Artificial selection for food colour preferences

Abstract

Colour is an important factor in food detection and acquisition by animals using visually based foraging. Colour can be used to identify the suitability of a food source or improve the efficiency of food detection, and can even be linked to mate choice. Food colour preferences are known to exist, but whether these preferences are heritable and how these preferences evolve is unknown. Using the freshwater fish Poecilia reticulata, we artificially selected for chase behaviour towards two different-coloured moving stimuli: red and blue spots. A response to selection was only seen for chase behaviours towards the red, with realized heritabilities ranging from 0.25 to 0.30. Despite intense selection, no significant chase response was recorded for the blue-selected lines. This lack of response may be due to the motion-detection mechanism in the guppy visual system and may have novel implications for the evolvability of responses to colour-related signals. The behavioural response to several colours after five generations of selection suggests that the colour opponency system of the fish may regulate the response to selection.

Developmental plasticity in vision and behavior may help guppies overcome increased turbidity

“…Long-wavelength sensitivity has been shown to be important in motion detection for a number of fish species (Schaerer and Neumeyer 1996;Krauss and Neumeyer 2003), including the guppy (Anstis et al 1998;Cole and Endler 2015). Additionally, foraging efficiency was improved under long-wavelength lighting environments in cutthroat trout (Oncorhynchus clarkii) and Dolly Varden trout (Salvelinus malma), emphasizing the important role of long wavelength light in motion detection (Henderson and Northcote 1985).…”

Stabilizing selection on sperm number revealed by artificial selection and experimental evolution

“…We used in combination these two latter approaches, namely artificial selection and experimental evolution, to investigate the evolutionary response of sperm production to the simultaneous action of pre-and postcopulatory sexual selection in the guppy, Poecilia reticulata, a livebearing fish characterized by high levels of polyandry (Hain and Neff 2007). The guppy has been largely used as model system in artificial selection experiments on both sexual traits (Houde 1994;Hall et al 2004;Van Eeckhoven et al 2017) and nonsexual traits, such as brain size (Kotrschal et al 2013;Kotrschal et al 2015), light sensitivity , and food color preference (Cole and Endler 2015), and it is probably the first species in which experimental evolution has been used to investigate sexual traits (Endler 1980). While rarely used together (e.g., Edward et al 2010;Perry et al 2016;Skrzynecka and Radwan 2016), the combination of artificial selection and experimental evolution should allow to investigate how a sexual trait responds to artificial selection when the effect of sexual selection is removed, and secondly to determine the strength and direction of the overall sexual selection (total sexual selection sensu Hunt et al 2009;Evans and Garcia-Gonzalez 2016) operating on the trait when it is away from equilibrium (i.e., the initial mean and variance for a trait).…”

The Right Light: Tiger Salamander Capture Rates and Spectral Sensitivity

“…Nocturnally migrating birds, for example, are more attracted to red and white lights than other colors even though birds are most sensitive to green wavelengths (Poot et al 2008). Teleost fish, such as Cumaná guppy, are biased toward red and orange colors, even though they, like eastern tiger salamanders, have low spectral sensitivity to red and orange wavelengths (Watson et al 2011, De Serrano et al 2012, Cole and Endler 2015. Several hypotheses have been proposed to account for this apparent discrepancy between spectral sensitivities and sensory bias in behaviors: increased motion detection at longer wavelengths (White et al 2005, Cole andEndler 2015), increased absorption at longer wavelengths (Makino et al 1999), or 'hidden' biases occurring during processing of photoreceptor signals in neural networks (Phelps 2007).…”