THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER ISLAND ECOREGION: A BASELINE FOR MANAGEMENT AND CONSERVATION
Doctorado en Biología y Ecología Aplicada
Programa cooperativo entre la Universidad Católica del Norte y la Universidad de la Serena
by
Naití Andrea Morales Serrano
Supervisors: Dr. Carlos F. Gaymer and Dr. Alan M. Friedlander
Coquimbo, 2020
FACULTAD DE CIENCIAS DEL MAR UNIVERSIDAD CATÓLICA DEL NORTE DOCTORADO EN BIOLOGÍA Y ECOLOGÍA APLICADA
“THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER ISLAND ECOREGION: A BASELINE FOR MANAGEMENT AND CONSERVATION”
por: Naití Andrea Morales Serrano
Departamento Biología Marina
Fecha: 17 de noviembre de 2020
Aprobado Comisión de Calificación
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Juan Macchiavello Armengol Carlos F. Gaymer Decano Facultad Ciencias del Mar Profesor Guía
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Alan M. Friedlander Marcelo Rivadeneira Profesor Guía Comité tutorial
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Guillermo Luna-Jorquera David Véliz Comité Tutorial Profesor Externo
Tesis entregada como un requisito para obtener el título de Doctor en Biología y Ecología Aplicada en la Facultad de Ciencias del Mar. Universidad Católica del Norte. Sede Coquimbo.
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FACULTAD DE CIENCIAS DEL MAR UNIVERSIDAD CATÓLICA DEL NORTE DOCTORADO EN BIOLOGÍA Y ECOLOGÍA APLICADA
Departamento de Biología Marina
“THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER ISLAND ECOREGION: A
BASELINE FOR MANAGEMENT AND CONSERVATION”
Actividad de Titulación presentada
para optar al Título de Doctor en
Biología y Ecología Aplicada
NAITÍ ANDREA MORALES SERRANO
Coquimbo, noviembre de 2020
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FACULTAD DE CIENCIAS DEL MAR UNIVERSIDAD CATÓLICA DEL NORTE DOCTORADO EN BIOLOGÍA Y ECOLOGÍA APLICADA
DECLARACIÓN DEL AUTOR
Se permiten citas breves sin permiso especial de la Institución o autor, siempre y cuando se otorgue el crédito correspondiente. En cualquier otra circunstancia, se deberá solicitar permiso
de la Institución o el autor.
Naití Andrea Morales Serrano
Firma
2020
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ABSTRACT
Top predators are a key component of marine ecosystems and play an important role in top-down ecosystem regulation of lower trophic levels. The continuing decline of their populations has increased the concern about their conservation and the possible effects on the entire ecosystem.
To develop effective management and conservation strategies for these predators, it is imperative to better understand their ecology, from community-level (e.g., species assemblage composition and their interactions) to individual species-specific aspects (e.g., abundance, distribution, and spatial dynamics).
Rapa Nui (Easter Island) and Salas y Gómez Island are considered the most isolated islands and the south-eastern coral most reefs in the Pacific Ocean. The marine ecosystems in this area have been understudied in comparison to other locations in the Pacific. Previous studies showed contrasting top-predator assemblages between the two islands. Rapa Nui displays signs of historical overfishing and the reef fish assemblage is dominated by smaller planktivorous species.
In contrast, at Salas y Gómez Island species such as the Galapagos shark (Carcharhinus galapagensis) and jacks (Seriola lalandi, Caranx lugubris, and Pseudocaranx dentex) are abundant and dominate the ecosystem. However, despite its remoteness, Salas y Gómez Island has not escaped from anthropogenic impacts. Sharks at Salas y Gómez Island are small and cautious. Some individuals have been observed with fishing hooks in their mouths, suggesting that recent illegal fishing is likely occurring in the area.
Using multiple approaches, this thesis provides the basic information needed for developing science-based management strategies across top predator species within the Easter Island ecoregion. A general introduction, including background and specific aims is presented in
CHAPTER 1, following by four chapters which used different approaches to answer a variety of ecological questions:
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CHAPTER 2.- To characterize the assemblage of top predator species around Rapa Nui, a baited remote underwater video system (BRUVS) was used for one year. I found significant spatial and seasonal differences in the fish assemblage around the island. The southern coast of Rapa Nui, which is the most exposed to large swells, was different in fish assemblage structure from the other sites and concentrated more top predator species. Winter season was distinct from the other seasons, probably associated to the strong oceanic swells and winds coming from the south during that time of year. Weather conditions also limits fishing pressure which could contribute to the higher concentration of these predators. These findings are essential in the implementation of conservation and management strategies, such as the newly created Rapa Nui multiple uses coastal marine protected areas (MUMPA).
CHAPTER 3.- To determinate the trophic position and to define the trophic interaction of sympatric species, δ13C and δ15N isotopic signatures of large fishes inhabiting Rapa Nui were used. The results suggest that not all large fishes sampled should be considered as top predator species, and that there is a high degree of overlap among the isotopic niche of four of these species, suggesting potential interspecific competition. This chapter contributes to correctly identifying the role of large fishes inhabiting Rapa Nui in order to create a wider understanding of how these species interact in an isolated and species-poor ecosystem.
CHAPTER 4.- To study the movement patterns of the two more abundant two predator species and then shed lights about the effectiveness of the borders of the Motu Motiro Hiva Marine Park
(MMHMP) around Salas y Gómez Island, I studied the movement patterns of the two most abundant top predators: the Galapagos shark, Carcharhinus galapagensis, and the yellowtail amberjack, Seriola lalandi. Specimens from both species were tagged around Salas y Gómez
Island using miniPAT satellite tags. The results showed that most of the satellite geolocations come from inside the MMHMP. However, all the individuals crossed the park borders at some point during the tracking time. These findings, thus, endorse the expansion of the current border iii
to the west in order to protect mobile species. Additionally, a female Galapagos shark travelled a maximum linear distance of 236 km extending the maximum distance previously reported for juveniles of this species (< 50 km). These results raise the question whether adults are maintaining genetic connectivity between Rapa Nui and Salas y Gómez Island through long distance movements.
CHAPTER 5.- To determine the levels of genetic connectivity of the Galapagos shark between
Rapa Nui and Salas y Gómez Island, a genome-wide neutral Single Nucleotide Polymorphism
(13496 neutral SNP) and a section of the mtDNA (636 pb) markers were used. The results suggested that individuals inhabiting both islands belong to the same population and could be considered as one conservation unit. This study highlights the importance of the MMHMP, a non- take zone, in preserving the local populations of the Galapagos shark. In addition, the comparatively low genetic diversity found at the Easter Island Ecoregion suggests the occurrence of few colonization events due to the isolation of the area.
CHAPTER 6.- In this section I discussed the most significant results found through the thesis in a general context. Additionally, I discussed the limitation of the study, and the importance of comparative studies between Rapa Nui and Salas y Gómez Island. Finally, I included recommendations for future studies regarding top predator biodiversity and their protection at the
Easter Island Ecoregion.
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RESUMEN
Los depredadores topes son un componente clave de los ecosistemas marinos y desempeñan un papel fundamental en la regulación del ecosistema top-down de los niveles tróficos inferiores.
El continuo declive de sus poblaciones ha generado una creciente preocupación por su conservación y por los posibles efectos en el ecosistema. Para desarrollar estrategias efectivas de manejo y conservación para estas especies, es imperativo comprender mejor su ecología, desde el nivel de comunidad (por ejemplo, la composición del conjunto de especies y sus interacciones) hasta los aspectos específicos de cada especie (por ejemplo, abundancia, distribución y dinámica espacial).
Rapa Nui (también conocida como Isla de Pascua) y la Isla Salas y Gómez albergan los arrecifes de coral más al sureste del Océano Pacífico. Estudios previos dan cuenta de una gran diferencia en la abundancia de depredadores topes entre ambas islas. Rapa Nui por su parte muestra signos de una sobrepesca histórica y donde el ensamble de peces de arrecife está dominado por especies planctívoras. En contraste, el ecosistema de Salas y Gómez está dominado por especies como el tiburón de Galápagos (Carcharhinus galapagensis) y carángidos (Seriola lalandi, Caranx lugubris y Pseudocaranx dentex). Sin embargo, y a pesar de su lejanía, la isla
Salas y Gómez también se ha visto afectada con impactos antropogénicos. Los tiburones de la isla Salas y Gómez son pequeños y cautelosos. Se ha observado a algunos individuos con anzuelos en la boca, lo que sugiere la ocurrencia de pesca ilegal reciente en el área.
Utilizando múltiples enfoques, esta tesis proporciona la información básica necesaria para desarrollar estrategias de manejo basadas en conocimiento científico de las principales especies de depredadores topes dentro de la ecorregión de Isla de Pascua. En el CAPÍTULO 1 se presenta una introducción general, que incluye antecedentes y objetivos específicos, seguida de cuatro
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capítulos que utilizan diferentes enfoques para responder a una variedad de preguntas ecológicas:
CAPÍTULO 2.- Para caracterizar el ensamblaje de los peces pelágicos de Rapa Nui, se utilizó un sistema de video submarino remoto con carnada (BRUVS) durante un periodo de un año. Dentro de los resultados principales se destaca la presencia de diferencias espaciales y estacionales significativas alrededor de la isla. La costa sur de Rapa Nui fue diferente en la estructura del ensamble de peces con respecto a los otros sitios y concentró más especies de depredadores topes. La temporada de invierno también fue estadísticamente diferente a las otras temporadas, probablemente debido a las fuertes marejadas oceánicas y los vientos provenientes del sur durante esa época del año. Las condiciones climáticas también limitan la presión de pesca, lo que podría contribuir a una mayor concentración de estos depredadores en la costa sur. Estos hallazgos son esenciales para la implementación de estrategias de conservación y manejo, como el área marina protegida costeras de múltiples usos de Rapa Nui (MUMPA) recientemente creada.
CAPÍTULO 3.- Para determinar la posición trófica y definir las interacciones tróficas de los grandes peces que habitan Rapa Nui, se analizaron sus señales isotópicas de δ13C y δ15N. Los resultados sugieren que no todas las especies muestreadas deben considerarse depredadores topes. Los resultados también sugieren un alto grado de superposición de nicho isotópico entre
Thunnus albacares y Katsuwonus pelamis, lo que podría significar una competencia interespecífica. Este capítulo contribuye a identificar correctamente el papel de grandes peces que habitan en Rapa Nui con el fin de crear una comprensión más amplia de cómo estas especies interactúan en un ecosistema aislado y pobre en recursos.
CAPÍTULO 4.- En este capítulo busqué investigar los patrones de movimiento del tiburón de
Galápagos, Carcharhinus galapagensis, y la vidriola, Seriola lalandi para evaluar la efectividad
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de los límites actuales del Parque Marino Motu Motiro Hiva (MMHMP) en la protección de especies móviles. Para esto se utilizaron marcas satelitales miniPAT y marcas convencionales.
Los resultados mostraron que todos los individuos estudiados pasan la mayor cantidad de tiempo dentro del MMHMP. Sin embargo, todos los individuos cruzaron los límites del parque en algún momento durante el tiempo de seguimiento. Estos hallazgos, por tanto, avalan la idea de una expansión hacia el oeste de los limites actuales. Además, una hembra de tiburón de Galápagos recorrió una distancia lineal máxima de 236 km aumentando la distancia máxima previamente reportada para los juveniles de esta especie (<50 km). Estos resultados plantean la pregunta de si los adultos son capaces entonces de mantener una conectividad genética entre Rapa Nui y la isla Salas y Gómez a través de movimientos de larga distancia. Por último, cuantificamos la presión de pesca industrial en el área. Los resultados sugieren que no existe pesca ilegan dentro de la Zona Económica Exclusiva (ZEE). Sin embargo, producto del aislamiento de esta area y el constante avistamiento de barcos industriales dentro de la ZEE por parte de los habitantes de
Rapa Nui se hace necesario un aumento en la fiscalización por parte de las autoridades.
CAPÍTULO 5.- Para determinar los niveles de conectividad genética del tiburón de Galápagos entre Rapa Nui y la isla Salas y Gómez, utilicé marcadores de polimorfismo de nucleótido único
(SNP) neutro de todo el genoma (13496 SNP neutro) y una sección del ADN mitocondrial
(mtDNA; 636 pb). Los resultados obtenidos en este capítulo sugieren que los individuos que habitan en ambas islas pertenecen a la misma población y podrían ser considerados como una sola unidad de conservación. Este estudio destaca la importancia del MMHMP para preservar las poblaciones locales del tiburón de Galápagos. Además, la diversidad genética comparativamente baja encontrada en la Ecorregión de Isla de Pascua sugiere la ocurrencia de pocos eventos de colonización debido al aislamiento del área.
CAPÍTULO 6.- En este apartado expuse los principales resultados encontrados durante la tesis en un contexto general. Adicionalmente, discutí las limitaciones del estudio, y la importancia de vii
los estudios comparativos entre Rapa Nui e Isla Salas y Gómez. Finalmente, incluí recomendaciones para estudios futuros sobre la biodiversidad de las principales especies de depredadores topes y su protección en la Ecorregión de Isla de Pascua.
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PUBLICATIONS FROM THIS THESIS
Published chapters
Chapter 2.- Morales NA, Easton EE, Friedlander AM, Harvey ES, Garcia R, Gaymer CF. 2019.
Spatial and seasonal differences in the top predators of Rapa Nui: Essential data for implementing the new Rapa Nui multiple‐uses marine protected area. Aquatic Conserv: Mar Freshw Ecosyst.29:
118-129; 1–12. https://doi.org/10.1002/aqc.3068
Chapter 4.- Morales NA, Heidemeyer M, Bauer R, Hernandez S, Acuña E, Friedlander AM,
Gaymer CF. In press. Residential movements of top predators at Chile’s most isolated Marine
Protected Area: implications for the conservation of the Galapagos shark, Carcharhinus galapagensis, and the yellowtail amberjack, Seriola lalandi. Aquatic Conserv: Mar Freshw
Ecosyst.
Related articles
Morales NA, Coghlan AR, Hayden G, Guajardo P. First sighting of a tropical benthic reef shark species at Rapa Nui: chance dispersal or a sign of things to come? J Fish Biol. 2019; 95: 642–
646. https://doi.org/10.1111/ jfb.13977.
Thiel Martin, Guillermo Luna-Jorquera, Rocio Álvarez-Vargas, Camila Gallardo, Ivan A Hinojosa,
Nicolás Luna, Diego Miranda-Urbina, Naiti Morales, Nicolas Ory, Aldo Pacheco, Matias Portflitt-
Toro, Carlos Zavalaga. 2018. Impacts of marine plastic pollution from continental coasts to subtropical gyres – Fish, seabirds and other vertebrates in the SE Pacific. Frontiers in Marine
Science. 5(238).
