Species, habitat and daily activity pattern.
The vertebrates’ retina has a highly conserved laminar organization of 10 alternating nuclear and plexiform layers. Species differences in the retinal specializations, i.e., areas of higher cell density, among the species, represent specific regions of the visual field of higher importance for a better spatial resolution and indicate distinct evolutionary pressures on the structures of the visual system, which can be related to many aspects of the species evolutionary history. In this chapter, we analyzed the density and distribution of cells of the retinal ganglion cell layer (GCL) and estimated the upper limits of the spatial resolving power of 12 species of snakes from the Colubridae family, 6 diurnal and 6 nocturnal, which inhabit different habitats. Our results revealed lower visual acuity in nocturnal species, compared to diurnal, and we observed different types of retinal specialization, horizontal streak, area centralis, or scattered distribution, with higher cell density in different retinal regions, depending on the species. These variations may be related to ecological and behavioral features, such as daily activity pattern, habitat, and substrate preferentially occupied, hunting strategies and diet. This comparative study indicates the complexity of the adaptive strategies of the snakes’ visual system.
- visual ecology
- visual acuity
- ganglion cells
1.1. The visual system
The sensory systems allow the animals to interact properly with their environment and with other organisms. The perception of the surrounding environment is essential for the animals’ survival, and in most vertebrates, the visual system plays a crucial role in basic activities such as foraging behavior, sheltering, flight from predators, and breeding. Functional and anatomical differences of the visual structures often reflect distinct selective pressures implied by the ecological niches.
In all vertebrates, three layers of tissue concentrically arranged form the eyes. The sclera is the outermost layer composed by highly interconnected collagen fibers that support the eye. In the anterior part of the eye, the fibers of the sclera assume an orderly conformation that confers transparency to the sclera, forming the cornea, a lens through which the light can pass. The cornea, together with the crystalline lens, located between the anterior chamber and the vitreous humor, a gelatinous substance that fills the eyeball, enable the focusing of the image in the retina. The second layer is the uvea, formed by the iris, ciliary body, and choroid, and provides nutrients and oxygen to third and innermost layer, the retina, a tissue formed by a network of nerve and glial cells [1, 2].
1.1.1. The retina
In all vertebrates, the retina has an organizational pattern of 10 layers of body cells, nerve plexuses, limiting membranes, pigment epithelium, and nerve fibers (Figure 1). This laminar tissue, responsible for capturing and initiating the processing of luminous information for image formation, has a complex organization, with five main types of neurons: photoreceptors, bipolar cells, horizontal cells, amacrine cells, and ganglion cells. The neuroanatomist Santiago Ramón y Cajal was the first to describe, in 1893 , this thin neural tissue, with a 10-layered division.
The retinal pigment epithelium is the outermost layer, formed by epithelial cells with pigment granules, and has a number of metabolic functions essential for retinal homeostasis and activity, such as nutrients and oxygen supply, and cycling of the photosensitive chromophore (retinal) [3, 4, 5, 6]. The photoreceptor layer (PL) is formed by the outer and inner segments of these neurons (cones and rods), specialized in capturing and converting the light energy into electrochemical energy and transmitting this information to the cells of the following layer. The outer limiting membrane (OLM), located below the PL, is formed by the extensions of Müller cells (glial cells) and is followed by the outer nuclear layer (ONL), with the photoreceptors nuclei. These first-order neurons make synaptic contact with second order neurons, bipolar and horizontal cells, in the outer plexiform layer (OPL). Bipolar, horizontal, and amacrine cell bodies are located in the inner nuclear layer (INL), and these cells make synaptic contact with the ganglion cells (third order neurons), in the inner plexiform layer (IPL). The cell bodies of ganglion cells and displaced amacrine cells form the ganglion cell layer (GCC). The ganglion cell axons form the nerve fiber layer (NFL) and come together to form the optic nerve, which conducts information from the retina to the higher visual centers in the brain. The inner limiting membrane (ILM) is also composed of laterally contacting extensions of Müller cells .
