Morphological Changes in the Visual Sensory System of Congeneric Fishes Inhabiting Clear and Turbid Aquatic Environments

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Theses and Dissertations

Spring 2020

Habeeb Mohsin Hammadi Alsudani

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Recommended Citation Alsudani, H. M.(2020). Morphological Changes in the Visual Sensory System of Congeneric Fishes Inhabiting Clear and Turbid Aquatic Environments. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/5939

This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. MORPHOLOGICAL CHANGES IN THE VISUAL SENSORY SYSTEM OF CONGENERIC FISHES INHABITING CLEAR AND TURBID AQUATIC ENVIRONMENTS

Habeeb Mohsin Hammadi Alsudani

Bachelor of Fisheries & Marine Resources University of Basrah, 1998

Master of Fish Physiology & Histology University of Basrah, 2009

Submitted in Partial Fulfillment of the Requirements

For the Degree of Doctor of Philosophy in

Biological Sciences

College of Arts and Sciences

University of South Carolina

2020

Accepted by:

Joseph M. Quattro, Major Professor

Roger Sawyer, Major Professor

Soumitra Ghoshroy, Committee Member

Daniel Speiser, Committee Member

Wayne Carver, Committee Member

ii DEDICATION

To the souls of my parents and brothers, my beloved wife, the flowers of my life

(My sons Mohsin, Asif, and Elias), all my family and all my teachers and friends without whom it was almost impossible to finish this work.

iii ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to my dissertation advisor Prof. Roger

Sawyer and Prof. Joseph M. Quattro for their great support and encouragement throughout my doctoral study on the scientific and personal level. I am grateful especially for Dr.

Soumitra Ghoshroy, who has taught me the initials in the field of Electron Microscopy. I would like to extend my gratitude to my dissertation committee members Dr. Daniel

Speiser and Dr. Wayne Carver for their guidance and discussion.

I gratefully acknowledge the financial support provided by the Iraqi Ministry of

Higher Education and Scientific Research for covering all my study cost at the University of South Carolina. I owe my appreciation to my professors and colleagues at the University of Basrah and the Iraqi Cultural Office at Washington DC, for their continuous encouragement during my study. Special thanks to Dr. Basim Jasim, Dr. Fatima Sultan,

Dr. Jassim Mohsen Abed and Dr. Amjed K. Resen for their support and encouragement.

Many thanks to Fritz Rohde (NOAA Federal) without him would not have been possible getting the samples, and my former and current colleagues in Department of Biological

Sciences for their friendship and sharing ideas especially Matthew Greenwold.

No words can describe my deepest gratitude to my brothers with their families especially Ali Alsudany and Dr. Salah Alsudany for being always with me and for me.

From deep in my heart, I would love to express my heartful thanks to my wife Hind and my sons Mohsin, Asif, and Elias for their patience, support, and encouragement.

iv ABSTRACT

This study examined the histological aspects of the visual system of six species of fish. Menidia extensa and Fundulus waccamensis are found in Lake Waccamaw, which is a unique aquatic environment of the Atlantic coastal plain characterized by low turbidity, higher pH and clarity. Menidia beryllina, Menidia menidia, Fundulus diaphanus and

Fundulus heteroclitus are found in the Waccamaw River, which is characterized by high turbidity. These species have undergone morphological changes likely related to inhabiting two ecological systems that differ in their environmental characteristics. We tested the hypothesis that fish modify their visual system to adapt to various environments.

The advantage of the present study was that all parts of the retina were examined using the modified method of Alsudani et al 2018. The number of rods and cones calculated in the whole retina. It was found that light availability has a significant influence on the quality of the visual system. The number of cones are higher in bright environment of both genera. It was observed that a higher number of rods across species in Waccamaw River found to have acclimatized to low light environments. Furthermore, these species are characterized by having a pattern of photoreceptors called double layers that are considered as a method of optimal exploitation of light available in improving vision. Also, the investigation was to evaluate the effects that the quantity of light available in a specific habitat has on the development of the optic nerve and optic tectum in six species of fish.

The results suggest that light has a specific influence on the development of both optic nerve and tectum. The number of optic nerve fibers in clear water fish are higher than for

dark water fish. The size of the optic tectum is smaller in fish that inhabit dark water compared to that for clear water fish.

TABLE OF CONTENTS

DEDICATION…………………………………………………………………………... iii

ACKNOWLEDGEMENTS……………………………………………………………... iv

ABSTRACT……………………………………………………………………………… v

LIST OF TABLES………………………………………………………………………. ix

LIST OF FIGURES……………………………………………………………………… xi

CHAPTER 1. INTRODUCTION…………………………………………………………1

INTRODUCTION……………………………………………….………...…...…. 2

CONVERGENCE AND PARALLELISM EVOLUTION…………..………….… 3

CONVERGENT EVOLUTION………………...……………………...…………. 6

CONVERGENT EVOLUTION OF FISH………………….……………………... 8

CONVERGENT EVOLUTION OF EYE………………..………………………. 11

RESEARCH OBJECTIVES……………………………….…………………….. 14

CHAPTER 2. IMPROVED BIOLOGICAL TISSUE PREPARATION PROCEDURE FOR SCANNING ELECTRON MICROSCOPIC IMAGING…………...…….…17

INTRODUCTION……………….………………………………………………. 18

RESULTS AND DISCUSSION…………………………………………….…… 18

vii

CHAPTER 3. DIFFERENCES EXIST IN THE HISTOLOGICAL STRUCTURE OF THE EYE MANIFESTED IN THE MOSAIC STRUCTURE OF THE RODS AND CONE…………………………………………………………………………..... 24

INTRODUCTION………………….……………………………………………. 25

METHODS…………………..…………………………………………………... 29

RESULTS…………….……….…………………………………………………. 30

DISCUSSION …………………………………………………………………... 36

CHAPTER 4. DIFFERENCES EXIST IN THE HISTOLOGICAL STRUCTURE OF THE OPTIC NERVE, WHICH INCLUDES THE NUMBER AND SIZE OF THE NERVE FIBERS WITHIN THE OPTIC NERVE………………………………. 56

INTRODUCTION…………………………..…………………………………… 57

METHODS……………………..…………………………………………….….. 60

RESULTS…………………………………………..……………………………. 62

DISCUSSION ……………………………………………………………………64

CHAPTER 5. SUMMARY AND CONCLUSIONS ……...…………………………… 81

SUMMARY AND CONCLUSIONS …………………………………………... 82

REFERENCES………………………………...……………………………...…………83

viii

LIST OF TABLES

Table 3.1: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (C/R) with mean (M) ± standard deviation (SD) of different individuals of Menidia extensa...... 42

Table 3.2: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (R/C) with average (A) ± standard deviation (SD) of different individuals of Menidia beryllina...... 43

Table 3.3: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (R/C) with mean (M) ± standard deviation (SD) of different individuals of Menidia menidia...... 44

Table 3.4: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (R/C) with mean (M) ± standard deviation (SD) of different individuals of Fundulus waccamensis...... 45

Table 3.5: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (R/C) with mean (M) ± standard deviation (SD) of different individuals of Fundulus diaphanus...... 46

Table 3.6: Total Length of Fish (TL) mm, The Diameter of Eyeball (DE) µm, % Diameter of Eyeball to Total Length (D/L), Number of Rod (NR), Number of Cone (NC), Total Number of Photoreceptor (NP), % Rod to Photoreceptor (R\P), % Cone to Photoreceptor (C/P), and % Rod to Cone (R/C) with mean (M) ± standard deviation (SD) of different individuals of Fundulus heteroclitus...... 47

Table 4.1: Total length of Fish (TL) mm , The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2 , Area of optic tectum (AL) µm2, Area of brain (AB) µm2, and % optic tectum to brain area (AT/AB) of different individuals of Menidia extensa...... 69

Table 4.2: Total length of Fish (TL) mm , The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2 , , Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of different individuals of Menidia beryllina...... 70

LIST OF FIGURES

Figure 2.1: Image of SEM stage with three mounting substrates...... 20

Figure 2.2 : SEM images of the uncoated retina of fish eyes in low-vacuum loaded on aluminum plate (a) and carbon sticky tab (b)...... 20

Figure 2.3: SEM images of mice lung coated with gold in high-vacuum, loaded on aluminum plate (a), stickycarbon tab (b), and glass slide (c)...... 21

Figure 2.4: SEM images of fish eye lens (a), fish skin (b) and fish eye iris (c) tissues coated with gold in high-vacuum, loaded on an aluminum plate with varying magnifications... 22

Figure 2.5: SEM images of uncoated mouse lung in low-vacuum, loaded on aluminum plate (a), carbon sticky tab (b), and glass slide (c)...... 23

Figure 3.1: Map of Collecting Sites for Specimens Used in This Study. Arrows point to individual sample collection sites (map from Stager and Cahoon 1987)...... 48

Figure 3.2: Relation between The Diameter of Eyeball (DE) and Number of Rods (NR) and Number of Cones (NC) for M. extensa, M. beryllina and M. menidia...... 49

Figure 3.3: Relation between The Diameter of Eyeball (DE) and Number of Rods (NR) and Number of Cones (NC) for F. waccamensis, F. diaphanus and F. heteroclitus...... 50

Figure 3.4: Mean number of Cones and Rods of M. extensa from Lake Waccamaw, M. beryllina and M. menidia from Waccamaw River...... 51

Figure 3.5: Mean number of Cones and Rods of F. waccamensis from Lake Waccamaw, F. diaphanus and F. heteroclitus from Waccamaw River...... 52

Figure 3.6: Mean ratio of Rods to Cones of F. waccamensis from Lake Waccamaw, F. diaphanus and F. heteroclitus from Waccamaw River and M. extensa from Lake Waccamaw, M. beryllina and M. menidia from Waccamaw River...... 53

Figure 3.7: The double Layer patterns of photoreceptors of both genera, Menidia species, and Fundulus species...... 54

Figure 3.8: The Morphology of Rods and Cones of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River...... 54

Figure 3.9: A number of Rods and Cones of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River...... 54

Figure 3.10: The Morphology of Rods and Cones of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River...... 55

Figure 3.11: A number of Rods and Cones of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw Rive...... 55

Figure 4.1: Scheme of Map to Lake Waccamaw and Waccamaw River the arrows are pointing samples collection sites (Stager and Cahoon 1987)...... 75

Figure 4.2: Relation between the diameter of eyeball (DE) and the total number of optic nerve Fibers for F. waccamensis from Lake Waccamaw, F. diaphanus and F. heteroclitus from Waccamaw River as well as M. extensa from Lake Waccamaw, M. beryllina and M. menidia from Waccamaw River...... 76

Figure 4.3: The total number of optic nerve fibers (NF) for F. waccamensis, M. extensa from Lake Waccamaw and F. diaphanus, F. heteroclitus, M. beryllina and M. menidia from Waccamaw River...... 77

Figure 4.4: The ratio of optic lobe to brain area (AT/AB) for F. waccamensis from Lake Waccamaw, F. diaphanus and F. heteroclitus from Waccamaw River and M. extensa from Lake Waccamaw, M. beryllina and M. menidia from Waccamaw River...... 78

Figure 4.5: The cross-section of the Optic Nerve of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River...... 79

Figure 4.6: The number of Optic Nerve Fibers of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River...... 79

Figure 4.7: The brain size of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River...... 79

Figure 4.8: The cross section of Optic Nerve of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River...... 80

Figure 4.9: The number of Optic Nerve Fibers of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River...... 80

xii

Figure 4.10: The brain size of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River...... 80

xiii

CHAPTER 1

INTRODUCTION

Life started as single-celled forms three billion years ago. During the past 600 million years ago, organisms used the reiterated parts strategy to evolve into more complex lifeforms (McShea 1996). The fossil records indicate that evolution has passed through three stages. The first stage was the appearance of unicellular ancestors, such as bacterial and algal forms (Bonner 1998). The second stage was the multicellular (organisms became more complex form consisting of several various cell types (Bonner 1988a; Bonner 1998b;

Valentine et al. 1994) to have organs with specialized functions). In third stage, the existing forms of organisms that have appeared from the multicellular complexes during different eras such as the Cambrian, Devonian, Carboniferous, Cretaceous, and Tertiary (Kenrick and Crane 1997; Bell 1998). Therefore, it is believed that the evolution of organisms has used two possible mechanisms to change their size and complexity (McShea 1994). First, non-random, by determining the evolution trend towards complexity or increased size

(Wagner 1996). Second, randomly evolved body size away from the first minima by changing their complexity and diversity (Stanley 1973; Gingerich 2001; Gould 1988).

Moreover, evolutionary trends are affected by two types of mechanisms. It can either be altered externally influenced by selection such as environment or ecology, or internal subjected to genetic changes, such as biomechanical or developmental control (Gingerich

2001; Carroll 2001).

Evolution is an essential factor of all organisms that discoverable taxonomic by sharing a mutual inheritance which comes from a very long past of accumulated changes to ancestry (Hallgrímsson and Hall 2011). Body mass estimates of American fossils of mammal species indicate similar increases within matched pairs of younger and older

species (MacFadden 1986; Alroy 1998). The evolution could reflect an interaction between organisms and the environment (Carroll 2001). That would divide it into parallel and convergent evolution, but there is widespread controversy among scientists with regards to the use of the terms parallel and convergent evolution. Where some of them argue that there is no distinction between the two, due to all organisms sharing a common ancestor

(Arendt and Reznick 2008), while other scientists suggest that there are still significant distinctions between parallel and convergent evolution (Pearce 2011).

CONVERGENCE AND PARALLELISM EVOLUTION

Parallel and convergent have been used to distinguish between the different views on evolution within the taxonomic relationships of organisms where “parallelism” is applied to close relatives and “convergence” to more-distant family members. Some studies use genetic mechanisms to study parallel and convergent evolutionary (Sapp 2012).

If the organisms use the same genetic mechanisms, then they are undergoing parallel evolution, while organisms using different genetic mechanisms are viewed as undergoing convergent evolution (Sapp 2012). Determining the degree of convergence between two species of organisms is not easy because all organisms share a common ancestor.

Consequently, it is difficult to distinguish between convergent and parallel evolutionary because there is no clear boundary between them. Parallel evolution is defined as related species evolving similar traits, whereas convergent evolutionary is defined as unrelated species evolving the same characters (Arendt and Reznick 2008). One evidence of evolutionary developmental biology is increases in the numbers of animal lineages that are currently living in cave habitats compared to what had existed in the past (Mitchell et al.

1977). The existing lineage of cave animals derived from surface-dwelling ancestors

provides an opportunity to investigate the developmental mechanisms underlying parallelism and convergence evolution (Jeffery 2001). The neural crest (NC) possess many similarities among distantly related organisms (unrelated species). The inter-relationships of various organisms are using to understand the origin of features through convergent and parallel evolution (Panaram et al. 2005; Gross 2012). Where, the natural selection drives to double the probability of the parallel evolution of two populations living in the same environment compare to that of neutrality selection (Orr 2005; Chevin et al. 2010).

The study of phylogenetics is beneficial for interpreting phenotypic similarity based on the knowledge of adaptive processes. For instance, the shape and size of fish bodies are correlated to surrounding ecological and behavioral conditions (Bravo et al. 2014).

Whereas convergent evolution of phenotype can be used to explain the common response of populations to different environmental challenges; it often is as a result of similar genetic changes (Stern 2013). Besides, phylogenetic niche conservatism is commonly known as a similarity across an ancestor's related species (Losos 2008; Wiens et al. 2010; Crisp and

Cook 2012). It is called convergent evolution when a similarity across an ancestor's unrelated species evolved independently to become similar to each other (Stayton 2006;

Losos 2011). Many studies suggest that Phylogenetic Niche Conservatism (PNC) is happening as a result of genetic changes and as a response to changing conditions (Wiens and Graham 2005; Losos 2008; Crisp and Cook 2012). Also, many closely related organisms tend to use the different genetic pathway to respond to ecological problems. For example, the species of Drosophila from three the continents of Australia, Europe, and

South America all have a similar body size. The body size is achieved by increasing the cell numbers, by increasing cell size keeping the number of cells the same or by using both.

In addition to the genetic mechanisms that are utilized in these processes (James et al. 1995;

De Moed et al. 1997; Zwaan et al. 2000; Arendt and Reznick 2008), the same genetic mechanisms are used by the distantly related organisms to achieve the same phenotype.

For example, the Pitx1 gene, which is responsible for changes in pelvic structure, is involved in three-spined stickleback species. It was observed that the Pitx1 gene influences three different populations of fish that are living on the west coast of Canada and populations from Iceland (Shapiro et al. 2004) and several inhabitances from Alaska

(Cresko et al. 2004). Organisms utilize different pathways to produce a variety of phenotypes from similar genes. For instance, they are co-opting entire gene Cascades

(Keys et al. 1999), gene duplication and functional divergence (Holland et al. 1994), and altering the expression pattern of single-copy genes (Sucena and Stern 2000; Jeffery 2001).

Genetic changes could happen in two ways. First, by parallel evolution, which occurs when similar or identical mutations evolve in independent lineages, and second, a collateral genetic evolution which takes place when separate lineages of alleles evolve among populations (Stern 2013). Much evidence has been found for parallel and collateral evolution in many taxa (Stern 2013). There are many examples of organisms that have evolved similar solutions as a result of ecologic challenges; such as insects, birds, and bats, all of whom possess wings. Also, vertebrates and spiders have evolved focusing eyes

(Endler 1986; Losos 2011). The quantitative methods in experimental studies of morphogenesis will boost a realization of the needed procedures to identify what genetic changes that could influence anatomical formation (Cooper and Albertson 2008).

CONVERGENT EVOLUTION

Convergent evolution is an independent process that results in the development of similar features in organisms of different lineages. This process can happen through different developmental pathways, and results in analogous structures that have similar form or function. The most typical example of the convergent evolution is flying insects, birds, and bats since they fly by using their independently evolved wings (Lauder 1981;

Ogura et al. 2004). On the other hand, the organism might employ the same strategy to perform a similar function in different environmental conditions. Sharks and pinnipeds use the same actions (flapping and gliding) that are also used by birds for locomotion (Gleiss et al. 2011). Furthermore, the number of vertebrae in mammals has increased in comparison to fish which are relatively symmetrical to vertebral columns of fossil (Carroll

2001). Convergent evolution results in optimization of function with lower expanded energy cost in complex systems such as sensory, neuronal, and mechanical systems

(MacIver et al. 2010). Convergent evolution has been used to understand how natural selection forms the organisms to be fit with the environment conditions (Losos 2011)

(Ingram and LukeMahler 2013). Natural selection often leads to convergence of animals that are occupying comparable selective environments. However, convergence might be a result of another reason that natural selection on a similar feature of different animals living in one habitat. In addition to that, taxa can react to similar selective environments by evolving nonconvergent traits (Losos 2011). The evolution possesses the limited possibility of constraints on forms of organism traits (Sapp 2012). Placental mammals from

North America and Marsupials from Australia have taken a similar pathway of evolution to possess several common features such as adaptation to the same climate or locomotor

skill. Despite this, they have a significant temporal and geographical separation to serve as vital evidence for convergent evolution (Zihlman 2001). The convergence of form and various genetic changes for cave-dwelling populations lead to a unique phenotype, whereas their morphological similarity might be the result of similar developmental processes

(Dowling et al. 2002). Phylogenetic comparative study of Caribbean Anolis lizards has appeared that there is a high morphological convergence for the features within several members living in different habitats (Harmon et al. 2005).