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Easton EE, Sellanes J, Gaymer CF, Morales NA, Gorny M & Berkenpas E. 2017. Diversity of deep-sea fishes of the Easter Island Ecoregion. Deep Sea Research Part II: Topical Studies in
Oceanography, 137, 78-88.
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Esta tesis está dedicada a todos los amantes del mar, especialmente
a mi abuelo Raúl y a mis dos amores, Chris y Lukas…
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AGRADECIMIENTOS
Esta tesis no podría haberse llevado a cabo sin el constante apoyo de muchas personas e instituciones. Me gustaría partir agradeciendo a mi familia, especialmente a mis padres quienes siempre han apoyado mis sueños. Mis amigos de la vida y los nuevos que hice durante estos años alrededor del mundo quienes han tenido la paciencia para escucharme hablar por horas del mar. Un agradecimiento especial a Francisco Concha quien desde pequeña me inspiró en el amor por los tiburones.
Esta tesis de investigación forma parte del Núcleo Milenio de Ecología y Manejo Sustentable de
Islas Oceánicas (ESMOI) y tiene como objetivo contribuir a brindar la información necesaria para apoyar las estrategias de manejo y conservación marina para la Ecorregión de Isla de Pascua.
Por lo que me gustaría agradecer a todos los integrantes de la casita ESMOI quienes me ayudaron en la toma de muestras y el planeamiento de los diferentes capítulos de esta tesis.
Además, agradecer el compañerismo y la buena onda que sin duda hicieron de este proceso uno mucho más fácil y entretenido. Mas que compañeros muchos de ellos se transformaron en amigos y familia. Agradecer también a todos aquellos que me ayudaron en la Isla: Michel Garcia,
Orca Dive Center, Alex Tuki, entre muchos otros.
Agradecer por supuesto a mis profesores tutores Carlos Gaymer y Alan Friedlander quienes siempre estuvieron disponibles para cualquier problema o consulta que tuviera y supieron guiar esta tesis de manera ejemplar. Gracias también a la comisión por las grandes contribuciones que hicieron en cada uno de los capítulos.
Finalmente me gustaría agradecer al Gobierno de Chile quien mediante las Becas de Doctorado
Nacional financio todos estos años de estudio. A Save Our Seas Foundation e Idea Wild por creer en mis locuras y financiar el primer capítulo de la tesis que dio origen a todos los demás.
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TABLE OF CONTENTS
DECLARACIÓN DEL AUTOR ...... i
ABSTRACT ...... ii
RESUMEN ...... v
PUBLICATIONS FROM THIS THESIS...... ix
AGRADECIMIENTOS ...... xii
LIST OF FIGURES ...... xvi
LIST OF TABLES ...... xvii
LIST OF ACRONYMS ...... xviii
CHAPTER 1. Background ...... 1
1.1 Introduction ...... 1 1.1.1 Research questions ...... 2 1.2 Specific Aims ...... 4 1.2.1 Diversity, abundance, and distribution of top predators around Rapa Nui ...... 4 1.2.2 Species trophic position and interaction among sympatric species top predator species at Rapa Nui ...... 4 1.2.3 Movement patterns and connectivity of top predators inhabiting the Easter Island Ecoregion ...... 5 CHAPTER 2. Spatial and Seasonal Differences in the Top Predators of Rapa Nui: Essential Data Used as a Key Tool for Implementing the New Rapa Nui Multiple-Uses MPA ...... 8
2.1 Abstract...... 8 2.2 Introduction ...... 9 2.3 Methods ...... 12 2.3.1 Study area ...... 12 2.3.2 Sample collection ...... 13 2.3.3 Data analyses ...... 15 2.3.4 Environmental data collection and analysis ...... 16 2.4 Results ...... 17 2.4.1 Spatial differences ...... 19 2.4.2 Seasonal differences ...... 20 2.4.3 Environmental analysis ...... 21 2.5 Discussion ...... 21 xiii
2.5.1 Spatial and temporal patterns ...... 21 2.5.2 Conservation actions ...... 26 CHAPTER 3. The Trophic Role of Large Fishes Inhabiting the Easter Island Ecoregion ...... 28
3.1 Abstract...... 28 3.2. Introduction ...... 28 3.3 Methods ...... 30 3.3.1 Sample collection ...... 30 3.3.2 Sample preparation and stable isotopes analysis ...... 31 3.3.3 Trophic positions estimations...... 31 3.3.4 Trophic structure and isotopic niche ...... 32 3.4 Results ...... 33 3.4.1 Trophic position ...... 35 3.4.2 Isotopic niche...... 36 3.5. Discussion ...... 39 3.5.1 Stable isotope signatures ...... 39 3.5.2 Trophic position ...... 40 3.5.3 Isotopic niche...... 42 3.5.4 Conservation aspects ...... 44 CHAPTER 4. Residential Movements of Top Predators at Chile’s Most Isolated Marine Protected Area: Implications for the Conservation of the Galapagos Shark, Carcharhinus galapagensis, and the Yellowtail Amberjack, Seriola lalandi...... 46
4.1 Abstract...... 46 2.2 Introduction ...... 47 4.3 Methods ...... 51 4.3.1 Study area ...... 51 4.3.2 Capture and tagging ...... 52 4.3.4 Analysis of vertical behaviour ...... 56 4.4 Results ...... 57 4.4.1 Horizontal behaviour ...... 57 4.4.2 Vertical behaviour ...... 60 4.5 Discussion ...... 65 4.5.1 Horizontal and vertical migratory behaviour...... 65 4.5.2 Fishing activities around Rapa Nui and Salas y Gómez ...... 66 4.5.3 Future Perspectives of the MMHMP ...... 69 CHAPTER 5. Genetic Connectivity of the Galapagos Shark, Carcharhinus galapagensis, in the Easter Island Ecoregion...... 73
5.1 Abstract...... 73 5.3 Methods ...... 77 5.3.1 Sample collection ...... 77 5.3.2 DNA extraction and sequencing for SNP ...... 77
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5.3.3 SNPs filtering and Outlier detection ...... 78 5.3.4 Genetic structure analysis...... 78 5.3.5 Mitocondrial (mtDNA) extraction, amplification, sequencing, and alignment ...... 79 5.4 Results ...... 80 5.4.1 SNPs analysis ...... 80 5.4.2 Mitochondrial DNA analysis ...... 80 5.5 Discussion ...... 82 5.5.1 Connectivity within the Easter Island Ecoregion ...... 82 5.5.3 Implication for the conservation of the species ...... 86 CHAPTER 6. Conclusions ...... 88
6.1 Main findings and conservation implications ...... 88 6.2 Limitation of the Study and Suggestions for Future Research Directions ...... 91 REFERENCES ...... 94
APPENDIXES ...... 117
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LIST OF FIGURES
Figure 1.1: Flow diagram outlining the background and general structure of the thesis...... 3
Figure 2.2: Map of Rapa Nui (Easter Island) and Salas y Gómez Island showing sampling locations...... 14
Figure 2.2. Canonical analysis of principal coordinates (CAP) ordination of the variation in fish assemblage ...... 20
Figure 3.1. δ13C and δ15N signatures of large fishes inhabiting Rapa Nui...... 34
Figure 3.2. Isotopic niche space of four species sampled during the study and density plots showing the credibility interval of Bayesian standard ellipses areas...... 38
Figure 4.1. Bathymetry of the Easter Island Ecoregion...... ¡Error! Marcador no definido.
Figure 4.2. Temporal coverage data per deployed tag ...... 58
Figure 4.3. Geolocations of each individual combined and kernel densities ...... 59
Figure 4.4. Distance between geolocations and their distance from Salas y Gómez per species ...... 60
Figure 4.5. Diel vertical movement patterns of the five fish tagged...... 61
Figure 4.6. Vertical behaviour patterns for the vertical behaviour clusters and their spatial distribution ...... 62
Figure 5.1. Location of Rapa Nui and Salas y Gómez Island ...... 75
Figure 5.2. Principal Coordinates Analysis ...... ¡Error! Marcador no definido.
Figure 5.3. Number of cluster (k) suggested by BIC values ...... ¡Error! Marcador no definido.
Figure 5.4. Haplotype network based on the informative mtDNA control region ...... 81
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LIST OF TABLES
Table 2.1: List of 15 species recorded using BRUVS at Rapa Nui...... 18
Table 2.2. Summary of fish sightings and relative abundance...... 18
Table 3.1. Summary of mean and standard deviation (SD) of stable isotopes (δ13C and δ15N) composition of large fishes included in this study...... 35
Table 3.2. Summary of outputs from trophic positions (TP) models (model-1: “oneBaseline” and model-2: “twoBaseline”)...... 36
Table 3.3 Isotopic niche area ...... 37
Table 4.1. Metadata of the individual tagged during this study. .... ¡Error! Marcador no definido.
Table 5.1. Genetic diversity determinate by mtDNA Control Region ...... 81
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LIST OF ACRONYMS
CAP: Analysis of Principal Coordinates BRUVS: Baited Remote Underwater Video Systems BIC: Bayesian Information Criterion CR: Control Region cMax: Corrected Maxn Seac: Corrected Version of Standard Ellipse Area DistLM: Distance-Based Linear Modelling dbRDA: Distance-Based Redundancy Analysis EEZ: Economic Exclusive Zone EBM: Ecosystem-Based Management FDR: False Discovery Rate HWE: Hardy–Weinberg Equilibrium IUU: Illegal, Unreported, And Unregulated Fishing LD: Linkage Disequilibrium MPAs: Marine Protected Areas Maxn: Maximum Number of Individuals CD: Mean Distance to the Centroid MAF: Minor Allele Frequencies MtDNA: Mitochondrial DNA PERMANOVA: Permutational Multivariate Analysis of Variance PAT: Pop-Up Archiving Tags PCA: Principal Component Analyses PDT: Profiles of Depth at Temperature Rapa Nui MUMPA: Rapa Nui Multiple Uses Coastal Marine Protected Areas SST: Sea Surface Temperature SNP: Single Nucleotide Polymorphisms SIA: Stable Isotope Analysis SDNND: Standard Deviation of Nearest Neighbour Distance
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SEA: Standard Ellipse Area TAD: Time-At-Depth TAT: Time-At-Temperature TA: Total Area TL: Total Length TEF: Trophic Enrichment Factor TP: Trophic Position UVC: Underwater Visual Census
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CHAPTER 1. BACKGROUND
1.1 INTRODUCTION
Top predators, species that represent the highest trophic level in a community, are a key component of marine ecosystems and play an important role in top-down ecosystem regulation
(Stevens et al., 2000; Roff et al. 2016). For example, top predators (e.g., sharks, jacks, groupers, and tunas) have been found to be efficient in controlling demography, life history, and behaviour
(risk effect) of organisms from lower trophic levels (Steven et al., 2000; Shears et al., 2002;
Daskalov et al., 2007; Heithaus et al., 2008; Ruttenberg et al., 2011). This control can have profound impacts on the structure of marine communities by increasing ecosystem stability and preventing phase shifts in fragile ecosystems such as coral reefs (Hughes et al., 2010) or algal forests (Estes & Duggins, 1995; Shears et al., 2002).
The continuing decline of top-predator populations, principally due to overfishing and habitat degradation (Myers et al., 2003; Robbins et al., 2006; Daskalov et al., 2007; Ferretti et al., 2010;
Dulvy et al., 2014), has led to concerns about both predator conservation and possible effects on the ecosystem as a whole (Myers et al., 2003; Daskalov et al., 2007; Myers et al., 2007; Sandin et al., 2008; 2010; Dulvy et al., 2014). In this context, several authors have suggested that the effective management and conservation of top predators relies in part on a more thorough understanding of their ecology from a community level (e.g., species composition and species interaction) to individual species-specific aspects (e.g., abundance, distribution, and spatial dynamics) (Garla et al., 2006; Dale et al., 2010; Simpfendorfer et al., 2010; Vaudo et al., 2017).
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1.1.1 Research questions
Little is known about the marine biodiversity and the marine ecosystem health of the Easter Island
Ecoregion (Friedlander et al., 2013). Only a few recent surveys have examined the fishes from
Rapa Nui and the associated seamounts, with most of these studies focusing on benthic, rather than pelagic species (Fernandez et al., 2014). Moreover, virtually no information is available on the ecology of pelagic top predator fishes at the ecosystem scale making it difficult to understand the dynamic of reef communities and to predict the ecological consequences of common threats, such as fishing. Nowadays, multispecies studies are needed to implement ecosystem-based management, because it gives a more comprehensive understanding of the ecosystem, which can lead to better management (Toonen et al., 2011).
Therefore, the general objective of this thesis was to study some important ecological aspects of the marine top predator assemblage needed for developing science-based management and effective conservation strategies across species within the Easter Island Ecoregion. Here, I present the specific aims that are addressed in subsequent chapters (Fig. 1.1). Each chapter is written as a stand-alone manuscript to facilitate publication; thus, chapters have their specific introduction sections and consequently may include some elements of the background information presented here.
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Figure 1.1. Flow diagram outlining the background and general structure of the thesis.
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1.2 SPECIFIC AIMS
1.2.1 Diversity, abundance, and distribution of top predators around Rapa Nui
Precise and accurate information on the diversity and abundance of fish populations is important for studying most aspects of their ecology, and therefore is widely sought after for management and conservation purposes (Pita et al., 2014). Moreover, changes in these parameters usually indicate alteration in the community structure in response to ecological, climatic, or anthropological changes (Schlosser, 1990; Westera et al., 2003; Jeppesen et al., 2010; Dale et al., 2011). In Chapter 2, I characterized the top predator assemblage of Rapa Nui throughout a year around the island of Rapa Nui using a novel and non-lethal technique (Baited Remote
Underwater Video System [BRUVS]). This technique has been widely used in the study of fish assemblage structure in a variety of environments including both temperate and tropical reefs
(Harvey et al., 2012, Langlois et al., 2010). BRUVS increase the number of sampled species,
(Stobart et al., 2007, Willis & Babcock, 2000), since the bait attracts the fishes into the field of view of a camera so they can be identified and counted (Dorman et al. 2012; Hardinge et al.
2013), and therefore, are especially effective in the study of cryptic and rare predators, such as sharks and fishery-target species (Harvey et al., 2012). Determining which species inhabit Rapa
Nui and how they are distributed will contribute to the identification of priority conservation areas within the recently created Rapa Nui multiple use marine protect area (Rapa Nui MUMPA).
