Connectivity between natural populations of the sea urchin ...

Connectivity between natural populations of the sea urchin

Echinometra lucunter lucunter (Echinodermata: Echinoidea:

Echinometridae) throughout the Caribbean region

Milena Benavides Serrato

Universidad Nacional de Colombia

Instituto de Estudios en Ciencias del Mar - CECIMAR

Convenio Universidad Nacional de Colombia - INVEMAR

Santa Marta, D.T.C.H., Colombia

2020

Connectivity between natural populations of the sea urchin

Echinometra lucunter lucunter (Echinodermata: Echinoidea:

Echinometridae) throughout the Caribbean region

Milena Benavides Serrato

Thesis presented as a partial requirement to qualify for the title of

Doctor en Ciencias-Biología

Director

Dr. rer. nat. Néstor Hernando Campos

Codirector

Dr. Lina M. Barrios Gardelis

Research line:

Marine Biology

Research Group:

Fauna Marina Colombiana: Biodiversidad y Usos

Universidad Nacional de Colombia

Instituto de Estudios en Ciencias del Mar - CECIMAR

Convenio Universidad Nacional de Colombia - INVEMAR

Santa Marta, D.T.C.H., Colombia

2020

A mi mami, nuevo ángel en el cielo que me

ilumina cada día...

Mónica, por su apoyo incondicional y toda su

fuerza dada en este proceso…

Padre, porque eres mi ejemplo para seguir...

Acknowledgements

I want to say thanks to my two thesis directors, the professor Dr. Néstor Hernando Campos

and Dra. Lina María Barrios Gardelis. Thanks to both for their dedication and effort in the

advice of this dissertation. I received from both their guidance and help to the different steps

of this study.

Several people contributed to this study through their participation. To members of the

research group Fauna Marina Colombiana: Biodiversidad y Usos and to all professors and

students of the Instituto De Estudios en Ciencias del Mar – CECIMAR, Universidad

Nacional de Colombia, Caribbean Campus for their support and constant company during

this process. To Ana Milena Cardenas for her excellent collaboration in all the

administrative processes of the University. Specially thanks to Gilberto Orozco and to Juan

Carlos Narváez Barandica (Universidad Nacional de Colombia) for their invaluable help to

develop the process and obtaining the results of chapter 2 and 3; in the same way, to the

members of the molecular laboratory of the Universidad del Magdalena, specially to Juan

Carlos Aguirre, for their invaluable support in the results and analysis of chapter 3.

This research was supported by MinCIENCIAS fellowship call 267-2012 (PhD grant) and

MinCIENCIAS research grant No. 116171450647. Thanks to the Master student and young

research supported by the MinCIENCIAS project, Yully Fernanda Contreras, for her

collaboration in the obtention of the results reflected specially in the chapter 2. Thanks to

CEMarin, Corporation Center of Excellence in Marine Science (Contract No 12-03) with the

support reflected in the chapter 3.

Thanks to Professor Dr. Richard Preziosi for his help and administrative support during the

development of my internship in their molecular genetic laboratory of the Metropolitan

Manchester University (MMU). To the research group of the same university, Ecological

Genetics and Conservation and their members, specially to Dra. Sarah Griffiths, Thomas

Hughes and Dr. Graeme Fox, for their support on microsatellites development and all work

VIII Connectivity between natural population of Echinometra lucunter lucunter throughout

Caribbean region

related with molecular laboratory and correct use of Bioinformatic Tool Galaxy. From MMU

was received a support through grant R60010-sampling.

Numerous people and institutions helped with sample collection and logistics throughout

the Caribbean - Cape Verde (Africa) and permits: Mayra Nuñez (Instituto Nacional de

Conservación y Desarrollo Forestal, Áreas Protegidas y Vida Silvestre, Honduras:

Permit_Resolution-DE-MP-080-2015), Ricardo Betancur (Universidad de Puerto Rico, Rio

Piedras Campus, Departamento de Recursos Naturales y Ambientales: DRNA 2016-IC-

052), Nikolaos Schizas and Monica Alfaro (Universidad de Puerto Rico, Mayagüez

Campus), Ana Carolina Peralta (Universidad Simón Bolívar, Venezuela), Foltz Zachary of

Caribbean Coral Reef Ecosystems Program - Smithsonian National Museum of Natural

History, Carrie Bow Marine Field Station and Belize Fisheries Department (000039-17

Marine Scientific Research Permit), Owen Wangensteen (Universitetet I Troms, Noruega),

Adriana Osorno (INVEMAR, Colombia), Ministry of Environment and Sustainable

Development (Colombia- National Authority of Environmental Licenses (ANLA): Resolution

number 255, 12 of March of 2014 and Export permit No. 00848, Parques Nacionales

Naturales de Colombia with their collect authorizations No. 004-2017.

Thanks to The Colombian Marine Research Institute (INVEMAR) specially to the staff of

the Biodiversity and Marine and Ecosystems Research Program (BEM) for allowing the use

of their molecular laboratory at the Museum of Marine Natural History of Colombia –

MAKURIWA. To Roxana Martínez Canseco, student of Marine Biology of the Universidad

del Mar, Ecology and Reproduction of Echinoderms (Mexico), who was definitive during

laboratory sessions that are part of chapter 2 and 3.

Abstract and Resumen IX

Abstract

One of the basic requirements for the management and conservation of species is the

correct definition of their population structure, meaning, how many populations may be in

a particular area and how they connect with each other. This can be achieved more

effectively using genetic approaches to determine the connectivity (gene flow) between the

sites of interest (e.g. marine protected areas, fishing areas). In the Caribbean marine

province, four connectivity regions have been proposed based on complex hydrodynamic

current models and genetic data from fish studies especially from the Eastern Caribbean,

Western Caribbean, Bahamas and Panama–Colombia. Dispersal is extensive within

regions, but dispersal across their boundaries is unknown. Efforts to define the marine

connectivity and the genetic structure among populations have been focused

overwhelmingly on corals, sponges and fish populations, however, in other well

represented marine taxa in the Caribbean, data of their genetic structure and gene flow are

lacking. The sea urchin Echinometra lucunter lucunter was selected as the model species

for this study, mainly due to its wide distribution throughout the Caribbean Sea and its

biological and ecological characteristics. The main objective of this research was to

postulate connections at the genetic level between natural populations of marine

invertebrates throughout the Caribbean Sea, using the chosen model specie to detect

whether the marine discontinuities identified for other taxa affect their connectivity, acting

as barriers to gene flow. With this preamble, it was sought to answer the following questions

using microsatellites as molecular markers: 1) How is the genetic structure of Echinometra

lucunter lucunter through the Caribbean Sea? and 2) Is there any influence from marine

discontinuities previously detected to others taxa in the connectivity between natural

populations of Echinometra lucunter lucunter? In order to reach the objectives, 26 specific

microsatellites for E. lucunter lucunter were designed, ten of them were used to detect the

genetic structure of E. lucunter lucunter populations through the Caribbean Sea. The

results from all loci and all localities, showed lower heterozygosity than expected. This

deficit in heterozygosity was identified by the significant deviation from the Hardy-

X Connectivity between natural population of Echinometra lucunter lucunter throughout

Caribbean region

Weingberg Equilibrium and the high/positive inbreeding coefficient (FIS) values in all sites.

Several reasons could potentially explain this result: 1) the assortative matting linked to

different gamete recognition proteins, 2) the presence of spatial breeding groups linked to

stochasticity in reproductive success, 3) patchiness in gamete distribution and 4) the

collective dispersal of genetically related larvae in the plankton. The Caribbean sites

chosen for this study show a clear evidence of genetic structure and gene flow for E.

lucunter lucunter through the Caribbean Sea. It is also evident the presence of three genetic

different groups or populations: one corresponding to the Colombian Caribbean Sea sites

(with significant differences among sites), a second related to the area of Venezuela and

Belize, and a third group in Puerto Rico. The clear evidence of structure and gene flow

between the populations of E. lucunter lucunter is apparently related to marine

discontinuities found in other marine taxa studied before, such as the influence of physical

factors (e.g. marine currents and the geomorphological breaks) and their variation through

the area, as well as the biological characteristics of the specie. The results of this research

demonstrate the influence of discontinuities (marine currents and geomorphological

breaks) in the performance of Echinometra lucunter lucunter early life stages, evidenced in

the heterogeneous spatial mosaics of marine dispersion through the Colombian Caribbean

Sea vs. the homogeneous scenario shown in the genetic connexion through the West

Caribbean Sea. The results of this work have important implications for the application of

conservation and management strategies of Marine Protected Areas, because they

generate important inputs for researchers and decision makers.

Keywords: Gene flow, Caribbean Sea, genetic population structure, molecular markers,

microsatellites, echinoderms.

Abstract and Resumen XI

Resumen

Uno de los requerimientos básicos para el manejo y la conservación de las especies es la

correcta definición de sus poblaciones, es decir, cuántas poblaciones puede haber en un

área particular y cómo se conectan entre sí. Esto se puede lograr de manera más efectiva

utilizando enfoques genéticos en las poblaciones a estudiar de una especie objeto para

determinar la conectividad (flujo de genes) entre sitios de interés (por ejemplo, áreas

marinas protegidas, áreas de pesca). En la provincia marina del Caribe, se han propuesto

cuatro regiones de conectividad basados en complejos modelos hidrodinámicos de

corrientes y datos genéticos especialmente de estudios de peces arrecifales del Caribe:

Caribe Oriental, Caribe occidental, Bahamas y Panamá-Colombia. La dispersión es

extensa dentro de las regiones, pero se desconoce la dispersión a lo largo de los bordes

de esas regiones. Los esfuerzos para definir la conectividad marina y la estructura genética

entre poblaciones se han centrado abrumadoramente en corales, esponjas y poblaciones

de peces. En otros taxa marinos bien representados para el Caribe, faltan datos sobre la

estructura genética de sus poblaciones y el flujo de genes. El erizo de mar Echinometra

lucunter lucunter fue seleccionado como modelo para este estudio, principalmente debido

a su amplia distribución en todo el Gran Caribe y sus características biológicas y

ecológicas. El objetivo principal de esta investigación fue postular conexiones a nivel

genético entre poblaciones naturales de invertebrados marinos a través de la región del

Caribe con base en la especie modelo escogida, detectando si las discontinuidades

marinas que se han identificado para otros taxa afectan esa conectividad, actuando como

barreras para el flujo genético. Con este preámbulo, se buscó responder las siguientes

preguntas utilizando microsatélites como marcadores moleculares: 1) ¿Cómo es la

estructura genética de Echinometra lucunter lucunter a través del Mar Caribe? y 2) ¿Existe

alguna influencia de discontinuidades marinas previamente detectadas a otros taxa en la

conectividad entre poblaciones naturales de Echinometra lucunter lucunter?. Para

alcanzar los objetivos, se diseñaron 26 microsatélites específicos para la especie basados

en técnicas de secuenciación (NGS); diez de ellos se utilizaron para detectar la estructura

genética de las poblaciones de E. lucunter lucunter a través del área, con base en varios

índices genéticos y paquetes estadísticos. Los resultados mostraron en todos los loci y en

todas las localidades, una heterocigosidad observada inferior a la esperada, mostrando un

XII Connectivity between natural population of Echinometra lucunter lucunter throughout

Caribbean region

alto déficit de heterocigosidad reflejado por la desviación significativa del equilibrio de

Hardy-Weingberg y los valores altos y positivos del coeficiente de endogamia (FIS) en todas

las poblaciones y en todos los sitios. Hay varias razones discutidas por varios autores que

podrían explicar potencialmente este comportamiento: 1). La variedad de opciones de

apareamiento ligado al reconocimiento de proteínas de gametos, 2) la presencia de grupos

de reproducción espacial vinculados a la estocasticidad en el éxito reproductivo, 3) parches

en la distribución de gametos y 4) la dispersión colectiva de larvas genéticamente

relacionadas en el plancton. Los sitios dentro del mar Caribe escogidos para este estudio

muestran una clara evidencia de estructuración y flujo genético para E. lucunter lucunter a

lo largo del Mar Caribe. Es evidente la presencia de tres grupos genéticamente distintos,

uno con las estaciones del mar Caribe colombiano (con diferencias significativas entre los

sitios), un segundo grupo con las estaciones de Venezuela y Belice y un tercer grupo con

la estación de Puerto Rico. La estructuración y el flujo genético evidenciado entre las

poblaciones de E. lucunter lucunter aparentemente están relacionados con las

discontinuidades marinas encontradas, tales como la influencia de factores físicos (como,

por ejemplo, las corrientes marinas y las rupturas geomorfológicas), así como las

características biológicas de la especie, los cuales también han sido detectados en otros

taxa marinos estudiados en el Caribe. Con los resultados de esta investigación, se

demuestra la influencia de discontinuidades (corrientes marinas y rompimientos

geomorfológicos) en el comportamiento de la etapa larval de Echinometra lucunter

lucunter, evidenciado con los mosaicos espaciales heterogéneos de dispersión marina de

la especie a través del mar Caribe colombiano frente al escenario homogéneo que se

muestra en la conexión genética a través del Mar Caribe Occidental. Los resultados de

este trabajo tienen implicaciones importantes para la aplicación de estrategias de

conservación y manejo de áreas marinas protegidas, y pueden generar aportes

importantes para investigadores y tomadores de decisiones.

Palabras clave: Flujo Genético, Mar Caribe, estructura genética poblacional, marcadores

moleculares, microsatélites, equinodermos.

Content XIII

Content

Abstract…………………………………………………………………………………………..IX

Resumen………………………………………………………………………………………….XI

Introduction………………………………………………………………………………………1

1. Echinometra lucunter lucunter, model specie for the study ................................. 5 1.1 Generalities of Echinoidea Class ........................................................................ 5 1.2 Characteristics of Family Echinometridae ........................................................... 7

2. Isolation and characterization of 26 polymorphic microsatellite loci for the sea urchin Echinometra lucunter lucunter ......................................................................... 17

2.1 INTRODUCTION .............................................................................................. 17 2.2 MATERIALS AND METHODS .......................................................................... 20

2.2.1 Development of microsatellites ...................................................................... 20 2.2.2 Microsatellite screening ................................................................................. 22 2.2.3 Data analysis ................................................................................................. 23

2.3 RESULTS AND DISCUSSION ......................................................................... 24

3. Genetic structure of Echinometra lucunter lucunter populations in the Caribbean Sea: Is there any influence of marine discontinuities in their

connectivity? ................................................................................................................. 31 3.1 INTRODUCTION .............................................................................................. 31

3.1.1 Physic characteristics of the Caribbean ......................................................... 32 3.2 MATERIALS AND METHODS .......................................................................... 34

3.2.1 Samples collection ......................................................................................... 34 3.2.2 Genotyping .................................................................................................... 35 3.2.3 Microsatellites amplification, sequencing and genotyping .............................. 36 3.2.4 Statistical and molecular analysis .................................................................. 37

3.3 RESULTS ........................................................................................................ 38 3.3.1 Genetic diversity ............................................................................................ 38 3.3.2 Population differentiation and structure .......................................................... 40

3.4 DISCUSSION ................................................................................................... 45 3.4.1 Genetic diversity ............................................................................................ 45 3.4.2 Population differentiation and structure .......................................................... 46 3.4.3 Influence of marine discontinuities different to Currents present in the Caribbean Sea ......................................................................................................... 53 3.4.4 Marine connectivity scenarios in the Caribbean Sea ...................................... 55

XIV Connectivity between natural population of Echinometra lucunter lucunter

throughout Caribbean region

4. Conclusions and Recommendations ................................................................... 57

4.1 Conclusions ...................................................................................................... 57 4.2 Recommendations ............................................................................................ 59

References………………………………………………………………………………………60

Introduction

Human activities, such as overfishing, shoreline alteration, tourism, contamination, planning

or installation of outfalls in coastal areas and in the continental shelf affects a great variety

of marine organisms due to fragmentation and deterioration of their habitats (Alonso et al.,

2008; Sala and Knowlton, 2006). The decreased abundance of some marine populations

is so remarkable that not only is producing local or regional commercial extinctions of

marine fisheries resources, but also deepening alterations to ecosystem structure and

performance (Alonso et al., 2008). As a result, a rational management of these resources

is currently imperative. Such resource management needs to be verified and

complemented with information on the biology of the organisms to be protected as well as

their environment.

One of the basic requirements for the management and conservation of species is the

correct definition of their populations and how they connect with each other. This can be

achieved more effectively using genetic approaches to determine the connectivity (gene

flow) between the sites of interest (for example, marine protected areas, fishing areas). This

approach requires studies that allow determining the genetic structure of populations, in

such a way that the connection between the different populations can be quantified and

whether their degree of isolation can have consequences in the management of their

conservation (Palumbi, 2004).

Gene flow between marine populations could be affected by physical oceanography of the

area, biological features and the ecology-physiology of the species of interest; i.e. elements

of their life history such as reproductive behaviour, bathymetric and geographic distribution,

larval stages and larval dispersal, among others. Indeed, marine discontinuities can reduce

marine connectivity between populations, but those marine barriers could be permeable for

some species for their life history or behaviour.

2 Introduction

In the Caribbean marine province, four connectivity regions have been proposed based on

complex hydrodynamic current models and genetic data from fish specially: Eastern

Caribbean, Western Caribbean, Bahamas and Panama–Colombia (Cowen et al., 2006).

Dispersal is extensive within regions, but it is unknown outside of their boundaries. Efforts

to define connectivity through population’s genetic structure have been focused

overwhelmingly on corals, sponges and fish populations (Taylor and Hellberg, 2003;

Andras et al., 2013; Chaves-Fonnegra et al., 2015). In other well represented marine taxa,

such as molluscs and echinoderms in the Caribbean, data on genetic structure and diversity

are lacking.

In this study, Echinometra lucunter lucunter is the species that was used as model.

Although there is genetic information focused in this sea urchin that reflect the high gene

flow throughout the Caribbean Sea (McCartney et al., 2000), it does not show the genetic

connectivity in detail that exist between their Caribbean populations. This species is

recognized for its ecological role as a very capable grazer and bioeroder of carbonate

substrata on coral reefs and rocky shores (Abbott et al., 1974; Odgen, 1976; Lozano-Cortés

et al., 2011) and its activity related to abrasion, which results in the formation of cavities

and burrows (Odgen, 1977; Hoskin et al., 1986; Bak, 1990; Lozano-Cortés et al., 2011).

The main objective of this research was to postulate the genetic connectivity between

natural populations of sessile marine invertebrates throughout the Caribbean Sea,

identifying if the discontinuities that have been considered for other taxa in the area could

also influence the connexion of this species. With this preamble, we seek to answer the

following questions using molecular markers as tools: 1) How is the genetic structure of

Echinometra lucunter lucunter through the Caribbean Sea? and 2) Is there any influence of

marine discontinuities previously detected for others taxa in the connectivity between

natural populations of Echinometra lucunter lucunter?

To answer these questions, this study was organized under three chapters. The first one

shows the general characteristics of the model species Echinometra lucunter lucunter. It

explains generalities of Echinoidea Class, characteristics of the Family Echinometridae and

the biological and ecological features of the species. The second chapter describes, step

by step, the isolation and characterization of 26 polymorphic microsatellite loci which were

developed to detect the genetic structure of the model species through the Caribbean

Introduction 3

region, being the latest point described in the third chapter. In this latest chapter there is

also a discussion of how the marine discontinuities that have affected other marine taxa

influence in the same way the connexion or genetic structure of Echinometra lucunter

lucunter populations.

1. Echinometra lucunter lucunter, model specie for the study

1.1 Generalities of Echinoidea Class

The class Echinoidea, commonly known as sea urchins, sand dollars, sea biscuits or heart

urchins (depending on their shape), is the third most common group within the phylum

Echinodermata, after Ophiuroidea and Asteroidea. Echinoids occur in almost every marine

habitat, from the poles to the equator, and from coastal waters to the deep sea (Borrero-

Pérez et al., 2012). Like all echinoderms, echinoids have a skeleton composed of

calcareous plates embedded in their skin. They are benthic free-moving organisms and are

characterized by a rigid test, arranged into columns radiating from an aboral-apical zone to

the mouth. In regular echinoids the test is globular and perfectly pentaradiate (Figure 1-1),

but in irregular echinoids (Figure 1-2) there is a secondary bilateral symmetry superimposed

upon the primary pentaradiate pattern (Smith and Kroh, 2011).

There are around 800 extant species distributed worldwide (Smith and Kroh, 2011),

classified within two subclasses, 16 orders and 57 families (Kroh and Mooi, 2019). Subclass

Cidaroidea is represented only by regular sea urchins, while the Subclass Euechinoidea is

represented by regular organisms (Orders Aspidodiadematoida, Diadematoida,

Micropygoida, Pedinoiida, Salenioida, Arbacioida, Camarodonta and Stomopnesutida) and

irregular ones (Infraclass Irregularia). Both subclasses are present in Caribbean waters and

include 14 orders, 30 families and up to 61 species (Borrero-Pérez et al., 2012; Alvarado

and Solís-Marín, 2013; Kroh and Mooi, 2019).

6 Connectivity between natural population of Echinometra lucunter lucunter


Figure 1-1: External anatomy of a regular sea urchin. A. Oral view. B. Aboral view. Modified from Grosjean (2001) and originally in: Ruppert and Barnes, 1994.

Figure 1-2: Morphological characters of an irregular sea urchin (Heart urchin). A. Aboral view. B. Oral aspect of generalized spatangoid with Loven’s numbering system superimposed. C. Posterior aspect of generalized spatangoid. Modified from Serafy, 1979.

Echinometra lucunter lucunter, model specie of the study 7

Although Mortensen (1928-1951), compiled the available information for the Class

Echinoidea that exist around the world in a monograph composed of several volumes,

marine expeditions such as H. M. S. Challenger (1872-1876), Blake (1877-1886) and

Albatross (1899-1900) are considered the pioneering studies for Caribbean echinoderms

(Benavides-Serrato and Borrero-Pérez, 2010). Within the Caribbean region, Farfante

(1959) made a compilation about sea urchins from Cuba, Chesher (1968) described in

detail several Caribbean species belonging to the Order Spatangoida, and Serafy (1979)

worked with species from the Gulf of Mexico and adjacent waters. Moreover, Hendler et al.

(1995) made a complete taxonomic and ecological revision of different species within the

Phyllum Echinodermata present in Caribbean waters, based on samples collected in the

Florida Keys and Bahamas. Alvarado (2010) analysed the diversity patterns for 433

Caribbean echinoderms species, from which 76 were within the Class Echinoidea.

There are 54 species in the Colombian Caribbean (Borrero-Pérez et al., 2012) and,

according to Pérez-Ruzafa et al. (2013), Colombia occupies the third place in diversity of

echinoids, after Mexico and Cuba. Currently, all the updated taxonomic information is found

in www.marinespecies.org/echinoidea/index.php (Kroh and Mooi, 2019).

1.2 Characteristics of Family Echinometridae

Regular and irregular sea urchins differ considerably in lifestyle, which is related to their

differences in shape. Regular forms in shallow waters are predominantly grazers in a wide

range of intertidal and subtidal habitats, such as coral reefs, rocky shores and sometimes

seagrass and mangroves roots. The grazing by the sea urchins has major biological and

geological effects on these habitats. Biological effects include alteration of the distribution

and abundance on the community of plants and animals and limitation of coral growth while

geological effects include sediment production and the erosion of carbonate rock (Muthiga,

1996).

All echinometrids, apart from the genus Selenechinus, are littoral forms inhabiting rocky

shores or coral reefs, particularly nestling in cavities in the rocks or among the corals,

possibly made by their own burrowing activities (Mortensen, 1943b). They are mainly



distributed on tropical and temperate waters worldwide, including the Caribbean Sea

(Mortensen, 1943b; Smith and Kroh, 2011; Kroh and Mooi, 2019).

According to Kroh and Mooi (2019), Echinometridae includes 23 species classified in nine

genera: Caenocentrotus H.L. Clark, 1912 (1 species), Colobocentrotus Brandt, 1835 (3),

Echinometra Gray, 1825 (6), Echinostrephus A. Agassiz, 1863 (3), Evechinus Verrill, 1871

(1), Heliocidaris L. Agassiz & Desor, 1846 (5), Heterocentrotus Brandt, 1835 (2),

Selenechinus de Meijere, 1904 (1) and Zenocentrotus A. H. Clark, 1932 (2). However,

Echinometra is the only genus of this family present in the Caribbean Sea.

Mortensen (1943b) mention that the main morphological features for this family include a

circular or ovate test in outline, distinctly flattened on the oral side and trigeminate or

polygenimate ambulacral plates. The interambulacral area is characterized by primary

tubercles from very regular vertical series. On the oral side, the tubercles become rapidly

smaller in the forms, with petaloid pore-zones, and the interambulacral area is constricted

and narrower than the ambulacral area. The peristome is usually conspicuously larger than

the apical system, particularly in the elongate forms, where it is also distinctively elongated.

Pelagic larvae of this family can be distinguished from those of all other sea urchins by the

presence of a double recurrent rod (Pearse and Cameron, 1991).

▪ Genus Echinometra

The genus Echinometra belong to a tropical regular sea urchin around the world. It has a

usually elongated and strong test, with sizes from moderate to rather large. There are 4-10

pore-pairs of ambulacra polyporous in each arc, except on the aboral side, where the

number of pore-pairs is only 3 at the peristomial edge (Mortensen, 1943b). This genus

forms a monophyletic group composed of two clades, the Indo-West Pacific species and

the Eastern Pacific to Atlantic species (Kinjo, et al., 2004). Species diversity is likely to show

a pattern, as other tropical taxa, with the highest genetic and species diversity in the West

Indo-Pacific, near Indonesia, decreasing with the distance from this centre area (Palumbi

et al. 1997). The total number of species and species-level taxonomy for the genus has yet

to be completed, particularly for the central Indo-Pacific (Palumbi, 1996; Kroh and Mooi,

2019). According to Kroh and Mooi (2019), Echinometra has six recognized extant species:


Echinometra insularis, which is an endemic species on Easter Island in the central Pacific

(Fell, 1974); Echinometra mathaei and Echinometra oblonga in the Indo-West Pacific,

Echinometra vanbrunti in the Eastern Pacific from the Gulf of California to Peru,

Echinometra lucunter in the Atlantic Ocean and Echinometra viridis in the Caribbean Sea

(McClananhan and Muthiga, 2013). In addition, the same authors reported two additional

species that need to be described formally: E. tsumajiroi and E. ryukensis for the Indo-West

Pacific (Figure 1-3).

Both Caribbean and Indo-Pacific species appeared to have formed during the Pleistocene

with speciation associated with decreases in sea level (Palumbi, 1996; McCartney et al.,

2000). The two sympatric Caribbean species of Echinometra formed 1.27-1.62 million years

ago (mya) (McCartney et al., 2000), while the Indo-Pacific Echinometra formed from 1.5 to

3 mya (Palumbi, 1996). Indo-Pacific and Atlantic lineages split between 3.3 and 4.2 mya,

shortly before the rise of the Isthmus of Panama (McCartney et al., 2000). The more broadly

distributed Caribbean species, E. lucunter, has an Atlantic and a Caribbean subspecies

(polypora and lucunter respectively), which were estimated to have separated between

200.000 and 250.000 years ago (McCartney et al., 2000; Geyer and Lessios, 2009) (Figure

1-3).

▪ Echinometra lucunter lucunter

Echinometra lucunter lucunter is specially found on hard substrates, often in exposed,

shallow reef habitats, including the mid-littoral beach-rock zone (Hendler et al., 1995),

where there is a wide food availability, based on benthonic and floating algae (McPherson,

1969; Odgen 1976). It has two basic feeding modes: 1) catching algal drift and 2) benthic

grazing. Probably most individuals use both modes at any time (McClanahan and Muthiga,

2013). The broad diets and tolerance of E. lucunter lucunter to variable habitats has

resulted in their use in controlling the fouling of suspended bivalve aquaculture nets in

Venezuela (Lodeiros and Garcia, 2004).



Figure 1-3: Maximum-likelihood quartet phylogeny of Echinometra COI sequences. Only nodes with > 50% support are labelled. Vertical bars show major clades. Termini is labelled with source locality of individuals (Modified of McCartney et al., 2000).


Abundance patterns

Echinometra lucunter lucunter, as other Echinometra species, has a distribution in patchy

form, with densities varying from 0.1 to 100 individuals per m2 over very short distances

(McClanahan and Muthiga, 2013). This patchiness is not always easy to explain. For

example, the distribution of E. lucunter lucunter throughout the Caribbean Sea is

gregarious, but the number per square meter is variable. For example, Pompa et al. (1989)

recorded an average of 11 ind/ m2 in Cariaco Gulf (Venezuela), Romero (2009) reported

19 ind/m2 in Bahía Concha (Tayrona National Natural Park, Colombia), while Monroy-López

and Solano (2005) reported higher densities in Zapzurro (Chocó, Colombia) and Inca Inca

(Santa Marta, Colombia), with 69 and 65 ind/m2 respectively. Changes in density may

depend on several factors such as predation, environmental factors, water movement and

food availability among others (McClanahan and Muthiga, 2013).

Echinometra’s burrows are a common feature on the upper margin of the rocky intertidal

and reef areas where they form a distinct zone at mean low water along with other species

that have high strength of attachment (Schoppe and Werding, 1996). In Colombia, it has

been reported that the cavities formed by E. lucunter lucunter can create microhabitats for

other marine vertebrates and invertebrates, such as the ofiuroid Ophiothrix synoecina,

which is an endemic and vulnerable species (Monroy-López and Solano, 2008), the

porcelanid Clastotoechus vanderhorsti, the red clingfish Acyrtus rubiginosus and the flat

worm Armatoplana divae (Schoppe, 1993; Schoppe and Werding, 1996; Monroy-López

and Solano, 2005).

E. lucunter lucunter has important ecological roles. It is capable of grazing and abrading

the carbonate substrata on coral and littoral reefs (Ogden, 1976; Ogden, 1977; Hoskin et

al., 1986). The effects of grazing by this sea urchin on the erosion of coral reefs and other

carbonate surfaces and their contribution to the production of carbonate sediments have

long been known (McLean, 1967; Hunt, 1969; Ogden, 1977). Ogden (1977) defines this

sea urchin as a “grazer” because during their feeding they rasp, bite or scrape off pieces of

the substrate and subsequently excreting it as sediment, just as other marine vertebrates

and invertebrates do. Grazers may have dramatic effects on the benthic community

including algae, corals, and invertebrates. Their constant disturbance of the substrate

interferes with the settlement and growth of a wide range of coral-reef organisms. Many



reef areas of the Caribbean are heavily scoured and show the scrape marks of the teeth of

sea urchins. The grazers, as a group, are herbivores, and are the dominant animals of the

reef in terms of biomass (Ogden et al, 1973; Sammarco et al., 1974).

Growth and longevity

Growth estimates for Echinometra lucunter lucunter has been reported from a variety of

sources, including estimates from size distributions (McClanahan and Muthiga, 2013).

Ebert et al. (2008) found a relationship between maximum body length and latitude for this

species distributions in the Atlantic. The authors mentioned that E. lucunter was relatively

slow growing and a long-lived species in contrast to other sea urchins species with an

average life expectancy of over 10 years. The same authors found that the two latitudinal

extremes, Bermuda and Brazil, had the largest maximum sizes, while the smallest sizes

were in Panama. They also found a U-shaped relationship between maximum body length

and latitude for E. lucunter distributions in the Atlantic. Site factors associated with latitude

and associated environmental factors were also expected to influence these life history

parameters (McClanahan and Muthiga, 2013).

Reproductive biology and ecology

Echinometra lucunter lucunter as other species of the same genus has a sexual

reproduction, without external sexual dimorphism and a general sex ratio of 1:1 (Espinoza

et al., 2008; McClanahan and Muthiga, 2013). This species has two phases during their life

cycle. The larval phase, which has a duration of approximately two weeks according to in

vitro studies (Cameron, 1986; Astudillo et al., 2005) and a second phase, where the animal

settles in the substrate after the larval phase development. During the adult phase, the

animal usually has a sedentary behaviour on mid-littoral beach-rock zones or coral reefs.

Larval structure and development have been described extensively by Mortensen (1921,

1937).

In Echinometra, the protein binding influences the attachment of sperm to eggs. Eggs show

strong selection of sperm with a binding genotype similar to their own, suggesting a strong

linkage between female choice and male trait loci (Palumbi, 1999). According to McCartney


and Lessios (2004), eggs of E. lucunter have developed a mechanism to avoid fertilization

by sperm of other species, whereas eggs of E. viridis, a sympatric species of E. lucunter,

has not develop this feature. The same authors suggest that different selection processes

act to varying degrees within species.

Echinometra lucunter lucunter spawning seasons and pelagic larval dynamics

The factors that influence the timing of the reproductive cycle of Echinometra species are

not well understood (Muthiga, 1996; Muthiga and Jaccarini, 2005). For E. lucunter lucunter

there is a difference between spawning seasons on tropical areas close to the equator,

where it is continuous, such as in Panama (Lessios, 1981), while in other localities it is

seasonal to some range of time (Table 1-1). E. mathaei also has a spawning season

restricted to high latitudes, but it is continuous for populations that are close to the equator,

where environmental conditions, presumably temperature, are more stable (Pearse, 1969).

Like other Echinometra species, latitude does not always influence the reproductive cycle

continuity.

In Echinometra species there are other factors that play a modifying role including

population density and the physical environment. Lessios (1981) showed that spawning

synchrony is tighter in sparsely populated populations of E. viridis and less important in

dense populations of E. lucunter lucunter. He postulated that salinity could act as a

proximate cue for controlling the timing of spawning, as it coincided with increased salinity

for E. lucunter lucunter and E. viridis at Fort Randolph, in the Caribbean. Spawning also

occurs in E. lucunter lucunter during a discrete period from July to October in wave swept

areas and occurred during several periods in populations living in calm waters (Lewis and

Storey, 1984). In Brazilian populations of E. lucunter polypora, spawning occurred in the

late spring and early summer, although differences occurred in the length of the spawning

period, being longer in populations on coral reefs than on rocky shores (Ventura et al.,

2003) (Table 1-1).



Table 1-1: Caribbean Echinometra species: spawning seasons.

Specie Locality Spawning

season Reference

E. lucunter lucunter

Bermuda Jul–Oct Harvey (1947) In: McClanahan and

Muthiga, 2013

South Florida Jun-Aug McPherson (1969)

Puerto Rico Aug-Oct Cameron (1986)

Barbados Jul-Oct Lewis and Storey (1984)

Panama Continuous Lessios (1981)

E. lucunter polypora Brazil Dec-May Ventura et al (2003)

E. viridis

South Florida Jun-Ago McPherson (1969)

Puerto Rico Sept-Nov Cameron (1986)

Panama Apr-Dec Lessios (1981)

Larval structure and development of E. lucunter were described by Mortensen (1921, 1937).

Several factors, including temperature, salinity, carbon dioxide and food availability may

affect the rate and fate of larval development, but few field data are available (McClanahan

and Muthinga, 2013). Estimates of the larval period have been reported for E. lucunter

lucunter (23 days), E. vanbrunti (18 days) and E. viridis (30 days) (Cameron, 1986; Emlet

et al., 1987).

The rate of larval development in laboratory studies of Echinometra species is strongly

affected by diet (Emlet et al., 1987). Larvae of E. lucunter lucunter increases in size with

increased food ration (Metaxas and Young, 1998). Poor dietary conditions affect the shape,

the densities and locomotors abilities of echinoid larvae, eventually affecting recruitment

(Metaxas and Young, 1998).

Events that occur at or near the time of echinoid settlement and the first few days of benthic

life may cause significant mortality, but the causes are not well understood (McClanahan

and Muthiga, 2013). Caribbean species, E. lucunter lucunter and E. viridis, show

seasonality, spawning in August and September and recruiting in September and October

(Cameron, 1986). Although no recruitment pulse was observed for E. lucunter lucunter in

Puerto Rico, E. viridis recruited in several seasons (Pearse and Cameron, 1991). It is

necessary to stablish the larval duration and settlement of Echinometra Caribbean species


in Colombian Caribbean Sea to identify the particular conditions for the southwest part of

the Caribbean.

2. Isolation and characterization of 26 polymorphic microsatellite loci for the sea urchin Echinometra lucunter lucunter

2.1 INTRODUCTION

There is a wide variety of molecular markers related to population biology that help in the

study of intraspecific genetic diversity and its distribution, depending on the type of

research, species being studied, and questions being addressed (Sunnucks, 2000). In

order to answer the principal goal of the present research, the first step was to develop and

characterize the specific genetic markers for Echinometra lucunter lucunter, the specie

model chosen for this study. Microsatellites have several advantages over other markers

when it comes to answering particular ecological questions. This kind of markers provides

relevant information for genetic processes that take place between populations of a

particular species such as patterns of gene flow or connectivity, generation of genetic

neighborhoods and the incidence of genetic drift, and identification of levels of exchange

between individuals of spatially separated populations (Hellberg et al., 2002). On the other

hand, microsatellites generally have high-mutation rates resulting in high genetic diversity

and, consequently, are ideal for small or recently bottlenecked populations, or for

demography or connectivity patterns in the present day, detecting changes in the recent

past (0-100 generations) (Selkoe and Toonen, 2006).

Microsatellites are taxa-specific, meaning primers must often be developed de novo for

each species or genus, traditionally an expensive and time-consuming process. However,

developments in sequencing technology have transformed the field of microsatellite



isolation (Griffiths et al., 2016). For example, Next Generation Sequencing (NGS) allows

millions of sequences to be read for a much cheaper per-base cost than traditional

methods, and far more quickly (Griffiths, 2013). NGS has offered a useful tool in

microsatellites characterization to research groups that may have originally been

constrained by cost and time. Nevertheless, effectively processing the huge amount of data

resulting from NGS can be challenging for groups. In this sense, Griffiths et al. (2016)

created a complete microsatellite development pipeline for raw Illumina paired-end data

that incorporates existing computer programs and a novel filtering script (pal_filter) method

that allows the development of microsatellites markers quicker and less expensive that

traditional methods. Recently, Fox et al. (2019), incorporated shotgun next-generation

sequencing data sets from multiple individuals of the same species to develop a new

method named multi-individual microsatellite identification (MiMi), for the optimal design of

microsatellite markers. According to them, this new tool increases the rate at which suitable

candidate markers are directly compared and selected by 58% facilitating an estimated

16% reduction in costs associated with producing a novel microsatellite panel. This method

enables the visualization of each microsatellite locus in a multiple sequence alignment,

allowing several important quality checks to be made. Polymorphic loci can be identified

and prioritised. Loci containing fragment‐length‐altering mutations in the flanking regions,

which may invalidate assumptions regarding the model of evolution underlying variation at

the microsatellite, can be avoided. Priming regions containing point mutations can be

detected and avoided, helping to reduce sample‐site‐marker specificity arising from genetic

isolation, and the likelihood of null alleles occurring.

Microsatellites have been used in a variety of ways to study sea urchins’ populations and

species in several spatial scales. Strongylocentrotus franciscanus, an important herbivore

in kelp forest ecosystems along the west coast of North America, has an important

commercial interest due its gonads, which are considered a delicacy in many markets

(Benham et al., 2012). Miller et al. (2004) described 11 novel, highly polymorphic di- and

trinucleotide loci microsatellites that were used by Benham et al. (2012) to detect genetic

structure and connectivity between their populations throughout their distribution. García-

Cisneros et al. (2013) characterized ten (10) polymorphic di-, tri-, tetra- and

pentanucleotides microsatellite of Arbacia lixula, an amphiatlantic thermophilus sea urchin

Isolation and characterization of 26 polymorphic microsatellite loci for the sea

urchin Echinometra lucunter lucunter

19

that recently, was listed among those being favoured by the warming Mediterranean

(Wangensteen, 2013). Pérez-Portela et al. (2018) found a genetic homogeneity in the

Mediterranean Sea, implying that intraspecific genetic features and/or potential impact of

barriers to gene flow in this region will not limit a potential expansion of this keystone’s

species. Calderón et al. (2009) also isolated nine highly polymorphic di-, tri- and

tetranucleotides microsatellites in the common Mediterranean Sea urchin, Paracentrotus

lividus.

In the Caribbean Sea, there have been several studies of microsatellite isolation and

genetic characterization. Meoma ventricosa, commonly known as red heart urchin, is

afflicted by a bacterial disease, causing localized mass mortality (Nagelkerken et al., 1999;

Przeslawski et al., 2008). Eight moderately high polymorphic dinucleotides microsatellite

loci were characterized in order to provide information on genetic diversity of this species

for conservation purposes (Jossart et al., 2015). Another example of genetic studies of

Caribbean Sea species is the case of Diadema antillarum. Caribbean populations of this

sea urchin have undergone two mass mortality events since the 1980s affecting 93%-100%

of individuals. Chandler et al. (2017) designed eight microsatellites loci to detect genetic

structure of D. antillarum throughout the Florida Keys, assessing the genetic differentiation

between brood-stock and natural populations. The authors found that natural populations

are genetically similar to captive bred individuals, suggesting that those animals could be

used for reintroduction to natural populations.

This chapter aims to describe 26 microsatellites, isolated and characterized during the

development of this research, under the methods developed by Griffiths et al. (2016) and

Fox et al. (2019). It is necessary to clarify that the isolation, characterization and design of

the microsatellite loci was developed at Manchester Metropolitan University (MMU) with

Next Generation Sequencing (NGS) technology and support from researchers and students

of the Department of Natural Sciences and the Ecological Genetics group, part of the

Faculty of Science and Engineering. On the other hand, to detect the genetic structuring of

Echinometra lucunter lucunter through the Caribbean, a subgroup of ten (10) loci were

selected (Table 2-5), as it is explained in chapter three.



2.2 MATERIALS AND METHODS

As a first step, a literature review was performed to search for already developed

microsatellite loci of Echinometra lucunter lucunter. The search showed no results of this

type of markers for the species, the sister species or even for the Echinometridae family.

For this reason, novel microsatellite loci were designed.

2.2.1 Development of microsatellites

Microsatellites were designed from eight individuals from Honduras (3), Puerto Rico (3) and

Venezuela (2) (Table 2-1). Tube feet of each individual were processed to extract Genomic

DNA using Qiagen® DNeasy Blood & Tissue kit. The extractions were used to create one

paired-end library using the Illumina Nextera® DNA Sample Preparation Kit, with adaptors

for identification purposes. Each of the eight samples were pooled together and sequenced

on the same flowcell. This step was carried out in The Illumina Mi Seq platform of the

Genetics Core Facility at the University of Salford, Manchester, UK. The left and right raw

reads were separated into two files, and converter to FASTQ format.

The Griffiths et al. (2016) workflow (Figure 2-1) was used to detect microsatellites in the

sequence data and design PCR primers. Multi-individual microsatellite identification (MiMi),

(Fox et al., 2019) was used subsequently as a further quality control on potential markers.

This technique was used to improve the rate at which loci that are identified previously could

be successfully amplified by PCR and produced informative genotype data.



21

Table 2-1: Samples used to isolate and characterize the specific microsatellites for Echinometra lucunter

Country Coordinates Collection

date

Sites/No

samples Approval organization

samples / approval number

Honduras 15°58'17.92'' N

86°28'14.36'' W 14/05/2015

Cayos

Cochinos/3

Instituto Nacional de Conservación y Desarrollo Forestal Áreas Protegidas y Vida Silvestre (ICF)/ Resolution-DE-MP-080-2015

Puerto

Rico

17°57'08.61'' N

67°03'24.30'' W 15/06/2016 Cayo Enrique/3

Departamento de Recursos Naturales y Ambientales/ DRNA: 2016-IC-052

Colombia

12°27'3.6'' N

72°7'17.4'' W 01/07/2016 Cabo de la Vela/1 Ministerio de Ambiente y

Desarrollo Sostenible, República de Colombia, Autoridad Nacional de Licencias Ambientales (ANLA): Resolution number 255, 12 of March of 2014 Export permit #00848

8°41'8.2'' N

77°19'12.7'' W 15/06/2016 Capurganá/2

12°32'34'' N

81°44'45'' W 14/08/2016 San Andrés/3

14°17'4.52'' N

80°22'55'' W 18/08/2016

Isla Cayo

Serrana /1

Venezuela 10°41'20.7'' N

63°52'29.11'' W 15/02/2016 Islote Lobos/2

Universidad Simón Bolívar/ by-product of the artisanal fishery of the Araya Peninsula in Venezuela

Cape

Verde-

Africa

16°35'39.4''N

22°53'42.6'' W 11/08/2011

Santa María, Sol

Island/8

Donation by Owen Wangensteen, Department of Animal Biology of University of Barcelona.

Figure 2-1: Pipeline processes (in boxes) with software used (in bold) and pipeline output. *novel wrapper enabling process step to be run in Galaxy. **novel program developed by Griffiths et al. (2016). Modified from Griffiths et al. (2016).



The Illumina MiSeq run produced 27.043.607 paired-end reads. Later, a list of 308

Potentially Amplifiable Loci (PAL) was obtained using the pipeline processes described in

Figure 2-1, from which 26 PAL´s were chosen for screening: 14 di-, 11 tri- and 1 tetra-

nucleotide.

To check for amplification success, the selected filtered primers were tested on the same

eight (8) extractions used to design the primers plus eight samples more from the

Colombian Caribbean Sea. The process followed the specifications of kit Type-it®

Microsatellite PCR in a reaction volume of 5μl, with a thermal cycle as follows: Step 1:

95°C/5 minutes; Step 2: 28 cycles x (95°/30 seconds for denaturation, 61°C/90 seconds for

annealing and 72°C/30 seconds for elongation); Step 3: 60°C/30 minutes; Step 4: endless

file at 4°C/∞. Five microliters (5μl) of PCR product solution was checked on 1.8% agarose

gel against Hyperladder IV size standard (Bioline) to determine successful PCR

amplification and the approximate size range of fragments. All the 26 microsatellite loci had

successful results.

To have a good distribution of loci sizes and to facilitate multiplexing in the next steps, each

forward primer locus was labelled with one of four (4) universal tail primers: Blacket A,

Blacket C, Blacket D and M13 mod B (Blacket et al., 2012; Culley et al., 2013). Each locus

was labelled with the fluorochromes 6FAM, TAMRA, HEX and PET respectively.

Amplification success was tested in eight (8) DNA extractions from the Tropical Eastern

Atlantic (Cape Verde-Sal Island: 16°35’39.4’’N- 22°53’42.6’’W). Thermal cycles and PCR

features used were the same as in earlier steps. All the 26 SSR’s had successful results.

2.2.2 Microsatellite screening

Twenty-six primers previously tested were used to assess polymorphic level of the

microsatellites loci isolated. For this step, 23 extractions from Cape Verde and Caribbean



23

localities were carried out (Table 2-1). Six (6) multiplex (containing 18 microsatellites) and

eight (8) as singleplex (Table 2-2) were designed using the same thermal cycles and PCR

features explained in the earlier steps.

Reactions were genotyped on an Applied Biosystems 3730 DNA analyser of the Genetic

Core facility at the University of Manchester (UK). Length and allele’s scoring of PCR

products were estimated using GeneMapper software 5 (ThermoFisher Scientific) and were

confirmed using software Peakscanner v1.0 (Applied Biosystems). This process identified

13 primers (out the originally 26) with good behaviour at polymorphic level.

In order to confirm microsatellite screening of the 13 markers selected on the six (6)

multiplex designed before, we used Multiplex Manager 1.2 software (Holley and Geerts,

2009), using DNA extractions of eight (8) individuals from three (3) localities of Caribbean

Sea populations: Honduras, Puerto Rico and Venezuela. As a result, we obtained five (5)

multiplex as is shown in Table 2-3. Eleven markers from this step had successful results.

On the other hand, six (6) singleplex of Table 2-2 had a correct behaviour.

2.2.3 Data analysis

Twenty-three (23) individuals were used as one big population, comprising samples from

several localities in the Caribbean and at Cape Verde (Africa) (Table 2-1). Data were edited

in excel tables, converted to input files with Microsatellite Toolkit to have gene-pop data for

other analysis software. To detect and estimate genotyping error, data were probed using

Microchecker software. Hardy Weingberg Equilibrium (HWE), number of alleles per loci and

population, observed heterozygosity (Ho) and expected heterozygocity (He) were

calculated with GenAlEx v.6.503 (Peakall and Smouse, 2006). The polymorphic Information

Content (PIC) of each locus was estimated using the program Cervus 3.0.7 (Marshall et al.,

1998). Genotypic Linkage disequilibrium was estimated using Genepop on the Web version



4.2 (Raymond and Rousset, 1995). The Bonferroni correction method was used to adjust

significant levels for HWE and linkage disequilibrium.

2.3 RESULTS AND DISCUSSION

Loci behaviour

The pipeline for raw Illumina paired data created by Griffiths et al. (2016) generated a total

of 27.043.607 reads. After the pal filtering process from the Galaxy Platform, it was obtained

a further list of 308 potentially amplifiable loci (PAL), from which 26 PAL’s were selected for

screening: 14 di-, 11 tri- and one (1) tetra-nucleotide (Table 2-2); all of them had good

results in the samples that were probed in single way, demonstrating that new method

created by Fox et al. (2019), known as multi‐individual microsatellite identification -MiMi- is

very effective, reducing time in the laboratory, cost of materials and reagents necessary for

the isolation and design of microsatellite loci. Fox et al. (2019) demonstrated that this

method reduces the number of markers that must be tested for polymorphism in the

laboratory and achieves an improved rate of successful marker development. The process

was quicker and less expensive than traditional markers isolation methods that have been

tested in other biological models (Griffiths, 2013).



25

Table 2-2: Characteristics of 26 nuclear microsatellite markers developed for Echinometra lucunter. F: Forward. R: Reverse. F.L: Fluorescent Label. Allele Size Range: bp. N. E: Not Expressed under the Multiplex design performed.

Locus Universal Tail used Primer sequences F.L. Repeat motif

bp

Sin

gle

ple

x

EL1 F: GCCTCCCTCGCGCCA AATGCTCCCCAAATTTCTCG

6-FAM AAG*81 N. E. R: GCCTGTTACAATTTCCTGGGG

EL2 F: GCCTCCCTCGCGCCA ATGGCTTTTGGGACAGATGG

6-FAM AAG*111 394-434 R: TCCTCAGAAATTATGCCCGC

EL3 F: GCCTCCCTCGCGCCA CGATGTGAGACTAGAAATGTACGG

6-FAM AAC*93 390-407 R: CGACAACTGGTACCTGGACG

EL4 F: GCCTCCCTCGCGCCA GCATGTTCACGATTTGAAGG

6-FAM AAG*90 241-269 R: GCAGATGGTGGAGAAGAGG

EL5 F: GCCTCCCTCGCGCCA CTTTCAAAGACCCGATACAACG

6-FAM AG*90 296-324 R: TGCACCGAATAATGATGAGC

EL6 F: GCCTCCCTCGCGCCA TGATCGAAACGGTGACATCC

6-FAM AAAC*104 209-253 R: AGTCAGATCACCGCCATGC

EL7 F: GCCTCCCTCGCGCCA TGGGACAAAGAGAGAGCTTGG

6-FAM AG*72 162-171 R: AGCGGATGTTGATTTACGGC

EL26 F: CGGAGAGCCGAGAGGTG TTCATCCAACTCTGTCACATCC

HEX AAG*147 N. E. R: GCAGACGATATTCTGGGTCC

Mu

ltip

lex1 EL8

F: CAGGACCAGGCTACCGTG GGCTTATGGTCACAGGACTGG 6-FAM AG*68 N. E.

R: GGAAGAGCTCGCTTGATTCG

EL14 F: CACTGCTTAGAGCGATGC TCTTCTCGATCCCTCTTTGTCC

PET AAG*84 333-345 R: TGAAGGTGCACTGATGGAGG

EL20 F: CGGAGAGCCGAGAGGTG CGCCATTAATGTCAACACCG

HEX ATC*69 249-318 R: AGCATCTGAATCCCCACCC

Mu

ltip

lex2 EL9

F: CAGGACCAGGCTACCGTG CCGTCTTGAGAGCTATCGGC 6-FAM AGT*72 N. E.

R: GCGTTTAAGATTCCCTTTGCC

EL15 F: CACTGCTTAGAGCGATGC GTTTCACATCGGTCCGTCG

PET AAG*93 346-400 R: CTCCATAGCAACATGACGGG

EL21 F: CGGAGAGCCGAGAGGTG GCAGTATCATCATCGCCAGC

HEX ATC*126 315-561 R: TCAGGGATTGTGTCTTTGCG

Mu

ltip

lex3 EL10

F: CAGGACCAGGCTACCGTG TCAGCCTAAGTGTGTTGGAAGC 6-FAM AG*114 N. E.

R: CTCTGTGCAAAGAAGGTTAAGTGC

EL16 F: CACTGCTTAGAGCGATGC TGAAGACGTTCGTTGATTGC

PET AG*52 182-272 R: GCACTTACATCAGTGGATTGC

EL22 F: CGGAGAGCCGAGAGGTG ACATTGAAAACACTGGGGCG

HEX AAG*129 156-294 R: ATGCATTTTCATGTGCACCC

Mu

ltip

lex4 EL11

F: CAGGACCAGGCTACCGTG TGATGCTCTACATTTGAGCCC 6-FAM AG*68 N. E.

R: TCTCCCTAGCGTTACAGGGG

EL17 F: CACTGCTTAGAGCGATGC AAGTGGGTAGGCCAACGCTACACG

PET AG*96 217-247 R: ACGCGCATACTATAGCGTGGTGG

EL23 F: CGGAGAGCCGAGAGGTG GAGACAGAATGAAAATCGCTGC

HEX TG*38 297-367 R: CGAAGGGACTATGAAACGGG

Mu

ltip

lex5 EL12

F: CAGGACCAGGCTACCGTG TGGTGCAAATGTAGCAGTCG 6-FAM AGT*75 N. E.

R: CGTGGTACATTGCTTACAGCC

EL18 F: CACTGCTTAGAGCGATGC GCTCATTGGCAGATCAAACG

PET AG*60 217-399 R: CTTGCTCTCTTGTTCTATTCCCC

EL24 F: CGGAGAGCCGAGAGGTG CGGTGACCCTACAGTAAACTTGC HEX AG*54 300-474 R: TCCCTCTTTCTCCCACGC

Mu

ltip

lex6 EL13

F: CAGGACCAGGCTACCGTG CCATGCCCAATAATAACGCC 6-FAM AG*128 N. E.

R: ATCCTTGGGCCATCTTTCC

EL19 F: CACTGCTTAGAGCGATGC CAAACTAAAGACGCCCAGCC

PET AG*40 156-186 R: ATGAGGCTTCCCTGAATCCC

EL25 F: CGGAGAGCCGAGAGGTG TAAACCAGGCCAGAATGACG

HEX TC*72 N. E. R: TGGGAGCTTTTAGTCCGAGC



Table 2-3: Characteristics of 13 nuclear microsatellite markers developed for Echinometra lucunter. F: Forward. R: Reverse. F.L: Fluorescent Label. Allele Size Range: bp. N.E.: Not Expressed under the Multiplex design performed.

Locus Universal Tail used Primer sequences F.L Repeat motif

bp

Mu

ltip

lex1

EL8 F: CAGGACCAGGCTACCGTG GGCTTATGGTCACAGGACTGG

6-FAM AG*68 N. E.

R: GGAAGAGCTCGCTTGATTCG

EL22 F: CGGAGAGCCGAGAGGTG ACATTGAAAACACTGGGGCG

HEX AAG*129 156-294

R: ATGCATTTTCATGTGCACCC

EL15 F: CACTGCTTAGAGCGATGC GTTTCACATCGGTCCGTCG

PET AAG*93 346-400

R: CTCCATAGCAACATGACGGG

Mu

ltip

lex2

EL9 F: CAGGACCAGGCTACCGTG CCGTCTTGAGAGCTATCGGC

6-FAM AGT*72 N. E.

R: GCGTTTAAGATTCCCTTTGCC

EL23 F: CGGAGAGCCGAGAGGTG GAGACAGAATGAAAATCGCTGC

HEX TG*38 297-367

R: CGAAGGGACTATGAAACGGG

EL16 F: CACTGCTTAGAGCGATGC TGAAGACGTTCGTTGATTGC

PET AG*52 182-272

R: GCACTTACATCAGTGGATTGC

Mu

ltip

lex3

EL24 F: CGGAGAGCCGAGAGGTG CGGTGACCCTACAGTAAACTTGC

HEX AG*54 300-474

R: TCCCTCTTTCTCCCACGC

EL17 F: CACTGCTTAGAGCGATGC AAGTGGGTAGGCCAACGCTACACG

PET AG*96 217-247

R: ACGCGCATACTATAGCGTGGTGG

Mu

ltip

lex4

EL19 F: CACTGCTTAGAGCGATGC CAAACTAAAGACGCCCAGCC

PET AG*40 156-186

R: ATGAGGCTTCCCTGAATCCC

EL21 F: CGGAGAGCCGAGAGGTG GCAGTATCATCATCGCCAGC

HEX ATC*126 315-561

R: TCAGGGATTGTGTCTTTGCG

EL14 F: CACTGCTTAGAGCGATGC TCTTCTCGATCCCTCTTTGTCC

PET AAG*84 333-345

R: TGAAGGTGCACTGATGGAGG

Mu

ltip

lex5

EL20 F: CGGAGAGCCGAGAGGTG CGCCATTAATGTCAACACCG

HEX ATC*69 249-318

R: AGCATCTGAATCCCCACCC

EL18 F: CACTGCTTAGAGCGATGC GCTCATTGGCAGATCAAACG

PET AG*60 217-399

R: CTTGCTCTCTTGTTCTATTCCCC



27

From the 26 microsatellites tested under the screening techniques, 17 markers confirmed

good behaviour through the process under the chosen Multiplex design (Table 2-4).

However, several loci tested had not good performance, but they were submitted to different

combinations with the program Multiplex Manager 1.2 software (Holley and Geerts, 2009)

(Table 2-2; Table 2-3). The results suggest that fluorescent dye 6-FAM (Blue colour) has

good results when it was used under singleplex, as it is seen in the Figure 2-2A for marker

EL3, or the most of the singleplex runs (Table 2-2). However, the combination of this dye

under some multiplex combination influenced their behaviour (Figure 2-2B; Table 2-2; Table

2-3). As it is mentioned by Lluvia-Flores and Andrews (2013), it is frequent that some picks

have strong fluorescence signal, creating artefact peaks in one channel derived from the

strong signal intensity in another, called “bleedthrough” or “pull up”. Consequently, artefact

peaks are not practicable for analysis. The interaction between dyes is not always the

same, it depends on the samples used, which is the reason why the multiplex screening

needs to be performed to confirm a potential interaction. In this case, the results suggest

that the interaction occurred between fluorescent dye HEX (green colour) and 6-FAM (blue

colour it affected more the later one). For this reason, all markers with the fluorescent dye

6-FAM were left for singleplex runs.



Figure 2-2: Development of 6-FAM dye: A. Singleplex with EL3. Honduras, sample 6. B. Multiplex 1, Cabo de la Vela, sample 8. Length and allele scoring were carried out with using software Peakscanner v1.0 (Applied Biosystems).



29

Table 2-4: Characteristics of 17 markers developed for Echinometra lucunter. R. motif: motif of repetition, Ta: Anelling temperature, N: sample size, Na: Allele number, Ne: Allele effective number, Ho: Observed heterozygosity, He: Expected heterozygosity. PIC: Polymorphism Information Content. pHWE: Hardy-Weingberg Equilibrium and pHWEbc: Hardy-Weingberg Equilibrium with Bonferroni Correction method. *: p<0.05, **: p<0.01; *** p<0.001.

Characteristics of 17 loci isolated

The allele number per locus (Na) ranged from 4 to 24. Observed (Ho) and expected (He)

heterozygosity ranged from 0.682 to 1, and 0.609 to 0.9304, respectively. Two loci out of

17 deviated from HWE after Bonferroni correction for multiple testing (p<0.05). Homozygote

excess related to the presence of null alleles or stochasticity due to the different locality

sources for one population might explain these deviations. Polymorphic information content

(PIC) ranging from 0.530 to 0.930 shows normal polymorphism behaviour. After Bonferroni

correction, no linkage disequilibrium was detected between any locus pair.

To detect the genetic structure of E. lucunter lucunter populations through the Caribbean

Sea, a set of ten (10) loci were used (Table 2-5). Eight loci are part of the 17 described

before and the two additional are part of the 26 original markers isolated (Table 2-2). The

analysis developed with these 10 markers is explain in detail in the chapter three, using 12

Locus Forward primer Reverse Primer R. motif Size range Ta N Na Ne Ho He PIC pHWE pHWEbc

EL2 ATGGCTTTTGGGACAGATGG TCCTCAGAAATTATGCCCGC (AAG)111 394-434 61°C 7 7.000 4.900 0.857 0.796 0.772 0.817 1.000

EL3 CGATGTGAGACTAGAAATGTACGG CGACAACTGGTACCTGGACG (AAC)93 390-407 61°C 8 6.000 4.414 1.000 0.773 0.744 0.509 1.000

EL4 GCATGTTCACGATTTGAAGG GCAGATGGTGGAGAAGAGG (AAG)90 241-269 61°C 8 6.000 4.000 1.000 0.750 0.712 0.559 1.000

EL5 CTTTCAAAGACCCGATACAACG TGCACCGAATAATGATGAGC (AG)90 296-324 61°C 6 10.000 9.000 1.000 0.889 0.878 0.600 1.000

EL6 TGATCGAAACGGTGACATCC AGTCAGATCACCGCCATGC (AAAC)104 209-253 61°C 8 4.000 2.844 0.875 0.648 0.592 0.204 1.000

EL7 TGGGACAAAGAGAGAGCTTGG AGCGGATGTTGATTTACGGC (AG)72 162-171 61°C 8 4.000 2.560 0.875 0.609 0.530 0.495 1.000

EL14 TCTTCTCGATCCCTCTTTGTCC TGAAGGTGCACTGATGGAGG (AG)68 333-345 61°C 22 5.000 3.625 0.682 0.724 0.681 0.002** 0.034*

EL15 GTTTCACATCGGTCCGTCG CTCCATAGCAACATGACGGG (AAG)93 346-400 61°C 20 16.000 10.256 0.800 0.903 0.895 0.014* 0.238

EL16 TGAAGACGTTCGTTGATTGC GCACTTACATCAGTGGATTGC (AG)52 182-272 61°C 16 19.000 15.059 0.875 0.934 0.930 0.144 1.000

EL17 AAGTGGGTAGGCCAACGCTACACG ACGCGCATACTATAGCGTGGTGG (AG)96 217-247 61°C 19 13.000 8.022 0.789 0.875 0.864 0.590 1.000

EL18 GCTCATTGGCAGATCAAACG CTTGCTCTCTTGTTCTATTCCCC (AG)60 217-399 61°C 21 22.000 11.919 0.810 0.916 0.911 0.024* 0.408

EL19 CAAACTAAAGACGCCCAGCC ATGAGGCTTCCCTGAATCCC (AG)40 156-296 61°C 11 10.000 5.261 0.909 0.810 0.792 0.275 1.000

EL20 CGCCATTAATGTCAACACCG AGCATCTGAATCCCCACCC (ATC)69 249-318 61°C 20 16.000 10.000 0.850 0.900 0.892 0.021* 0.357

EL21 GCAGTATCATCATCGCCAGC TCAGGGATTGTGTCTTTGCG (ATC)126 315-561 61°C 22 24.000 13.444 1.000 0.926 0.921 0.229 1.000

EL22 ACATTGAAAACACTGGGGCG ATGCATTTTCATGTGCACCC (AAG)129 156-294 61°C 15 13.000 9.783 0.733 0.898 0.889 0.001*** 0.017*

EL23 GAGACAGAATGAAAATCGCTGC CGAAGGGACTATGAAACGGG (TG)38 297-367 61°C 19 18.000 11.108 0.842 0.910 0.904 0.017* 0.289

EL24 CGGTGACCCTACAGTAAACTTGC TCCCTCTTTCTCCCACGC (AG)54 300-474 61°C 10 13.000 10.000 0.800 0.900 0.892 0.130 1.000



localities distributed through the Caribbean Sea. The microsatellite loci include di- (6), tri-

(3) and tetranucleotide (1) motifs, which exhibited allele lengths ranging from 84 to 434 bp.

Table 2-5: Characteristics of 10 markers selected to determine the genetic structure to Echinometra lucunter lucunter through the Caribbean Sea. R. motif: motif of repetition, Ta: Anelling temperature, N: sample size, Na: Allele number, Ne: Allele effective number, Ho: Observed heterozygosity, He: Expected heterozygosity. PIC: Polymorphism Information Content. pHWE: Hardy-Weingberg Equilibrium and pHWEbc: Hardy-Weingberg Equilibrium with Bonferroni Correction method. *: p<0,05, **: p<0,01; *** p<0,001.

The microsatellite markers developed for E. lucunter will allow understanding of how the

genetic diversity is distributed in this species, considering its wide range of geographical

distribution. Information on population genetics of the species is crucial for the assessment

of ecological threats and recovery potential, following disease episodes and population

decline, and for the developing of management strategies when necessary.

Locus Forward primer Reverse Primer R. motif Size range Ta N Na Ne Ho He PIC pHWE pHWEbc

EL2 ATGGCTTTTGGGACAGATGG TCCTCAGAAATTATGCCCGC (AAG)111 394-434 61°C 7 7.000 4.900 0.857 0.796 0.772 0.817 1.000

EL3 CGATGTGAGACTAGAAATGTACGG CGACAACTGGTACCTGGACG (AAC)93 390-407 61°C 8 6.000 4.414 1.000 0.773 0.744 0.509 1.000

EL6 TGATCGAAACGGTGACATCC AGTCAGATCACCGCCATGC (AAAC)104 209-253 61°C 8 4.000 2.844 0.875 0.648 0.592 0.204 1.000

EL7 TGGGACAAAGAGAGAGCTTGG AGCGGATGTTGATTTACGGC (AG)72 162-171 61°C 8 4.000 2.560 0.875 0.609 0.530 0.495 1.000

EL8 GGCTTATGGTCACAGGACTGG GGAAGAGCTCGCTTGATTCG (AG)68 194-284 61°C 26 7.333 5.572 0.257 0.815 0.869 0.000*** 0.000***

EL10 TCAGCCTAAGTGTGTTGGAAGC CTCTGTGCAAAGAAGGTTAAGTGC (AG)114 84-174 61°C 26 5.667 3.535 0.406 0.657 0.889 0.089 0.886

EL13 CCATGCCCAATAATAACGCC ATCCTTGGGCCATCTTTCC (AG)128 126-178 61°C 27 8.250 5.900 0.553 0.816 0.804 0.040* 0.400

EL16 TGAAGACGTTCGTTGATTGC GCACTTACATCAGTGGATTGC (AG)52 182-272 61°C 16 19.000 15.059 0.875 0.934 0.930 0.144 1.000

EL19 CAAACTAAAGACGCCCAGCC ATGAGGCTTCCCTGAATCCC (AG)40 156-296 61°C 11 10.000 5.261 0.909 0.810 0.792 0.275 1.000

EL25 TAAACCAGGCCAGAATGACG GCAGACGATATTCTGGGTCC (AAG)147 228-318 61°C 26 6.667 5.135 0.411 0.799 0.913 0.013* 0.129

3. Genetic structure of Echinometra lucunter lucunter populations in the Caribbean Sea: Is there any influence of marine discontinuities in their connectivity?

3.1 INTRODUCTION

For many years, it has been assumed that dispersion of marine organisms works as an

open system so that the dispersive phase of the species, mainly during the larval period,

allows the colonization of large coastal areas (Shanks et al., 2003; McPherson and

Raventos, 2006). The expected pattern is that species with high dispersal potential (i.e. with

planktotrophic larvae) have little genetic structure and high gene flow (Palumbi and Wilson,

1990; Lacson, 1992; Ovenden et al., 1992; Russo et al., 1994; Uthicke and Benzie, 2003;

Duran et al., 2004a). Conversely, species with low dispersal potential (i.e. with lecitotrophic

larvae) are expected to have clear genetic structure (Janson and Ward, 1984; Day and

Bayne, 1988; McMillan et al., 1992; Duffy, 1993; Hunt, 1993; Duran et al., 2004b). However,

molecular studies in marine invertebrates and fish have demonstrated that gene flow

between populations could be restricted to small scales (Duran et al., 2004a; Carreras-

Carbonell et al., 2006; Calderón et al., 2007; Galarza et al., 2009) due to several factors

(biological, physical, ecological, etc.) or discontinuities. Thus, to establish genetic

connectivity between natural marine populations in an area it is essential to know whether

potentially present marine discontinuities may have any influence in this connectivity.

Marine discontinuities could reduce the population connectivity or increase it, depending on

barrier permeability or influence of biological features of the species. Among barriers



identified as factors that affect the gene flow one could cite the physical distance between

populations of a particular species (Geyer and Lessios, 2009), the influence of ocean

currents (Shulman and Bermingham, 1995; Galarza et al., 2009), physicochemical factors

(Chollett et al., 2012) and mouth of rivers in a study area (Carlin et al., 2003). Life history

of species is an important point to consider, as larval behaviour could change under the

influence of marine discontinuities and affect the connectivity between populations

(Shulman and Bemingham, 1995; Galarza et al., 2009; Fisher et al., 2014).

3.1.1 Physic characteristics of the Caribbean

The Caribbean Sea is a semi-enclosed basin of the Western Atlantic Ocean. It has an

approximate area of 2.754.000 km2, a volume of nearly 6.5 x 106 km3, and over 13.500 km

of coastline (Miloslavich et al., 2010). Towards the east and northeast, the closely spaced

chain of islands, banks, and sills of the Antilles islands arc separates the Caribbean from

the Atlantic Ocean and acts as a sieve for the inflow of Atlantic water (Andrade and Barton,

2000). Towards the northwest, the Yucatan Channel links the Caribbean to the Gulf of

Mexico. It is also bounded by the coasts of Central America on the west and South America

on the south. The Caribbean Sea floor is divided into five basins (Grenada, Venezuela,

Colombia and Yucatan Basins and the Cayman Trough) separated from each other by

underwater ridges and sills (Miloslavich et al., 2010). Half of the waters in the Caribbean

are deeper than 3.600 m and 75% are deeper than 1.800 m. Volcanic activity and

earthquakes are common in the Caribbean, as are destructive hurricanes, most of which

originate in the Central Atlantic (Miloslavich et al., 2010).

Features of the Caribbean Sea current system have a strong influence on connectivity

patterns between ecosystems and marine populations in the area. The currents are

characterized by three main systems: 1- The Caribbean Central Current (CCC), 2- the

Antilles Current (AC) and 3- the counter-current from Panama to Costa Rica (Lessios et al.,

1984; Gyory et al., 2013). According to Jossart et al. (2017), the speed and the direction of

the Caribbean Sea currents (e.g., East to West CCC, South to North AC) may have

implications for the genetic patterns among populations (e.g., direction of gene flow).

Genetic structure of Echinometra lucunter lucunter populations in the Caribbean

Sea: Is there any influence of marine discontinuities in their connectivity?

33

The Colombian Caribbean currents are influenced, in addition by the trade winds, by three

general conditions: 1- the presence of the westward CCC, 2- the Panama-Colombia

eastward counter-current (PCE) with intensities that vary depending on depth, and 3- the

deep and eastward Caribbean undercurrent with slow movement over the slope and

abyssal bottom (Corredor, 1979; Pujos et al., 1986; Blanco 1988; Álvarez-León et al., 1995;

Andrade-Amaya et al., 2003). In this area, the behaviour of the currents is seasonal and

determined by the presence of trade winds, which promote the coastal upwelling in the

Guajira department and in front of the Sierra Nevada de Santa Marta. The upwelling is

favoured by the particular direction of the coast, northeast-southwest (Andrade-Amaya et

al., 2003). The surface current is not stable and shows swirls with cyclonics and anti-

cyclonics movements of different sizes (Andrade and Barton, 2000). According to Lozano-

Duque et al. (2010), the marine current system in the south of the Colombian Caribbean

Sea is dominated by the PCE and Panama-Colombia gyre (PCG) (Andrade, 2001).

Caribbean waters are mainly clear and warm (22-29°C), with low tides (< 0.4 m) (Kinder et

al., 1985). The water column is stratified in the first 1200 m depth by bottom elevations of

the Antilles Arc, with consequently depth waters flux in the basin (Gordon, 1967). The

salinity varies in the first 1000 m from the surface of the water. Higher salinities are present

eastward of the Caribbean, close to Less Antilles, due to the influence of Atlantic waters

(major salinity). On the west part of the Caribbean the salinity is lower, as a consequence

of major precipitation and runoff, as well as freshwater contributions from the rivers that

flow into this area (Gordon, 1967).

Integrating the physical features of the Caribbean to studies focused on detecting the

genetic structure and marine connectivity of different organisms as models have contributed

to understand that this region is not as homogeneous as it was believed. Most of these

studies have centred on endangered or charismatic inhabitants of reef fish, corals, some

crustaceans and sponges (Avise, 1992; Schultz and Cowen, 1994; Carlin et al., 2003;

Taylor and Hellberg, 2003, 2006; Paris and Cowen, 2004; Baums et al., 2005; Cowen et

al., 2006; Ospina‐Guerrero et al., 2008; Landínez-García et al., 2009; Betancur-R et al.,

2011; Díaz-Ferguson et al., 2011; Márquez‐Fernández et al., 2013; Truelove et al., 2015;



Chaves-Fonnegra et al., 2015). Based on these studies, several connectivity regions have

been proposed (Carlin et al., 2003; Baums et al., 2005; Cowen et al., 2006; Taylor and

Hellberg, 2003, 2006; Betancur-R et al., 2011) indicating different scenarios. However,

genetic variation patterns for other taxa in this area such as echinoderms, have not been

properly addressed.

In order to complete the connectivity patterns in the Caribbean Sea is necessary to use

other kind of models and environments to understand the connexion between the physical

and biological characteristics of this area. This study used Echinometra lucunter lucunter

as a model due to its broad distribution throughout the Atlantic Ocean and Caribbean Sea,

and aspects of their life history such as reproductive biology, larval development and

Pelagic Larval Duration - PLD (McClanahan and Muthiga, 2013), which has not been well

understood through their geographical Caribbean distribution. The acquisition of this

information is not only essential for conservation and management, but also for

understanding population dynamics and community assembly in Caribbean rocky shore

ecosystems (Hellberg et al., 2002; Dethier et al., 2003; Siegel et al., 2008).

3.2 MATERIALS AND METHODS

3.2.1 Samples collection

A total of 329 samples were obtained from tube feet of E. lucunter lucunter in 12 places of

the Caribbean Sea through manual collection on rocky littoral shore (Table 3-1, Figure 3-

1). Sea urchins tissue were immersed in 96% ethanol upon returning to the wet laboratory

in each site and stored at -20°C for further transport and genotyping.



35

3.2.2 Genotyping

Genomic DNA was extracted from tube feet using the DNeasy Blood & Tissue kit in the

molecular biology laboratories at the Museo de Historia Natural Marina de Colombia

(MAKURIWA) of INVEMAR (Santa Marta) and at Manchester Metropolitan University -

MMU- (United Kingdom). Confirmation of the DNA quantity and quality extracted was

calculated with NanoDrop 2000 (Thermo Scientific) spectrophotometer and electrophoresis

with agarose gels.

Table 3-1: Information of Echinometra lucunter lucunter samples collected through the Caribbean Sea for this study: CCC: Caribbean Central Current, AC: Antilles Current, PCE: Panama-Colombia eastward counter-current and Panama-Colombia gyre: PCG.

Country Marine current

influence Locality Coordinates

Collection date

Number of samples per site

Colombia

PCG-PCE

Capurganá (CAP) 8.691155 -77.320196

15/06/2016 31

San Andrés (SAN) 12.526096 -81.729065

14/08/2016 30

Alburquerque (ALB) 12.165383 -81.840133

27/09/2018 19

CCC-PCG

Punta Betín (PB) 11.250415 -74.078800

10/05/2016 20

Cabo de la Vela (CV) 12.450992 -72.121496

01/07/2016 32

Banco Serrana (SER) 14.284730 -80.367044

18/08/2016 31

Nenguange (NEG) 11.328958 -74.078833

22/03/2018 20

Playa Blanca (PBL) 10.214131 -75.619041

08/04/2016 19

Playetas (PLY) 10.190911 -75.632772

15/11/2017 24

Venezuela Islote Lobos - Venezuela (VEN) 10.689166 -63.874722

15/02/2016 29

Belize Carrie Bow Cay - Belize (BLZ) 16.799797 -88.083329

04/11/2017 33

Puerto Rico

AC Cayo Enrique – Puerto Rico (PR) 17.954840 -67.052626

15/06/2016 41



Figure 3-1: Location of sampling stations, marked as red stars, through the Caribbean Sea of the sea urchin Echinometra lucunter lucunter used for this study. Parenthesis number is individuals used by locality. PR(41): Puerto Rico, VEN(29): Venezuela, CV(32): Cabo de la Vela, NEG(20): Nenguanje, PB(20): Punta Betín, PBL(19): Playa Blanca, PLY(24): Playetas, CAP(31): Capurganá, ALB(19): Alburquerque, SAN(30): San Andrés, SER(31): Serrana, BLZ(33): Belize.

3.2.3 Microsatellites amplification, sequencing and genotyping

Ten microsatellite loci (Table 2-5) developed specifically for Echinometra lucunter

(Benavides-Serrato et al., 2020) were used as a molecular tool to identify the populations

structure of the species. Amplification (PCR) cycling conditions included specifications of

kit Type it® Microsatellite PCR in a reaction volume of 5µl, with a thermal cycle as follows:

Step 1: 95°C/5 minutes; Step 2: 28 cycles x (95°/30 seconds for denaturation, 61°C/90

seconds for annealing and 72°C/30 seconds for elongation); Step 3: 60°C/30 minutes; Step

4: endless file at 4°C/∞. Five microliters (5μl) of PCR product solution was checked with

electrophoresis agarose gels to determine successful PCR amplification and the

approximate size range of fragments. The amplification products were analysed by capillary

electrophoresis QIAxcel Advance (QIAGEN), using a high-resolution kit (High Resolution

Kit QIAGEN) and a molecular weight ladder of known concentration (DNA Size Marker 50-

800 bp v2.0 QIAGEN). The size of each amplified product was determined with the



37

ScreenGel QIAxcel v1.0 QIAGEN program, which allows quantifying the weight of each

band thus distinguishing loci sizes of samples used (Orozco-Berdugo and Narváez-

Barandica, 2014). Micro-Checker v.2.2.3 (Van Oosterhout et al., 2004) was run to detect

potential genotyping errors.

3.2.4 Statistical and molecular analysis

The number of alleles (Na) per locus, Effective number of alleles (Ne), observed

Heterocigosity (Ho) and expected (He), were calculated to estimate the genetic diversity of

E. lucunter lucunter with GenAlEx v.6.503 (Peakall and Smouse, 2006). The inbreeding

coefficients (FIS) per loci and the pairwise Fst value were calculated with ARLEQUIN

v.3.5.2.2 (Excoffier et al., 2005). Test for departures from Hardy-Weinberg Equilibrium

(HWE) and linkage disequilibrium (LD) were estimated using Genepop on the Web version

4.2 (Raymond and Rousset, 1995). The sequential Bonferroni correction was applied to

adjust the statistical significance in multiple comparisons (Rice, 1989).

Different approaches based on Bayesian clustering, genetic distances and Principal

Component Analysis (PCA) were applied. Whereas methods based on genetic distances

(e.g pairwise value-FST) are affected by the HWE and assume absence of linkage

disequilibrium among all loci within populations, other multivariate methods are free from

these two assumptions (Pérez-Portela et al., 2018). Therefore, here it was used two

methods to minimize potential bias of using only one approach. The genetic differentiation

between populations was determined using phiPT and Jost’s Dest values (Meirmans and

Hedrick, 2011) with GenAlEx v.6.503 (Peakall and Smouse, 2006). The program

ARLEQUIN v.3.5.2.2 (Excoffier et al., 2005) was used to perform an Analysis of Molecular

variance (AMOVA) with 10.000 permutations assuming all localities of the Caribbean Sea

as a big population, which allows the software to identify real and potential groups. After

exploring the genetic structure of the samples, it was tested the hypothesis of geographic

isolation using a Mantel test (Mantel, 1967) implemented in ARLEQUIN v.3.5.2.2 (Excoffier

et al., 2005). Patterns of genetic structure were further explored, using the diploid



genotypes of 10 loci (20 variables) in 329 individuals, which were submitted to PCA using

the R package Adegenet (Jombart, 2008).

The software STRUCTURE v 2.3.4 (Pritchard et al., 2000) was used to infer an optimal

number of homogeneous genetic units (K) based on Bayesian clustering analyses. It was

run with the whole data set, with a K number from one (1) to 15 (to include three artificial

populations as a negative control in the analysis) and 1.000.000 Markov Chain Monte Carlo

(MCMC) steps were performed following 80.000 burn-in iterations under the “admixture

model” and the “correlated allele frequencies mode” and the LOCPRIOR option for

detecting relatively weak population structures (Hubisz et al., 2009). Each analysis was

repeated 20 times for each simulated K-value. For the best estimation of genetic stocks (K),

the ∆K ad hoc statistic (Evanno et al., 2005) was calculated using STRUCTURE

HARVESTER (Earl and VonHoldt, 2012). Then, CLUMPP v. 1.1.2b (Jakobsson and

Rosenberg, 2007) and DISTRUCT v.1.1 (Rosenberg, 2004) were used, respectively, to

summarize the result of independent STRUCTURE runs and plot the Q-matrix obtained in

a histogram, displaying the individual ancestry of each population.

3.3 RESULTS

3.3.1 Genetic diversity

The set of 10 loci amplified in the samples studied. The effective number of alleles (Ne) per

locus varied from 1 to 7.680 (Table 3-2). In all loci and in all localities, observed

heterozygosity was lower than expected, showing a high deficit of heterozygosity, reflected

by the significant deviation from the HWE in all sites (Table 3-2). There was association

among the genotypes of 10 loci, suggesting that the analyses could be performed assuming

statistical independence of the loci. FIS values per population were high, positive and

significant (FIS: 0.436, P: 0.000) (Table 3-2).



39

Table 3-2: Genetic diversity per locus of 10 loci in Echinometra lucunter lucunter from Caribbean Sea. N: Number of samples; Na: Number of alleles; Ne: Effective number of alleles; Ho: Observed Heterozygosity; He: Expected Heterozygosity; FIS: inbreeding coefficient. Values in bold denote significant departures from Hardy-Weinberg equilibrium (HWE) after Bonferroni correction. Sampling sites: CAP: Capurganá, SAN: San Andrés, ALB: Albuquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas, VEN: Venezuela, BLZ: Belize, PR: Puerto Rico.

Site (N) EL13 EL10 EL19 EL6 EL7 EL8 EL2 EL16 EL25 EL3 Across loci

CAP (31)

Na 9.000 6.000 7.000 5.000 6.000 8.000 7.000 9.000 7.000 5.000 6.900

Ne 5.555 3.696 5.175 2.359 2.877 6.253 5.502 5.59 5.471 4.534 4.701

Ho 0.323 0.452 0.207 0.400 0.516 0.103 0.333 0.267 0.567 0.621 0.379

He 0.820 0.729 0.807 0.576 0.652 0.840 0.818 0.821 0.817 0.779 0.766

FIS 0.617 0.395 0.751 0.321 0.225 0.881 0.605 0.684 0.322 0.220 0.502

SAN (30)

Na 7.000 4.000 8.000 5.000 6.000 8.000 7.000 7.000 8.000 7.000 6.700

Ne 5.882 2.187 4.625 4.455 4.478 6.509 4.962 5.674 5.682 5.939 5.039

Ho 0.667 0.643 0.429 0.679 0.533 0.296 0.448 0.100 0.360 0.318 0.447

He 0.830 0.543 0.784 0.776 0.777 0.846 0.798 0.824 0.824 0.832 0.783

FIS 0.213 -0.167 0.468 0.143 0.329 0.661 0.453 0.884 0.577 0.632 0.419

ALB (19)

Na 8.000 6.000 5.000 2.000 4.000 7.000 6.000 8.000 7.000 7.000 6.000

Ne 6.505 1.675 3.180 1.870 1.837 6.224 4.500 6.400 5.255 3.400 4.085

Ho 0.632 0.294 0.313 0.632 0.526 0.368 0.667 0.313 0.471 0.412 0.463

He 0.846 0.403 0.686 0.465 0.456 0.839 0.778 0.844 0.810 0.706 0.683

FIS 0.279 0.298 0.566 -0.333 -0.129 0.579 0.171 0.649 0.443 0.441 0.297

PB (20)

Na 10.000 4.000 7.000 5.000 6.000 8.000 6.000 8.000 6.000 8.000 6.800

Ne 6.897 1.867 4.612 2.930 2.703 4.762 5.014 5.311 4.563 5.838 4.450

Ho 0.450 0.400 0.357 0.600 0.400 0.400 0.526 0.111 0.667 0.556 0.447

He 0.855 0.464 0.783 0.659 0.630 0.790 0.801 0.812 0.781 0.829 0.740

FIS 0.493 0.172 0.57 0.115 0.387 0.513 0.366 0.870 0.174 0.355 0.402

CV (32)

Na 9.000 8.000 8.000 5.000 6.000 8.000 7.000 7.000 7.000 8.000 7.300

Ne 7.161 4.380 4.954 4.243 4.752 6.628 5.936 4.216 5.806 5.523 5.360

Ho 0.750 0.133 0.194 0.677 0.344 0.323 0.313 0.310 0.433 0.407 0.388

He 0.860 0.772 0.798 0.764 0.790 0.849 0.832 0.763 0.828 0.819 0.807

FIS 0.144 0.832 0.764 0.130 0.575 0.630 0.634 0.604 0.490 0.516 0.532

SER (31)

Na 9.000 8.000 7.000 5.000 6.000 8.000 7.000 8.000 7.000 8.000 7.300

Ne 6.471 5.066 4.052 4.258 5.014 5.661 4.310 5.443 4.992 6.250 5.152

Ho 0.484 0.241 0.286 0.586 0.667 0.214 0.400 0.345 0.323 0.467 0.401

He 0.845 0.803 0.753 0.765 0.801 0.823 0.768 0.816 0.800 0.840 0.801

FIS 0.441 0.708 0.632 0.250 0.184 0.748 0.495 0.589 0.607 0.458 0.511

NEG (20)

Na 9.000 8.000 6.000 5.000 5.000 7.000 7.000 8.000 7.000 7.000 6.900

Ne 6.667 5.970 4.596 2.418 2.689 6.299 5.586 6.017 5.776 4.846 5.086

Ho 0.450 0.500 0.389 0.778 0.500 0.300 0.500 0.158 0.368 0.474 0.442

He 0.850 0.833 0.782 0.586 0.628 0.841 0.821 0.834 0.827 0.794 0.780

FIS 0.490 0.421 0.524 -0.301 0.231 0.658 0.415 0.820 0.573 0.426 0.426



Table 3.2: Continuation. Genetic diversity per locus of 10 loci in Echinometra lucunter lucunter from Caribbean Sea. N: Number of samples; Na: Number of alleles; Ne: Effective number of alleles; Ho: Observed Heterozygosity; He: Expected Heterozygosity; FIS: inbreeding coefficient. Values in bold denote significant departures from Hardy-Weinberg equilibrium (HWE) after Bonferroni correction. Sampling sites: CAP: Capurganá, SAN: San Andrés, ALB: Albuquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas, VEN: Venezuela, BLZ: Belize, PR: Puerto Rico.

Site (N) EL13 EL10 EL19 EL6 EL7 EL8 EL2 EL16 EL25 EL3 Across loci

PBL (19)

Na 10.000 4.000 7.000 5.000 6.000 8.000 6.000 8.000 7.000 7.000 6.800

Ne 6.876 3.880 4.050 3.267 4.688 6.149 4.738 4.222 4.412 4.011 4.629

Ho 0.684 0.278 0.167 0.421 0.526 0.294 0.294 0.316 0.176 0.316 0.347

He 0.855 0.742 0.753 0.694 0.787 0.837 0.789 0.763 0.773 0.751 0.774

FIS 0.225 0.643 0.790 0.416 0.355 0.666 0.645 0.604 0.784 0.597 0.572

PLY (24)

Na 10.000 8.000 8.000 5.000 6.000 8.000 7.000 8.000 8.000 6.000 7.400

Ne 7.680 5.975 6.411 2.822 2.844 5.628 4.429 3.833 6.817 3.984 5.042

Ho 0.708 0.545 0.409 0.636 0.292 0.348 0.263 0.261 0.091 0.364 0.392

He 0.870 0.833 0.844 0.646 0.648 0.822 0.774 0.739 0.853 0.749 0.778

FIS 0.206 0.365 0.532 0.038 0.565 0.592 0.675 0.660 0.898 0.531 0.506

VEN (29)

Na 6.000 3.000 6.000 2.000 6.000 6.000 5.000 5.000 4.000 1.000 4.400

Ne 3.234 2.039 3.504 1.533 2.496 3.834 1.950 4.553 3.301 1.000 2.744

Ho 0.500 0.500 0.483 0.310 0.448 0.107 0.464 0.417 0.286 0.000 0.352

He 0.691 0.510 0.715 0.348 0.599 0.739 0.487 0.780 0.697 0.000 0.557

FIS 0.294 0.037 0.340 0.125 0.268 0.860 0.065 0.483 0.602 N.A. 0.342

BLZ (33)

Na 6.000 4.000 7.000 4.000 6.000 6.000 8.000 6.000 6.000 2.000 5.500

Ne 4.109 2.117 3.612 1.933 3.293 4.296 2.387 4.711 5.112 1.942 3.351

Ho 0.515 0.364 0.344 0.606 0.500 0.100 0.500 0.129 0.581 0.276 0.391

He 0.757 0.528 0.723 0.483 0.696 0.767 0.581 0.788 0.804 0.485 0.661

FIS 0.333 0.325 0.536 -0.242 0.296 0.874 0.155 0.841 0.293 0.446 0.386

PR (41)

Na 6.000 5.000 5.000 2.000 6.000 6.000 9.000 10.000 6.000 5.000 6.000

Ne 3.422 3.571 3.735 1.772 2.687 4.618 5.388 7.949 4.430 3.383 4.095

Ho 0.475 0.525 0.378 0.538 0.650 0.225 0.585 0.091 0.610 0.425 0.450

He 0.708 0.720 0.732 0.436 0.628 0.783 0.814 0.874 0.774 0.704 0.717

FIS 0.340 0.283 0.494 -0.224 -0.023 0.719 0.293 0.899 0.224 0.407 0.341

3.3.2 Population differentiation and structure

The approaches for measuring genetic differences revealed contrasting results. The

Bayesian analysis showed the presence of three genetic groups (∆K=3): one with

Colombian Caribbean Sea sites, another with VEN and BLZ and other with PR station



41

(Figure 3-2). However, the AMOVA showed that the variation observed within each group

was explained by the significant differences among the sites of each group (Fixation Index

FST: 0.06342 p value: 0.00), PhiPT and Jost’s Dest tests, with the presence and absence of

genetic differentiation among the pairwise population comparisons (Table 3-3) among

places. The further PCA showed differences among four spatial groups: 1). CAP, SAN,

ALB, PB; 2). CV, SER, PBL, PLY, NEG, 3). BLZ, VEN and 4). PR (Figure 3-2B). The Mantel

test showed no correlation between the genetic and geographic distances (R2 = 0.000023;

P = 0.004). This means that the distance does not have any influence on the genetic

structuration found in this study.

A.

∆K: 3

B.

Figure 3-2: Population structure of Caribbean Sea localities suggested by STRUCTURE. A: Bar plot of population ancestry coefficients estimated by STRUCTURE is provided for ∆K = 3 and Q-matrixes were consensus estimates produced by CLUMPP across 20 iterations of STRUCTURE. B: Principal Component Analysis. CAP: Capurganá, SAN: San Andrés, ALB: Alburquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas, VEN: Venezuela, BLZ: Belize, PR: Puerto Rico.

Table 3-3: Pairwaise PhiPT (upper diagonal) and Jost’s Dest (below diagonal) values among samples of E. lucunter lucunter from the Caribbean Sea. Sampling sites: CAP: Capurganá, SAN: San Andrés, ALB: Albuquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas, VEN: Venezuela, BLZ: Belize, PR: Puerto Rico. To PhiPT values: Value: Big difference (0.15-1.25); Value: Moderate difference (0.05-0.14); Value: Low difference (<0.05) (Wright, 1978). p: significant value. Red values: Non-significant pairwise values, per Bonferroni correction p < 0.004.

CAP p SAN p ALB p PB p CV p SER p NEG p PBL p PLY P VEN p BLZ p PR p

CAP 0.000 - 0.038 0.001 0.092 0.001 0.039 0.001 0.074 0.001 0.072 0.001 0.061 0.001 0.063 0.001 0.030 0.006 0.080 0.001 0.083 0.001 0.130 0.001

SAN 0.089 0.001 0.000 - 0.072 0.003 0.040 0.005 0.087 0.001 0.076 0.001 0.064 0.001 0.087 0.001 0.059 0.001 0.074 0.001 0.061 0.001 0.141 0.001

ALB 0.101 0.001 0.092 0.001 0.000 - 0.023 0.074 0.129 0.001 0.131 0.001 0.050 0.005 0.114 0.001 0.081 0.001 0.063 0.001 0.040 0.007 0.113 0.001

PB 0.037 0.076 0.045 0.060 0.022 0.187 0.000 - 0.076 0.001 0.084 0.001 0.042 0.004 0.072 0.001 0.033 0.006 0.067 0.001 0.035 0.008 0.110 0.001

CV 0.143 0.001 0.233 0.001 0.304 0.001 0.214 0.001 0.000 - 0.000 0.466 0.030 0.012 0.000 0.445 0.017 0.037 0.165 0.001 0.138 0.001 0.107 0.001

SER 0.141 0.001 0.154 0.001 0.279 0.001 0.213 0.001 0.053 0.027 0.000 - 0.022 0.033 0.000 0.427 0.024 0.017 0.177 0.001 0.146 0.001 0.121 0.001

NEG 0.058 0.026 0.145 0.001 0.091 0.002 0.072 0.014 0.073 0.020 0.069 0.036 0.000 - 0.010 0.183 0.005 0.280 0.102 0.001 0.069 0.001 0.052 0.001

PBL 0.131 0.004 0.220 0.001 0.243 0.001 0.173 0.001 0.018 0.283 0.048 0.087 0.019 0.281 0.000 - 0.019 0.064 0.140 0.001 0.113 0.001 0.073 0.001

PLY 0.084 0.001 0.175 0.001 0.186 0.001 0.150 0.001 0.084 0.003 0.125 0.001 0.021 0.214 0.062 0.028 0.000 - 0.113 0.001 0.098 0.001 0.054 0.001

VEN 0.290 0.001 0.291 0.001 0.246 0.001 0.226 0.001 0.464 0.001 0.480 0.001 0.274 0.001 0.263 0.001 0.323 0.001 0.000 - 0.009 0.160 0.130 0.001

BLZ 0.195 0.001 0.177 0.001 0.121 0.001 0.099 0.001 0.356 0.001 0.353 0.001 0.159 0.001 0.220 0.001 0.251 0.001 0.052 0.001 0.000 - 0.103 0.001

PR 0.211 0.001 0.280 0.001 0.209 0.001 0.205 0.001 0.267 0.001 0.290 0.001 0.105 0.001 0.234 0.001 0.169 0.001 0.343 0.001 0.236 0.001 0.000 -

Colombian Caribbean Sea Group

To understand better the results of Colombian Caribbean Sea, it was taken the Colombian

sites in one separate group. The Bayesian analysis showed the presence of two genetic

groups (∆K=2) that were detected in the general analysis made before: one group with

CAP-SAN-ALB-PB and a second group with CV-SER-NEG-PBL-PLY (Figure 3-3). With

AMOVA, the variation observed within the groups was explained by the significant

differences among the sites of each group (Fixation Index FST: 0.02750 p value: 0.00),

PhiPT and Jost’s Dest tests with the presence and absence of genetic differentiation among

the pairwise population comparisons among places (Table 3-4), confirmed by the

discriminant PCA analysis (Figure 3-3B).

Table 3-4: Pairwaise PhiPT (upper diagonal) and Jost’s Dest (below diagonal) values among samples of E. lucunter lucunter from the Colombian Caribbean Sea. Sampling sites: CAP: Capurganá, SAN: San Andrés, ALB: Albuquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas. To PhiPT values: Value: Big difference (0.15-1.25); Value: Moderate difference (0.05-0.14); Value: Low difference (<0.05) (Wright, 1978). p: significant value. Red values: Non-significant pairwise values, per Bonferroni correction p < 0.004.

CAP p SAN p ALB p PB p CV p SER p NEG p PBL p PLY p

CAP 0.000 - 0.038 0.002 0.092 0.001 0.039 0.003 0.074 0.001 0.072 0.001 0.061 0.001 0.063 0.001 0.030 0.005

SAN 0.089 0.001 0.000 - 0.072 0.001 0.040 0.006 0.087 0.001 0.076 0.001 0.064 0.001 0.087 0.001 0.059 0.001

ALB 0.101 0.003 0.092 0.003 0.000 - 0.023 0.091 0.129 0.001 0.131 0.001 0.050 0.003 0.114 0.001 0.081 0.001

PB 0.037 0.086 0.045 0.072 0.022 0.202 0.000 - 0.076 0.001 0.084 0.001 0.042 0.005 0.072 0.001 0.033 0.012

CV 0.143 0.001 0.233 0.001 0.304 0.001 0.214 0.001 0.000 - 0.000 0.447 0.030 0.014 0.000 0.461 0.017 0.054

SER 0.141 0.001 0.154 0.001 0.279 0.001 0.213 0.001 0.053 0.028 0.000 - 0.022 0.048 0.000 0.433 0.024 0.023

NEG 0.058 0.035 0.145 0.001 0.091 0.003 0.072 0.017 0.073 0.012 0.069 0.029 0.000 - 0.010 0.183 0.005 0.311

PBL 0.131 0.002 0.220 0.001 0.243 0.001 0.173 0.001 0.018 0.292 0.048 0.090 0.019 0.282 0.000 - 0.019 0.065

PLY 0.084 0.005 0.175 0.001 0.186 0.001 0.150 0.002 0.084 0.005 0.125 0.001 0.021 0.214 0.062 0.040 0.000 -



A.

∆K: 2

B.

Figure 3-3: Population structure of Colombian Caribbean Sea localities suggested by STRUCTURE. A: Bar plot of population ancestry coefficients estimated by STRUCTURE is provided for ∆K = 2 and Q-matrixes were consensus estimates produced by CLUMPP across 20 iterations of STRUCTURE. B: Discriminant Analysis of Principal Components. CAP: Capurganá, SAN: San Andrés, ALB: Alburquerque, PB: Punta Betín, CV: Cabo de la Vela, SER: Serrana, NEG: Nenguanje, PBL: Playa Blanca, PLY: Playetas.



45

3.4 DISCUSSION

3.4.1 Genetic diversity

The results show a high degree of genetic diversity in the observed populations of E.

lucunter lucunter. However, there was a strong deficit of heterozygotes and high/positive

inbreeding coefficient (FIS) values in all populations, with significant departure from HWE.

These results are similar to other studies previously developed on sea urchins in the

Mediterranean Sea and Eastern Atlantic Ocean (Calderón and Turon 2010, Couvray and

Coupé 2018, Pérez-Portela et al., 2018), where the authors explain the strong deficit of

heterozygotes under several arguments. In this study, there are several reasons that

potentially could explain the strong deficit of heterozygotes and high/positive FIS values: 1)

the assortative matting linked to different gamete recognition proteins (Pérez-Portela et al.,

2018), 2) the presence of spatial breeding groups linked to stochasticity in reproductive

success, 3) patchiness in gamete distribution and 4) the collective dispersal of genetically

related larvae in the plankton (Broquet et al., 2013; Couvray and Copé, 2018) and 5)

hydrological and biological factors that may limit or facilitate the dispersion of larvae

(Calderón et al., 2008).

Examples of gamete recognition in sea urchins linked to proteins have been reported by

diverse authors (Metz et al., 1998; Zigler and Lessios, 2003; McCartney and Lessios, 2004;

Zigler et al., 2005; Lessios et al., 2012), the sperm protein, “Bindin” is implicated in the

fertilization of sea urchins eggs as regular and irregular forms (Zigler and Lessios, 2003).

Calderón and Turon (2010) showed that assortative mating, linked to selected positions in

the bindin gene of Paracentrotus lividus, could explain inter-cohort differentiation observed.

In a similar way, Pérez-Portela et al. (2018) explained the deficit of heterozygotes obtained

in their research as a consequence of biological behaviour of Arbacia lixula that favours

gamete recognition and assortative mating. Thus, similar processes to the ones described

above could be happening with E. lucunter lucunter in the Caribbean Sea.



The performance of the “Bindin” protein has been amply studied in the genus Echinometra.

McCartney and Lessios (2004), found that the eggs of E. lucunter have developed through

evolution a strong fertilization blocking by sperm of its neotropical congeners, E. viridis and

E. vanbrunti. Their results show that positive selection is concentred along the E. lucunter

bindin linage. Although it has not yet been demonstrated that this behaviour could be taking

place at intra-specific levels within E. lucunter, the results of this study suggest it may be

possible that this situation may be influencing the high heterozygosity deficit and the

positive/significant FIS values found. Indo-West Pacific (IWP) species of Echinometra (Metz

and Palumbi, 1996) and species of Strongylocentrotus from temperate seas (Biermann,

1998) also show high Bindin sequence divergence within and between species. Thus, the

results of this study also suggest that E. lucunter lucunter is favouring positive selection

and particular genetic pools within their populations.

3.4.2 Population differentiation and structure

The sites chosen for this study show a clear evidence of genetic structure and gene flow

between populations of E. lucunter lucunter through the Caribbean Sea. The genetic

diversity pattern observed in this study contrasts with the results of McCartney et al. (2000).

The authors, who used COI as genetic marker, found that E. lucunter was behaving as only

one genetic population within the Caribbean. However, the COI does not allow and easy

identification of structure within a particular species, which explains why they found only

one group for the species. In the results of this study, the use of microsatellites markers

allowed to detect more clearly the difference between populations of the same species and

gene flow within each group. On other hand, it has been demonstrated by other studies in

the Caribbean sea (Avise, 1992; Schultz and Cowen, 1994; Carlin et al., 2003; Taylor and

Hellberg, 2003, 2006; Paris and Cowen, 2004; Baums et al., 2005; Cowen et al., 2006;

Ospina‐Guerrero et al., 2008; Landínez-García et al., 2009; Betancur-R et al., 2011; Díaz-

Ferguson et al., 2011; Márquez‐Fernández et al., 2013; Truelove et al., 2015; Chaves-

Fonnegra et al., 2015), that connectivity is not influenced only by larval transport via the



47

main Caribbean current, as suggested by McCartney et al. (2000). Apparently, aspects

related to the biological characteristics of E. lucunter lucunter and the influence of physical

factors in the area such as marine currents (Lozano-Duque et al., 2010; Gyory et al., 2013),

geomorphological breaks (Betancur-R et al., 2010; Rangel-Buitrago and Idárraga-García,

2010) and their variation through the area, are affecting the genetic structure of this species.

The strong connexion between BLZ and VEN (Table 3-3, Figure 3-2, Figure 3-4), the two

more distant points in this study, is not surprising. McCartney et al. (2000) already

demonstrated the ample range of distribution of the genus Echinometra through the Indo

West Pacific and Atlantic Sea. Based on molecular analysis with COI gen, the authors

presented an overall picture of the three neotropical Echinometra species. They

demonstrated the existence of gen flow between Eastern and Western Atlantic for E.

lucunter polypora. In similar way, they identified the presence of one population of E.

lucunter lucunter through the Caribbean Sea. One explanation that could be attributed to

the gen flow between VEN and BLZ is the larval transport facilitated to the strongest (70

cm s-1) westward CCC found across the southern third of the Caribbean Sea (Gordon 1967;

Kinder 1983) (Figure 3-4). The current is originated across the barrier produced by the

Antilles and the portion of the current influencing the flow comes from the southern part and

moves through the centre of the Caribbean towards Belize, in the northwest of the

Caribbean. The connexion between BLZ and VEN also suggest that the Venezuela Basin

and Cayman Trough (Miloslavitch et al, 2010) are connected through the CCC, at least for

semi-sessile species with planktonic larvae like E. lucunter lucunter.

On the other hand, the clear difference between PR (Table 3-3, Figure 3-2, Figure 3-4) and

the rest of places chosen in this study is reflecting the existence of a break between

Western and Eastern regions of the Caribbean Sea, influenced by complex hydrodynamic

currents present in the area (Cowen et al., 2006); previous studies suggest that this locality

is part of the Lesser Antilles or Grenada Basin (Miloslavitch et al. 2010). In this case,

although is the same CCC, the origin of their influence is very different, and here,

apparently, it is acting as a barrier with the other localities. The overall speed of the water



traveling north-east from Aves Ridge to Florida has been calculated to be slower, about 30-

40 cm s-1 (Gyory et al., 2013) (Figure 3-4). Besides, AC may mediate larval influx through

the Antilles Islands Arc, involving Greater and Lesser Antilles (Díaz-Ferguson et al., 2010).

Previous studies reported similarities through the Eastern Caribbean Sea promoted by the

northward flow of the AC (Galindo et al., 2006). Genetic similarities between the same areas

have also been observed for the goby Elacatinus evelynae and the rocky shore trochid

gastropod, Cittarium pica (Taylor and Hellberg, 2003; Díaz-Ferguson et al., 2010).

The other localities would correspond to the Colombian Caribbean Sea Group. The analysis

shows that there is a heterogeneous spatial mosaic of marine dispersion through the

Colombian Caribbean Sea (Table 3-3, Figure 3-2, Figure 3-4) confirmed by the specific

results carried out to Colombian Caribbean Sea with the same results (Table 3-4, Figure 3-

3). Apparently, the results are reflected by the biological behaviour of E. lucunter lucunter

in early stages, geomorphological breaks and physical characteristics of the area

(Betancur-R et al. 2010; Rangel-Buitrago and Idárraga-García, 2010; McClanahan and

Muthiga, 2013). These differences are evident in the PCA (Figure 3-2B; Figure 3-3B), where

the south-west of the Colombian Caribbean (CAP, SAN, ALB, PB) tends to create a

different group from the North-East part (CV, SER, PBL, PLY, NEG), but the differences

are not strong enough to define clear groups with the other analyses. In this case, the speed

and direction of the CCC westward, PCG and PCE (Corredor, 1979; Pujos et al., 1986;

Blanco 1988; Álvarez-León et al., 1995; Andrade et al., 2003; Lozano-Duque et al., 2010)

jointly to the early life history of the species, have had implications for the genetic patterns

among populations of taxa previously studied in this area (Ospina‐Guerrero et al., 2008;

Landínez-García et al., 2009; Márquez‐Fernández et al., 2013; Narváez et al., 2015a,

Narváez et al., 2015b; Almanza-Bernal et al., 2016).

• Group 1: CAP, SAN, ALB, PB:

The genetic divergence observed in this group could be attributed to differences in

geomorphological aspects through the continental margin of the coast join to the CCC



49

westward and PCG (Andrade and Burton, 2000; Andrade, 2012; Gyory et al., 2013) that

favour rather than limit gene flow. Under this principle, the Caribbean Sea has in common

that is a closed sea. The individuals of Echinometra lucunter lucunter live on the intertidal

zone, where the presence of calcareous rock is abundant. Here, the specie inhabits on

holes that they burrow for their own protection. The Caribbean Colombian rocky intertidal

is very reduced in contrast to others Caribbean areas (Díaz, 1991), consequently, the

distribution of Echinometra lucunter lucunter is patchy. Monroy-López and Solano (2005)

determined the condition of E. lucunter lucunter population in the Colombian Caribbean

Sea, finding differences in their abundances due to riverine discharges. Under their study

Zapsurro and Inca-Inca presented the highest density values with no influence of riverine

discharges, while Acandí and Punta Betín (PB), influenced by the Atrato and Manzanares

rivers respectively, presented the lowest values. The gene flow between PB and the other

localities observed in this study could be facilitated by the CCC, the PCG and by the local

riverine influence, shown in the study of Monroy-López and Lozano (2005) (Figure 3-4).

This Colombian Caribbean scenario was similar to the one presented by Benavides et al.

(2013) for the sea urchin Eucidaris tribuloides. Here the authors found not influence of the

Magdalena river mouth on E. tribuloides populations, with gene flow between Tayrona and

the Darien region (Benavides et al., 2013). In this study, the PCA and the STRUCTURE

show similar results (Figure 3-2; Figure 3-3) but is evidencing, in contrast to PhiPT and

Jost’s values the possible gene flow between CAP, SAN and ALB. This congruence among

distribution patterns suggests the influence of the PCG (Andrade-Amaya and Burton, 2000;

Andrade, 2012; Gyory et al., 2013), as has been described to other marine taxa through

the Colombian Caribbean Sea (Landínez-García et al., 2009; Marquez et al., 2013).



Figure 3-4: Study area with sampled locations marked as red stars. PR: Puerto Rico, VEN: Venezuela, CV: Cabo de la Vela, NEG: Nenguanje, PB: Punta Betín, PBL: Playa Blanca, PLY: Playetas, CAP: Capurganá, ALB: Alburquerque, SAN: San Andrés, SER: Serrana, BLZ: Belize. White lines represent the Caribbean Central Current (CCC), blue lines Panama-Colombia Gyre (PCG), pink lines Panama-Colombia eastward current (PCE) and green lines: Antilles Current (AC). Yellow dashed lines represent the division of the three ∆K: 3. Speed of some currents is included (Adapted from Andrade, 2012; Gyory et al., 2013).

• Group 2: CV, SER, PBL, PLY, NEG:

This group is more evident in the PCA, although the analysis suggests the separation

between two subgroups, CV-SER and PLY-PBL-NEG, condition that is more evident in

STRUCTURE and PhiPT and Jost’s Dest values (Table 3-4, Figure 3-3). The pattern found

in this group, can also be attributed to the biological behaviour of Echinometra lucunter

lucunter in the area, which has been described to other sea urchins in the Mediterranean

and Atlantic sea (Pérez-Portela et al., 2018; Calderon and Turon, 2010). Their behaviour

involves assortative mating, facilitation of gamete recognition proteins, as well the presence

of spatial breeding groups, linked to stochasticity in reproductive success, patchiness in

gamete distribution and the collective dispersal of genetically related larvae in the plankton

(Broquet et al., 2013; Couvray and Coupé, 2018). These processes are facilitated by the

oceanographic characteristics of the area and the littoral shore in patchiness form that are



51

present in the Colombian Caribbean Sea (Monroy-López and Solano, 2005; Andrade,

2012). The connexion PLY-PBL-NEG observed in this study was previously found by

Benavides et al. (2013) for Eucidaris tribuloides demonstrating the gene flow between the

Tayrona and Darien regions.

The interruption of gene flow that has been perceived between CV, SAN, ALB and PB may

be explained through geomorphologic and oceanographic features of the area. The

principal geomorphological break present between CV and PB may be by the presence of

the geological progression of the Santa Marta Massif (SMM) described by Betancur-R et al

(2010). This progression is limited on the western side by Oca and the Santa Marta faults

along the northwest (Rangel-Buitrago and Idárraga-García, 2010). Between the

oceanographic reasons could be cited the different influence of trade winds on each locality

and the strong influence of the CCC in the centre of the Caribbean Sea, previously

mentioned by several authors (Miloslavich et al., 2010; Díaz-Ferguson et al., 2011; Cowen

et al., 2006 between others). According to Lozano-Duque et al (2010), trade winds create

a seasonal current behaviour, determining upwelling areas of superficial cold waters in front

of Guajira and Santa Marta mountain formations (Sierra Nevada de Santa Marta) with

strong influence of coast direction. The superficial current is not stable, and it has gyres

with cyclonic and anti-cyclonic forms with several sizes (Andrade and Barton, 2000).

Several studies have shown gene restriction associated to a break between TAY and GUA,

registered specially to fish (Betancur-R et al., 2010; Almanza-Bernal et al., 2016; Narváez

et al., 2015a; Narváez et al., 2015b). In addition to the SMM effect, the interruption between

populations of TAY and GUA has been attributed to the combined effects of the narrow

coastal shelf and cold up-welling waters influencing the region (Lozano-Duque et al., 2010).

NEG, PB, PBL and PLY were selected for their presence close to or inside Marine Protected

Areas (MPAs). Two sites (NEG and PB) were in the boundaries of the Parque Nacional

Natural Tayrona (PNNT), the other two (PBL and PLY) were related to Parque Nacional

Natural Los Corales del Rosario y San Bernardo (PNNCRSB). The AMOVA showed no

variation among populations (Fixation Index FST: 0.01444 p value: 0.77810), which is



demonstrated in similar way by PhiPT and Jost.s Dest tests (Table 3-3, Table 3-4). According

with these results, the MPA has not influence on the connexion or genetic structure of

Echinometra lucunter lucunter. To detect some kind of pression to the population (group) it

would be necessary to increase the size of the park or to develop regional parks.

The Seaflower Biosphere Reserve, Oceanic area

The genetic differences between oceanic localities SER with SAN and ALB may result from

the patterns of distribution of the CCC westward, the superficial currents non-uniformly

distributed among the atolls (Richardson, 2005) and the influence of the Cayman through

(Miloslavitch et al 2010) at the north of the reserve. The Seaflower Biosphere Reserve,

shows a complex and different oceanography from the other areas, due to the interaction

with the underwater topography, the different geoforms composed by carbonated platforms,

mountains, underwater valleys, guyots and abyssal plains between others (INVEMAR-

ANH, 2012; Ricaurte-Villota et al., 2017). The ocean circulation depends mainly on the

limitation offered by the Chibcha pass to the waters of the CCC that flow to Cayman Sea

(Andrade et al, 1997; Andrade, 2009). They also depend on the bathymetric configuration

of the region that forces the recirculation of the waters that come from the East, and in the

area they rotate every once more to the South, until it reaches the Gulf of Mosquitos where

an almost permanent cyclone resides (Andrade, 2001). The Caribbean Current is divided

just between the islands and cays in such a way that a part (~ 60%) continues its journey

to the Cayman Sea and the other recirculates towards the southwestern Caribbean forming

the PCG (Andrade, 2012). The clear connexion between CV-SER could be facilitated by

the pass of the CCC towards the Cayman Sea, that occurs mainly through the straits that

exist between the Central American Elevation and the Pedro bank (Andrade, 2012). The

clear connexion between CV-SER could be facilitated by the pass of the CCC towards the

Cayman Sea, that occurs mainly through the straits that exist between the Central American

Elevation and the Pedro bank (Andrade, 2012).



53

3.4.3 Influence of marine discontinuities different to Currents present in the Caribbean Sea

Evidence of connexions between populations of the most extreme localities VEN-BLZ and

between Colombian Caribbean Sea populations (Table 3-3, Figure 3-2, Figure 3-4)

demonstrate the high capacity of E. lucunter lucunter larvae movement through high

distances (McCartney et al., 2000). Despite factors such as changes in temperature,

salinity, carbon dioxide and food availability that could affect the rate and fate of larval

development (McClanahan and Muthiga, 2013), there is evidence in these results of the

gene flow between populations.

According to Sewell and Young (1999), the geographical distribution of E. lucunter lucunter

through the Caribbean Sea does not appear to be limited by temperatures at which normal

developments occurs in the area: 18°C, North Carolina in winter to summer monthly

maximum of 27.5°C and 25.5°C reported in Florianopolis (Brazil). Perricone and Collin

(2019) used warming tolerance and thermal safety margins on larval stages of seven

species of tropical sea urchins common to Caribbean. The authors identify that

Echinometra genus has significantly higher thermal tolerance than the other sea urchins.

In addition, based on the six physicochemical providences proposed by Chollett et al.

(2012) and the genetic structure found in the present study (Table 3-5, Figure 3-5), E.

lucunter lucunter larvae share several providences without boundaries to their connectivity.



Table 3-5: Localities chosen in this study on three physicochemical providences proposed by Chollett et al. (2012) with the description of each one. PR: Puerto Rico, VEN: Venezuela, CV: Cabo de la Vela, NEG: Nenguanje, PB: Punta Betín, PBL: Playa Blanca, PLY: Playetas, CAP: Capurganá, ALB: Alburquerque, SAN: San Andrés, SER: Serrana, BLZ: Belize.

Main oceanographic features Clusters Localities chosen in this study

Description

Southern Caribbean upwelling 11 VEN and CV

Wind-driven upwelling occurs along eastern Colombia and most of the Venezuelan coastline, although the Guajira (Andrade and Barton, 2000) and the south-eastern Venezuela (Müller-Karger et al, 2004) are the best-known upwelling areas in the region

Inner Caribbean

1 NEG currents in a clockwise flow (Hofmann and Worley1986)

2 ALB, SAN, SER

3 BLZ and PLY

6 PBL and PB

10 PR

Low Salinity, Panama–Costa Rica runoff region

9 CAP

Under the influence of river discharge and runoff (Imbach et al, 2010) driven by strong rainfall in the area (Portig 1965)

Figure 3-5: Localities chosen for this study on the spatial arrangement proposed for Chollett et al. (2012) to the Caribbean Sea. The 16 clusters were formed with sea surface temperature, water clarity and salinity data of the Caribbean Sea. Map modified from Chollett et al (2012). Sampled locations marked as red stars. PR: Puerto Rico, VEN: Venezuela, CV: Cabo de la Vela, NEG: Nenguanje, PB: Punta Betín, PBL: Playa Blanca, PLY:

Playetas, CAP: Capurganá, ALB: Alburquerque, SAN: San Andrés, SER: Serrana, BLZ: Belize.



55

The present analysis supports that the principal marine discontinuity that is influencing the

gene flow and populations genetic structure of E. lucunter lucunter in the Caribbean Sea is

the presence of marine currents, mainly the CCC, AC, PCG and PCE in combination with

the biological behaviour of the species. These results are in contradiction of the physical-

chemical providences proposed by Chollett et al. (2012), because there is no evidence in

the present study suggesting that this kind of factors are influencing the genetic behaviour

of the species. One clear example of this is the presence of Southern Caribbean upwelling

on VEN and CV locations, which are part of different groups.

3.4.4 Marine connectivity scenarios in the Caribbean Sea

The results from this study demonstrate the high grade of geographical segregation and

how heterogeneous the Caribbean Sea can be to the gene flow between marine

populations. The gene flow in the Caribbean Sea reflects a combination of several results

that have been obtained with different taxa and marine connectivity models proposed by

several authors (Baums et al., 2005; Cowen et al., 2006; Ospina-Guerrero et al., 2008;

Landínez-García et al., 2009; Díaz-Ferguson et al, 2010; 2011; Betancur-R et al., 2011;

Chaves-Fonnegra et al., 2015; Truelove et al., 2016; Almanza-Bernal et al., 2016; Márquez-

Fernández et al., 2013 between others).

In contrast to other studies on echinoderms previously done in the Caribbean Sea, where

a homogenous genetic pattern through the study area was demonstrated (McCartney et

al., 2000; Chandler et al., 2017; Jossart et al., 2017), it is difficult to establish a stable

connectivity scenario for the sea urchin E. lucunter lucunter in the region. The contrasting

scenarios show that connectivity or genetic interruption depend on a mix of biological

features for each particular species such as pelagic larval phase-adult development, habitat

and feeding preference, as well as physical factors such as continental hydric discharges,

current systems and physicochemical treats (Ospina-Guerrero et al., 2008; Landínez-

García et al., 2009).



After integrating the Caribbean marine currents into an oceanographic model, four

connectivity regions have been proposed (Cowen et al, 2006): Eastern Caribbean, Western

Caribbean, Bahamas and Panama-Colombia. This regional pattern leads to the prediction

of high dispersal potential of marine larvae within each region, but limited exchange across

regions (Cowen et al., 2006). More recently, Kool et al (2010) refined these connectivity

regions, sometimes in weak agreement with geographic distances. The three new regions

defined by this second model are the Lesser Antilles, Bahamas-Northern Cuba and

Panama-Nicaragua. All these models were built based on data taken from coast localities.

The three groups obtained for E. lucunter lucunter (∆K: 3) (Figure 3-2) show in general, a

biogeographical separation between Western and Eastern Caribbean, evidenced in the

structuring of PR as a separate group, the connexion between VEN and BLZ, confirming

the gene flow through Western Caribbean for the presence of CCC and a third group with

the typical Colombian Caribbean Sea, where coastal and oceanic features are integrated.

The connexion favoured by PCG (Andrade, 2001; Salas et al., 2010; Andrade, 2012)

between the limit gene flow of the southwest Caribbean Sea area is confirmed in our study,

as is showed with other models taxa (Landínez-García et al., 2009; Marquez et al., 2013;

Chavez-Fonnegra et al., 2015).

4. Conclusions and Recommendations

4.1 Conclusions

This study demonstrates the high level of adaptation of Echinometra lucunter lucunter early

life stage, evidenced in the heterogeneous spatial mosaics of marine dispersion through

the Colombian Caribbean Sea vs the homogeneous scenario shown in the genetic

connexion between BLZ and VEN and the population in PR. In the first (Colombia) and third

case (PR), it is clearly demonstrated that the life strategy of E. lucunter lucunter is adapted

to marine currents and the presence of suitable habitat through their pelagic larval duration.

The second group shows that if the habitat is not available, this specie has the capacity to

remain in the water through long distances, despite different marine boundaries, such as

river discharges, upwelling, or changes in temperatures and salinity.

There is a clear influence of marine discontinuities previously detected for other marine

taxa in the connectivity and the genetic populations structure of Echinometra lucunter

lucunter through the Caribbean Sea. The discontinuities are specially attributed to marine

currents and their different manifestation in the area (e.g. variation in speed, direction and

forms of gyres, swirls with cyclonic and anti-cyclonic movements), the biological

characteristics of the species, the geomorphological breaks and their influence in the

region.



This research postulates a new marine connectivity scenario for a sessile marine

invertebrate throughout the Caribbean Sea. It evidences a biogeographical separation

between Western and Eastern Caribbean, demonstrated in the structuration of the

Echinometra lucunter lucunter PR population and in the connexion between VEN and BLZ,

confirming the gene flow through the Western Caribbean. The evident third group

conformed by the Colombian Caribbean Sea, where coastal and oceanic features of the

area are integrated, it is clearly influenced by the westward CCC, the PCG and the PCE.

The results of this study also prove the significant influence of local Colombian currents on

the genetic connexion or break between populations through the Colombian Caribbean

Sea. Besides, it was possible to prove the connexion favoured by the oceanographic gyre

Panama-Colombia and the Panama-Colombia eastward counter-current between the limit

gene flow of the southwest Caribbean Sea area, as is showed with other models taxa.

Another important result was the development of the first 26 microsatellites loci for

Echinometra lucunter useful for studies of connectivity and genetic populations of sea

urchins through the Caribbean Sea and or other marine area. From those, 10 markers were

used to confirm the influence of marine barriers to gen flow among populations of E.

lucunter lucunter throughout the Caribbean Sea.

This PhD thesis has important implications for the application of conservation and

management strategies of Marine Protected Areas, as it can generate important inputs for

researchers and decision makers. The features of the behaviour of Echinometra lucunter

lucunter demonstrated through this study have suggested to amply the borders of the MPAs

taken account here. The results need to be interpreted with care for the low number of

individuals in the locations chosen (NEG, PB, PLB and PLY).

Conclusions and Recommendations 59

4.2 Recommendations

It is necessary to continue the study of marine connectivity and population genetics through

the Caribbean Sea, especially focused in ecosystems that are scarce, such as the rocky

shore ecosystem. Even though rocky shore invertebrates have been used as model

systems to understand genetic structure in some marine environments, the knowledge of

connectivity in Caribbean communities is based overwhelmingly on studies of tropical fishes

and corals.

Although all the 26 microsatellites loci developed to Echinometra lucunter are potentially

useful for marine connectivity and population genetics research, they need to be tested to

confirm their effectiveness. It is suggested to use them in future studies with its congeners

present in the Caribbean and Pacific Sea.

An important advice is to strength the new marine connectivity scenario proposed in this

research, using more Caribbean localities and other sessile invertebrates as a model, to

detect in more detail the performance of marine discontinuities on the connectivity and

genetic structuration of marine taxa through the study area.

Biological and ecological studies of E. lucunter lucunter are not enough yet. It is important

to include their broad marine distribution to continue the knowledge of the connexion

between their populations. It is recommended to continue the studies on this species to

determine how the larval phase duration in different localities of the Caribbean Sea join to

the seasonal reproductive behaviour.

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