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Social and ecological factors influencing movement and organizational patterns in
sheep
Habiba, Caitlin Barale, Ipek Kulahci,
Rajmonda Sulo and Khairi Reda
complementary approaches from ecology and computer science
How do we identify the key individuals in a group?
•Personality 'types’•Group social dynamics•Group movement patterns
Approaches
Ecology• Direct observation
• Information on:• Proximity
• Individual movement patterns
• Behavior
• Interactions
• Individual state
Computer Science• Remote sensing
• Information on:• Proximity
• Group-level movement patterns
• Clustering
• Velocity
• Turning angle
How can we use concepts from computer science to study animal behavior?
•Association visualizations
•Static networks
•GPS clustering algorithms
•Dynamic networks
•Social network analysis
•Agent-based modeling
Personality Analysis
• PCA to generate personality ‘types’
• Static social networks
• Association visualizations
Push/pullPush/pushedFollowed pullsPull/followerPush/follower
Lactation state
Join time in the morningsGroup joiningAlone grazingLocation- frontLocation- backLocation- edgeLocation- front during movement
Personality components
Push/pullPush/pushedFollowed pullsPull/followerPush/follower
Lactation state
Join time in the morningsGroup joiningAlone grazingLocation- frontLocation- backLocation- edgeLocation- front during movement
Personality components
PCA component 1 - spatial
PCA component 2 - behavioral
PCA 2Push/pull
Push/pushedFollowed pulls
PCA1Location- backLocation- edge
Location- front during movement
Personality scores
Pull-follow and displacement networks
What does the network look like from the perspective of individuals?
Key individuals – pull network
Size: # of times an individual pulls
Color: PCA category
Lactating
Not-lactating
Key individuals – push network
Size: # of times an individual is pushed
Color: PCA category
Lactating
Not-lactating
Key individuals – nearest neighbor network
Size: # of times two individuals are
neighbors while aligned-grazing
Color: PCA category
Lactating
Not-lactating
GPS clustering
What is an edge ?
Use k-means algorithm to spatially cluster gps readings. Any two sheep in the same cluster have an edge between them.
Use the Simple Structure Index (SSI) to determine the number of optimal clusters in each time interval
Using different graph theoretic measures, the goal is to identify sampling rates at which the measures contain minimal noise while still being informative of the underlying dynamics in the network.
What is the right temporal resolution?
Apply information theoretic concepts to quantify noise and information and compute the best trade-off across different sampling rates.
Algorithm TWIN
For w < wmax
* Compute the interaction network.
* Compute the different structure measures such
as density, and centrality, etc.Quantify the amount of noise and information inherent
in the network measure.Compute a measure of goodness of fit based on a
trade-off between the two.
Community Identification
•A dynamic community is a subset of individuals that stick together over time.
•NOTE: Communities ≠ Groups
5 4 32 1
5
4
5
4
1
4
12 3 4
5 2
2 3
5 2 3 1
t=1
t=2
t=3
t=4
t=5
Approach: Assumptions
• Individuals and groups represent exactly one community at a time.
• Concurrent groups represent distinct communities.
Desired
Required
•Conservatism: community affiliation changes are rare.•Group Loyalty: individuals observed in a group belong to the same community.•Parsimony: few affiliations overall for each individual.
• Interesting behavioral aspects to model– Social foraging• Alignment• Sub-group formation from local interactions• “Exploratory” random walks
– Foraging strategy• How do sheep find good food in patchy landscape?• How does information about food resources spread in the
herd?• Competition
– Pull and push
Agent-based model
• Interesting behavioral aspects to model– Social foraging• Alignment• Sub-group formation from local interactions• “Exploratory” random walks
– Foraging strategy• How do sheep find good food in patchy landscape?• How does information about food resources spread in the
herd?• Competition
– Pull and push
Agent-based model
– Sheep watch closest N neighbors and react to their behavior
– No personality variance among agents– Agents assumed to be “hungry”. They graze
continuously without rest– Agents have 3 states: GRAZE, WALK, PANIC– Low vegetation quality and patchy landscape;
agents need to move frequently to maximize gain
Assumptions
• Agents switch back and forth between grazing and social walking
• State transition governed by a Gaussian timer– Grazing bouts: mean=6 sec, SD=2 sec– Social walking: mean=2-8 sec (depending on
isolation), SD=2.5 sec
• Certain events cause agents to switch state– Agents minimize grazing and take longer walks if
they are isolated from subgroup
Timing
Local rules
Collision avoidance Cohesion Alignment
Random biased-walksP = 0.05 (when walking)
Pulling
Sheep 15 – an influential individual??
• Central in the observational networks– High betweeness and degree– Highest number of successful pulls
• Role in correlational plots– Often an outlier– Makes trends more significant
• GPS – doesn’t stand out• Why?? Missing data…
Sheep 19 – a loner??
• Static networks
• Not well connected– >>>
• Animation• Little interaction with others in herd
• GPS – consistently low spread values
Are social network measures of individuals consistent across the three networks?
Push & pull networks: + correlation (betweenness & closeness)Push & nearest neighbor networks: - correlation (eigenvector &
closeness)
Network comparison
Is there consistency between observational subgroups and GPS clustering data?
Is there consistency between personality analysis obtained from observations and that obtained from GPS data aggregated at the “optimal” time interval?
CS future directions
•Community structure evolution.
•Compare the observational data with “unsupervised” data collection and interpretation methods.
•Network comparison of different population to discover their social dynamics independent of their environment and other factors.