1.2.2 Species trophic position and interaction among sympatric species top predator species at Rapa Nui
The estimation of trophic levels has become increasingly useful in the analysis of marine food webs (Stergiou & Karpouzi, 2002, Young et al., 2010) because it facilitates the understanding of ecosystem function and the relation among sympatric species (Frisch et al., 2016). For instance,
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trophic interactions are known to influence the larger patterns of community dynamics, such as species composition, abundance, biomass, distribution, and others (Polovina et al., 2001;
DeMartini & Friedlander 2006; Dale et al., 2011, Speed et al., 2012). Large fishes are usually considered as top predators because their size and behaviour; however, it is well known that is not always the case (Frisch et al., 2016), and that the trophic role of species can change (e.g., among habitats; Ferreira et al., 2017). The misclassification of the trophic level and their relation within other sympatric species can lead to erroneous conclusions about ecosystem dynamics, and therefore, ineffective management strategies may be implemented (Heithaus et al., 2008;
Frisch et al., 2016). In Chapter 3, I used stable isotope analysis to determine the trophic position and the isotopic niche of large fishes inhabiting Rapa Nui. The study of trophic relationships will clarify the ecological role of large predatory fishes previously classified as top predators, while the isotopic niche will provide insights of how resources are being partitioned among sympatric species.
1.2.3 Movement patterns and connectivity of top predators inhabiting the Easter Island
Ecoregion
Oceanic islands and seamounts often aggregate highly mobile species (Holland et al., 1999;
Worm et al., 2003; Morato et al., 2010; Garrigue et al., 2015). They facilitate the dispersion of organisms between distant areas (Wilson & Kaufman, 1987; Friedlander et al., 2013) by serving as navigational marks for resting and/or feeding areas (Rogers, 1994; Garrigue et al., 2015). Rapa
Nui and Salas y Gómez Island are connected, by several dozen seamounts that could play the role of stepping-stones (Newman & Foster 1983; Friedlander et al., 2013), and thus creating a biological corridor. Despite their potential, little is known about the connectivity between these two neighbouring islands.
5
Marine protected areas (MPAs) have become an effective tool for the protection of biodiversity
(Botsford et al., 2003; Lubchenco & Grorud-Colvert, 2015; Sala et al., 2018); however, many of these MPAs were implemented without sufficient empirical knowledge of the species inhabiting the area (Botsford et al., 2003; Pasmiño et al. 2017). Therefore, understanding the association between species and their habitat requirements is crucial to create MPAs that include appropriate habitat types, and are large enough to provide effective, long-term protection for the species of concern (Botsford et al., 2003; Friedlander et al., 2007; Meyer et al., 2007). Additionally, the clarification of complex processes, such as ecosystem connectivity (e.g., source-sink dynamics), are becoming increasingly recognized in the protection of key species and in the implementation of effective MPAs (Wilson et al., 2006; Simpfendorfer et al., 2010; Papastamatiou et al., 2010;
Espinoza et al., 2014; Vaudo et al., 2017).
To study the connectivity of large predators between both islands, I used two different approaches, each one corresponding to a different chapter. In Chapter 4, I studied the horizontal and vertical movement patterns of two abundant top predator species in the Easter Island
Ecoregion, the Galapagos shark (Carcharhinus galapagensis) and the yellowtail amberjack
(Seriola lalandi) using satellite tags. Pop-up archiving tags (PAT) record horizontal and vertical movements, through depth and time, plus positional water temperature (Brill et al., 2002; Luo et al., 2006). PAT pop-up tags have been widely used to address questions of large-scale movements and behaviour of species that do not spend enough time at the surface (e.g., Wilson et al., 2006; Holmes et al., 2014). Moreover, I examined if the MMHMP´s borders were appropriate for protecting these species from fishing pressure outside of the MPA.
Finally, in Chapter 5, I studied the population genetic of the Galapagos shark using single nucleotide polymorphisms (SNP) and mitochondrial DNA (mtDNA). SNPs have proven to be informative markers for Galapagos shark population structure (Pazmiño et al., 2017; 2018).
Determining the degree of population connectivity among geographic areas, with the estimated 6
location of genetic breaks, enables assessing the appropriate scale at which management strategies for marine species should be applied to continue demographic exchange and prevent local extinctions (Crowder & Norse, 2008; Toonen et al., 2011). In this context, the establishment of a synergistic source-sink relationship between the marine ecosystems of Rapa Nui and Salas y Gómez Island would increase the value of the existing MMHMP for the health and sustainability of the entire ecosystem.
7
CHAPTER 2. SPATIAL AND SEASONAL DIFFERENCES IN THE TOP PREDATORS
OF RAPA NUI: ESSENTIAL DATA USED AS A KEY TOOL FOR IMPLEMENTING
THE NEW RAPA NUI MULTIPLE-USES MPA
2.1 ABSTRACT
Reef fishes are an important component of marine biodiversity and changes in the composition of the assemblage structure may indicate ecological, climatic, or anthropogenic disturbances. To examine spatial differences in the reef fish assemblage structure around Rapa Nui, we sampled eight sites during autumn and summer 2016-2017 with Baited Remote Underwater Video systems
(BRUVs). To determine seasonal changes, we conducted quarterly seasonal sampling at five of those eight sites. A total of 15 pelagic species of fishes were recorded during this study, some of which have not previously been recorded in SCUBA surveys, including the Galapagos shark
(Carcharhinus galapagensis) and tunas (Scombridae). Significant spatial and seasonal differences were found in the fish assemblage. Fish assemblages from the south coast differed significantly from those along the west and the east coasts, mainly due the occurrence of top predators. Winter differed from other seasons, especially along the south coast were the island is more exposed to large oceanic swells and winds from Antarctica. Due to the variety and high relative abundance of species recorded during this survey, BRUVs seemed to be an effective method for studying top predators at Rapa Nui. Future studies should examine deeper zones around the island and the surrounded seamounts. The identification of priority zones for the protection of top predator species represent an important contribution of this study, in order to develop management and conservation strategies to be implemented in the newly created Rapa
Nui multiple uses coastal marine protected areas (Rapa Nui MUMPA).
8
2.2 INTRODUCTION
Reef fishes play an important role in ecosystem function (Stevens et al., 2000), and are the target of recreational, commercial, and subsistence fisheries in many coastal locations (Henry & Lyle,
2003; Kingsford et al., 1991). Precise and accurate information on the diversity and abundance of fish populations is important for understanding their ecology and is critical for developing effective management and conservation strategies (Pita et al., 2014). Changes in the fish assemblage composition usually indicate alteration in the community structure in response to ecological, climatic, or anthropogenic drivers (Jeppesen et al., 2010; Schlosser, 1990; Westera et al., 2003).
Reef fish assemblages vary spatially and temporally in response to biotic variables, such as food availability (Tickler et al., 2017), predation or competition (Almany, 2004), and abiotic variables, such as habitat complexity and environmental characteristics like wave exposure and temperature
(Anderson & Millar, 2004; Coles & Tarr, 1990; Curley et al., 2003; Friedlander & Parrish, 1998).
For example, spatial variation in reef fish assemblages can occur on scales of meters to kilometers (Connell & Jones, 1991; Curley et al., 2003; Malcolm et al., 2007), and are usually associated with habitat complexity and the environmental conditions that structure that habitat
(Asher et al., 2017; Coles & Tarr, 1990; Friedlander & Parrish, 1998). Seasonal changes are more evident in reef ecosystems from sub-tropical latitudes because of greater environmental variability
(Coles & Tarr, 1990; Friedlander & Parrish, 1998). However, these influences differ by location.
For example, Coles & Tarr (1990) found that the large variation in temperature between winter and summer (about 20ºC) in the Western Arabian Gulf determines the richness and abundance of inshore species. In Hawaii, Friedlander & Parrish (1998) observed that fish assemblages responded to high wind and wave energy during winter by taking refuge at deeper depths and in more complex habitats. Understanding the natural variations in the fish assemblage provides essential baseline information for designing and evaluating the effectiveness of marine protected
9
areas (MPA) (Charton et al., 2000). Having accurate information of where to protect is especially valuable in highly urbanized areas, where area protection is constrained owing to conflicts among multiple users (Curley et al., 2003).
Marine Protected Areas (MPAs) have been shown to be a highly effective means of conserving biodiversity and managing fisheries, while also restoring and preserving overall ecosystem functions (Gaines et al., 2010; Lubchenco & Grorud-Colvert, 2015). Through the establishment of fishing regulations such as minimum size, effort control and/or regulation of total catches (Botsford et al., 2003; Hilborn et al., 2006), MPAs are usually associated with the increase of abundance, biomass and size of focal species (Micheli et al., 2004) as well as catch-per-unit-effort (CPUE) in adjacent areas (Roberts et al., 2000). In Chile, 23 MPAs have been created in the last decade, protecting over 41% of its economic exclusive zone (EEZ) (Petit et al., 2017). The most recent three MPAs were announced during the 2017 International Marine Protected Areas Congress
(IMPAC4 2017): Islas Diego Ramirez-Paso Drake, Juan Fernandez archipelago and Rapa Nui.
The Rapa Nui Multiple Uses Coastal Marine Protected Area (MUMPA) covers the entire Easter
Island Ecoregion and extends from the Rapa Nui coastline to the limit of the EEZ, embracing
~579,000 km2.
Easter Island, also known by its Polynesian name Rapa Nui, is the most south-eastern coral reef ecosystem in the Pacific Ocean and harbours a unique fish assemblage with a high level of endemism (Randall & Cea, 2011). Rapa Nui is one of the most isolated inhabited islands in the
Pacific Ocean; yet, long-term overfishing has dramatically reduced the abundance of targeted species (Aburto et al., 2015; Friedlander et al., 2013; Randall & Cea, 2011; Zylich et al., 2014).
Modern fishing equipment and the demand for local fish from increasing tourism has compounded the effects of overfishing (Randall & Cea, 2011; Zylich et al., 2014). There have been a limited number of surveys of fishes around Rapa Nui (e.g., Easton et al., 2018; Fernández et al., 2014;
Friedlander et al., 2013), with most of these studies focusing on reef fishes, rather than pelagic 10
species. Using underwater visual census (UVC), Friedlander et al. (2013) found contrasting reef fish assemblages between Rapa Nui and its nearest neighbour, Salas y Gómez, a small island located ~390 km to the northeast. Salas y Gómez is one of the most isolated islands in the Pacific
Ocean and is fully protected from fishing as part of the Motu Motiro Hiva Marine Park. Sharks, primarily the Galapagos shark (Carcharhinus galapagensis), and jacks account for more than
40% of the fish biomass around Salas y Gómez, whereas Rapa Nui is dominated by smaller planktivorous species, with top predators virtually absent (Friedlander et al., 2013).
In the past, ecological studies of fishes at Rapa Nui have relied on fishery-dependent data from commercial fisheries and UVC, performed by scuba divers (Acuña et al., 2018). The use of fishery-dependent sampling is destructive (Skomal, 2007) and inefficient due to sampling biases from gear selectivity and different fishing effort between species, habitats, seasons, and vessels
(Bishop, 2006; Murphy & Jenkins, 2010; Thorson & Simpfendorfer; 2009). Additionally, this technique is less effective in locations with insufficient and inaccurate landing information, like
Rapa Nui (Aburto & Gaymer, 2018). UVC is the most-used observational technique for reef ecosystems (Medley et al., 1993; Samoilys & Carlos, 2000). However, it also has several well- documented limitations and problems, including intra- and inter-observer variability (Thompson &
Mapstone, 1997) and the effect of divers on the species behaviour (Chapman et al., 1974; Cole,
1994; Emslie et al., 2018; Gray et al., 2016; Kulbicki, 1998; Lindfield et al., 2014). In contrast, remote underwater video systems, such as Baited Remote Underwater Video Systems (BRUVs), are effective, non-destructive fishery-independent techniques used to sample fish assemblages without these diver-associated problems.
BRUVs attract a wide range of marine species from different trophic groups into the field of view of a camera so that they can be identified and counted (Dorman et al., 2012; Hardinge et al.,
2013). BRUVs increase the number of sampled species (Stobart et al., 2007; Willis & Babcock,
2000), and are especially effective in the detection of cryptic and rare predators, such as sharks 11
and fishery-targeted species, that are not well sampled using UVC (Brooks et al., 2011a; Harvey et al., 2012; Malcolm et al., 2007; Watson et al., 2005). Pelagic BRUVs are even more novel than traditional BRUVs, allowing the study of species that inhabit the water column, including highly mobile species (Santana-Garcon et al., 2014; Santana‐Garcon et al., 2014a). Pelagic species are ecologically important to marine ecosystems (Freon et al., 2005) and highly valuable for the fishing industry (Pauly, 2002; Worm et al., 2006). Despite their importance and that they are constantly threatened by multiple factors, such as pollution, climate change, and overfishing (see
Game et al., 2009), the pelagic ecosystems, at a community scale, are still data poor worldwide.
Given the lack of quantitative data on the pelagic fish assemblages of Rapa Nui, the fragility of the marine ecosystem, and the importance of baseline information for the implementation of conservation strategies, the general objective of this study was to characterize the assemblage of marine top predator inhabiting Rapa Nui. The specific objectives were: (1) to assess spatial and seasonal variability in the pelagic fishes around Rapa Nui using BRUVs; (2) to determine which environmental factors best explain the observed differences; and (3) to provide key data for advising management and conservation of the coastal areas, with particular emphasis on zoning the recently created MUMPA.
2.3 METHODS
2.3.1 Study area
Rapa Nui (27°13´S and 109°37´W) has a land area of 166 km2 and ~5600 inhabitants. Located
2250 km east from Pitcairn Island and 3760 km south-west from mainland Chile, it is one of the most isolated places on earth. The nearest island is Salas y Gomez Island (26º28`S and
105º21`W), which is an uninhabited volcanic island with a total area of 0.15 km2. Both islands and more than several dozen seamounts are part of the Salas y Gómez Ridge, which extends
12
2232 km before reaching the Nazca Ridge in the south-eastern Pacific Ocean (Randall & Cea,
2011; Friedlander et al., 2013).
2.3.2 Sample collection
Mid-water BRUVs were constructed according to Santana-Garcon et al. (2014b). Each BRUVs was constructed using a single GoPro Hero 4 camera (mono-camera) held in their own underwater housing. GoPros were set to record a wide-angle of view and 1080p. A mix of fresh local fishes (~300 gr) and one can of Chilean jack mackerel (Trachurus murphyi) were used as bait. Deployments were carried out during daylight hours, avoiding dusk and dawn. Four simultaneous 1-h deployments (replicates), having a minimum separation of 500 m to avoid plume dispersion overlap (Santana-Garcon et al., 2014b), were conducted at a depth of ~25 m at each site; a minimum of six deployments were conducted per site. Local knowledge, previous studies and limitations related to weather conditions were used to guide the spatial coverage of sites.
Date, hour and location (latitude and longitude) were recorded during every deployment. To study spatial differences around Rapa Nui, eight sites were sampled during autumn and summer 2017
(Fig. 2.1). To determine seasonal changes in the fish assemblage, quarterly seasonal sampling was undertaken at five of those sites during 2016-2017.
