Quantifying social attributes and their consequences in yellow-bellied marmots

Tina W. Wey1 & Daniel T. Blumstein2

1 University of California, Davis; twwey@ucdavis.edu2 University of California, Los Angeles; marmots@ucla.edu

Tom Uhlman

Causes and consequences of sociality

Sociality is widespread and varied Questions

Why is there social variation?

What are the consequences of social variation?

Multiple levels Proximate and ultimate causes of sociality

Individual and group variation

Many ways to quantify sociality

Interactions are important

INTERACTIONS

RELATIONSHIPS

SOCIAL STRUCTURE

Hinde 1976

Social network variation

Outline

Social network structure and consequences in yellow-bellied marmots How are marmot social networks structured at

multiple levels?

Do individual social attributes have fitness correlates?

Yellow-bellied marmots

Rocky Mountain Biological Laboratory

Marmota flaviventris

Fieldwork

Data Trapping

Adriana Maldonado-Chaparo

Karisa Tang

Fieldwork

Data Observations

2003-2008

Brian Smith

Carol Glazer

Marmot social networks

Quantifying network variation

Affiliative (cohesive, “friendly”) vs. agonistic (competitive, “unfriendly”) interactions

Multiple levels of analysis Networks

Dyads

Individuals

vs

Network structure – “Friendly” and “unfriendly” networks differ

Measure Affiliative Agonistic

Network structure – “Friendly” and “unfriendly” networks differ

Measure Affiliative Agonistic

Density

N = 24Z = -3.741P < 0.001

Network structure – “Friendly” and “unfriendly” networks differ

Measure Affiliative Agonistic

Density

Reciprocity

N = 24Z = -3.741P < 0.001

N = 24Z = -3.657P < 0.001

Network structure – “Friendly” and “unfriendly” networks differ

Measure Affiliative Agonistic

Density

Reciprocity

Clusteringcoefficient

N = 24Z = -3.741P < 0.001

N = 24Z = -3.657P < 0.001

N = 24Z = -0.343P < 0.001

Dyadic structure

Wey & Blumstein 2010 Animal Behaviour

Age and kinship influence affiliation preference Marmots are more friendly to others in the same age class and

closer relatives

Less consistent “rules” for who fights

Friendship Fighting

Age class

Sex

Kinship

Individual variation

Age and sex influence social tendencies Older and male marmots are crankier

Older marmots receive fewer affiliations, and initiate more and receive less aggression

Females receive affiliation from more partners, and males initiate aggression to more

Affiliative Agonistic

Initiation Reception Initiation Reception

Age

Sex

Wey & Blumstein 2010 Animal Behaviour

Social variation can have associated consequences

Individual social variation influences reproductive success

Social bonds can improve fitness, especially for females

Aggression and dominance can result in differential fitness

Increased parasitism is often a cost of sociality Increase with group size (Côté & Poulin 1995, Bordes et al. 2007)

Social behavior is expected to influence parasites (Møller et al. 1993, Altizer et al. 2003)

Consequences of individual social variation in marmots

Background: Dominance rank is associated with reproductive success in

males but not females (Huang et al. 2011 Ethology)

Larger males tend to be dominant

Social cohesion predicts female dispersal (Blumstein et al. 2009 Proc B)

Social variation is heritable (Lea et al. 2010 PNAS)

Predictions: Social cohesion would improve female reproductive success

Aggression would predict males reproductive success

More social individuals would have more parasites

Consequences of individual social variation in marmots

Defined social attributes

Measured fitness correlates Annual reproductive success

Parasites – fleas and intestinal parasites

Fit separate mixed models with social variables as predictors Female and male reproductive success analyzed separately

Included a spatial component

Burrow overlap

Defining social attributes

Many possibly measures of connectivity, which reflect different but related aspects of social connectivity

Degree Number of social partners

Out-degree Number social partners initiating to

In-degree Number social partners receiving from

Strength Total interactions (weighted degree)

Out-strength Total interactions initiated

In-strength Total interactions received

Closeness Overall connectivity in the network

Betweenness Importance as a connection point in the network

Embeddedness Social integration

Defining social attributes

Network Social Variable Description

Affiliative PC1: Connectivity Overall affiliative connectedness

PC2: Affiliation strength

Weighted affiliation

Agonistic PC1: Victimization Received aggression, embeddedness

PC2: Bullying Initiated aggression,Betweenness

Spatial Overlap Crowding experienced

Social cohesion did not improve female reproductive success

Friendlier females had fewer offspring

Crowded females had fewer offspring

Affiliationstrength

Annualreproductivesuccess

Spatialoverlap

Annualreproductivesuccess

N = 228P < 0.001

N = 228P = 0.014

Size and aggression were important for male reproductive success

Bigger males had more offspring

More aggressive males had more offspring

Relativemass

Annualreproductivesuccess

BullyingAnnualreproductivesuccess

N = 44P < 0.001

N = 44P = 0.044

Parasites were not a cost of sociality

Social factors did not influence ectoparasite or endoparasite infection

Ectoparasites: fleas (N = 185) Males had more fleas (P = 0.030)

Endoparasites: gut protozoa (N = 428) No significant trends

Sociality Parasites

Summary Social attributes explained reproductive success, but not

parasites Friendlier females had lower reproductive success

Contrasts with more highly social species

Constraints and competition?

Male bullies had higher reproductive success Consistent with results from dominance

Parasites do not appear to be a cost of sociality in marmots Results differ from findings in other systems (Corner et al. 2003)

Consistent with previous research on group measures in marmots (Arnold & Lichtenstein 1993, Van Vuren 1996)

Conclusions

Quantified and distinguished biologically important attributes of sociality examine structure and possible consequences

Examining social network variation offered complimentary insights to other approaches

AcknowledgementsCollaboratorsBrian HuangFerenc JordánAmanda LeaJulien MartinWeiwei ShenKarisa Tang

Adriana Maldonado

Help & FeedbackRick GrannisPeter NonacsPeter Narins

Blumstein labUCLA ATS stats consulting