Convergent evolution at the genetic level occurs as a result of one of three processes. In the first process, an independent genetic mutation arises in diverse species or populations. In the second process, organisms duplicate the genes of an allele in a related species. In the third process, evolution has occurred as a result of an introduction of an allele from one population into another by hybridization. Merging the second and third processes is known as collateral genetic evolution (Stern 2013). Convergent evolution might occur as a result of the same gene. For instance, lizards, several birds, various felids, pocket mice, and black bears have a similar gene, MC1R, which is responsible for pale or dark coloration (Ritland et al. 2001; Theron et al. 2001; Eizirik et al. 2003; Nachman et al.

2003; Rosenblum et al. 2004; Mundy et al. 2004; Arendt and Reznick 2008).

Genes can be classified as affecting a single specialized function or pleiotropic, where the gene influences more than two functions or traits of an organism. The classification depends on a pathway in which a gene contributes to various characteristics.

The gene participates in two functions to reduce specialization in one of them at the expense of the other because pleiotropy would significantly disrupt each other’s function.

Pleiotropy tends to reduce their evolving, with genes specializing in the trait that is

currently more important than others. Genes become pleiotropic as result of new surrounding conditions (Guillaume and Otto 2012). Pleiotropy is considered as a restriction on evolution because it decreases the adaptive capability of an organism (Orr 2000; Welch and Waxman 2003). The organisms tend to reduce pleiotropy to improve their capacity to deal with surrounding challenges (Hansen 2003). Skeletal elements of an animal are often changeable at the beginning or during the development process (Franz-Odendaal and

Vickaryous 2006). For example, exogenous retinoic acid (RA) plays a significant role in conserving the typical structure of the cranium, from its skeletal elements. At the same time, it might cause hypomorphic mutations of the gene encoding (the RA-degrading enzyme CYP26B1) that could result in craniofacial and skeletal abnormalities (Laue et al.

2011). In addition to this role, it could affect the development of scleral ossicles of different organisms (Franz-Odendaal and Hall 2006; Franz-Odendaal 2008). The optic cup of the mouse plays a vital role in regulating the scleral cartilage development where differentiation pathway is initiated by the expression of Cart1 and Sox9 genes (Thompson et al. 2010).

CONVERGENT EVOLUTION OF FISH

Although fish and cephalopods differ in their basic anatomy, they have evolved strategies for a common function, namely locomotion. For example, fish depend on undulation of the body and tail, while squid depends on jet propulsion. Cuttlefish, another member of the Cephalopoda, use both strategies. Octopi also use two methods of locomotion. First, they use jet propulsion for the purpose of escape, and secondly, they utilize their tentacles for crawling over rocks, which is their primary form of locomotion

(Chapman and Hall 1978). Moreover, flying fish and squid have independently evolved a

kind of locomotion, which involves a burst of acceleration (Chapman and Hall 1978).

Much teleost fish, such as knifefish, have evolved a manner of swimming using undulation of their dorsal or anal fins (Albert and Crampton 2005). This method is beneficial for shifting swimming directions rapidly (MacIver et al. 2001; Ruiz-Torres, 2013). Knifefish and carangiform fish produce a linked vortex as an undulatory wave to propel them forward, but the vortex rings of knifefish might be distorted due to their elongated fin

(Neveln 2014). Also, buoyancy strategies have evolved independently in the ancestors of both fish and squid, where fat storage and swim bladders are used by fish, while squid depend on ionic change with limited fat storage to control buoyancy. More recent lineages of organisms tend to evolve smaller body sizes with high levels of activity and increased brain size to avoid predators (Stanley 1973; Carroll 2001).

Environmental conditions play a significant role in determining the functional consequences of evolutionary changes as either beneficial or detrimental. For example, the constructive cavefish shift such as the feeding apparatus (jaws, taste buds, and teeth) as well as the mechanosensory system of cranial neuromasts have been affected by the homeobox gene Prox1 (Jeffery 2001). As an example of regressive changes is eye degeneration where adult cavefish lack functional eyes as a result of stopping the formatting of eyes during embryogenesis due to apoptosis. Many studies propose that the

Pax6 gene is controlling cavefish eye degeneration (Jeffery 2001). Moreover, two forms of

Astyanax mexicanus fish exist; first, one is surfaced dwelling, and second is cave-living blind. They had divided from a common ancestor in the relatively short period about one million years ago (Jeffery 2001). Lack of scleral ossification of Astyanax cavefish is the result of multifaceted evolutionary and genetic patterns (O’Quin et al. 2015). Study of

lateral line system among populations of Australian-western rainbowfish has revealed that specialized sensory systems are essential for animals to be able to react with specific surrounding environmental factors, which drive the organisms to develop a particular behavioral pattern (Spiller et al. 2017).

Convergent phenotypes are beneficial for studying the genetic adaptations to a new environment, thus to convergent evolution research at the molecular level in distant lineages (Christin et al. 2010). Many examples illustrate how studying genetics and phylogenetics is critical to detecting a repeated evolution of adaptive characteristics. For instance, the different shapes of eyes (Fernald 2006; Kozmik et al. 2008), using echolocation in dolphins and bats (Liu et al. 2010; Li et al. 2010), and the variation of pigmentation in vertebrates (Protas et al. 2006; Hubbard et al. 2010). It is worth mentioning that the total number of genes in a genome does not indicate the extent of morphological complexity. For example, zebrafish have a higher number of genes than humans. Compared to humans, genomic complexity plays a significant role in the complexity evolutionary of organisms. Kauffman (1995) has pointed out that the number of existing gene expression does not represent the number of gene expression that possibly obtained in any organism

(Carroll 2001). A hybrid phenotype from cavefish and surface fish utilize to understand the genetic architecture of independent craniofacial alterations that have evolved separately from loss of eye (Gross et al. 2014). Furthermore, gene or protein expression are using to label the differences of a range of bones, organs, tissues, or even developmental traits that are possible to measure (Eberhart et al. 2008; Lam et al. 2005). The investigations indicate the vital role of fgf8 signaling in left and right patterning of visceral organs and craniofacial skeleton patterning of embryonic zebrafish (Albertson and Pamela 2005). Furthermore, the

fgf8 is participating in the proper asymmetric development of the heart, brain, and gut

(Albertson and Pamela 2007). The misexpression study of the various Fgf ligands and receptors of mice provides evidence for the significant role of Fgf signaling to development and growth the skeleton bones (Govindarajan and Overbeek 2006; Valta et al. 2006). One of the evolutionary patterns of organisms is a gradual loss of useless trait in relation to the surrounding environment (Coghill et al. 2014). Losing characteristics without a functional feature is a result of a significant accumulation of mutations caused by a long time of isolation (Protas et al. 2007). Losing phenotypic traits of organisms is considered a distinct advantage of evolution (Gross 2012). For example, how that natural selection, pleiotropy, and recurrent mutation with genetic drift have effected eye regression of cave animals

(Culver 1982), which is in turn, correlated genetically with other adaptive traits of organisms that are residents of a dark environment (Dufton et al. 2012; Yoshizawa et al.

2012).

CONVERGENT EVOLUTION OF EYE

The eyes of organisms range from a few light-sensitive cells to very complex structures, depending on the need of the organism for this function (Ogura et al. 2004). It is believed that the high degree of similarity between the human and octopus’ eye has evolved independently after the divergence of the two lineages during the Precambrian period (Harris 1997). There are many similarities between the eye of Cephalopods and vertebrates, one being the camera type of eye, which is composed of more than a million retinal cells. There are some differences, as well. Cephalopods cannot pass vast quantities of information between retinal and brain because of the simple structure of their optic nerve as compared to the optic nerve of fish. Also, fish possess several types of the photoreceptor

in a directed position whereas Cephalopods have one type of photoreceptor cell in an inverted position. Fish use retinal pigment migrations to control the light that reaches the retina while Cephalopods rely on their iris which is an irregular horizontal slit (Chapman and Hall 1978). Moran et al. (2015) found that the eyes and optic tectum of fish with eyes had a higher metabolism when compared to the metabolism of eyeless fish. They also discovered that the metabolism of fish decreases during growth as the eye and brain size decline (Moran et al. 2015). Caves are considered as limited energy ecosystems comparative to surface ecosystems (Poulson, and White 1969). All species of blind cavefish started adapting to cave habitats of North-Eastern Mexico at the mid-Pleistocene.

They have evolved from at least two distinct ancestral surface-dwelling species

(Mukhametov et al. 1977; Borowsky 2008; Gross 2012). Cave animals tend to take various ways such as eye regression and pigmentation to be in great unison with surrounding environmental conditions. For example, as a result of high energy cost needed to maintain eyes, that are useless in cave conditions, many animals that live in these habitats have reduced or absent visual systems as a way to reduce the energy expenditure (Protas et al.

2007; Culver 1982; Eigenmann 1909). At the genetic level, quantitative analysis has indicated that mutations in multiple gene sites caused evolutionary deterioration of the eye, which happened independently at least three times (Wilkens 1988; Dowling et al. 2002;

Strecker et al. 2004; Protas et al. 2007). Retinal degeneration (loss of eye function) and degenerative visual diseases (retinitis pigmentosa and anophthalmia/ microphthalmia) are results of genetic mutations (O’Quin et al. 2013).

The evolution level of various parts of the same organ is different depending on gene expression and the evolution of genes expressed in the camera eye might be distinct

from the evolution of its morphological structures (Ogura et al. 2004). The eyes of all organisms is a result of many genes working together, and each one is specialized in providing specific information about how to form part of the eye such as the gene that is responsible for the development of a light-sensitive pigment (Campbell 2014). Mutations of various participating genes of eye regression indicate that cavefish populations have evolved independently in multiple occasions (Wilkens 1988; Jeffery 2001). Some of them can restore parts of vision structures by hybridization cavefish with related species of surface fish (Niven 2008; Coghill et al. 2014). The regressive evolutionary phenotype forces cave animals to obtain an excellent adaptation to the new environment condition

(Jeffery 2009). The generation of neuronal cells from retinal layers is controlled by a group of cellular and genetic factors (Chow and Lang 2001). In addition, some specialist tissues from different developmental origins engaged in development and formatting eye through tightly controlled mutual signaling (Gage et al. 2005). Hedgehog (Hh) gene expression is responsible for eye degeneration in blind cavefish, by reducing a size of the optic vesicle and losing ventral sector of the optic cup (Yamamoto et al. 2004). Pax6 is considered one of the most significant genes that play a significant role in the formation of the eye. The ancestral Pax6 gene contributed to the evolution of a straightforward eye, such as the compound eye of insects, which uses a group of many light-sensing parts to construct a full image. To be camera eye with the intricate structure of iris and lens, liquid interior, and retina (vertebrate eyes) (Tomarev et al. 1997; Ogura et al. 2004). Where we know evolution increased the number of information that is transferred from a single Pax6. However, the convergent evolution of eyes of squid and humans is not entirely clear (Campbell 2014).

The information has passed through the generations by reading and then copy the gene

instructions in DNA code into a different kind of code. Addition to information that comes by RNA code which can be modified by splicing, which means merge the two ends of RNA after cut off a piece of its middle? Thus get on a new RNA code like Pax6 RNA splicing that has been contributed to evolving a camera eye in the lineage of squid, cuttlefish, and octopus the cephalopods. For example, a similarity between the eyes of humans and squids result of same genes that used to evolve them. A master control gene is defined as a coordinator gene to eye structure. Where, it provides information about a time and place that were needed to construct and assemble, in addition, it is controlling the process of eye formation (Arendt and Reznick 2008). Genes do not usually take simple pathways of development but often are part of developmental networks where several genes work together to develop a single phenotype (Friedman and Perrimon 2007). Also, the convergent evolution of the camera eye is the result of many conserved genes and gene networks. Where there are 1019 out of the 1052 genes, have found in the popular predecessors to Bilateria. Also, humans and octopi share around 875 conserved genes

(Ogura et al. 2004). The genome-wide linkage map and quantitative trait association analyses are recently used to understanding the genetic basis of organism characteristics.

For example, the linkage map of four separate crosses between surface and cavefish species

(hybrid forms) allowed to identify four candidate genes have related to the number of ribs and eye size (Grossa et al. 2008).

RESEARCH OBJECTIVES

A strong association exists between aquatic environmental conditions and the development of fish vision (Leech and Johnsen, 2009). For example, fish that are sight feeders or depend on vision for recognition and locating cover possesses a significantly

more developed visual system compared with that of fish that inhabit turbid, very deep waters, or subterranean waters (Etnier and Starnes, 1993).

The vision of all vertebrates occurs when light enters the eye at the cornea, passes through the pupil to reach the lens, which then focuses the light onto the retina. The retina contains the photoreceptors that can convert the light into neural impulses which travel through the optic nerve to the brain. There are two types of photoreceptors referred to as rods and cones. Cones are less sensitive to light than rods but allow for color vision and are usually used during the daylight. Bright-colored vision is accomplished by having four morphological subtypes of cones each one of them is sensitive to specific wavelengths.

Rods are more sensitive to light and are used for low-light vision (Pankhurst, 1989;

Raymond et al., 1995; Shand, 1997; Fishelson et al., 2004). The structural features of photoreceptors (size and packing density) and mosaic pattern (ratio of cones to rods) play an important role in the acuity of vision within different environments (Wikler and Rakic,

1997; Arikawa et al., 1992; Winkler et al., 1997). In the transition from one environment to another, several morphological changes have been documented in the eyes of aquatic animals (Sabbah, 2012). For example, fish that are living in high - transmission UV/blue light (for example a clear water spring) possess a high density of UV and violet cones compared with fish living in low-transmission UV/blue light (for example a swamp) (Fuller et al., 2003). Furthermore, the type of habitat has a profound effect on the structure of optic nerves where fish living in clear water have twice the number of optic nerve fibers compared to fish inhabiting turbid water (Huber and Rylander, 1992a). The objectives of this study were to: 1) develop a novel microscopic approach to more effectively analyze tissue specimens including eye samples (Chapter 2) and: 2) quantitatively correlate

differences in the structure of the visual system to habitat (water turbidity) in fish (Chapters

3 and 4).

CHAPTER 2

IMPROVED BIOLOGICAL TISSUE PREPARATION PROCEDURE FOR

SCANNING ELECTRON MICROSCOPIC IMAGING1

1Alsudani, H., Ghoshroy, S., Quattro, J., Greenwold, M., & Sawyer, R. 2018. Microscopy and Microanalysis, 24(S1), 1308-1309. Reprinted here with permission of publisher.

INTRODUCTION

Scanning Electron Microscope (SEM) is widely used to investigate surface architecture of a variety of specimens. Observation of most biological specimens under the

SEM needs tissue preparation which is well established fact (Ratnayake et al., 2012). Light microscopic observation of sectioned specimens that are paraffin embedded has its limitations. Low-vacuum scanning electron microscopy (SEM) has been used to observe biological samples by avoiding the “charging” even in non-coated samples (Tanaka, 1996) with an occasional compromise of resolution. In the current study, the conventional protocol of biological sample preparation has been modified by merging a light microscope preparation protocol with that for SEM. This enables evaluation of internal structures of biological specimens by using SEM imaging technique.

RESULTS AND DISCUSSION

Paraffin embedded biological specimens were sectioned using a Microm rotary microtome at a thickness range of 10-60µm and the ribbon was collected on an albumin coated thin aluminum support/plate, carbon sticky tab, and superfrost plus glass slide. The paraffin from the sections was removed using xylene, dried with Hexamethyldisilazane

(HMDS) and all three substrates containing sections were mounted on a standard aluminum stub mount with the sections side facing up (Fig. 2.1). The sectioned specimens were either gold coated with a Denton Desk II sputter coater and observed under a Tescan Vega 3 SEM outfitted with a secondary detector (SE) under high vacuum or viewed uncoated using low vacuum SEM mode and a backscattered electron detector.

A variety of biological specimens that were loaded onto a thin aluminum plate, gold coated and observed under high vacuum mode with a SE detector, showed no “charging”

effect and structural details of the specimens such as blood cells and epithelial tissue was clearly visible (Fig. 2.3a, and 2.4a, b, c). The sections of mice lung, fish skin, and fish eye that were loaded onto sticky carbon tabs (Fig. 2.3b,) or glass slide (Fig. 2.3c), gold coated and imaged under high vacuum with SE detector showed considerable charging effects

(bright spots in the images). Low vacuum environmental SEM imaging with different magnifications for non-gold coated specimens demonstrated a high variation in the image quality between aluminum plates (Fig. 2.2a and Fig. 2.5a), sticky carbon tabs (Fig. 2.2b and Fig. 2.5b), and glass slide (Fig. 2.5c).

This study demonstrated that paraffin embedded sectioned tissues from various organisms loaded on a thin aluminum plate, coated with gold, and observed under high vacuum with varying magnifications provided highest quality images when compared with sectioned specimens loaded on carbon tabs or glass slides. The gold coated sections fixed on albumin coated aluminum plates also stored well for several months in glass desiccators.

This improved method eliminates the need for a variable pressure SEM for imaging paraffin embedded specimens and therefore would be highly effective for structural analysis in many areas of biology including histology, pathology, and embryology.

Figure 2.1: Image of SEM stage with three mounting substrates.

Figure 2.2 : SEM images of the uncoated retina of fish eyes in low-vacuum loaded on aluminum plate (a) and carbon sticky tab (b).

Figure 2.3: SEM images of mice lung coated with gold in high-vacuum, loaded on aluminum plate (a), stickycarbon tab (b), and glass slide (c).

Figure 2.4: SEM images of fish eye lens (a), fish skin (b) and fish eye iris (c) tissues coated with gold in high-vacuum, loaded on an aluminum plate with varying magnifications.

Figure 2.5: SEM images of uncoated mouse lung in low-vacuum, loaded on aluminum plate (a), carbon sticky tab (b), and glass slide (c).

