Person:
Henzi, S. Peter

Profile Picture
Email Address
Affiliation
ORCID
Birth Date
Job Title
Last Name
Henzi
First Name
S. Peter
Creator of
Editor of
Reviewer of
Copyright Holder of
Data Contributor of
Funder of
Translator of
Other Contributor of

Filters

20171
Reset filters

Settings

Search Results

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Data package
    Data from: Direction matching for sparse movement datasets: determining interaction rules in social groups
    (2017-01-03) Bonnell, Tyler R.; Dostie, Marcus; Clarke, Parry M.; Henzi, S. Peter; Barrett, Louise
    It is generally assumed that high-resolution movement data are needed to extract meaningful decision-making patterns of animals on the move. Here we propose a modified version of force matching (referred to here as direction matching), whereby sparse movement data (i.e., collected over minutes instead of seconds) can be used to test hypothesized forces acting on a focal animal based on their ability to explain observed movement. We first test the direction matching approach using simulated data from an agent-based model, and then go on to apply it to a sparse movement data set collected on a troop of baboons in the DeHoop Nature Reserve, South Africa. We use the baboon data set to test the hypothesis that an individual’s motion is influenced by the group as a whole or, alternatively, whether it is influenced by the location of specific individuals within the group. Our data provide support for both hypotheses, with stronger support for the latter. The focal animal showed consistent patterns of movement toward particular individuals when distance from these individuals increased beyond 5.6 m. Although the focal animal was also sensitive to the group movement on those occasions when the group as a whole was highly clustered, these conditions of isolation occurred infrequently. We suggest that specific social interactions may thus drive overall group cohesion. The results of the direction matching approach suggest that relatively sparse data, with low technical and economic costs, can be used to test between hypotheses on the factors driving movement decisions.