13
Figure 2.1: Map of Rapa Nui (Easter Island) and Salas y Gómez Island showing sampling locations (a) Map of Rapa Nui (Easter Island) and Salas y Gómez Island in relation to South America. Dark lines represent the exclusive economic zone. (b) Sampling locations around Rapa Nui for seasonal variability (yellow dots). Purple dots represent the 3 extra sites used for assessing spatial variability during summer and autumn.
Every BRUVs was deployed for a minimum of 70 minutes. Following the recommendations of
Acuña -Marrero et al. (2018), we discarded the first and the last 5 minutes from every video to avoid any potential influence caused by the presence of the boat. Species assignments were made following Randall & Cea, (2011), FishBase (ver. 02/2018, R. Froese & D. Pauly, see www.fishbase.org, accessed 2018), and consultations with world fish specialists. Each species 14
was assigned to a functional group (herbivores, planktivores, secondary consumers, and top predators) following Friedlander et al. (2013) and FishBase (ver. 02/2018, R. Froese & D. Pauly, see www.fishbase.org, accessed 2018). Additionally, all the species were classified as “Target
Species” or “Not Target Species” according to Zylich et al. (2014) and discussions by the first author with local fishermen. The maximum number of individuals of the same species appearing in a video frame at the same time (MaxN), plus any other individual that was uniquely and clearly distinguishable from the other individuals, was used as an estimate of relative abundance or a corrected MaxN (cMaxN; see Acuña-Marrero et al., 2018). MaxN is a conservative measurement of relative abundance that avoids any error associated with recounting the same fish (Cappo et al., 2003; Priede et al., 1994; Willis et al., 2003); however, it usually underestimates the real abundance in a single deployment (Kilfoil et al., 2017). By including any other individual that was undoubtedly distinguishable within the deployment and that was not already included in the MaxN calculation, cMaxN tends to solve, in part, the underestimation problem of sampled species. cMaxN per hour was used to standardize effort across deployments of different soak times, as suggested by Santana-Garcon et al. (2004a). Measurement of length was not considered during this study; therefore, a biomass calculation could not be included in the analysis.
2.3.3 Data analyses
All statistical analyses were conducted in PRIMER v. 7.0.13 software package (Clarke & Gorley,
2006) with the PERMANOVA+ add-on (Anderson et al., 2008), unless otherwise specified. A
Bray–Curtis similarity matrix was created on the 4th-root transformed cMaxN data. All permutational multivariate analysis of variance (PERMANOVA) tests were run with default settings and 9999 permutations to obtain p-values (Anderson et al., 2008). Statistically significant
(p < 0.05) interactions were further explored with appropriate post hoc pairwise tests. To test spatial variance around Rapa Nui, cMaxN data of each site were analyzed using “Sites” as a fixed factor in a PERMANOVA. To test seasonal difference on fish assemblage, data were analyzed
15
using seasons (winter, spring, summer and autumn) and five sites as fixed factors. A canonical analysis of principal coordinates (CAP) was used as a general test to evaluate structural differences in overall fish assemblage. CAP maximizes group differences finding the axis that best separates each group (Anderson et al., 2008). CAP analyses were run on the resemble matrix of average values between sites and seasons.
2.3.4 Environmental data collection and analysis
To determine the role of seasonal and spatial environmental variation on the fish assemblage structure, sea surface temperature (SST), long-term and recent wave energy, distance of each deployment site from the shore, and shelf width were considered. For each site, SST MUR (Multi- scale Sea Surface Temperature) satellite data at a 1 km spatial resolution
(https://mur.jpl.nasa.gov) were used after we verified the accuracy of these satellite data with in situ SST data collected at Omohi, Motu Tautara, Ovahe and Kari Kari sites by Dr. Evie Wieters
(unpublished data) from deployed temperature sensors (Onset, tidbit) set to record SST every ten minutes at 12-15 m depth. Long-term and recent wave energy were computed from NOAA’s Wave
Watch III (WWIII; http://polar.ncep.noaa.gov/waves), were binned into 16 discrete sectors each spanning 22.5 degrees. The long-term wave energy ranged from Jan 2010 to Jul 2015, meanwhile recent wave energy was calculated using mean values corresponding to the month each deployment was made. Distance from shore and shelf width were calculated for each site using
Google Earth Pro (http://earth.google.com) (Table S2.1). For seasonal analysis, only wave energy, long-term wave energy, and SST were considered. Environmental and biological data were analysed using distance-based linear modelling (DistLM) and a distance-based redundancy analysis (dbRDA). DistLM is a routine for analysing and modelling the relationship between a multivariate data cloud, as described by a resemblance matrix, and one or more predictor variables. The dbRDA analysis was used to visualize the given model in a multi-dimensional
16
space (Anderson et al., 2008). Environmental values used in the DistLM-dbRDA are shown in
Table S2.2.
2.4 RESULTS
Fifteen species were recorded during the study (Table 2.1). Planktivores and herbivores were the largest components of the pelagic fish assemblage at Rapa Nui, accounting for 73.8% and 16.9%, respectively (Table 2.2). The most abundant species around Rapa Nui were Xanthichthys mento and Chromis randalli. Both occurred at every site-season combination, except at Vaihu during spring. Top predators, while having the highest species richness (9 species), were not well represented in abundance except at Vaihu. Fistularia commersonii was the most abundant species among top predators, followed by Seriola lalandi (Table 2.2). Some species such as
Aulostomus chinensis and Caranx lugubris showed seasonal occurrence and other species such as Carcharhinus galapagensis and Pseudocaranx dentex displayed more site-specific occurrences. Nine target species were recorded, seven of which were top predators. The most abundant and well distributed was Kyphosus sandwicensis, which was abundant along the east and west coasts of Rapa Nui year-round; however, low abundances were reported at Vinapu, and it was absent at Vaihu. The black trevally C. lugubris was rare during the entire study.
17
Table 2.1: List of 15 species recorded using BRUVs at Rapa Nui Family Species Rapa Nui name Trophic level Target
Carcharhinidae Carcharhinus galapagensis Mango Top predator Yes Aulostomidae Aulostomus chinensis Toto amo Top predator No Fistulariidae Fistularia commersonii Toto amo hiku kio´e Top predator No Carangidae Pseudocaranx dentex Po´opo´o Top predator Yes Carangidae Caranx lugubris Ruhi Top predator Yes Carangidae Seriola lalandi Toremo Top predator Yes Carangidae Decapterus muroadsi ature Planktivores Yes Kyphosidae Kyphosus sandwicensis Nanue Herbivorous Yes Chaetodontidae Chaetodon litus Tipi tipi uri Secondary consumer No Pomacentridae Chromis randalli Mamata Planktivores No Sphyraenidae Sphyraena helleri Barracuda Top predator Yes Scombridae Thunnus albacares Kahi Top predator Yes Scombridae Katsuwonus pelamis Bonito Top predator Yes Balistidae Xanthichthys mento Kokiri Planktivores No Monacanthidae Aluterus scriptus Paoa Secondary consumer No
Table 2.2. Summary of fish sightings and relative abundance recorded by Baited Remote Underwater Video systems (BRUVS) at Rapa Nui. cMaxN: corrected MaxN. Trophic level Total no. Individuals % of total Highest cMaxN
Top predator 685 8,12 Carcharhinus galapagensis 112 1,33 21 Aulostomus chinensis 27 0,32 2 Fistularia commersonii 147 1,74 4 Caranx lugubris 12 0,14 4 Pseudocaranx dentex 78 0,92 12 Seriola lalandi 108 1,28 5 Sphyraena helleri 25 0,30 25 Katsuwonus pelamis 1 0,01 1 Thunnus albacares 175 2,07 133 Sec. Cons 97 1,15 Chaetodon litus 47 0,56 9 Aluterus scriptis 50 0,59 3 Planktivore 6227 73,80 Chromis randalli 2838 33,63 163 Xanthichthys mento 3279 38,86 140 Decapterus muroadsi 110 1,30 43 Herbivore 1429 16,94 18
Kyphosus sandwicensis 1429 16,94 241 Total 8438 100
2.4.1 Spatial differences
PERMANOVA revealed that the fish assemblages differed significantly among sites (Pseudo-F =
4.795, p < 0.001). Sites along the south-east side of Rapa Nui, Ana hukahu, Vaihu and Vinapu, were significantly different from all the other sites around the island (Table S3). CAP illustrates the difference in the fish assemblage found using PERMANOVA (Fig. 2.2a). The size of the first two axes were δ1 = 0.9823 and δ2 = 0.9339, respectively, over 5 (m) principal coordinate axes.
The estimation of misclassification error indicates low allocation success (31%); however, most of the misclassifications occurred within two groups (Fig. 2.2a): (1) Vinapu-Vaihu-Ana hukahu, and (2) Ovahe-Omohi-Poike-Kari Kari-Motu Tautara (Table S2.2). Vaihu was the only site with
100% allocation success. Vector length and direction from CAP revealed that the abundance of a few species such as C. galapagensis, F. commersonii and P. dentex drove the differences between Vaihu-Vinapu-Ana hukahu, and all the other sites (Fig. 2.2a). The occurrence of Thunnus albacares and Decapterus muroadsi distinguished Poike from other sites (Fig. 2.2a), meanwhile the occurrence of Katsuwonus pelamis was a consequence of the differences at Omohi.
19
Figure 2.2. Canonical analysis of principal coordinates (CAP) ordination of the variation in fish assemblage among (a) sites and (c) seasons. (b) and (d) CAP loadings shown graphically.
2.4.2 Seasonal differences
Highest richness and abundances were found in autumn and summer. Fish assemblages during winter significantly differed from the other seasons (Pseudo-F = 3.366, p < 0.001, Table S2.3).
Principal axes values from CAP were δ1 = 0.909 and δ2 = 0.546, over m = 3 principal coordinate axes (Fig. 2.2b). The overall estimation of misclassification error showed an allocation success of only 60%. Winter had the highest allocation success with 80%, while success for autumn (60%),
20
summer (60%), and spring (40%) were lower. In general, the occurrence and abundance of species such as X. mento, A. chinensis and S. lalandi, were associated with winter, while Aluterus scriptus and C. lugubris were associated with the summer season.
2.4.3 Environmental analysis
DistLM-dbRDA ordination showed that shelf width explained 26.6% of the spatial variation in the fish assemblage around Rapa Nui (p = 0.002). Recent wave energy and distance from the coast, when considered alone, explained 15.4 %, (p=0.028) and 14.5% (p= 0.039) of the variation, respectively. Long-term wave energy was the only variable explaining significant seasonal variability (~ 17.2% of the variation, p = 0.031) (Table S2.4).
2.5 DISCUSSION
2.5.1 Spatial and temporal patterns
This study is the first on spatial and temporal patterns of the pelagic fish assemblage at Rapa Nui, highlighting the importance of specific areas of occurrence and abundance. We found the pelagic fish assemblage at Rapa Nui to be dominated numerically by two small planktivore species, C. randalli and X. mento, followed by the herbivorous K. sandwicensis. The numerical dominance of planktivorous and herbivorous species observed in our study is consistent with Friedlander et al.
(2013) findings that these two trophic groups accounted for 40% and 31% of the total reef fish biomass, respectively. Top predator species, although less abundant, constituted the richest trophic group in our study (nine species). In contrast, Friedlander et al. (2013) only observed six species of this trophic group, and with lower abundances. These differences in richness and abundance of top predator species might be explained by differences in sampling methods. UVCs is a reliable observational technique (Medley et al., 1993; Samoilys & Carlos, 2000), and it is widely used for sampling reef-associated species at shallow, nearshore habitats. However, the effect of divers on animal behaviour has led to the underestimation of some species abundance,
21
such is the case of cryptic and fishery-target species within fishing areas (Chapman et al., 1974;
Cole, 1994; Gray et al., 2016; Kulbicki, 1998; Lindfield et al., 2014), especially pelagic species
(De Girolamo & Mazzoldi, 2001; Stanley & Wilson, 1995). The higher occurrence of rare species and species undersampled by UVCs, such as C. galapagensis, K. pelamis, T. albacare and C. lugubris, during our study proved the effectiveness of BRUVs in studying the pelagic fish assemblages at Rapa Nui, especially top predators.
Top predators play an important role in the top-down ecosystem regulation (Stevens et al., 2000), yet these species are the most vulnerable to overfishing and their removal could lead to environmental changes affecting ecosystem function in fragile ecosystems (Hughes et al., 2010;
Shears & Babcock, 2002). The continued decline of top-predator populations at Rapa Nui has likely caused a phase shift from a healthy community dominated by large top predators, such as at Salas y Gómez, to a disturbed community dominated by smaller planktivorous species
(Friedlander et al., 2013). Seven of the nine species of top predators recorded in this study are targeted by fisheremen at Rapa Nui. Together with the herbivorous Pacific rudderfish, K. sandwicensis, top predators like S. lalandi, S. helleri and T. albacares are the most targeted pelagic fishes at Rapa Nui (Zylich et al., 2014). Subsistence catches are also dominated by K. sandwicensis and other jacks such as C. lugubris and P. dentex (Zylich et al., 2014). According to local residents, C. lugubris was abundant in the past, but now is uncommon. Similarly, the
Galapagos shark, which is currently classified as Near Threatened on the IUCN Red List, has been reported by local residents to have declined considerably around Rapa Nui, possibly as a result of direct and indirect fishing impacts (Zylich et al., 2014; N. Morales, pers. obs), although the overfishing of prey may also be contributing to this decline (DiSalvo et al., 1988). Even though fishermen on Rapa Nui do not directly target C. galapagensis, they seem to be susceptible to bycatch in coastal and offshore fisheries. Likewise, their population has declined considerably in
22
Central America (Bennett et al., 2003), where the major threat comes from bait-fishing activities around islands and seamounts (Bennett et al., 2003; Zylich et al., 2014).
Carcharhinus galapagensis is the most common coastal shark around Rapa Nui (Randall & Cea,
2011; Zylich et al., 2014), and it was the only species of shark observed during the current study.
A similar BRUVs study in the Galapagos Archipelago found that the C. galapagensis was also the most abundant among 12 species of sharks in the area (Acuña-Marrero et al., 2018). In that study, C. galapagensis showed a similar mean cMaxN (0.52) per deployment to our observations
(0.58), despite the fact that the highest cMaxN found in the Galapagos (8) was almost three times lower than in the current study (21). Total number of individuals observed was 334 in the
Galapagos Archipelago, and 112 in the current study. These contrasting numbers could be a result of a higher local (i.e., site) concentration of this species but a lower regional (i.e., island) abundance at Rapa Nui than at the Galapagos Archipelago.