The photoreceptors contain visual photopigments, which are responsible for capturing luminous information and initiating visual processing. Two main types of photoreceptors are usually present in vertebrate retinas, cones, and rods. The outer segments of these cells consist of stacked membranous disks containing the visual photopigments. The latter are formed by a membrane protein, opsin or rhodopsin, coupled to a chromophore, responsible for the absorption of photons and the beginning of the visual processing [7, 8]. The higher number of photopigments in rods provides greater absorption capacity of photons, which makes these cells more sensitive to light compared to cones. Rods are responsible for the scotopic (nocturnal) vision system, which is highly sensitive, with a large capacity of light capturing and signal amplification generated by a single photoizomerization event and the great synaptic convergence, with many rods attached to one ganglion cell, through the bipolar cells, but with a low visual acuity, due to the high degree of convergence. The photopic (diurnal) visual system mediated by cones has less sensitivity but greater visual acuity [7, 8, 9]. Under high luminous intensity, rods are saturated, while cones are activated. During the night, rods are activated with the illumination below the activation threshold of cones . Nocturnal animals usually have retinas with predominance of rods, whereas diurnal animals possess greater amount of cones. Different types of photopigments capture maximally photons with different wavelengths. The presence of distinct photoreceptors in the retina, with different opsin types, together with a postreceptor mechanism capable of comparing the signal transmitted by these neurons, is the first step to enable the color vision .
More recently, a third photoreceptor class was described in the inner retina of many vertebrates [12, 13, 14]. The melanopsin-containing ganglion cells, known as intrinsically photosensitive retinal ganglion cells (ipRGCs), are activated directly by light. These cells give rise to circuits that process important physiological functions, such as the circadian rhythm synchronization and pupillary light reflex [15, 16, 17]. They constitute the nonimage forming visual system. The ipRGCs represent about 1–3% of the retinal ganglion cells in mammals [14, 18]. In other vertebrates, as fish [19, 20] and birds [21, 22], melanopsin-containing neurons were described not only in the GCL but also in the other retinal layers.
The ganglion cells are on average larger than the other retinal neurons and have myelinated axons, with large diameters, capable of transmitting the electrical messages of the visual signal generated by the photoreceptors and processed in the inner retina , to the receptive areas of the brain, many millimeters or centimeters away from the retina. Their density and topographic distribution in the retina are important factors in determining the upper limits for the spatial resolution power of the eye [23, 24, 25, 26].
In short, the highly complex and standardized laminar pattern of the retina is observed in all vertebrates. However, remarkable differences related to the specific cell types, and their density and distribution in the retina, the so-called retinal specializations, are observed among the different species and are related to specific habitats, behaviors, and the species’ visual ecology.
1.1.2. Retinal specializations
A higher concentration of retinal neurons is observed in regions of greater demand for a good image quality [24, 25, 27, 28, 29, 30, 31, 32, 33, 34]. Some studies have shown that cell distribution correlates better with species behavior and habitat than with phylogeny, and that phylogenetically related species may have different patterns of distribution and organization of the neural elements and vice versa [28, 29, 30, 35, 36]. The retinal specializations are areas of higher cell density compared to neighboring areas and include visual streaks,
Many species have a circular area of higher cell density called
According to Hughes’ “terrain theory” , terrestrial animals inhabiting open fields generally have a horizontal streak with high density of photoreceptors and ganglion cells. Since the streak provides a panoramic view of the environment, there is no need for eye movements for detection of objects along the horizon line, an appropriate feature for the field extension vision and perception of the approach of predators. Arboreal species or those from dense forests generally have an
Some primates, reptiles, and birds have a fovea, a specialization of the
1.1.3. Spatial resolving power
Variations in the visual acuity may reflect ecological differences among species and are limited by the diffraction and optical aberration characteristics of the eye, the density of photoreceptors and ganglion cells, and by variables such as refraction error, ambient illumination, and contrast . Lisney and Collin  analyzed the retinas of several species of elasmobranchs (sharks and rays) and observed that species with lower resolution power tend to be relatively less active and feed on benthic invertebrates and small fish, while more active, predatory species that usually feed on larger prey have a greater eye resolving power.
The visual acuity of an animal can be measured using different approaches, such as behavioral tests, response to a stimulus, ocular movements (preferential look), electrophysiological recording, or it can be estimated from anatomical data . Because the ganglion cells constitute the final output of visual information from the retina to the higher visual centers, their density represents a limiting factor of the spatial resolution power of the eye and the ability of the animal to distinguish fine details of the objects . Thus, the peak density of ganglion cells in combination with the eye focal length may be used to infer the maximum spatial resolution power of an animal [24, 25, 33, 34, 53]. These estimated values are usually very close to the acuity values obtained from more direct methods for many species in which both measurements were compared [54, 55, 56, 57, 58].
