“Peak shift” is a behavioral response bias arising from discrimination learning in which animals display a directional, but limited, preference for or avoidance of unusual stimuli. Its hypothesized evolutionary relevance has been primarily in the realm of aposematic coloration and limited sexual dimorphism. Here, we develop a novel functional approach to peak shift, based on signal detection theory, which characterizes the response bias as arising from uncertainty about stimulus appearance, frequency, and quality. This approach allows the influence of peak shift to be generalized to the evolution of signals in a variety of domains and sensory modalities. The approach is illustrated with a bumblebee (Bombus impatiens) discrimination learning experiment. Bees exhibited peak shift while foraging in an artificial Batesian mimicry system. Changes in flower abundance, color distribution, and visitation reward induced bees to preferentially visit novel flower colors that reduced the risk of flower-type misidentification. Under conditions of signal uncertainty, peak shift results in visitation to rarer, but more easily distinguished, morphological variants of rewarding species in preference to their average morphology. Peak shift is a common and taxonomically wide-spread phenomenon. This example of the possible role of peak shift in signal evolution can be generalized to other systems in which a signal receiver learns to make choices in situations in which signal variation is linked to the sender’s reproductive success.

Lynn, S.K., J. Cnaani, and D.R. Papaj. 2005. Peak shift discrimination learning as a mechanism of signal evolution. Evolution 59(6):1300-1305.

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The “signals approach” is an articulation of signal detection theory (SDT) as a model of decision-making in behavioral ecology. Though previous models of decision-making have taken into account variation in the quality of resources among which choices are made, variation in cues that signal quality has remained unaddressed. Treating stimuli as signals, accounting for stimulus variation as a source of uncertainty, reveals that such variation can have significant consequences on choice behavior. The signals approach functions alongside traditional models to produce a more full understanding of decision making. Here, I apply SDT in novel ways to predator response to aposematic prey, mimicry, discrimination learning, and sexual selection.

Using data from existing literature, I show that the signals approach offers an account of predator response to aposematic prey alternative to traditional explanations based on associative learning. The mistakes that predators make may be better characterized as “false alarm” attacks rather than due to poor associative learning. Under SDT, the number of false alarms is expected to rise as aposematic prey abundance rises from rare to moderate levels. This increase in attacks is contrary to expectations based on associative learning, wherein the mistakes are expected to decrease or remain constant. SDT explains otherwise enigmatic empirical data.

I develop a novel expression of SDT by questioning the “integrated signals” assumption. Changing this assumption extends the applicability of signal detection theory, providing a model of generalization and discrimination learning. This model is contrasted to associative learning and yields a novel explanation of the “peak shift” phenomenon. Peak shift can be characterized as a directional preference for novel stimuli under conditions of signal uncertainty.

In flower discrimination learning experiments designed within a signal detection framework, bumblebees (Bombus impatiens) demonstrated peak shift. Peak shift has the potential to act as an agent of selection; pollinator selection of flower morphology and sexual selection of exaggerated traits provide examples.

As a model of decision-making, signal detection theory can yield insight into receiver (e.g., predator) choice behavior and the consequences of that choice behavior on the subsequent evolution of the signals (e.g., prey appearance) upon which decisions are made.

Lynn, S.K. 2003. The Signals Approach to Decision-making in Behavioral Ecology. Ph.D. dissertation, Univerisity of Arizona.

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