Spatial and seasonal differences in the composition of pelagic fish species were found during this study. Species composition along the south coast (Ana hukahu, Vaihu and Vinapu) was significantly different from the east and west coasts of the island. Spatial differences in assemblage structure were driven by the occurrence and abundance of the top predators such as C. galapagensis, F. commersonii, and P. dentex, which showed more site specificity, suggesting the presence of specific habitat characteristics unique to certain areas. Habitat structure and complexity have been indicated as important characteristics in the composition of fish assemblages, e.g., more complex habitats provide greater food availability and refuge
(Anderson & Millar, 2004; Asher et al., 2017; Coles & Tarr, 1990; Curley et al., 2003; Heupel &
Hueter, 2002). Shelf width was the most influential pelagic fish assemblage driver. Along the southern coast of the island, the shelf break (30 m) occurs further from the coastline creating an extended shallow platform (Table S2.2). The sharks observed during this study were likely juveniles (less than 200 cm TL; Wetherbee et al., 1996), based on size estimates of those sharks 23
that closely approached bait canisters (used for scale), suggesting juveniles have an apparent strong association with that shallow shelf habitat. Our observations suggests that the south-east coast of Rapa Nui could be serving as a nursery area for juvenile Galapagos sharks, which is consistent with nursery areas for Carcharhinus species often occurring in shallow waters
(Springer, 1967) with a low-predation environment and ample prey availability (Branstetter, 1990;
Heupel & Hueter, 2002; Simpfendorfer & Milward, 1993).
Abiotic (environmental) variables also influence the abundance of fish species within an area, leading to spatial variability within the ecosystem (Felley & Felley, 1986). Wave energy has been noted as an important driver of reef habitats and benthic communities at Rapa Nui where the dominance of different coral species depends on the degree of exposure (Easton, et al., 2018;
Friedlander et al., 2013). Wave energy came mainly from the south-west (202°) (Table S2.1); however, it only explained a small amount of the spatial variability in the pelagic fish assemblage.
These results may be explained by the low resolution of the satellite data for each site, which probably did not reflect the real effect of wave energy in the total area. Furthermore, in situ measurement of this environmental variable may provide finer resolution and explanatory power.
Although, top predator species are often associated with high-energy environments, the occurrence of top predators and target species at the south-easternmost part of the island (From
Vinapu to Poike) could be also explained by the effect of adverse weather conditions (e.g., wind, currents, and wave energy) on the local fishing effort, forcing fishing into more sheltered areas.
Conversely, the most abundant target species K. sandwicensis was rare on the south coast and virtually absent between Vaihu and Ana hukahu. The nanue (Rapanui name for the K. sandwicensis) is an herbivore species that feeds primarily on red algae. At Rapa Nui, the occurrence of algae is concentrated at the most protected sites (north-east) of the island (see
Easton et al., 2018). On the other hand, this species is one of the most prized species on Rapa
Nui and is considered over-exploited by local people (Gaymer et al., 2013). According to Acuña 24
et al. (2018), nanue are usually caught by traditional shoreline fishing and spearfishing, especially from Vinapu to Hanga Nui, where shoreline access is easier and fishing pressure is higher. The heavy fishing pressure together with the species habitat preference could explain the localized depletion in these areas.
Seasonal variability in pelagic fish assemblage structure was evident during this study, with winter been significantly different from the other seasons. Autumn and spring are transition seasons, as has been described from other subtropical areas (Friedlander & Parrish, 1998). Sites located along the coasts most exposed to winter swells and winds (Ana hukahu, Vaihu and Vinapu) showed higher variability among seasons in comparison with more protected sites. Similar results were found by Coles & Tarr (1990) in the western Arabian Gulf, and by Friedlander & Parrish
(1998) in the Hawaiian Archipelago. In both cases, the authors noticed that some mobile fishes seem to migrate from exposed to more protected and deeper locations that provide refuge from high wave energy during winter. In contrast, more protected sites seem to have more stable assemblages throughout the year. Asher et al. (2017) also found an increase in abundance of jacks and sharks in shallow and mesophotic reefs in the Hawaiian Archipelago with increasing depth, due probably to the avoidance of environmental (e.g., wave energy) and anthropogenic factors (e.g., fishing) in shallow waters. Rapa Nui has been understudied in comparison to other islands in the Pacific Ocean, and studies at deeper depths are even more limited (Easton et al.,
2017). Seriola lalandi and P. dentex were recorded at ~280 m and ~170 m, respectively, using
ROV (remotely operated vehicle) and Drop-Cams around Rapa Nui and the surrounding seamounts (Easton et al., 2017). The occurrence of inshore species at deeper depths could also suggest that deeper habitats are being used as a refuge from natural and anthropogenic influences. The presence of particular species during certain seasons and at certain sites could be explored by expanding the survey area in order to include mesophotic zones and incorporate surrounding seamounts in future designs.
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2.5.2 Conservation actions
Randall & Cea (2011) proposed the establishment of marine reserves around Rapa Nui to allow resident fishes to grow until they reached full reproductive maturity. Some of the areas suggested for reserves were Motu Nui and Motu Iti (in front of Kari-Kari), Ovahe, Motu Tautara, Hanga Nui, and Motu Marotiri. The last two areas correspond to the southeast side of the island, close to where the greatest abundance of top predators was recorded and a possible nursery area for C. galapagensis was identified. Carcharhinus galapagensis show ontogenetic segregation, where juveniles are more likely to inhabit shallow coastal waters, meanwhile adults occur in deeper waters away from the coast (Acuña-Marrero et al., 2018; Kohler et al., 1998; Wetherbee et al.,
1996). Areas used by early life stages are vital for population stability and recovery (Bonfil, 1997), and therefore, their protection is necessary.
Several initiatives have proposed other strategies to protect marine coastal and offshore ecosystems at Rapa Nui. An effort has been made in the last seven years to raise awareness and capacity building in the Rapanui community (Aburto et al. 2017; Gaymer et al., 2013). These efforts ultimately resulted in a participatory process that lead to the creation of a Multiple Uses coastal Marine Protected Area, MUMPA, around the entire EEZ of Easter and Salas and Gómez islands, completing the protection initially provided by the MMHMP in 2010. In order to implement this large-scale MPA, a participatory management plan has to be built, which includes the zoning of the MUMPA in both the coastal and offshore areas. Zoning will include establishing fully no- take coastal areas that could allow recovery of some over-exploited target fishes, but also to protect areas were top predators (such as C. galapagensis) are concentrated. Top predators play a crucial role in ecosystem function (Friedlander & DeMartini, 2002), thus their protection is necessary for maintaining ecological processes and ecosystem services. The current study is an important contribution for planning the management and conservation strategies to be implemented in the newly created Rapa Nui MUMPA. A Marine Council, with a majority of
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Rapanui-elected members, will place the administration of this area under a co-management strategy, in which is an unprecedented model of MPA administration in Chile (Aburto et al., 2017)
Over the last decades, there has been an increasing awareness of the added value that ecosystem services and sustainable management can offer to small human communities that inhabit coastal areas (Arkema et al., 2006). Biodiversity has been recently recognized as an economic resource (Admiraal et al. 2013), enhancing ecotourism and helping local inhabitants shift from non-sustainable practices (overfishing) to a broader array of sustainable activities with added value such as community-based ecotourism. In this sense, the year-round occurrence of the Galapagos shark in one specific area of the island could be considered a shark-based ecotourism spot, where local operators benefit from long-lived animals ensuring decades of incomes. Thus, not only the protection of the Galapagos shark, but also its potential for ecotourism
(e.g., shark-watching by SCUBA divers), should be key elements for taking into account for the zoning of the Rapa Nui MUMPA, that will allow activities such as traditional fishing practices, ecotourism, scientific research and others that should be defined in the management plan.
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CHAPTER 3. THE TROPHIC ROLE OF LARGE FISHES INHABITING THE EASTER
ISLAND ECOREGION
3.1 ABSTRACT
Large fishes are culturally and economically valuable in the remote island of Rapa Nui.
Nonetheless, historical overfishing resulting in the removal of top predators could affect the abundance and distribution of other species from lower trophic position within the ecosystem due to top-down regulation. Large fishes are usually assumed to be top predators; however, their trophic role needs to be evaluated on each location where they occurred. Here, we used δ13C and
δ15N isotopic signatures of large reef fishes previously listed as top predator species to: (1) determine the trophic position of large fishes and highlight their role in the ecosystem; and to (2) define the trophic niche of these species to evaluate the potential for dietary overlap. Our results indicate that not all large fishes inhabiting Rapa Nui can be considered as top predator species.
It is also shown that there is a high overlap in isotopic signatures among studied species, suggesting the potential for interspecific competition. The current study highlights the needed for multispecies studies to elucidate the tropho-dynamics of this isolated and understudied ecosystem.
3.2. INTRODUCTION
Top predators exert strong top-down influences on communities, controlling the demography of other organisms (Steven et al., 2000; Heithaus et al., 2008; Ruttenberg et al., 2011). The continued decline of these predators has led to increased concerns about how flow-on effects could impact the abundance and distribution of other species from lower trophic position (Stevens et al., 2000; Myers et al., 2003; Dulvy et al., 2014; Myers et al., 2007).
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Since the emergence of stable isotope technology, stable isotope analysis (SIA) are been increasingly used to investigate the tropho-dynamics within food webs (Cherel et al., 2008). The stable isotope signatures of carbon (δ13C) and nitrogen (δ15N) reflects nutrients assimilated over many feeding events (Futuyma & Moreno 1988). As the isotopic signature of carbon (δ13C) undergoes very little enrichment as trophic position increases, it is a useful indicator of the source of primary production (Harrigan et al 1989; Sweeting et al., 2007), whereas nitrogen (δ15N) signatures increase by 3-4 ‰ from prey to predator providing an index of trophic position (Vander
Zanden et al., 1997; Sweeting et al., 2007; Frisch et al., 2014). Using this technique, relevant information on trophic interaction such as trophic position, trophic niche, and intra- or inter-specific competition can be easily achieved (Post 2002; Papastamatiou et al., 2006). Reef sharks and larger bony fishes are typically assumed to have the highest trophic position in their food webs
(Sandin et al., 2008; Roff et al., 2016), mainly because of their relatively large body size and predatory behaviour (Frisch et al., 2014; 2016). The misclassification of a species trophic position has the potential to result in erroneous conclusions on the food web dynamic of coral reefs, and therefore, inappropriate management decisions (Heithaus et al., 2008; Frisch et al., 2016). The trophic niche in a food web provides insight into how a community is structured within an ecosystem (Layman et al., 2007), and how the available resources are partitioned among sympatric species (Papastamatiou et al., 2006). Niche dynamics are susceptible to environmental and biotic changes such as intra- and inter-specific competition and prey abundance (Bearhop et al., 2004). This information is especially relevant in ecosystems where anthropogenic stressors impact community structure (Layman et al., 2007).
Rapa Nui is one of the most isolated islands in the Pacific Ocean. Due to its geological age, small size, and high latitude location, this island is characterized by a low number of shore species compared to other islands in the Pacific Ocean (Randall & Cea, 2011). Fishes at Rapa Nui are culturally and economically valuable for the rapanui people. However, historical local overfishing
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has led to a decline of commercially important fishes over the pass years (Aburto et al. 2015;
Friedlander et al., 2013; Zylich et al., 2014). Fisheries landings predominately consist of large pelagic fishes like tunas, jacks, and swordfishes, among others, although the herbivorous rudderfish Kyphosus sandwicensis is also an important and culturally valued inshore species
(Zylich et al., 2014; Easton et al., 2017; Friedlander, 2018). Species like the Galapagos shark,
Carcharhinus galapagensis, are not commercially targeted; however, its population is also decreasing mainly due to incidental fisheries take (Zylich et al., 2014). In addition, some (legal) catches of large pelagic fishes such as swordfishes, oceanic sharks, and marlins commonly occur inside (Vega et al., 2009) and outside (Morales unpublished data) the EEZ of the Easter Island
Ecoregion. As a result, large species are either absent or in extremely low abundance around the island, raising questions about the health of the entire ecosystem (Friedlander et al., 2013).
Trophic interactions can have a strong influence on certain aspects of community ecology such as demography (Myers et al., 2007). Therefore, determining tropho-dynamics is an important step in understanding ecosystem functioning (Cherel et al., 2008). Here, we used stable isotope signatures of δ13C and δ15N to: (1) determine trophic position of large fishes and clarify their role within the Rapa Nui ecosystem; and (2) define the trophic niche of sympatric species, evaluate the potential for dietary overlap and hypothesize about potential competition for food in this nutrient poor ecosystem.
3.3 METHODS
3.3.1 Sample collection
Muscle samples were collected from specimens caught by local fishermen around Rapa Nui from
2016-2019. Our study focused on the top predator species described by Friedlander et al., (2013), and those observed in Morales et al. (2019a). Muscle samples were labelled and stored at -20°C
30
after collection. Body size could not be measured for each individual therefore, were not considerate in the analyses.
3.3.2 Sample preparation and stable isotopes analysis
Approximately 10 mg of wet muscle tissue was dissected, washed with milli-Q water, and placed in pre-combusted vials. All samples were oven-dried (60º C for 48 h) and then ground to a fine power in an agate mortar. Lipids, which can affect isotopic composition, were dissolved with cyclohexane according to Lorrain et al., (2011). Approximately 0.5 mg were transferred to tin capsules (5 x 9 mm) and stored in a desiccator until analysed for stable isotopes.
Analyses of carbon (δ 13C) and nitrogen (δ15N) stable isotope ratios were conducted at the School of Biological Sciences, Washington State University, using a Eurovector elemental analyzer, coupled to a Micromass Isoprime isotope ratio mass spectrometer. Stable isotope ratios were reported in the δ notation as the deviation relative to international standards (Vienna Pee Dee
13 15 13 15 Belemnite for δ C and atmospheric N 2 for δ N), so δ C or δ N = [(R sample /R standard) – 1] ×
10 3, where R is 13C/12C or 15N/14N, respectively. Typical precision of the analyses was ± 0.1 ‰ for both δ 15N and δ 13C.
3.3.3 Trophic positions estimations
Calculations of trophic positions of consumers were performed using “oneBaseline” (model 1) and
“twoBaseline” (model 2) models, based on the trophic enrichment factor (TEF) for nitrogen detailed by Post (2002). We then incorporated uncertainty through Bayesian inference using the tRophicPosition package in R software v0.98.977 (R Core Team 2019; Quezada-Romegialli et al., 2018):
(Model 1-oneBaseline):
15 15 δ Nc = δ Nb1 + ΔN (TP – λ)
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(Model 2-twoBaseline):
δ15Nc = ΔN (TP + λ) + α (δ15Nb1 + δ15Nb2) – δ15Nb2
α = (δ13Cc – δ13Cb2) / (δ13Cb1 – δ13Cb2)
Where δ15Nc and δ13Cc refer to the δ15N and δ13C values of consumers, respectively. ΔN corresponds to the trophic enrichment factor (TEF) for nitrogen and λ is the trophic position of the baseline. The δ15Nb1 and δ13Cb1 are the nitrogen and carbon signatures for baseline 1 and
δ15Nb2 and δ13Cb1 for baseline 2. α is the proportion of nitrogen derived from baseline 1. The models were run with 2 chains, 20.000 adapting samplings and 20.000 iterations.
