and environmental processes. [project page]

Dynamic networks

Dynamic Network Analysis

Finding patterns of social interaction within a population has applications from epidemiology and marketing to conservation biology and behavioral ecology. An intrinsic characteristics of societies is their continual change. Yet, few analysis methods are explicitly dynamic. We are working on novel conceptual and computational frameworks to accurately describe the social context of an individual at time scales matching changes in individual and group activity. Current projects in this direction include finding communities and critical individuals in dynamic networks, and fine-grained interaction prediction in dynamic networks. [wiki]

Supported by:
  • NSF CAREER Award IIS-0747369 "Computational Tools for Population Biology"
  • NSF grant 0705822 "Computational Methods for Understanding Social Interactions in Animal Populations"

  • Microsoft

  • Sibling reconstruction

    Sibling Relationship Reconstruction

    Please see our Wiki Page for more information.

    Falcons and other birds of prey are extremely secretive about their lives. Sharks are hard to catch and study because they live in water. Cowbirds leave eggs in other birds' nests and let them raise the cowbird chicks. One of the things common to all these species is that it is difficult to study their mating system. It is even difficult to identify which animals or plants are siblings. Yet, this simple fact is necessary for conservation, animal management, and understanding of evolutionary mechanisms.

    Our team has developed the first efficient computational method for reconstruction of sibling relationships from genetic data geared especially to wild populations. Now, given a genetic sample of the individuals from the same generation, we can identify full siblings without any information on the parents. Unlike other methods, ours does not assume anything about the data beyond the simple Mendelian laws of inheritance and does not require any a priori knowledge about the population. Our method reconstructs known sibling groups exactly on data from such diverse species as cod, shrimp, flies, and radishes. Biologists now can use our method to find out more about mating systems by just taking a genetic sample from chicks in nests or young shark in shallow waters.

    Supported by:
  • NSF CAREER Award IIS-0747369 "Computational Tools for Population Biology"
  • NSF grant 0612044 "Computational Methods for Kinship Reconstruction"

  • ZebraID

    Automatic Individual Animal Identification from Photographs

    In wild animal populations, collecting behavioral data about a species often entails identifying individual animals between sightings taken at different places and times. This is a primitive operation in ecological analysis that underlies broader aspects of animal behavior research. Electronic tracking devices embedded in animals are one ap proach to identifying individual animals, but can be prohibitively expensive and difficult to design for field conditions, and involve considerable cost and risk for larger an imals. Researchers are therefore left with no alternative other than to manually record data in the field us ing, for example, genetic markers in excrement, capture recapture techniques, or manual identification from photographs. Advances in hardware and the correspond ing drop in prices of digital cameras have resulted in an increase in the availability of digital photographs of wild animal sightings at high resolutions and qualities, making it an attractive candidate for fully-automatic or computer-assisted animal identification.

    In collaboration with the Princeton Equid research group, we have developed StripeSpotter, a program to perform automatic individual animal identification of zebras and other striped animals. It is an ongoing project, and has currently been deployed at various nature conservancies in Kenya.

    Supported by:
  • NSF CAREER Award IIS-0747369 "Computational Tools for Population Biology"
  • NSF grant 0705822 "Computational Methods for Understanding Social Interactions in Animal Populations"

  • UIC Provost's Award (Mayank Lahiri)
  • Camera trap

    Computational tools for camera trap analysis

    This project will create computational methods to detect animals in camera trap images and to identify species of detected animals to support wildlife tracking for biology research. [abstract]