Comparative system identification of flower tracking performance in three hawkmoth species reveals adaptations for dim light vision

Abstract

Flight control in insects is heavily dependent on vision. Thus, in dim light, the decreased reliability of visual signal detection also prompts consequences for insect flight. We have an emerging understanding of the neural mechanisms that different species employ to adapt the visual system to low light. However, much less explored are comparative analyses of how low light affects the flight behaviour of insect species, and the corresponding links between physiological adaptations and behaviour. We investigated whether the flower tracking behaviour of three hawkmoth species with different diel activity patterns revealed luminance-dependent adaptations, using a system identification approach. We found clear luminance-dependent differences in flower tracking in all three species, which were explained by a simple luminance-dependent delay model, which generalized across species. We discuss physiological and anatomical explanations for the variance in tracking responses, which could not be explained by such simple models. Differences between species could not be explained by the simple delay model. However, in several cases, they could be explained through the addition on a second model parameter, a simple scaling term, that captures the responsiveness of each species to flower movements. Thus, we demonstrate here that much of the variance in the luminance-dependent flower tracking responses of hawkmoths with different diel activity patterns can be captured by simple models of neural processing.This article is part of the themed issue 'Vision in dim light'.

Hawkmoth flight in the unsteady wakes of flowers

“…Prior to experiments, moths were marked with a dot of white paint (1:1 ratio of tempera and acrylic paint) on the ventral side of the thorax for tracking. Only the thorax point was used since head tracking is not significantly different (Stöckl et al 2017). Once the moth was feeding ( Fig.…”

Tuning movement for sensing in an uncertain world

“…Unlike energy-constrained proportional betting, entropy minimization locally minimizes the entropy (maximizes the EID) of the posterior belief about the target location, in this case leading it to the nearby distractor by following the gradient. (C-E) An illustration of an animal tracking an object constrained to move in a line, in this case a hypothetical moth following a flower swaying in a breeze in a manner approximated by a 1-D sinusoid-a natural behavior reported in (Sponberg et al, 2015;Stockl et al, 2017). We simulate the tracking of the flower using energy-constrained proportional betting.…”

“…89 Figure 2C-E illustrates the emergence of oscillatory sensing-related motions in one-dimensional 90 tracking behavior simulated with EIH, here for a hypothetical moth tracking a flower swaying lat-91 erally in the wind in order to feed from the flower's nectary (Stockl et al, 2017). A key behavioral 92 signature of EIH-increased sensor wiggling as the signal weakens-is evident in this illustration.…”

“…One approach, called infotaxis (Vergassola et al, 2007), 46 similarly generates trajectories that, at each move on a grid of locations, maximizes information 47 87 does perfect proportional betting on the EID. We therefore call our approach ergodic information 88 harvesting (hereafter EIH).89 Figure 2C-E illustrates the emergence of oscillatory sensing-related motions in one-dimensional 90 tracking behavior simulated with EIH, here for a hypothetical moth tracking a flower swaying lat-91 erally in the wind in order to feed from the flower's nectary (Stockl et al, 2017). A key behavioral 92 signature of EIH-increased sensor wiggling as the signal weakens-is evident in this illustration.…”

“…For evaluation of the moth data where the target moves in a more complex sum-of-sines stimu- weak signal) and simulation ( Fig. 4N, = 120 for strong signal and = 120 for weak signal), which is 197 the same range used in (Stockl et al, 2017). We show the spectrum in Fig.…”