The retinal specializations and the spatial resolution power of the eye are closely associated with the animals’ visual ecology. Studies on these aspects of the visual system, which include the analysis of the density and distribution of retinal neurons and the specific area of higher degree of visual acuity, bring valuable information on the species biology and are often more related to ecological and behavioral features than to phylogeny. In order to better understand the evolution and functioning of this complex sensory system, it is of great value to compare closely related species with ecological differences. An excellent model for this type of comparative study is the group of snakes, given their great diversity and the variety of ecological niches occupied by phylogenetically close species.
1.2. Snakes: characteristics of the group and adaptations of the visual system
The infraorder Serpentes is characterized by body stretching, absence of limbs, eyelids and external ears, and the presence of forked tongue  and is subdivided into two main groups. The Scolecophidia group (blind snakes) is composed by small fossorial snakes with reduced eyes that feed on small prey as termites and ants. The Alethinophidia group is composed by a greater diversity of species, with two major groups, the paraphyletic Henophidia group, with about 180 species, including pythons and boas, and the Caenophidia group, with about 2500 species [60, 61]. Snakes from the Caenophidia group are found in virtually every portion of the biosphere, except for the poles, some islands, and the ocean deep . The great diversity of this group, with species adapted to a great variety of habitats, can be explained by the occurrence of a number of adaptations that favored their dispersion [63, 64, 65] and the specialization of their sensory systems that evolved to allow their survival and adaptive radiation. The Caenophidia group is therefore characterized by a great diversity of species, with differences in the circadian activity patterns and in the habitats occupied, including terrestrial, arboreal, cryptozoic or fossorial, as well as aquatic environments, which include marine or fresh water habitats .
Despite the great diversity of snake’ species and ecological and behavioral features, very few studies have investigated their retinal specializations. To date, only three studies described the distribution of neurons in snakes’ retinas. Wong  described a visual streak for cones and GCL cells in retinas of the terrestrial
The upper limits of spatial resolving power, estimated based on the ganglion cell peak density and the eye focal length, varied between 2.3 and 2.8 cpg in diurnal and terrestrial snakes [36, 67] and were lower in marine species, ranging between 1.1 and 2.3 cpd . The lower values of marine snakes were attributed to reduced eye size and differences in the photic properties of water compared to air. A higher visual acuity, 4.9 cpd, was measured by recording evoked responses from telencephalon in the aquatic snake
In short, the diversity of species and the variability of habitats used by snakes point to important adaptations of their visual system. Studies on the characteristics and adaptations of the visual system of snakes are extremely scarce in view of the large number of species. Based on their ecological diversity, caenophidian snakes represent a good model for testing hypotheses of correlations between retinal specializations and behavioral ecology. Thus, we analyzed and compared the density and distribution of the GCL cells and estimated the eye spatial resolving power of 12 species from the Colubridae family, with variety regarding their daily activity pattern, and the preferential substrate: arboreal, terrestrial, fossorial, or aquatic. The analysis revealed the presence of different specialization types, visual streak or
2. Assessing cell density and topographic distribution across the retinas and estimating the spatial resolving power
In this study, retinas of 12 Colubridae snakes, 6 considered as primarily nocturnal and 6 as primarily diurnal (Table 1), were collected and dissected for wholemount and Nissl-staining technique. The adult specimens were obtained at the Butantan Institute, São Paulo, Brazil, and were euthanized with a lethal injection of sodium thiopental (thiobarbiturate ethyl sodium, 30 mg/kg). Following euthanasia, the eyes were enucleated, and the axial length was measured. The cornea, ciliary body, and lens were removed, and the lens diameters were measured. A small radial incision was made in the dorsal region of each eyecup, for retinal orientation. The retinas were dissected from the eyecup, the pigment epithelium was separated, and the vitreous humor was removed. The retinas were fixed in 10% formalin. After these procedures, the specimens were fixed in 10% formaldehyde and preserved in the herpetological collection of the Butantan Institute. The animal procedures were done in accordance with the ethical principles of animal management and experimentation established by the Brazilian Animal Experiment College (COBEA). Species daily activity pattern and ecological features were established based on [77, 78, 79] (Table 1).
