How do you make choices in a data-poor environment? Imagine being in a dark room in total silence. Every few seconds, a tiny flash of light appears. You might keep your eyes open as long as possible to avoid missing any of them. You might watch the flashes over time to see if there’s a pattern. If you see a pattern, you might deduce it will lead to further information.

The ability to navigate this way in a dim world is called a summation strategy. “This slowing visual response is consistent with temporal summation, a visual strategy whereby the visual integration time (or ‘shutter time’) is lengthened to increase visual reliability in dim light,” Eric Warrant explains in Science. He’s discussing how hawkmoths perform “Visual tracking in the dead of night,” and he’s clearly impressed by how amazingly well insects “defy the possible” as they move through the world:

Nocturnal insects live in a dim world. They have brains smaller than a grain of rice, and eyes that are even smaller. Yet, they have remarkable visual abilities, many of which seem to defy what is physically possible. On page 1245 of this issue, Sponberg et al. reveal how one species, the hawkmoth Manduca sexta, is able to accurately track wind-tossed flowers in near darkness and remain stationary while hovering and feeding. [Emphasis added.]

The hawkmoth has some peers on the Olympic award platform:

Examples of remarkable visual abilities include the nocturnal central American sweat bee Megalopta genalis, which can use learned visual landmarks to navigate from its nest — an inconspicuous hollowed-out twig hanging in the tangled undergrowth — through a dark and complex rainforest to a distant source of nocturnal flowers, and then return. The nocturnal Australian bull ant Myrmecia pyriformis manages similar navigational feats on foot. Nocturnal South African dung beetles can use the dim celestial pattern of polarized light around the moon or the bright band of light in the Milky Way as a visual compass to trace out a beeline when rolling dung balls. Some nocturnal insects, like the elephant hawkmoth Deilephila elpenor, even have trichromatic color vision.

These insects pack a lot of computing power in brains the size of a grain of rice. How do they do it? Part of the answer lies in the fine-tuning between object and sensor:

It turns out that even though the hawkmoths must compromise tracking accuracy to meet the demands of visual motion detection in dim light, the tracking error remains small exactly over the range of frequencies with which wind-tossed flowers move in the wild. The results reveal a remarkable match between the sensorimotor performance of an animal and the dynamics of the sensory stimulus that it most needs to detect.

A tiny brain imposes real-world constraints on processing speed. The hawkmoth, so equipped, faces limits on sensorimotor performance: how sensitive its eyes are in dim light, how quickly it can perceive motion in the flower, and how fast it can move its muscles to stay in sync. The moth inserts its proboscis into the flower, and if a breeze moves the flower about, the moth has to be able to keep up with it to get its food. To meet the challenge, its brain software includes the “remarkable” ability to perform data summation and path integration fast enough to move with the flower while it feeds.

In their experiments, Sponberg et al. observed hawkmoths in a specially designed chamber. They were able to control light levels and move artificial flowers containing a sugar solution at different speeds. “During experiments, this flower was attached to a motorized arm that moved the flower from side to side in a complex trajectory,” Warrant says.

The component movement frequencies of this trajectory varied over two orders of magnitude and encompassed the narrower range of frequencies typical of wind-tossed flowers. A hovering moth fed from the flower by extending its proboscis into the reservoir, rapidly flying from side to side to maintain feeding by stabilizing the moving flower in the center of its visual field.

The experiment allowed the researchers to cross the line from possible to impossible, showing at what point the moth could not keep up. Dimmer light requires longer integration time, while faster motion requires quicker muscle response. Still, these little flyers “tracked the flower remarkably well” by using the temporal summation strategy.

Hummingbirds feed on moving flowers, too, but usually in broad daylight. To find this ability to track a moving food source in a tinier creature possessing a much smaller brain is truly amazing — especially considering that it has less light to see by.

This strategy has recently been demonstrated in bumblebees flying in dim light and has been predicted for nocturnal hawkmoths. Although temporal summation sacrifices the perception of faster objects, it strengthens the perception of slower ones, like the slower movement frequencies (below ~2 Hz) of the robotic flower.

… and that just happens to be the maximum speed of the natural flowers in the moth’s environment. How did this perfect match arise? Why, natural selection, of course. Here comes the narrative gloss:

By carefully analyzing the movements of several species of flowers tossed by natural winds — including those favored by hawkmoths — Sponberg et al. discovered that their movements were confined to frequencies below ~2 Hz. Thus, despite visual limitations in dim light, the flight dynamics and visual summation strategies of hovering hawkmoths have evolved to perfectly match the movement characteristics of flowers, their only food source.

The implications of the study go far beyond this particular species. It shows how in small animals like hawkmoths, with limited nervous system capacities and stretched energy budgets, the forces of natural selection have matched sensory and motor processing to the most pressing ecological tasks that animals must perform in order to survive. This is done not by maximizing performance in every possible aspect of behavior, but by stripping away everything but the absolutely necessary and honing what remains to perform tasks as accurately and efficiently as possible.

Do the experimenters agree with this narrative? They actually have little to say about evolution. Near the end of the paper, they speculate a little:

The frequencies with which a moth can maneuver could provide a selective pressure on the biomechanics of flowers to avoid producing floral movements faster than those that the moth can track in low light. The converse interaction — flower motions selecting on the moth — could also be important, suggesting a coevolutionary relationship between pollinator and plant that extends beyond color, odor, and spatial features to include motion dynamics.

The evolutionary narrative, though, is unsatisfying. It is only conceptual, not empirical (nobody saw the flower and moth co-evolve). Additionally, flowers could just as well thrive with other pollinators that operate during the daytime. Or, the moth could simply adjust its biological clock to feed in better light, too. The theory, “It is, therefore it evolved” could explain anything.

Warrant also mischaracterizes natural selection as a force. Natural selection is more like a bumper than a force; it’s the hub in the pinball game, not the flipper that an intelligent agent uses. It’s far easier for a moth to drop the ball in the hole (i.e., go extinct) than to decide what capacities it must stretch to match the hubs in its game. The hub, certainly, cares nothing about whether the player wins or not. It’s not going to tell the moth, “Pssst … strip away everything that’s not absolutely necessary, and hone what remains, and you might win!” Personifying natural selection in this way does not foster scientific understanding.

Worst of all, the evolutionary explanation presupposes the existence of highly complex traits that are available to be stripped or honed: flight, a brain, muscles — the works. You can’t hone what isn’t there.

What we observe is a tightly adapted relationship between flower and moth that reaches to the limits of the possible. Any stripping or honing comes not from the environment, but from internal information encoded in the organism. Intelligent causes know how to code for robustness, so that a program can work in a variety of circumstances. Seeing this kind of software packed into a computer the size of a grain of rice makes the design inference even more compelling.

Illustra Media’s documentary Metamorphosis showed in vibrant color the remarkable continent-spanning migration of the Monarch butterfly. Their new documentary, Living Waters (coming out this summer), shows dramatic examples of long-distance migration and targeting in the oceans and rivers of the earth, where the lack of visual cues makes finding the target even more demanding. The film makes powerful arguments against the abilities of natural selection, and for the explanatory fruitfulness of intelligent design.

Image: Macroglossum stellatarum, by IronChris (Wikipedia. See other versions) [GFDL, CC-BY-SA-3.0 or CC BY-SA 2.5-2.0-1.0], via Wikimedia Commons.