Species-Level Spatial and Temporal Dynamics of Coral Reef Fish Larvae in the Gulf of Aqaba

Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY”

Naama Kimmerling Berenshtein

Submitted to the Senate of Ben-Gurion University of the Negev

December 2016 Beer-Sheva, Israel

Species-Level Spatial and Temporal Dynamics of Coral Reef Fish Larvae in the Gulf of Aqaba

Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY”

Naama Kimmerling Berenshtein

Submitted to the Senate of Ben-Gurion University of the Negev

Approved by the advisors:

Dr. Moshe Kiflawi Date: May 21st 2018

Prof. Claire Paris Date: May 21st 2018

Approved by the Dean of the Kreitman School of Advanced Graduate Studies

December 2016 Beer-Sheva, Israel

This work was carried out under the supervision of

Dr. Moshe Kiflawi

The Department of Life Sciences Faculty of Natural Sciences Ben-Gurion University of the Negev

and

Prof. Claire Paris

The Department of Ocean Sciences The Rosenstiel School of Marine and Atmospheric Science University of Miami

Research-Student's Affidavit when Submitting the Doctoral Thesis for Judgment

I, Naama Kimmerling, whose signature appears below, hereby declare that (please mark the appropriate statements):

√ I have written this Thesis by myself, except for the help and guidance offered by my Thesis Advisors.

√ The scientific materials included in this Thesis are products of my own research, culled from the period during which I was a research student.

√ This Thesis incorporates research materials produced in cooperation with others, excluding the technical help commonly received during experimental work. Therefore, I am attaching another affidavit stating the contributions made by myself and the other participants in this research, which has been approved by them and submitted with their approval.

Date: _Dec, 4th 2016___ Student's name: Naama Kimmerling

Signature:______

Acknowledgments

This PhD was an amazing learning experience for me. First I thank my supervisor, Dr. Moshe Kiflawi, for his tremendous academic support and guidance, and for giving me this wonderful opportunity. Moshe with his endless patience and insightful discussions taught me to think critically, drastically raised my standard for good science and at the same time taught me not to “wave my hands” even when it is tempting to do so. Thank you for always being available when I needed advice and for always being so thoughtful and kind.

My profound gratitude goes to Prof. Claire Paris who revealed to me the astonishing world of larval fish taxonomy (by far my favorite part of the research) and provided insight from her extraordinary experience in the field.

I thank Nalani Schnell, for sharing her taxonomic expertise so diligently and for offering friendly advice about the challenging balance between motherhood and research.

I am also hugely appreciative of Ben-Gurion University and especially Prof. Nadav Shashar, for offering the much-needed opportunity to write my thesis in the beautiful and quiet dolphin reef lab, and in general for always “being on our side” academically and financially. I would like to thank the IUI staff and students for making the IUI a great place for work and research. Special thanks are due to the SAM RV staff for going far beyond the call of duty during those long days of sampling. Warm thanks go to Malka who made the IUI a pleasant and homely place by her warm morning hugs and by pampering me with her delicious homemade sweets when I most needed them. I thank my parents, Diana and Baruch, who raised me to seek after my interests, and were the best role models always showing me that anything is possible with their determination and bravery. My brother,

Eli, was the first to light up my interest in living creatures during our excursions into the wild as kids, and later encouraged me to study biology. To my sister, Shira, I am grateful for always reminding me what the important things in life are and for giving me much inspiration for life after academia.

Special thanks go to my dear spouse, Igal, a true partner who lovingly stood by my side and contributed all the way from catching larvae to providing meaningful insights to my research, while raising a family together.

And of course a particularly sweet thanks goes to Tom and Orr, my own two larvae, who persistently tried to sabotage my work by consistently waking me up at night and by catching the flue at the worst possible timing. You two have filled my life with a special kind of love and with endless joy and laughter.

In memory of my loving, supportive, witty and brave dad, Baruch Kimmerling.

Abstract

The life cycle of coral reef fish is composed of an adult stage and a larval stage. While the adult fish are permanent dwellers of the reef, the larval-stage sets out to the open sea for a few days to a few weeks. At the end of their pelagic larval duration (PLD) they recruit to a reef and undergo metamorphosis. The larval stage of sedentary reef fishes constitutes the dispersal phase —a relatively short but important part of their life history. This early pelagic phase contributes to the demographic and ecological characteristics of the adult fish community. Thus far, community-level studies of coral reef fish larvae have mainly been conducted at the family level due to technical limitations. This work uses a combination of large-scale year-round sampling, morphological identification, single-organism barcoding, and next generation high- throughput metagenomics, to generate novel quantitative estimations of species-level spatial and temporal dynamics of 16, 695 coral reef fish larvae from more than 400 species in the Gulf of Aqaba. This is the first community- level ecological study of the larval pool at a species taxonomic resolution, shedding light on some of the lingering questions related to the dynamics of coral reef fish larvae in general and on those concerning the Gulf of Aqaba in particular. We find significant correlations between the abundance of adult coral reef fish and their larvae, suggesting that larval supply (i.e. the amount of larvae produced by adult populations and settling back to the reef) plays a major role in determining local adult densities. Unexpectedly, we discover larval influx of species never sighted in the region, suggesting settlement or recruitment barriers as major causes for the absence of the adult stage. We also find that species-specific spatio-temporal distributions of larvae are correlated with environmental parameters (mainly salinity, light intensity, chlorophyll concentration and temperature) such that even closely related species differ in their early life preference for specific bio-physical conditions. Lastly, we are able to map larval distributions throughout ontogeny of three Pseudanthias species. Interestingly, the vertical spread of

Pseudanthias squamipinnis decreases with ontogeny as they migrate closer the surface, an inverse vertical migration pattern to that observed in other localities. The combination of extensive stratified ichthyoplankton sampling, traditional larval taxonomy, and high-resolution molecular identification represents a significant advance in larval fish ecology and provides an unprecedented and powerful tool for studying the dynamics of coral reef fish communities.

Keywords: Coral reef fish larvae, GOA, larval pool, species level identification, species composition, non-native species, spatio-temporal distribution, environmental variables, ontogeny.

Table of Contents

1. Introduction...... 1 1.1. Larval Fish General Introduction...... 1 1.2. Current Fundamental Questions in the Field of Larval Fish Ecology ...... 2 1.3. The Challenges in Using Quantitative Metagenomics to Advance the Field of Coral Reef Fish Larvae………………………………………………………………………………………………………………4 1.4. Previous Works Conducted on the Local Larval Pool ...... 5 1.5. Larval Distributions and Influential Environmental Variables ...... 6 1.6. Larval Spatio-Temporal Distributions Throughout Ontogeny ...... 11

2. Research Goals and Objectives ...... 13

3. Materials and Methods ...... 14 3.1. Study Site 14 3.2. Ichtyoplankton Sampling 14 3.3. Sample Preservation, Handling and Sorting ...... 16 3.4. Illuminated and Silhouette Images of Larvae Samples ...... 16 3.5. Morphology-Based Taxonomic Assignment of Larvae Families ...... 17 3.6. Collection of Adult Fish and COI Barcoding ...... 22 3.7. List of Fish Known to Dwell in the Gulf of Aqaba and the Red Sea ...... 22 3.8. Compilation of COI Reference Barcode Database ...... 22 3.9. DNA Extraction, Library Preparation and Sequencing ...... 23 3.10. De-Novo Assembly and Classification of COIs ...... 23 3.11. Species Identification from Metagenomic COI Reads ...... 23 3.12. From Larval Area to Abundance ...... 24 3.13. Comparison between Larval Pool and Benthic Assemblage ...... 26 3.14. Spatio-Temporal Patterns in Larval Distribution ...... 27 3.15. Larval Distributions and Influencing Environmental variables ...... 28 3.16. Spatiotemporal Distributions Throughout Ontogeny ...... 32

4. Results ...... 36 4.1. Intensive Larval Sampling and Genomic Barcode Collection ...... 36 4.2. Unbiased Metagenomics of Larvae Samples ...... 36 4.3. Quantitative Metagenomics Reveals Species Abundance ...... 38 4.4. Larval Species Composition and Relative Abundance ...... 38

4.5. Comparison Between Larval Pool and Benthic Assemblage ...... 40 4.6. High Resolution Spatio-Temporal Larval Distribution ...... 43 4.7. Larval Distributions and Influential Environmental variables ...... 46 4.8. Spatiotemporal Distributions Throughout Ontogeny ...... 54

5. Discussion ...... 56 5.1. Comparison between Larval Pool and Benthic Assemblage ...... 56 5.2. High-Resolution Spatio-Temporal Larval Distribution ...... 58 5.3. Larval Distributions and Influential Environmental variables ...... 59 5.4. Spatiotemporal Distributions Throughout Ontogeny ...... 61

6. Conclusions and Significance ...... 66

7. Contribution and Support ...... 67

8. Published Work ...... 71

9. References ...... 72

Table of Figures

Figure 1. Temperature and Chlorophyll-a change in the Gulf of Aqaba 10 Figure 2. The Gulf of Aqaba study site 15 Figure 3: Sample 126 16 Figure 4. Size-based model for quantitative inference 26 Figure 5. Ranges of environmental variables and date 29 Figure 6. Northern tip polygon 32 Figure 7. Pseudanthias squamipinnis 33 Figure 8. Focal Pseudanthias species 34 Figure 9. Larval species' relative abundance distribution 39 Figure 10. Relationships between adults and their larvae 40 Figure 11. Discrepancy between relative abundance in larval vs. adult pools 41 Figure 12. Relationship between larval incidence and density 43 Figure 13. Spatio-temporal distribution 45 Figure 14. Tabasco plot - according to species dissimilarity 48 Figure 15. Tabasco plot - according to appearance 50 Figure 16. Environmental variables and date affecting Chromis pelloura denseties 51 Figure 17. Environmental variables affecting pooled Pomacentridae 52 Figure 18. Percentage of particles remaining in the Gulf 54 Figure 19. Spatio-temporal distributions of Pseudanthias 55 Figure 20. Salinity profiles 58

List of Tables

Table 1. Larval morphological features for identification…………………..….....…19 Table 2. List of non-native species and their life style……………..…………………...42 Table 3. ANOVA table of PERMANOVA analysis of environmental variable …46 Table 4. ANOVA table of PERMANOVA analysis of taxonomic affiliation…… .49 Table 5. ANOVA table of the GAM analysis on C. pelloura………...…………..…….52 Table 6. ANOVA table of the GAM analysis on Pomacentridae……..….…...... ….53 Table 7. Quantile regression analysis (annual) P. squamipinnis…….……………55 Table 8. Quantile regression analysis (fall) P. squamipinnis………….………...... 55 Appendix Table I………………………………………………………………….…………………..79

1. Introduction

1.1. Larval Fish General Introduction Coral reef fish have a complex life cycle that includes two main stages: the adult phase, in which the fish are permanent dwellers of the reef environment, and the larval period, which is pelagic. Typically, the fish progeny leave the reef environment into the pelagic one either as eggs or as newly hatched larvae depending on the species. The pelagic duration is species-specific as well, and may span from days to weeks, at the end of which the larvae undergo metamorphosis and recruit into a reef environment as juveniles (Leis 1991). In the past fish larvae were thought to passively drift, promoting the belief that the integration of the pelagic larval duration and the velocity of the currents encountered by the larvae throughout this period, alone, determine larval dispersal (Roberts 1996). However, in recent years studies have shown this is not the case. While the larvae hatch with limited behavioural capabilities, they gradually develop horizontal swimming and orientation abilities during the pelagic stage, enabling them to directly influence their dispersal trajectories to varying extent (Leis 2006). Furthermore, larvae were shown to control their vertical positioning from a very early stage by gas bladder adjustments and active swimming (Leis 2006, 2010; Huebert 2008), which may facilitate indirect control over trajectories (discussed in detail later on; Lagarde`re et al. 1999; Forward and Tankersley 2001; Paris and Cowen 2004). The pelagic larval stage constitutes the main dispersive phase of coral reef fishes (Leis 1991). The supply of larvae arriving from the open sea environment into the reef is vital for the persistence of local fish populations (Milicich and Doherty 1994). More specifically, the outcome of larval dispersion largely dictates temporal and spatial patterns in the size and stability of adult populations (Victor 1986), consequently influencing community composition and functionality. In spite of the fact that larval ecology is important for understanding adult coral reef fish population and community dynamics, the knowledge on the larval phase is greatly lacking (Leis and McCormick 2002). The scarcity of knowledge stems mainly from difficulties in larval fish identification (Leis 2015), since (1) the larvae are minute in size; (2) they exhibit a high degree of morphological similarity among species; (3) the larvae are dramatically different from their adult stage (as they are incompletely

developed and possess structures specialized for pelagic life that are later lost). For this reason morphology-based identification of individual larvae to a species-level resolution is highly challenging and frequently impossible (Ko et al. 2013; Leis 2015). Consequently, two types of ecological studies have been conducted on coral reef fish larvae to date. The first, research focusing on a few focal species (e.g. Paris and Cowen 2004; Leis et al. 2006). The second comprised studies exploring the larval pool (involving numerous species) at a relatively poor taxonomic resolution, in which individuals are typically assigned to the family to which they belong, rather than to actual species (e.g. Limouzy-Paris et al. 1994; Carassou et al. 2008; Irisson et al. 2009; Huebert et al. 2011).

1.2. Current Fundamental Questions in the Field of Larval Fish Ecology 1.2.1. Taxonomic discrepancy between life stages One of the most fundamental and long-lasting problems in the field of coral reef fish ecology stems from poor larval taxonomic resolution. Namely, a considerable gap exists in the taxonomic resolution used in studies examining the larval pool and those dealing with the adult community. This taxonomic discrepancy between life stages makes it unfeasible for researchers to relate between the structure and dynamics of the adult reef community and the ecological patterns and processes occurring at the larval pool supplying it. Therefore, the association between larval and adult abundance is unknown. Studies dealing with this relationship often use larval settlement and recruitment as a proxy for larval supply, i.e. the degree to which local larval pool supplies individuals to the adult pool, via recruitment. Frequently using light traps, SMURFS (Standard Monitoring Unit for the Recruitment of Fish), and visual census of the benthic habitat; (Cowen 2002; Ammann 2004). However, the two pools may be decoupled (Pineda et al. 2010), in part because the larval pool spans a far larger spatial extent (up to thousands of kilometers) than local populations (Kinlan and Gaines 2003). More specifically, since locally produced offspring do not necessarily return to their natal community and the larval supply of the community is the product of a larger-scale metapopulation (Leibold et al. 2004), the structure and reproductive efforts of the local adult community may be uncorrelated with recruitment success

(Pineda et al. 2010). To date, there is no knowledge on the degree to which the species diversity in benthic assemblages is matched by the larval pool in coral reef fish.

1.2.2. Species-specific vertical distributions In addition to the lack of ability to relate between larval and adult stages, another fundamental problem arises due to the poor taxonomic resolution. Pooling together different taxonomic groups (i.e. grouping con-familial species together) may result in over-simplification of a complex multi-species system and the obscuring of patterns and processes occurring at finer taxonomic scales (Leis 2015). Studies of larval pool spatiotemporal distributions, which are typically conducted at a coarse taxonomic resolution, possibly entail important ecological patterns beyond what can currently be revealed at the family level. Larval vertical distribution is of special interest due to its great potential to influence larval dispersal. The pelagic environment is immensely different from that occupied by the adult fish. It is extremely dynamic, with water currents varying spatially and temporally in both the horizontal and the vertical planes (Sponaugle et al. 2002). Since the velocity of currents is vertically stratified, larvae having different depth distributions are exposed to a different set of bio-physical factors that vary with depth. This results in differential survival, dispersal trajectories and demographic connectivity (Paris and Cowen 2004; Huebert et al. 2011; Corell et al. 2012). Paris and Cowen (2004) illustrate how specific depth distributions influences dispersal outcome. The larvae of their focal species, Stegastes partitus, increased their chances of local retention by migrating to deeper water strata that are characterized by an onshore flow. Generally, while macro scale (>100km) circulation contributes to larval dispersal away from their natal reefs, meso- and sub-mesoscale (20-2km) processes tend to facilitate local retention and self-recruitment (Paris and Cowen, 2004). Species-specific larval fish spatiotemporal information is required by dispersal modellers to study the interactions between the larvae and the currents in the environments that they occupy, in order to reveal connectivity and dispersal routes (Cowen 2002; Leis 2004). Therefore, studying larval vertical distributions at fine taxonomic resolutions has the potential to greatly improve our understanding of larval dispersal mechanisms.

1.3. The Challenges in Using Quantitative Metagenomics to Advance the Field of Coral Reef Fish Larvae

In order to resolve the above-mentioned limitations in the field of larval pool ecology of reef fish, which stem from poor taxonomic resolution, DNA meta-barcoding is increasingly being advocated as an alternative for the classical taxonomy to produce species-level identification (Hubert et al. 2015; Comtet et al. 2015; Kress et al. 2015). The meta-barcoding approach involves the biodiversity assessment of bulk environmental samples by the use of next generation sequencing (NGS) techniques identifying individuals at the species level according to their unique Cytochrome C Oxidase subunit I (COI) mitochondrial gene (~650bp). However, thus far, three main challenges impede the success of this approach:

(1) There is a need for taxonomically comprehensive DNA reference libraries of the local coral reef fish, to which larval barcodes could be compared for species assignment. Significant global effort is underway to map the COI barcodes for all world fish species as part of the “fish barcode of life (FISH-BOL)” project, so far covering ~11,000 out of the >32,000 known fish species (Ratnasingham and Hebert 2007). However, the COI barcodes of coral reef fish species of the GOA are currently largely lacking.

(2) Producing non-biased sequencing. Meta-barcoding methods typically use PCR technique to amplify the COI region from the DNA pooled from all organisms in the sample (Leray and Knowlton 2015). Such an approach was shown to be prone to introduce strong amplification biases due to differences in primer annealing efficiencies, as well as generation of artefactual barcode chimeras (Qiu et al. 2001; Galan et al. 2012), preventing accurate quantitative assessment of species abundance and leading to frequent misidentification of species (Zhou et al. 2013; Deagle et al. 2014).

(3) Generating quantitative estimates of larval fish abundance, rather than presence/absence inferences. More specifically, resolving whether the DNA amounts detected come from a single large individual, numerous small individuals or individuals of various sizes (Leis 2015).

These challenges were overcome by our collaborative work with the Sorek Lab (The Weizmann Ins.) and the Holzman Lab (TAU and IUI), and enabled us to quantitatively characterize the local larval pool at the species level and to clarify some of the above-mentioned fundamental questions in coral reef larval fish ecology.

1.4. Previous Works Conducted on the Local Larval Pool In the Gulf of Aqaba very few studies have focused on larval fish ecology; the following is a short outline: (A) Cushnir (1991) was the first to describe icthyioplankton taxonomic diversity and characterize spatial and temporal trends in the north-western tip of the Gulf. The study described the distribution of larvae at the family level. However, it did not provide sufficient spatial resolution, as ichthyoplankton sampling took place mostly in the upper 10m of the water column, while deeper sampling had a coarse resolution of 100m depth range per sample. (B) Froukh (2001) characterized larval biodiversity at the family level off the Jordanian coast of the Gulf and provided novel data about the larval assemblages. However, since samples were collected by light traps, which attract mostly late-stage phototactic taxa, the study provided an unrepresentative subset of the natural larval pool. Light-trap studies should be used to complement studies that are based on net tows (Hickford and Schiel 1999) rather than as an independent sample collection method in biodiversity studies. (C) An entirely different approach, employed by Ben-Tzvi et al. 2008, was used to infer about larval trajectories of two pomacentrid species in the Gulf. This was done by examining the trace element composition in the otoliths of new recruits.

In the Gulf of Aqaba, as in other sites across the world, the taxonomic resolution limitations persists and so larval fish studies are either conducted at the family level or focus on a reduced number of species.

1.5. Larval Distributions and Influential Environmental Variables The pelagic environment is dynamic and largely characterized by frequent changes in conditions such as temperature, food concentration, predator distribution, salinity and light intensity at various spatio-temporal scales (hours and meters to years and hundreds of kilometres) (Sverdrup et al. 1942). Multiple studies indicate that the spatial distribution of larval fishes is influenced by these various biological and physical factors (e.g. Cha et al. 1994).

1.5.1. Environmental variables potentially influencing larval dynamics 1.5.1.1. Temperature Temperature has been shown to enhance larval growth rates and locomotion abilities (Green and Fisher 2004), thus increasing survivability during this vulnerable life stage. Vollset et al. (2009) and Hurst et al. (2009) have experimentally shown that temperate larval cod fish of certain developmental stages favour warmer water strata. Batty (1994) studied herring larvae distribution in their natural surroundings, and revealed that they congregate near the thermocline. In warmer climatic regions, such as south-eastern Australia (Gray and Kingsford 2003), the Florida Straits (Huebert et al. 2010) and French Polynesia (Irisson et al. 2010), no significant influence of temperature and the thermoclines on the vertical distribution of larval fishes was found, suggesting no larval preference to either warmer or colder waters. Conversely, several other studies in the tropics have demonstrated a correlation between the appearance of specific coral reef taxonomic groups and the surrounding water temperature (Wilson and Meekan 2001; Carassou et al. 2008), and a strong influence of temperature on larval spatial positioning (Rodríguez et al. 2006; Garrido et al. 2009).

1.5.1.2. Chlorophyll a Although not a direct measure, chlorophyll a concentrations are often used as a proxy for phytoplankton concentrations (Huot et al. 2007) and therefore also as larval

food concentrations (Heath 1992). Food availability may serve as a key factor in larval dynamics (Meekan et al. 2003), since feeding success is crucial for survival and recruitment to the reef (Østergaard et al. 2005). Some studies show that larval abundance correlates with Chl-a concentrations, while other studies refuted such association. For instance, Espinosa-Fuentes et al. (2009) showed that in the Gulf of Mexico the most larval-dense water strata coincided with the Chl-max layer. In addition, several studies illustrate that particular species respond to Chl gradients while other species do not. One such study, conducted by Cowen et al. (2003), showed that a single species of coral reef fish, Thalassoma bifasciatum, was consistently found several meters above the Chl-max layer near the Island of Barbados. Similarly, Garrido et al. (2009) demonstrated that out of all taxonomic groups analyzed, the sardine larvae were the only ones influenced by Chl concentrations in the Iberian coast. In general, oceanographic processes such as turbulence, upwelling, and internal waves may cause nutrient-rich water to rise to shallower strata and facilitate primary productivity. Those processes are considered to enhance the efficiency of food webs in the tropics where reefs are situated in the midst of an oligotrophic environment (Meekan et al. 2006 and references therein). In Western Australia, where small-scale turbulence and upwelling characterize the coast, late-stage larvae were found in high densities in chlorophyll-rich strata. Chlorophyll a is not only considered to influence larval spatial distribution, but has also been hypothesized to influence the taxonomic composition of larval assemblages. In the waters surrounding New Caledonia, Carassou et al. (2008) investigated the influences of water column variables on the structure of larval assemblages, indicating that concentrations above and below a certain threshold yielded utterly different larval assemblages, therefore they concluded that larval assemblages strongly coincide with specific Chl-a levels. Conversely, Huebert et al. (2010) found no relationship between Chl levels and abundance in the Straits of Florida, since larvae concentrated in shallow waters and the Chl-max was consistently located much deeper.

1.5.1.3. Light intensity An additional factor that may influence the spatial distribution of larval fish is light intensity (Cha et al. 1994), which exponentially attenuates with depth and varies

according to the level of water turbidity that is, in turn, related to chlorophyll concentration (Fiksen et al. 2002). Depth and turbidity may therefore affect larval feeding efficiency and risk of predation (Job and Bellwood 2000; Fiksen et al. 2002). One example supporting this hypothesis is provided by Huebert et al. (2010), who, while investigating what environmental features influence larval vertical positioning, discovered that the depth at which larval Pomacentrids are found is correlated to surface light intensities in all three seasons examined (spring, summer and fall). 1.5.1.4. Salinity Salinity has been also shown to affect larval distributions. Carassou et al. (2008), for example, showed that different salinities (along with Chl a levels and temperature) coincided with particular larval assemblages in New Caledonia. Lougee et al. (2002) experimentally demonstrated that larval Pacific herring avoid high salinity. Irisson et al. (2010) found that larval fish belonging to the Lethrinidae and Blennidae families were consistently deeper when the halocline was deeper, suggesting that haloclines may form boundary layers limiting larval fish distributions. In contrast, Huebert et al. (2010) did not find any consistent effect of salinity on larval distributions in the Straits of Florida. Interestingly, salinity is a conservative parameter, and is therefore a good indicator of distinctive water masses, therefore, larvae found in waters of different salinity may indicate larval preference (or differential survival), as well as conveying water masses with which the larvae arrived (e.g. Alemany et al. 2006; Muhling et al. 2013). From the above review, it is apparent that the influence of each of the different environmental variables on the occurrence and positioning of fish larvae is not a global phenomenon; rather, each factor is more common for particular taxonomic groups and appears to occur in some places more than others. It therefore seems that larval distribution is influenced by complex interactions between multiple factors, and should be examined locally.

1.5.2. Potential influence of various environmental variables on the spatiotemporal distribution of larval fish in the Gulf of Aqaba

Temperature and chlorophyll a change seasonally in the Gulf of Aqaba and may influence larval dynamics. Although sub-tropical marine ecosystems have been noted for relatively minor seasonal changes, these have been shown to control many ecological processes (McClanahan 1988). In the GOA, seasonality is characterized by differential thermocline and mixing depths (Biton and Gildor 2011c), and differential water temperatures, salinities, light intensities, Chl-a concentrations and other nutrient concentrations; some of these parameters are interdependent. Typically, during the summer the water column is thermally stratified and is considered to be stable. As the atmospheric temperature decreases, the upper sea layer is cooled resulting in convective mixing, which deepens as winter temperatures continue to decrease. The phytoplankton community has been shown to follow this stable vs. mixed water column pattern in terms of densities and distribution (Genin et al. 1995). During the summer, chlorophyll a concentrations are low and possess a vertical structure, having a distinct Chl-max layer (NMP - the National Monitoring Program data base, 2011). During the winter Chl-a concentrations somewhat increase and the spatial structure is replaced by a uniform vertical distribution due to the mixing (Genin et al. 1995). With the commencement of spring Chl-a levels peak, and the structure is built up again. Once concentrations level off, the seasonal cycle begins again (Figure 1). Another potentially influencing factor in the GOA is light intensity. The gulf is located in an arid area characterized by clear sky, absence of clouds for most of the year and low water turbidity; these features enable light to penetrate to relatively deep waters. Maximal light penetration has been recorded during the stratified season, and minimal – during the spring blooms (photic depth of 127m and 60m, respectively; Dishon et al. (2012)). The seasonal changes in water clarity, caused by water column mixing and chlorophyll concentrations, can potentially derive seasonal alterations in larval vertical positioning. Larval transport via advection could also act as a mechanism for observed correspondence between larval distributions and environmental variables, specifically salinity, which is the most conservative factor among the parameters mentioned above. Low-salinity water parcels enter the GOA from the Red Sea (Biton and Gildor 2011a), and although their salinity level changes through the seasons, it still remains relatively low in comparison to the high-salinity waters of the northern tip of the gulf due to increased evaporation (Biton and Gildor 2011a). Water parcels originating in the south

may contain different larval assemblages than local water masses.

Figure 1. Temperature and Chlorophyll-a change in the Gulf of Aqaba across seasons (with May, August, November and February representing the spring, summer, fall and winter, respectively) and across the gulf (West coast vs. East coast).

Studies examining the relation between environmental variables (such as temperature, Chl-a concentration, light intensity and salinity) and larval dynamics across the world have done so either on several species or on the whole larval community, though in coarse taxonomic resolutions, due to identification limitations (discussed earlier). In the GOA, the extent of association between local environmental variables and larval spatio-temporal distribution and assemblage is currently unknown. In this chapter we provide a first examination of potential influences of local bio-physical factors on coral reef larval fish dynamics at the species level, and of the entire larval pool community throughout one year.

1.6. Larval Spatio-Temporal Distributions Throughout Ontogeny At the beginning of their larval stage, coral reef ﬁsh have limited behavioral abilities that gradually improve, so that by the end of the pelagic phase larval swimming, orientation and cue detection capabilities are well developed (Leis 2006; Montgomery et al. 2006). Nonetheless, since larvae can regulate their vertical positioning from about the time they hatch and due to the vertical stratification in current velocities in the water column, larval vertical position has the potential to substantially inﬂuence dispersal throughout the pelagic phase (Leis 2010). More specifically, larvae may choose their vertical positioning throughout ontogeny according to their preferred current speed and direction at each developmental stage; hence they either catch a ride in a favorable direction (e.g. towards or away from the reef) or exploit slow currents to stay in a specific location such as the reef vicinity in general (Huebert et al. 2011) or their natal reef (Paris and Cowen 2004). Thus, they exploit current velocities in an energetically efficient manner (Paris and Cowen 2004). This ontogenetic vertical zonation behavior may greatly affect larval transport distance and direction. Therefore, introducing ontogenetic changes in larval vertical distributions (as a vertical position probability function, see e.g. Paris et al. 2013) into dispersal models has great potential in refining the knowledge on dispersal and connectivity (Leis et al. 2006).

Larval fish vertical positioning has been shown to change throughout ontogeny (Neilson and Perry 1990; Paris and Cowen 2004; Leis et al. 2006; Lopera-Barrero et al. 2008; Irisson et al. 2010; Leis 2010; Huebert et al. 2011). The following is a review of the relevant literature:

Irisson et al. (2010) showed that as the coral reef fish larvae develop, they occupy a wider depth range. That is, while younger larvae typically occupy the shallow waters, older larvae are more spread in the water column and are found in shallow as well as deep waters.

Leis et al. (2006) showed that larvae of Argyrosomus japonicus (a non-reef associated Sciaenidae) and Pagrus auratus (Sparidae) moved deeper with increasing size while Acanthopagrus australis (a non-reef associated Sparidae) exhibit an ontogenetic ascent to the surface.

Huebert et al. (2011) showed an ontogenetic vertical migration in ten taxa in which the larvae gradually moved deeper with growth and development. Furthermore, in some cases this migration resulted in extensive differences in transport routes among larvae of different developmental stages.

Paris and Cowen (2004) showed the existence of an ontogenetic downward migration (of ~ 60m) of Stegastes partitus (Pomacentridae) in the west coast of Barbados, suggesting a retention mechanism for locally spawned larvae, since deeper waters are characterized by an onshore flow in that location.

Vertical positioning is likely influenced by the interplay between biophysical factors such as temperature, light intensity, predation risks, prey availability and current regime (Leis 2004; Leis et al. 2006). In general, ontogenetic migration to deeper water with development is thought to coincide with an ontogenetic increase in visual sensitivity, which enables older larvae to occupy deeper water without compromising their prey/predator detection capacities (Job and Bellwood 2000).

While a few works have demonstrated that even small variation in vertical positioning substantially affects the dispersal outcome of coral reef fish, only few studies have actually examined these distributions throughout ontogeny, and those were done mostly at the family level. In this chapter we provide a high-resolution, year- round description of the ontogenetic vertical distribution of three Serranidae species in the Northern tip of the Gulf of Aqaba.

2. Research Goals and Objectives

My work aims at significantly expanding the knowledge on coral reef fish larval ecology in general and providing insight on the spatial and temporal dynamics of coral reef fish larvae in the Gulf of Aqaba in particular. For this purpose, the first large-scale, year-round and finely stratified sampling was conducted in the Gulf, and the taxonomic resolution currently used in larval pool studies was increased using a metagenomic approach.

Specific objectives: 1. To provide the first description of an entire larval pool in time and space at the species level by using our novel quantitative metagenomic approach.

2. To examine whether the relative abundance of species in benthic assemblages is matched by the larval pool supplying it.

3. To compare the diversity of the larval pool with that of the adult reef community. More specifically, to examine whether non-native species exist in the larval pool.

4. To examine the extent of within family (among species) variation in vertical distribution.

5. To inspect the extent to which local environmental variables are associated with species-level larval spatiotemporal distributions and assemblages of the entire larval pool community.

6. To examine the species-specific ontogenetic spatio-temporal distributions of three Pseudanthias species (P. squamipinnis, P. taeniatus and P. Heemstrai).

3. Materials and Methods COI barcoding approach was used to identify all sampled larvae. Because the large number of larvae in this study rendered amplification and sequencing of the COI from each individual larva unfeasible, a high coverage metagenomic approach was used. The entire pooled genomic DNA from each of the samples was sequenced using the Illumina HiSeq technology. Paired-end reads were generated in this study, a portion of which were mapped to the COI barcode sequence. Then, the COI reads were compared in each sample with each of the COI barcodes in our reference library composed of: (1) sequences of adult fish collected in the Gulf, (2) sequences available in public databases, (3) De-novo COI barcode sequencing from adult fish collected in the Gulf. Only reads providing high-confidence identification of species were used for species assignment. Silhouette Images of Larvae Samples was done to get a proxy for the tissue size of fish which is used later for optimal matching of photographed silhouettes (individuals) to their respective COI sequences. Then classic morphological identification was carried out to verify the validity of the molecular identifications.

3.1. Study Site The Gulf of Aqaba (GOA) is a deep narrow extension of the Red Sea. It is 180 km long and 14-26 km wide; with an average depth of 800 m and a maximum depth of 1,800 m. Fringing coral reefs are abundant along the eastern and western coasts of the gulf and extend from very shallow waters to depths of tens of meters and more (Brokovich et al. 2006a). A complex hydrological regime governs the gulf with velocities exceeding 15–20 cm/s (Berman et al. 2000). In the northern edge, although the currents vary spatially and temporally, their general direction is mainly north to south along the main axis of the gulf.

3.2. Ichtyoplankton Sampling An extensive ichtyoplankton sampling effort has been carried out along the western and eastern coasts of the northern tip of the Gulf of Aqaba (29.26-32ºN, 34.54- 59ºE). The sampling commenced on July 2010 in a bimonthly format of two sampling days, so that the first day of sampling was carried out on the western and the following

day on the eastern side of the coast (Figure 2). In December 2010 the sampling format was altered into a monthly layout of two sampling days, and continued until May 2011. The sampling efforts were reduced towards the end of autumn along with the decline of the major reproductive season. Plankton samples were collected using a Multiple Opening and Closing Net and Environmental Sensing System (MOCNESS; Wiebe et al. 1985), with 1m2 opening and mounted with 600µm nets.

Figure 2. The Gulf of Aqaba study site (the location of which is marked by an arrowhead in the inset). Extensive stratified larval fish MOCNESS sampling was carried out between 2010-2011. X and Y axes represent longitude and latitude coordinates, respectively. The sampling sites: NR - Nature Reserve, MSS - Marine Science Station, NB - North Beach, MG - Mid-Gulf deep water. Within each site, sampling was conducted over three bottom depths (therefore 3 sub-sites per site) except for the MG site. Sampling depth ranges are indicated.

The sampling transects run parallel to the north-western shore of the gulf; over a bottom depth of ~70, ~170, ~250 and ~500m. The nets were first lowered to the maximum depth and then opened sequentially, so that for the nearest-to-shore transect (above a bottom depth of ~70m) a single net sampled the depth range between 25-0m; for the next transect (above a bottom depth of ~170m) four nets sampled the depth ranges of 100-75m, 75-50m, 50-25m and 25-0m; and for the two farthest-from-shore transects (above bottom depths of ~250m and ~500m) the nets sampled the depths between 180-140m, 140-100m, 100-75m, 75-50m, 50-25m and

25-0m. The MOCNESS was towed at a speed of approximately 2 knots, for 5 minutes per net. The number of larvae per net was normalized to the volume of water filtered to yield larval concentrations (Individuals Per 1,000m3).

3.3. Sample Preservation, Handling and Sorting The ichthyoplankton samples were instantly preserved on board in 80% ethanol, which was replaced by fresh absolute ethanol the following day. EtOH preservation allows subsequent morphological and molecular identification. The samples were stored in a 4oC cold room. Then the ichthyoplankton was manually separated from the rest of the plankton.

3.4. Illuminated and Silhouette Images of Larvae Samples The larvae of each of the 383 nets (samples) were photographed using a Panasonic DMC-G5 camera directly mounted on a Nikon SMZ1500 dissecting-scope. Photos were taken under two sets of conditions: the first entailed full illumination to provide high-quality pictures in case re-examination of larval morphological characteristics was needed at a later time (Figure 3a). The second set of conditions involved a bottom illumination source alone to create a silhouette image of the larvae next to a 10mm scale (Figure 3b). The latter photographs were used for measuring larval area, which was later utilized in the automatic quantification procedure.

Figure 3: Sample126 captured on Oct 6st 2010 at the Mid-gulf site at depths between 50-75m. (A) Fully illuminated photograph for further inspection of individuals for future identification purposes. (B) Silhouette image for area measurements to be used in quantification procedure. Both pictures include a 10mm scale.

3.5. Morphology-Based Taxonomic Assignment of Larvae Families Classic taxonomic identification for a subset of larvae (3,736 larvae from 238 samples) was done in most cases down to the family level (with some exceptions where the identification was made possible down to either the order or the genus level). Morphological identification was necessary for the following purposes: (1) Validation of species identified by sequencing. (2) Taxonomic ascription of individuals whose COI sequences were missing from the local as well as public databases. (3) Calibration and assessment of the quantification method. (4) Removal of the most abundant non-reef-associated taxa in order to reduce sequencing costs and to prevent the masking of the less common reef associated sequences. Specifically, larval cohorts visually identified as belonging to one of five abundant pelagic taxa – Myctophidae, Phosichthyidae, Paralepididae, Trichiuridae, and Sternoptychidae – were discarded from the set.

[In addition, one visually identified morphotype of larvae, encompassing the genus Cirrhilabrus and the species Paracheilinus octotaenia (n=285 larvae), were manually excluded from the larvae samples for the purpose of a separate study].

Morphological identification was conducted under a Nikon SMZ1500 dissecting-scope, using the identification guides of Leis and Carson-Ewart (2004) and that of Richards (2006). Larvae were morphologically identified according to the following criteria (see Table 1 for examples): (A) General body shape – relating body depth to length (e.g. very elongated, elongated, moderate, deep, very deep, ventrally flattened, dorso-ventrally flattened, globular). (B) Gut morphology (e.g. striated or smooth, straight [long, moderate, short] or coiled). (C) Meristic count (e.g. myomere, vertebra, fin, spine and ray counts).

(D) Size proportions and location of specific body sections (e.g. head [relating head to body length (small/moderate/large)] /eye [relating diameter to head length] / location of fin relative to vent). (E) Eye shape (round, narrow, wide, presence of choroid tissue). (F) Special features (e.g. special ornamentation such as exceptionally long rays or spines, teeth plates, head spination, width of caudal fin base, sucking disk). (G) Pigmentation (melanophores) - (inner or outer pigments, pigment shape blotches/lines/spots/stellate). (H) Photophores (light-producing organs). ** Ontogenetic development is also accounted for (e.g. timing of gut coiling, fin formation, notochord flexion etc.).

3.6. Collection of Adult Fish and COI Barcoding In order to construct a reference database of local COIs to which larval fish COIs could be compared for identification, fin clips and muscle tissue samples were collected from adult individuals belonging to 203 local reef species (sampling procedure detailed in Kimmerling et al. in revision). These were preserved in analytical grade absolute ethanol. From each sample, DNA was extracted and COI gene was amplified following PCR with universal primer cocktails and sequenced using SANGER sequencing following (Ivanova et al. 2007; Hubert et al. 2010).

3.7. List of Fish Known to Dwell in the Gulf of Aqaba and the Red Sea To assess whether the identified species are known to dwell in the GoA and/or the Red Sea, a list of fish known to dwell in the Gulf of Aqaba and the Red Sea was compiled from the primary literature (Baranes and Golani 1993; Ben-Tuvia 1993; Russell and Golani 1993; Hensley 1993; Randall and Van Egmond 1994; Randall and Golani 1995; Golani 2001; Khalaf and Kochzius 2002; Khalaf 2004; Kimura et al. 2006; Golani and Lerner 2007; Brokovich et al. 2008; Klöppel et al. 2013; Herler et al. 2013). For this purpose, only published, peer-review articles and species reports that included the physical collection and identification of species in the area were used. In addition, two fish guide books and one graduate thesis published by fish taxonomists were also used (Pietsch and Grobecker 1987; Khalaf and Disi 1997; Brokovich 1999). Finally, fish identified as adults during our adult sampling between 2011-2013 were also included in the list of known fish. Species names were verified with fishbase.org.

3.8. Compilation of COI Reference Barcode Database In addition to the set of COI reads sequenced as part of this study, publicly available COI barcodes of Red Sea fish were obtained from NCBI and BOLD (Ratnasingham and Hebert 2007). COIs were collected from public records in two main steps: (1) COIs of Red Sea fish were collected by searching the organism name, and a representative COI was chosen. (2) A BLAST search with the ‘nt’ database [download on June 2014] was performed for reads that could not be associated with any COI in the database. COIs that yielded hits with at least 98% identity over more than 90 bps were added to the database .

3.9. DNA Extraction, Library Preparation and Sequencing For DNA extraction from mixed larvae samples, each sample was gently centrifuged (1,000g) and washed 3 times with 1ml of PBS buffer (pH 7.2, 50 mM potassium phosphate, 150 mM NaCl) in order to discard residual ethanol. The DNA was then extracted from each sample separately by using the DNeasy Blood & Tissue kit (Qiagen) according to the manufacturer protocol for purification of total DNA from animal tissues using spin columns. In a few cases, when the amount of larvae exceeded 100 larvae, samples were split during the morphological identification step. Genomic DNA was measured using the Qubit dsDNA fluorometric assay (QuBit, ThermoFisher Scientific). The integrity of the genomic DNA was assessed by the 2,200 Tape station instrument (Agilent Technologies). Extracted DNA samples were prepared for sequencing according to Blecher- Gonen et al. (2013) with modifications as described in Kimmerling et al. (in revision). The DNA libraries were pooled and sequenced at paired end 100bp lanes of Illumina HiSeq 2500 instrument.

3.10. De-Novo Assembly and Classification of COIs De-novo COI barcode sequences were constructed from the pool of unmapped metagenomic COI reads by an iterative procedure detailed in Omer Zuquert’s thesis (Zuqert 2014). To taxonomically classify the 158 COI sequences that were constructed using the de-novo assembly method, sequences were first compared to all COI sequences present in the BOLD database (Ratnasingham and Hebert 2007). Other sequences were identified by their phylogenetic association and the rest by family- level morphological identifications performed on a subset of 238 samples. Assembled COIs that were consistently present in multiple samples in which a morphologically observed family was absent from the sequence identification, and where no other explanation was possible, were assigned to that family.

3.11. Species Identification from Metagenomic COI Reads The procedure of species identification according to COI reads is detailed in Zuqert (2014). In general, all metagenomic larval reads were aligned to all barcodes in our COI database. Reads having a single best hit to one of the COIs in the reference

barcode set with an alignment length >90bps and percent identity >98% were assigned to that COI barcode.

3.12. From Larval Area to Abundance First, the area of each larva was inferred from the silhouette images by using the Fiji software (Schindelin et al. 2012). The relative area of a given individual was calculated as a fraction of the sum of all areas in the sample. Next, in order to refine the model for quantitative estimation of the larvae abundance, a set of 47 samples, in which all larvae were taxonomically assigned based on morphology, was used for learning a linear model aiming to normalize the amount of COI reads expected per unit area for each separate taxonomical classification (family, genus or order). In order to do so, the fully illuminated high-quality pictures of each of the 47 samples were visually re- examined to match individuals to specific species or families that were known to compose the sample from the sequencing output. Subsequently, a statistical method for inferring species abundance was developed by Omer Zuqert and described in detail in his MSc thesis (Zuqert 2014). The method relied on the observation that the relative size of each larva is highly correlated with the fraction of COI reads obtained from it. In general, the model fits the amount of DNA reads obtained from each species into larval areas (obtained from the silhouette images) in the best possible way, providing larval abundance data for each sample. The performance of this algorithm was assessed by comparing its results with the results derived from morphology assignments (Figure 4). The following explanation is after Zuqert (2014), for more details see Zuqert (2014): We used a maximum likelihood function to score each assignment (Z) of n larvae and s observed species the observed data is r_1,..,r_s – the number of observed COI reads per species and s_1,..,s_n the relative size of each larva. The reads are therefore a result of sampling 푅 = Σ푟i reads with probabilities p1,..,ps which are the relative size of each species in the sample (i.e pi=∑ j: larvae of sp. i s j)

Then we score each assignment Z of the n larvae to s species (Zij=1 if larvae i is assigned to species j). For each such Z the relative sizes\sampling probabilities

is calculated. From this point, log likelihood is computed based on multinomial distribution:

Finding the optimal Z: For small samples we computed all the possible assignments of the n larvae to s species and to choose the one that maximize the likelihood, once we found the best Z we have the identity of each larva in the sample and we got the numbers. The problem is that the number of assignment grows extremely fast and we can’t test all assignments in a reasonable computation time (the number of assignment is in order of sn as any larvae can be assigned to any species) thus we need some heuristic method to scan the assignment space to find reasonable assignment in a feasible time. Heuristic method for finding best Z: 1. Start with a random assignment Z 2. Calculate l(Z) 3. Sample another assignment Z’ (sampling process is explained later) a. calculate l(Z’) b. if l(Z’) > l(Z): set Z:=Z’ 4. Repeat step 3 until convergence (some arbitrary threshold of number of sampling without improvement) 5. Repeat step 1 (new starting points) and choose the end point with the maximum likelihood. (best resulting assignment of all trials) Sampling Z’:

Figure 4. Size-based model for quantitative inference of species abundance. (A) Silhouette picture of larvae from sample 97, collected on October 19th from a depth layer of 25-50m from the mid-gulf (MG) site. Family identity per larva was inferred with visual inspection based on morphological, meristic and pigmentation criteria. (B) Species identified within sample 97 based on COI mapping. Sizes of larvae were computationally inferred from the silhouette picture using the Fiji software (Schindelin et al. 2012). (C) Correlation between larval size fraction and the fraction of mapped COI reads in 47 samples in which all larvae (n=303) were taxonomically assigned by both morphology and sequencing. Blue, correlation with raw reads; Red, correlation with read numbers normalized to account for biases typical to specific families. (D) Differences between the number of larvae according to the morphological assignments and the estimated numbers based on COI sequencing and quantitative model. The data was based on a larger set of 3736 larvae from 234 samples that were taxonomically assigned to the family level using morphology criteria.

3.13. Comparison between Larval Pool and Benthic Assemblage 3.13.1. Species representation in a local assemblage and the larval pool The total number of adult sightings during replicated censuses along the north- western GOA was used as a quantitative proxy for population size and stability. The data was collected during two periods: September 1999 – September 2000 and December 2003 – April 2006. For the first period we took the mean of the total counts from 42 belt-transects (2x50m), with the mean calculated across four seasonal replicates (Brokovich et al. 2006b). For the second period we took the total counts from

42 non-replicated belt-transects (2x25m), conducted at depths of down to 65m (Brokovich et al. 2008). The counts from both periods were summed (by species). Log- transformed counts were used along with log-transformed total larval-density estimates in six separate major-axis regression models; one for each of the six families for which we had adult-sighting and larval-density data for at least nine species. The analysis was run with the 'lmodel2' command of the R-package "lmodel2" (Price et al. 2009).

3.13.2. Non-native species in the larval pool Summing larval densities across samples (i.e. across space and time), we treated total larval density, per species, as a proxy for propagule pressure, a reliable factor in predicting colonization success (Hufbauer et al. 2013). As a means of comparing propagule pressure of GOA-native and non-native species, a simple ANCOVA model was used to test the relationship between total larval density, per species, and species' incidence. Equality of slopes was tested first, using an interaction term for the covariate and status, followed by a test for the equality of intercepts. To further evaluate the similarity between GOA natives and non-natives, model- based cluster-analysis was performed with the ‘Mclust’ function in the R library ‘mclust’ (Fraley et al. 2016). Sampling-site (Figure 2) and sampling-month were entered as descriptor variables; BIC was used as the criteria for choosing amongst competing mixture models. The quality of the selected clustering was evaluated using the adjusted Rand Index, which takes a value of zero when the agreement between the observed and modelled classification is no better than chance. The analysis was limited to 34 species from 11 genera that included at least one species from each category (20 GOA natives and 14 non-natives).

3.14. Spatio-Temporal Patterns in Larval Distribution To capture the spatio-temporal dynamics of multi-species larval composition we used heat maps and dendrograms with the heatmap.2 command of the R-package "gplots"(Warnes et al. 2015). The analyses were limited to species represented in at least five sampled nets, with clustering based on Bray-Curtis dissimilarity and average-

linkage. A simple Mantel test (R package "vegan" according to Oksanen et al. 2013) on the distances matrices was used to look for an association between depth distribution and time of year. Differences in sampling effort (i.e. in the number of hauls per depth strata or month) were controlled for by considering average per-haul densities. Spatial: for each month, total density per depth strata was divided by the number of hauls in that strata; averages were then summed across months and expressed as a proportion of the grand-total (i.e. the sum, of the monthly sums, across the four depth strata of the upper 100m). Temporal analysis: for each month, total density was divided by the number of hauls for that month and expressed as a proportion of the sum of the monthly averages.

3.15. Larval Distributions and Influencing Environmental variables In this section we sought to examine the relationship between larval abundance and environmental variables, and therefore, only samples with CTD data were used (205 samples out of 384, Supplementary table S4). For the Tabasco and PERMANOVA analyses (see below), data was filtered so that only the 43 most abundant samples and species were analyzed, using species abundance normalized to sample volumes (species densities), in order to capture the key species and sites in the system (and remove the effect of the rare species, and poorly-abundant samples). Figure 5 demonstrates the variability in the environmental variables and time throughout the samples. Temperature, irradiance, chlorophyll a and salinity varied both due to seasonal and sample-depth variability (Figure 5e). As poor samples were filtered out, not all months are represented (Figure 5f). PAR (Photosynthetic Active Radiation) was computed according to

, where PARz is the PAR at the sample’s depth, PAR0 is the PAR measured at the meteorological station in the IUI (29°30.211’ N 34°55.068’E), z is the sample’s depth (m), and K is the monthly attenuation coefficient following Dishon et al. (2012). Chlorophyll was computed from a calibration equation (extracted from the NMP data) translating fluorescence measurements from the CTD (SBE 911plus, Sea-Bird Electronics) to chlorophyll a concentration measured in μgr/L. The same fluorescence sensor that was used for the measurements was used in the calibrations of the NMP.

Figure 5. Ranges of environmental variables and date (month parameter) measured in the samples used for the environmental variables analysis, i.e. 43 most abundant samples and species.

To examine the relationship between species composition within samples and the environmental variables, we used two methods: (1) A quantitative method: Permutational Multivariate Analysis of Variance Using Distance Matrices (“PERMANOVA” function from Vegan library, Oksanen et al. 2015), and (2) A graphical exploratory method “Tabasco” function from Vegan library (Oksanen et al. 2015). Both analyses were applied for the data by: (1) samples, where factors represent the different environmental and saptio-temporal conditions of each sample. (2) by species, where factors represent the taxonomic family of each species. The former was performed to determine if the different samples are characterized by specific species assemblage given the samples’ factors, while the latter was performed to examine whether species of the same family have the tendency to appear in the similar samples.

PERMANOVA: The Permutational Multivariate Analysis of Variance Using Distance Matrices (PERMANOVA function) is a permutation MANOVA (multivariate analysis of variance) that uses distance matrices (in our case “Bray Curtis” dissimilarity, Anderson 2001). The function utilizes permutations to find significant partitioning of the data according to a-priori defined tests (from R package “vegan”: Oksanen et al. 2015). PERMANOVA is advantageous since it uses the full multivariate space of dissimilarity without reducing dimensionality and losing data (as opposed to other methods e.g. PCA, RDA etc.). It can be used for factorial as well as continues variables (Oksanen et al. 2015).

Tabasco (Oksanen et al. 2015) is a graphical exploratory function in R (R core team 2015) that enables a more comprehensive visualization of a complex dataset. The Tabasco plot is produced as follows: (1) The similarity between the sites was computed using a dendrogram with UPGMA (Unweighted Pair Group Method with Arithmetic Mean) average linkage method and Bray-Curtis dissimilarity distances. The average group linkage method fuses the two most similar nodes into a cluster, and then the distance between the clusters is determined according to the distance between the clusters’ unweighted centroids. (2) The species are then re-arranged according to the first correspondence analysis axis, which joins similar species to each other, and similar sites to each other, resulting in a diagonal representation of the data. (3) We then manually pasted the table of environmental variables and used color-coding to visualize their values/categories.

3.15.1. General Additive Models (GAMs) The relationship between species abundance and environmental variables is not necessarily linear; on the contrary, it is likely that certain species will have a preferred/optimal range of environmental variables (e.g. temperature or salinity) beyond which species abundance decreases, giving rise to non-linear relationships. Such nonlinearities are not “well captured” when using the conventional linear regression methods. We therefore chose to use General Additive Models (GAMs) approach to examine the correspondence between single species and environmental

variables (mgcv package, Wood 2011). Briefly, the generalized additive model is a generalized linear model with a linear predictor which uses an optimizing algorithm of additive non-parametric smoothing functions (in our case: Locally Weighted Scatterplot Smoothing) to achieve the best fit for the data. Following the parsimony principal, the model “penalizes” (reduction of the likelihood of the fit) for increasing number of smoothing functions (Wood 2011); otherwise infinite number of smoothing functions will result in a maximal fit. The advantage of GAM is the flexibility in finding non-linear relationships, while controlling for the degree of model complexity. In the GAM analysis, in contrast to the previous analyses, all the sites (nets) with available CTD data where examined (n=205).

3.15.2. Flushing time of the Northen tip of the GoA using Lagrangian modelling Water masses that are transported along the Gulf (Biton and Gildor 2011c) also transport larvae. Therefore, it is plausible to assume that specific conveying water masses will be characterized, to a certain extent, by the larvae that were originally present in them, resulting in observed correlation between larval species composition and the water mass conditions (e.g. salinity). For this reason, we performed multiple simulations of passive tracers in the Northern tip of the Gulf, to assess how long particles actually stay in the study area (the Norhtern tip of the GOA).

Particles were released from the northern tip of the GOA (Figure 6) from depths of 0-200m, and transported using the 3D velocity field (U, V, W) and turbulence. The velocity field was obtained from running the MITgcm model (Marshall et al. 1997), which was applied for the Gulf previously in several other works (e.g. Biton and Gildor 2011a; Biton and Gildor 2011b; Biton and Gildor 2011c). For our simulation we used high-resolution air temperature and wind forcing data produced from running a WRF model (Weather and Forecasting model, Skamarock et al. 2001). The particle trajectories were modeled using PATATO (Fredj et al. 2016), which is basically a particle tracking algorithm that integrates particles’ position through time and space, according to the velocity field and turbulence (Berenshtein et al. in review). At the 1st of each month, 10000 were released and tracked for 10 days and the number of particles that remained in the northern tip of the GOA was recorded, essentially

assessing the residence time of water masses in our region across the 12 months of 2010.

Figure 6. Northern tip polygon, which was used to release 10000 passive tracers on the 1st of each month during 2010, examining the residence time of particles in the northern tip of the Gulf.

3.16. Spatiotemporal Distributions Throughout Ontogeny 3.16.1. Focal species The three Pseudanthias sp. (Family: Serranidae) on which I focus in this chapter are Pseudanthias squamipinnis (Sea Goldie; Figure 7), Pseudanthias taeniatus (Striped

Anthias) and Pseudanthias heemstrai (Orangehead Anthias). These are small and colorful planktivorous reef-associated fish found in aggregations is shallow waters (0- 40m) (Lieske and Myers 2004). Pseudanthias sp. spawn pelagic eggs (Leis and Carson- Ewart 2004). Their larvae hatch at 1.2-1.4 mm with unpigmented eyes and a large yolk sac (the mouth is formed later). While P. squamipinnis is one of the most abundant fish in the north-western tip of the Gulf of Aqaba (3rd in relative abundance rank (0.090); according to Brokovich, unpublished), P. heemstrai and P. taeniatus are less common (relative abundance of 0.015 and 0.009, respectively).

Figure 7. Pseudanthias squamipinnis. On the left a mature specimen (~8cm TL). On the right a larval specimen (~9.5mm SL) captured on Nov 25th 2010 in the Gulf of Aqaba.

3.16.2. Pseudanthias larvae handling and morphological identification Larval fish were captured, documented and identified as discussed in the first chapter. Samples containing Pseudanthias DNA were selected and the morphologies of all larvae in the sample were inspected from the photographs to identify the individuals belonging to the Pseudanthias genus. This was done according to the following distinctive Pseudanthias characteristics (according to Leis and Carson-Ewart 2004; Richards 2006): • Fin element counts [dorsal X-XI, 15-17; anal III,6-9; pectoral 15- 20; pelvic I,5; caudal 9+(13-15)]. • Vertebrae count [(10-11)+(15-16)=26]. • Head shape - large, wide, deep, rugose with extensive spination (including a large serrated interopercular spine (Table 1, Page 20), small smooth inner preopercular spines, supracleithral and posttemporal spines). o Mouth - large with small teeth, reaching beyond the middle of the eye. o Eyes - large and round. o Snout - short and round. • Body shape - deep and short (preflexion stages seem hunchbacked) with a narrow caudal peduncle and a coiled gut, unattached gill membranes.

In cases where more than one Pseudanthias sp. was represented in the DNA output, larval morphologies were further inspected from the photographs in order to identify which specimen belongs to which of the three species: P. squamipinnis, P. taeniatus or P. heemstrai. Morphological identification down to the species level was possible only for the large postflexion larvae and was based on melanophore pigmentation patterns (Figure 8). For Pseudanthias squamipinnis four pigmentation lines or blotches were identified: ventral to dorsal spines (beneath spines IV-VII), ventral to posterior base of dorsal fin, mid-lateral anterior to the peduncle and posterior to anal fin base. Pseudanthias taeniatus has a similar pigmentation pattern with the following exceptions: absence of ventral to posterior base of dorsal fin pigmentation and the presence of a pigment spot on anterior opening of vent. Pseudanthias heemstrai is characterized by being less pigmented with only mid-lateral anterior to the peduncle and posterior to anal fin base pigmentation spots.

Figure 8. The three Pseudanthias species: P. squamipinnis, P. taeniatus and P. heemstrai (from left to right). Red arrows indicate characteristic pigmentation for each species.

In cases where species identification could not be ascertained morphologically, we relied on the correlation between larval relative size and DNA read count. First, larval areas were calculated from the silhouette images using ImageJ software. Then DNA reads were matched with larval areas by computational partitioning (into two groups). The procedure considers all possible combinations of DNA to area possibilities, and selects the combination that produces the best agreement in area to DNA read count ratio. This process was carried out using the "Combinat" R package. Due to computational power limitations, only samples with two Pseudanthias species were used. For samples where partial species assignment by morphological identification was performed, only combinations in agreement with the classical taxonomic identification were considered. Following species assignment to individual larvae, larval length was measured

as a proxy for ontogenetic stage, since actual developmental stage (e.g preflexion, flexion, postflexion) designation was unattainable, as notochord flexion was frequently unobservable in the pictures. The standard length (SL) of each larva was calculated by using the 10mm scale present in each silhouette image. SL was defined as the distance from the tip of the snout along the midline to the vertical line through the posterior edge of the hypural plate (according to Carson-Ewart and Leis 2004). The vertical distributions of the three Pseudanthias species were analyzed separately for each of the 4 seasons. Seasons were defined according to water column parameters, where the summer (July-October) was characterized by water column stratification, fall (November-December) – by water column mixing (temperature being vertically uniform) with relatively high temperatures (24-260C), winter (Jan- April) – by water column mixing with relatively low temperatures (22-230C), and spring (May) – by the commencement of stratification. In order to assess ontogenetic patterns in the vertical distributions of the larvae, Quantile Regression Analysis was performed using R statistical software (R Core Team 2015, Koenker 2015). This analysis captures the trends of various quantiles of the data, and is applicable in cases of high heteroscedasticity. Quantile Regression was used since the relationship between larva size and depth was generally characterized by a heteroscedasticity, such that smaller larvae were distributed across the entire water column, while larger larvae were mostly distributed at shallow waters. The 0.75 quantile was arbitrarily chosen to represent an upper quartile. Quantile Regression was performed only for cases where sub-sample size (species and season) was larger than n=40.

4. Results

4.1. Intensive Larval Sampling and Genomic Barcode Collection To obtain high-resolution insights into the ecology of coral-reef fish larvae in the Gulf of Aqaba, we conducted an intensive ichthyoplankton sampling over a period of 11 months in 2010-2011 (Figure 2). This sampling program yielded 383 discrete samples, from 10 geographical sites across the northern tip of the Gulf of Aqaba, an area of roughly 50 km2. Each site was sampled either once or twice per month, sampling 1 to 6 depth layers per site at 25m intervals for the upper 100m (e.g. 0-25m, 25-50m) and 40m intervals at deeper waters (100-180m). Overall, ~130,000 m3 of water were filtered during this sampling, yielding 16,695 fish larvae. Larval cohorts visually identified as belonging to one of five abundant pelagic taxa were discarded from the set, leaving 10,099 fish larvae suspected as belonging to reef-associated and benthic species. To identify the sampled larvae based on their COI sequence barcode, there was first a need to establish a reference COI sequence library of the local GOA species. COI sequences of 377 (82%) of the 461 reef-associated fish species were obtained either by sequencing of DNA extracted from tissue samples collected locally from adult fish or from online public repositories, providing a near-complete coverage of the COI sequence space in this species-rich ecological region (Supplementary table S2). To account for the possibility that some of the larvae would be of pelagic species, or dispersed from the adjacent basin (south of the straits of Tiran), this database was supplemented with all the available COI sequences of additional fish known to reside in the entire Red Sea, covering about 70% of the known species in this larger basin.

4.2. Unbiased Metagenomics of Larvae Samples We set out to use the COI barcode approach to accurately identify all sampled larvae. Since the large amount of larvae in this study rendered amplification and sequencing of the COI from each individual larva unfeasible, we turned to using a metagenomic high-coverage approach. To avoid the strong amplification biases inherent to PCR-based metagenomics, unbiased, high- coverage metagenomic sequencing was used, that was not based on direct

amplification of the COI barcode. The entire pooled genomic DNA from each of the 383 samples (spanning up to 492 larvae per sample, as shown in Supplementary table S1) was sequenced using the Illumina HiSeq technology. The sequence coverage for each sample was adjusted to yield ~20 COI-derived reads per larva, so that samples containing more larvae were proportionally sequenced more deeply. In total, 3.54x109 paired-end reads (2*101bps) were generated in this study, 201,145 of which were mapped to the COI barcode sequence (denoted hereafter “COI reads”). The COI reads in each sample were compared with each of the COI barcodes in the reference set. Only reads providing high-confidence identification of species (showing unique best mapping with up to two mismatches) were used for species assignment. Surprisingly, only 71.5% of the COI reads showed such high confidence mapping to one of the COIs in the reference set, in contradiction to the expectation based on the 87% coverage of the reef-associated species space in the barcode database. This is possibly due to the fact that some of the larvae may belong to abundant pelagic species, the COIs of which were not targeted in the adult sampling effort. To account for these species, new COI barcodes were assembled de-novo (detailed in Zuqert 2014). About half of the de-novo assembled barcodes (88 of 158, 56%) were taxonomically assigned based on sequence phylogeny and morphological inspection, showing that at least 19,970 previously unassigned reads indeed map to common pelagic taxa (Supplementary table S3). Overall, 184,826 of the 201,145 COI reads (91.8%) mapped to the extended COI barcode set (including the de-novo assembled ones), providing a framework for high- resolution, high-accuracy species identification. To verify the accuracy of the taxonomic identifications, we selected 96 samples, each comprising between 1 and 15 larvae. For 474 of the larvae in these samples it was possible to visually determine the taxonomic family based on meristic, morphological and pigmentation criteria, overall identifying larvae belonging to 13 orders and 33 different families. The results of the metagenomic analysis were compared with the morphology-based taxonomic assignments. The metagenomic procedure mapped 92% of the COI reads onto barcodes of known species in these 96 samples, identifying 138 individual species. 412 (86%) of the 474 morphologically identified larvae were accurately accounted for in the

metagenomic identifications. For additional 21 larvae, discrepancies were recorded between the morphology-based and barcode-based taxonomic assignments, but upon re-examination of the morphology data these cases were resolved as clear morphological misclassifications, verifying the barcode-based classification. Therefore, the consistency of our barcode-based approach with the morphology-based taxonomic assignment was at least 91.4%, and with a much higher taxonomic resolution. Only 41 (8.6%) morphologically identified larvae were not supported in the sequencing data, most probably accounting for species whose COI was missing from our barcode database.

4.3. Quantitative Metagenomics Reveals Species Abundance Since the current metagenomic approach does not involve PCR amplification of the COI barcode, the fraction of COI reads derived from each individual larva were found to be proportional to the relative tissue mass of that larva in the sample. Based on this finding, a heuristic statistical model was developed by Zuqert (2014), which, based on the COI reads inferred from the metagenomic sequencing and the larvae sizes computationally inferred from silhouette pictures, assigns the most probable species abundance in each sample (for more details see Zuqert 2014). The accuracy of this model was estimated by examining the resulting abundance profiles in samples where the taxonomy was also inferred based on morphological features. The results indicate high accuracy of the computational abundance inference, with errors rarely exceeding 1-2 larvae per species per sample when compared to the actual abundances (Figure 4D).

4.4. Larval Species Composition and Relative Abundance Applying the above method to the 383 samples we collected resulted in taxonomic classification of 9,262 larvae, 5,388 of which were classified to the species level (Supplementary table S3). Overall, larvae belonging to 278 species were identified, 254 of which were categorized as reef-associated or demersal. The classified larvae overall covered 186 genera and 79 different taxonomic families, accounting for almost all known families in the region. This species-level taxonomic

identification allowed, for the first time, high-resolution ecological inferences that are described henceforth. Only 24 of the coral reef species were found to have a relative abundance of 0.01 or higher (calculated as the abundance of the species in question divided by the total abundance of the species), while 224 of the species were found to have a relative abundance lower than 0.01 (Figure 9). The distribution of species abundance in the larval community shows that the larval pool is composed of a high number of rare species and low number of abundant species (90.3% and 9.7% of the species respectively).

Figure 9. Species’ relative abundance distribution of the local larval pool. Most species have abundances lower than 0.01.

4.5. Comparison Between Larval Pool and Benthic Assemblage 4.5.1. Species representation in a local assemblage and the larval pool The major-axis regression models found a significant cross-species correspondence between adult sightings and larval abundance of varying strengths for three out of the six families considered (R2=0.69 and P<0.004; R2=0.33 and P<0.01; R2=0.3 and P<0.037 for the Serranidae, Labridae and Pomacentridae families respectively; Figure 10).

Figure 10. Relationships between the abundance of adults and their larvae in common families of reef fish in our set. Adult abundances were estimated from sightings of adults from multiple benthic surveys across Eilat reefs (Brokovich et al. 2008). Families for which adult-sighting and larval- density data was available for at least nine species are included in this analysis. Larval abundances were calculated as summed densities across all Eilat (Israeli) sampling sites. Major-axis regression model is presented for each family. Solid symbols depict species present in the larval community but not in the local assemblage.

Other cases, however, demonstrate a large discrepancy between the relative abundance of adults and larvae (Figure 11).

Figure 11. Species having a large discrepancy between their relative abundance in the larval and adult pools. (A) Cases with higher adult abundance. (B) Cases with higher larval abundance.

4.5.2. Documentation of non-native species The current research revealed 15 non-native species in the larval pool from five different orders and 10 different families. Of the 15 species, 12 were reef- associated (Table 2; Supplementary tables S2 & S3). Notably, the actual number is likely to be higher, as not all of the known local species were recovered as larvae in our samples, and, in addition, our COI reference database does not contain all the species of the Red Sea. It is important to note that although our identification criteria were extremely conservative and meticulous there are some sources which may introduce errors in the identification of fish. One example is the existence of sequences in the public databases which were incorrectly assigned due to cryptic species, sexual and developmental dimorphism etc. These mistakes could later be transferred on when compared with larval fish sequences for identification purposes. By using public sequences which had more than one source of identification we reduce such errors in our results. In addition, some disputes exist regarding the presence of certain species in the GOA however in cases where their occurrence is not supported by the primary literature, they were treated as non- native (the species in dispute are also documented in Table 2).

Table 2. List of non-native species and their life style.

The relationship between total larval density and incidence was not different between native and non-native species (Pslope=0.89, Pintersept=0.36), with mostly overlapping distributions of incidence (i.e. the number of samples in which a species was present – Figure 12). The model demonstrates that both native and non-native species have similar levels of propagule pressure. In addition, cluster analysis showed an Adjusted Rand index of 0.03, meaning natives and non-natives did not differ with respect to the time and location in which they were collected.

Figure 12. The relationship between larval incidence and density. Red, species known to occur as adults in the Gulf of Aqaba (GOA); blue, species not observed as adults in the Gulf of Aqaba. The Y axis represents log (natural) individuals per 1000 m3. Inset: distributions of incidence for native and non- native species.

4.6. High Resolution Spatio-Temporal Larval Distribution The heat maps and dendrograms of the spatial and temporal distribution of larvae demonstrate large differences in the distributions of different species belonging to the same family of the five families examined (Figure 13; Labridae, Serranidae, Gobiidae, Pomacentridae and Apogonidae, which include about 60% of the reef-associated larvae sampled). For example, a clear bi-modal depth distribution is observed for the family Gobiidae (Figure 13). Whereas the larvae of about half of the species belonging to this family in our samples tend to dwell in surface water (0-25m) and mostly appear during the month of July, the other half dwell in deeper water (50- 75m) and have a higher tendency to also appear between September and November. Similarly, the larvae of Plectranthias winniensis (family: Serranidae) frequently reside in depth ranges of 50-100m, although larvae of its confamilial species Pseudanthias squamipinnis and Pseudanthias taeniatus are only rarely found deeper than 50m. Interestingly, clear differences can be observed also between larvae belonging to the same genus. The larvae of Chromis viridis, a major zooplanktivorous fish in the habitat, were found in deep water, mostly between July

and December, whereas the larvae of its kin species Chromis pelloura reside almost exclusively in surface water and are abundant between January and April (Figure 13). It is notable that the larval depth distribution of these species is opposite to that of the adults, as the adults of Chromis viridis tend to live in shallower water than those of Chromis pelloura (Allen and Randall 1980; Brokovich 1999).

Figure 13. Spatio-temporal distribution of species from the five most abundant families of reef- associated fish in our set. The abundance of each species in each rubric is given as the proportion from the total number of individuals from that species, normalized to the sampling effort (volume of water filtered and the number of hauls in each depth stratum or month). Clustering is based on average-linkage and Bray-Curtis dissimilarity between samples. Only species represented in at least five hauls were included in the analysis (Supplementary tables S1 & S3).

4.7. Larval Distributions and Influential Environmental variables Permutational Multivariate Analysis of Variance Using Distance Matrices (PERMANOVA; Oksanen et al. 2015; for more details see the Methods section) indicated that the environmental variables explained 47% of the variability in the species composition (Table 3) with salinity, PAR, temperature and chlorophyll explaining most of the variance. Note that the month parameter, which was used as a continuous parameter, accounted for only ~3% of the variation.

Table 3. ANOVA table of PERMANOVA analysis: Permutational Multivariate Analysis of Variance Using Bray-Curtis Distance Matrices of 43 most abundant sites and species. Df – degrees of freedom, SumsOfSqs – sum of squares, MeanSqs – mean squares.

The association between species composition and environmental characteristics is expressed in Figure 14. The dendrogram in the figure is a hierarchical dendrogram demonstrating sample resemblance in species composition dissimilarities (Bray-Curtis distances; for more details see the Methods section). The dendrogram is unconstrained; hence it is constructed solely according to the

dissimilarities in species composition (independently of the environmental variables). The high degree of adjacent similar-colored cells within each column reflects the lack of randomness with respect to the environmental variables, as also demonstrated in the PERMANOVA analysis.

Figure 14. Tabasco plot - classification of samples according to species composition dissimilarity; warm colors in the heat map represent high relative abundance. The dendrogram represents hierarchical clustering using average linkage method. The table at the bottom reprseents the environmental parameters; different colors represent different values/ groups. The data is log transformed, and filtered for the 43 most abundant species and samples in which they occur.

In order to determine whether there is an effect of the different taxonomic levels on the samples’ larval species composition, we transposed the community matrix and used the “Family” taxonomic classification as a factor. Permutational Multivariate Analysis of Variance Using Distance Matrices on log-transformed densities (Oksanen et al. 2015; for more details see the Methods section) indicated that the taxonomic family explains ~43% of the variation in species composition dissimilarity (viewed as the structure of samples in which species appear in, Table 4). This finding indicates that, to some extent, species of similar families tend to appear in similar samples. This is further demonstrated in Figure 15, where con-familial species tend to appear in adjacent cells (e.g. Chromis agilis, Genicanthus caudovittatus and Chromis viridis, which all belong to the Pomacentridae family).

Table 4. ANOVA table of PERMANOVA: Permutational Multivariate Analysis of Variance Using Bray- Curtis Distance Matrices of 43 most abundant sites and species. Df – degrees of freedom, SumsOfSqs – sum of squares, MeanSqs – mean squares.

Figure 15. Tabasco plot - classification of the 43 most abundant species according to the dissimilarity in their appearance in the different samples; warm colors in the heat map represent high relative abundance. Dendrogram represents hierarchical clustering using the average linkage method. The table on the left is the families’ classification; different colors represent different families. The data is log transformed.

The relationship between species density (abundance normalized to sample volume) and environmental variables can be also examined at the species level. For example, the variation in environmental variables and date explained ~73% of the variation in Chromis pelloura densities using General Additive Models analysis (Figure 16, Table 5; for more details about this analysis see the Methods section). It is evident that the density of C. pelloura is higher between March and May, peaking in April, which corresponds to a specific temperature range (22.2-22.7 °C). This pattern diminishes when examining this correspondence in other species, e.g. Chromis viridis (~1% of the variation explained, Figure 16, Table 5), or at the family (Pomacentridae) resolution (~33% of the variation explained, Figure 17, Table 6).

Figure 16. The effect of environmental variables and date (Month2) on Chromis pelloura densities from the General Additive Model analysis. Grey areas represent confidence bands for smooths (±1 se). On the Y-axis, the number next to the covariate name is the estimated number of degrees of freedom of the smooth. All nets with CTD data (N=205) were used for this analysis.

Table 5. ANOVA table of the General Additive Model analysis examining the effect of environmental variables and date (Month2) on Chromis pelloura densities. EDF – estimated degrees of freedom for each model parameter.

Figure 17. The effect of environmental variables and date (Month2) on pooled Pomacentridae species densities from the General Additive Model analysis. Grey areas represent confidence bands for smooths (±1 se). On the Y-axis, the number next to the covariate name is the estimated number of degrees of freedom of the smooth. All nets with CTD data (N=205) were used for this analysis.

Table 6. ANOVA table of the General Additive Model analysis examining the effect of environmental variables and date (Month2) on the pulled densities of Pomacentridae species. EDF – estimated degrees of freedom for each model parameter.

A water parcel tracing simulation (Figure 18, based on the model described in Berenshtein et al. in review) illustrates that a water mass in the gulf, if sampled and then resampled 24 hours later, will have an 80% identity in water particles. Therefore, the high similarity in taxonomic composition in sample pair 158 and 173, for example, which were captured on consecutive days, is most likely due to spatio-temporal auto- correlation. Furthermore, as the GOA is characterized by a predominant chain of eddies (e.g. Biton and Gildor 2016), consisting of anti-cyclonic “entraining” eddies, it is likely that such an eddy entrains larvae from its proximate region (scales of ~40 km, and ~2 days) and “flushes” the contents of the eddy upon breakdown. Flushing was increased during the winter times, perhaps due to increased flux out of the GoA (Biton and Gildor 2011).

Figure 18. Percentage of particles remaining in the northern tip of the Gulf of Aqaba across a ten- day simulation (beginning on the 1st of each month). 10000 passive tracers were released at the beginning of each month during 2010.

4.8. Spatiotemporal Distributions Throughout Ontogeny 4.8.1. Temporal distribution As shown in Figures 13 and 19, the larvae of Pseudanthias squamipinnis were uniformly found throughout the summer, fall and winter. P. taeniatus larvae were rare during the summer, increased in numbers during the fall and were most abundant during the winter. The larvae of P. heemstrai occurred mostly during the summer with decreasing numbers in the fall and were not present in winter samples at all. None of the species were found in the spring samples. However, this could be due to low sampling effort in this season (see Appendix Table I detailing the sampling effort).

4.8.2. Vertical distributions Smaller larvae of Pseudanthias squamipinnis were somewhat more vertically spread than the later-stage larvae; this pattern was significant for both the total annual distribution and the fall distribution using quantile regression (Tables 7 & 8; Figure 19). The 0.75 quantile regression analysis found no significant trends for the distributions of P. teaniatus and P. heemstrai.

Figure 19. Spatio-temporal distributions of the Pseudanthias species: P. squamipinnis, P. taeniatus and P. heemstrai (pictures taken by Joe Paul, John Randall and Maroof Khalaf, respectively).

Table 7. The 0.75 quantile regression analysis for the annual distribution of Pseudanthias squamipinnis

Table 8. The 0.75 quantile regression analysis for the fall distribution of Pseudanthias squamipinnis

Moreover, during the winter the distribution appears to further extend to deeper water for the earlier-stage P. squamipinnis (down to the 100-140m depth layer) and P. taeniatus (down to the 140-180m depth layer; Figure 19). The vertical distribution of P. heemstrai extends from the surface water to the 50-75m depth layer during the summer, yet during the fall it was found to occupy the shallowest depth layer only (0-25m; Figure 19).

5. Discussion

Our aim was to obtain high-resolution insights into reef-fish larval ecology. Specifically, we sought to quantify the species-level distribution patterns of the entire community of reef-fish larvae at the Gulf of Aqaba across space and time, a task that was never accomplished before, mainly due to technological limitations.

5.1. Comparison between Larval Pool and Benthic Assemblage 5.1.1. Species representation in a local assemblage and the larval pool The size and stability of populations at small spatial scales depends on a set of hierarchical processes involving larval supply (which species arrive to the site), settlement and post-settlement processes (which larvae survive after arrival), which ultimately determine the number of recruits replenishing the population (Roughgarden et al. 1988; Pineda et al. 2010). The relative importance of larval supply is a central question in reef-fish ecology, but to date it could not be directly assessed, certainly not for a wide range of species. Here we report on the first comparison between the numeric representation of species in a regional larval pool (i.e. larval supply) and their representation in a local assemblage (Brokovich et al. 2008). A positive cross-species relationship of varying strengths was found within the Serranidae, Labridae and Pomacentridae families (Figure 10). The relationship was found despite a >4 year separation between the sampling of the adult and larval data sets. As the larval pool probably represents the reproductive output of a meta-community spanning an area at least an order of magnitude larger than the local adult assemblage, we view these relationships as indicative that, at least in some families, the larval supply plays a major role in determining the local adult density. However, interesting exceptions from this correlation were also observed. Cases where species are abundant in the adult community but rare in the larval pool (Figure 11a) could be explained either by inter-annual differences in larval supply or by the unsuitability of the sampling technique to specific species (e.g. species located near the bottom layer during their early life history, where the

MOCNESS cannot sample, or species performing DVM in which they ascend to shallower water layers only at night and were therefore not included in our samples). On the other hand, cases where the larval pool abundance was much greater than that of the adult fish were also observed (Figure 11b), suggesting that the rarity of adults is not determined by lack of larval supply but rather by local ecological conditions disfavoring these species in the specific area. An extreme case is where the larvae are present in the pelagic environment but no adult population is established.

5.1.2. Documentation of non-native species One of the advantages of unbiased metagenomic sequencing is the ability to discover, in the larval pool, species whose presence as adults has never been documented in the habitat of interest (Limouzy-Paris et al. 1994). The fish fauna of the Gulf of Aqaba consists of a sub-sample of the species known from the Red Sea, and a long-standing question is whether the missing species have failed to arrive or failed to become established (Kiflawi et al. 2006; Dibattista et al. 2015). Using strict criteria, we searched the larval pool for Red Sea species that have never been documented as adults in the Gulf of Aqaba. Surprisingly, we found species in the larval pool that are unknown in the adult fish communities of the Gulf. Total larval densities, our proxy for propagule pressure, did not differ significantly between native (Gulf of Aqaba) and non-native (Red Sea) species. Specifically, both shared the same relationship between total density and incidence. In addition, little distinction was found between native and non-native species, based on the location and time of samples in which they were captured. Together these findings indicate that for multiple Red Sea species, their absence from the Gulf of Aqaba is not due to the lack of larval supply, and thus may indicate failure to colonize due to ecological barriers at or post-settlement. A possible mechanism explaining the observed influx of species arriving from the Red Sea to the GOA is water mass transport in this direction, which is supported by the salinity profile (Figure 20; provided by The Israel National Monitoring Program at the Gulf of Eilat; Shaked and Genin 2015). In general, lower salinity waters are thought to arrive from the Red Sea (Biton and Gildor 2011c). It is evident that during the

sampling year (2010-2011) there was a mass influx of water coming from the south, characterized by lower salinity; this may be related to the local water mixing depth or water temperatures in the south. This transport of water occurs periodically at varying strength throughout the years and may facilitate repeated introductions of non-native species arriving from the Red Sea.

Figure 20. Salinity (‰) profiles at Station A (on the Israeli/ Jordanian/Egyptian border at ca. 700 meters depth; northern GOA) from 2004 to 2014. Figure provided by The Israel National Monitoring Program at the Gulf of Eilat (Shaked and Genin 2015).

5.2. High-Resolution Spatio-Temporal Larval Distribution Fish larvae can actively control their vertical position in the water column from early ontogenetic stages (Leis 2006, 2010; Irisson et al. 2010). While it is well established that vertical position can affect larval dispersal trajectories (e.g., when current velocity is vertically non-uniform) (Paris and Cowen 2004; Leis 2007), little is known of the extent to which species differ in their preferred depths. Moreover, there is almost no account of the vertical distribution of reef fish larvae among seasons (Gray 1996). We examined the spatio-temporal distributions of larvae from the five most

abundant families of reef-associated fish in our set, encompassing ~60% of the reef-associated larvae we identified. Importantly, our data shows species-specific depth distributions in all families examined (Figure 13), and in some cases even between congeneric species (e.g. differences between Chromis viridis and Chromis pelloura). The existence of species-specific spatio-temporal distribution varying even amongst closely related taxa emphasizes the need for increasing the taxonomic resolution in such studies. This kind of species- specific information is crucial for connectivity and dispersal modeling efforts.

5.3. Larval Distributions and Influential Environmental variables In this chapter, we examined the influence of multiple environmental variables on the distributions and assemblages of coral reef larval fish at the species-level taxonomic resolution. Local environmental variables explained almost half of the variability in larval pool structure with salinity, light intensity, chlorophyll and temperature responsible for most of the variance. Samples that are similar with respect to their taxonomic composition are often found in similar environmental conditions (e.g. sample pairs in Figure 14: 312 and 318 with similar Chl, salinity and light intensity; 242 and 316 with similar temperature, salinity and light intensity). The high resemblance in different biophysical factors between samples sharing similar taxa is often seen in samples that were captured in a relatively high temporal proximity (e.g. sample pair 158 and 173, captured on consecutive days). However, high similarities among environmental variables between samples having highly similar assemblages were also observed in temporally distinct samples (e.g. sample pairs 242 and 316, 156 and 230, with each sample pair captured about two months apart). While the former similarity in assemblages may merely reflect a spatio- temporal auto-correlation due to repeated sampling of the same water parcel, supported by simulations tracing water mass flushing time in the gulf (Figure 18), the later similarity between assemblages is likely to reveal larval preferences for specific environmental conditions, leading to behavior differences (or differential survival).

A further examination of Figure 14 reveals that some species appear in a specific range of environmental conditions, such as Scarus fuscopurpureus (Scaridae), which is consistently found in high-chlorophyll, highly saline and low-temperature waters, or Corythoichthys flavofasciatus (Syngnathidae), which is consistently found in water conditions characterized by low chlorophyll, moderate salinity and high temperature. Other species, however, such as Pseudanthias squamipinnis, are present in a wide range of bio-physical conditions (Figure 14). The appearance of species in particular environmental settings could be attributed to a combination of species-specific preferences for certain conditions and reproductive seasonality, which at longer time scales is also affected by the optimality of environmental variables. Figure 15 shows the similarities among species with respect to the samples they were collected in, revealing that con-familial species to some extent tend to appear in similar samples. However, it is evident that while some species that were consistently captured together are closely related (for example the Pseudanthias congeners P. squamipinnis and P. taeniatus, belonging to the Serranidae family), others are more taxonomically distant (for instance Scarus fuscopurpureus and Chromis pelloura belonging to the Scaridae and Pomacentridae families respectively). Species that are consistently found together may have similar affinities to specific environmental conditions at the pelagic phase and a reproductive seasonality that at least partially coincides. These species, such as the discussed above S. fuscopurpureus and C. pelloura, may share similar ecological roles in the pelagic system (e.g. having similar physiological capabilities, consuming similar prey and avoiding the same type of predators). These assumed similarities in ecological roles, however, are likely to change as each species undergoes metamorphosis and adapts to its respective benthic habitat. In the case of S. fuscopurpureus and C. pelloura, the former becomes a shallow water (2-20m) algal scraper (Lieske and Myers 2004) and the latter becomes planktivorus and occupies deeper water (30-50m), hence occupying different ecological niches as adult fish in the reef. The density of the Pomacentridae family was analyzed with respect to potentially influential environmental variables (Figure 17). It is apparent that at the family level higher abundance occurs in high salinity conditions. However, if we focus on Chromis pelloura alone (a member of the family), it is evident that while individuals are observed in a wide range of conditions, the densities are highly correlated with

temperatures of ~22.50C during March to May. The persistence of high densities across several months supports the idea that the observed correspondence between larval composition and environmental variables is not strictly due to auto-correlation, since during the course of the auto-correlated peak in Feb-May (~60 days, Figure 18), the northern tip of the GOA could have been “flushed” approximately 5 times. This further supports the existing paradigm of active swimming and dispersal of coral reef fish larvae (e.g. Leis 2010). The distinctiveness of C. pelloura’s distribution patterns with respect to environmental conditions in comparison with the entire Pomacentrid family further highlights the importance of species-level resolution in studies examining larval fish ecology. In this chapter, we have examined how various environmental conditions correspond with larval dynamics at an unprecedented taxonomic level of species. We found that larval fish spatio-temporal distributions and assemblages are considerably correlated with the local bio-physical factors, suggesting larval preferences for specific environmental conditions. In some cases, species of the same family were shown to have a tendency to coincide with similar environmental conditions. We hypothesize that similarity among samples collected at close temporal proximity may be partially related to auto- correlation of the water mass. Understanding how different factors in the pelagic environment affect larval dynamics is essential for reef ecosystem conservation initiatives. Specifically, this knowledge can potentially allow scientists to predict possible influences of global changes in temperature, salinity etc. on the local larval pool and hence on the adult benthic fish community at the reefs. In addition, the different spatio-temporal characteristics at the species- vs. family level, should be applied and compared in biophysical models. This would shed important light on the difference in larval dispersal and connectivity between these two taxonomic resolution levels.

5.4. Spatiotemporal Distributions Throughout Ontogeny In this part of the study, we aimed to shed light on the ontogenetic patterns in the spatial distributions of three Pseudanthias species in the

northern tip of the Gulf of Aqaba. The study shows that in the case of P. squamipinnis the vertical distribution of the younger larvae is to some extent more spread than that of the more developed individuals. More specifically, while the larvae in their early stages are present in a wide range of depths, they narrow their depth distribution into shallower waters as they develop. This means that smaller larvae experience a wider range of conditions that have the potential to inﬂuence growth, survival and dispersal, such as temperature, light intensity, food availability, predators and current velocity, which vary vertically (Sverdrup et al. 1942). In contrast, the more mature larvae chose a more specific set of conditions by narrowing their depth range. The narrowing of the vertical distribution through ontogeny and the apparent differential use of shallower water by larger larvae is opposite to the patterns described in other coral reef locations, where older larvae mostly descend (e.g. Huebert et al. 2011) and widen their vertical spread (e.g. Irisson et al. 2010). This difference in ontogenetic depth choice may be due to either variations in behavior between different locations (Leis and Carson-Ewart 1999; Trnski 2002; Leis 2004; Leis et al. 2006), or to difference in the species examined (Leis 2004), or possibly to a combination of both. Current velocity is often vertically heterogeneous (Sponaugle et al. 2002); generally the current becomes weaker with depth, and so it has been hypothesized that it is advantageous for larvae favoring to stay in a specific region to occupy deeper layers (according to their physiological capabilities that develop throughout ontogeny, i.e. vision). One example of the benefits of occupying deeper currents is that from the Straits of Florida (SOF) where larvae at shallow waters can potentially get carried north by the stronger flow of the Florida Current, and hence may exit their habitat range (Huebert et al. 2011). Therefore, in this location a downward ontogenetic shift may facilitate increased settlement success and hence play an adaptive role. Another example is that of Paris and Cowen (2004), where the utilization of deeper water was found to be a mechanism enabling larvae to return to their natal reef, thus facilitating self-recruitment.

Exceptions for the widespread ontogenetic downward shift were exhibited by the Pomacentridae family in French Polynesia (Irisson et al. 2010) and the serranins of the Serranidae family in the SOF (Huebert et al. 2011), which had displayed an upward shift similar to the pattern demonstrated by P. squamipinnis in the current study. On the other hand, the anthiinae sub-family of the Serranidae (SOF), which is most closely related to our focal species (Pseudanthias sp.), shared the widespread ontogenetic downward shift pattern, opposite to our findings. While the current regime in the gulf was shown to be vertically stratified (Carlson et al. 2012) with stronger currents mostly characterizing the shallow strata, as observed in other locations, this may not affect larval dispersal as it does in other systems. I suggest that the possible inverse ontogenetic shift of the local P. squamipinnis larvae (which was observed both annually and in the fall season) may be related to the unique, enclosed and narrow (14-26 km) shape of the GOA. As all locations within the gulf are relatively near a fringing reef, the risk of being lost by unfavorable advective currents is relatively low in comparison to other systems. Therefore, all else equal, there may be no benefit for older larvae to be vertically positioned in deep water. On the contrary, it is possible that shallow larval positioning is a local biophysical mechanism to ensure maximal dispersion via rapid larval transport, resulting in increased population connectivity. The idea is supported by the lack of genetic structure for P. squamipinnis in the GOA (Froukh 2007) and by simulations conducted for other meroplankton organisms (e.g. corals) showing exceptionally high connectivity in the area (Meir et al. 2005; Fuchs et al. 2006). Note however that Increased dispersal is not strictly advantageous in comparison to larval retention, as the latter may be advantageous due to the fact that the parents successfully reproduced, indicating – to a certain extent – that the habitat is suitable. While the more developed P. squamipinnis larvae seem to occupy shallow waters, the younger larvae are vertically spread, occupying a wide depth range from the surface to 100m (and even deeper in winter). Hence, light intensity does not seem to be a limiting factor preventing these younger and less visually developed larvae from penetrating deeper layers in the

clear oligotropic waters of the Gulf. The spread of earlier stages into deeper waters may be related to their swimming performance, since it might be advantageous for smaller larvae having limited swimming capabilities and energetic resources to dwell in weaker currents (Irisson et al. 2010). This is supported by the fact that the Serranidae family (to which P. squamipinnis belongs) is characterized by having extremely low swimming speeds in the early larval stages (up to 7-9mm) in comparison to other coral reef taxonomic groups (Leis 2010). Their poor swimming capacity, however, improves rapidly, and by the time the larvae are pre-settlement stage they are described as one of the fastest coral reef fish. The exceptionally good swimming abilities may enable the later-stage larvae to endure and utilize the faster shallow currents they were found in. Food availability may also account for differential vertical distributions among species and among developmental stages, as it has been shown that coral reef fish larvae have species-specific diets that change throughout ontogeny (Llopiz and Cowen 2009). However, it is likely that the ontogenetic vertical distributions are derived from a trade-off between local biophysical parameters such as current velocities, food availability and predation. A probable scenario is that the younger larvae balance the trade- off between slower currents and low predation at depth versus higher food concentrations at shallower waters. In addition to the ontogenetic upward shift, a weak seasonal ontogenetic distribution pattern is also apparent. In the winter season the vertical spread of P. squamipinnis and P. taeniatus somewhat increases and penetrates waters down to the 100-140m and 140-180m strata, respectively (note however that the sampling effort in winter at these depths (deeper than 100m) was higher than during the summer and fall: 19 vs. 16 and 9 samples respectively). The water column in the Gulf of Aqaba is stratified in summer and deeply mixed in winter (Genin et al. 1995; Fine et al. 2013). During mixing, planktonic organisms, which cannot resist vertical mixing currents, are mixed along with the water column (Farstey et al. 2002). This suggests that the younger Pseudanthias sp. in our study site,

which were most probably nourished by the smaller and less current resistant plankton, were either mixed along with it or were following their prey. It should be noted that while the patterns observed may reflect an active narrowing of depth range by larval vertical positioning behavior as discussed above, it may also be the result of selective mortality in case larvae occupying deeper strata do not survive (Irisson et al. 2010; Huebert et al. 2011). In addition, larvae at different developmental stages may interact with the bottom for shelter and enhance retention. Such larvae would not be captured in our sampling as we kept at least 20 m, safety distance from the bottom. Finally, due to the fairly wide net mesh size (640m) the sampling missed out very small (and young) larvae. It is likely that soon after hatching, newly hatched larvae will be found in shallow waters due to eggs positive buoyancy.

6. Conclusions and Significance

The metagenomic technique has allowed unbiased quantitative analysis of large ichthyoplanktonic communities at a taxonomic resolution never previously possible, yielding important ecological insights. True quantitative estimation of the number of individuals per sample is a basic parameter needed in ecological studies (Lawton 1999; Hubert et al. 2015; Leis 2015). Our method enables direct quantification of larval supply, both in terms of species composition and of their relative influx, and, crucially, provides the ability to consider hundreds of species simultaneously to assess community-level ecological processes, revealing the following ecologically meaningful discoveries:

• The larval supply of some taxonomic groups plays a major role in shaping the local adult density. However, on the other side of the scale, other taxonomic groups exhibit large discrepancies between larval and adult relative abundance. The extreme cases of this phenomenon naturally lead to the next discovery.

• The surprising finding of a high influx of non-native species into this habitat. This capacity to detect potentially invasive species at their dispersal stage, before establishing viable populations, could be a strong asset in conservation and management of reef and other marine ecosystems.

• Species-specific spatio-temporal distribution was found, which in some cases included also closely related species. The results strengthen the idea that species sharing the same familial affiliation should not be considered as having the same behaviors and therefore distributions and dispersal outcomes.

• The distributions and taxonomic assemblages of larval fish across space and time are significantly correlated with specific environmental parameters, highlighting larval preferences for specific conditions.

• Some species have a similar tendency to coincide with other con-familial species with regard to their affinity to the various environmental conditions.

• We assume that similarity among samples collected at close temporal proximity may be partially related to auto- correlation of the water mass.

• Different developmental stages within a species may behave differently. Increasing the knowledge on the behaviors of specific species throughout their development will greatly improve our understanding of the journey and determinants for success of coral reef fish larvae.

7. Contribution and Support

This research was a part of a large collaboration between the following four research labs:

The labs of both of my supervisors, Dr. Moshe Kiflawi (Ben-Gurion University and the Interuniversity Institute for Marine Sciences in Eilat [aka IUI]) and Prof. Claire Paris (Rosenstiel School of Marine and Atmospheric Science), the labs of Dr. Roi Holzman (Tel-Aviv University and the IUI) and Prof. Rotem Sorek (Weizmann Institute of Science).

More specifically,

The Kiflawi lab led the larval sampling as well as the ecological interpretation and analyses.

The Paris and Kiflawi labs led traditional ichthyoplankton taxonomic work of visual identification of keys and morphological characteristics, for the construction of the image library,

The Holzman lab led the sampling and sequencing of the local ichthyofauna, for the construction of a reference COI library (Methods sections 3.9-3.10).

The Sorek lab had directed the development and performance of the metagenomic sequencing pipeline, bioinformatics and the quantification model (Methods sections 3.11).

Igal Berenshtein from Kiflawi lab performed the Lagrangian tracking simulations (Methods sections 3.16).

The staff of IUI and MSS assisted in the larval sampling field work.

The study was supported by the United States – Israel Binational Science Foundation (BSF grant 2008/144) to MK and CP and by the Israeli Ministry of the Environment (grant 111-51-6) to MK and RH, as well as the Nancy & Stephen Grand Israel National Center for Personalized Medicine. Field sampling was supported in part by the World Bank, as part of the Red Sea–Dead Sea Water Conveyance Study Program.

8. Published Work

Kimmerling N, Zuqert O, Amitai G, Gurevich T, Armoza-Zvuloni R, Kolesnikov I, Berenshtein I, Melamed S, Gilad S, Benjamin S, Rivlin A, Ohavia M, Paris CB, Holzman R, Kiflawi M, Sorek R (2017) Large scale, species-level ecology of reef fish larvae via quantitative metagenomics. . Nat. Ecol. Evol., 1. doi:10.1038/s41559-017-0413-2.

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Appendix

Table I. Sampling effort for each season according to depth Depth Range (m) Summer Fall Winter Spring

0-25 63 24 31 5

25-50 42 16 21 3

50-75 43 16 21 3

75-100 22 7 18 3

100-140 9 7 11 0

140-180 7 2 8 0

Number of Sampling days 11 5 9 2

תקציר

מחזור החיים של דגי שונית האלמוגים הינו דו-שלבי ומורכב משלב החיים הבוגר ומהשלב הלרוולי. בעוד שהדגים הבוגרים שוכני קבע בשונית, צאצאיהם יוצאים מסביבת השונית אל הים הפתוח לתקופה של מספר ימים עד שבועות )התקופה הלרוולית(, בסופה הלרוות עוברות מטמורפוזה ומתגייסות לשונית. השלב הלרוולי הינו שלב בעל חשיבות רבה מכיוון שמהווה את שלב ההפצה של דגי השונית, ועל כן מכתיב את המאפיינים הדמוגרפיים והאקולוגיים של חברת הדגים הבוגרת. עד כה מחקרים העוסקים בחברת לרוות דגי השונית נעשו ברמת המשפחה בשל מגבלות טכניות של זיהוי הדגים. בעבודה זו, באמצעות שילוב של דיגום נרחב, זיהוי מורפולוגי של לרוות, ריצוף פרטני של דגים בוגרים, ריצוף מתקדם מטהגנומי של דגימות הלרוות והערכתן הכמותית, הצלחנו ללכוד ולחקור 16,695 לרוות דגים המייצגות למעלה מ 400- מִ ינים. העבודה מציגה מחקר ראשון מסוגו העוסק בפן האקולוגי של כלל המאגר הלרוולי ברמת המין, ושופך אור על מספר שאלות אקולוגיות מהותיות עליהן לא ניתן היה לענות לפני כן העוסקות בדינאמיקת לרוות דגי שונית האלמוגים בכלל ובמפרץ אילת בפרט. ראשית, מצאנו מתאם משמעותי בין שכיחויות הדגים הבוגרים והלרוות שלהם, דבר המראה כי אספקה לרוולית ממלאת תפקיד מרכזי בקביעת צפיפות הדגים הבוגרים בשונית. יתר על כן, העבודה מתעדת לראשונה את הימצאותן במפרץ של לרוות של מינים אשר שלבם הבוגר מעולם לא נצפה באזור, דבר המצביע על מחסומי גיוס כגורם מרכזי בהעדר הבוגרים בחברות הדגים המקומיות. כמו כן, אנו חושפים את פיזורם הספציפי למין של לרוות החברה המקומית בזמן ובמרחב, ובכך מראים שגם מינים קרובים נבדלים בהעדפת עומק. בנוסף, אנו מראים כי ישנה התאמה בין הפיזור וההרכב הלרוולי לבין פרמטרים סביבתיים )בעיקר מליחות, עוצמת אור, כלורופיל וטמפרטורה(, דבר המדגיש את העדפתם של המינים לתנאים ביו-פיזי ספציפיים בשלב הלרוולי. לבסוף, אנו מדגימים את הפיזור הלרוולי לאורך האונטוגנזה של שלושה מיני פזיות. ומראים כי הפזית הים סופית צמצמת את פיזורה האנכי לאורך האונטוגנזה כך שבשלבים המאוחרים יותר מתרכזת במים רדודים ובכך מדגימה דפוס הפוך לזה שנצפה בדרך כלל באזורי שונית אחרים. השילוב של דיגום נרחב, זיהוי מורפולוגי, וזיהוי מולקולארי כמותי ברזולוציה גבוהה מספק כלי רב עצמה חסר תקדים לחקר האקולוגיה של לרוות דגים ודינאמיקת חברות הדגים.

מילות מפתח: לרוות דגי שונית אלמוגים, מפרץ אילת, המאגר הלרוולי, זיהוי ברמת המין, הרכב מינים, מינים לא מקומיים, הפצה במרחב ובזמן, גורמים סביבתיים, אונטוגנזה.

הצהרת תלמיד המחקר עם הגשת עבודת הדוקטור לשיפוט

אני החתום מטה מצהיר/ה בזאת: )אנא סמן(:

__√_ חיברתי את חיבורי בעצמי, להוציא עזרת ההדרכה שקיבלתי מאת מנחה/ים.

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__√_ בעבודה נכלל חומר מחקרי שהוא פרי שיתוף עם אחרים, למעט עזרה טכנית הנהוגה בעבודה ניסיונית. לפי כך מצורפת בזאת הצהרה על תרומתי ותרומת שותפי למחקר, שאושרה על ידם ומוגשת בהסכמתם.

תאריך ____23/12/2016__ שם התלמיד/ה ___נעמה קימרלינג_____

חתימה ______

העבודה זו נעשתה בהדרכתם של:

ד"ר משה כפלוי המחלקה למדעי החיים הפקולטה למדעי הטבע אוניברסיטת בן גוריון בנגב

פרופ' קלייר פריס המחלקה למדעי האוקיינוס בית ספר רוזנסטייל למדעי הים והאטמוספרה אוניברסיטת מיאמי

דינאמיקה בזמן ובמרחב במאגר לרוות דגי השונית ברמת המין במפרץ אילת

מחקר לשם מילוי חלקי של הדרישות לקבלת תואר "דוקטור לפילוסופיה"

מאת

נעמה קימרלינג ברנשטיין

הוגש לסינאט אוניברסיטת בן גוריון בנגב

אישור המנחה ______בתאריך: 21 במאי 2018

אישור דיקן בית הספר ללימודי מחקר מתקדמים ע"ש קרייטמן

______

תשרי תשע"ז דצמבר 2016

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