Signaling in the Cleaner Shrimp-Client Fish Mutualism Combining Behavior, Network Analysis, and Sensory Physiology


Meeting Abstract

15-3  Thursday, Jan. 4 11:00 – 11:15  Signaling in the Cleaner Shrimp-Client Fish Mutualism: Combining Behavior, Network Analysis, and Sensory Physiology CAVES, EM*; GREEN, PA; JOHNSEN, S; Duke University; Duke University; Duke University eleanor.caves@gmail.com http://eleanorcaves.weebly.com/

Researchers often refer to acts or structures as signals without first testing for a signaling function. To be a signal, an act or structure should at the very least (1) be perceptible by the receiver, and (2) result in a behavioral change in the receiver. Thus, before studying signal form or function, the act or structure in question must be shown to be a signal. In mutualisms, signaling between partners can impact the interaction’s success, which makes them a good system for exploring potential signaling behaviors. We examined interactions between cleaner shrimp (Ancylomenes and Lysmata spp) and their client fish to determine if cleaning interactions are mediated by visual signals. Cleaner shrimp remove ectoparasites from their reef fish clients, and although up to half of client visits are by fish that eat crustaceans, cleaner shrimp are rarely eaten during cleaning interactions. One hypothesis is that cleaners and clients use visual signals to identify themselves as beneficial partners. Cleaner shrimp display complex color patterns and exhibit stereotyped behaviors in the presence of clients. Clients, in turn, adopt stereotyped poses and often change color at cleaning stations. To address the two criteria outlined above, we (1) incorporated measures of cleaner and client spectral sensitivity and spatial acuity to examine how they may appear to their mutualistic partners, and exposed cleaners to black-or-white geometric stimuli on a screen to test how they respond to purely visual stimuli. We then (2) recorded and annotated 152 hours of video of cleaner-client interactions in the Caribbean and Red Seas to identify candidate signaling behaviors. To those annotations, we applied network analysis to show which candidate behaviors altered receiver behavior.

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