CHAPTER 3

DIFFERENCES EXIST IN THE HISTOLOGICAL STRUCTURE OF THE EYE

MANIFESTED IN THE MOSAIC STRUCTURE OF THE RODS AND CONES

INTRODUCTION

Convergent evolution occurs when animals living in similar ecological conditions evolve similar characteristics independently as to function adequately in their respective environments (Arendt and Reznick 2008). Convergent evolution of fish occupying the natural large bay-lakes in North Carolina, Lake Waccamaw, and Lake Phelps, revealed morphological differences among species within the same genus of fishes that migrated from Waccamaw River to live in Lake Waccamaw. These morphological differences were related to environmental characteristics such that the lake species have elongated, and skinny bodies compared to stream species and thus reflect a difference in the relative eye size to body mass ratio (Krabbenhoft et al., 2009). Waccamaw River is approximately 140 miles long, in southeastern North Carolina and eastern South Carolina and it is characterized by high turbidity (ranges from 5 to 10 mph) and dark color (ranges from 180 to 250 PtCo units) with pH values ranging from 5 to 7 (Coastal Carolina University –

Parameters, 2015). Lake Waccamaw is oval in shape measuring 5.2 miles by 3.5 miles with an average depth of 7.5 feet (Stager and Cahoon, 1987). Lake Waccamaw is characterized by clear water, high pH, and alkalinity (pH 6.8-7.1; alkalinity 12.0 mg/liter) due to the weathering of limestone (Cahoon and Cooke, 1992). Morphological evidence exists for at least four species endemic to Lake Waccamaw, including a darter, catfish, silverside, and killifish (Krabbenhoft 2009), and genetic evidence suggests that the Waccamaw Lake endemics have differentiated from their stream counterparts, and, presumably owe their origins to the formation of the lake less than 15,000 years ago (Stager and Cahoon 1987)

Figure 3.1.

To determine whether these morphological changes are accompanied by changes in the visual system, this work will focus on two species: (Menidia extensa and Fundulus waccamensis) found in Lake Waccamaw and four species (Menidia beryllina, Menidia menidia, Fundulus diaphanus and Fundulus heteroclitus) found in the Waccamaw River.

Menidia extensa is a freshwater, pelagic fish that is only found in Lake Waccamaw in North

Carolina with a maximum length of 8.0 cm and usually occurs in large schools near the surface of open water over dark sandy bottoms. Fundulus waccamensis is a freshwater, benthopelagic and non-migratory fish with a maximum body length of 10.0 cm. Fundulus waccamensis is usually found in sandy areas with vegetation and swims a few inches below the surface. Menidia beryllina and Menidia menidia live in marine, freshwater, brackish and pelagic-neritic waters. Menidia beryllina and M. menidia have a maximum body length of 15.0 cm. Fundulus diaphanus and Fundulus heteroclitus inhabit freshwater, brackish, benthopelagic water. Fundulus heteroclitus has a maximum body length of 15.0 cm.

Fundulus diaphanous maximum length is 13.0 cm and is found in shallow, quiet margins of lakes, ponds and sluggish streams, usually over sand or mud and often near vegetation.

Fundulus diaphanus forms schools a few inches below the surface and is found in North

Carolina Rivers such as the Waccamaw River.

Most vertebrates possess two separate visual systems, the scotopic system providing vision under low light conditions and the photopic system providing vision under well-lit conditions and color perception (Jacobs, 1993). This means they contain two types of photoreceptors referred to as rods and cones. Cones are less sensitive to light but allow for color vision, usually used for daylight, bright-colored vision whereas the rods are more sensitive to light and used for low-light vision (Pankhurst, 1989; Fishelson et al., 2004;

Shand, 1997). Retinas dominated by rods have relatively large cones, but the cones only make up 3 to 5 percent of the photoreceptors (Mustafi et al., 2009; Purves et al., 2001).

Concomitant with changes in life history, the retina of most teleost fish appears to have a mosaic form in response to different ambient light characteristics (Hawryshyn et al., 2003).

The arrangement pattern of photoreceptors also referred to as mosaic is defined as the ratio of cones to rods within the retina and this may play a significant role in color vision

(Winkler et al., 1997). For instance, the retinas of some fish lose short single cones that are sensitive to short wavelengths, and this happens when fish change their position to be deeper depth (Bowmaker and Kunz, 1987; Loew and Wahl, 1991; Beaudet et al., 1993).

The photoreceptor development stages of retina differ within subtypes through growth stages of fish species resulting in different patterns of mosaicism (Shand et al., 1999).

An organism’s light environment influences the expression of visual pigment opsins in the retina. An increased ratio of double cones (long wavelength-sensitive) has been observed in fish that inhabit short wavelength-reduced conditions (Shand et al., 2008).

These changes in opsin expression have been observed in different species of fish such as zebrafish (Takechi and Kawamura, 2005), pink salmon (Cheng and Flamarique, 2007), eel

(Hope et al., 1998), rainbow trout and cichlids (Spady et al., 2006). The color vision system is affected by epigenetic adaptive processes as a result of differences in spectral composition that occurred in their surrounding environment (Wagner and Kroger, 2005).

One of these differences is the variation in the L/M cone ratio where vertebrates utilize neural factors (ganglion cells, horizontal cells, bipolar cells, and amacrine cells) to modify the optic changes that occurred as a response to variation of available wavelengths for animals (Brainard et al., 2001). The exogenous thyroid hormone (TH) which plays a central

role in controlling many of the alterations in physiology, morphology and behavior (Hoar,

1988), influences the topographic distribution of the cone mosaic and regeneration of UVS

(ultraviolet-sensitive cones) during the post-juvenile period of Rainbow Trout (Hawryshyn et al., 2003).

The morphological trait evolution (e.g., elongated body shapes) indicate that the Lake

Waccamaw endemic fishes, Fundulus waccamensis, and Menidia extensa are an apparent case of convergent evolution. Fundulus and Menidia are unrelated genera (belong to different Orders, Cyprinodontiformes, and Atheriniformes, respectively) that inhabit the same environmental conditions and have evolved this identical feature (Gleiss et al. 2011).

In the current study, we are testing the hypothesis that fish modify their vision system to adapt to various environments. Species from Waccamaw River with a low light environment have migrated to live in lake Waccamaw that has higher light availability. All vertebrate retinas contain two types of photoreceptors, rods, and cones. Rods are very sensitive to light, generally used for low-light vision, and cones are lower sensitivity to light, usually used for daylight, bright-colored vision. The variations among animal eyes reveal adaptations to different environments. It is expected that species transition from

Waccamaw River (dark environment) to Lake Waccamaw (clear environment) could be concurrent with an increase in the number of cones with a decrease in the number of rods of the retina. Therefore, the ratio of rods to cones which make up the so-called mosaic patterns is lower for Waccamaw lake species comparative to river species, which gives a higher capacity to colored vision (Wagner et al., 1998).

METHODS

3.1.1 Fieldwork

Fish samples (M. extensa, F. waccamensis) and (M. beryllina, M. menidia, F. diaphanus, and F. heteroclitus) were taken from shallow water near the edge of two bodies of water (Lake Waccamaw and Waccamaw River). A basic seine net was used to collect individual samples of each species. The collected specimens were either fixed in

Glutaraldehyde immediately or brought back to the lab live using a small aquarium with an air pump.

3.1.2 Histological Examination

A Scanning Electron Microscope (SEM) was utilized to test the density of rods, cones, and the ratio of cones to rods (mosaic patterns) within the retina, but due to these structures being concentrated in a small area called the fovea, it was impossible to obtain these areas using the general procedure for electron microscopy preparation. Therefore, I have modified the general procedure by integrating the SEM and paraffin protocols, which allowed me to examine the whole retina, including the fovea region, as well as the cross- sections of the optic nerve. The process includes fixation with 2% glutaraldehyde, dehydrating the samples with different concentrations of ethanol (30%, 50%, 70%, 80%,

95% and 100%), usage xylene as a transitional solvent, embedding the samples in paraffin to make blocks, sectioning the blocks by using microtome with 10-50 um of eyeball. Once the blocks are made and sectioned, the sections are loaded onto aluminum plates, using xylene to remove the paraffin. Then the specimens are dried using HMDS and were gold coated using a Denton Desk II sputter coater before examination under a Tescan Vega 3

SEM (Alsudani et al. 2018).

Photoreceptors (cones and rods) were enumerated throughout the retina. The entire eyeball had been sectioned longitudinally and others transversely with the eye axis at a thickness of 10-30µm. All sections were examined by scanning electron microscopy to calculate the number of cones and rods within random boxes with known size to estimate the total numbers of each type of photoreceptors in the whole retina.

3.1.3 Statistical analysis

The main objective of Statistical analysis is testing the significance of the results by exploring the relationship of the data that relates to different species. The one-way analysis of variance (ANOVA) is one way used to examine how organisms interact with environmental conditions by investigating the effect environmental light has on variations in visual systems by using SPSS version 16.0 Software. Correlation analysis was used to explain the relationship between one dependent variable (the diameter of eyeball (DE)) and independent variables such as the number of rod (NR), number of cone (NC), and the ratio of rod to cone (R/C). The Bonferroni correction used to test error that might get a higher chance for a false positive when rejecting the null hypothesis when it should be accepted.

RESULTS

3.1.4 The ratio of eyeball to body length

The ratio of eyeball diameter to the total length of fish has been used to determine the variation among species of two genera in our current study instead of just a diameter of the eyeball. The diameter of eyeball is strongly correlated to the total length of individuals within the genus Menidia (r = 0.737, p = 0.000) and Fundulus (r = 0.834, p =

0.000) (Table 3.1, 3.2, 3.3, 3.4, 3.5, 3.6). However, correlations between eyeball diameter

(DE) and the number of rods (NR) or the number of cones (NC) individually by species

(12 tests) or pooled by genera (4 tests) were not significant (all p > 0.05) except in two instances (M. extensa DE vs NR, r = 0.89, p = 0.017; M. menidia DE vs NR, r = 0.93, p =

0.008) (Fig. 3.2, 3.3). No correlations were significant after Bonferroni correction for multiple tests (with the resulting adjusted p-value of 0.003 for individual comparisons).

Given a lack of any correlation between eye diameter and the number of rods or cones in these data, statistical tests of mean differences in the number of rods or cones between species within genera did not use a covariate (eye diameter).

A one-way ANOVA determined a significant difference among Fundulus species living in lake Waccamaw and species inhabiting the Waccamaw River in the ratio of eye diameter to total length [F (2, 14) = 59.19, p = 0.000]. The result of a Tukey post hoc test for Fundulus species showed that the ratio of the eyeball to the total length of F. waccamensis was statistically significantly lower (mean (푥) = 4.75 ± 0.222 (standard deviation)) compared to F. diaphanus (푥 = 5.33 ± 0.078) and F. heteroclitus (푥 = 6.06 ±

0.259). Also, there was a significant difference between F. diaphanus and F. heteroclitus

(p = 0.001) (Table 3.4, 3.5, 3.6). Variation in the ratio of eyeball diameter to the total length of fish was not statistically significant different at the p < 0.05 level among three species of Menidia from lake Waccamaw and the Waccamaw River [F (2, 15) = 3.65, p = 0.051].

The mean ratio of eye diameter to the total length of individuals of Menidia extensa living in clear water (푥 = 4.49 ± 0.292) was higher than M. beryllina that has the lowest ratio (푥 =

4.05 ± 0.706) but lower than that observed in M. menidia which had the highest ratio (푥 =

4.74 ± 0.083) (Table 3.1, 3.2, 3.3).

3.1.5 Number of Rods

A one-way between-subjects ANOVA was conducted to compare the number of rods in the retina among three species of Menidia as well as among three species of

Fundulus that are living in two different environmental conditions (clear water lake and dark water river). There was a significant difference in the total number of rods among species of Menidia [F (2, 15) = 85.96, p = 0.000]. Post hoc comparisons using the Tukey

HSD test indicated that the mean rod numbers for M. extensa (푥 = 572,130.0 ± 82,994.9) was significantly lower than that observed M. beryllina (푥 = 1,164,384.5 ± 7,5401.1) as well as M. menidia (푥 = 854,146.3 ± 76,179.2) (Fig. 3.4, 3.5), (Table 3.1, 3.2, 3.3).

Likewise, there was a statistically significant difference in the total number of rods in the retina among species of Fundulus [F (2, 14) = 41.78, p = 0.000]. Post hoc comparisons utilizing the Tukey HSD test showed that the mean number of rods in the retina for F. waccamensis (푥= 569,637.0 ± 41,699.9) was significantly different and lower compared to

F. diaphanus (푥= 752,576.5 ± 4,1271.2) and F. heteroclitus (푥 = 731,782.6 ± 25,325.0)

(Fig. 3.4, 3.5), (Table 3.4, 3.5, 3.6).

3.1.6 The number of cones

There was a statistically significant difference in the total number of cones among

Menidia species as determined by one-way ANOVA [F (2, 15) = 38.54, p = 0.000]. A

Tukey post hoc test of Menidia species revealed that the number cones for M. extensa were statistically significantly larger (푥 = 146,141.5 ± 23,697.6) compared to M. beryllina (푥=

73,922.3 ± 9,340.5) and M. menidia (푥 = 87,188.7 ± 6,434.1). There was no statistically significant difference between M. beryllina and M. menidia (p = 0.312) (Fig. 3.4, 3.5)

(Table 3.1, 3.2, 3.3). The same scenario was also observed for Fundulus species, where a

significant difference among the three species living in the two environmental conditions at the p < 0.05 level determined by one-way ANOVA [F (2, 14) = 225.35, p = 0.000]. A

Tukey post hoc test indicated that F. waccamensis have a significantly higher total number of cones in the retina (푥= 601,781.7 ± 48,777.6) compared to F. diaphanus (푥= 214,845.0

± 20,225.6) and F. heteroclitus (푥= 254,148.2 ± 25,572.0) whereas no significant difference was found between F. diaphanus and F. heteroclitus (p = 0.179) (Fig. 3.4, 3.5),

(Table 3.4, 3.5, 3.6). In general, species found in clear lakes, M. extensa and F. waccamensis, showed a larger number of cones in the retina relative to their congeners found in turbid rivers.

3.1.7 The ratio of rods to cones (R/C)

A statistically significant difference in the ratio of rods to cones was found among three species of the genus Menidia at the p < 0.05 level as determined by one-way ANOVA

[F (2, 15) = 127.85, p = 0.000]. A Tukey post hoc test of Menidia species revealed that the ratio for M. extensa was statistically significantly lower (푥 = 4.17 ± 1.18) compared to M. beryllina (푥 = 15.89 ± 2.36) and M. menidia (푥 = 9.84 ± 1.29). Furthermore, a significant difference was found between M. beryllina and M. menidia (p = 0.000) (Fig. 3.6) (Table

3.1, 3.2, 3.3). Fundulus species showed a significant difference among three species inhabiting the two environmental conditions at the p < 0.05 level determined by one-way

ANOVA [F (2, 14) = 73.69, p = 0.000]. A Tukey post hoc test indicated that F. waccamensis had a lower value of the ratio (푥 = 0.95 ± 0.01) comparated to F. diaphanus

(푥 = 3.54 ± 0.26) and F. heteroclitus (푥 = 2.91 ± 0.17), as well as a significant difference has been found between F. diaphanus and F. heteroclitus (p = 0.041) (Fig. 3.6) (Table 3.4,

3.5, 3.6). In general, Lake Species showed a decrease in the ratio of rods to cones compared to river species.

3.1.8 The total number of Photoreceptor

A statistically significant difference was found among the three species of Menidia in the total number of photoreceptors at the p<0.05 level as determined by one-way

ANOVA [F (2, 15) = 65.98, p = 0.000]. A Tukey post hoc test indicated that M. extensa have a significantly higher number of photoreceptors (푥= 718,271.5 ± 77,249.3) compared to the species belong to the same genera, M. beryllina (푥= 1,236,164.0 ±81,635.3) and M. menidia (푥= 941,335.0 ± 76,018.5) (Table 3.1, 3.2, 3.3). Furthermore, there was a statistically significant difference between M. beryllina and M. menidia (p = 0.001) that are living in the same environmental conditions. Fundulus species displayed a significant difference among the single species from lake Waccamaw and the two species from the nearby Waccamaw River at the p < 0.05 level determined by one-way ANOVA [F (2, 14)

= 67.84, p = 0.000]. A Tukey post hoc test indicated that F. waccamensis possessed a significantly higher number of photoreceptors (푥= 1,171,418.7 ± 49,238.3) compared to F. diaphanus (푥= 967,421.5 ± 23,139.6) and F. heteroclitus (푥= 983,858.6 ± 13,678.5), whereas no significant difference was found between F. diaphanus and F. heteroclitus (p

= 0.700) (Table 3.4, 3.5, 3.6). It found the number of photoreceptors of all species was unrelated to eye size, it is related to the species. Moreover, individuals with big eyes have larger photoreceptors, but as a result of a very high number of photoreceptors, any small change in their size reflects a wide variety of eye sizes. That the reason it is found no significant variation among photoreceptors size.

3.1.9 Photoreceptor patterns

The mosaic patterns are a distribution of one particular type or several photoreceptors across any specific part of the retina. This study focused on the two types of photoreceptors (rods and cones). In general, all species in this study have similarities in the formation of the rods as well as the cones. The rods of adults are cylindrical in shape with a diameter that could reach about 1.7 um in all parts. The cones are conical shaped that has a base diameter up to a triple the base of rods. They gradually narrow to be a cylindrical form with a diameter in the higher part is lower than to that for rods. We discovered that the base of the cones possesses grooves that extend only along the base, increasing the surface area of the base (Fig. 3.8, 3.9, 3.10, 3.11). Which is speculated to enhance the area of communicating with neighbor cells and transfer of nutrients. All species possess a design known as double layers (one following another) (Fig. 3.7). The cones make the first layer of the photoreceptor from the internal eyeball, followed by the layer of rods. It believed that this pattern contributes to the maximum use of the light reaching the retina to achieve a reasonable vision. Teleost characterized by having grouped retina is an arrangement of the rods lie in a light-scattering medium behind the center of the reflecting cup of cones (Braekevelt, 1982). The photoreceptors of electric fish inhabit dark environment conditions arranged in two spared bundles (rods bundle and cones bundle). This pattern of photoreceptors serves as a filter and reduces the contrast, which helps to observe fast-moving organisms even in a very dim environment (Pusch et al.,

2013). Furthermore, it found all species lack a fovea. A fovea provides optically to fine- tune vision acuity. Birds, some reptiles and fish have deep foveal pits that may cause a

simple degree of image magnification from refraction on pit walls (Reymond, 1987). A lack of Fovea is not to be interpreted as indicative of poor vision (Marmor et al. 2008).