The retinas were flattened on gelatinized slides, with the GCL side facing up. Small radial incisions were made to allow the retinas to flatten and adhere to the slide. To label the retinal ganglion cells, we used Nissl-Staining procedures as described previously . Glial cells were identified by their dark staining, small size, and rounded profile [34, 67, 83] and were not included in the counts. However, ganglion cells and displaced amacrine cells could not be reliably differentiated from each other, and both were included in the GCL cell counting [36, 80, 81, 82, 83, 84, 85]. To analyze the density and distribution of GCL cells in wholemount retinas, we used a systematic random sampling and the fractionator principle [53, 82, 86, 87, 88]. The coordinates of the retinal edges were plotted on an Excel spreadsheet, and cells were counted at regular intervals defined by a sampling grid, ranging from 220 × 220 μm up to 680 × 680 μm, depending on the size of each retina (Table 2). The coordinates of the sampled fields were plotted on the same Excel spreadsheet, as well as the number of cells counted per field. The counting was performed directly under a Leica DMRXE microscope with a 100× oil objective (numerical aperture, NA = 1.25), equipped with a Nikon Digital Sight DS-U3 DSRi1 camera and the software NIS-Elements AR Microscope Imaging (Nikon Instruments, Melville, NY, USA). A counting frame at 74 × 74 μm was imposed on each sampled frame (Table 2). Cells were counted when inserted fully inside the counting frame or if touched the acceptance lines, without touching the rejection lines . The number of cells quantified at each sampled field was entered in the Excel spreadsheet and converted into density value of cells per mm2, by dividing the number of cells by the frame sampling area. The total number of GCL cells was estimated by multiplying the total number of cells counted by the inverse of the area sampling fraction (asf). The asf is calculated dividing the area of the counting frame by the area of the sampling grid, according to the algorithm: N total = ΣQ × 1/asf, where ΣQ is the number of counted cells [53, 89, 90] (Table 2). The average cell density of each retina was estimated from the average density values of each sampled field. The coefficients of error (CE) were calculated using the method proposed by Scheaffer et al.  and were <0.02 for all retinas, indicating that the total cell number estimates had a high degree of accuracy [53, 87, 92].
|Species||Counting Frame (μm × μm)||Grid (μm × μm)||Area sampling fraction||Number of sites counted|
|74 × 74||620 × 620||0.014||154|
|74 × 74||520 × 520||0.020||125|
|74 × 74||320 × 320||0.054||217|
|74 × 74||320 × 320||0.054||155|
|74 × 74||680 × 680||0.010||190|
|74 × 74||320 × 320||0.050||184|
|74 × 74||230 × 230||0.100||55|
|74 × 74||220 × 220||0.110||56|
|74 × 74||340 × 340||0.048||160|
|74 × 74||310 × 310||0.057||117|
|74 × 74||320 × 320||0.053||133|
|74 × 74||220 × 220||0.115||272|
The coordinates of each sampled frame and the cell density values were used to elaborate the topographic maps, with the software OriginPro 8.1 (Northampton, MA, USA). The position of the retina was determined based on the radial incision made in the dorsal region during the dissection procedures and based on the optic nerve located in the ventral and temporal retinal quadrant in snakes. The reconstructed images were processed using the software Adobe Photoshop CS3 (Adobe Systems, Inc.).
We estimated the theoretical upper limits of the spatial resolving power based on the peak density of presumed ganglion cells and the estimated focal length of the eye. The focal length of the eye is represented by the posterior nodal distance (PND), which corresponds to the distance from the lens center to the choroid-retina border [93, 94]. In a broad analysis of different vertebrate species, Pettigrew and colleagues  proposed that the PND of diurnal vertebrates has a mean of 0.67 of the eye’s axial length and that of nocturnal vertebrates has a mean of 0.52 of the eye’s axial length. However, Hauzman and colleagues  estimated a focal length of 0.52 for diurnal colubrids. In this study, we accessed the eye’s focal length of two colubrids, the diurnal
To estimate the theoretical peak of spatial resolving power, we applied the method proposed by Hart , wherein the distance
Statistical analyses were performed using the program SPSS v.20.0 Statistic (IBM Corporation, Armonk, NY, USA), to compare the population of GCL cells and the estimated upper limit of the spatial resolving power in diurnal and nocturnal colubrids, using the parametric t test and the nonparametric Mann-Whitney test. All data were log 10 transformed prior to analysis. The distribution of values for each variable in each group was evaluated by the Kolmogorov-Smirnov test, and the homoscedasticity between the groups was assessed by the Levene test. The t test for independent samples was performed to verify possible differences between the mean of the groups, for each variable analyzed. There was no disagreement in terms of statistical significance between the Mann-Whitney tests performed, which reinforces the results of the t tests. The level of significance for all comparisons was 5%.
