Tracking sickness effects on social encounters via continuous proximity-sensing in wild vampire bats

Sickness behaviors can slow the spread of pathogens across a social
network. We conducted a field experiment to investigate how sickness
behavior affects individual connectedness over time using a dynamic social
network created from high-resolution proximity data. After capturing adult
female vampire bats (Desmodus rotundus) from a roost, we created ‘sick’
bats by injecting a random half of bats with the immune-challenging
substance, lipopolysaccharide, while the control group received saline
injections. Over the next three days, we used proximity sensors to
continuously track dyadic associations between 16 ‘sick’ bats and 15
control bats under natural conditions. Compared to control bats, ‘sick’
bats associated with fewer bats, spent less time near others, and were
less socially connected to more well-connected individuals (sick bats had
on average a lower degree, strength, and eigenvector centrality).
High-resolution proximity data allow researchers to flexibly define
network connections (association rates) based on how a particular pathogen
is transmitted (e.g., contact duration of >1 vs >60 minutes,
contact proximity of <1 vs <10 meters). Therefore, we
inspected how different ways of measuring association rates changed the
observed effect of LPS. How researchers define association rates
influences the magnitude and detectability of sickness effects on network
centrality.