Seeing with your brain
Donohue, Cohen, & Cronin show that mantis shrimp have functional photoreceptors in their brains, adding to the long list of animals that express opsins in unintuitive places (including abdomens, feet and genitals).
The currently unsurpassed diversity of photoreceptors found in the eyes of stomatopods, or mantis shrimps, is achieved through a variety of opsin-based visual pigments and optical filters. However, the presence of extraocular photoreceptors in these crustaceans is undescribed. Opsins have been found in extraocular tissues across animal taxa, but their functions are often unknown. Here, we show that the mantis shrimp Neogonodactylus oerstedii has functional cerebral photoreceptors, which expands the suite of mechanisms by which mantis shrimp sense light. Illumination of extraocular photoreceptors elicits behaviors akin to common arthropod escape responses, which persist in blinded individuals. The anterior central nervous system, which is illuminated when a mantis shrimp’s cephalothorax protrudes from its burrow to search for predators, prey, or mates, appears to be photosensitive and to feature two types of opsin-based, potentially histaminergic photoreceptors. A pigmented ventral eye that may be capable of color discrimination extends from the cerebral ganglion, or brain, against the transparent outer carapace, and exhibits a rapid electrical response when illuminated. Additionally, opsins and histamine are expressed in several locations of the eyestalks and cerebral ganglion, where any photoresponses could contribute to shelter-seeking behaviors and other functions.
Version 1.4 of pavo is working its way through CRAN, and will become available over the next day or so. Lots of tweaks and fixes:
- getspec() can now read OceanOptics
- added the visual system of Ctenophorous ornatus, the (trichromatic) ornate dragon lizard
- getspecf (and the argument fast = TRUE in getspec) have been deprecated
- summary.rspec() returned incorrect values for S7. If you use S7, please re-run your analyses
MINOR FEATURES AND BUG FIXES
- summary.rspec() now properly outputs
NA for monotonically decreasing spectra
- fixed warning when subset.rspec was provided with a logical vector
- fixed harmless warning when summary.colspace() was used on a tcs object
by argument in merge.rspec() is no longer ignored
- fixed bug in voloverlap() when plot = TRUE
- fixed bug in vismodel() when transmission has more than one column
- fixed bug in vismodel() that applied von Kries correction to achromatic channel
- added argument fill=FALSE in voloverlap()
- fixed bug in jndplot() when suppressing the plotting of arrows
- better handling of subset data when using summary.colspace() and summary.vismodel()
- fixed bug in coldist() when
noise = "quantum" and
achro = TRUE were used
- fixed bug in jndplot() when
arrow = "none" and
achro = TRUE
- spec2rgb() now takes into account the 390-400 nm wavelength range into account when possible
- as.rspec() no longer fails with tibbles
- bin option of procspec() now works for all values of bins
- non-relative quantum catches from dataframe object were not correctly scaled in “di”, “tri”, “categorical” and “coc” colspaces
- fixed a bug in colspace where it would incorrectly infer a preference for a general trichromatic space, when a cie model is more appropriate
- fixed a bug so that cie color matching functions can be more easily be used in a general trichromatic space (i.e. maxwell triangle)
1.1-billion-year-old porphyrins establish a marine ecosystem dominated by bacterial primary producers
The oceans of Earth’s middle age, 1.8–0.8 billion years ago, were devoid of animal-like life. According to one hypothesis, the emergence of large, active organisms was restrained by the limited supply of large food particles such as algae. Through the discovery of molecular fossils of the photopigment chlorophyll in 1.1-billion-year-old marine sedimentary rocks, we were able to quantify the abundance of different phototrophs. The nitrogen isotopic values of the fossil pigments showed that the oceans were dominated by cyanobacteria, while larger planktonic algae were scarce. This supports the hypothesis that small cells at the base of the food chain limited the flow of energy to higher trophic levels, potentially retarding the emergence of large and complex life.
Robots can learn a lot from nature if they want to ‘see’ the world, via The Conversation:
To model a biological system and make it useful for robots, you typically need to understand both the behavioural and neural basis of that vision system. The behavioural component is what you observe the animal doing and how that behaviour changes when you mess with what it can see, for example by trying different configurations of landmarks. The neural components are the circuits in the animal’s brain underlying visual learning for tasks, such as navigation.
Applications for The University of Sydney Postdoctoral Fellowships are now open for expressions of interest! It’s a wonderful place to work and live, and the fellowships offer generous support.