In the retinal wholemounts, we were able to differentiate the neuron population (ganglion cells and displaced amacrine cells) from the nonneuron cell population (glial cells) (Figure 4). Diurnal and nocturnal colubrid snakes differed statistically in the total population of GCL cells and the estimated spatial resolution but not in the mean density of GCL cells. The average cell population in the GCL was 57.856 ± 27.815 cells in the retinas of nocturnal snakes and 288.974 ± 186.079 cells in retinas of diurnal snakes (t(10) = 4.7, p = 0.001) (Table 3 and Figure 5). The mean cell density was 6.739 ± 2.530 cells/mm2 in nocturnal species and 7.729 ± 1.318 cells/mm2 in diurnal species (t(10) = 1.2, p = 0.28) (Table 3 and Figure 5). The mean spatial resolution assuming a hexagonal arrangement was 1.3 ± 0.4 cpd in nocturnal snakes and 2.5 ± 0.6 cpd in diurnal snakes (t(10) = 3.9, p = 0.003) (Table 3 and Figure 5). Similar values were obtained for the assumption of a square lattice: 1.2 ± 0.4 cpd and 2.3 ± 0.6 cpd, in nocturnal and diurnal species, respectively (Table 3).
|Species||Retinal area (mm2)||Total number of cells||CE||Mean cell density (cells/mm2)||Eye axial length (mm)||Peak density of GCL cells (cells/mm2)||Visual acuity (cpd)*||Visual acuity (cpd)**|
|59||394,096||0.01||6721 ± 1634||5.5||11,623||2.9||2.7|
|33||247,386||0.01||7474 ± 2308||4.5||12,176||2.4||2.3|
|22||172,525||0.01||7842 ± 2034||4.0||12,281||2.2||2.0|
|16||110,153||0.01||7016 ± 2258||3.0||13,381||1.7||1.6|
|88||614,553||0.01||7025 ± 1355||7.0||10,815||3.5||3.3|
|19||195,131||0.01||10,297 ± 1934||4.0||14,759||2.4||2.2|
|39 ± 28||288,974 ± 186,079||7729 ± 1318||4.7 ± 1.4||12,506 ± 1389||2.5 ± 0.6||2.3 ± 0.6|
|3||31,404||0.02||11,019 ± 2410||1.3||16,788||0.8||0.8|
|3||23,007||0.02||8490 ± 2177||1.8||11,992||1.0||0.8|
|18||97,524||0.02||5341 ± 1189||3.5||7933||1.5||1.4|
|11||59,285||0.02||5327 ± 1140||2.9||8798||1.3||1.2|
|14||58,621||0.02||4270 ± 1175||3.9||7195||1.6||1.5|
|13||77,298||0.01||5987 ± 1200||3.7||9531||1.8||1.6|
|10 ± 6||57,856 ± 27,815||6739 ± 2530||2.9 ± 1.1||10,373 ± 3550||1.3 ± 0.4||1.2 ± 0.4|
The ganglion cell isodensity contour maps showed different types of retinal specializations, which may be related to species daily activity pattern and differences in habitat preferentially used (Figure 6). Poorly defined horizontal streaks with higher cell densities in the temporal region were observed in diurnal species that inhabit distinct habitats: the arboreal species
3.1. Density of GCL cells and the estimated spatial resolving power
To our knowledge, this is the first study to evaluate and describe the density and distribution of neurons in retinas of nocturnal Colubridae snakes. The average of the total population of GCL cells was significantly lower in nocturnal species (57,856 ± 27,815 cells) compared to diurnal species (288,974 ± 186,079 cells). No significant difference of the mean density of GCL cells was observed between diurnal and nocturnal species, although this may reflect the low species sampling, with a high sampling variability (Figure 5). Among the nocturnal snakes, the fossorial
Data from the literature show similar density values for diurnal and terrestrial snakes. The population of GCL cells in the semiarboreal
The upper limits of the spatial resolving power were also significantly higher in diurnal snakes (2.5 ± 0.6 cpd), which points to the importance of a better image quality in snakes with diurnal habits that actively forage during photoperiods of higher illumination. Similar values were reported for other terrestrial and diurnal species: 2.6 cpd in
Morphological studies revealed that diurnal snakes from the Caenophidia group, which include the Colubridae and Hydrophiidae families, have pure cone retinas, with no typical rod-like photoreceptor [2, 34, 36, 67, 96, 97, 98], and a lower photoreceptor density, compared to nocturnal species [36, 98]. The presence of only cones in retinas of diurnal snakes should contribute to higher spatial resolution , given the lower convergence from cones to ganglion cells.