Assuming that the Rapa Nui marine ecosystem could be supported by both pelagic and benthic
N2-fixation pathways (e.g., N2-fixing pelagic cyanobacteria and diazotrophic symbiont in corals, respectively), a two-baseline model based in nitrogen signatures was used. (Zapata et al. unpublished data). The δ15Nb1 signature was estimated as the average from three herbivorous zooplankton taxa (i.e., calanoid and cyclopoid copepods and euphausids) (baseline 1) and the coralivorous gastropod Coraliophilla violacea (baseline 2). The ΔN and ΔC were taken from global meta-analyses and correspond to muscle tissue signatures (ΔN=2.9‰ ±0.3 and ΔC=1.3+0.3‰)
(McCutchan et al., 2003).
3.3.4 Trophic structure and isotopic niche
To determine the trophic diversity between marine assemblages, a sample-size corrected version of standard ellipse area (SEAc) was utilized as a measure of the mean core of the isotopic niche occupied by different taxa in each assemblage (Jackson et al., 2011). This metric represents a measure of the total amount of niche occupied in the isotopic space and allows for robust statistical comparisons between data sets with different sample sizes and corrects for bias when sample sizes are small (Jackson et al., 2011). Moreover, this metric allowed for calculating the 32
overlapping area of the standard ellipses (and their respective %) between assemblages
(employing 95% of data) and was used as a measure of trophic partitioning between different assemblages. Additionally, other measurements of isotopic niche widths and trophic structure proposed by Layman et al., (2007), were calculated for each assemblage: (1) the total area (TA) occupied was calculated as the area of the convex hull that incorporated all individuals and represents a measure of niche width and reflects the isotopic diversity of a group (Vaudo &
Heithaus, 2011); (2) mean distance to the centroid (CD) represents the average degree of trophic diversity within the species. CD is calculated by determining the Euclidean distance of each individual to the δ13C and δ15N centroid of all individual; and (3) standard deviation of nearest neighbour distance (SDNND) giving a measure of evenness of spatial density and packing. These metrics were estimated using the SIBER package in R (Jackson et al., 2011).
3.4 RESULTS
In total, 105 individuals from 10 large fish species were sampled and analyzed for isotopic composition (Table 3.1). Mean δ15N and δ13C signatures for all fish species are shown on a bivariate plot in Figure 3.1. Thunnus albacares had the largest range of δ13C (6 ‰), while
Acanthocybium solandri and Pseudocaranx dentex had the largest ranges of δ15N (7.5 ‰).
Species-specific mean δ13C signatures ranged from -18.40 ± 1.29 ‰ (Katsuwonus pelamis) to
16.45 ± 0.67‰ (Carcharhinus galapagensis), and mean signatures of δ15N range from 13.85 ±
1.75‰ (Pseudocaranx dentex) to 17.53 ± 0.93‰ (Coryphaena hippurus), although the single sample of Alustomus chinensis measured 11.6‰. Statistical comparisons showed significant differences between species (Pseudo-F = 5.148; df = 9; P< 0.001). The most significant differences were from P. dentex and C. galapagensis to the other species inhabiting the island
(Table S3.1).
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Figure 3.1. δ13C and δ15N signatures of large fishes inhabiting Rapa Nui. Data points are group means, and error bars are standard deviations. Open symbols represent species with less than five samples. Species abbreviations are defined in Table 3.1.
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Table 3.1. Summary of mean and standard deviation (SD) of stable isotopes (δ13C and δ15N) composition of large fishes included in this study. Abbreviation (Abb), sampling size (n).
δ13C δ15N Taxón Abb n Mean SD Range Mean SD Range
Acanthocybium solandri AS -17.77 0.97 4.2 16.03 1.58 7.5 31
Aulostomus chinensis AC ------1
Carcharhinus galapagensis CG -16.45 0.67 1.5 15.62 1.26 2.9 6
Coryphaena hippurus CH -17.79 0.93 2.4 17.53 0.93 2.4 7
Katsuwonus pelamis KP -18.40 1.29 5.5 15.94 1.65 6.6 19
Pseudocaranx dentex PD -17.20 1.32 4.8 13.85 1.75 7.5 19
Seriola lalandi SL -18.10 0.34 0.7 14.15 2.51 5.3 4
Kajikia audax KA -17.28 0.44 0.9 15.74 1.30 3 5
Thunnus albacares ThA -18.38 1.06 6 16.01 1.67 6.3 36
Thyrsites atun TyA -18.16 1.09 3.3 16.21 0.84 2.2 7
3.4.1 Trophic position
Large fishes inhabiting Rapa Nui encompassed three trophic positions: secondary (3.0-3.9), tertiary (4.0-4.9), and quaternary (≥5.0) consumers. Only one species was recoded as a secondary consumer, seven as tertiary, and one as quaternary (Table 3.2). Within the tertiary consumers, Pseudocaranx dentex and Seriola lalandi had the lowest trophic positions (4.2 and
4.4, respectively), while Katsuwonus pelamis, Thunnus albacares, Thyrsites atun, and
Acanthocybium solandri had the highest signatures, nearly approximating quaternary consumers.
Only one species, Coryphaena hippurus, encompassed the quaternary group.
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Table 3.2. Summary of outputs from trophic position (TP) models (model-1: “oneBaseline” and model-2: “twoBaseline”). Mode signatures and 95% Bayesian confidence interval are presented. TP from Fishbase are based on food items.
Taxon TP-model 1 TP-model 2 TP from Mode Lower Upper Mode Lower Upper Fishbase Acanthocybium solandri 5.0 4.3 6.0 4.9 4.2 5.9 4.3 ± 0.2
Aulostomus chinensis 3.4 2.0 9.3 3.3 2.0 9.3 4.2 ± 0.7 Carcharhinus 4.8 4.0 6.0 4.6 3.9 5.7 4.2 ± 0.4 galapagensis
Coryphaena hippurus 5.4 4.6 6.8 5.4 4.5 6.5 4.4 ± 0.0 Katsuwonus pelamis 4.9 4.2 6.0 4.8 4.1 5.8 4.4 ± 0.5 Pseudocaranx dentex 4.2 3.6 5.0 4.1 3.6 5.0 3.9 ± 0.6 Seriola lalandi 4.3 2.6 6.5 4.1 2.3 6.1 4.2 ± 0.1 Kajikia audax 4.8 4.0 6.2 4.8 3.8 6.1 4.5 ± 0.7 Thunnus albacares 4.9 4.2 5.9 4.8 4.2 5.7 4.4 ± 0.4 Thyrsites atun 4.9 4.3 6.2 4.9 4.2 5.9 3.6 ± 0.3
3.4.2 Isotopic niche
The isotopic niche of large fishes inhabiting Rapa Nui differed in size but not much in position, indicating a low to moderate degree of trophic diversity (Fig. 3.2). The ellipse areas (SEAc) was smaller for Acanthocybium solandri (4.16) and larger for Pseudocaranx dentex (7.4). Except for
A. solandri, all groups had similar CD signatures. NND ranged from 0.46 (T. albacares) to 0.82
(K. pelamis and P. dentex), and SDNND ranged from 0.49 (T. albacares) to 1.21 (K. pelamis)
(Table 3.3).
The niche overlap was high (≥50%) between A. solandri, K. pelamis, and T. albacares.
Pseudocaranx dentex was the only species that showed less overlap with the others, suggesting that this species feeds on different prey than the other species examined (Table 3.3; Fig.3.2a).
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Table 3.3. Isotopic niche area described by Standard Ellipse Area (SEA), corrected SEA (SEAc), total area of the convex hull (TA) and sample size (n) for the four species with sample size over 10 as was recommended by Jackson et al. (2011). Acanthocybium solandri (AC); Katsuwonus pelamis (KP); Pseudocaranx dentex (PD); Thunnus albacares (ThA). Other abbreviations are defined in methods.
Metrics AS KP PC ThA SEA 4.01 6.55 6.99 5.58 SEAc 4.16 6.93 7.40 5.75 TA 17.65 23.70 22.75 21.03 CD 1.27 1.55 1.84 1.74 NND 0.49 0.82 0.82 0.46 SDNND 0.55 1.21 0.72 0.49 n 31 19 19 36
Ellipse overlap (%) Species 1 Species 2 AS KP PD ThA AS - 49.9 10.0 51.6
KP 83.2 - 15.2 98.4
PD 17.8 16.3 - 17.5
ThA 71.3 81.5 13.5 -
*Data are the percentage of SEAc of species 1 that is occupied by the SEAc of species 2.
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Figure 3.2. Isotopic niche space of four species sampled during the study and density plots showing the credibility intervals of Bayesian standard ellipses areas. (a) Isotopic niche space of four species sampled during the study. (b) Density plots showing the credibility interval of Bayesian standard ellipses areas (SEA). Black circles and red crosses indicate mode SEAB and small sample size corrected (SEAC), respectively. Shaded boxes indicate the 50, 75, and 95% credibility intervals for each species. We only considered species with > 10 samples for these analyses. Acanthocybium solandri (AS); Katsuwonus pelamis (KP); Pseudocaranx dentex (PD); Thunnus albacares (ThA).
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3.5. DISCUSSION
3.5.1 Stable isotope signatures
Large fishes from Rapa Nui are broadly distributed throughout δ13C - δ15N space (Fig. 3.1), indicating a broad range of trophic diversities. In general, it is well established that inshore
(benthic) and offshore (pelagic) systems have enriched and depleted δ13C, respectively (Speed et al., 2012). The δ13C signatures found in this study suggests that most of the studied species are more related to a planktonic (pelagic) rather than benthic food source. Only Pseudocaranx dentex and Carcharhinus galapagensis appeared to be more closely related with the benthic coral reef resource.
Thunnus albacares displayed the largest range of δ13C (6‰), suggesting a broad carbon source.
The wide range and high mean signature could be explained by its migratory behaviour (Bearhop et al., 2004), and high trophic position, due to the integration of different carbon sources along their movement routes (Hecky & Hesslein, 1995; Estrada et al., 2003). During these migrations,
T. albacares may consume both benthic prey from islets and seamounts, in addition to pelagic prey from the open ocean. Moreover, the latitudinal variation in food carbon signatures to which this species encounters during their migrations can also explain the wide range of δ13C found here (Kelly et al., 2006; Cherel & Hobson, 2007). In contrast, Acanthocybium solandri and P. dentex showed the largest range of δ15N (7.5 ‰), which indicates that they feed over a broad range of trophic positions (Table 3.1; Fig. 3.1). Large ranges of δ15N signatures are more common on inshore species due to the higher availability of prey from different trophic positions (Link 2002;
Estrada et al., 2003). On Rapa Nui, P. dentex is a benthic feeder that feeds primarily on bivalves, gastropods, and small fishes (Randal & Cea, 2011). However, personal observations by divers in our study area indicate that P. dentex also eats the feces (coprophagia) of larger fishes, like the
Galapagos sharks. This interaction could explain the broad range of δ15N values despite its low
39
mean. Coryphaena hippurus showed the highest δ15N signatures (17.53 ± 0.93) suggesting that this species is at the top of the food web and feeds on high trophic position prey.
3.5.2 Trophic position
Derived trophic position (TP) from δ15N signatures showed that most large fishes at Rapa Nui have high positions in the trophic web (TP= 4.1 – 5.4). Only Aulostomus chinensis, which was previously reported as a top predator at Rapa Nui (Friedlander et al., 2013), was not a top predator based on trophic position (TP= 3.4). However, this should be interpreted with caution since we only analysed one sample of this species. Even though, the TP of A. chinensis (and the size of its mouth) seems consistent since they are much smaller than the other fishes sampled here
(maximum size = ~76 cm total length; Randall & Cea, 2011), and therefore are limited to smaller prey, which are generally low-level consumers. Pseudocaranx dentex and Seriola lalandi had a
TP of 4.2 and 4.3, respectively. Frisch et al., (2016) suggest that species with TP between 4.0 and 4.5 should be considerate more as high-level mesopredators, an alternative trophic group that better fits their trophic role. The highest trophic positions in our study correspond to C. hippurus (TP = 5.4) indicating that in the South Pacific it feeds on prey with a high trophic position, such as pelagic fishes and epipelagic cephalopods (Olson & Galván-Magaña, 2002).
Nevertheless, Teffer et al., (2015) suggested that C. hippurus and other species such as tuna have similar diets and might be competing for available resources. The differences between C. hippurus and the other top predators studied here may be a result of their foraging habits (e.g., vertical migration and maximum depth, feeding hours, etc.; Moteki et al., 2001). Menard et al.,
(2007) also suggested that sympatric species with similar diets, but slightly different TP´s might be results of one species feeding over larger specimens of the same prey species. The two species of tuna (Katsuwonus pelamis and T. albacares) displayed close δ15N signatures, and therefore TP´s suggesting a similar diet. These two species are mainly piscivorous with similar feeding behaviour, which could be explaining our results and those findings by Koladinovic et al.,
40
(2008). Even though we could not sample other large species inhabiting the area, such as
Prionace glauca (blue shark) and Isurus oxyrinchus (Mako shark), we hypothesize that these large roving sharks might also be occupying the role of apex predators in the area as has been suggested for other locations (Estrada et al. 2003; Bugoni et al., 2010)
The differences found between our TP estimates and those from Fishbase could be explained by different hypothesis: (1) a close relationship between body size and d15N enrichment has been proposed (Estrada et al., 2006; Papastamatiou et al., 2010; Speed et al., 2012), meaning that larger individuals eat higher trophic position prey. We were unable to collect the length measurements of our fishes which may explain these differences since some specimens were already processed when landed; (2) different habitats could also have different prey, and thus display different isotopic signatures within the same species. Ferreira et al., (2017) found that
Galeocerdo cuvier (tiger shark), a widely distributed species occupies different trophic position according to the habitat (offshore vs. inshore) due to different prey availability; and (3) TPs from
Fishbase were calculated using stomach content, and therefore, they might be showing different time-frames than our results. Stomach content analysis usually underestimates the presence of rapidly digested prey such as pelagic species (clupeids and engraulids; Frisk et al., 2016) resulting in a possible underestimation of TP (Hussey et al., 2012). That said, our results are consistent with other TPs from stable isotope surveys, such as for T. albacares (TP= 4.8; Graham
2010, and TP= 4.7; Bugoni et al., 2010).
Carcharhinus galapagensis appears to have a high TP in the Rapa Nui ecosystem (TP = 4.8) even though only juveniles were sampled. According to our results, its presence is important and should be considered a priority for the health and stability of the entire ecosystem, especially considering that it is the only resident shark species inhabiting the area (Morales et al., 2019a).
