The sea urchinDiadema africanumuses low resolution vision to find shelter and deter enemies

Abstract

Many sea urchins can detect light on their body surface and some species are reported to possess image-resolving vision. Here, we measure the spatial resolution of vision in the long-spined sea urchin , using two different visual responses: a taxis towards dark objects and an alarm response of spine-pointing towards looming stimuli. For the taxis response we used visual stimuli, which were isoluminant to the background, to discriminate spatial vision from phototaxis. Individual animals were placed in the centre of a cylindrical arena under bright down-welling light, with stimuli of varying angular width placed on the arena wall at alternating directions from the centre. We tracked the direction of movement of individual animals in relation to the stimuli to determine whether the animals oriented towards the stimulus. We found that responds by taxis towards isoluminant stimuli with a spatial resolution in the range of 29-69 deg. This corresponds to a theoretical acceptance angle of 38-89 deg, assuming a contrast threshold of 10%. The visual acuity of the alarm response of was tested by exposing animals to different sized dark looming and appearing stimuli on a monitor. We found that displays a spine-pointing response to appearing black circles of 13-25 deg angular width, corresponding to an acceptance angle of 60-116 deg, assuming the same contrast threshold as above.

Hard to get, easy to lose: Evolution of mantle photoreceptor organs in bivalves (Bivalvia, Pteriomorphia)

“…In both case, cap eyespots and invaginated eyes may be related to shelter‐seeking behaviors, which can be enhanced by even moderate visual abilities (Blevin and Johnsen 2004; Yerramilli and Johnsen 2010; Kirwan et al. 2018; Sumner‐Rooney et al. 2020).…”

Photoresponses in the radiolar eyes of the fan worm, Acromegalomma vesiculosum (Montagu)

“…Are these eyes capable of visual tasks beyond simple shadow detection, such as low-resolution vision (Nilsson, 2013;Nilsson and Bok, 2017)? Certainly, the majority of sabellids and serpulids seemingly lack the compound eye organisational sophistication required for image-forming vision (Bok et al, 2016(Bok et al, , 2017b, though recent work on sea urchins suggests that complex eyes are not necessarily required for coarse spatial vision tasks (Kirwan et al, 2018). However, species of Acromegalomma, as well as the serpulid Christmas tree worm, Spirobranchus corniculatus, have large, consolidated eyes with over 1000 facets each.…”

“…These sensory systems present unique visual challenges when compared with the consolidated, pairedcephalic-eyed visual systems found in most animals. Many-eyed or distributed visual systems and the behaviours they control have been described in jellyfish (Nilsson et al, 2005;Garm et al, 2007a), starfish (Garm and Nilsson, 2014;Petie et al, 2016a), sea urchins (Kirwan et al, 2018), scallops (Land, 1965;Speiser and Johnsen, 2008), arc clams (Nilsson, 1994) and chitons (Kingston et al, 2018). These visual systems have typically been implicated in phototactic orientation, navigation and obstacle avoidance, or shadow-response behavioural tasks.…”

“…Acromegalomma vesiculosum is an accessible species for experimentation and a promising model to examine the functional properties of many-eyed visual systems and startle responses in general. Many-eyed visual systems are common in marine organisms including echinoderms, bivalves and jellyfish (Land, 1965;Nilsson et al, 2005;Garm and Nilsson, 2014;Kirwan et al, 2018). This visual approach presents unique challenges and opportunities to their function in aquatic habitats.…”

Three-dimensional shape and velocity changes affect responses of a locust visual interneuron to approaching objects

“…Animals living in complex, spatiotemporally dynamic visual environments require robust collision-detection systems to successfully orient amongst stationary objects and conspecifics as well as to avoid threats, such as an approaching predator. Behavioural and neural mechanisms underlying collision detection and avoidance have been well studied in taxonomically diverse animals, including humans (Gray and Regan, 1998;Poljac et al, 2006;Vallis and McFadyen, 2005) and other primates (Cléry et al, 2017), cats (Liu et al, 2011b), mice (De Franceschi et al, 2016;Shang et al, 2015;Zhao et al, 2014), birds (Cao et al, 2004;Sun and Frost, 1998), frogs (Yamamoto et al, 2003), fish (Dunn et al, 2016;Preuss et al, 2006;Temizer et al, 2015), crustaceans (Carbone et al, 2018;Oliva et al, 2007;Scarano et al, 2018), insects (Gabbiani et al, 1999;von Reyn et al, 2017;Robertson and Johnson, 1993;Santer et al, 2012;Sato and Yamawaki, 2014;Thyselius et al, 2018;Wang et al, 2018) and sea urchins (Kirwan et al, 2018). Findings suggest that common neural coding strategies exist across these groups and demonstrate the utility of a tractable system to address questions of how complex visual stimuli are detected and how the information is used to drive downstream motor elements to produce adaptive behavioural responses.…”