DISCUSSION

Lake Waccamaw and Waccamaw River are considered an ideal model environment for studying the changes of fish to become unison with the surrounding environmental conditions. The endemic fish species of Lake Waccamaw have independently evolved morphological features comparative to their very closely related species inhabiting

Waccamaw River. That has been reflected in their vision systems, as we found that light availability has a significant influence on the quality of the visual system. We consistently observed a higher number of cones in the higher light environment across species. In contrast, we observed higher numbers of rods across species that have acclimatized to low light environments as found in Lake Waccamaw species. This is consistent with several studies that examine visual systems across species often find that dim environments are associated with reducing susceptibility to the color vision. There are morphological and histological differences in the eyes among fish species as a result of the varying light levels within the water column (Wikler and Rakic, 1990). Light levels are considered a major physical factor in the evolution of eye shape. Fish inhabiting clear water possess larger eyes compared with those inhabiting turbid water (Huber and Rylander, 1992b). The eyes of nocturnal fishes are different than diurnal's fishes. Nocturnal fish have a larger relative eye size, higher optical ratio, and large, rounded pupils (Schmitz and Wainwright, 2011).

Histologically, nocturnal fish eyes have variations in the location, number, and function of photoreceptor cells (Chiu et al., 1995; Burnside, 1976). Shallow-water fish have evolved to maintain color vision as a result of the shallow aquatic environment, but they possess

fewer than four cone classes (Sabbah et al., 2013). In a study on three species of fish by

Sandkam et al. (2015), they found that color vision differs among populations more than among fish species. Fish use one of many ways to control the amount of light that enter the eyes such as swimming toward or away from the source of illumination, a pigment in the cornea or lens, contractile irises or the movement of pigments (Helfman et al., 2009). The retina of fish is characterized by its ability to continue to grow during the embryonic phase compared with other animals; all parts involve significant changes, including the cell types of the photoreceptors. The sensitivity of the cone to different wavelengths of light varies over time, starting with scotopic vision through the final differentiation, which is a photopic vision. However, photoreceptors are missing rods until the juvenile stage. (Valen et al.,

2016). The rods of mammalian vertebrates are considered an evolutionary extension of the

S-cone lineage (Kim et al. 2016). Furthermore, fishes inhabiting different lighting levels showed various histological structures for lens and photoreceptors (Darwish et al., 2015).

Species of fish may produce new photoreceptor cell types or change the expression of visual pigments during the metamorphoses of the larva into a juvenile (Evans and Fernald,

1990; Stenkamp, 2007). This difference in the number of cones and rods is similar to that found in nocturnal birds in addition to the variance in the type of cones (Höglund et al.,

2019). Besides, this is confirmed by the regressive evolution of the mammalian eye that occupies dim-light environments (Emerling and Springer 2014). The photoreceptors of bottom feeder fish consist only of rod cells (Atta, 2013). Cone visual pigments are divided into four groups, each with a different wavelength absorption capability (photopic vision).

Contrastingly, the rod visual is responsible for the scotopic vision that can produce a reaction from even a single photon (Imamoto and Shichida, 2014). Many hypotheses have

been used to explain the function of the retina such as the role of the high density of photoreceptors, interneurons, or retinal ganglion cells in providing high visual resolution of the optical system (Collin, 1999). Moreover, another hypothesis indicates the heterogeneous distribution of the photoreceptor cell types across the retina (Rowe and

Stone, 1976). The third hypothesis posits both the physiological and morphological divergence of photoreceptor cells (Tancred, 1981).

Our analysis revealed that the ratio of cone and rod to photoreceptors cells is one indication of influence of light on color vision. The ratio of cones to photoreceptors increase for species inhabiting in a high level of brightness as that for lake species. On the contrary, the proportion of rods to photoreceptors is higher for species living in limited lighting conditions, because the cones require high light to perform their functions.

Vertebrates vary substantially in visual sensory demands. Different visual sensory components have passed multiple evolutionary stages to meet current sensory requirements and improve visual behavioral performance (Moore et al. 2017). Vision is a sensory system that is usually reduced under the effects of nutrient limitations (Niven and Laughlin 2008).

Neural tissue of the visual system is considered an expensive tissue which forced cave animals to abandon vision as a result of lack of food and their limited effectiveness with surrounding environmental conditions (Moran et al., 2015). For example, a vision of deep water fish requires different visual adaptations such as focal length, radius, and optical quality of the lenses (Gagnon et al., 2013). Deep-sea teleost lineages developed a vision by increasing the number of single rod opsin rhodopsin 1 cells (RH1), which reflect to maximize their visual sensitivities allowing them to see in dim light (Musilova et al., 2019).

The retina of Archerfish is characterized by a high density of photoreceptors which

increases the visual acuity that fish need to distinguish a small prey from a complex background (Temple et al. 2013). A study on eight species of fish living at different depths reveals that a decrease in the ratio of cones to rods was correlated with maximum habitat depths (Hunt et al., 2015). Furthermore, fish inhabiting turbid water undergo multiple changes in the ratio of rods to cones that enable them to live in low-light conditions

(Francke et al. 2014). The ratio of rods to cones varies among different animals; for example the rate in mice is 97% (Carter-Dawson and LaVail, 1979), it is 92% in bovine

(Krebs and Friedrich, 1982), 65% in salamanders (Mariani, 1986), 40% in chickens

(Meyer and May, 1973), and 4% in ground squirrels (West and Dowling, 1975).

Furthermore, this ratio may also vary during different age phases, for instance, the retina of zebrafish larvae is considered cone-dominant (Bilotta et al., 2001; Branchek, 1984) whereas the retina of adults shifts to become characterized as rods-dominant (Warrant,

2015).

The advantage of the present study was that all parts of the retina were examined using the modified method of Alsudani et al. (2018). The eyeball was sectioned into slices that were examined by a scanning electron microscope. The number of the rods and cones was calculated in an area of known size around the whole retina. The size and arrangement of photoreceptor cells (rods and cones) play a role in visual acuity. Fish species in our study have a pattern of photoreceptors called double layers that are considered as a method of optimal exploitation of light available in improving vision. It believe that mutations and rearrangements in the genes are responsible for the absorption spectra of the pigments, the number of rods and cone types, the mosaic of photoreceptors, and the functionality and sensitivity of rods and cones (Neitz and Neitz, 2011). Fish might respond to the available

amount of light by changing the frequencies of spectral cone types or the cone outer segment lengths (Kröger et al., 1999). The sensitivities of photoreceptors are different in the organisms depending on the arrangement and the density of cones, rods, and Müller cells (Agte et al., 2018; Lindenau et al., 2019) Photoreceptor cells have responded to the dynamics of vertebrate evolution by changes in photoreceptor cell expression through numerous gains and losses of opsin. For instance, the nocturnal lifestyle of some organism is a result of the absence of cone receptor types (Musser and Arendt, 2017). Many studies have indicated the relation of the photic environment of fish habitats to the mosaic pattern of cones and rods as well as the type of photoreceptors (Salem, 2016). The retinal layer structures of moray eels inhabiting shallow water are significantly different from three deep-sea species, which is likely the result of the diversity of the intensity/spectral quality of the light abundance (Wang et al. 2011). Changing the distribution of photoreceptor types in the retinal regions is considered a common strategy that fish use to deal with different visual tasks in different light intensity (Crawford, 1977; De Monasterio et al., 1985). The retina photoreceptors of the mormyridae family species (electric fish) are that living in blackwater streams causes a particular characterization where the rods are grouped in bundles located at the back of cone bundles within a light-scattering area of the retina

(Pusch et al., 2013). The visual system of fish that possesses two separate layers of cones and rods shows characteristics of a band-pass filter and reduces the contrast, which might enable fish to see fast-moving prey even in complete darkness (Pusch et al., 2013). This arrangement of photoreceptors where the rods lie in a light-scattering medium behind the center of the reflecting cup of cones, named the grouped retina, has been found only in teleosts (Braekevelt, 1982). The structure of cones consists of the outer segment, which is

an adjusted primary cilium highly augmented with proteins involved in visual signal transduction located at the distal end acting as a light-sensing organelle. The inner segment contains two main sub-compartments, the ellipsoid and the myoid. The ellipsoid is situated directly below the connecting cilium and is enriched with mitochondria that assist satisfy the metabolic requirements of the cone photoreceptors (Pearring et al. 2013). Addition to a high level of glucose metabolism (Linton et al. 2010). Which means a morphological adaptation is proposed to make them a more efficient supplier to the energy-demanding outer segments (Stone et al. 2008).

Taken together, the results revealed a difference in the number of rods and cones in the six fish species included in the current study. A significant variation was observed between species of both genera that inhabit a clear environmental condition (Lake

Waccamaw) and species that have adapted to a dark environmental condition (Waccamaw

River). Lake Species obtain a lower number of rods and higher number of cones compared to that for river species. The study of Menidia species is revealed that species M. beryllina have the highest number of rods followed by M. menidia, and the lowest number is to M. extensa. Likewise, river species of Fundulus such as F. diaphanus and F. heteroclitus have a higher number of rods than to that of lake species F. waccamensis, oppositely what has found for the number of cones. The statistical results are displayed that the species in the current study not only change the number of cones or rods to get a vision that meets the demands of life in the surrounding environment conditions but also a change in eyeball size

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P C/R

47 2365.9 5.0 603101 100421 703522 85.7 14.3 6.0

63 2748.5 4.4 670329 141016 811345 82.6 17.4 4.75

46 2098.3 4.6 487577 163336 650913 74.9 25.1 2.98

42 1893.8 4.5 523019 153884 676903 77.3 22.7 3.39

61 2597.5 4.3 489916 159158 649074 75.5 24.5 3.71

62 2635.8 4.3 658838 159034 817872 80.6 19.4 4.14

M± 2389.9 4.49 572130± 146142± 718272± 79.4± 20.6± 4.16± SD ±0.29 82994.9 23697.6 77249.3 4.27 4.27 1.18

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P R/C

59 2414 4.1 1102372 58005 1160377 95.1 4.9 19.00

80 2710.3 3.4 1136417 73175 1209592 93.9 6.1 15.53

68 2633.4 3.9 1252077 83932 1336009 93.7 6.3 14.91

39 2113 5.4 1061543 69441 1130984 93.8 6.2 15.28

66 2460.7 3.7 1215146 79397 1294543 93.8 6.2 15.30

67 2564.8 3.8 1218752 79584 1285479 94.8 6.2 15.31

M± 2481.7 4.05± 1164384.5 73922± 1236164 94.2± 5.9± 15.89± SD 0.70 ±75401.1 9340.5 ±81635.4 0.60 0.53 2.36

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P R/C

53 2539.1 4.8 794196 96975 891171 89.1 10.9 8.19

59 2814.5 4.8 983236 84028 1067264 92.1 7.9 11.70

57 2619.9 4.6 846475 90501 936976 90.3 9.7 9.35

52 2512.9 4.8 763425 77694 841119 90.8 9.2 9.82

55 2583.8 4.7 873307 87032 960339 90.9 9.1 10.03

57 2701.6 4.7 864239 86902 951141 90.8 9.2 9.94

M± 2628.6 4.73± 854146± 87188± 941335± 90.7± 9.3± 9.94± SD 0.08 76179.2 6434.1 76018.5 0.97 0.97 1.29

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P R/C

50 2494.3 4.9 489564 652361 1141925 45.3 54.7 0.75

50 2473.8 4.9 579102 530941 1110043 51.4 48.6 1.09

65 2940.3 4.5 603522 653556 1257078 48.6 51.4 0.92

66 2997.7 4.5 577928 596701 1174629 49.5 50.5 0.97

49 2417.5 4.9 600765 563427 1164192 51.1 48.9 1.06

68 3126.7 4.6 566941 613704 1180645 48.7 51.3 0.92

M± 2741.7 4.75± 569637± 601782± 1171418 49.1± 50.9± 0.95± SD 0.22 41699.9 48777.6 ±49238.3 2.20 2.21 0.01

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P R/C

59 3072.9 5.2 749448 210724 960172 78.1 21.9 3.56

53 2870.2 5.4 683320 242844 926164 73.8 26.2 2.81

44 2369.8 5.4 738427 234389 972816 75.9 24.1 3.15

54 2843.7 5.3 764043 208760 972803 78.5 21.5 3.65

55 2931.9 5.3 806859 188163 995022 81.1 18.9 4.29

49 2629.4 5.4 773362 204190 977552 79.1 20.9 3.79

M± 2786.3 5.32± 752576± 214845± 967421± 77.8± 22.3± 3.54± SD 0.08 41271.2 20225.6 23139.6 2.56 2.56 0.26

(TL) (DE) D/L (NR) (NC) (NP) R\P C/P R/C

53 2555.5 6.4 698480 280274 978754 71.4 28.6 2.49

54 2591.4 6.2 764375 213042 977417 78.2 21.8 3.59

54 2641.3 6.2 717710 249627 967337 74.2 25.8 2.88

59 2670.9 5.8 732586 267951 1000537 73.2 26.8 2.73

55 2601.4 5.8 745762 259847 995248 74.9 26.1 2.87

M± 2612.1 6.05± 731782± 254148± 983858± 74.4± 25.8± 2.91± SD 0.26 25325.1 25572.1 13678.5 2.51 2.49 0.16

Lake Waccamaw 34.257840-78.503187 Lake Waccamaw 34.316520-78.525023

Waccamaw River 33.668366 -79.061312 Waccamaw River 34.048779 -78.582503 Waccamaw River 33.911332 -78.715027

Figure 3.1: Map of Collecting Sites for Specimens Used in This Study. Arrows point to individual sample collection sites (map from Stager and Cahoon 1987).

3000  M. menidia  M. extensa M. beryllina

2800

2600

2400

2200 EYE DIAMETER (um) DIAMETEREYE

2000

1800 40000 60000 80000 100000 120000 140000 160000 180000 # CONES

3000  M. menidia  M. extensa M. beryllina

2800

2600

2400

EYEDIAMETER (um) EYEDIAMETER 2200

2000

1800 400000 600000 800000 1000000 1200000 # RODS

Figure 3.2: Relation between The Diameter of Eyeball (DE) and Number of Rods (NR) and Number of Cones (NC) for M. extensa, M. beryllina and M. menidia.

3200  F. diaphanus  F.waccamamensis F. heteroclitus 3100

3000

2900

2800

2700

2600 EYE DIAMETER (um) DIAMETEREYE

2500

2400

2300 100000 200000 300000 400000 500000 600000 700000 # CONES

 F. diaphanus  F.waccamamensis F. heteroclitus 3200

3100

3000

2900

2800

2700

EYE DIAMETER (um) DIAMETEREYE 2600

2500

2400

2300 450000 500000 550000 600000 650000 700000 750000 800000 850000 # RODS

Figure 3.3: Relation between The Diameter of Eyeball (DE) and Number of Rods (NR) and Number of Cones (NC) for F. waccamensis, F. diaphanus and F. heteroclitus.

1300000 Rods 1200000

1100000

1000000

900000

800000

700000

600000

500000

400000

200000 Cones 180000

160000

140000

120000

100000

80000

60000

40000

20000 M. extensa M. beryllina M. menidia 0

Figure 3.4: Mean number of Cones and Rods of M. extensa from Lake Waccamaw, M. beryllina and M. menidia from Waccamaw River.

900000 Rods 850000

800000

750000

700000

650000

600000

550000

500000

450000

400000

700000 Cones

600000

500000

400000

300000

200000

F. waccamensis F. diaphanus F. heteroclitus 100000

Figure 3.5: Mean number of Cones and Rods of F. waccamensis from Lake Waccamaw, F. diaphanus and F. heteroclitus from Waccamaw River.

5 Rods/Cones 4.5

3.5

2.5

1.5

0.5 F. waccamensis F. diaphanus F. heteroclitus 0

20 Rods/Cones 18

2 M. extensa M. beryllina M. menidia 0

Figure 3.7: The double Layer patterns of photoreceptors of both genera, Menidia species, and Fundulus species.

A B C

Figure 3.8: The Morphology of Rods (thin arrow) and Cones (wide arrow) of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River.

A B C

Figure 3.9: A number of Rods and Cones of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River.

A B C

Figure 3.10: The Morphology of Rods and Cones of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River.

A B C

Figure 3.11: A number of Rods and Cones of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw Rive.

CHAPTER 4

DIFFERENCES EXIST IN THE HISTOLOGICAL STRUCTURE OF THE

OPTIC NERVE, WHICH INCLUDES THE NUMBER AND SIZE OF THE

NERVE FIBERS WITHIN THE OPTIC NERVE

INTRODUCTION

Convergent evolution is the independent evolution of structures, which possess a similar form or function in different animals living in similar environmental conditions

(Arendt and Reznick 2008). For example, fishes of the genera Menidia and Fundulus have undergone morphological changes after colonization of Lake Waccamaw from the nearby

Waccamaw River (Krabbenhoft et al., 2009). Waccamaw River is characterized by high turbidity (ranges from 5 to 10 mph) and dark color (ranges from 180 to 250 PtCo units) with pH values ranging from 5 to 7. It extends to a length of approximately 140 miles between southeastern North Carolina and eastern South Carolina (Coastal Carolina

University – Parameters, 2015). Whereas, Lake Waccamaw is characterized by clear water, high pH, and alkalinity (pH 6.8-7.1; alkalinity 12.0 mg/liter) due to the weathering of limestone (Cahoon and Cooke, 1992). Lake Waccamaw measures 5.2 miles by 3.5 miles oval in shape with an average depth of 7.5 feet (Stager and Cahoon, 1987). There is morphological evidence indicating that some species of fish have adapted to Lake

Waccamaw conditions, including silverside, and killifish (Krabbenhoft et al., 2009), and genetic evidence suggests that the Waccamaw Lake species have discriminated from very closely related Waccamaw river species 15,000 years ago (Stager and Cahoon 1987) (Fig.

4.1).

Attending the morphological change of the Genus Menidia and Fundulus might be similar patterns of convergence in the visual system. This work will focus on two species:

(Menidia extensa and Fundulus waccamensis) found in the Lake Waccamaw and four species (Menidia beryllina, Menidia menidia, Fundulus diaphanus and Fundulus heteroclitus) found in the Waccamaw River. Menidia extensa is a freshwater, pelagic fish

that is only found in Lake Waccamaw in North Carolina with a maximum length of 8.0 cm and usually occurs in large schools near the surface of open water over dark sandy bottoms.