Several studies have noted the influence of reef shark species on the structure of different ecosystems. For example, Barley et al., (2017), found that on shark-depleted reefs, mesopredator 41
species were more abundant and consumed a different diet (fishes and squids), than those inhabiting shark-rich reefs. In the latter situation, mesopredators were less abundant and consumed mainly benthic invertebrates. It is worth remembering that sharks sampled during this study were juveniles and most likely young-of-year. Juvenile elasmobranchs keep their mothers isotopic signal during their first year of life (and up to two years; Matchi et al., 2010), especially when slow incorporation rate tissues (e.g., muscle) are sampled (Matich et al., 2010; Olin et al.,
2011). As a result, our findings were most likely the signature of the mother’s isotopic signal and not the juveniles. Additionally, most of elasmobranchs maintain high levels of urea in their tissues as a response to osmoregulation processes (Olson 1999). These nitrogenous waste products could interfere when comparing δ15N signatures between individuals or taxa (Frisch et al., 2012) by creating lower δ15N signatures (0.1-1.4‰; Hussey et al., 2012), and therefore, lower trophic position (-0.2 TL; Churchill et al., 2015) than expected. This may mean that C. galapagensis could eventually occupy an even higher TP in the Rapa Nui ecosystem.
3.5.3 Isotopic niche
Katsuwonus pelamis and P. dentex showed the widest isotopic niche (TA and SEAc) among the study species suggesting a more generalist diet than T. albacares and A. solandri. If other conditions remain the same, trophic generalist species seem to be less susceptible to extinction, and therefore, more resilient to environmental changes due to their ability to change prey (Layman et al., 2007). Nevertheless, the utilization of TA (convex hull area) to describe the isotopic niche width should be used with caution, since wider niches are usually related with larger populations
(Bond et al., 2016) and/or larger sample sizes (Jackson et al., 2011). Our results suggest that P. dentex and K. pelamis have the widest isotopic niches despite their low sample sizes among the study species (Table 3.3). Pseudocaranx dentex is a species frequently inhabiting nearshore areas and feeds mainly on benthic prey (Randal & Cea, 2011), but interacts with larger fishes as mention above, and therefore, we expect this species to have a wider isotopic niche. Katsuwonus
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pelamis and T. albacares are sympatric species that frequently occur in mixed pelagic schools
(Sardenne et al., 2016). Adults of T. albacares usually displays a deeper vertical distribution than
K. pelamis and preys upon a larger size range of prey (Graham & Dickson, 2004). However, during our study, we sampled T. albacares of small size (~ 10 kg) called “pitufos” (smurfs in english) by the rapanui and only a few large individuals (~ 40 kg) were sampled. The diving ability of T. albacares increases with size, and therefore, we would expect that larger T. albacares to display wider isotopic niches than K. pelamis.
The high isotopic overlap found between the studied species could be a result of feeding on species from similar TPs and sources of production (Frisk et al., 2014), and does not necessarily suggests interspecific competition. Usually, species partition (spatially and temporally) available resources in order to avoid competition and enable coexistence (Frisch et al., 2014; 2016; Kinney et al., 2011; Layman et al., 2012). Muscle tissue of pelagic bony fishes only reveals the diet from
3-4 months (Maruyama et al., 2001) up to 6 months for the stable isotope rations of a fish to reflect consuming prey within a specific area (e.g., Rapa Nui) giving insights into past diet but not the current one. In this sense, highly migratory species with high niche overlap are not necessarily competing for food, but instead might have consumed prey from similar TPs or even the same species at different location.
Nevertheless, Rapa Nui is an impoverished ecosystem in terms of habitat types and species number compared to other oceanic island in the Pacific Ocean (Randall & Cea, 2011; Friedlander et al., 2013), with fewer prey species. In this sense, our results might suggest a degree of interspecific competition between taxa. A high degree of interspecific competition is especially concerning at Rapa Nui since the depletion of important species populations such as the nanue
(Kyphosus sandwicensis; Friedlander et al., 2013) could trigger a shift in the ecosystem due a bottom-up regulation. For example, Myers et al., (2007) suggested that after a reduction of primary prey, the consumer could display a decrease in the breeding performance and a reduction 43
in their population size. Additionally, the occurrence of new species in the ecosystem, such as
Triaenodon obesus (Morales et al., 2019b) and Seriola rivoliana (N. Morales in prep.), could eventually cause a change in the ecosystem structure through a niche expansion or “ecological release”. Bolnick et al., (2010) suggested that niche expansion occurs when invading species arrive in species–poor habitats, such as oceanic islands. Invading species access resources that were previously monopolized by former competitors, forcing them to expand their niche and diet.
Thus, future studies should include different turn-over tissues and stomach contents studies in order to elucidate if migratory and resident species are effectively competing for resources at
Rapa Nui. Tagging programs also would help to determinate important ecological aspects that could explain the coexistence of sympatric species such as feeding areas, feeding time, and depth, among others.
3.5.4 Conservation aspects
Coral reef ecosystems are at risk due to the effects of both local stressors such as overfishing and habitat lost (Wilson et al. 2010), and global stressors such as global warming and ocean acidification (Hoegh-Guldberg et al., 2007). The biodiversity at Rapa Nui is especially susceptible to global changes. Its subtropical location makes tropical species vulnerable to extended periods of cool sea temperature, while subtropical species are vulnerable to long periods of warm sea temperature (Randall & Cea, 2011). Moreover, historical high fishing pressure on large fishes is putting pelagic stocks at risk, causing a drastic decline of their population (Myers & Worm 2003).
Thus, the decrease of both high and low trophic position species could cause both a bottom-up and/or top-down shift with unexpected consequences for the entire ecosystem.
Understanding the role of co-occurring species and how those species use inshore areas is crucial to implementing effective strategies to manage and conserve both the habitats and the species inhabiting them (Gallagher et al., 2016). This current study contributes to the
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understanding of the role of large fishes at Rapa Nui, how they interact, and the relationships occurring within this isolated and understudied area. Moreover, this information could be used to improve conservation actions such as the implementation of no-take areas where key species occur, or the implementation of management strategies for economically important species.
Finally, future studies should concentrate on comparing both Rapa Nui, a shark-depleted ecosystem, and Salas y Gómez island, a nearly pristine ecosystem full of reef sharks (Friedlander et al., 2013; Morales pers. obs.), in order to determine if Rapa Nui corresponds to a “new” state of organization of food web caused by anthropogenic pressure.
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CHAPTER 4. RESIDENTIAL MOVEMENTS OF TOP PREDATORS AT CHILE’S
MOST ISOLATED MARINE PROTECTED AREA: IMPLICATIONS FOR THE
CONSERVATION OF THE GALAPAGOS SHARK, CARCHARHINUS
GALAPAGENSIS, AND THE YELLOWTAIL AMBERJACK, SERIOLA LALANDI.
4.1 ABSTRACT
Marine Protected Areas (MPAs) are becoming a widely used tool for the conservation of biodiversity and fishery management. However, most of these areas are designed without knowledge of the basic ecological aspects of the species they are trying to protect. This study investigated the movement of two top predators: Galapagos shark, Carcharhinus galapagensis, and the yellowtail amberjack, Seriola lalandi in and around the Motu Motiro Hiva Marine Park
(MMHMP) using miniPAT satellite tags to determine the effectiveness of this MPA for the protection of these species. The Galapagos sharks (n = 4) spent most of their tag deployment periods inside the MMHMP. However, high intraspecific variability was observed in their movement dynamics. Daily individual maximum movements ranged from 17 to 58 km and the maximum distance from Salas y Gómez Island, the only emergent island within the MMHMP, was
31 to 139 km. The maximum linear distance travelled for a female juvenile Galapagos shark (152 cm TL) was 236 km, which is greater than the maximum distance previously documented for juveniles of this species (< 50 km). For yellowtail amberjack (n = 1), 91% of the satellite geolocations were within the MMHMP, with a maximum daily distanced travelled of 6 km.
Maximum distance travelled between points was 111 km and the maximum distance from Salas y Gómez Island was 62 km. All archival-tagged fish spent most of their time at depths <50 m and never left the epipelagic zone. Day vs night-time differences were pronounced in all individuals but showed high inter-individual variability. This study provides a baseline on the movement of
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these two top predators in the MMHMP and provides valuable insights for the creation of MPAs in the region and elsewhere.
2.2 INTRODUCTION
The establishment of Marine Protected Areas (MPAs) has become a widely applied tool for the conservation of biodiversity and fishery management (Botsford, Micheli, & Hastings, 2003;
Pendleton et al., 2018). Numerous studies have demonstrated their capacity to protect a variety of marine fauna, including mobile species (e.g., White et al. 2017), while restoring and preserving overall ecosystem functions (Gaines et al., 2010; Lubchenco & Grorud-Colvet, 2015). Features such as isolation, size (> 100 km2), age, enforcement, and the establishment of fully protected no-take zones have been shown to be key features for the success of MPAs (Edgar et al., 2014).
However, at least 94% of the world’s MPAs allow some form of fishing activity (Costello &
Ballantine, 2015), which negatively impacts biodiversity within these MPAs and the fisheries benefits due the reduced reproductive output and adult spillover (Boonzaier & Pauly, 2015;
Hilborn, 2016; Klein et al., 2016).
Fishing activities outside an MPA may also negatively affect populations and biodiversity inside the MPA (Moffitt, Botsford, Kaplan, & O'Farrell, 2009). This particularly applies to mobile species such as tunas and sharks that are wide-ranging and can move between national and international boundaries. As a result, many mobile species within MPAs are considered to be overfished because of poorly managed fisheries operating in international waters (Sala et al., 2018), and within national boundaries (Agnew et al., 2010). Evaluating the efficacy of MPAs thus becomes imperative for the long-term conservation of marine ecosystems. For this to occur, it is imperative to have an in-depth knowledge of species habitat utilization patterns, habitat requirements
(Roberts, 2000; Field et al., 2011), and the degree of overlap between the habitat and the area under protection (Knip, Heupel, & Simpfendorfer, 2012).
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With the rapid technological advancement of remote tracking applications, it is now possible to gain insights into the movement patterns of marine organisms and its relevance for marine conservation. Satellite tagging studies on whales, sea turtles, and sharks have significantly improved our understanding of their migratory routes and habitat use, revealing specific areas and corridors as biodiversity hotspots (Block et al., 2011; Robinson et al., 2016). Furthermore, satellite telemetry has been employed to develop management strategies that minimize anthropogenic impacts, such as incidental mortalities in high-seas fisheries (Shillinger et al., 2011;
Poisson et al., 2016), vulnerability of species to certain fishing gears (Cortés et al., 2010), and the effectiveness of modified gears by tracking the survival of released fisheries bycatch (Moyes et al., 2006; Swimmer et al., 2006). With the increase in available information, satellite data have allowed us to evaluate the effectiveness of established MPAs for the protection of top predator species. For example, White et al. (2017) overlaid the habitat utilization of grey reef sharks
(Carcharhinus amblyrhynchos) at Palmyra Atoll MPA with the boundaries of the no-take U.S
Pacific Remote Island Marine National Monument and found that while the MPA was somewhat effective, it did not cover the species’ entire home range. This highlights the need for much larger
MPAs to guarantee the conservation of highly migratory species. In a similar study by Queiroz et al., (2016), tracking data of coastal and pelagic shark species, including critically endangered
(Sphyrna mokarran and S. lewini), endangered (Isurus oxyrinchus), and near-threatened species
(Galeocerdo cuvier, Prionace glauca) overlapped so extensively with the fishing efforts of longline vessels fishing in international waters, that the creation of effective MPAs in the high seas was considered infeasible due to socio-economic reasons.
Chile is among the top ten fish producers globally and has among the most productive waters worldwide (Daneri et al., 2000). However, the overall conservation of Chile’s marine biodiversity is at risk, especially along its coastline, where anthropogenic impacts are greatest and often conflict with established or planned MPAs (Tognelli, Fernández, & Marquet, 2009; Cárcamo et
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al., 2011). To confront the fishing pressure imposed on Chile’s marine biodiversity and to protect critical ecosystems, in 2010 the Chilean government created the Motu Motiro Hiva Marine Park
(MMHMP), a large-scale (150,000 km2) no-take marine protected area surrounding Salas y
Gómez Island (Figure 4.1). It is considered one of the most isolated islands in the world, with the south-easternmost distribution of coral reef systems (Randall & Cea, 2011; Friedlander et al.,
2013; Arana, 2014). This ecosystem is dominated by top predator species such as the Galapagos shark (Carcharhinus galapagensis) and three species of jacks (Seriola lalandi, Caranx lugubris, and Pseudocaranx cheilio). The marine ecosystem surrounding Salas y Gómez Island is in sharp contrast to Rapa Nui (Easter Island) where historical overfishing has depleted these top predator species (Friedlander et al., 2013).
Figure 4.1. Bathymetry of the Easter Island Ecoregion. Rapa Nui, Salas y Gómez Island, Motu Motiro Hiva Marine Park (violet), Exclusive Economic Zone (white).
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Top predators, such as sharks, jacks, groupers, and tunas are key elements for the maintenance of ecosystem health, as they regulate the demography and behaviour of organisms from lower trophic levels (Myers et al., 2007; Heithaus et al., 2008; Ferretti et al., 2010; Ruttenberg et al.,
2011). However, these large predators are becoming increasingly scarce in coral reef and pelagic ecosystems, largely due to overfishing and habitat degradation (Steven et al., 2000; Myers &
Worm, 2003; Dulvy et al., 2014).
Carcharhinus galapagensis, is one of the most abundant reef sharks inhabiting the Tropical Indo-
Pacific Ocean (Duffy, 2016). It has a circumglobal, albeit patchy, distribution and is associated with warm and temperate waters and oceanic islands (Wetherbee Crow, & Lowe, 1996; Kohler,
Casey, & Turner, 1998; Meyer, Papastamatiou, & Holland, 2010; Duffy, 2016). Their slow growth, fragmented geographical distributions, and unknown levels of local connectivity have likely contributed to their population declines in areas of high fishing pressure in the Pacific and Atlantic oceans (Kyne et al. 2019). Carcharhinus galapagensis was recently reassigned as “Least concern” by the IUCN Red List; however, the habitat specificity of this species make it susceptible to localized depletion (Kyne et al. 2019). Major threats to this species come from fishing activities around islands and seamounts (Zylich et al., 2014; Kyne et al. 2019). Carcharhinus galapagensis accounts for 26% of the biomass at Salas y Gómez Island (Friedlander et al., 2013). At Rapa Nui, their population has declined as a result of direct and indirect fisheries impacts (Zylich et al.,
2014), although a resident population still inhabits the area (Morales et al., 2019a). Another important predator in the Easter Island Ecoregion is the yellowtail amberjack Seriola lalandi, a coastal pelagic species restricted to subtropical waters (Randall & Cea, 2011; Martinez-Takeshita et al., 2015). It is currently classified as ‘Least Concern’ (IUCN, 2018), with no apparent population declines (Smith-Vaniz & Williams, 2015). In total, Seriola lalandi together with Caranx lugubris and Pseudocaranx cheilio account for 19% of the total biomass at Salas y Gómez Island, and as
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with C. galapagensis, their populations around Rapa Nui are virtually absent (Friedlander et al.,
2013).
