Dmitry G. Kuzmin

Bio: Dmitry G. Kuzmin is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Absorbance. The author has an hindex of 1, co-authored 1 publications receiving 898 citations.

In search of the visual pigment template.

Abstract: Absorbance spectra were recorded by microspectrophotometry from 39 different rod and cone types representing amphibians, reptiles, and fishes, with A1- or A2-based visual pigments and λmax ranging from 357 to 620 nm. The purpose was to investigate accuracy limits of putative universal templates for visual pigment absorbance spectra, and if possible to amend the templates to overcome the limitations. It was found that (1) the absorbance spectrum of frog rhodopsin extract very precisely parallels that of rod outer segments from the same individual, with only a slight hypsochromic shift in λmax, hence templates based on extracts are valid for absorbance in situ; (2) a template based on the bovine rhodopsin extract data of Partridge and De Grip (1991) describes the absorbance of amphibian rod outer segments excellently, contrary to recent electrophysiological results; (3) the λmax/λ invariance of spectral shape fails for A1 pigments with small λmax and for A2 pigments with large λmax, but the deviations are systematic and can be readily incorporated into, for example, the Lamb (1995) template. We thus propose modified templates for the main “α-band” of A1 and A2 pigments and show that these describe both absorbance and spectral sensitivities of photoreceptors over the whole range of λmax. Subtraction of the α-band from the full absorbance spectrum leaves a “β-band” described by a λmax-dependent Gaussian. We conclude that the idea of universal templates (one for A1- and one for A2-based visual pigments) remains valid and useful at the present level of accuracy of data on photoreceptor absorbance and sensitivity. The sum of our expressions for the α- and β-band gives a good description for visual pigment spectra with λmax > 350 nm.

Animal colour vision — behavioural tests and physiological concepts

Abstract: Over a century ago workers such as J. Lubbock and K. von Frisch developed behavioural criteria for establishing that non-human animals see colour. Many animals in most phyla have since then been shown to have colour vision. Colour is used for specific behaviours, such as phototaxis and object recognition, while other behaviours such as motion detection are colour blind. Having established the existence of colour vision, research focussed on the question of how many spectral types of photoreceptors are involved. Recently, data on photoreceptor spectral sensitivities have been combined with behavioural experiments and physiological models to study systematically the next logical question: 'what neural interactions underlie colour vision?' This review gives an overview of the methods used to study animal colour vision, and discusses how quantitative modelling can suggest how photoreceptor signals are combined and compared to allow for the discrimination of biologically relevant stimuli.

Comparing entire colour patterns as birds see them

Abstract: Colour patterns and their visual backgrounds consist of a mosaic of patches that vary in colour, brightness, size, shape and position. Most studies of crypsis, aposematism, sexual selection, or other forms of signalling concentrate on one or two patch classes (colours), either ignoring the rest of the colour pattern, or analysing the patches separately. We summarize methods of comparing colour patterns making use of known properties of bird eyes. The methods are easily modifiable for other animal visual systems. We present a new statistical method to compare entire colour patterns rather than comparing multiple pairs of patches. Unlike previous methods, the new method detects differences in the relationships among the colours, not just differences in colours. We present tests of the method's ability to detect a variety of kinds of differences between natural colour patterns and provide suggestions for analysis.

pavo : an R package for the analysis, visualization and organization of spectral data

Abstract: Summary Recent technical and methodological advances have led to a dramatic increase in the use of spectrometry to quantify reflectance properties of biological materials, as well as models to determine how these colours are perceived by animals, providing important insights into ecological and evolutionary aspects of animal visual communication. Despite this growing interest, a unified cross-platform framework for analysing and visualizing spectral data has not been available. We introduce pavo, an R package that facilitates the organization, visualization and analysis of spectral data in a cohesive framework. pavo is highly flexible, allowing users to (a) organize and manipulate data from a variety of sources, (b) visualize data using R's state-of-the-art graphics capabilities and (c) analyse data using spectral curve shape properties and visual system modelling for a broad range of taxa. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyse spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies. As an example of pavo's capabilities, we compare the colour patterns of three African glossy starling species, two of which have diverged very recently. We demonstrate how both colour vision models and direct spectral measurement analysis can be used to describe colour attributes and differences between these species. Different approaches to visual models and several plotting capabilities exemplify the package's versatility and streamlined workflow. pavo provides a cohesive environment for handling spectral data and addressing complex sensory ecology questions, while integrating with R's modular core for a broader and comprehensive analytical framework, automated management of spectral data and reproducible workflows for colour analysis.