Fundulus waccamensis is a freshwater, benthopelagic, and non-migratory fish with a maximum body length of 10.0 cm. Fundulus waccamensis is usually found in sandy areas with vegetation and swims a few inches below the surface. Menidia beryllina and Menidia menidia live in marine, freshwater, brackish, and pelagic-neritic waters. Menidia beryllina and M. menidia have a maximum body length of 15.0 cm. Fundulus diaphanus and

Fundulus heteroclitus inhabit freshwater, brackish, benthopelagic water. Fundulus heteroclitus has a maximum body length of 15.0 cm. Fundulus diaphanous have a maximum body length of 15.0 cm maximum length is 13.0 cm and is found in shallow, quiet margins of lakes, ponds and sluggish streams, usually over sand or mud and often near vegetation. Fundulus diaphanus forms schools a few inches below the surface and is found in North Carolina Rivers such as Waccamaw River.

The evolution of the sensory systems occurs through the adaptation of communication signals or the development of senses and signals as a response to the influence of many factors (Osorio and Vorobyev, 2008). Organisms interact with their environment through the acquisition of prey, avoidance of predators, schooling, communication, and reproduction. Furthermore, organisms adapt to their environment through varying evolutionary mechanisms. Fish use one or more sensory systems to interact with their surrounding environmental conditions while expending the lowest possible amount of energy. Recent studies suggest that the energy consumed to maintain a functioning visual system is too “costly” and not adaptive in nutrient-poor cave environments (Moran et al., 2015). Therefore, cave-dwelling species depend on other

sensory systems, such as the lateral line or olfactory system (Heuschele et al., 2009).

Furthermore, histological and morphological variation exists for these neural systems. For example, the nasal olfactory organs differ histologically and morphologically among fish species that live in different environmental conditions related to depth and, therefore, light penetration (Zeiske et al., 1979; Fishelson et al., 2010; Chakrabarti and Ghosh, 2011). The optic nerve connects the eye to the brain and carries electrical signals from the retina in the eye to the vision centers of the brain. It consists of nerve fibers that differ in number among fish species (Tapp, 1974). The optic nerve is characterized by the ability to regenerate throughout the life of Teleost fish (Northmore, 1989; Callahan and Mensinger, 2007).

There are four stages of regeneration that the optic nerves of fish go through; neurite sprouting, axonal elongation, synaptic refinement, and functional recovery. This process of regeneration involves an increased number of retinal ganglion cells, photoreceptors and nuclear layers (Kato et al., 2013). Parrilla et al., (2009) found that adult fish still possess

Pax2 which is a known transcription factor that regulates optic nerve development. This could relate to the continuous addition of new ganglion cell axons and new glial cells.

There are several factors that affect the optic nerve fiber rearrangements that happen within the optic nerve in the optic tracts or on the tectum (Bunt, 1982). Varying types of habitats have specific effects on the optic nerves in that fish living in clear water have twice the number of optic nerve fibers compared to fish inhabiting turbid water. However, no differences are observed in the number and diameter of optic nerve fibers per retinal area among these species for the two habitats (Huber and Rylander, 1992b). Also, the number of optic nerve fibers increases with the age of fish (Easter et al., 1981). It can be concluded that fish living in clear water are more developed within the part of the brain that is

responsible for vision compared to turbid water fish (Huber and Rylander, 1991).

Understanding the capability of fish to adapt to the variations of surrounding environmental conditions will contribute to understanding the function of all parts of the visual system to terrestrial vertebrates that broadly have a similar structure to that of fish eyes. On this basis, our understanding can be used to address many visual system questions for all vertebrates.

In this particular study, our goal is to test the hypothesis that all parts of the visual system could be changed as a result of changing environmental conditions. It is expected that the number of nerve fibers and cross-section of the optic nerve would be increased for species inhabiting Lake Waccamaw (clear environment) compared to species living in Waccamaw

River (dark environment). That would coincide with an increase in the size of the optic lobe for lake species compared to river species to cover a high quality vision requirements

(Huber and Rylander, 1991).

METHODS

4.1.1 Fieldwork

Samples were collected from two different areas, namely clear water represented by the Lake Waccamaw and dark waters represented by Waccamaw River. A basic seine net was used to collect six species belonging to two genera: Menidia beryllina, Menidia menidia, Fundulus diaphanus and Fundulus heteroclitus from the river and Menidia extensa, Fundulus waccamensis from the lake. A number of the specimens were immediately fixed in Glutaraldehyde, and the others are brought back to the lab alive in a small aquarium equipped with an air pump.

4.1.2 Histological Examination

Laboratory work ensured that the phenotypic measurements (Total length of Fish

(TL) cm , The diameter of eyeball (DE) µm) of each individual fish was labeled and calculated. The fish were then dissected by using a dissecting microscope to extract the eyes, the optic nerve, and the brain from the cranium. A modified method by Alsudani et al. 2018 was used in the preparation of optic nerve specimens, including cutting samples preserved in paraffin with a thickness of 30-60 microns by using a rotary microtome, and then removal of the paraffin and drying the samples utilizing HMDS to prepare them for imaging under the a Tescan Vega 3 scanning electron microscopy. The area of the optic nerve sections was measured in the regions that differed from the point of contact with the eye, and nerve fibers were calculated within the cross-sections of the optic nerve. The cross-section of the optic nerve were measured by SEM software. The whole area of cross- section were divided into squares known size and then the number of optic nerve fiber was calculated in random squares. In the end, was estimated the total numbers of optic nerve fiber in the whole cross-section.

An anatomical microscope was used to visualize the fish brain. Image J program was used to measure the area of the optic tectum (OT) and the total area of the brain.

4.1.3 Statistical analysis

A one-way analysis of variance (ANOVA), Analystsoft program for mac, Version v7 with a standard deviation (significance level p<0.05) was used to test the power of light availability in the fish habitats, including the changes that occur to the optical system of different fish species, by examining the significant variations between different groups of

fish. Correlation analysis was used to determine the relationship between the diameter of an eyeball (DE) and a number of optic nerve fibers (NF).

RESULTS

4.1.4 Number of Optic Nerve fibers

No correlations were significant between eyeball diameter (DE) and the number of optic nerve fibers (NF) for three species of Menidia (M. extensa, r = 0.605, p = 0.202; M. beryllina r = 0.338, p = 0.512 and M. menidia r = 0.192, p = 0.714). Similarly, no significant correlations were found between eyeball diameter (DE) and the number of optic nerve fibers (NF) within the three species of Fundulus ( F. waccamensis, r = 0.371, p =

0.468; F. diaphanous, r = 0.013, p = 0.979; and F. heteroclitus, r = 0.497, p = 0.394). (Fig.

4.2). As a result of no correlation between eye diameter and the number of optic nerve fibers, statistical tests of mean differences in the number of optic nerve fibers between species within genera did not use a covariate (eye diameter).

There was a significant difference in the mean number of optic nerve fibers among species of Menidia [F (2, 15) = 135.2, p = 0.000]. Post hoc comparisons that used the

Tukey HSD test indicated that the mean number of fibers for M. extensa (푥 = 52,484 ±

5,362.93) was significantly higher than M. beryllina (푥 = 20,045.8 ± 3,167.59) as well as

M. menidia (푥 = 23,368 ± 1,902.33) (Fig. 4.3, 4.6), although there was not a significant difference between M. beryllina and M. menidia (p = 0.305) (Table 4.1, 4.2, 4.3).

Similarly, there was a significant difference in the mean number of optic nerve fibers among species of Fundulus [F (2, 14) = 392.28, p = 0.000]. Post hoc comparisons that used the Tukey HSD test indicated that the mean number of fibers for F. waccamensis are significantly higher (푥 = 100,699 ± 2,838.27) compared to those of river species F.

diaphanus (푥 = 42,694 ± 2,832.27) and F. heteroclitus (푥 = 59,109 ± 5,237.1) (Fig. 4.3,

4.9), (Table 4.4, 4.5, 4.6).

4.1.5 Cross-section of Optic nerve

A statistically significant difference in mean cross-section area of the optic nerve has been found among three species of Menidia as determined by one-way ANOVA [F (2,

15) = 24.48, p = 0.000]. A Tukey post hoc test of Menidia species revealed that the area of the cross-section of the optic nerve for M. extensa was statistically significantly larger (푥

= 78,038.9 ± 3,240.4) compared to M. beryllina (푥 = 64,747.6 ± 6,114.1) and M. menidia

(푥 = 54,019.2 ± 7,654.9) (Fig. 4.5) (Table 4.1, 4.2, 4.3). Similarly, the mean area of the optic nerve was statistically significantly different among the three species of Fundulus [F

(2, 14) = 6.12, p = 0.012]. A Tukey post hoc test revealed that the area of the cross-section of the optic nerve for F. waccamensis was larger (푥 = 77,684.1 ± 17,362.37) compared to

F. diaphanus (푥 = 52,782.2 ± 10,035.3) and F. heteroclitus (푥 = 67,186.5 ± 5,762.09).

However, there was no statistically significant difference between F. diaphanus, and F. heteroclitus (p= 0.367) (Fig. 4.8) (Table 4.4, 4.5, 4.6).

The optic nerves of all species in this study are characterized by a ligament folded longitudinally, which appears as a semi-circular shape in cross-section. The number and thickness of folds are different depending on the species (Fig. 4.5, 4.8).

4.1.6 The Brain and optic tectum

Significant variation in the ratio of optic tectum area relative to brain size was found within genera and among species within genera among species. A one-way ANOVA of the ratio of the optic tectum relative to brain size revealed a significant difference among the three Fundulus species [F (2, 14) = 261.5, p = 0.000]. A Tukey post hoc test for Fundulus

species showed that the ratio of the optic tectum size relative to brain size of F. waccamensis was statistically significantly larger (푥 = 55.7 ± 1.08) compared to F. diaphanus (푥 = 49.7 ± 0.78) and F. heteroclitus (푥 = 43.5 ± 0.66) and a significant difference between F. diaphanus and F. heteroclitus (p = 0.000) (Fig. 4.4, 4.10) (Table 4.4,

4.5, 4.6). Moreover, a one-way ANOVA of the ratio of the optic tectum relative to the brain revealed a significant difference among the three species of Menidia [F (2, 15) =

95.62, p = 0.000]. A Tukey post hoc test for Menidia species showed that the ratio of the optic tectum size relative to brain size of M. extensa was statistically significantly larger

(푥 = 56.2 ± 0.46) than that in M. beryllina (푥 = 51.3 ± 0.72) and M. menidia (푥 = 50.5 ±

0.99). There was no statistically significant difference between the mean the ratio of the optic tectum size relative to brain size of M. beryllina and M. menidia (p = 0.197) (Table

4.1, 4.2, 4.3), (Fig. 4.4, 4.7).

DISCUSSION

The current study reinforces the theory that the environmental factors surrounding fish play a significant role in the development or inhibition of sensory systems that can be used effectively with the least cost of energy. Studying very closely related species here has revealed the effect of light intensity on the number of neurotransmitters that carry visual guidance between the eyes and the brain. The number of nerve fibers in clear water species increased compared to species living in dark water to meet the need for improved transport of optical signals due to increased dependence on the visual system. This is reflected in the size of the cross-section of the optic nerve, where the area increased in species living in bright fields. The enhanced visual signal coming from the eyes via the optic nerve requires a large processing unit of the optic lobe of the brain, which has

increased in magnitude in species that have a more significant number of nerve fibers due to inhabiting in clear water. The mammalian visual system consists of many neurons types, each specializing in performing a specific role in processing visual images. They start in the photoreceptor cells of the retina and end with brain cells (Masland, 2012). The optic tectum and the number of retinal axons in the optic nerve of the adult cavefish brain have been reduced compared to those in surface fish. In addition, the optic nerves increased when transplantation a lens from an embryonic surface fish to an embryonic cavefish eye, which accompanies the development of eye structure (Soares et al. 2004). Furthermore, adult cavefish show a reduction in the number of fiber bundles in the optic nerve as compared to that of a surface fish; nevertheless, both surface fish and cavefish possess approximately the same quantity of fiber nerves of each fiber bundle (Rétaux, et al. 2015).

The number of nerve fibers in a specimen’s optic nerve may undergo variations as a result of changing surrounding conditions, such as an increase in the number of nerve fibers for cavefish after lens transplantation (Gallman et al. 2019). The efficient visual performance of organisms depends on the retina, optic nerve, and optic tectum and are affected by surrounding environmental conditions. It has been observed that fish species inhabiting clear water had more enormous eyes and almost twice as many optic nerve fibers than other species of the same genera which live in turbid water (Huber and Rylander 1992b).

Additionally, the number of optic nerve fibers changes during the different stages of a fish’s life, increasing with age, as well as possibly replacing damaged fibers (Easter et al. 1981).

The anatomic study of fibers shows that the vast majority of optic nerve fibers from one eye crosses over the midline to enter the opposite lobe of the optic tectum (Bergmann et al.

2018). Neuroanatomical studies of the visual organ of deep-sea fish revealed a decrease in

the total number of optic nerve axons accompanied by an increase in the olfactory tract. As a result, these fish depend heavily on chemosensation, especially olfaction instead of vision

(Lisney et al. 2018).

The optic nerves of eyes cross over to becoming the optic tracts, each of which enters the front of its corresponding tectum lobe. The anatomical study displays that the information from each eye travel straight to the opposite side of tectum lobe. It is the primary visual center in the brain where the optic nerve fibers and the axons of retinal ganglion cells end (Lisney et al. 2018). The optic nerves of fish extend between the eyes and brain and form a folded ribbon in order to increase the section space (Maggs, and

Scholes, 1990). The optic nerves of fish are composed of longitudinally folded ribbons, with their nerve fibers stretching parallel to the longitudinal axis (Maggs and Scholes

1986). The relationship is significantly positive between the optic tectum and the optic nerve as well as the acute visual of fish species (Lust and Tanaka 2019). The results of a study on related species revealed that there is a close relationship between the optical nerve and the intensity of illumination available to the fish (Hedberg-Buenz et al. 2016). The shallow-water fish are visually orientated and featuring well-developed forebrains, optic tecta and large brains. The brain size of marine fish reveals depth gradients (Yanagitsuru et al., 2018). Adult fish are characterized by a regenerative capacity when the optic nerve is subjected to an injury (Bollaerts et al., 2019). The organization of the optic nerve in fish as a ribbon folder surrounded by conjunctive tissue consists of young and mature axons

(Velasco et al. 2019). The optic nerve, which extends from the retina into the brain, consists of Axons of retinal ganglion cells (RGCs) and glia, which transfer complex visual information into the visual centers of the brain (Harvey et al. 2019).

Fish brains and sensory organs are morphologically different among species. This diversity reflects how brains and neural systems respond to ecology. It has been shown that the brain size and the lobes for vision are significantly reduced in bathypelagic in addition to the deep-sea benthic and turbid environment in shallow water as a result of lowering the capacity for vision (Kotrschal et al., 1998). The relative size of the areas of the brain are considered an authoritative indicator of their relative significance to the organism (Kotrschal and Pauzenberger, 1992; Schellart and Prins, 1993). The optic tectum is the essential visual center in fish and composes the upper part of the midbrain. It is responsible for eye movements, approach, and avoidance movements in addition to be an extremely developed neural processor. The sensory differentiation and rapid decisions required for behavioral reactions are indispensable for survival and reproduction (Suzuki et al. 2019). The mesencephalon consists of a pair of optic lobes (tectum opticum), and the brains of most fish are considerably smaller than the available brain cavity, which may occupy only 6% in some fish species (Kruska, 1988). Morphological diversification in the brains of fish may occur because of the higher request for sensory systems in aquatic environments as a result of the physical properties of water comparative to terrestrial animals (Fay and Tavolga, 2012). Fish that feed on small items and live mostly in mid- water are characterized by having brains that feature well-developed visual centers, compared to the moderate size of lateral line and taste centers (Zaunreiter et al., 1991). The size of fish brain areas is affected by predation, habitat choice, and availability of prey

(Huber et al., 1997). The evolution of sensory and brain structures is controlled by the physical characteristics of the habitat. For example, fish that live in an area with limited light availability will possess a low number of photoreceptors, which requires very little

processing in a small optic lobe compared to that of fish inhabiting an area with abundant light (Yopak et al., 2019). Moreover, there is a significant variation in stratification and the size of the optic tectum between fish species that inhabit clear water and their closely related species living in turbid water. The optic lobes of the clear water species are more significant than that of the turbid water species (Huber and Rylander 1991).

Table 4.1: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2 , Area of optic tectum (AL) µm2, Area of brain (AB) µm2, and % optic tectum to brain area (AT/AB) of different individuals of Menidia extensa.

(TL)mm (DE)µm (NF) (AO)µm2 (AL)µm2 (AB)µm2 (AT/AB)

47 2365.9 55771 78296.8 1696688.6 3006107.5 56.4

63 2748.5 48840 79808.6 2295823.4 4137756.9 55.4

46 2098.3 47486 76819.8 1790218.2 3165821.9 56.5

42 1893.8 61781 72786.1 1563488.7 2798345 55.8

61 2597.5 51027 77986.4 1897956.3 3387869.2 56.0

62 2635.8 49997 82535.7 1986905.8 3505889.6 56.6

M± (SD) 2389.9 52484.6± 78038.9± 1871846.8 3333631.7 56.2±0.46 5362.93 3240.4 ±255383.9 ±469296.5

Table 4.2: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2, Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of different individuals of Menidia beryllina.

(TL)mm (DE)µm (NF) (AO)µm2 (AT)µm2 (AB)µm2 (AT/AB)

59 2414 21656 60132.9 794279.2 1558281.4 50.9

80 2710.3 16311 75471.3 974586.5 1862541.8 52.3

68 2633.4 22377 65803.7 911857.3 1801392.4 50.6

39 2113 15645 57966.9 787485.6 1509237.5 52.1

66 2460.7 22263 63242.1 908658.7 1790834.1 50.7

67 2564.8 22023 65868.8 818743.1 1597756.9 51.2

M± (SD) 2481.7 20045.8± 64747.6± 865935.1 1686674.0 51.3±0.72 3167.59 6114.1 ±76492.2 ±148871.6

Table 4.3: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2, Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of different individuals of Menidia menidia.

(TL)mm (DE)µm (NF) (AO)µm2 (AT)µm2 (AB)µm2 (AT/AB)

53 2539.1 24621 41481.3 5321080.9 10455130.6 50.9

59 2814.5 21514 62304.1 7174296.5 14092272.9 50.9

57 2619.9 26328 55601.6 6065253.2 11658055.9 52.0

52 2512.9 21434 48635.5 4845947.3 9687220.32 50.0

55 2583.8 22659 56776.2 5958990.8 11877955.6 50.1

57 2701.6 23655 59316.6 6236454.7 12694879.2 49.1

M± (SD) 2628.6 23368.5± 54019.2± 5933670.6 11744252.4 50.5±0.99 1902.33 7654.9 ±800898.8 ±1570695.0

Table 4.4: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2, Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of different individuals of Fundulus waccamensis.

(TL)mm (DE)µm (NF) (AO)µm2 (AT)µm2 (AB)µm2 (AT/AB)

50 2494.3 102489 62500.4 7845947.3 13968220.2 56.1

50 2473.8 95592 65153.4 8310531.3 15292667.6 54.3

65 2940.3 102095 83853.4 9078711.4 16559988.9 54.8

66 2997.7 99357 96503.7 9074296.5 15992272.9 56.7

49 2417.5 101352 60023.9 6065253.2 10658055.9 56.9

68 3126.7 103310 98069.8 9213401.8 16730449.8 55.0

M± (SD) 2741.7 100699.2± 77684.1± 8264690.3 14866942.5 55.7±1.08 2838.27 17362.37 ±1202756.6 ±2293865.9

Table 4.5: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2, Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of Fundulus diaphanus.

(TL) (DE) (NF) (AO)µm2 (AT)µm2 (AB)µm2 (AT/AB)

59 3072.9 41380 71293.5 18464542.7 36949390.2 49.9

53 2870.2 42321 49652.8 6910531.3 13792667.6 50.1

44 2369.8 40646 43677.2 4775545.3 9388214.32 50.8

54 2843.7 47522 50532.8 7174296.5 14392272.9 49.8

55 2931.9 39896 56012.7 7278711.4 14989988.9 48.5

49 2629.4 44400 45524.1 5932157.5 12050701.2 49.2

M± 2786.3 42694.1± 52782.2± 8422630.8± 16927205.8± 49.7± (SD) 2832.27 10035.3 5009930.5 10015074.4 0.78

Table 4.6: Total length of Fish (TL) mm, The diameter of eyeball (DE) µm, Number of Fiber within Optic (NF), Area of Optic nerve (AO) µm2, Area of optic tectum (AT) µm2, Area of brain (AB) µm2, and % Optic tectum to the brain area (AT/AB) of different individuals of Fundulus heteroclitus.

(TL)mm (DE)µm (NF) (AO)µm2 (AT)µm2 (AB)µm2 (AT/AB)

53 2555.5 56430 58762.1 5297159.1 12360605.7 42.8

54 2591.4 51475 65990.6 5385421.2 12254135.6 43.9

54 2641.3 64019 66105.3 5667721.2 12801241.2 44.2

59 2670.9 60149 73531.1 6075525.5 13813488.7 43.9

55 2601.4 63475 71543.5 5937556.1 12985617.7 42.8

M± (SD) 2612.1 59109.6± 67186.5± 5672676.6 12843017.8 43.5± 5237.04 5762.09 ±337639.6 ±621167.7 0.66

Lake Waccamaw 34.257840-78.503187 Lake Waccamaw 34.316520-78.525023

Waccamaw River 33.668366 -79.061312 Waccamaw River 34.048779 -78.582503 Waccamaw River 33.911332 -78.715027

Figure 4.1: Scheme of Map to Lake Waccamaw and Waccamaw River the arrows are pointing samples collection sites (Stager and Cahoon 1987).

3200

3100

3000

2900

2800 F. diaphanus 2700 F. heteroclitus

Eye Diameter (um) Diameter Eye 2600 F. waccamensis

2500

2400

2300 30000 40000 50000 60000 70000 80000 90000 100000 110000 # Fibers in Optic Nerve

3000

2800

2600

2400 M. beryllina M. extensa 2200 M. menidia Eye Diameter (um) Diameter Eye 2000

1800 10000 20000 30000 40000 50000 60000 70000 # Fibers Optic Nerve

110000 NF 100000

90000

80000

70000

60000

50000

40000 F. waccamensis F. diaphanous F. heteroclitus 30000

70000 NF

60000

50000

40000

30000

20000

M. extensa M. beryllina M. menidia 10000

Figure 4.3: The total number of optic nerve fibers (NF) for F. waccamensis, M. extensa from Lake Waccamaw and F. diaphanus, F. heteroclitus, M. beryllina and M. menidia from Waccamaw River.

60 AT/AB 58

42 F. waccamensis F. diaphanous F. heteroclitus 40

57 AT/AB 56

50 M. extensa M. beryllina M. menidia 49

A B C

Figure 4.5: The cross-section of the Optic Nerve of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River.

A B C

Figure 4.6: Micrograph of Optic Nerve Fibers of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River.

A B C

Figure 4.7: The brain size of M. extensa (A) from Lake Waccamaw, M. beryllina (B) and M. menidia (C) from Waccamaw River.

A B C

Figure 4.8: The cross section of Optic Nerve of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River.

A B C

Figure 4.9: The number of Optic Nerve Fibers of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River.

A B C

Figure 4.10: The brain size of F. waccamensis (A) from Lake Waccamaw, F. diaphanus (B) and F. heteroclitus (C) from Waccamaw River.

CHAPTER 5

SUMMARY AND CONCLUSIONS

When members of these fish genera (Menidia and Fundulus) migrated from the

Waccamaw River (turbid water) to Lake Waccamaw (clear water), convergent evolution occurred to their body morphology corresponding to their new environmental conditions.

It was expected that there were also adaptations to the visual system commensurate with the amount of available light within the new ecological environment. Previous studies have demonstrated that differences do exist in the overall eye shape and size, lenses shape and pupil diameter between clear and turbid water fishes. In our current study, differences have been observed in the histological structure of the optic nerve, which includes the number and size of the nerve fibers within the optic nerve. As was expected, the optic nerve diameter and the number of nerve fibers are greater in fish inhabiting a clear water environment and differences also exist in the histological structure of the eye and mosaic structure of the rods and cones. The number of rods is greater in fish in turbid water and in contrast, the number of cones are greater in clear water fish. Differences may also be present in the cone subtypes between fish living in different habitats.

REFERENCES

Agte, S., Savvinov, A., Karl, A., Zayas-Santiago, A., Ulbricht, E., Makarov, V.I.,

Reichenbach, A., Bringmann, A. and Skatchkov, S.N., 2018. Müller glial cells

contribute to dim light vision in the spectacled caiman (Caiman crocodilus fuscus):

Analysis of retinal light transmission. Experimental eye research, 173, pp.91-108.

Albert, J.S. and Crampton, W.G., 2005. Diversity and phylogeny of Neotropical electric

fishes (Gymnotiformes). In Electroreception (pp. 360-409). Springer, New York,

NY.

Albertson, R.C. and Yelick, P.C., 2005. Roles for fgf8 signaling in left–right patterning of

the visceral organs and craniofacial skeleton. Developmental biology, 283(2), pp.310-

321.

Albertson, R.C. and Yelick, P.C., 2007. Fgf8 haploinsufficiency results in distinct

craniofacial defects in adult zebrafish. Developmental biology, 306(2), pp.505-515.

Alroy, J., 1998. Cope's rule and the dynamics of body mass evolution in North American

fossil mammals. Science, 280(5364), pp.731-734.

Alsudani, H., Ghoshroy, S., Quattro, J., Greenwold, M. and Sawyer, R., 2018. Improved

Biological Tissue Preparation Procedure for Scanning Electron Microscopic

Imaging. Microscopy and Microanalysis, 24(S1), pp.1308-1309.

Arendt, J. and Reznick, D., 2008. Convergence and parallelism reconsidered: what have

we learned about the genetics of adaptation? Trends in ecology & evolution, 23(1),

pp.26-32.

Arikawa, K., Molday, L.L., Molday, R.S. and Williams, D.S., 1992. Localization of

peripherin/rds in the disk membranes of cone and rod photoreceptors: relationship to

disk membrane morphogenesis and retinal degeneration. The Journal of Cell

Biology, 116(3), pp.659-667.

Atta, K.I., 2013. Morphological, anatomical and histological studies on the olfactory

organs and eyes of teleost fish: Anguilla anguilla in relation to its feeding habits. The

Journal of Basic & Applied Zoology, 66(3), pp.101-108.

Beaudet, L., Browman, H.I. and Hawryshyn, C.W., 1993. Optic nerve response and retinal

structure in rainbow trout of different sizes. Vision research, 33(13), pp.1739-1746.

Bell, G., 1998. Volvox: Molecular-Genetic Origins of Multicellularity and Cellular

Differentiation. Science, 282(5387), pp.248-249.

Bergmann, K., Meza Santoscoy, P., Lygdas, K., Nikolaeva, Y., MacDonald, R., Cunliffe,

V. and Nikolaev, A., 2018. Imaging neuronal activity in the optic tectum of late stage

larval zebrafish. Journal of developmental biology, 6(1), p.6.

Bilotta, J., Saszik, S. and Sutherland, S.E., 2001. Rod contributions to the

electroretinogram of the dark‐adapted developing zebrafish. Developmental

dynamics: an official publication of the American Association of Anatomists, 222(4),

pp.564-570.

Bollaerts, I., Beckers, A., Lemmens, K., Vanhunsel, S., De Groef, L. and Moons, L., 2019.

Prior Exposure to Immunosuppressors Sensitizes Retinal Microglia and Accelerates

Optic Nerve Regeneration in Zebrafish. Mediators of inflammation, 2019.

Bonner, J.T., 1988a. The evolution of complexity by means of natural selection. Princeton

University Press.

Bonner, J.T., 1998b. The origins of multicellularity. Integrative Biology: Issues, News, and

Reviews: Published in Association with The Society for Integrative and Comparative

Biology, 1(1), pp.27-36.

Borowsky, R., 2008. Restoring sight in blind cavefish. Current biology, 18(1), pp. R23-

R24.

Bowmaker, J.K. and Kunz, Y.W., 1987. Ultraviolet receptors, tetrachromatic colour vision

and retinal mosaics in the brown trout (Salmo trutta): age-dependent changes. Vision

research, 27(12), pp.2101-2108.

Braekevelt, C.R., 1982. Photoreceptor fine structure in the goldeye (Hiodon

alosoides)(Teleost). Anatomy and embryology, 165(2), pp.177-192.

Brainard, G.C., Hanifin, J.P., Greeson, J.M., Byrne, B., Glickman, G., Gerner, E. and

Rollag, M.D., 2001. Action spectrum for melatonin regulation in humans: evidence

for a novel circadian photoreceptor. Journal of Neuroscience, 21(16), pp.6405-6412.

Branchek, T., 1984. The development of photoreceptors in the zebrafish, brachydanio rerio.

II. Function. Journal of Comparative Neurology, 224(1), pp.116-122.

Bravo, G.A., Remsen Jr, J.V. and Brumfield, R.T., 2014. Adaptive processes drive

ecomorphological convergent evolution in antwrens

(Thamnophilidae). Evolution, 68(10), pp. 2757-2774.

Bunt, S.M., 1982. Retinotopic and temporal organization of the optic nerve and tracts in

the adult goldfish. Journal of Comparative Neurology, 206(3), pp.209-226.

Burnside, B., 1976. Microtubles and actin filaments in teleost visual cone elongation and

contraction. Journal of supramolecular structure, 5(3), pp.257-275.

Cahoon, L.B. and Cooke, J.E., 1992. Benthic microalgal production in Onslow bay, North

Carolina, USA. Marine Ecology Progress Series, pp.185-196.

Callahan, M.P. and Mensinger, A.F., 2007. Restoration of visual function following optic

nerve regeneration in bluegill (Lepomis macrochirus) pumpkinseed (Lepomis

gibbosus) hybrid sunfish. Visual Neuroscience, 24(3), pp.309-317.

Campbell M 2014. Humans and squids evolved the same eyes using the same genes. PHYS.

ORG. https://phys.org/news/2014-05-humans-squids-evolved-eyes-genes.html.

Carroll, S.B., 2001. Chance and necessity: the evolution of morphological complexity and

diversity. Nature, 409(6823), p.1102.

Carter‐Dawson, L.D. and Lavail, M.M., 1979. Rods and cones in the mouse retina. I.

Structural analysis using light and electron microscopy. Journal of Comparative

Neurology, 188(2), pp.245-262.

Chakrabarti, P. and Ghosh, S.K., 2011. Histological and ultrastructural studies of the

olfactory epithelium of spotted butter fish Scatophagus argus (Linnaeus). Folia

morphologica, 70(2), pp.74-79.

Cheng, C.L. and Flamarique, I.N., 2007. Chromatic organization of cone photoreceptors in

the retina of rainbow trout: single cones irreversibly switch from UV (SWS1) to blue

(SWS2) light sensitive opsin during natural development. Journal of Experimental

Biology, 210(23), pp.4123-4135.

Chevin, L.M., Martin, G. and Lenormand, T., 2010. Fisher's model and the genomics of

adaptation: restricted pleiotropy, heterogenous mutation, and parallel

evolution. Evolution: International Journal of Organic Evolution, 64(11), pp.3213-

3231.

Chiu, J.F., Mack, A.F. and Fernald, R.D., 1995. Daily rhythm of cell proliferation in the

teleost retina. Brain research, 673(1), pp.119-125.

Chow, R.L. and Lang, R.A., 2001. Early eye development in vertebrates. Annual review of

cell and developmental biology, 17(1), pp.255-296.

Christin, P.A., Weinreich, D.M. and Besnard, G., 2010. Causes and evolutionary

significance of genetic convergence. Trends in Genetics, 26(9), pp.400-405.

Coastal Carolina University – Parameters, 2015. https://www.coastal.edu/wwa/vm/

Coghill, L.M., Hulsey, C.D., Chaves-Campos, J., de Leon, F.J.G. and Johnson, S.G., 2014.

Next generation phylogeography of cave and surface Astyanax mexicanus. Molecular

Phylogenetics and Evolution, 79, pp.368-374.

Collin, S.P., 1999. Behavioural ecology and retinal cell topography. In Adaptive

mechanisms in the ecology of vision (pp. 509-535). Springer, Dordrecht.

Cooper, W.J. and Albertson, R.C., 2008. Quantification and variation in experimental

studies of morphogenesis. Developmental biology, 321(2), pp.295-302.

Crawford, M.L.J., 1977. Central vision of man and macaque: cone and rod

sensitivity. Brain research, 119(2), pp.345-356.

Cresko, W.A., Amores, A., Wilson, C., Murphy, J., Currey, M., Phillips, P., Bell, M.A.,

Kimmel, C.B. and Postlethwait, J.H., 2004. Parallel genetic basis for repeated

evolution of armor loss in Alaskan threespine stickleback populations. Proceedings

of the National Academy of Sciences, 101(16), pp.6050-6055.

Crisp, M.D. and Cook, L.G., 2012. Phylogenetic niche conservatism: what are the

underlying evolutionary and ecological causes? New Phytologist, 196(3), pp.681-694.

Culver, D.C., 1982. Cave life: evolution and ecology. HARVARD UNIVERSITY PRESS,

CAMBRIDGE, MA(USA). 1982.

Darwish, S.T., Mohalal, M.E., Helal, M.M. and El-Sayyad, H.I., 2015. Structural and

functional analysis of ocular regions of five marine teleost fishes (Hippocampus

hippocampus, Sardina pilchardus, Gobius niger, Mullus barbatus & Solea

solea). Egyptian journal of basic and applied sciences, 2(3), pp.159-166.

De Moed, G.H., De Jong, G. and Scharloo, W., 1997. The phenotypic plasticity of wing

size in Drosophila melanogaster: the cellular basis of its genetic

variation. Heredity, 79(3), p.260.

De Monasterio, F.M., McCrane, E.P., Newlander, J.K. and Schein, S.J., 1985. Density

profile of blue-sensitive cones along the horizontal meridian of macaque

retina. Investigative Ophthalmology & Visual Science, 26(3), pp.289-302.

Dowling, T.E., Martasian, D.P. and Jeffery, W.R., 2002. Evidence for multiple genetic

forms with similar eyeless phenotypes in the blind cavefish, Astyanax

mexicanus. Molecular Biology and Evolution, 19(4), pp.446-455.

Dufton, M., Hall, B.K. and Franz-Odendaal, T.A., 2012. Early lens ablation causes

dramatic long-term effects on the shape of bones in the craniofacial skeleton of

Astyanax mexicanus. PLoS One, 7(11), p.e50308.

Easter, S., Rusoff, A.C. and Kish, P.E., 1981. The growth and organization of the optic

nerve and tract in juvenile and adult goldfish. Journal of Neuroscience, 1(8), 793-811.

Eberhart, J.K., He, X., Swartz, M.E., Yan, Y.L., Song, H., Boling, T.C., Kunerth, A.K.,

Walker, M.B., Kimmel, C.B. and Postlethwait, J.H., 2008. MicroRNA Mirn140

modulates Pdgf signaling during palatogenesis. Nature genetics, 40(3), p.290.

Eigenmann, C.H., 1909. Cave vertebrates of America: a study in degenerative

evolution (No. 104). Carnegie Institution of Washington.

Eizirik, E., Yuhki, N., Johnson, W.E., Menotti-Raymond, M., Hannah, S.S. and O'Brien,

S.J., 2003. Molecular genetics and evolution of melanism in the cat family. Current

biology, 13(5), pp.448-453.

Emerling, C.A. and Springer, M.S., 2014. Eyes underground: regression of visual protein

networks in subterranean mammals.Molecular phylogenetic and evolution,78 260,270

Endler, J.A., 1986. Natural selection in the wild (No. 21). Princeton University Press.

Etnier, D.A. and Starnes, W.C., 1993. The fishes of Tennessee. Newfound Press.

Evans, B.I. and Fernald, R.D., 1990. Metamorphosis and fish vision. Journal of

neurobiology, 21(7), pp.1037-1052.

Fay, R.R. and Tavolga, W.N. eds., 2012. Sensory biology of aquatic animals. Springer

Science & Business Media.

Fernald, R.D., 2006. Casting a genetic light on the evolution of eyes. Science, 313(5795),

pp.1914-1918.

Fishelson, L., Ayalon, G., Zverdling, A. and Holzman, R., 2004. Comparative morphology

of the eye (with particular attention to the retina) in various species of cardinal fish

(Apogonidae, Teleostei). The Anatomical Record Part A: Discoveries in Molecular,

Cellular, and Evolutionary Biology: An Official Publication of the American

Association of Anatomists, 277(2), pp.249-261.

Fishelson, L., Golani, D., Galil, B. and Goren, M., 2010. Comparison of the nasal olfactory

organs of various species of lizardfishes (Teleostei: Aulopiformes: Synodontidae)

with additional remarks on the brain. International Journal of Zoology, 2010.

Francke, M., Kreysing, M., Mack, A., Engelmann, J., Karl, A., Makarov, F., Guck, J.,

Kolle, M., Wolburg, H., Pusch, R. and von der Emde, G., 2014. Grouped retinae and

tapetal cups in some Teleostian fish: Occurrence, structure, and function. Progress in

retinal and eye research, 38, pp.43-69.

Franz‐Odendaal, T.A. and Hall, B.K., 2006. Skeletal elements within teleost eyes and a

discussion of their homology. Journal of Morphology, 267(11), pp.1326-1337.

Franz‐Odendaal, T.A. and Vickaryous, M.K., 2006. Skeletal elements in the vertebrate eye

and adnexa: morphological and developmental perspectives. Developmental

dynamics: an official publication of the American Association of Anatomists, 235(5),

pp.1244-1255.

Franz‐Odendaal, T.A., 2008. Scleral ossicles of teleostei: Evolutionary and developmental

trends. The Anatomical Record: Advances in Integrative Anatomy and Evolutionary

Biology: Advances in Integrative Anatomy and Evolutionary Biology, 291,2.161-168.

Friedman, A. and Perrimon, N., 2007. Genetic screening for signal transduction in the era

of network biology. Cell, 128(2), pp.225-231.

Fuller, R.C., Fleishman, L.J., Leal, M., Travis, J. and Loew, E., 2003. Intraspecific

variation in retinal cone distribution in the bluefin killifish, Lucania goodei. Journal

of Comparative Physiology A, 189(8), pp.609-616.

Gage, P.J., Rhoades, W., Prucka, S.K. and Hjalt, T., 2005. Fate maps of neural crest and

mesoderm in the mammalian eye. Investigative ophthalmology & visual

science, 46(11), pp.4200-4208.

Gagnon, Y.L., Sutton, T.T. and Johnsen, S., 2013. Visual acuity in pelagic fishes and

mollusks. Vision research, 92, pp.1-9.

Gallman, K.E., Rivera, D. and Soares, D., 2019. Evolutionary increases in catecholamine

signaling may underlie the emergence of adaptive traits and behaviors in the blind

cavefish, Astyanax mexicanus. bioRxiv, p.724179.

Gingerich, P.D., 2001. Rates of evolution on the time scale of the evolutionary process.

In Microevolution Rate, Pattern, Process (pp. 127-144). Springer, Dordrecht.

Gleiss, A.C., Jorgensen, S.J., Liebsch, N., Sala, J.E., Norman, B., Hays, G.C., Quintana,

F., Grundy, E., Campagna, C., Trites, A.W. and Block, B.A., 2011. Convergent

evolution in locomotory patterns of flying and swimming animals. Nature

communications, 2, p.352.

Gould, S.J., 1988. Trends as changes in variance: a new slant on progress and directionality

in evolution. Journal of Paleontology, 62(3), pp.319-329.

Govindarajan, V. and Overbeek, P.A., 2006. FGF9 can induce endochondral ossification

in cranial mesenchyme. BMC developmental biology, 6(1), p.7.

Gross, J.B., 2012. The complex origin of Astyanax cavefish. BMC evolutionary

biology, 12(1), p.105.

Gross, J.B., Krutzler, A.J. and Carlson, B.M., 2014. Complex craniofacial changes in blind

cave-dwelling fish are mediated by genetically symmetric and asymmetric

loci. Genetics, 196(4), pp.1303-1319.

Gross, J.B., Protas, M., Conrad, M., Scheid, P.E., Vidal, O., Jeffery, W.R., Borowsky, R.

and Tabin, C.J., 2008. Synteny and candidate gene prediction using an anchored

linkage map of Astyanax mexicanus. Proceedings of the National Academy of

Sciences, 105(51), pp.20106-20111.

Guillaume, F. and Otto, S.P., 2012. Gene functional trade-offs and the evolution of

pleiotropy. Genetics, 192(4), pp.1389-1409.

Hallgrímsson, B. and Hall, B.K. eds., 2011. Variation: a central concept in biology.

Elsevier.

Hansen, T.F., 2003. Is modularity necessary for evolvability?: Remarks on the relationship

between pleiotropy and evolvability. Biosystems, 69(2-3), pp.83-94.

Harmon, L.J., Kolbe, J.J., Cheverud, J.M. and Losos, J.B., 2005. Convergence and the

multidimensional niche. Evolution, 59(2), pp.409-421.

Harris, W.A., 1997. Pax-6: where to be conserved is not conservative. Proceedings of the

National Academy of Sciences, 94(6), pp.2098-2100.

Harvey, B.M., Baxter, M. and Granato, M., 2019. Optic nerve regeneration in larval

zebrafish exhibits spontaneous capacity for retinotopic but not tectum specific axon

targeting. PloS one, 14(6), p.e0218667.

Hawryshyn, C.W., Martens, G., Allison, W.T. and Anholt, B.R., 2003. Regeneration of

ultraviolet-sensitive cones in the retinal cone mosaic of thyroxin-challenged post-

juvenile rainbow trout (Oncorhynchus mykiss). Journal of Experimental

Biology, 206(15), pp.2665-2673.

Hedberg-Buenz, A., Christopher, M.A., Lewis, C.J., Fernandes, K.A., Dutca, L.M., Wang,

K., Scheetz, T.E., Abràmoff, M.D., Libby, R.T., Garvin, M.K. and Anderson, M.G.,

2016. Quantitative measurement of retinal ganglion cell populations via histology-

based random forest classification. Experimental eye research, 146, pp.370-385.

Helfman, G., Collette, B.B., Facey, D.E. and Bowen, B.W., 2009. The diversity of fishes:

biology, evolution, and ecology. John Wiley & Sons.

Heuschele, J., Mannerla, M., Gienapp, P. and Candolin, U., 2009. Environment-dependent

use of mate choice cues in sticklebacks. Behavioral Ecology, 20(6), pp.1223-1227.

Hoar, W.S., 1988. The physiology of smolting salmonids. In" Fish Physiology Vol XIB"

Ed by WS Hoar, DJ Randall.

Höglund, J., Mitkus, M., Olsson, P., Lind, O., Drews, A., Bloch, N.I., Kelber, A. and

Strandh, M., 2019. Owls lack UV-sensitive cone opsin and red oil droplets, but see

UV light at night: Retinal transcriptomes and ocular media transmittance. Vision

research, 158, pp.109-119.

Holland, P.W., Garcia-Fernàndez, J., Williams, N.A. and Sidow, A., 1994. Gene

duplications and the origins of vertebrate

development. Development, 1994(Supplement), pp.125-133.

Hope, A.J., Partridge, J.C. and Hayes, P.K., 1998. Switch in rod opsin gene expression in

the European eel, Anguilla anguilla (L.). Proceedings of the Royal Society of London.

Series B: Biological Sciences, 265(1399), pp.869-874.

Hubbard, J.K., Uy, J.A.C., Hauber, M.E., Hoekstra, H.E. and Safran, R.J., 2010. Vertebrate

pigmentation: from underlying genes to adaptive function. Trends in Genetics, 26(5),

pp.231-239.

Huber Robert, Rylander Kent, 1992b. Quantitative histological study of optic nerve in

species of Minnows (Cyprinidae, Teleostei) inhabiting clear and turbid water. Brain

Behav Evol 40: 250 – 255.

Huber, R. and Rylander, M.K., 1991. Quantitative histological studies of the optic tectum

in six species of Notropis and Cyprinella (Cyprinidae, Teleostei). Journal fur

Hirnforschung, 32(3), pp.309-316.

Huber, R. and Rylander, M.K., 1992a. Brain morphology and turbidity preference in

Notropis and related genera (Cyprinidae, Teleostei). Environmental biology of

fishes, 33(1-2), pp.153-165.

Huber, R., van Staaden, M.J., Kaufman, L.S. and Liem, K.F., 1997. Microhabitat use,

trophic patterns, and the evolution of brain structure in African cichlids. Brain,

Behavior and Evolution, 50(3), pp.167-182.

Hunt, D.E., Rawlinson, N.J.F., Thomas, G.A. and Cobcroft, J.M., 2015. Investigating

photoreceptor densities, potential visual acuity, and cone mosaics of shallow water,

temperate fish species. Vision research, 111, pp.13-21.

Imamoto, Y. and Shichida, Y., 2014. Cone visual pigments. Biochimica et Biophysica Acta

(BBA)-Bioenergetics, 1837(5), pp.664-673.

Ingram, T. and Mahler, D.L., 2013. SURFACE: detecting convergent evolution from

comparative data by fitting Ornstein‐Uhlenbeck models with stepwise Akaike

Information Criterion. Methods in Ecology and Evolution, 4(5), pp.416-425.

Jacobs, G.H., 1993. The distribution and nature of colour vision among the

mammals. Biological Reviews, 68(3), pp.413-471.

James, A.C., Azevedo, R.B. and Partridge, L., 1995. Cellular basis and developmental

timing in a size cline of Drosophila melanogaster. Genetics, 140(2), pp.659-666.

Jeffery, W.R., 2001. Cavefish as a model system in evolutionary developmental

biology. Developmental biology, 231(1), pp.1-12.

Jeffery, W.R., 2009. Regressive evolution in Astyanax cavefish. Annual review of

genetics, 43, pp.25-47.

Kato, S., Matsukawa, T., Koriyama, Y., Sugitani, K. and Ogai, K., 2013. A molecular

mechanism of optic nerve regeneration in fish: the retinoid signaling

pathway. Progress in retinal and eye research, 37, pp.13-30.

Kauffman, S.A., 1995. At home in the universe Oxford University Press Oxford.

Kenrick, P. and Crane, P.R., 1997. The origin and early evolution of plants on

land. Nature, 389(6646), p.33.

Keys, D.N., Lewis, D.L., Selegue, J.E., Pearson, B.J., Goodrich, L.V., Johnson, R.L.,

Gates, J., Scott, M.P. and Carroll, S.B., 1999. Recruitment of a hedgehog regulatory

circuit in butterfly eyespot evolution. Science, 283(5401), pp.532-534.

Kotrschal, K. and Palzenberger, M., 1992. Neuroecology of cyprinids: comparative,

quantitative histology reveals diverse brain patterns. In Environmental biology of

European cyprinids (pp. 135-152). Springer, Dordrecht.

Kotrschal, K., Van Staaden, M.J. and Huber, R., 1998. Fish brains: Evolution and

anvironmental relationships. Reviews in Fish Biology and Fisheries, 8(4), p.373-408.

Kozmik, Z., Ruzickova, J., Jonasova, K., Matsumoto, Y., Vopalensky, P., Kozmikova, I.,

Strnad, H., Kawamura, S., Piatigorsky, J., Paces, V. and Vlcek, C., 2008. Assembly

of the cnidarian camera-type eye from vertebrate-like components. Proceedings of the

National Academy of Sciences, 105(26), pp.8989-8993.

Krabbenhoft, T.J., Collyer, M.L. and Quattro, J.M., 2009. Differing evolutionary patterns

underlie convergence on elongate morphology in endemic fishes of Lake Waccamaw,

North Carolina. Biological Journal of the Linnean Society, 98(3), pp.636-645.

Krebs, W. and Friedrich, I., 1982. Quantitative morphology of bovine retina. The structure

of the eye, pp.175-182.

Kröger, R.H.H., Bowmaker, J.K. and Wagner, H.J., 1999. Morphological changes in the

retina of Aequidens pulcher (Cichlidae) after rearing in monochromatic light. Vision

research, 39(15), pp.2441-2448.

Kruska, D.C., 1988. The brain of the basking shark (Cetorhinus maximus). Brain, Behavior

and Evolution, 32(6), pp.353-363.

Lam, S.H., Sin, Y.M., Gong, Z. and Lam, T.J., 2005. Effects of thyroid hormone on the

development of immune system in zebrafish. General and comparative

endocrinology, 142(3), pp.325-335.

Lauder, G.V., 1981. Form and function: structural analysis in evolutionary morphology.

Paleobiology, 7(4), pp.430-442.

Laue, K., Pogoda, H.M., Daniel, P.B., van Haeringen, A., Alanay, Y., von Ameln, S.,

Rachwalski, M., Morgan, T., Gray, M.J., Breuning, M.H. and Sawyer, G.M., 2011.

Craniosynostosis and Multiple Skeletal Anomalies in Humans and Zebrafish Result

from a Defect in the Localized Degradation of Retinoic Acid. The American Journal

of Human Genetics 89: 595–606.

Leech, D.M. and Johnsen, S., 2009. Light, biological receptors. Encyclopedia of inland

waters, 2, pp.671-681.

Li, Y., Liu, Z., Shi, P. and Zhang, J., 2010. The hearing gene Prestin unites echolocating

bats and whales. Current Biology, 20(2), pp.R55-R56.

Lindenau, W., Kuhrt, H., Ulbricht, E., Körner, K., Bringmann, A. and Reichenbach, A.,

2019. Cone-to-Müller cell ratio in the mammalian retina: A survey of seven mammals

with different lifestyle. Experimental eye research, 181, pp.38-48.

Linton, J.D., Holzhausen, L.C., Babai, N., Song, H., Miyagishima, K.J., Stearns, G.W.,

Lindsay, K., Wei, J., Chertov, A.O., Peters, T.A. and Caffe, R., 2010. Flow of energy

in the outer retina in darkness and in light. Proceedings of the National Academy of

Sciences, 107(19), pp.8599-8604.

Lisney, T.J., Wagner, H.J. and Collin, S.P., 2018. Ontogenetic Shifts in the Number of

Axons in the Olfactory Tract and Optic Nerve in Two Species of Deep-Sea Grenadier

Fish (Gadiformes: Macrouridae: Coryphaenoides). Frontiers in Ecology and

Evolution, 6, p.168.

Liu, Y., Cotton, J.A., Shen, B., Han, X., Rossiter, S.J. and Zhang, S., 2010. Convergent

sequence evolution between echolocating bats and dolphins. Current Biology, 20(2),

pp.R53-R54.

Loew, E.R. and Wahl, C.M., 1991. A short-wavelength sensitive cone mechanism in

juvenile yellow perch, Perca flavescens. Vision research, 31(3), pp.353-360.

Losos, J.B., 2008. Phylogenetic niche conservatism, phylogenetic signal and the

relationship between phylogenetic relatedness and ecological similarity among

species. Ecology letters, 11(10), pp.995-1003.

Losos, J.B., 2011. Convergence, adaptation, and constraint. Evolution: International

Journal of Organic Evolution, 65(7), pp.1827-1840.

Lust, K. and Tanaka, E.M., 2019. A comparative perspective on brain regeneration in

amphibians and teleost fish. Developmental neurobiology.

MacFadden, B.J., 1986. Fossil horses from “Eohippus”(Hyracotherium) to Equus: scaling,

Cope's Law, and the evolution of body size. Paleobiology, 12(4), pp.355-369.

MacIver, M.A., Patankar, N.A. and Shirgaonkar, A.A., 2010. Energy-information trade-

offs between movement and sensing. PLoS computational biology, 6(5), p.e1000769.

Maciver, M.A., Sharabash, N.M. and Nelson, M.E., 2001. Prey-capture behavior in

gymnotid electric fish: motion analysis and effects of water conductivity. Journal of

experimental biology, 204(3), pp.543-557.

Maggs, A. and Scholes, J., 1986. Glial domains and nerve fiber patterns in the fish

retinotectal pathway. Journal of Neuroscience, 6(2), pp.424-438.

Maggs, A.O. and Scholes, J., 1990. Reticular astrocytes in the fish optic nerve: macroglia

with epithelial characteristics form an axially repeated lacework pattern, to which

nodes of Ranvier are apposed. Journal of Neuroscience, 10(5), pp.1600-1614.

Mariani, A.P., 1986. Photoreceptors of the larval tiger salamander retina. Proceedings of

the Royal society of London. Series B. Biological sciences, 227(1249), pp.483-492.

Marmor, M.F., Choi, S.S., Zawadzki, R.J. and Werner, J.S., 2008. Visual insignificance of

the foveal pit: reassessment of foveal hypoplasia as fovea plana. Archives of

Ophthalmology, 126(7), pp.907-913.

Masland, R.H., 2012. The neuronal organization of the retina. Neuron, 76(2), pp.266-280.

McShea D W 1994. Mechanisms of large-scale evolutionary trends. Evolution 1747–1763.

McShea D W 1996. Metazoan complexity and evolution: is there a trend? Evolution 50:

477–492.

Meyer, D.B. and May Jr, H.C., 1973. The topographical distribution of rods and cones in

the adult chicken retina. Experimental eye research, 17(4), pp.347-355.

Mitchell, R.W., Elliott, W.R. and Russell, W.H., 1977. Mexican eyeless characin fishes,

genus Astyanax: environment, distribution, and evolution. Texas Tech Press.

Moore, B.A., Tyrell, L.P., Kamilar, J.M., Collin, S., Dominy, N.J., Hall, M.I., Heesy, C.P.,

Lisney, T.J., Loew, E.R., Moritz, G.L. and Nava, S.S., 2017. Structure and function

of regional specializations in the vertebrate retina. In Evolution of nervous systems:

The evolution of the nervous systems in nonmammalian vertebrates (pp. 351-372).

Academic Press.

Moran, D., Softley, R. and Warrant, E.J., 2015. The energetic cost of vision and the

evolution of eyeless Mexican cavefish. Science advances, 1(8), p.e1500363.

Mukhametov, L.M., Supin, A.Y. and Polyakova, I.G., 1977. Interhemispheric asymmetry

of the electroencephalographic sleep patterns in dolphins. Brain research.

Mundy, N.I., Badcock, N.S., Hart, T., Scribner, K., Janssen, K. and Nadeau, N.J., 2004.

Conserved genetic basis of a quantitative plumage trait involved in mate choice.

Science, 303(5665), pp.1870-1873.

Musilova, Z., Cortesi, F., Matschiner, M., Davies, W.I., Patel, J.S., Stieb, S.M., de

Busserolles, F., Malmstrøm, M., Tørresen, O.K., Brown, C.J. and Mountford, J.K.,

2019. Vision using multiple distinct rod opsins in deep-sea fishes. Science, 364(6440),

pp.588-592.

Musser, J.M. and Arendt, D., 2017. Loss and gain of cone types in vertebrate ciliary

photoreceptor evolution. Developmental biology, 431(1), pp.26-35.

Mustafi, D., Engel, A.H. and Palczewski, K., 2009. Structure of cone photoreceptors.

Progress in retinal and eye research, 28(4), pp.289-302.

Nachman, M.W., Hoekstra, H.E. and D'Agostino, S.L., 2003. The genetic basis of adaptive

melanism in pocket mice. Proceedings of the National Academy of Sciences, 100(9),

pp.5268-5273.

Neitz, J. and Neitz, M., 2011. The genetics of normal and defective color vision. Vision

research, 51(7), pp.633-651.

Neveln, I.D., Bale, R., Bhalla, A.P.S., Curet, O.M., Patankar, N.A. and MacIver, M.A.,

2014. Undulating fins produce off-axis thrust and flow structures. Journal of

Experimental Biology, 217(2), pp.201-213.

Niven, J.E. and Laughlin, S.B., 2008. Energy limitation as a selective pressure on the

evolution of sensory systems. Journal of Experimental Biology, 211(11), pp.1792-

1804.

Niven, J.E., 2008. Evolution: convergent eye losses in fishy circumstances. Current

Biology, 18(1), pp.R27-R29.

Northmore, D.P.M., 1989. Quantitative electrophysiological studies of regenerating

visuotopic maps in goldfish—I. Early recovery of dimming sensitivity in tectum and

torus longitudinals. Neuroscience, 32(3), pp.739-747.

O’Quin, K.E., Doshi, P., Lyon, A., Hoenemeyer, E., Yoshizawa, M. and Jeffery, W.R.,

2015. Complex evolutionary and genetic patterns characterize the loss of scleral

ossification in the blind cavefish Astyanax mexicanus. PloS one, 10(12), p.e0142208.

Ogura A, Ikeo K and Gojobori T 2004. Comparative Analysis of Gene Expression for

Convergent Evolution of Camera Eye between Octopus and Human. Spring Harbor

Laboratory Press ISSN 14:1555–1561.

O'Quin, K.E., Yoshizawa, M., Doshi, P. and Jeffery, W.R., 2013. Quantitative genetic

analysis of retinal degeneration in the blind cavefish Astyanax mexicanus. PloS one,

8(2), p.e57281.

Orr, H.A., 2000. Adaptation and the cost of complexity. Evolution, 54(1), pp.13-20.

100

Orr, H.A., 2005. The probability of parallel evolution. Evolution, 59(1), pp.216-220.

Osorio, D. and Vorobyev, M., 2008. A review of the evolution of animal colour vision and

visual communication signals. Vision research, 48(20), pp.2042-2051.

Panaram, K. and Borowsky, R., 2005. Gene flow and genetic variability in cave and surface

populations of the Mexican tetra, Astyanax mexicanus (Teleostei: Characidae).

Copeia, 2005(2), pp.409-416.

Pankhurst, N.W., 1989. The relationship of ocular morphology to feeding modes and

activity periods in shallow marine teleosts from New Zealand. Environmental Biology

of Fishes, 26(3), pp.201-211.

Parrilla, M., Lillo, C., Herrero-Turrion, M.J., Arévalo, R., Lara, J.M., Aijón, J. and Velasco,

A., 2009. Pax2 in the optic nerve of the goldfish, a model of continuous growth. Brain

research, 1255, pp.75-88.

Pearce, T., 2011. Convergence and parallelism in evolution: a neo-Gouldian account. The

British Journal for the Philosophy of Science, 63(2), pp.429-448.

Pearring, J.N., Salinas, R.Y., Baker, S.A. and Arshavsky, V.Y., 2013. Protein sorting,

targeting and trafficking in photoreceptor cells. Progress in retinal and eye research,

36, pp.24-51.

Poulson, T.L. and White, W.B., 1969. The cave environment. Science, 165(3897), pp.971-

981.

Protas, M., Conrad, M., Gross, J.B., Tabin, C. and Borowsky, R., 2007. Regressive

evolution in the Mexican cave tetra, Astyanax mexicanus. Current biology, 17(5),

pp.452-454.

101

Protas, M.E., Hersey, C., Kochanek, D., Zhou, Y., Wilkens, H., Jeffery, W.R., Zon, L.I.,

Borowsky, R. and Tabin, C.J., 2006. Genetic analysis of cavefish reveals molecular

convergence in the evolution of albinism. Nature genetics, 38(1), p.107.

Purves, D., Augustine, G.J. and Fitzpatrick, D., 2001. Anatomical Distribution of Rods and

Cones, editors. Neuroscience. 2nd edition. Sunderland (MA): Sinauer Associates.

Pusch, R., Kassing, V., Riemer, U., Wagner, H.J., von der Emde, G. and Engelmann, J.,

2013. A grouped retina provides high temporal resolution in the weakly electric fish

Gnathonemus petersii. Journal of Physiology-Paris, 107(1-2), pp.84-94.

Ratnayake, K., Joyce, D.C. and Webb, R.I., 2012. A convenient sample preparation

protocol for scanning electron microscope examination of xylem-occluding bacterial

biofilm on cut flowers and foliage. Scientia Horticulturae, 140, pp.12-18.

Raymond, P.A., Barthel, L.K. and Curran, G.A., 1995. Developmental patterning of rod

and cone photoreceptors in embryonic zebrafish. Journal of Comparative Neurology,

359(4), pp.537-550.

Rétaux, S., Alié, A., Blin, M., Devos, L., Elipot, Y. and Hinaux, H., 2015. Neural

development and evolution in Astyanax mexicanus: comparing cavefish and surface

fish brains. Biology of the Mexican Cavefish. San Diego, CA: Elsevier, pp.227-241.

Reymond, L., 1987. Spatial visual acuity of the falcon, Falco berigora: a behavioural,

optical and anatomical investigation. Vision research, 27(10), pp.1859-1874.

Ritland, K., Newton, C. and Marshall, H.D., 2001. Inheritance and population structure of

the white-phased “Kermode” black bear. Current Biology, 11(18), pp.1468-1472.

Rosenblum, E.B., Hoekstra, H.E. and Nachman, M.W., 2004. Adaptive reptile color

variation and the evolution of the MCIR gene. Evolution, 58(8), pp.1794-1808.

102

Rowe, M.H. and Stone, J., 1976. Properties of ganglion cells in the visual streak of the cat's

retina. Journal of Comparative Neurology, 169(1), pp.99-125.

Ruiz-Torres, R., Curet, O.M., Lauder, G.V. and MacIver, M.A., 2013. Kinematics of the

ribbon fin in hovering and swimming of the electric ghost knifefish. Journal of

Experimental Biology, 216(5), pp.823-834.

Sabbah, S., 2012. Functional diversity in colour vision of fish (Doctoral dissertation).

Sabbah, S., Troje, N.F., Gray, S.M. and Hawryshyn, C.W., 2013. High complexity of

aquatic irradiance may have driven the evolution of four-dimensional colour vision in

shallow-water fish. Journal of Experimental Biology, 216(9), pp.1670-1682.

Salem, M.A., 2016. Structure and function of the retinal pigment epithelium,

photoreceptors and cornea in the eye of Sardinella aurita (Clupeidae, Teleostei). The

Journal of Basic & Applied Zoology, 75, pp.1-12.

Sandkam, B.A., Young, C.M., Breden, F.M.W., Bourne, G.R. and Breden, F., 2015. Color

vision varies more among populations than among species of live-bearing fish from

South America. BMC evolutionary biology, 15(1), p.225.

Sapp, J., 2012. Evolution replayed.

Schellart, N.A. and Prins, M.A.A.R.T.E.N., 1993. Interspecific allometry of the teleost

visual system: a new approach. Netherlands journal of zoology, (3-4).

Schmitz, L. and Wainwright, P.C., 2011. Nocturnality constrains morphological and

functional diversity in the eyes of reef fishes. BMC Evolutionary Biology, 11(1).338.

Shand, J., 1997. Ontogenetic changes in retinal structure and visual acuity: a comparative

study of coral-reef teleosts with differing post-settlement lifestyles. Environmental

Biology of Fishes, 49(3), pp.307-322.

103

Shand, J., Archer, M.A. and Collin, S.P., 1999. Ontogenetic changes in the retinal

photoreceptor mosaic in a fish, the black bream, Acanthopagrus butcheri. Journal of

Comparative Neurology, 412(2), pp.203-217.

Shand, J., Davies, W.L., Thomas, N., Balmer, L., Cowing, J.A., Pointer, M., Carvalho,

L.S., Trezise, A.E., Collin, S.P., Beazley, L.D. and Hunt, D.M., 2008. The influence

of ontogeny and light environment on the expression of visual pigment opsins in the

retina of the black bream, Acanthopagrus butcheri. Journal of Experimental Biology,

211(9), pp.1495-1503.

Shapiro, M.D., Marks, M.E., Peichel, C.L., Blackman, B.K., Nereng, K.S., Jónsson, B.,

Schluter, D. and Kingsley, D.M., 2004. Genetic and developmental basis of

evolutionary pelvic reduction in threespine sticklebacks. Nature, 428(6984), p.717.

Soares, D., Yamamoto, Y., Strickler, A.G. and Jeffery, W.R., 2004. The lens has a specific

influence on optic nerve and tectum development in the blind cavefish Astyanax.

Developmental neuroscience, 26(5-6), pp.308-317.

Spady, T.C., Parry, J.W., Robinson, P.R., Hunt, D.M., Bowmaker, J.K. and Carleton, K.L.,

2006. Evolution of the cichlid visual palette through ontogenetic subfunctionalization

of the opsin gene arrays. Molecular biology and evolution, 23(8), pp.1538-1547.

Spiller, L., Grierson, P.F., Davies, P.M., Hemmi, J., Collin, S.P. and Kelley, J.L., 2017.

Functional diversity of the lateral line system among populations of a native

Australian freshwater fish. Journal of Experimental Biology, 220(12), pp.2265-2276.

Stager, J.C. and Cahoon, L.B., 1987. The age and trophic history of Lake Waccamaw,

North Carolina. Journal of the Elisha Mitchell Scientific Society, 103(1), pp.1-13.

Stanley, S.M., 1973. An explanation for Cope's rule. Evolution, 27(1), pp.1-26.

104

Stayton, C.T., 2006. Testing hypotheses of convergence with multivariate data:

morphological and functional convergence among herbivorous lizards. Evolution,

60(4), pp.824-841.

Stenkamp, D.L., 2007. Neurogenesis in the fish retina. International review of cytology,

259, pp.173-224.

Stern, D.L., 2013. The genetic causes of convergent evolution. Nature Reviews Genetics,

14(11), p.751.

Stone, J., van Driel, D., Valter, K., Rees, S. and Provis, J., 2008. The locations of

mitochondria in mammalian photoreceptors: relation to retinal vasculature. Brain

research, 1189, pp.58-69.

Strecker, U., Faúndez, V.H. and Wilkens, H., 2004. Phylogeography of surface and cave

Astyanax (Teleostei) from Central and North America based on cytochrome b

sequence data. Molecular phylogenetics and evolution, 33(2), pp.469-481.

Sucena, É. and Stern, D.L., 2000. Divergence of larval morphology between Drosophila

sechellia and its sibling species caused by cis-regulatory evolution of ovo/shaven-

baby. Proceedings of the National Academy of Sciences, 97(9), pp.4530-4534.

Suzuki, D.G., Pérez-Fernández, J., Wibble, T., Kardamakis, A.A. and Grillner, S., 2019.

The role of the optic tectum for visually evoked orienting and evasive movements.

Proceedings of the National Academy of Sciences, 116(30), pp.15272-15281.

Takechi, M. and Kawamura, S., 2005. Temporal and spatial changes in the expression

pattern of multiple red and green subtype opsin genes during zebrafish development.

Journal of Experimental Biology, 208(7), pp.1337-1345.

105

Tanaka, K., 1996. Application of low vacuum scanning electron microscope in cytological

field. J. Electron Microsc., 45, p.344.

Tancred, E., 1981. The distribution and sizes of ganglion cells in the retinas of five

Australian marsupials. Journal of Comparative Neurology, 196(4), pp.585-603.

Tapp, R.L., 1974. Axon numbers and distribution, myelin thickness, and the reconstruction

of the compound action potential in the optic nerve of the teleost: Eugerres plumieri.

Journal of Comparative Neurology, 153(3), pp.267-274.

Temple, S.E., Manietta, D. and Collin, S.P., 2013. A comparison of behavioural (Landolt

C) and anatomical estimates of visual acuity in archerfish (Toxotes chatareus). Vision

Research, 83, pp.1-8.

Theron, E., Hawkins, K., Bermingham, E., Ricklefs, R.E. and Mundy, N.I., 2001. The

molecular basis of an avian plumage polymorphism in the wild: a melanocortin-1-

receptor point mutation is perfectly associated with the melanic plumage morph of the

bananaquit, Coereba flaveola. Current Biology, 11(8), pp.550-557.

Thompson, H., Griffiths, J.S., Jeffery, G. and McGonnell, I.M., 2010. The retinal pigment

epithelium of the eye regulates the development of scleral cartilage. Developmental

biology, 347(1), pp.40-52.

Tomarev, S.I., Callaerts, P., Kos, L., Zinovieva, R., Halder, G., Gehring, W. and

Piatigorsky, J., 1997. Squid Pax-6 and eye development. Proceedings of the National

Academy of Sciences, 94(6), pp.2421-2426.

Tuček, S., 1978. Acetylcholine synthesis in neurons. Chapman and Hall.

106

Valen, R., Eilertsen, M., Edvardsen, R.B., Furmanek, T., Rønnestad, I., van der Meeren,

T., Karlsen, Ø., Nilsen, T.O. and Helvik, J.V., 2016. The two-step development of a

duplex retina involves distinct events of cone and rod neurogenesis and

differentiation. Developmental biology, 416(2), pp.389-401.

Valen, R., Eilertsen, M., Edvardsen, R.B., Furmanek, T., Rønnestad, I., van der Meeren,

T., Karlsen, Ø., Nilsen, T.O. and Helvik, J.V., 2016. The two-step development of a

duplex retina involves distinct events of cone and rod neurogenesis and

differentiation. Developmental biology, 416(2), pp.389-401.

Valentine, J.W., Collins, A.G. and Meyer, C.P., 1994. Morphological complexity increase

in metazoans. Paleobiology, 20(2), pp.131-142.

Valta, M.P., Hentunen, T., Qu, Q., Valve, E.M., Harjula, A., Seppänen, J.A., Väänänen,

H.K. and Härkönen, P.L., 2006. Regulation of osteoblast differentiation: a novel

function for fibroblast growth factor 8. Endocrinology, 147(5), pp.2171-2182.

Velasco, A., Lillo, C., Jimeno, D., Cid, E., Weruaga, E., Aijón, J. and Lara, J.M., 2020.

Detection of macroglial cells in the fish optic nerve by intracellular injection in fixed

tissue. European Journal of Anatomy, 5(2), pp.89-95.

Wagner, A.D., Stebbins, G.T., Masciari, F., Fleischman, D.A. and Gabrieli, J.D., 1998.

Neuropsychological dissociation between recognition familiarity and perceptual

priming in visual long-term memory. Cortex, 34(4), pp.493-511.

Wagner, H.J. and Kröger, R.H., 2005. Adaptive plasticity during the development of colour

vision. Progress in retinal and eye research, 24(4), pp.521-536.

Wagner, P.J., 1996. Contrasting the underlying patterns of active trends in morphologic

evolution. Evolution, 50(3), pp.990-1007.

107

Wang, F.Y., Tang, M.Y. and Yan, H.Y., 2011. A comparative study on the visual

adaptations of four species of moray eel. Vision Research, 51(9), pp.1099-1108.

Warrant, E.J., 2015. Photoreceptor evolution: ancient ‘cones’ turn out to be rods. Current

Biology, 25(4), pp.R148-R151.

Welch, J.J. and Waxman, D., 2003. Modularity and the cost of complexity. Evolution,

57(8), pp.1723-1734.

West, R.W. and Dowling, J.E., 1975. Anatomical evidence for cone and rod‐like receptors

in the gray squirrel, ground squirrel, and prairie dog retinas. Journal of Comparative

Neurology, 159(4), pp.439-459.

Wiens, J.J. and Graham, C.H., 2005. Niche conservatism: integrating evolution, ecology,

and conservation biology. Annu. Rev. Ecol. Evol. Syst., 36, pp.519-539.

Wiens, J.J., Ackerly, D.D., Allen, A.P., Anacker, B.L., Buckley, L.B., Cornell, H.V.,

Damschen, E.I., Davies, T.J., Grytnes, J.A., Harrison, S.P. and Hawkins, B.A., 2010.

Niche conservatism as an emerging principle in ecology and conservation biology.

Ecology letters, 13(10), pp.1310-1324.

Wikler, K.C. and Rakic, P., 1990. Distribution of photoreceptor subtypes in the retina of

diurnal and nocturnal primates. Journal of Neuroscience, 10(10), pp.3390-3401.

Wikler, K.C., Rakic, P., Bhattacharyya, N. and Macleish, P.R., 1997. Early emergence of

photoreceptor mosaicism in the primate retina revealed by a novel cone‐specific

monoclonal antibody. Journal of Comparative Neurology, 377(4), pp.500-508.

Wilkens, H., 1988. Evolution and genetics of epigean and cave Astyanax fasciatus

(Characidae, Pisces). In Evolutionary biology (pp. 271-367). Springer, Boston, MA.

108

Winkler, J.R., Wittung-Stafshede, P., Leckner, J., Malmström, B.G. and Gray, H.B., 1997.

Effects of folding on metalloprotein active sites. Proceedings of the National

Academy of Sciences, 94(9), pp.4246-4249.

Yamamoto, Y., Stock, D.W. and Jeffery, W.R., 2004. Hedgehog signalling controls eye

degeneration in blind cavefish. Nature, 431(7010), p.844.

Yanagitsuru, Y.R., Akanyeti, O. and Liao, J.C., 2018. Head width influences flow sensing

by the lateral line canal system in fishes. Journal of Experimental Biology, 221(21),

p.jeb180877.

Yopak, K.E., McMeans, B.C., Mull, C.G., Feindel, K.W., Kovacs, K.M., Lydersen, C.,

Fisk, A.T. and Collin, S.P., 2019. Comparative Brain Morphology of the Greenland

and Pacific sleeper sharks and its Functional Implications. Scientific reports, 9(1),

pp.1-15.

Yoshizawa, M., Yamamoto, Y., O'Quin, K.E. and Jeffery, W.R., 2012. Evolution of an

adaptive behavior and its sensory receptors promotes eye regression in blind cavefish.

BMC biology, 10(1), p.108.

Zaunreiter, M., Junger, H. and Kotrschal, K., 1991. Retinal morphology of cyprinid fishes:

a quantitative histological study of ontogenetic changes and interspecific variation.

Vision research, 31(3), pp.383-394.

Zeiske, E., Breucker, H. and Melinkat, R., 1979. Gross morphology and fine structure of

the olfactory organ of rainbow fish (Atheriniformes, Melanotaeniidae). Acta

Zoologica, 60(3), pp.173-186.

Zihlman Adrienne L 2001. Adapted from The Human Evolution Coloring Book, 2d ed.

Produced by Coloring Concepts Inc. New York: HarperCollins.

109

Zwaan, B.J., Azevedo, R.B., James, A.C., van't Land, J. and Partridge, L., 2000. Cellular

basis of wing size variation in Drosophila melanogaster: a comparison of latitudinal

clines on two continents. Heredity, 84(3), p.338.

110