These important differences in the retinal morphology and the upper limits of the spatial resolving power between diurnal and nocturnal snakes and between aquatic and terrestrial snakes indicate how the variety of environments and circadian activity patterns plays a role in the adaptation of the visual system and influence essential aspects of vision.
3.2. Distribution of neurons in the retina and visual ecology of snakes
This comparative study on the distribution of GCL neurons in retinas of Colubridae snakes revealed the variety of retinal specializations, which indicates the complexity of the adaptive strategies of the snakes’ visual system. In the literature, only three studies described the density and topography of neurons in snake retinas [34, 36, 67], and this is the first study to describe the distribution of neurons in retinas of nocturnal snakes. In general, the diurnal species had a poorly defined visual streak extending along the meridional axis of the retina with peak density of cells in the temporal region, while nocturnal species had an anisotropic
|Subfamily||Species||Biome||Habitat||Substrate||Activity pattern||Diet||Retinal specialization|
|CE, AF||F, O||Te||N||mo||Diffuse—central|
|CE, AF||F, O||Te||D||sn||HS|
|CE||F, O||Te||N||ma, li||AC—temporal|
|CE, AF||F||Ar/Te||D||an, ma||HS*|
|CE, AF||O||Te||D||an, ma||AC—ventral*|
According to the terrain theory proposed by Hughes , species that inhabits open areas where the visual field is dominated by the horizon should preferably have a horizontal streak, which favors the panoramic view of the environment, without the constant need for eye or head movements. On the other hand, animals that live in forested areas, where the visual field is obstructed by foliage and should have an
In the literature, we find some studies that showed the presence of a horizontal streak in species of mammals where the horizon is not a relevant feature of their habitat or the absence of this type of distribution in species that inhabit open fields [30, 100, 101]. Stone  suggested that the topography of the retina must be a phylogenetically inherited trait and does not necessarily represent an adaptive condition of a lifestyle of a given species. However, if this proposition was applied to snakes, one would expect to observe the same pattern of cell distribution in the retinas of the phylogenetically close-related species
Based on the results obtained for
A horizontal streak was observed in different diurnal snakes that inhabit a variety of habitats and occupy different substrates: terrestrial, arboreal, or aquatic (Table 4). This type of retinal specialization results in the formation of a sharper image that favors the visual acuity along the naso-temporal axis, and is related to the ability of a panoramic view of the visual field, without the need for head movements , which would reveal the location of the snake for a possible prey or for visually oriented predators. Thus, a visual streak would be an important adaptation for locomotion and foraging in different substrates. In the literature, a visual streak was observed in the semiarboreal
In summary, the variation of the types of retinal specialization in snakes may be related to ecological and behavioral features such as the daily activity pattern, the habitat and substrate preferentially occupied, hunting strategies, and diet. Snakes that actively forage during the day and prey on fast and visually oriented preys may benefit from a horizontal streak that enables the screening of the environment without the constant need for eye and head movements. On the other hand, snakes are active during the night spend most of the day camouflaged resting  and may not have the necessity of a panoramic view of the environmental provided by a visual streak. The presence of an
4. Conclusion and future directions
In conclusion, this broad study reveals that the retinal topography in Colubridae snakes may have suffered influences not only from the preferential habitat, microhabitat, and substrate used by the species but also to a range of features and behaviors such as daily activity pattern, foraging strategies, and diet. We suggest that horizontal streak with higher cell density in the temporal retina is a more common feature of primarily diurnal snakes from forested areas, which feed on fast moving preys. An
It is also important to emphasize that these anatomical and morphological analyses of the visual system should be expanded not only to a broader species sampling but should also be combined with other studies, which include electrophysiological and behavioral approaches. Electrophysiological recordings, for instance, can be performed to compare the visual acuity measured with a more direct technique, with the upper limits of the eye spatial resolving power estimated from anatomical data. In addition, behavioral tests can be designed to verify how a specific morphological feature of the retina is ultimately associated to certain behaviors and to the species’ visual ecology.
The authors are grateful to Francisco Luís Franco for access to the snakes. This project was funded by the Foundation of Research Support in the State of Sao Paulo (FAPESP) with Fellowships to EH (PhD 2010/51670-8 and post doc 2014/25743-9) and DMOB (post doc 2011/17423-6) and research grants to DFV (2008/58731-2, 2014/26818-2 and 2009/06026-6). DFV is a CNPq 1 A Productivity Fellow.