Dataset Information

Visual perception of light organ patterns in deep-sea shrimps and implications for conspecific recognition.

ABSTRACT: Darkness and low biomass make it challenging for animals to find and identify one another in the deep sea. While spatiotemporal variation in bioluminescence is thought to underlie mate recognition for some species, its role in conspecific recognition remains unclear. The deep-sea shrimp genus, Sergestes sensu lato (s.l.), is one group that is characterized by species-specific variation in light organ arrangement, providing us the opportunity to test whether organ variation permits recognition to the species level. To test this, we analyzed the visual capabilities of three species of Sergestes s.l. in order to (a) test for sexual dimorphism in eye-to-body size scaling relationships, (b) model the visual ranges (i.e., sighting distances) over which these shrimps can detect intraspecific bioluminescence, and (c) assess the maximum possible spatial resolution of the eyes of these shrimps to estimate their capacity to distinguish the light organs of each species. Our results showed that relative eye size scaled negatively with body length across species and without sexual dimorphism. Though the three species appear capable of detecting one another's bioluminescence over distances ranging from < 1 to ~6 m, their limited spatial resolution suggests they cannot resolve light organ variation for the purpose of conspecific recognition. Our findings point to factors other than conspecific recognition (e.g., neutral drift, phenotypic constraint) that have led to the extensive diversification of light organs in Sergestes s.l and impart caution about interpreting ecological significance of visual characters based on the resolution of human vision. This work provides new insight into deep-sea animal interaction, supporting the idea that-at least for these mesopelagic shrimps-nonvisual signals may be required for conspecific recognition.

SUBMITTER: Schweikert LE

PROVIDER: S-EPMC7487218 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Visual perception of light organ patterns in deep-sea shrimps and implications for conspecific recognition.

Schweikert Lorian E LE Davis Alexander L AL Johnsen Sönke S Bracken-Grissom Heather D HD

Ecology and evolution 20200807 17

Darkness and low biomass make it challenging for animals to find and identify one another in the deep sea. While spatiotemporal variation in bioluminescence is thought to underlie mate recognition for some species, its role in conspecific recognition remains unclear. The deep-sea shrimp genus, <i>Sergestes</i> sensu lato (s.l.), is one group that is characterized by species-specific variation in light organ arrangement, providing us the opportunity to test whether organ variation permits recogni ...[more]

PMID: 32953078

Similar Datasets

Project description:Although decapod crustaceans are widespread in the oceans, only Natantia (shrimps) are common in the Antarctic. Because remoteness, depth and ice cover restrict sampling in the South Ocean, species distribution modelling is a useful tool for evaluating distributions. We used physical specimen and towed camera data to describe the diversity and distribution of shrimps in the Ross Sea region of Antarctica. Eight shrimp species were recorded: Chorismus antarcticus; Notocrangon antarcticus; Nematocarcinus lanceopes; Dendrobranchiata; Pasiphaea scotiae; Pasiphaea cf. ledoyeri; Petalidium sp., and a new species of Lebbeus. For the two most common species, N. antarcticus and N. lanceopes, we used maximum entropy modelling, based on records of 60 specimens and over 1130 observations across 23 sites in depths from 269 m to 3433 m, to predict distributions in relation to environmental variables. Two independent sets of environmental data layers at 0.05° and 0.5° resolution respectively, showed how spatial resolution affected the model. Chorismus antarcticus and N. antarcticus were found only on the continental shelf and upper slopes, while N. lanceopes, Lebbeus n. sp., Dendrobranchiata, Petalidium sp., Pasiphaea cf. ledoyeri, and Pasiphaea scotiae were found on the slopes, seamounts and abyssal plain. The environmental variables that contributed most to models for N. antarcticus were depth, chlorophyll-a concentration, temperature, and salinity, and for N. lanceopes were depth, ice concentration, seabed slope/rugosity, and temperature. The relative ranking, but not the composition of these variables changed in models using different spatial resolutions, and the predicted extent of suitable habitat was smaller in models using the finer-scale environmental layers. Our modelling indicated that shrimps were widespread throughout the Ross Sea region and were thus likely to play important functional role in the ecosystem, and that the spatial resolution of data needs to be considered both in the use of species distribution models.

Project description:Visually inferring material properties is crucial for many tasks, yet poses significant computational challenges for biological vision. Liquids and gels are particularly challenging due to their extreme variability and complex behaviour. We reasoned that measuring and modelling viscosity perception is a useful case study for identifying general principles of complex visual inferences. In recent years, artificial Deep Neural Networks (DNNs) have yielded breakthroughs in challenging real-world vision tasks. However, to model human vision, the emphasis lies not on best possible performance, but on mimicking the specific pattern of successes and errors humans make. We trained a DNN to estimate the viscosity of liquids using 100.000 simulations depicting liquids with sixteen different viscosities interacting in ten different scenes (stirring, pouring, splashing, etc). We find that a shallow feedforward network trained for only 30 epochs predicts mean observer performance better than most individual observers. This is the first successful image-computable model of human viscosity perception. Further training improved accuracy, but predicted human perception less well. We analysed the network's features using representational similarity analysis (RSA) and a range of image descriptors (e.g. optic flow, colour saturation, GIST). This revealed clusters of units sensitive to specific classes of feature. We also find a distinct population of units that are poorly explained by hand-engineered features, but which are particularly important both for physical viscosity estimation, and for the specific pattern of human responses. The final layers represent many distinct stimulus characteristics-not just viscosity, which the network was trained on. Retraining the fully-connected layer with a reduced number of units achieves practically identical performance, but results in representations focused on viscosity, suggesting that network capacity is a crucial parameter determining whether artificial or biological neural networks use distributed vs. localized representations.

Dataset Information

Visual perception of light organ patterns in deep-sea shrimps and implications for conspecific recognition.

Publications

Visual perception of light organ patterns in deep-sea shrimps and implications for conspecific recognition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets