Fish Movement in the Red Sea and Implications for Marine Protected Area Design

Home , Acanthurus blochii, Acanthurus gahhm, Acanthurus leucosternon, Amblygobius phalaena, Bluespotted cornetfish, Caesio suevica

Thesis by

Irene Antonina Salinas Akhmadeeva

In Partial Fulfillment of the Requirements

For the Degree of

Master of Science

King Abdullah University of Science and Technology

Thuwal, Kingdom of Saudi Arabia

April, 2021

EXAMINATION COMMITTEE PAGE

The thesis of Irene Antonina Salinas Akhmadeeva is approved by the examination committee.

Committee Chairperson: Prof. Michael L. Berumen Committee Co-Chair: Dr. Alison Green Committee Members: Dr. Darren Coker, Prof. Rusty Brainard

Irene Antonina Salinas Akhmadeeva

ABSTRACT

Fish Movement in the Red Sea and Implications for Marine Protected Area Design

Irene Antonina Salinas Akhmadeeva

The Red Sea is valued for its biodiversity and the livelihoods it provides for many. It now faces overfishing, habitat degradation, and anthropogenic induced climate-change. Marine Protected Areas (MPAs) became a powerful management tool to protect vulnerable species and ecosystems, re-establish their balance, and enhance marine populations. For this, they need to be well designed and managed. There are 15 designated

MPAs in the Red Sea but their level of enforcement is unclear. To design an MPA it is necessary to know if it will protect species of interest by considering their movement needs. In this thesis I aim at understanding fish movement in the Red Sea, specifically home range (HR) to inform MPA size designation. With not much empirical data available on HR for Red Sea fish, I used a Machine Learning (ML) classification model, trained with empirical literature HR measurements with Maximum Total Length (L Max), Aspect Ratio (AR) of the caudal fin, and Trophic Level as predictor variables. HR was classified into 5 categories: <.1 km, 0.1- 1.0 km,

2.0- 5.0 km, 5.0- 20 km, and >20 km. The model presents a 74.5% degree of accuracy. With it, I obtained the

HR category for 337 Red Sea fish species. Having MPAs with a maximum linear dimension of at least 10km will meet the requirements of 90% of fish species evaluated in the model, which were small to medium size families (damselfishes, butterflyfishes, small wrasses, cardinalfishes, gobies and blennies). This percentage does not include larger species likely to move over much greater distances (10s, 100s or 1000s of km) (e.g., medium to large jacks, snappers,, groupers, sharks and rays). 60% of the Red Seas designated MPAs have the potential, if enforced as a No Take Area (NTA), to benefit more than 95% of reef fishes. However, larger

MPAs will be required to protect more wide-ranging species. TRSP project in Al Wadj is proposing to close the entire SEZ to fishing. If they are successful in implementing and enforcing this fishing ban, TRSP will be the largest no take area in the Red Sea (~160 km long) that is likely to not only protect all of the species evaluated in the model, but also most wide-ranging species. Therefore, TRSP is not only likely to achieve and surpass its stated goal of increasing current fish biomass by 30%, but also to provide benefits to surrounding areas through the spillover of adults, juvenile and larvae to fished areas. 5

ACKNOWLEDGEMENTS

I would like to thank my committee chair, Prof. Michael L. Berumen, my co-chair Dr.

Alison Green, and my committee members, Dr. Darren Coker, Prof. Rusty Brainard for their guidance and support. I would like to thank as well Dr. Susana Carvahlo and Ute

Langner from the Red Sea Research Center.

My appreciation also goes to my KAUST family and my colleagues at KAUST, their continuous support was key to the development of my research. I also want to extend my gratitude to the KAUST University Library for always providing a suitable work space and for being the place where all of this thesis was written.

Finally, my eternal gratitude to my parents who keep encouraging a supporting me from a distance.

TABLE OF CONTENTS

EXAMINATION COMMITTEE PAGE ...... 2 COPYRIGHT ...... 3 ABSTRACT ...... 4 ACKNOWLEDGEMENTS ...... 5 TABLE OF CONTENTS ...... 6 LIST OF ABBREVIATIONS ...... 8 LIST OF ILLUSTRATIONS ...... 9 LIST OF TABLES ...... 12 Chapter 1. INTRODUCTION ...... 13 1.1. Marine Protected Areas...... 13 1.2. Marine Protected Areas in the Red Sea ...... 16 1.3. Fish Movement for Marine Protected Area Design in the Red Sea ...... 22 1.4. Machine Learning to Inform Fish Movement ...... 25 1.5. Objectives ...... 26 Chapter 2. METHODS ...... 28 2.1. Red Sea Fish Home Range ...... 28 2.1.1. Empirical Movement Training Data Set ...... 29 2.1.1.1. Response Variable ...... 29 2.1.1.2. Predictor Variables ...... 32 a. Size ...... 32 b. Aspect Ratio of the Caudal Fin ...... 33 c. Trophic Level ...... 34 2.1.2. Prediction model for Home Range ...... 34 2.1.2.1. Classification model ...... 35 2.1.3. Red Sea Fish Home Range Predictions ...... 36 2.2. Capabilities of Current Red Sea Marine Protected Areas ...... 37 2.3. Case Study: The Red Sea Project Potential ...... 38 Chapter 3. RESULTS ...... 40 3.1. Predicting Home Ranges of Red Sea Reef Fishes ...... 40 3.1.1. Characteristics of Empirical Movement Training Data Set...... 40 3.1.1.1. Response Variable ...... 41 7

3.1.2. Prediction model for Home Range ...... 42 3.1.3 Red Sea Fish Home Range Predictions ...... 43 ...... 48 3.2. Recommended sizes of NTAs for Red Sea Reef Fishes ...... 49 3.3. Capabilities of Current Red Sea Marine Protected Areas ...... 50 3.4. Case Study: The Red Sea Project Potential ...... 54 Chapter 4. DISCUSSION ...... 58 4.1. Red Sea Fish Home Range ...... 58 4.2. Utility of Models and ML for Predicting Reef Fish Movement ...... 59 4.3. Capabilities of Red Seas existing Marine Protected Areas ...... 63 4.4. Case Study: The Red Sea Project Potential ...... 64 4.5. Recommendations and future directions ...... 66 Chapter 5. CONCLUSIONS ...... 68 Bibliography ...... 70 APPENDIX 1. Empirical data set extracted from Green et al., 2015 used to train the HR prediction ML model...... 79 APPENDIX 2. Red Sea data based used to obtain the HR predictions, and their IUCN status (https://www.iucnredlist.org/). Input data (Trophic Level, Maximum Length, and Aspect Ratio)...... 83 APPENDIX 3. Poster with recommended size of no take areas to protect some iconic fish species in the Red Sea ...... 93

LIST OF ABBREVIATIONS

AR Aspect Ratio (of the caudal fin of fish)

CBD Convention on Biological Diversity

COP Conference of the Parties

HR Home range

KSA Kingdom of Saudi Arabia

L Max Maximum length (maximum total length ever reported for a

fish species)

ML Machine Learning

MPA Marine Protected Area

NTA No Take Area

SEZ Special Economic Zone

SL Supervised Learning

PERSGA Regional Organization for the Conservation of the Environment

of the Red Sea and Gulf of Aden

TRSP The Red Sea Project

TRSDC The Red Sea Development Company

UVC Under Water Visual Census

LIST OF ILLUSTRATIONS

Figure 1.1. Map of the Red Sea showing the location of proposed, designated and inscribed MPAs. The statud of each MPA is provided in Table 1.1. MPAs represented in this map are officially listed in the World Database on Protected Areas (IUCN & UNEP-WCMC, 2019; UNEP-WCMC, 2016). Map by Ute Langer...... 20

Figure 1.2. Location of The Red Sea Project with the delimitation of the Boundary of The Red Sea Project Special Economic Zone(SEZ). (TRSDC, 2019). Map by Ute Langer...... 21

Figure 1.3. “Linear scale of movement of coral reef and coastal pelagic fish species. Number colors are: black (daily movements: home ranges, territories and core areas of use); blue (ontogenetic shifts); red (spawning migrations); and green (long-term movements of undetermined cause). 1, Seahorses (Hippocampus spp.); 2, anemonefishes (Amphiprion spp.); 3, most damselfishes (e.g. Dascyllus spp.); 4, most butterflyfishes (Chaetodon spp.); 5, some angelfishes (e.g. Centropyge spp.); 6, some wrasses (e.g. Halichoeres garnoti); 7, some surgeonfishes (e.g. Acanthurus lineatus); 8, orangespotted filefish (Cantherhines pullus); 9, some soldierfishes/squirrelfishes (Holocentrus spp./Myripristis spp.); 10, moray eels (Gymnothorax spp.); 11, bignose unicornfish (e.g. Naso vlamingii); 12, some snappers (e.g. Lutjanus carponotatus); 13, some groupers (most Cephalopholis spp.); 14, some groupers (Ephinephelus spp.); 15, some butterflyfishes (e.g. C. striatus); 16, some surgeonfishes (e.g. A. coeruleus and Ctenochaetus striatus); 17, some angelfishes (Holocanthus/Pomacanthus spp.); 18, some parrotfishes (some Scarus/Sparisoma spp.); 19, some snappers (e.g. L. ehrenbergii); 20, yellow tang (Zebrasoma flavescens); 21, twotone tang (Z. scopas); 22, some rabbitfishes (e.g. Siganus lineatus); 23, goatfishes; 24, bluespine unicornfish (N. unicornis); 25, some parrotfishes (e.g. Scarus rivulatus); 26, some grunts (e.g. Haemulon sciurus); 27, squaretail coralgrouper (Plectropomus areolatus); 28, Bermuda sea chub (Kyphosus sectatrix); 29, some parrotfishes (Chlorurus spp.); 30, ember parrotfish (S. rubroviolaceus); 31, goldspotted sweetlip (Plectorhinchus flavomaculatus); 32, some groupers (e.g. P. leopardus); 33, bigeye trevally (Caranx sexfasciatus); 34, some wrasses (e.g. Coris aygula); 35, some surgeon/unicornfishes (e.g. A. blochii and N. lituratus); 36, shoemaker spinefoot (S. sutor); 37, red snapper (L. campechanus); 38, some groupers (e.g. C. sonnerati and E. coicoides); 39, some emperors (e.g. Lethrinus nebulosus); 40, silver drummer (Kyphosus sydneyanus); 41, kingfishes (Seriola spp.); 42, giant trevally (C. ignobilis); 43, lemon sharks (Negaprion spp.); 44, blue-barred parrotfish (S. ghobban); 45, Indonesian shortfin eel (Anguilla bicolor bicolor); 46, bumphead parrotfish (Bolbometopon muricatum); 47, humphead wrasse (Cheilinus undulatus); 48, green jobfish (Aprion virescens); 49, leopard coralgrouper (P. leopardus); 50, whitetip reef shark (Triaenodon obesus) and nurse shark (Ginglymostoma cirratum); 51, grey triggerfish (Balistes capriscus); 52, gag grouper (Mycteroperca microlepis); 53, blacktip reef shark (Carcharhinus melanopterus); 54, manta rays (Manta spp.); 55, Galapagos shark (C. galapagensis); 56, Nassau grouper (E. striatus); 57, trumpet emperor (L. miniatus); 58, mangrove red snapper (L. argentimaculatus); 59, tuna; 60, marlin/swordfish; 61, tiger shark (Galeocerdo cuvier). Most illustrations were modified from Randall, Allen & Steene (1997), B. muricatum was modified from Gladstone (1986)”. Figure and quote from Green et al., 2015...... 24

Figure 2.1. Number of fish species per home size range (km) in the empirical data set modified from Green et al., 2015...... 30

Figure 2.2. Number of fish species in each of five revised home size ranges (km) categories (see Table 1). These categories are modified from Green et al., 2015...... 31

Figure 2.3. Aspect ratio (A = h2/s, where h = height of the caudal fin; s = surface area of fin) of a pelagic fish (A = 7.5) and a bottom dweller (B = 0.6). (Taken from Froese & Pauly, 2018; Pauly, 1989)...... 33 10

Figure 2.4. Supervised ML approach with a Bagged Decision Trees model followed to obtain a prediction of HR for Red Sea fish species. In order to train the model a training data set composed of predictor variables (Max Length, Aspect Ratio, and Trophic Level) with the empirical response (HR of fish species) was used. The model was validated with a 5 fold cross validation method. Once trained the model was used with predictor data from fish species in the Red Sea to obtain a prediction of HR for each...... 36

Figure 2.5. Location of sites in TRSP from which monitoring data was used to analyze the potential biomass increase. The light blue polygon represents the Special Economic Zone Boundary (SEZ). (TRSDC, 2019)...... 39

Figure 3.1. 3D scatter plot of Trophic Level versus AR versus L Max. Each point represents a fish species and the color represents its empirical HR...... 41

Figure 3.2. Confusion matrix for the number of observations obtained for each class. Blue colors indicate the amount of correct prediction, while red tones indicate the number of mistakes in the prediction...... 42

Figure 3.3. Confusion matrix showing the True Positive Rates of prediction and the False Negative Rates (%) for class groups 1to 5, which are the HR categories. Blue colors indicate a correct prediction, while red tones indicate an error in the prediction...... 43

Figure 3.4. 3D Scatter plot of Trophic Level versus AR versus L Max. Each point represents a fish species, and the color represents the HR classified prediction...... 44

Figure 3.5. Out of the Bag Permuted Predictor Importance for the model used. Each predictor has an estimated level of influence over the model prediction power...... 44

Figure 3.6. Percentage of fishery valuable families in each size category in the model...... 45

Figure 3.7. Percentage of Red Sea reef fish species used in the model in each IUCN Red List category in each HR category. CR- Critically Endangered; EN- Endangered; VU- Vulnerable; NT, Nearly Threatened; LC- Least Concern, DD- Data Deficient; N.E.- Not Evaluated, being CR the highest endangerment level for this list of species, with the rest of the categories following consecutively. ... 46

Figure 3.8. Fishery Important fisheries species based on fish market surveys in Saudi Arabia (Shellem et al 2021) sharks of the Red Sea, and their predicted home ranges (km). This includes 44 of the 337 Red Sea reef fish species in the model (Appendix 1). Species are grouped and colored coded by family...... 48

Figure 3.9. Red Sea fish species linear HR obtained with the ML Model, represented next to the sizes and nationalities of designated MPAs in the Red Sea. Note that this figure only displays representative and iconic Red Sea fish from the study. We can observe that fish like damselfish, small wrasses, and small groupers are more likely to be protected by MPAs that measure up to 2 km long, these are fish with a high territoriality but small movement ranges, mostly within a reef. Bigger fish that move further distances include medium size snappers, groupers and parrot fish will be likely to be protected by MPAs with a length of up to 10 km. Big fish with wider movement ranges, like small sharks, big wrasses, big parrotfish, groupers and trevallies will be protected my MPAs with lengths of up to 40 km. Wider ranging fish, like big sharks, tunas, and barracudas will potentially be protected by MPAs with a length above 40 km, but because this wide ranging fish can move longer distances and potentially migrate into other areas it is recommended that other management approaches are implemented to complement their protection. These can be fishing bans, and regulations over specific species. Red Sea’s larger MPAs (from 7 to 15) have the potential to protect more wide- ranging fish if managed as No Take Areas (NTAs)...... 51 11

Figure 3.10. Accumulation curve of species in the model protected by taking in account the double of their predicted HR (km). MPAS with a length closer to 10 km will potentially protect 90 % of the species studied in this thesis...... 53

Figure 3.11. Fish species biomass comparison between Sudan (dark blue) and Al Wajh (light blue) outer reefs. Bar graph on the left (a) shows the biomass percentage comparison between both areas. The bar graph on the right (b) shows the total biomass in per square meters (kg)...... 55

Figure 3.12. Biomass percentage for each fish family in Sudan and TRSP area in Al Wajh. The small map to the right of the graph shows the location of both areas in the Red Sea...... 56

LIST OF TABLES Table 1.1. List of the MPAs located in the Red Sea. This list includes areas that have been proposed, inscribed and officially designated in the different countries surrounding the Red Sea. (IUCN & UNEP- WCMC, 2019; UNEP-WCMC, 2016)...... 18

Table 2.1. Empirical home range (extracted from Green er al., 2015) recategorization, home range categories used in this study, and the number of species in each category (n)...... 31

Table 3.1. List of the species that represent the highest biomass in both Sudan and Al Wajh. As well as their predicted HR, the equivalent of this in a range of km and the recommended NTA length (km).. 57

Chapter 1. INTRODUCTION

1.1 Marine Protected Areas

Humans have impacted the planet historically, now marine ecosystems are facing increasing threats such as overfishing, habitat degradation, and anthropogenic induced climate-change. These impacts represent a major change in the ecosystem structure and functioning, jeopardizing its health and most importantly its resilience, making them vulnerable to a potential collapse (Hoegh-Guldberg et al., 2007; Jackson et al., 2001; Mcleod et al., 2019). Human use has altered oceans directly and indirectly, leaving almost no areas undisturbed (Halpern et al., 2008, 2015). Overfishing leads to the endangerment of a large number of marine species (Jackson et al., 2001;

Kappel, 2005), specifically, the decline of predatory fish worldwide causes cascade effects over the trophic ecology of coastal regions (Gladstone al., 2003; Myers &

Worm, 2003).

It is possible to help rehabilitate coastal marine ecosystems like reefs, by protecting physical areas that marine organisms inhabit, and by restoring basic ecosystem functions (Jackson et al., 2001; Lewis, 1986). One of the most efficient tools to achieve this are Marine Protected Areas (MPAs). A Protected Area is recognized by the

International Union for Conservation of Nature (IUCN) as “a clearly defined geographical space, recognized, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated 14

ecosystem services and cultural values” (Dudley, 2008). A Marine Protected Area is an equivalent constituent but established in a marine ecosystem. MPA goal vary but mainly embrace biodiversity’s protection, maintenance and enhancement of fishery species populations, and adaptation to climate change (Green et al., 2014), as well as re-establishing ecosystem balance (Hooker & Gerber, 2004).

In the last few decades MPAs became the marine management tool of preference in many places around the world (Halpern, 2003). They have proven to help maintain a stable and healthy coral cover (Selig & Bruno, 2010), enhance populations, increase biomass, recover ecosystem services, and provide spillover of fishing resources to surrounding areas (Aburto-Oropeza et al., 2011; Mumby & Harborne, 2010). But these benefits are not immediate, it takes time and continuous management for them to start showing some results, that is why their implementation must aim for a long term plan (Aburto-Oropeza et al., 2011; Selig & Bruno, 2010).

Unfortunately, MPAs are not secure against the effects of climate change, e.g. corals still have the potential to bleach inside MPAs (Jones et al., 2004).Still, MPAs play a very important role in aiding the ecosystem to adapt to and mitigate the effects of climate change (Simard et al., 2016). If well managed, they can play a crucial role in addressing small scale effects of climate change and build ecological resilience (which is the capacity to resist a disturbance or recover after a perturbation, while still maintaining its function) of areas by decreasing the impact of non-climatic change stressor points, juch as (overexploitation of resources, habitat destruction, water 15

pollution, and maintaining a positive ecological status) (Bates et al., 2019; IPCC, 2019;

Simard et al., 2016).

During the Paris Agreement of the United Nations Framework Convention on Climate

Change in 2016 it was announced that only 4% of the oceans was protected, but only

1.5-2% is strictly protected and correctly managed meeting conservation goals

(Edgar et al., 2014; Mora et al., 2006; Sala et al., 2018; Simard et al., 2016; UNFCCC,

2016). During the event, 195 signing countries agreed to a goal of protecting 30% of the oceans by 2030 and enhancing the management of existing MPAs is part of the goals (UNFCCC, 2016). Before this, the Strategic Plan for Biodiversity 2011-2020 was signed in 2010 by the Conference of the Parties (COP) during the Convention on

Biological Diversity (CBD) with the vision that by 2050 biodiversity would be valued, conserved, restored and wisely used (CBD, 2010). One of the 20 targets of this

Strategic Plan, Target 11, states that by 2020, at least 17% of terrestrial and inland waters, and 10% of coastal and marine areas are conserved by effective and equitable managed protected areas that take in account ecological representation and connectivity. This means that we will expect a rise in MPA establishment around the world. But, in order for them to achieve ecosystem enhancement and protection goals, they have to be well planned, designed, and managed (Green et al., 2015;

McLeod et al., 2009).

MPAs can exist with a variety of restrictions and allowed activities, such as: Partially protected areas, fishery reserves, fishery closures (if the closure is total they are 16

called No Take Areas (NTAs)), gear restriction zones, and buffer zones (Claudet,

2011). Each of them is unique and has different conservation or enhancement goals that depend on the needs of the ecosystem and/or the depending community. It is clear however, that the official designation of an MPA is not enough to assure that an ecosystem will be protected from human disturbances (Vine, 2019), a constant management and consisting monitoring need to take place.

1.2. Marine Protected Areas in the Red Sea

Described as one of the global hotspots of aquatic biodiversity, the Red Sea is home to a high number of endemic species (DiBattista et al., 2016). Considered as a living laboratory to research tropical marine systems and ensure the future of coral reefs, it is an area that needs to be effectively protected and managed (Kleinhaus et al., 2020).

This region is not only valued because of its biodiversity and uniqueness. Along its coastline more than 28 million people depend on the Red Sea as a source of income, food or recreation, and the number of people is continuously growing just as the need for food and an income source (Chalastani et al., 2020). The value of fisheries landing for the region is estimated to be 28 million USD per year while the income coming from tourism represents more than 12 billion USD each year (IOC-UNESCO & UNEP,

2016).

The need to sustain the source of these incomes is clear, since these resources are declining. Specifically, the fish composition of top predators (which represent many of the fishery important species) in Red Sea waters is fairly lower when compared to other places of the world (Khalil et al., 2017). Zooming in to Saudi Arabia’s coral reef fish resources, these are are overexploited compared to other Red Sea countries, e.g.,

Sudan (Kattan et al., 2017), with a trend that keeps inclining towards overexploitation

(Jin et al., 2012). Red Sea fish stocks and reefs do not have a bright future unless effective, science based MPAs with clear goals (Khalil, 2015) and good designs (Green et al., 2015; McLeod et al., 2009) are implemented and managed (Aburto-Oropeza et al., 2011).

The Red Sea is bordered by eight countries, from north to south: Israel, Jordan, Egypt,

Saudi Arabia, Sudan, Eritrea, Yemen, and Djibouti. In 1995, the Regional Organization for the Conservation of the Environment of the Red Sea and Gulf of Aden (PERSGA) was created with the mission to safeguard and manage coastal and marine ecosystems and resources (PERSGA, 2001). Initially, PERSGA identified 12 potential

MPAs for the creation of a Regional Network of MPAs for the Red Sea and Gulf of Aden, these were: Ras Mohammed National Park and Red Sea Islands in Egypt; Aqaba

Marine Park in Jordan; Farasan Marine Protected Area and Al Wejh Bank in Saudi

Arabia; the Straits of Tiran, a couple of sites in Djibouti; Aibat and Saad ad-Din Islands in Somalia; Sanganeb National Park, Dungonab Bay and Mukkawar Island in Sudan;

Socotra Islands Group National Protected Area, and the Bir Ali–Belhaf area in Yemen 18

(PERSGA, 2002; Salam, 2006). But only some were officially implemented, which means that not all of them made it to a designation level (Table 1.1)

Table 1.1. List of the MPAs located in the Red Sea. This list includes areas that have been proposed, inscribed and officially designated in the different countries surrounding the Red Sea. (IUCN & UNEP- WCMC, 2019; UNEP-WCMC, 2016).

Name of the MPA Country Designation Status

1 Tourism Development Area II Egypt Multiple Use Management Area Designated

Iles des Sept Freres ainsi que Ras Syan, 2 Djubuti Marine protected landscape Proposed Khor Angar et la foret de Godoria

3 RS Islands Egypt Developing Resources Protected Area Designated

4 Malahet Ras Shukeir Egypt Protected Area Proposed

5 El-Galala El-Qebalya Egypt Protected Area Proposed

6 Wadi El-Gemal - Hamata Egypt National Park Designated

7 Tourism Development Area I Egypt Multiple Use Management Area Designated

8 Elba Egypt Multiple Use Management Area Designated

9 Nabq Egypt Multiple Use Management Area Designated

10 Ras Mohammed Egypt National Park Designated

11 Abu Gallum Egypt Multiple Use Management Area Designated

12 Dahlac Eritrea Marine National Park Proposed

13 Ha-Yam Ha-Deromi Be-Elat Israel Nature Reserve Designated

14 Hof HaAlmogim BeElat Israel Nature Reserve Designated

15 Yanbu‘ Coastal Conservation Area Saudi Arabia Reserve Designated

16 Ra’s Kishran / Jazirat Sharifah Saudi Arabia Reserve Proposed

17 Ra’s Suwayhil / Ra’s al-Qasbah Saudi Arabia Resource Use Reserve Proposed

18 Al-Wajh Bank Saudi Arabia Resource Use Reserve Proposed

19 Makhshush Saudi Arabia Natural Reserve Proposed

20 Umm al-Qamari Islands Saudi Arabia Special Nature Reserve Designated

21 ‘Asir National Park Saudi Arabia National Park Designated

22 Farasan Islands Saudi Arabia Resource Use Reserve Designated

Sanganeb Marine National Park and World Heritage Site 23 Dungonab Bay - Mukkawar Island Marine Sudan Inscribed (natural or mixed) National Park

24 Ra’s Suwayhil / Ra’s al-Qasbah Saudi Arabia Resource Use Reserve Proposed 19

In 2016, two Red Sea sites were declared as World Heritage by UNESCO, Sanganeb

Atoll and the Dungonab Bay-Mukkawar Island Marine National Park, both in Sudan

(Vine, 2019). The plan to manage these areas was clear in previous PERSGA reports, but the last one was published in 2009 (Kleinhaus et al., 2020). The World Data Base of Protected Areas from the UNEP lists 24 MPAs for the Red Sea (IUCN & UNEP-

WCMC, 2019; UNEP-WCMC, 2016) (See Table 1.1. and Figure 1.1.). These areas have

3 different statuses; “Proposed”, “Designated”, and “Inscribed”, from which only 15 belong to the last two categories and there is no clear sign of any enforcement or fishing regulations in these areas. Each MPA belongs to different countries, most of which are in Egypt and Saudi Arabia being the countries with the highest numbers. 20

Currently, the Kingdom of Saudi Arabia is developing three Giga-projects to encourage economic transformation and draw international investments, which are expected to have high financial revenues in the long term (PIF, 2018). One of these developments is The Red Sea Project (TRSP) that includes the Al Wajh bank which will be managed as a Special Economic Zone (Figure 1.2) (TRSDC, 2019).

Figure 1.2. Location of The Red Sea Project with the delimitation of the Boundary of The Red Sea Project Special Economic Zone(SEZ). (TRSDC, 2019). Map by Ute Langer.

TRSP development plan consists of establishing a series of luxury hotels based on sustainable development and regenerative tourism, intending to recondition and restore its coastal ecosystems obtaining a net positive conservation benefit of up to

30% within the next 20 years. They are also committed to closing fishing throughout their entire Special Economic Zone (SEZ), which covers 5,373 km2 of ocean including some of the best reefs in the Red Sea (TRSDC, 2019). The project intends to benefit from this plan since ecotourism activities such as snorkeling and diving will be a major focus, continuing to grow Red Sea’s reefs as a major economic asset (TRSDC,

2019).

1.3. Fish Movement for Marine Protected Area Design in the Red Sea

When designing MPAs, it is important that some biophysical aspects are taken in account. Green et al., 2014 states that there are key ecological aspects that have to be taken in consideration: Habitat representation, risk spreading, protecting special and unique areas, connectivity, time of recovery, adapting to climate changes, and minimizing local threads.

To understand how the establishment and correct management of MPAs around the

Red Sea will benefit fish species of interest it is necessary to first understand the spatial needs of these species, taking in consideration their core area of use or the area they consistently use for their daily needs, also known as home range (HR)

(Kramer & Chapman, 1999), other types of movement, such as spawning migrations 23

or ontogenic habitat shifts, and the geographical locations where they move to.

Different species will have different needs (Green et al., 2015; Maypa, 2012; Mouillot et al., n.d.; Waldie et al., 2016; Weeks et al., 2017), see Figure 1.3.1. Many reef fishes, like groupers, parrotfish, and surgeonfishes tend to have small HRs (<0.5 to 1 km), while others such as the bumphead parrotfish (Bolbometapon muricatum) or the napoleon wrasse (Cheilinus undulatus) are more wide ranging (5–10 or 10–20 km, respectively) (Green et al., 2015). If an MPA, especially a NTA, is not big enough to account for the movement patterns of fish species of interest, these will move outside of the protected areas remaining vulnerable to fishing pressure and causing a reduction of the protected fish biomass inside the NTA (Green et al., 2014; Kramer &

Chapman, 1999).

In this thesis I will be focusing on one of many aspects that need to be taken in account when designing MPAs, their size. For marine reserves to give appropriate spatial protection to fish species of interest, Green et al (2015) recommended that the MPAs, particularly no take areas (NTAs), should be more than twice the size of their HR (in the appropriate habitat in all directions). In the case of having a species which HR exceeds the possible implementable size of an MPA, these have to be managed using alternative techniques, such as, fisheries restrictions (Green et al., 2014, 2015). All the recommendations and assumptions in this thesis refer to an MPA being a NTA, which provides complete protection from fisheries to the species habiting an area.

inchus inchus

groupers groupers

ggerfish (Balistes (Balistes ggerfish

ion ion virescens); 49,

is is aygula); 35, some

); 14, some

fishes fishes (Seriola spp.); 42,

gentimaculatus); gentimaculatus); 59, tuna;

ted sweetlip (Plectorhted sweetlip

us); us); 8, orangespotted filefish

striatus); 17, some angelfishesstriatus);some 17,

spine unicornfish (N. unicornis); (N. unicornfish spine

movements: movements: ranges,home territories

manta manta rays (Manta spp.); 55, Galapagos

napper (L. campechanus); 38, some groupers (e.g. C.

ome ome snappers (e.g. L. ehrenbergii); 20, yellow tang

term movements of undetermined cause). 1, Seahorses

es (e.g. Dascyllus spp.); 4, most butterflyfishes (Chaetodon spp.); 5, some

barred barred parrotfish (S. ghobban); 45, Indonesian shortfin eel (Anguilla bicolor

llustrations llustrations were modified from Randall, Allen & Steene (1997), B. muricatum was

aprion aprion spp.); 44, blue

from Green 2015. et al.,

; 26, some grunts (e.g. Haemulon sciurus); 27, squaretail coralgrouper (Plectropomus areolatus); 28, Bermuda Bermuda areolatus); 28, (Plectropomus coralgrouper squaretail 27, sciurus); Haemulon (e.g. grunts some 26, ;

and and quote

. Figure.

”

, , some soldierfishes/squirrelfishes (Holocentrus spp./Myripristis spp.); 10, moray eels (Gymnothorax spp.); 11, bignose

Linear scale of movement of coral reef and coastal pelagic fish species. Number colors are: black (daily

“

ralgrouper (P. leopardus); 50, whitetip reef shark (Triaenodon obesus) and nurse shark (Ginglymostoma cirratum); 51, grey tri grey 51, cirratum); (Ginglymostoma shark nurse and obesus) (Triaenodon shark reef whitetip 50, leopardus); (P. ralgrouper

. .

. 1

(Hippocampus (Hippocampus spp.); 2, anemonefishes (Amphiprion spp.); 3, most damselfish (Holocanthus/Pomacanthus spp.); 18, some parrotfishes (some Scarus/Sparisoma spp.); 19, s sonnerati and E. coicoides); 39, some emperors nebulosus); Lethrinus (e.g. 40, silver drummer sydneyanus); (Kyphosus 41, king shark (C. galapagensis); 56, Nassau grouper (E. striatus); 57, trumpet emperor (L. miniatus); 58, mangrove red snapper (L. ar Figure and core areas of use); blue (ontogenetic shifts); red (spawning migrations); and green (long angelfishes (e.g. Centropyge spp.); 6, some wrasses (e.g. Halichoeres garnoti); 7, some surgeonfishes (e.g. Acanthurus lineat (Cantherhines pullus); 9 unicornfish (e.g. Naso vlamingii); 12, some snappers (e.g. Lutjanus somespp.); 15, (Ephinephelus surgeonfishes butterflyfishes (e.g. some A. coeruleus 16, (e.g. C. striatus); and Ctenochaetus carponotatus); 13, some groupers (most Cephalopholis spp. blue 24, goatfishes; 23, lineatus); Siganus (e.g. rabbitfishes some 22, scopas); (Z. tang twotone 21, flavescens); (Zebrasoma rivulatus) Scarus parrotfishes (e.g. some 25, goldspot 31, rubroviolaceus); (S. parrotfish ember 30, spp.); (Chlorurus parrotfishes some sectatrix); 29, chubsea (Kyphosus flavomaculatus); 32, some groupers (e.g. P. surgeon/unicornfishes (e.g. A. blochii and N. lituratus); leopardus); shoemaker36, spinefoot (S. sutor); 37, red s 33, bigeye trevally (Caranx sexfasciatus); 34, some wrasses (e.g. giant Cor trevally (C. ignobilis); 43, lemon sharks (Neg bicolor); 46, bumphead parrotfish (Bolbometopon muricatum); 47, humphead wrasse (Cheilinus undulatus); 48, cogreen leopard jobfish (Apr capriscus); gag 52, grouper microlepis); blacktip(Mycteroperca 53, reef shark (Carcharhinus melanopterus); 54, 60, marlin/swordfish; 61, tiger shark (Galeocerdo cuvier). Most i Gladstone from (1986) modified 25

Implementing the best possible MPA size will still benefit certain species giving them a percentage of coverage (Krueck et al., 2018), even if this means having an outflow of some portions of the fish communities out of the area. In 2015, Green et al. compiled a literature review of existing empirical research regarding movement patterns of coral reef and coastal pelagic fishes. This review was then used to inform management and make recommendations to refine and redesign sizes of MPAs

(Green et al., 2015; Krueck et al., 2018). This approach has been adapted and redefined to inform MPA network design in many other places around the world e.g., the Gulf of California in Mexico, and Pohnpei in Micronesia (Munguia-Vega et al.,

2018; Weeks et al., 2017). To date, movement patterns of many reef fishes in the Red

Sea are unknown, since the methods to obtain this information are usually time consuming, expensive and require much fieldwork. While it may be possible to use expert advice regarding movement patterns of Red Sea species (e.g., based on empirical movements of sister species in the Indo-Pacific) this requires lots of expertise and field knowledge.

1.4. Machine Learning to Inform Fish Movement

Utilizing Machine Learning (ML) to detect ecological patterns in interactive or related processes and phenomena is starting to be widely utilized, but still requires some exploring (Recknagel, 2001; Ryo & Rillig, 2017; Xu et al., 2019). When referring to predictive power, it is known that ML techniques can outperform other methods and 26

algorithms, especially for natural resource management cases, where decisions need to be taken in brief amounts of time (Humphries & Huettmann, 2018). The implementation of these methods in ecological research and its great potential has been slowly getting more attention in the field (Humphries & Huettmann, 2018;

Thessen, 2016).

During this study I developed a method to obtain a prediction for linear HR movement of fish for which there is no empirical data available. Data deficiency in marine sciences and biology is a recurrent topic given the alien environment in which the ecological processes take place. Monitoring and obtaining data that is closest to reality to help answer questions regarding the functioning of marine ecosystems and the behavior of the organisms inhabiting them, continues to be a complex task to solve. Much research has to rely on the construction of models until accurate data is available. However, it is possible to build models to gain insight about reality. This has the potential to inform the possible benefits that MPAs will have in the long term.

1.5. Objectives

The primary objective of this study is to predict the home range of reef fish species of interest for conservation and fisheries management in order to refine ecological recommendations for marine protected area or replenishment zones size design, especially for the Red Sea. This was achieved by using Machine Learning to build a 27

model that predicts the HR of fish. This has not yet been attempted in the Red Sea. It will bring valuable insight regarding the spatial needs of important fish species, having the potential to assist and inform the management of MPAs.

Fish movement data and predictions can be used to inform the design of new MPAs to ensure they are large enough to protect focal species, or for adaptive management to refine the design of existing MPAs to improve their effectiveness (Green et al 2015).

The second objective was to use this information to understand the capabilities of existing and proposed MPAs in the Red Sea to provide protection to specific fish. This thesis will particularly study the case of TRSP to understand the potential effects that the enforcement of the planned fishing ban over the Al Wajh in the central Red Sea will have for the fish species that will theoretically be protected in the area.

Chapter 2. METHODS

2.1. Red Sea Fish Home Range

This study takes place in the Red Sea. The marine biodiversity of this narrow sea comprises 1,166 fish species from 156 families (Bogorodsky & Randall, 2019). It covers a geographic area of 480,385 km2. This unique place is described as a semi enclosed basin which has a limited exchange of water and nutrients with the Indian

Ocean because of the narrow Gulf of Aden strait in the south, causing high salinity gradients and high temperatures along the latitudinal and depth gradient (Raitsos et al., 2013).

The Red Sea’s particular conditions, such as, nutrient-rich cold water, the circulation patterns, and its narrow entrance in the South isolates it from the rest of the Gulf of

Aden and Arabian Sea and drive an historical divergence of fish populations explaining the high levels of endemism. (DiBattista et al., 2016). Specifically 14.7% of all fish species known are identified as endemic (Bogorodsky & Randall, 2019).

I order to build a model to predict fish movement in the Red Sea I used machine learning as described below.

2.1.1. Empirical Movement Training Data Set

To start building the training data set that would allow me to build a model to predict

HR of Red Sea species it was necessary to extract empirical data from studies which had confirmed HRs for different fish species.

2.1.1.1. Response Variable

I extracted empirical data for 212 species of fishes from Green et al.’s 2015 review

(Figure 1.3). This review features data from different parts of the world but still contained data for fish that are known to inhabit the Red Sea. Some of the data includes HRs, long term movement, core area of use, and in some cases ontogenic movement or spawning migration data, in linear distance. These are all ecologically important types of movements (Green et al., 2015). It was necessary to use data that could consistently provide a bigger data set with equivalent or the same data for each fish species. For this reason I only used HR for this analysis. Using only HR meant many species with long distance movements not corresponding to HR were excluded from the training data set (e.g., barracudas and tunas). After filtering, the data set was reduced to 141 fish species from 24 families (Appendix 1) that possessed a known

HR as reported in Table 1 by Green et al., 2015.

The HR was composed of 15 categories that ranged between 0.01 and 50 km (Figure

2.1). All the distances and HRs referred in this thesis are given in linear km to be consistent with the training data set. It is observable in Figure 2.1 that the data has an uneven distribution among the various categories of HR.

Figure 2.1. Number of fish species per home size range (km) in the empirical data set modified from Green et al., 2015.

To account for the uneven distribution of data, I decided to recategorize it into 5 categories (Figure 2.2). I originally accounted for 6 categories, aiming for an equitable distribution of data between them, but the models that I was attempting to train where having trouble distinguishing between the second and the third category, for this reason I decided to merge them into a single category, which became the new second category.

Figure 2.2. Number of fish species in each of five revised home size ranges (km) categories (see Table 1). These categories are modified from Green et al., 2015.

The five categories’ data distribution may be observed in Figure 2.2, while the actual distance ranges of each category can be observed in Table 2.1. There is a gap between some ranges, meaning that in the data extracted from Green et al., 2015 there where no fish with a HR sorresponding to those distances.

Table 2.1. Empirical home range (extracted from Green er al., 2015) recategorization, home range categories used in this study, and the number of species in each category (n).

Empirical HR Category Label n (km)

1 0.01-0.07 <.1 26

2 0.1-1 .1-1 67

3 2-5 2-5 33

4 10-20 >5-20 9

5 30-50 >20 6

2.1.1.2. Predictor Variables

In order to build the model, it is necessary to find data exclusive to each fish that could help predict or explain their HRs, this data needs the capacity of giving us an insight into each fishes’ life, behavior and daily activities. The features chosen to predict the

HRs were maximum total length (L Max), Trophic Level, and Aspect Ratio (AR) of the caudal fin. The data was extracted from FishBase (Froese & Pauly, 2018) using

RStudio with the package “Rfishbase” (Boettiger et al., 2012). The data was continuously managed and cleaned using a combination of RStudio and Excel software.

a. Size

The size measurement used in this analysis was L Max in centimeters. L Max is acknowledged in the literature as a strong feature to predict an organism’s life history

(Blueweiss et al., 1978) and has been used before in analysis regarding fish HRs

(Kramer & Chapman, 1999; Reiss, 1988). This measurement is the maximum total length ever recorded for a species.

b. Aspect Ratio of the Caudal Fin

AR of the caudal fin of a fish species closely correlates with its average level of activity

(Pauly 1989). In cases where AR is not available it was calculated by planimetry from the pixels in photographs available for fish labeling and identification in the FishBase data bases (Froese & Pauly, 2018).

A = h2 / s

AR is calculated as the square of the tail’s height over the surface of the fin. Where h is the height of the caudal fin and s its surface area (Fig. 2.3) (Note that original authors abbreviate AR as A, in this document it will be referred as AR to avoid confusions with other terms). The use of this piece of information relies on the organisms having a 34

tail. Therefore, fish in the corresponding superorder of Batoidea and the order of

Anguilliformes had to be excluded from the analysis.

c. Trophic Level

Trophic level represents the position in the food chain independent of the species or the group (Froese & Pauly, 2018). It is a numerical metric that represents a species’ diet, which refers to the food consumed my each organism allowing for comparisons and ecological interpretations (Elton, 1927; Kercher & Shugart, 1975).

Trophic Level 1 represent primary producers (e.g., , organisms that photosynthesize.

Subsequent levels (2-5) are then calculated as a mean of the trophic scores of food items in a species’ diet, weighted by quantity, plus one. The maximum Trophic Level that an organism may have is up to level 5. For fish, the highest level is 4.5 corresponding to both the tiger shark (Galeocerdo cuvier) and the great white shark

(Carcharodon carcharias) (Froese & Pauly, 2018; D Pauly, 1998).

2.1.2. Prediction model for Home Range

Supervised learning (SL) in Machine Learning (ML) uses an empirical or verified data set with its known response variables to build a model that predicts the response variable for an additional data input. These techniques were applied to generate a model that predicts HR for marine fish species. Specifically, I used it to predict HR for 35

Red Sea species compatible with the model (fish with an AR score). Two approaches where tested, Regression and Classification. This was done using the Statistics and

Machine Learning Toolbox from MATLAB R2021a. To perform the model trainings, prediction variables values where normalized.

2.1.2.1. Classification model

In order to obtain a prediction for the HR of species for which the information is not available, the incognita was treated as a Classification problem. After the empirical

HR were reorganized into 5 categories (<.1, 1, 5, 20, and >20 km) (Figure 2.2). No set of the data was left to validate the model due its reduced size. For this reason, I used

5 cross-validation folds to obtain a mean overall accuracy.

Using cross-validation folds prevents the model from overfitting the data by partitioning the data set and estimating accuracy in each fold. I used Bagged Decision

Trees to predict HRs, they had the highest degree of accuract. Decision Trees are trees that classify instances by sorting them based on feature values (Kotsiantis, 2007). I obtained a model of Bagged Decision Trees that could predict fish HR category with an overall accuracy of 74.5%. (See Figure 2.4). 36

2.1.3. Red Sea Fish Home Range Predictions

It was necessary to have a list of fish for which having HR data information would

result in a potential positive feedback to the species. That is, it would result in the

potential of informing local management and decision making regarding MPA design.

For this I gathered a list of fish that have a known physical presence in the Saudi

Arabian reefs, using a species list gathered via biodiversity assessments and transect

data from Underwater Visual Censuses (UVCs) from previous monitoring field trips

performed by the Reef Ecology Lab at KAUST and TRSDP.

It was necessary to have clarity over which fish species are most valuable and most targeted by fisheries to develop a stronger discussion around MPA design. I added to this list the fish that were previously identified as important for the Saudi Arabian local fish markets at Jeddah and Thuwal, Saudi Arabia (Shellem et al., 2021). This information was then confirmed by doing some visual identification at the Jeddah fish market. Some of the fish encountered in the markets where not originally encountered in the species list from previous UVCs and monitoring efforts, so I included them into the analysis.

The data base resulted in 337 species from 56 families of Red Sea reef fishes with all three predictor variables (Appendix 2). The data was used with the previously trained model, resulting in the prediction of the HR category for all 337 species (Appendix 2),

2.2. Capabilities of Current Red Sea Marine Protected Areas

I assessed the capability of MPAs registered for the Red Sea to determine their spatial capacity to account for the HR distances of Red Sea species. I did this analysis only for

15 MPAs of the ones previously mentioned. These 15 MPAs have a “Designated” and

“Inscribed” status (See Table 1.1).

In order to be able to compare the Red Sea MPAs with the linear HRs obtained through the model we obtained the antipodal maximum linear lengths of the MPAs. This was 38

done using the maximum length of the minimum bounding convex hull shapes of the polygon inputs of MPAs’ areas (this refers to the line that crosses the furthest vertices of each MPA’s geometric shape). This was done using the geographic information system analysis tool ARC-GIS.

2.3. Case Study: The Red Sea Project Potential

The next objective of this research is to study the case of TRSP in and around the Al

Wajh region, located at the Northern Central Saudi Arabian coast. TRSP expects to completely ban fishing in its SEZ aiming to have an increase in fish biomass of 30% in

20 years (TRSDC, 2019). In order to understand the potential of this happening and confirming if the size of TRSP would provide a sufficient spatial protection for fish of interest, I compared the fish biomass at the Al Wajh region with the fish biomass of

Sudanese reefs, which have been shown to be well preserved in comparison with other Red Sea regions (Kattan et al., 2017).

In order to implement biomass comparisons between both regions, I used data gathered in previous Under Water Visual Surveys (UVCs). Using only the biomass of fish species that overlapped in both sites to be able to have a clear comparison. The data consisted of 3 replicate transects per site per region. I focused the comparison of species likely to be protected within TRSP (linear distance= ~160 km) and compared the average biomass of the region calculated as the average biomass per 39

site and then per region. Data from Sudan was gathered from 16 reefs classified as

offshore and in proximity to the outer margin of the continental shelf. To be able to

make the best comparison possible, only data from 7 sites at the Al Wajh Lagoon were

selected. These sites are found outside of the lagoon. Total biomass for both sites was

compared.

Chapter 3. RESULTS

3.1. Predicting Home Ranges of Red Sea Reef Fishes

3.1.1. Characteristics of Empirical Movement Training Data Set

The data set of empirical fish movement was plotted to understand the patterns of the data. From Figure 3.1, the graph shows a predominance in data for smaller sizes of fish species, this is consistent with the fact that the data set contains only data for

HR, which has a tendency to have a higher amount of data for smaller fish species.

Most small fish species with an L Max <50 cm possess a low aspect ratio (<4) (Figure

3.2) and are shown to move small distances (less than 1km) (Figure 3.1). However, medium to large species with an L Max >100 cm, are more variable in terms of how far they moved irrespective of their AR, observing HRs >5km.

There is no distinguishable relationship between Trophic Level and AR variables

(Figure 3.1), except that species with the highest aspect ratio tend to be species in higher trophic levels. Trophic Level presents an influence over the scattering of the data influencing the distribution towards a higher Trophic Level as L Max increases.

Figure 3.1. 3D scatter plot of Trophic Level versus AR versus L Max. Each point represents a fish species and the color represents its empirical HR.

3.1.1.1. Response Variable

The empirical HR data set presents an n of 141 spp. (Appendix 1). Category 1 ( < .1 km), is composed of 4 different fish families, with Pomacentridae being the family with the most species. Category 2 (.1 – 1.0 km) has the highest diversity with 13 different families, with Chaetodontidae and Serranidae comprising the most species.

Category 3 (2.0 – 5.0 km) has a diversity of 10 families, with Serranidae being the one comprising the most species, followed by Scaridae. Category 4 (5.0 – 20 km) has 4 families, with Carcharhinidae comprising the most species. Category 5 (> 20 km) has

3 families, with Carcharhinidae comprising the most species. It is worth mentioning that the distribution of empirical data availability can’t be compared with any causal factor other than lower sampling effort.

3.1.2. Prediction model for Home Range

The selected classification model to predict HR was a Bagged Decision Tree. It demonstrates a 74.5% of accuracy. I used confusion matrix plots to understand the performance for each predicted linear HR class category (1, 2, 3, 4, 5), this was obtained once the model was trained (this means once we used the empirical HR data to construct the model). In the Figures 3.2 and 3.3 we are able to observe that there is a level of confusion between category 2 and 3.

Overall, in Figure 3.2 and Figure 3.3 we can observe that the first two categories have

high rates of prediction accuracy, that is 84.6% and 85.1 % respectively.. This is

potentially driven by the amount of data available to train the model.

3.1.3 Red Sea Fish Home Range Predictions

The obtained HR predictions for Red Sea fishes, show a similarity with the training

data set data scatter plotting. Figure 3.4 shows that fish in HR 1 are very close to the

Y axis signifying that most fish in this category have a small L Max. In contrast, fish

with a HR 4 and 5 present a bigger distribution of sizes. There does not appear to be

a close relationship between AR and L Max. We can see the relationship between

Trophic Level and L Max has a stronger relationship than the one observable for 44

Aspect Ratio and L Max. The predominance of data for smaller fish species remains

true and the data distribution is pulled towards a higher Trophic Level as L Max

increases. Smaller fish species, with smaller predicted HR patterns (<.1 km), are

clustered in the graph in lower trophic levels (<3.5) and smaller AR scores (<3), as

the predicted HR increases so does the fishes length and trophic level, this is not very

much observable for AR.

Figure 3.4. 3D Scatter plot of Trophic Level versus AR versus L Max. Each point represents a fish species, and the color represents the HR classified prediction.

Estimated Level of Influence of Level Estimated

L MAX

Figure 3.5. Out of the Bag Permuted Predictor Importance for the model used. Each predictor has an estimated level of influence over the model prediction power. 45

In order to understand the importance of the predictor variables in the model trained and in the HR prediction response, we obtained an Out of the Bag Permuted

Importance Estimate. This shows that just as it is observed in the scatter plots, L Max is the variable that accounts for the highest estimate value for our model, followed by

Trophic Level (Figure 3.5).

Home Range Size Category

Figure 3.6. Percentage of fishery valuable families in each size category in the model.

To further analyze the composition of the fishes trained in the model, I looked into the composition of fishery valuable families present in the five HR categories. A trend is observable in Figure 3.6, where only 2 families are present in the smallest HR (1 &

2), we see many fish species from the Labridae family with HR smaller than .1 km and many shark species (Carcharhinidae) dominating HRs larger than 20 km. The middle 46

categories (1 , 5, and 20 km) show a higher family diversity, with 5 km the most homogenous category.

To further analyze the management implications of the fish trained in the model I looked into the composition of their IUCN categorization in the five HR categories. It is observable in Figure 3.7 that species with higher endangerment categories are found to have HRs between 5, 20km and >20 km. Critically Endangered fish can be found in a HR of 5 – 20 km. Fish with a HR equal or lower to 5 km have a higher percentage of species with LC (Least Concern) category.

Home Range Size Category Figure 3.7. Percentage of Red Sea reef fish species used in the model in each IUCN Red List category in each HR category. CR- Critically Endangered; EN- Endangered; VU- Vulnerable; NT, Nearly Threatened; LC- Least Concern, DD- Data Deficient; N.E.- Not Evaluated, being CR the highest endangerment level for this list of species, with the rest of the categories following consecutively. 47

In Figure 3.8. we can see the predicted HR of a set of species identified in Shellem et al., 2021 as important for fisheries in central Saudi Arabia and the sharks in the database (Appendix 2) as they have an historical importance in fisheries in the Red

Sea (Spaet et al., 2016). This set of species is here to visually illustrate in a selected group of fishes the predicted HR movement patterns obtained. In Appendix 2 is possible to visualize the predicted HR movement pattern for all species studied

(n=337 spp.).

In Figure 3.8 we can observe that different species belonging to the same family can have different HRs. Sharks (family Carcharhinidae) a wide range of HRs, with some species moving long distances. Groupers (family Serranidae) include 7 spp. with a smaller HR (<1km) that correspond with smaller body lengths (~40-80 cm) (refer to

Appendix 2 for full list of species with their sizes and HR predictions), and larger species that have much larger HRs (e.g., Epinephelus malabaricus, on the other side, is a grouper that can measure up to 234 cm.) Other families that comprise small species

(e.g., butterflyfishes and damselfishes) generally have HRs smaller than 0.5 km these are usually fish of small size and established territories (Berumen, 2001; Jones, 2005).

3.2. Recommended sizes of NTAs for Red Sea Reef Fishes

MPAs (if implemented as a NTA) (Figure 3.9 demonstrates which fish species are likely to be protected in MPAs based in their predicted HR movements) with a length smaller than 0.2 km will protect small reef fish, like damselfish, e.g., Red Sea Clownfish

(Amphiprion bicinctus); small wrasses, e.g., Cleaner Wrasse (Labroides dimidiatus), some butterfly fish, e.g., (Chaetodon larvatus). MPAs with a length of <.2-2 km will protect medium size reef fish, like medium parrotfish, e.g., Bicolour Parrotfish

(Cetoscarus bicolor); small groupers, e.g., Summan Grouper (Epinephelus summana); surgeon fishes, e.g., Sohal Surgeonfish (Acanthurus sohal); small snappers, e.g.,

Blueliune snapper (Lutjanus kasmira); emperor fish, e.g., Sky Emperor (Lethrinus mahsena). MPAs with a length of 2-10km will protect bigger fish, like medium groupers, e.g., Red Sea Roving Coral Grouper (Plectropomus pessuliferus); big snappers, e.g., Two-spotted Red Snapper (Lutjanus bohar); medium parrotfish, e.g.,

Longnose Parrotfish or (Hipposcarus harid). MPAs with a length of 10-40 km will protect big parrotfish, e.g., (Bolbometapon muricatum); big wrasses, e.g., (Cheilinus undulatus); small sharks, e.g., Whitetip Reef Shark (Triaenodon obesus); big groupers, e.g., Malabar Grouper (Epinephelus malabaricus). MPAs longer than 40 km will benefit sharks, e.g., Tiger Shark (Galeocerdo cuvier); tunas, e.g., Dogtooth Tuna (Gymnosarda unicolor); barracudas, e.g., Great Barracuda (Sphyraena barracuda); but the maximum size needed to protect them efficiently is indefinite because these fish move very long distances and will require a complementary management like fishing regulations in 50

order to increase their protection. Refer to additional poster created (Appendix 3) to picture this information in a visual manner. .

3.3. Capabilities of Current Red Sea Marine Protected Areas

The geometric analysis of the existing Red Sea MPAs showed that 60 % (9 MPAs) of the designated MPAs have a linear distance longer than 40 km. This study has it’s biggest Fish HR category as >20km. This information combined gives us a potential of protection for more than 90% of the species evaluated in this study. In these large

MPAs, six of these large MPAs are inscribed in Egypt, 2 in Saudi Arabia and 1 in Sudan

(Figure 3.9).

but but

, , big

tunas, tunas, and

parrot fish

wras

h h a high territoriality

, , groupers and

small small sharks, big

snappers

have the potential to protect more wide

, , these are fish wit

We can observe that fish like damselfish, small like small damselfish, We thatobserve fish can

Wider Wider ranging fish, like big sharks,

movement movement ranges, like

40 40 km.

MPAs (from 7 to 15)

fish from the from fish study

larger

Big Big fish with wider

Red Sea

Red Sea’s

above 40 km, but because this wide ranging can fish longermove and distances

other other management approaches are implemented to complement their protection.

Bigger fish that move further distances include medium size

over specific species.

fish species HR ML linear the fish obtained species next Model, with to represented the MPAs and designated in sizes of the nationalities

managed as No Take Areas (NTAs). asTakemanaged No Areas

Red Sea

and and small groupers are more likely to be protected by MPAs that measure up to 2 km long

Note that this figure only displays representative and iconic and representative thatdisplays Notethis only figure

, ,

Figure parrotfish, groupers and trevallies will be protected my MPAs with lengths of up to Red Sea. wrasses small movement ranges, mostly within a reef. will be likely to be protected by MPAs with a length of up to 10 km barracudas will potentially be by protected MPAs with a length potentially migrate into other areas it is recommended that These can be fishing bans, and regulations ranging if fish

. .

From Figure 3.9 we can observe that MPAs that are longer than 40 km have the potential to protect the majority of the species analyzed in this thesis (This is more than 95%) and the smaller the existing MPA, the less species they protect.

In the Red Sea no designated MPA measures less than 0.2 km. “Hof HaAlmogim

BeElat”, “Ha-Yam Ha-Deromi BeElat” belonging to Israel, and “Umm al-Qamari

Islands” from Saudi Arabia enter the category of <.2-2 km. “Tourism Development

Area I” from Egypt and “Yanbu Coastal Conservation Area” from Saudi Arabia enter the 2-10 km category. “Tourism DeveloMPApment Length Area (km) II” from Egypt enters the 10-40 km category. “Nabq”, “Ras Mohammed”, “Red Sea Islands”, “Abu Gallum”, “Wasi El-

Gemal – Hamata”, and “Elba” from Egypt; “Asir National Park” and “Farsan Islands” from Saudi Arabia; and “Sanganeband Dungonab Bay – Mukkawar Island” from

Sudan; enter the category of longer than 40 km (Figure 3.9).

In Figure 3.10 I present the species accumulation curve for different sizes of MPAs in the Red Sea. Here we can observe that in order to protect 90% of the species analyzed in this study (refer to Appendix 2 to see the list), MPAs need to be at least 10 km long.

It is important to note that this analysis is done for NTA that are efficiently enforced and managed. Species that are not included in this 90% are most of the wide-ranging species.

The next list of species have a HR of >20 km, Pelagic thresher (Alopias pelagicus),

Silky shark (Carcharhinus falciformis), Blacktip shark (Carcharhinus limbatus),

Sandbar shark (Carcharhinus plumbeus), Rainbow runner (Elagatis bipinnulata),

Bluespotted cornetfish (Fistularia commersonii), Tiger shark (Galeocerdo cuvier),

Dogtooth tuna (Gymnosarda unicolor), Narrow-barred Spanish mackerel

(Scomberomorus commerson), Great barracuda (Sphyraena barracuda), Pickhandle

barracuda (Sphyraena jello), Blackfin barracuda (Sphyraena qenie), Great

hammerhead (Sphyrna mokarran), Houndfish (Tylosurus crocodilus). If the interest is

to protect any of these wide ranging species in the previous list, the MPA (as a NTA)

needs to be greater than 40 km long in all directions.

3.4. Case Study: The Red Sea Project Potential

A biomass comparison between the region of Sudan and the outer reefs of Al Wajh shows that there is 71.6% more biomass on the reefs in Sudan (Figure 3.11). The data

(refer to Table 3.1 and Figure 3.11) shows that the species with the higher biomass, specifically in the Sudan region (figure are: Naso hexacanthus, Naso unicornis,

Lutjanus bohar, Trachinotus blochii, Macolor niger, Pseudanthias squamipinnis,

Cheilinus quinquecinctus, Plectorhinchus gaterinus, Chromis dimidata, Lethrinus microdon, Plectropomus pessuliferus, Caranx melampygus, Cetoscarus bicolor, Naso elegans, Kyphosus cinerascens, Acanthopagrus bifasciatus, Lethrinus xanthochilus,

Balistoides viridescens, Lutjanus kasmira.

The species that were observed to account for most of the biomass in Sudan have a

predicted HR of 0.01-0.07 km, 0.1-1 km, and 2.0-5.0 km. This translated into NTA

requirements means these species require an area with a length between 2 to 10 km,

which is why they are more abundant in the MPAs in Sudan (Figure 3.11 and 3.12).

The area of continuous reef to be protected in TRSP area in Al Wajh has a length of

approximately 160 km. Meaning it has the potential to benefit the species that

currently have lower biomass in the Al Wajh region at present when compared to

Sudan.

Figure 3.12. Biomass percentage for each fish family in Sudan and TRSP area in Al Wajh. The small map to the right of the graph shows the location of both areas in the Red Sea.

In Figure 3.12 the contrast between the family fish compositions in both sites. It is

observable that Sudan has a higher biomass than Al Wajh for all the groups, but the

highest biomass percentage is for Serranidae, Lutjanidae, Labridae, and the sum of 57

other families, which are not represented. The families presented in the figure represent important families for reef fisheries.

Biomass (kg per m sq) Pred. HR - Recommended Family Species SUDAN AL WAJH Equivalent NTA length (km) (km) Acanthuridae Naso hexacanthus 277.88 20.40 2.0-5.0 4.0 - 10.0 Acanthuridae Naso unicornis 89.07 21.99 0.1-1 0.2- 2 Lutjanidae Lutjanus bohar 78.59 5.32 2.0-5.0 4.0 - 10.0 Carangidae Trachinotus blochii 61.85 27.87 2.0-5.0 4.0 - 10.0 Lutjanidae Macolor niger 60.25 19.51 2.0-5.0 4.0 - 10.0 Pseudanthias Serranidae 55.92 3.50 0.01-0.07 0.02- 0.14 squamipinnis Labridae Cheilinus quinquecinctus 39.23 0.86 0.1-1 0.2- 2 Haemulidae Plectorhinchus gaterinus 38.20 1.82 0.1-1 0.2- 2 Pomacentridae Chromis dimidata 36.30 4.32 0.01-0.07 0.02- 0.14 Lethrinidae Lethrinus microdon 29.43 17.58 2.0-5.0 4.0 - 10.0 Plectropomus Serranidae 27.34 5.14 2.0-5.0 4.0 - 10.0 pessuliferus Carangidae Caranx melampygus 25.99 8.93 2.0-5.0 4.0 - 10.0 Scaridae Cetoscarus bicolor 22.46 6.53 0.1-1 0.2- 2 Acanthuridae Naso elegans 20.69 6.92 0.1-1 0.2- 2 Kyphosidae Kyphosus cinerascens 19.06 2.15 0.1-1 0.2- 2 Acanthopagrus Sparidae 17.23 7.18 0.1-1 0.2- 2 bifasciatus Lethrinidae Lethrinus xanthochilus 16.92 6.84 0.1-1 0.2- 2 Balistidae Balistoides viridescens 16.63 4.99 0.1-1 0.2- 2 Lutjanidae Lutjanus kasmira 15.63 0.69 0.1-1 0.2- 2 Caesionidae Caesio lunaris 14.33 16.66 0.1-1 0.2- 2 Acanthuridae Acanthurus gahhm 13.65 16.12 0.1-1 0.2- 2 Serranidae Aethaloperca rogaa 11.47 3.12 0.1-1 0.2- 2

Chapter 4. DISCUSSION

4.1. Red Sea Fish Home Range

This thesis represents the first attempt to predict the HR movement patterns of fishes in the Red Sea. HR represents the daily territory and area use of a species in their daily movement patterns, but fish have many more types of movement that are not HR where they may move greater distances (Green et al., 2015). These also need to be taken in account when designing and establishing MPAs and NTAs. If this is not possible, additional management strategiesmay be required. An example of other types of movement can be: Ontogenic migrations, which are migrations that some species undergo during life stage changes; Spawning migrations, where fish migrate to a different area outside of their HR in order to aggregate with a bigger group of the same species to reproduce (see review in Green et al., 2015).

Taking in account other types of movement into the design of an MPA or the implementation of additional management strategies is very valuable. Species are the most vulnerable during these types of movements, either because they congregate or because it is part of much needed process for their development. In the Red Sea spawning aggregations for many fish are known to be exploited by fishermen.

Using only HR movement of fish in this thesis had some drawbacks. The first one was that I could only use HR empirical existing knowledge. This meant having to exclude 59

other types of movement that are significant, as mentioned above. As well, there are many species for which there is no knowledge regarding HR but there is existing literature regarding wider movements, which is true especially for wide ranging pelagic fish (e.g. barracudas and trevallies). These fish are also subject to fisheries, so taking into consideration that they will require much wider MPAs, or NTAs is important. If it is not possible to include their spatial needs into an MPA it will be necessary to implement additional management, as such species specific fishing bans, fishing method restrictions, or fish size restrictions to protect these wide ranging species.

4.2. Utility of Models and ML for Predicting Reef Fish Movement

The Machine Learning modeling method choose in this thesis proved to have potential to answer ecological questions to inform management. The obtained model had a prediction accuracy of 74.5%, this percentage. This percentage can be increased if working with a larger training data set. This study can be used as a base line for understanding Red Sea fish linear HR. Future movement studies can reference this thesis in order to verify and rectify with field work if the predicted HR is indeed correct or not, as well as to complement the training data set to obtain a better prediction accuracy.

The model used in this thesis can be refined and modified in the future analysis. I used the L_Max of each fish species in both the training data set and the Red Sea species for 60

which HR was predicted. Using this piece of data can become problematic because the main intention of this modelling is to provide an approximation of reality. The main problem is that the empirical HR were probably obtained for fish with different lengths. This may cause the model to present irregularities in the training. For future approaches it is suggested that the empirical data set is built using the actual sizes of the fishes used for the empirical studies to obtain HR. Registered L Max from databases may be much elevated than the lengths observed in a population that is being studied (Froese & Pauly, 2018). This means that the estimates derived from any analysis using this data have the potential to be unrealistic, and in the case of modeling, the analysis can be farther from reality. As an alternative we could use common length of a species, local common length, or average length, in the training, which may result in an increase of this value, but it will also be subjected to the effect of base line shifting or fish downsizing because of overfishing in the Red Sea (Daniel

Pauly, 1995; Daniel Pauly et al., 1998).

In this study L Max and Trophic Level had a positive relationship on predicted HR size, which is consistent with the previous literature (Ou et al., 2017; Romanuk et al.,

2011). Only using L Max as in Munguia-Vega et al., 2018 is not recommended, because of a potential loss of data regarding the fish behavior, which means that there might be big fish that in reality does not move long distances (e.g. groupers).

This study demonstrated thar using the AR of the fish caudal fin had the least influence on HR of the three factors used in the model. But there was no trial of 61

retraining the model without this piece of information to compare the prediction accuracy. Further experiments can be done to assess this since the cost of implementing this data as a prediction variable relies on excluding fish with no tail, like manta rays and moray eels. In many cases, fish like these are of interest for management and conservation (Kessel et al., 2017; Tzeng, 2014). For this reason I suggest using alternative data to help predict fish HR, such as the shape of the body or the shape of the tail. Other ecological and behavioral information that would complement a movement analysis are gregariousness and territoriality (Krueck et al.,

2018).

The classification of HR into 5 categories in this study was broad, especially for smaller HR sizes (1, 2), where most of fish where classified. The reason for merging the original categories 2 and 3 into a single one that afterward became the new 2 was because the model had trouble making a distinction between them. For a refined level of classification, it might be necessary to introduce new predictor variables that increase the understanding of fish behavior and help give the model a finer classification prediction. Also using such broad HR categories clustered species into bigger bins than how far we know they move based on empirical data, e.g., Cheilinus undulatus and Bolbometapon muricatum are known to move <10km. In this thesis, because of the category classification, they were classified into a HR of 5-20km, suggesting that they need larger MPAs (40km) than they really do (20 km: see Green et al., 2015). Therefore, I recommend complementing the predicted HR in my model with empirical data if it exists, particularly for wide ranging species not included in 62

the model (Appendix 3) If it does not, always take in account that modeled data is something close to reality but will not be totally correct until proven. Thus empirical studies of fish movement patterns in the Red Sea are a research priority to refine the model and provide advice for conservation and management.

Using this approach, it becomes difficult to understand the behavior of outlier data.

One example of data outliers in the RS: Whale sharks (Rhincodon typus). This shark possesses a Trophic Level of 3.5, an L Max of 2000 cm, and an AR of 4.25. We know that this shark feeds on plankton. Therefore the model could not accurately determine a HR higher than 5km for this iconic species, erroneously assigning it into a movement category of 2 to 5 km, which is much lower than empirical measurements of movement for this species of 1000s of kms (Green et al.,2015). This is because the model was trained with a high number of planktivorous species that have small HRs

(e.g., damselfishes). There are only other two fish that present features similar to the whale shark: Cetorhinus maximus, the basking shark, and Megachasma pelagios, the megamouth shark. These are oceanodromous fish that have been shown to use small core areas in different parts of the world (Rohner et al., 2020). I recommend that these types of species are treated separately, especially if planning to use the information for taking management decisions.

4.3. Capabilities of Red Seas existing Marine Protected Areas

Currently most MPAs in the Red Sea do not have the sufficient size to protect all species and their needs, and if they do, their enforcement is not clear becoming functioning. For example, most pelagic species (e.g., barracudas, sharks, tunas) will come close to the reefs to feed, meaning that the NTA needs to take this information into consideration in terms of the habitats to protect. Only areas that measure more than 100s and 1000s of km will be sufficient to protect these species that have such wide HRs (Green et al., 2015).

Currently 60% of the designated MPAs in the Red Sea have the potential to protect the majority of species assessed in this thesis, but only if their status of enforcement is of an NTA. Currently there is no sign of any enforcement of fishing regulation in these areas. Having larger MPAs will benefit more fisheries, rare, threatened, endangered and vulnerable reef fishes not included in the model (e.g., manta rays).

Ithe Red Sea species list I assessed, the most endangered fish, Carcharhinus longimanus is Critically Endengered and has a predicted HR of 5-20 km (refer to

Figure 3.13), meaning that a NTA of at least 10-40 km is needed to protect it.

The analysis made on these MPAs considers only their size. In order to have a better understanding of their potential it will be very valuable to assess if their geographical location includes necessary habitats for the protection of species of interest as well as their proximity to other areas in order to benefit the connectivity of the species. 64

Currently, the Saudi Arabian government is working on improving the state of the

MPAs in their waters. The information obtained from this thesis has the potential to inform such actions.

Results obtained from this thesis and complemented with empirical information from previous literature have the potential to inform MPA sizing establishment decisions in the Red Sea. To achieve an efficient communication t is necessary to create a policy brief with clear graphics to share key aspects and information that stakeholder and decision makers need to consider to make informed decisions regarding MPA design to achieve biodiversity and fisheries objectives. In other places providing information on home ranges has been a powerful tool for working with key stakeholders

(particularly fishermen and decision makers) to improve the design of MPAs (e.g.,

Weeks et al., 2017). In this thesis I provide a poster that can be used for this purpose in the Red Sea, which is based on my model predictions.

4.4. Case Study: The Red Sea Project Potential

There are many iconic (fisheries, rare and threatened) species in the Red Sea that move a lot further than the ones currently benefiting from the large MPA in Sudan.

These species do not appear to be more abundant in Sudan at present, possibly because the existing MPAs are not large enough to protect these species that can move

10s, 100s or even 1000s of km (e.g., sharks and other big predators). Even the large

MPAs in Sudan (which are longer than 40 km) may not be big enough to protect some 65

species. Therefore, the proposed NTA at Al Wajh may provide the only NTA large enough to protect these species in the Red Sea, since it will be 100s of km long (~160 km). So it is likely to show more benefits for more species of large carnivorous reef fishes, sharks and rays. Since these are primarily large bodies species, they should contribute to achieving the goal of increasing biomass by 30%, but it may take years or decades because populations of these species take longer to recover than most reef fishes (Abesamis et al., 2014).

If implemented successfully, TRSP will become the largest NTA in Red Sea, with a length of approximately 160 km. Meaning that it will have the potential to protect close to a 100% of the species analyzed in this thesis, and well as many more wide- ranging species not included in the model. Only with the biomass comparison done with Sudan we could see that if closed, TRSP has the potential to increase its biomass by 70%, which is much higher than their 30% goal. This is only taking in account the species that overlap between both areas.

It is known that when an NTA is effectively managed through time, species that disappeared in the past can return (Aburto-Oropeza et al., 2011). We can’t tell if there are species in the Al Wajh area that are no longer there because of fishing pressure because of a lack of historical baseline to compare it to. Currently, a significant amount of fish surveys is being recorded in the area, meaning that there is a potential to document the recovery of fish diversity and biomass over time.

It is necessary to have a broad understanding of the situation of an area that intends to be managed. An example is to better understand the interaction between people and fisheries resources. Therefore, it is recommended that more socio-ecological research is done combining data obtained directly from fisherman, like targeted species, fishing methods and geographical location of the areas of fishing interest.

Overlapping this knowledge with the known ecology of species would be very valuable to power conservation or management initiatives.

4.5. Recommendations and future directions

The enforcement of MPAs is necessary for them to act as the tool they are designed to be. The ecosystems found inside the areas will see benefits regarding the enforcement to fishing restrictions. This will potentially push the local communities to seek other places to obtain the equivalent product and these alternative places will have a greater fishing pressure.

In the long term MPAs have the potential to generate a spillover of fish to support fisheries in surrounding areas but this is true only after the area and populations involved have a chance to recover and increase their biomass and abundance. These positive changes require constant monitoring and enforcement of the MPAs in question, as well as a continuous ban or management of the fisheries in the area. A 67

clear parallel of fisheries and socio ecological understanding and management has to take place while both communities and ecosystem adapt to the management strategies.

Fisheries in the Red Sea are becoming heavily impacted, meaning that the Saudi

Arabian coastal ecosystems will experience a great unbalance if fishing regulations

(e.g. MPAs) are not being implemented and regularly monitored. To sustain fish populations for the future is necessary to first understand the existing pressure currently being exerted. This is yet to be outlined, but the first steps towards a better management design and strategy have been taken.

Overlapping this knowledge with the known ecology of species would be very valuable to power conservation or management initiatives.

Chapter 5. CONCLUSIONS

• ML is a good tool to predict ecological data necessary to take environmental

management decisions. This method can be improved by experimenting with

different data sets and predictor variables.

• L Max of a fish species if a very strong predictor or movement, but can be

complemented with additional variables to add additional behavioral

information.

• Existing Red Sea MPAs showed that 60 % (9 MPAs) of the designated MPAs

have a linear distance longer than 40 km. Which means that, if implemented

as a NTA these areas have the potential to protect most of fish species analyzed

in this thesis.

• MPA’s smaller than 10 km have the potential to protect 90% of the fish species

analyzed in this thesis, but this percentage does not include pelagic or wide-

ranging fishes that move 10s, 100s or 1000s of kms. The data presents itself

like this because my data set has a higher proportion of small ranging fish.

• The most vulnerable species (according to their endangerment status) in my

model require MPAs of at least 5 km. However, many more of these species

that were not in my model move over large distances (10s, 100s or 1000s of

km) and require much larger MPAs (see Green et al. 2015).

• To protect fish with a HR greater than 20 km it is necessary to complement

MPAs with additional management strategies like species specific fishing bans. 69

• In the Red Sea 53% of Designated MPAs belongs to Egypt, 26% to Saudi Arabia,

the rest to Israel and Sudan.

• Based on movement patterns of key species, and biomass of similar species in

unfished areas in Sudan, TRSP has the potential of increasing its fish biomass

more than 70% is implemented successfully.

• This study represent only one of the multiple factors that MPA establishment

needs to take in account, other factors are habitat representation, connectivity

and the social component. Well designed MPAs for the Red Sea will also need

to consider other biophysical and socioeconomic and cultural design criteria,

particularly the need for habitat representation and replication, protecting

critical areas (e.g., spawning and nursery areas), incorporating larval

dispersal, adapting to climate change, and ensuring food security and

livelihoods for coastal communities (Gajdzik et al., 2021; Green et al., 2014).

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APPENDIX 1. Empirical data set extracted from Green et al., 2015 used to train the HR prediction ML model.

Total Maximu Aspect Empiric HR Family Species Length m Length Ratio HR Categ. Acanthuridae Acanthurus blochii 2 45 2.61 3 3 Acanthuridae Acanthurus chirurgus 2.09 39 1.87 0.3 2 Acanthuridae Acanthurus coeruleus 2 39 3.49 0.3 2 Acanthuridae Acanthurus leucosternon 2 54 2.39 0.1 2 Acanthuridae Acanthurus lineatus 2 38 2.05 1 2 Acanthuridae Acanthurus nigrofuscus 2 21 1.82 3 3 Acanthuridae Ctenochaetus striatus 2 26 1.96 0.3 2 Acanthuridae Naso hexacanthus 3.06 83.25 3.56 5 3 Acanthuridae Naso lituratus 2.32 56.12 2.55 5 3 Acanthuridae Naso unicornis 2.17 77.7 2.85 1 2 Acanthuridae Naso vlamingii 2.16 60 2.39 0.1 2 Acanthuridae Zebrasoma flavescens 2 20 1.96 1 2 Acanthuridae Zebrasoma scopas 2 48.8 2.02 1 2 Apogonidae Cheilodipterus artus 4.14 22.81 2.18 0.01 1 Apogonidae Cheilodipterus quinquelineatus 3.89 13 2.25 0.01 1 Apogonidae Ostorhinchus doederleini 3.57 14 1.53 0.01 1 Carangidae Carangoides ferdau 4.31 70 3.64 5 3 Carangidae Carangoides fulvoguttatus 4.43 133.2 4.77 5 3 Carangidae Carangoides orthogrammus 4.25 75 4.52 5 3 Carangidae Caranx papuensis 3.98 88 4.05 5 3 Carangidae Caranx ruber 4.28 81.03 4.23 5 3 Carcharhinidae Carcharhinus amblyrhynchos 4.11 255 2.58 10 4 Carcharhinidae Carcharhinus amboinensis 4.28 280 2.80 30 5 Carcharhinidae Carcharhinus melanopterus 3.94 200 1.54 20 4 Carcharhinidae Carcharhinus perezi 4.5 300 1.89 40 5 Carcharhinidae Carcharhinus plumbeus 4.27 250 3.18 200 5 Carcharhinidae Carcharhinus sorrah 4.15 160 2.33 20 4 Carcharhinidae Galeocerdo cuvier 4.56 750 1.39 35 5 Carcharhinidae Negaprion acutidens 4.13 380 2.79 5 3 Carcharhinidae Negaprion brevirostris 4.27 340 1.07 5 3 Carcharhinidae Triaenodon obesus 4.19 213 7.05 10 4 Chaetodontida Chaetodon auriga 3.69 23 2.43 0.5 2 e 80

Chaetodontida Chaetodon kleinii 2.93 15 2.20 1 2 e Chaetodontida Chaetodon multicinctus 3.28 12 2.44 1 2 e Chaetodontida Chaetodon ornatissimus 3.34 20 2.39 1 2 e Chaetodontida Chaetodon plebeius 3.34 15 2.87 1 2 e Chaetodontida Chaetodon quadrimaculatus 4.18 16 2.75 1 2 e Chaetodontida Chaetodon rainfordi 2.77 15 2.88 1 2 e Chaetodontida Chaetodon striatus 3.53 16 2.56 0.3 2 e Chaetodontida Chaetodon trifasciatus 3.34 15 2.51 1 2 e Chaetodontida Chaetodon unimaculatus 3.28 20 2.20 1 2 e Chaetodontida Chelmon rostratus 3.5 20 2.67 1 2 e Epinephelidae Cephalopholis argus 4.32 60 1.45 0.1 2 Epinephelidae Cephalopholis cruentata 4.25 42.6 2.43 0.1 2 Epinephelidae Cephalopholis cyanostigma 4.17 40 1.35 0.1 2 Epinephelidae Cephalopholis fulva 4.05 44 1.29 0.1 2 Epinephelidae Cephalopholis hemistiktos 4.14 35 1.38 0.1 2 Epinephelidae Cephalopholis miniata 4.29 50 1.62 0.1 2 Epinephelidae Cephalopholis sonnerati 3.81 57 1.96 5 3 Epinephelidae Epinephelus coioides 4.04 120 1.28 5 3 Epinephelidae Epinephelus guttatus 3.84 76 0.98 0.3 2 Epinephelidae Epinephelus maculatus 3.98 60.5 1.31 5 3 Epinephelidae Epinephelus marginatus 4.1 150 1.51 3 3 Epinephelidae Epinephelus multinotatus 3.86 100 1.54 3 3 Epinephelidae Epinephelus tauvina 4.13 100 1.40 3 3 Epinephelidae Epinephelus adscensionis 3.75 65 1.20 0.1 2 Epinephelidae Mycteroperca microlepis 3.65 145 1.43 1 2 Epinephelidae Plectropomus areolatus 4.5 80 1.84 1 2 Epinephelidae Plectropomus leopardus 4.42 146.4 1.60 3 3 Epinephelidae Serranus cabrilla 3.35 48.8 1.34 2 2 Epinephelidae Variola louti 4.33 83 2.09 3 3 Gobiidae Amblygobius bynoensis 2.71 12.2 0.95 0.01 1 Gobiidae Amblygobius phalaena 3.63 15 0.85 0.01 1 Gobiidae Asterropteryx semipunctata 2.36 6.5 1.08 0.01 1 Gobiidae Istigobius goldmanni 3.28 7.32 1.02 0.01 1 Gobiidae Valenciennea muralis 3.5 19.52 0.79 0.01 1 Haemulidae Haemulon carbonarium 3.68 36 2.67 0.1 2 Haemulidae Haemulon chrysargyreum 3.53 23 1.45 0.1 2 81

Haemulidae Haemulon plumierii 3.59 53 3.00 1 2 Haemulidae Haemulon sciurus 3.48 46 2.47 1 2 Holocentridae Holocentrus adscensionis 3.11 61 2.11 0.1 2 Holocentridae Holocentrus rufus 3.51 35 2.31 0.1 2 Holocentridae Myripristis jacobus 3.39 25 2.77 0.1 2 Kyphosidae Kyphosus sectatrix 2 76 2.71 3 3 Kyphosidae Kyphosus sydneyanus 2.03 80 3.08 5 3 Labridae Bodianus rufus 3.71 40 1.62 0.1 2 Labridae Cheilinus undulatus 3.99 279.4 1.49 10 4 Labridae Coris aygula 3.73 120 1.18 5 3 Labridae Halichoeres garnoti 3.7 19.3 0.89 0.1 2 Lethrinidae Lethrinus nebulosus 3.76 87 1.84 5 3 Lutjanidae Aprion virescens 4.28 112 3.22 10 5 Lutjanidae Lutjanus apodus 4.25 87.8 1.78 1 2 Lutjanidae Lutjanus campechanus 3.92 100 1.43 1 2 Lutjanidae Lutjanus carponotatus 3.89 40 2.04 0.1 2 Lutjanidae Lutjanus ehrenbergii 3.85 35 1.94 0.1 2 Lutjanidae Lutjanus fulviflamma 3.79 35 2.14 0.1 2 Lutjanidae Lutjanus griseus 4.23 89 1.78 1 2 Lutjanidae Lutjanus johnii 4.2 97 1.50 3 3 Lutjanidae Lutjanus russellii 4.1 50 2.21 1 2 Lutjanidae Ocyurus chrysurus 4.03 86.3 1.74 0.1 2 Monacanthidae Cantherhines pullus 2.62 20 2.02 0.1 2 Mullidae Mulloidichthys flavolineatus 3.84 43 2.48 1 2 Mullidae Mulloidichthys martinicus 3.21 44.8 2.26 0.5 2 Mullidae Parupeneus cyclostomus 4.2 50 1.72 1 2 Mullidae Parupeneus porphyreus 3.98 51 2.18 0.5 2 Mullidae Parupeneus trifasciatus 3.54 35 1.23 1 2 Pomacanthidae Centropyge ferrugata 2.79 10 1.38 0.01 1 Pomacanthidae Holacanthus tricolor 3.01 35 1.10 0.3 2 Pomacanthidae Pomacanthus arcuatus 3.19 60 2.06 0.3 2 Pomacanthidae Pomacanthus paru 2.81 41.1 1.83 0.3 2 Pomacentridae Abudefduf saxatilis 3.82 22.9 1.54 0.2 2 Pomacentridae Chromis fumea 3.4 12.2 2.61 0.07 1 Pomacentridae Dascyllus albisella 3.14 13 1.24 0.02 1 Pomacentridae Dascyllus aruanus 3.35 10 1.68 0.02 1 Pomacentridae Dischistodus perspicillatus 2 18 1.60 0.02 1 Pomacentridae Dischistodus prosopotaenia 2.7 18.5 1.75 0.02 1 Hemiglyphidodon Pomacentridae 2 18 1.70 0.02 1 plagiometopon 82

Pomacentridae Microspathodon chrysurus 2.1 21 1.09 0.07 1 Pomacentridae Neoglyphidodon nigroris 3.02 13 1.21 0.02 1 Plectroglyphidodon Pomacentridae 2.22 10 1.43 0.02 1 lacrymatus Pomacentridae Pomacentrus adelus 2.69 8.5 1.81 0.02 1 Pomacentridae Pomacentrus bankanensis 2.68 9 1.65 0.02 1 Pomacentridae Pomacentrus burroughi 2 8.5 1.62 0.02 1 Pomacentridae Pomacentrus chrysurus 2.64 9 1.86 0.02 1 Pomacentridae Pomacentrus tripunctatus 2 9.15 1.95 0.02 1 Pomacentridae Pomacentrus wardi 2 9.76 1.81 0.02 1 Pomacentridae Stegastes adustus 2.49 15 1.28 0.02 1 Pomacentridae Stegastes apicalis 2.06 15 1.87 0.02 1 Scaridae Bolbometopon muricatum 2.67 130 1.37 10 4 Scaridae Chlorurus frontalis 2 50 1.53 3 3 Scaridae Chlorurus microrhinos 2 70 1.21 3 3 Scaridae Chlorurus perspicillatus 2 74.3 1.73 3 3 Scaridae Chlorurus sordidus 2.62 40 1.07 3 3 Scaridae Scarus ghobban 2 75 1.77 3 3 Scaridae Scarus iseri 2 35 1.32 0.1 2 Scaridae Scarus prasiognathos 2 70 2.02 3 3 Scaridae Scarus rivulatus 2 48.8 1.66 1 2 Scaridae Scarus rubroviolaceus 2 70 1.94 3 3 Scaridae Scarus taeniopterus 2.02 35 1.05 1 2 Scaridae Scarus vetula 2 61 1.52 0.1 2 Scaridae Scarus coeruleus 2 120 0.97 1 2 Scaridae Sparisoma rubripinne 2 47.8 1.20 0.5 2 Scaridae Sparisoma viride 2 64 1.13 0.5 2 Scaridae Sparisoma chrysopterum 2 46 1.35 0.1 2 Scombridae Scomberomorus cavalla 4.42 184 5.82 50 5 Siganidae Siganus doliatus 2 30.5 2.33 3 3 Siganidae Siganus fuscescens 2.03 40 2.22 3 3 Siganidae Siganus lineatus 2 43 3.14 1 2 Sphyrnidae Eusphyra blochii 4.15 186 1.25 20 4 Sphyrnidae Sphyrna lewini 4.08 430 1.85 10 4 Sphyrnidae Sphyrna tiburo 4 150 1.77 10 4

APPENDIX 2. Red Sea data based used to obtain the HR predictions, and their IUCN status (https://www.iucnredlist.org/). Input data (Trophic Level,

Maximum Length, and Aspect Ratio).

HR HR - Trophic Maximum Aspect Family Species Predicted Equivalent IUCN Level Length Ratio Category (km) Acanthuridae Acanthurus gahhm 2.76 40 3.12 2 0.1-1 LC Acanthuridae Acanthurus mata 2.53 50 2.28 2 0.1-1 LC Acanthurus Acanthuridae 2 21 1.82 3 2.0-5.0 LC nigrofuscus Acanthuridae Acanthurus sohal 2 40 2.00 2 0.1-1 LC Ctenochaetus Acanthuridae 2 26 1.96 2 0.1-1 LC striatus Acanthuridae Naso brevirostris 2.22 60 2.67 2 0.1-1 LC Acanthuridae Naso elegans 2 54.9 3.06 2 0.1-1 LC Acanthuridae Naso hexacanthus 3.06 83.3 3.56 3 2.0-5.0 LC

Acanthuridae Naso unicornis 2.17 77.7 2.85 2 0.1-1 LC Zebrasoma Acanthuridae 2 40 1.81 2 0.1-1 LC desjardinii Zebrasoma Acanthuridae 2 36.7 1.97 2 0.1-1 LC xanthurum Alopidae Alopias pelagicus 4.5 428 1.94 5 30.0-50.0 VU Aploactinidae Ptarmus gallus 3.13 3.5 1.27 1 0.01-0.07 N.E.

Ariidae Netuma thalassina 3.54 185 2.64 4 10.0-20.0 N.E. Balistapus Balistidae 3.37 30 1.95 2 0.1-1 N.E. undulatus Balistoides Balistidae 3.33 75 1.87 2 0.1-1 N.E. viridescens Canthidermis Balistidae 3.45 60 2.51 2 0.1-1 N.E. macrolepis Balistidae Melichthys indicus 3 25 1.90 2 0.1-1 N.E. Balistidae Odonus niger 3.22 50 1.98 2 0.1-1 N.E. Pseudobalistes Balistidae 2.78 60 1.74 2 0.1-1 N.E. flavimarginatus Pseudobalistes Balistidae 3.05 55 1.51 2 0.1-1 N.E. fuscus Balistidae Rhinecanthus assasi 3.5 30 2.10 2 0.1-1 N.E. Sufflamen Balistidae 3.4 22 2.33 2 0.1-1 N.E. albicaudatum Belonidae Tylosurus choram 4.39 120 1.86 2 0.1-1 N.E. Tylosurus Belonidae 4.43 150 2.06 5 30.0-50.0 LC crocodilus Caesionidae Caesio lunaris 3.4 40 3.53 2 0.1-1 LC

Caesionidae Caesio striata 3.4 25 2.69 2 0.1-1 N.E.

Caesionidae Caesio suevica 3.4 35 3.02 2 0.1-1 N.E. Caesionidae Caesio varilineata 3.4 40 4.10 2 0.1-1 N.E. 84

Gymnocaesio Caesionidae 3.4 18 2.19 2 0.1-1 LC gymnoptera Pterocaesio Caesionidae 3.4 21 2.54 2 0.1-1 LC chrysozona Carangidae Alepes vari 3.65 56 2.83 2 0.1-1 LC Carangidae Atule mate 4.22 30 3.48 2 0.1-1 LC Carangidae Carangoides bajad 3.97 55 4.35 3 2.0-5.0 LC Carangidae Carangoides ferdau 4.31 70 3.64 3 2.0-5.0 LC Carangoides Carangidae 4.43 133 4.77 3 2.0-5.0 LC fulvoguttatus Carangidae Caranx ignobilis 4.22 170 3.48 4 10.0-20.0 LC Carangidae Caranx melampygus 4.49 130 4.80 3 2.0-5.0 LC Carangidae Caranx sexfasciatus 4.5 120 4.21 3 2.0-5.0 LC Carangidae Decapterus russelli 3.68 45 2.90 2 0.1-1 LC

Carangidae Elagatis bipinnulata 4.27 180 3.29 5 30.0-50.0 LC Gnathanodon Carangidae 3.84 120 3.07 3 2.0-5.0 LC speciosus Carangidae Scomberoides lysan 4.04 110 3.94 3 2.0-5.0 LC Trachinotus Carangidae 3.56 60 3.14 2 0.1-1 LC baillonii Carangidae Trachinotus blochii 3.74 122 3.52 3 2.0-5.0 LC

Carangidae Ulua mentalis 3.68 100 3.60 3 2.0-5.0 LC Carcharhinus Carcharhinidae 4.21 300 3.02 4 10.0-20.0 VU albimarginatus Carcharhinus Carcharhinidae 4.11 255 2.58 4 10.0-20.0 NT amblyrhynchos Carcharhinus Carcharhinidae 4.36 350 2.03 5 30.0-50.0 VU falciformis Carcharhinus Carcharhinidae 4.37 275 2.00 5 30.0-50.0 NT limbatus Carcharhinus Carcharhinidae 4.2 400 2.47 4 10.0-20.0 CR longimanus Carcharhinus Carcharhinidae 3.94 200 1.54 4 10.0-20.0 NT melanopterus Carcharhinus Carcharhinidae 4.27 250 3.18 5 30.0-50.0 VU plumbeus Carcharhinidae Galeocerdo cuvier 4.56 750 1.39 5 30.0-50.0 NT Negaprion Carcharhinidae 4.13 380 2.79 3 2.0-5.0 EN acutidens Carcharhinidae Triaenodon obesus 4.19 213 2.05 4 10.0-20.0 NT Chaetodontidae Chaetodon auriga 3.69 23 2.43 2 0.1-1 LC Chaetodon Chaetodontidae 3.34 15.9 2.54 2 0.1-1 LC austriacus Chaetodontidae Chaetodon fasciatus 3.34 22 2.79 2 0.1-1 LC Chaetodontidae Chaetodon larvatus 3.38 12 3.32 1 0.01-0.07 LC Chaetodon Chaetodontidae 3.39 30 2.34 2 0.1-1 LC lineolatus Chaetodon Chaetodontidae 4.35 18 1.74 2 0.1-1 LC melannotus Chaetodon Chaetodontidae 3.34 13 2.74 1 0.01-0.07 LC melapterus Chaetodon Chaetodontidae 3.29 13 2.92 2 0.1-1 LC mesoleucos Chaetodon Chaetodontidae 3.17 14 2.57 2 0.1-1 LC paucifasciatus 85

Chaetodontidae Chaetodon pictus 3.33 17.2 2.43 2 0.1-1 LC Chaetodontidae Chaetodon semeion 2.73 26 2.50 2 0.1-1 LC Chaetodon Chaetodontidae 3.5 23 2.79 2 0.1-1 LC semilarvatus Chaetodon Chaetodontidae 3.34 18 2.50 2 0.1-1 NT trifascialis Chaetodon Chaetodontidae 3.28 20 2.20 2 0.1-1 LC unimaculatus Heniochus Chaetodontidae 3.4 21 2.25 2 0.1-1 LC diphreutes Heniochus Chaetodontidae 3.45 18 2.57 2 0.1-1 LC intermedius Chanidae Chanos chanos 2.4 220 3.59 3 2.0-5.0 LC Cirrhitichthys Cirrhitidae 4.01 10 1.69 1 0.01-0.07 LC oxycephalus Cirrhitidae Cirrhitus pinnulatus 3.68 30 1.39 2 0.1-1 LC Cirrhitidae Oxycirrhites typus 3.3 13 1.56 1 0.01-0.07 LC Paracirrhites Cirrhitidae 4.3 22 1.15 1 0.01-0.07 LC forsteri Cyclichthys Diodontidae 3.46 34 1.12 2 0.1-1 N.E. spilostylus Diodontidae Diodon holocanthus 3.85 50 0.79 2 0.1-1 LC Diodontidae Diodon hystrix 4.01 91 1.53 3 2.0-5.0 LC

Diodontidae Diodon liturosus 3.5 65 1.18 2 0.1-1 N.E. Echeneidae Echeneis naucrates 3.68 110 1.54 3 2.0-5.0 LC Ephippidae Platax boersii 3.53 40 3.16 2 0.1-1 N.E. Ephippidae Platax orbicularis 3.33 60 2.97 2 0.1-1 N.E. Ephippidae Platax teira 3.95 70 2.45 3 2.0-5.0 N.E. Fistularia Fistulariidae 4.26 160 1.76 5 30.0-50.0 LC commersonii Gerreidae Gerres oyena 2.72 30 2.07 2 0.1-1 LC Haemulidae Diagramma pictum 3.7 111 1.45 2 0.1-1 N.E. Plectorhinchus Haemulidae 4 122 1.50 3 2.0-5.0 N.E. albovittatus Plectorhinchus Haemulidae 3.99 50 2.00 2 0.1-1 N.E. gaterinus Plectorhinchus Haemulidae 3.57 75 1.78 2 0.1-1 LC gibbosus Plectorhinchus Haemulidae 3.33 90 1.93 3 2.0-5.0 N.E. playfairi Plectorhinchus Haemulidae 3.88 80 1.72 2 0.1-1 N.E. schotaf Plectorhinchus Haemulidae 3.99 60 1.57 2 0.1-1 N.E. sordidus Holocentridae Myripristis murdjan 3.39 60 2.37 2 0.1-1 LC Myripristis Holocentridae 3.41 20 2.52 2 0.1-1 N.E. xanthacra Neoniphon Holocentridae 3.62 32 2.25 2 0.1-1 LC sammara Sargocentron Holocentridae 3.89 25 3.08 2 0.1-1 LC caudimaculatum Sargocentron Holocentridae 3.37 17 1.98 1 0.01-0.07 LC diadema Sargocentron Holocentridae 3.58 32 2.44 2 0.1-1 LC rubrum Sargocentron Holocentridae 3.6 56.6 2.24 2 0.1-1 LC spiniferum 86

Kyphosidae Kyphosus bigibbus 2.05 75 3.20 3 2.0-5.0 LC Kyphosus Kyphosidae 2.85 56.3 3.43 2 0.1-1 LC cinerascens Kyphosidae Kyphosus vaigiensis 2.07 70 3.25 3 2.0-5.0 LC Anampses Labridae 3.37 42 1.54 2 0.1-1 LC caeruleopunctatus Labridae Anampses lineatus 3.37 13 1.49 1 0.01-0.07 DD Anampses Labridae 3.5 22 1.91 2 0.1-1 LC meleagrides Labridae Anampses twistii 3.5 18 1.35 1 0.01-0.07 LC Labridae Bodianus anthioides 3.44 29.3 1.40 2 0.1-1 LC Labridae Bodianus axillaris 3.42 24.4 1.24 2 0.1-1 LC Labridae Bodianus diana 3.4 20.6 0.92 2 0.1-1 LC Labridae Cheilinus abudjubbe 3.48 26.6 0.90 2 0.1-1 LC

Labridae Cheilinus lunulatus 3.6 50 0.98 2 0.1-1 LC Cheilinus Labridae 3.7 40 1.33 2 0.1-1 LC quinquecinctus Labridae Cheilinus undulatus 3.99 279 1.49 4 10.0-20.0 EN Labridae Cheilio inermis 3.49 61 0.95 2 0.1-1 LC Cirrhilabrus Labridae 3.4 16 0.96 1 0.01-0.07 LC blatteus Cirrhilabrus Labridae 3.4 7.5 1.21 1 0.01-0.07 LC rubriventralis Labridae Coris aygula 3.73 120 1.18 3 2.0-5.0 LC Labridae Coris caudimacula 3.38 20 1.17 2 0.1-1 LC Labridae Coris cuvieri 3.38 38 1.39 2 0.1-1 LC Labridae Coris formosa 3.35 60 1.16 2 0.1-1 LC

Labridae Coris variegata 3.5 20 1.44 2 0.1-1 LC Labridae Epibulus insidiator 4.01 65.9 1.62 2 0.1-1 LC Gomphosus Labridae 3.5 32 1.41 2 0.1-1 LC caeruleus Halichoeres Labridae 3.4 27 1.55 2 0.1-1 LC hortulanus Labridae Halichoeres iridis 3.38 11.5 1.47 1 0.01-0.07 LC Halichoeres Labridae 3.19 18 1.15 1 0.01-0.07 LC marginatus Halichoeres Labridae 3.34 12 1.30 1 0.01-0.07 LC nebulosus Halichoeres Labridae 3.5 20 1.24 2 0.1-1 LC scapularis Halichoeres Labridae 3.5 20 1.36 2 0.1-1 LC zeylonicus Hemigymnus Labridae 3.55 45.1 1.22 2 0.1-1 LC melapterus Hemigymnus Labridae 3.52 36.5 1.46 2 0.1-1 DD sexfasciatus Hologymnosus Labridae 4.2 40 1.79 2 0.1-1 LC annulatus Hologymnosus Labridae 3.79 50 1.47 2 0.1-1 LC doliatus Labridae Labroides bicolor 4.02 15 1.22 1 0.01-0.07 LC Labroides Labridae 3.46 14 1.08 1 0.01-0.07 LC dimidiatus 87

Larabicus Labridae 3.34 11.5 1.40 1 0.01-0.07 DD quadrilineatus Macropharyngodon Labridae 3.5 13 1.45 1 0.01-0.07 LC bipartitus Macropharyngodon Labridae 3.47 11.6 1.45 1 0.01-0.07 N.E. marisrubri Novaculichthys Labridae 3.25 30 1.61 2 0.1-1 LC taeniourus Oxycheilinus Labridae 3.69 48.8 1.32 2 0.1-1 LC digramma Oxycheilinus Labridae 3.78 20 1.33 2 0.1-1 LC mentalis Paracheilinus Labridae 3.4 9 1.72 1 0.01-0.07 LC octotaenia Pseudocheilinus Labridae 3.5 9 1.08 1 0.01-0.07 LC evanidus Pseudocheilinus Labridae 3.4 10 1.29 1 0.01-0.07 LC hexataenia Pseudodax Labridae 2.82 30 1.30 2 0.1-1 LC moluccanus Stethojulis Labridae 3.56 14 1.33 1 0.01-0.07 LC albovittata Stethojulis Labridae 3.37 13 1.18 1 0.01-0.07 LC interrupta Labridae Thalassoma lunare 3.5 45 1.08 2 0.1-1 LC Thalassoma Labridae 3.72 30 1.58 2 0.1-1 LC lutescens Thalassoma Labridae 3.66 46 1.55 2 0.1-1 LC purpureum Thalassoma Labridae 3.5 20 1.50 2 0.1-1 LC rueppellii Wetmorella Labridae 3.5 8 1.00 1 0.01-0.07 LC nigropinnata Gymnocranius Lethrinidae 3.48 80 2.70 2 0.1-1 LC grandoculis Lethrinus Lethrinidae 3.47 40 1.91 2 0.1-1 N.E. borbonicus Lethrinidae Lethrinus harak 3.59 50 2.31 2 0.1-1 LC Lethrinidae Lethrinus lentjan 3.94 52 2.31 2 0.1-1 LC Lethrinidae Lethrinus mahsena 3.43 65 2.02 2 0.1-1 N.E. Lethrinidae Lethrinus microdon 3.79 80 2.58 3 2.0-5.0 LC Lethrinidae Lethrinus nebulosus 3.76 87 1.84 3 2.0-5.0 LC

Lethrinidae Lethrinus obsoletus 3.89 60 2.39 2 0.1-1 LC Lethrinidae Lethrinus olivaceus 3.95 100 2.82 3 2.0-5.0 LC Lethrinus Lethrinidae 3.5 20 2.22 2 0.1-1 LC variegatus Lethrinus Lethrinidae 3.79 77.7 2.67 2 0.1-1 LC xanthochilus Monotaxis Lethrinidae 3.45 60 3.22 2 0.1-1 LC grandoculis Lutjanus Lutjanidae 3.58 150 1.57 4 10.0-20.0 LC argentimaculatus Lutjanidae Lutjanus bohar 4.27 90 2.81 3 2.0-5.0 LC Lutjanus Lutjanidae 3.85 35 1.94 2 0.1-1 LC ehrenbergii Lutjanus Lutjanidae 3.79 35 2.14 2 0.1-1 LC fulviflamma Lutjanidae Lutjanus fulvus 3.61 40 2.08 2 0.1-1 LC

Lutjanidae Lutjanus gibbus 4.12 50 2.16 2 0.1-1 LC 88

Lutjanidae Lutjanus kasmira 3.87 40 2.08 2 0.1-1 LC Lutjanus Lutjanidae 4.27 60 2.01 2 0.1-1 LC monostigma Lutjanidae Lutjanus rivulatus 4.13 80 1.83 2 0.1-1 LC Lutjanidae Macolor niger 4.01 75 1.95 3 2.0-5.0 LC Lutjanidae Aphareus rutilans 4.11 110 4.14 3 2.0-5.0 LC Malacanthus Malacanthidae 3.5 32 1.35 2 0.1-1 N.E. brevirostris Malacanthus Malacanthidae 3.5 54.9 1.99 2 0.1-1 N.E. latovittatus Monacanthidae Aluterus scriptus 2.81 110 0.86 2 0.1-1 LC Monacanthidae Amanses scopas 2.86 20 1.49 2 0.1-1 LC Cantherhines Monacanthidae 3.5 25 1.46 2 0.1-1 LC pardalis Oxymonacanthus Monacanthidae 3.6 7 2.13 1 0.01-0.07 N.E. halli Monacanthidae Pervagor randalli 2.91 8.17 2.08 1 0.01-0.07 N.E. Monodactylus Monodactylidae 2.95 32.9 3.33 2 0.1-1 LC argenteus Crenimugil Mugilidae 2.29 60 1.81 2 0.1-1 LC crenilabis Mugilidae Crenimugil seheli 2.32 60 2.06 2 0.1-1 N.E. Ellochelon Mugilidae 2.18 63 1.37 2 0.1-1 LC vaigiensis Mugilidae Plicomugil labiosus 2.1 48.8 1.29 2 0.1-1 N.E. Mulloidichthys Mullidae 3.84 43 2.48 2 0.1-1 LC flavolineatus Mulloidichthys Mullidae 3.6 38 4.26 2 0.1-1 LC vanicolensis Parupeneus Mullidae 4.2 50 1.72 2 0.1-1 LC cyclostomus Parupeneus Mullidae 3.54 28 2.69 2 0.1-1 N.E. forsskali Parupeneus Mullidae 3.5 40 1.92 2 0.1-1 LC macronemus Nemipteridae Scolopsis ghanam 3.62 30 2.44 2 0.1-1 N.E. Nemipteridae Scolopsis taeniata 3.66 36 2.39 2 0.1-1 N.E. Nemipteridae Scolopsis vosmeri 3.5 25 2.01 2 0.1-1 N.E.

Ostraciidae Ostracion cubicus 3.37 45 1.41 2 0.1-1 N.E. Ostraciidae Ostracion cyanurus 3.24 15 1.50 1 0.01-0.07 N.E. Pempheridae Pempheris oualensis 3.58 22 1.99 2 0.1-1 N.E. Pempheris Pempheridae 3.4 15 2.37 2 0.1-1 N.E. schwenkii Pempheris Pempheridae 3.45 20 2.24 2 0.1-1 N.E. vanicolensis Parapercis Pinguipedidae 3.55 29 1.20 2 0.1-1 N.E. hexophtalma Calloplesiops Plesiopidae 3.97 20 0.84 2 0.1-1 N.E. altivelis Plotosidae Plotosus lineatus 3.57 32 0.51 2 0.1-1 N.E. Apolemichthys Pomacanthidae 2.83 24.4 1.73 2 0.1-1 LC xanthotis Centropyge Pomacanthidae 2.79 14 1.69 1 0.01-0.07 LC multispinis Genicanthus Pomacanthidae 3.4 20 1.61 2 0.1-1 LC caudovittatus 89

Pomacanthidae Pomacanthus asfur 2.68 40 1.82 2 0.1-1 LC Pomacanthus Pomacanthidae 2.7 48.8 1.71 2 0.1-1 LC imperator Pomacanthus Pomacanthidae 2.67 61 1.29 2 0.1-1 LC maculosus Pygoplites Pomacanthidae 2.69 30.5 1.51 2 0.1-1 LC diacanthus Abudefduf Pomacentridae 2.7 19 2.45 2 0.1-1 LC sexfasciatus Pomacentridae Abudefduf sordidus 2.69 24 1.94 2 0.1-1 LC Abudefduf Pomacentridae 2.57 20 2.13 2 0.1-1 LC vaigiensis Amblyglyphidodon Pomacentridae 2.72 10 1.78 1 0.01-0.07 LC flavilatus Amblyglyphidodon Pomacentridae 2.72 10.1 1.54 1 0.01-0.07 LC indicus Amphiprion Pomacentridae 2.68 14 1.44 1 0.01-0.07 LC bicinctus Pomacentridae Chromis dimidiata 2.68 9 1.41 1 0.01-0.07 LC Pomacentridae Chromis flavaxilla 3.4 7.2 2.76 1 0.01-0.07 N.E. Pomacentridae Chromis pelloura 2.72 14 2.10 1 0.01-0.07 N.E. Pomacentridae Chromis pembae 2.67 13 1.68 1 0.01-0.07 LC Pomacentridae Chromis trialpha 2.68 6 1.33 1 0.01-0.07 N.E.

Pomacentridae Chromis viridis 2.92 10 1.11 1 0.01-0.07 N.E. Pomacentridae Chromis weberi 3.4 13.5 1.90 1 0.01-0.07 N.E. Chrysiptera Pomacentridae 2.75 8 2.03 1 0.01-0.07 N.E. annulata Chrysiptera Pomacentridae 2.08 10 1.10 1 0.01-0.07 LC unimaculata Pomacentridae Dascyllus abudafur 2.96 10.4 1.68 1 0.01-0.07 N.E. Dascyllus Pomacentridae 2.69 6 1.70 1 0.01-0.07 N.E. marginatus Dascyllus Pomacentridae 2.8 14 1.31 1 0.01-0.07 N.E. trimaculatus Neoglyphidodon Pomacentridae 3.43 18 1.63 1 0.01-0.07 N.E. melas Neopomacentrus Pomacentridae 3.43 10 1.66 1 0.01-0.07 N.E. cyanomos Neopomacentrus Pomacentridae 3.4 11 2.00 1 0.01-0.07 N.E. miryae Neopomacentrus Pomacentridae 3.4 6 1.52 1 0.01-0.07 N.E. xanthurus Plectroglyphidodon Pomacentridae 2.22 10 1.43 1 0.01-0.07 N.E. lacrymatus Plectroglyphidodon Pomacentridae 2 12 1.92 1 0.01-0.07 N.E. leucozonus Pomacentrus Pomacentridae 2.62 10.2 1.61 1 0.01-0.07 N.E. albicaudatus Pomacentrus Pomacentridae 2.73 12 1.52 1 0.01-0.07 N.E. aquilus Pomacentridae Pomacentrus leptus 2.69 7 1.49 1 0.01-0.07 N.E. Pomacentrus Pomacentridae 2.63 11 1.76 1 0.01-0.07 N.E. sulfureus Pomacentrus Pomacentridae 2.68 11 1.71 1 0.01-0.07 N.E. trichrourus Pomacentrus Pomacentridae 2.64 10 1.70 1 0.01-0.07 N.E. trilineatus Pristotis Pomacentridae 2.75 11 1.02 1 0.01-0.07 N.E. cyanostigma 90

Pomacentridae Stegastes nigricans 2.09 14 2.07 1 0.01-0.07 N.E. Pomacentridae Stegastes punctatus 2.03 15.9 2.10 2 0.1-1 N.E.

Priacanthidae Priacanthus hamrur 3.64 45 2.91 2 0.1-1 LC Pseudochromis Pseudochromidae 3.5 9 2.05 1 0.01-0.07 LC dixurus Pseudochromis Pseudochromidae 3.5 7.2 1.17 1 0.01-0.07 LC flavivertex Pseudochromis Pseudochromidae 3.5 6.3 1.17 1 0.01-0.07 N.E. fridmani Pseudochromis Pseudochromidae 3.5 9 1.19 1 0.01-0.07 N.E. olivaceus Pseudochromidae Pseudochromis pesi 3.54 10 1.00 1 0.01-0.07 VU Pseudochromis Pseudochromidae 3.5 5.5 0.95 1 0.01-0.07 N.E. springeri Nemateleotris Ptereleotridae 3.05 9 0.95 1 0.01-0.07 LC decora Ptereleotridae Ptereleotris evides 3.4 14 0.93 1 0.01-0.07 LC Ptereleotris Ptereleotridae 3.4 14 0.82 1 0.01-0.07 LC heteroptera Ptereleotris Ptereleotridae 3.38 13 0.67 1 0.01-0.07 N.E. microlepis Ptereleotridae Ptereleotris zebra 3.4 14.6 1.06 1 0.01-0.07 LC Rachycentron Rachycentridae 3.83 200 0.99 4 10.0-20.0 LC canadum Rhincodontidae Rhincodon typus 3.55 2000 4.25 3 2.0-5.0 EN Bolbometopon Scaridae 2.67 130 1.37 4 10.0-20.0 VU muricatum Calotomus Scaridae 2 25.6 1.30 2 0.1-1 LC viridescens Scaridae Cetoscarus bicolor 2 61 1.90 2 0.1-1 LC Chlorurus Scaridae 2 31 1.51 2 0.1-1 LC genazonatus Scaridae Chlorurus gibbus 2 70 1.65 3 2.0-5.0 LC Scaridae Chlorurus sordidus 2.62 40 1.07 3 2.0-5.0 LC Scaridae Hipposcarus harid 2 75 2.01 3 2.0-5.0 LC Leptoscarus Scaridae 2 35 1.13 2 0.1-1 LC vaigiensis Scaridae Scarus collana 2.1 33 1.69 2 0.1-1 LC

Scaridae Scarus falcipinnis 2 60 1.40 2 0.1-1 LC Scaridae Scarus ferrugineus 2 41 1.70 2 0.1-1 LC Scaridae Scarus frenatus 2 47 1.39 2 0.1-1 LC Scarus Scaridae 2.05 38 1.45 2 0.1-1 LC fuscopurpureus Scaridae Scarus ghobban 2 75 1.77 3 2.0-5.0 LC Scaridae Scarus niger 2 40 1.40 2 0.1-1 LC Scaridae Scarus psittacus 2 34 1.81 2 0.1-1 LC Scarus Scaridae 2 70 1.94 3 2.0-5.0 LC rubroviolaceus Scaridae Scarus scaber 2 37 1.86 2 0.1-1 LC

Scombridae Euthynnus affinis 4.26 111 5.62 3 2.0-5.0 LC Grammatorcynus Scombridae 4.18 111 2.93 3 2.0-5.0 LC bilineatus 91

Gymnosarda Scombridae 4.5 275 5.67 5 30.0-50.0 LC unicolor Rastrelliger Scombridae 3.19 42.1 4.45 2 0.1-1 DD kanagurta Scomberomorus Scombridae 4.5 266 3.13 5 30.0-50.0 NT commerson Inimicus Scorpaenidae 4.14 30.5 1.12 2 0.1-1 LC filamentosus Scorpaenidae Pterois cincta 3.73 42.7 1.19 2 0.1-1 LC Scorpaenidae Pterois miles 3.73 42.7 1.19 2 0.1-1 LC Scorpaenopsis Scorpaenidae 4.2 30 1.44 2 0.1-1 LC diabolus Scorpaenopsis Scorpaenidae 3.88 36 1.61 2 0.1-1 LC oxycephala Sebastapistes Scorpaenidae 3.82 10 1.30 1 0.01-0.07 LC cyanostigma Synanceia Scorpaenidae 4.2 48.8 1.40 2 0.1-1 LC verrucosa Serranidae Aethaloperca rogaa 4.2 60 1.52 2 0.1-1 LC Anyperodon Serranidae 3.94 65 1.44 2 0.1-1 LC leucogrammicus Serranidae Cephalopholis argus 4.32 60 1.45 2 0.1-1 LC Cephalopholis Serranidae 4.14 35 1.38 2 0.1-1 LC hemistiktos Cephalopholis Serranidae 4.29 50 1.62 2 0.1-1 LC miniata Cephalopholis Serranidae 4.05 30 1.52 2 0.1-1 LC oligosticta Cephalopholis Serranidae 4 50 1.52 2 0.1-1 LC sexmaculata Serranidae Diploprion drachi 4.06 14 1.35 1 0.01-0.07 LC Epinephelus Serranidae 3.74 47 1.78 2 0.1-1 LC areolatus Epinephelus Serranidae 3.99 80 1.63 2 0.1-1 LC chlorostigma Epinephelus Serranidae 3.72 40 1.56 2 0.1-1 LC fasciatus Epinephelus Serranidae 4.14 120 1.60 3 2.0-5.0 VU fuscoguttatus Epinephelus Serranidae 4 270 1.26 4 10.0-20.0 DD lanceolatus Epinephelus Serranidae 4.16 234 1.34 4 10.0-20.0 LC malabaricus Epinephelus Serranidae 3.99 110 1.34 3 2.0-5.0 VU polyphekadion Epinephelus Serranidae 3.75 38 1.43 2 0.1-1 LC stoliczkae Epinephelus Serranidae 3.83 52 1.39 2 0.1-1 LC summana Serranidae Epinephelus tauvina 4.13 100 1.40 3 2.0-5.0 DD

Serranidae Epinephelus tukula 4.2 200 1.07 4 10.0-20.0 LC Grammistes Serranidae 3.98 30 1.49 2 0.1-1 LC sexlineatus Plectropomus Serranidae 4.5 80 1.84 2 0.1-1 VU areolatus Plectropomus Serranidae 4.24 120 1.39 3 2.0-5.0 LC pessuliferus Pseudanthias Serranidae 3.4 15 1.41 1 0.01-0.07 LC squamipinnis Pseudanthias Serranidae 3.4 13 2.17 1 0.01-0.07 LC taeniatus Serranidae Variola louti 4.33 83 2.09 3 2.0-5.0 LC 92

Siganidae Siganus argenteus 2 40 3.82 2 0.1-1 LC Siganidae Siganus luridus 2 30 1.32 2 0.1-1 LC

Siganidae Siganus rivulatus 2 27 2.44 2 0.1-1 LC Siganidae Siganus stellatus 2.7 40 2.13 2 0.1-1 LC Pardachirus Soleidae 3.5 26 1.29 2 0.1-1 N.E. marmoratus Acanthopagrus Sparidae 3.5 90 2.29 3 2.0-5.0 LC berda Acanthopagrus Sparidae 3.39 44.2 2.46 2 0.1-1 LC bifasciatus Sphyraena Sphyraenidae 4.49 200 2.45 5 30.0-50.0 LC barracuda Sphyraena Sphyraenidae 3.76 60 2.97 2 0.1-1 N.E. flavicauda Sphyraenidae Sphyraena jello 4.5 150 2.55 5 30.0-50.0 N.E. Sphyraenidae Sphyraena obtusata 4.5 55 2.79 2 0.1-1 N.E.

Sphyraenidae Sphyraena qenie 4.52 170 2.56 5 30.0-50.0 N.E. Sphyrnidae Sphyrna mokarran 4.32 610 1.85 5 30.0-50.0 EN Stegostoma Stegostomatidae 3.1 354 0.46 4 10.0-20.0 EN fasciatum Corythoichthys Syngnathidae 3.6 12 0.74 1 0.01-0.07 LC flavofasciatus Synodontidae Saurida gracilis 4.19 39 2.43 2 0.1-1 LC Synodus Synodontidae 4.2 24 2.27 2 0.1-1 LC dermatogenys Synodontidae Synodus variegatus 4.2 40 2.11 2 0.1-1 LC Teraponidae Terapon jarbua 3.93 36 2.33 2 0.1-1 LC Arothron Tetraodontidae 3.36 30 1.60 2 0.1-1 LC diadematus Tetraodontidae Arothron hispidus 3.42 50 1.64 2 0.1-1 LC Tetraodontidae Arothron stellatus 3.65 120 1.34 3 2.0-5.0 LC Canthigaster Tetraodontidae 3.11 31.2 1.57 2 0.1-1 LC margaritata

APPENDIX 3. Poster with recommended size of no take areas to protect some iconic fish species in the Red Sea