Despite being the most common top predators within MMHMP, little information is currently available for C. galapagensis and S. lalandi within the Easter Island Ecoregion. Ecological information, such as spatial dynamics, habitat use, and connectivity, are essential in generating realistic and effective conservation plans for the protection of these species. Therefore, this current study aims to investigate the movement patterns and habitat use of both species within
MMHMP in order to give some insights into the effectiveness of the current MMHMP borders for the protection of these key species. Additionally, industrial fishing pressure in the area was examined and compared with the current MPA borders and predator movement patterns to explore the potential impact of fishing on these species.
4.3 METHODS
4.3.1 Study area
Salas y Gómez Island (26°27'S, 105°28'W) is a 0.15 km2 small, emergent rocky outcrop located
400 km east of Rapa Nui (Easter Island), and ~ 3,300 km from the Chilean coast (Figure 1).
Together, both islands are the only two exposed landmasses of the numerous submerged volcanic seamounts that make up the 2,232 km long Salas y Gómez Ridge (Ray et al. 2012;
Friedlander et al., 2013). Even though both islands are part of the Chilean territory, Rapa Nui and
Salas y Gómez Island are very different from continental Chile in terms of marine ecosystems and species composition (Aburto, Gaymer, & Cundill, 2017). Located at the eastern limit of the
South Pacific Gyre (Figure 1), this area belongs to the Easter Island Province and Ecoregion, which is characterized by oligotrophic waters (Pizarro et al., 2006; Andrade, Hormazábal, &
Correa-Ramírez, 2014).
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4.3.2 Capture and tagging
Fishes were caught and tagged in November 2015 and 2017, during two research trips to Salas y Gómez Island (during the CIMAR 21 cruise organized by the Chilean Navy and a research trip of ESMOI,). During each trip, approximately six handlines were equipped with #14/0 circle hooks, baited with chub mackerel (Scomber japonicus), and set between 15 to 30 m deep for ~ 10 min.
Sharks were secured head-forward alongside an inflatable boat and immobilised for less than five minutes, following common tagging practice of pelagic sharks (Brooks et al., 2011). Each individual shark was sexed and measured from snout to the tip of the tail in horizontal position to determine the animal’s total length (TL). Seriola were measured (TL) and tagged inside the inflatable boat.
Two types of tags were used during this study. To facilitate the identification of the fish after potential recapture, each individual was tagged with conventional dart tags with a stainless-steel metal blade anchor (model FH-69 SS, Floy Tag & Mfg., Inc.) inserted into the musculature at the base of the dorsal fin between the pterygiophores. To study the movements and habitat use of these top predators in the area, six large fishes (five C. galapagensis > 140 cm TL and one S. lalandi, measuring 124 cm TL) were equipped with antifouling coated pop-up satellite archival tags (model MiniPAT, Wildlife Computers, Redmond, Washington, USA), which were tethered to the fish next to the conventional tags, using either a plastic Domeier Dart or a stainless-steel metal blade anchor (Bradford et al., 2009; Table 4.1).
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Table 4.1. Metadata of each fish tagged during this study. Female (F), male (M), total length (TL), deployment and pop-up latitude (Lat.), longitude (Long.), total number of position estimated per individual (TNPE), resolution (Resol.), and percentage of position estimates inside the MMHMP (EGP) as well as the average overlap of the 50%, 95% and 99% likelihood areas with the MMHMP. We assumed ID 154066 died shortly after tagging and therefore it was not included in the analysis.
Deployment Pop-up Model % inside MMHMP Max linear distance (km) TNPE per Species Tag ID Anchor type Sex TL (cm) Duration Speed From Salas per between all Date Lat. Long. ind. Date Lat. Long. Resol. Score EGP 50% 95% 99% (days) (km/h) y Gómez day geolocations Seriola lalandi 154061 Plastic Dart - 124 04 Nov 15 -26,43 -105,38 148 296 31 Mar 16 -27,27 -105,53 600 3 87,48 91,3 84.1 73.4 70.2 62,2 6,2 111,3 Carcharhinus 154062 Plastic Dart F 160 02 Nov 15 -26,45 -105,35 35 69 07 Dec 15 -26,35 -105,22 600 3,5 79,26 72,2 68.6 63.6 62.6 31,3 29,6 47,6 galapagensis Carcharhinus 154064 Plastic Dart M 160 03 Nov 15 -26,38 -105,39 28 56 01 Dec 15 -26,46 -105,12 600 3 82,05 69 61.8 62.3 62.1 98,9 58 172,4 galapagensis Carcharhinus 154065 Plastic Dart F 170 05 Nov 15 -26,45 -105,39 18 37 23 Nov 15 -26,4 -105,4 600 3,5 79,07 42,1 54.6 52.9 53.2 35,4 17,5 32,5 galapagensis Carcharhinus 154066 Plastic Dart M 145 03 Nov 15 -26,62 -105,39 1 7 04 Nov 15 -26,62 -105,42 300 ------galapagensis Carcharhinus 173480 Steel blade F 152 21 Nov 17 -26,33 -105,39 95 190 24 Feb 18 -26,17 -105,43 600 3,5 73,49 79,2 79.0 79.9 78.6 138,6 45 235,8 galapagensis
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By default, MiniPATs record depth (1 – 1700 m, 0.5 m ± 1.0%), water temperature (−40 to +60°C;
0.05 ± 0.1°C), and light level (10-2- 10-10W/cm2 at 440 nm) time series data throughout their deployment period. The sampling interval thereby depends on the deployment duration, which in this case was every 15 sec for 365 days. If selected for transmission, only a subset of this time series data, as well as other user-specified (summary) data products, were transferred to the
ARGOS satellite system. Depth and temperature time series data at a temporal resolution of 10 min were transmitted to facilitate a fine-scale analysis of vertical behaviour (Bauer et al., 2017), and thus their potential vulnerability to commercial fisheries (e.g., longliners; Tolotti et al., 2017).
Long deployment durations can result in transmission gaps, especially in the transmitted depth time series data, due to the limited transmission capacity of the tag (Bauer, Forget, & Fromentin,
2015). Time-at-depth (TAD) and time-at-temperature (TAT) profiles (histogram data) represent a summary data product that can complement depth time series data analyses and can be transmitted in fewer messages. For this purpose, archived depth and temperature time series data from user-specified time intervals were aggregated by the tag into user specified bins. Here, a 24h TAD and TAT data with the following depth and temperature bins were selected: Depth bins: 0, 1, 5, 15, 30, 50, 70, 100, 200, 300, 500, >1000 m; Temperature bins: 0, 6, 9, 12, 15, 21,
24, 27, 30, >33°C. Other selected data products included daily Profiles of Depth at Temperature
(PDT), daily light curves, as well as minimum and maximum depth records. Given the long deployment duration programmed, it was decided that the summary and depth time series data should be transmitted on duty cycle (2 days of data to be transmitted, followed by 3 days of data gaps), in order to increase battery life during data transmission. In the event of premature release, transmission was programmed to initiate upon the tag being at the surface or remaining at depths of > 1800m (indicating death of the animal) for more than three days.
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4.3.3 Analysis of horizontal behaviour
Geolocation analysis was performed using the manufacturer’s proprietary Hidden Markov Model
(HMM, WC-GPE3, Wildlife Computers) to estimate positions. This approach uses a gridded HMM that computes posterior probability distributions to estimate the most likely state (positions, hereafter referred as geolocations) at each time point using light levels, sea-surface temperature
(SST), and bathymetric data. The model allows the user to define a typical travelling speed, deployment and pop-up locations of the tags. Travelling speeds are thereby used by the model to define the allowable distance moved per day, which restricts the daily diffusion kernels.
Different travelling speeds (1.9, 2.5, 3, and 3.5 km/h) were tested based on available literature values (Holland, Meyer, & Dagorn, 2009; Meyer et al., 2010; Palstra et al., 2015). First transmitted geolocations of each tag were thereby used as pop-up locations. The selection of the optimal speed and model was done based on the model score produced by the GPE3 software, with higher scores indicating better fits to the transmitted data. Transmitted data from all MiniPAT tags were de-coded with the manufacturer’s cloud-based portal software and analysed using the
Rchival Package (Bauer, 2020a) in the R Statistical Environment (v. 3.5; R Core Team, 2018).
Likelihood areas were generated from the netcdf files in the GPE3 model runs using the get_geopos function in the R-package RchivalTag (Bauer, 2020a). This function is based on a transformation suggested by Wildlife Computers (2015). In order to identify species-specific high- use areas within the study regions, kernel densities were calculated per species based on the daily geolocations of all related individuals, using the “kde2d”-function from the R-package
“MASS” with a search radius of 1 degree to account for the uncertainty in the geolocations
(Venables & Ripley, 2002; Teo et al., 2004).
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4.3.4 Analysis of vertical behaviour
Time-at-Depth and depth time series data, transmitted on duty cycle (two days of data to be transmitted, followed by three days of data gaps), facilitated a vertical behaviour analysis of the tagged fish. A preliminary analysis of the transmitted data sets revealed additional gaps in the
Time-at-Depth (and Time-at-Temperature) data of the amberjack (ID 154061) and one shark (ID
154065). By contrast, the depth time series data showed good data coverage with only some minor additional transmission gaps (Figure 2). Given this, as well as its higher informational value, further analysis focused on the depth time series data. To analyse the diel vertical behaviour of the tagged fish with respect to the time of sunrise and sunset, these moments were first estimated based on the “get_DayTimeLimits” and “classify_DayTime” functions in the R-package
RchivalTag (Bauer, 2020a), which uses daily geolocation estimates from the tags as input data.
To determine the general patterns of diel vertical behaviour per fish, their hourly changes in depth via boxplots were analysed. To assess regional differences in the dive patterns during daytime and night-time, daily geolocation estimates were mapped in relation to the maximum dive depths per day and night, the bathymetry of the study area, and the borders of the EEZ and MPA
(MMHMP), using the v-function in the R-Package “oceanmap” (Bauer, 2020b).
In order to identify species-specific dive behaviour patterns from the different Galapagos sharks, the average depth per hour of each deployment day and fish was first estimated, and then clustered the combined data matrix from all individuals using the k-means clustering method.
Cluster selection was done based on 30 different clustering indices that were provided by the
NbClust function of the same-named R-package (Charrad et al., 2014).
Finally, the distance of the clusters’ geolocations was estimated to the shore by applying the distHaversine function in the R-package “geosphere” (Hijmans, 2017).
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4.3.5. Commercial fishing pressure
Commercial fishing activities were calculated around the Easter Island Ecoregion (117°5´-
97°5´E, 32°5´-17°5´S) during the period 2012- 2016 using Automatic Identification System (AIS).
Daily data of vessel flagged nationality, type of fishing, and fishing hours were obtained from
Global Fishing Watch (www.globalfishingwatch.org). Finally, fishing hours were summed by 5° resolution according to flagged nationality and gear type.
4.4 RESULTS
Over both campaigns, 48 C. galapagensis and 18 S. lalandi (Table S4.1; Figure S4.1) were captured. Six MiniPATs were deployed on five C. galapagensis and one S. lalandi (Table 4.1;
Table S4.1). One of the five C. galapagensis (ID: 154066) died shortly after tagging and was therefore not included in the analysis. The five remaining MiniPAT had premature releases, due to unknown reasons, resulting in relatively short deployment periods of 18-148 days (Table 4.1).
None of the conventionally or archival-tagged fish have been recaptured to date; however, some sharks tagged in 2015 were observed swimming with the tags during a dive survey in 2017.
4.4.1 Horizontal behaviour
The GPE3 models yielded best fits for the 3 km/h (IDs 154061 and 154064) and 3.5 km/h (IDs
154062, 154065 and 173480) travelling speeds (Table 4.1). Thus, derived daily geolocations indicated that all fish left MMHMP at least once throughout the tag deployment period but remained within the EEZ (Figures 4.2, 4.3, S4.2-4.6), even taking in account different likelihood areas (Table 4.1, Figure S4.7-4.9) . For C. galapagensis (IDs 154062, 154064, 154065 and
173480), 42-79% of the estimated geolocation points were inside the MMHMP (Table 4.1), whilst
91% of the single S. lalandi track remained within the MMHMP. The range of all 99% likelihood area within the MMHMP, the most conservative estimate (large) of occurrence, was 53.2-78.6%,
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whereas the 95% likelihood was 52.9-79.9% and the 50% was 54.6-79% (Table 4.1). The maximum linear distance per geolocations for S. lalandi (ID 154061) was 6.2 km, and 17.5-58 km for C. galapagensis (Table 4.1; Figure 4.4).
Figure 4.2. Temporal coverage of available (green) Depth, TS data per deployed tag. Data gaps due to transmission failure or duty cycle are shown in red. Blue bars indicate the periods spent inside the Motu Motiro Hiva Marine Park.
The maximum linear distance from Salas y Gómez Island was 62 km for S. lalandi and 31-139 km for the Galapagos sharks. The maximum linear distance between distant points was 111 km for the S. lalandi and 235.8 km for C. galapagensis (Table 4.1; Figure 4.4). The kernel density analysis indicated a common high-use area for both species, which surrounded Salas y Gómez
Island but exceeded the western limits of the MMHMP (Figure 4.3).
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Figure 4.3. Geolocations of each individual combined (upper panel) and kernel densities (lower panel) indicating areas of high use for the four Galapagos sharks (left) and one amberjack (right). The borders of the EEZ and MMHMP are indicated in white and violet, respectively.
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Figure 4.4. Distance between subsequent geolocations (left) as well as their distance from Salas y Gómez (right) per species (amberjack indicated in grey).
4.4.2 Vertical behaviour
All archival-tagged fishes spent most of their time at depths <50 m (average depth <60 m) and never left the epipelagic zone (0-200 m; Table S4.2; Figure S4.10). Even though all fishes reached depths of ~100 m, only one C. galapagensis (ID 154064) descended to a depth of 195.5 m. The remaining fishes stayed within 0-131.5 m throughout the deployment period of their tags (18-148 days). The one Seriola lalandi (ID 154061) maximum depth was 100.5 m during night-time (Table
S4.2).
Day vs. night-time differences were pronounced in all individuals but showed high inter-individual variability (Figures 4.5 and S4.10). Clear diel vertical behaviour patterns were evident for S. lalandi (ID 154061) and two C. galapagensis (IDs 154062 and 173480), with the amberjack and one shark (ID 154062) staying in deeper waters during the night compared with the daytime. The vertical data from shark 173480 showed an opposite pattern. Strong fluctuations were observed in the diel vertical behaviour of sharks 154064 and 154065, leading to the assumption of changes in the diel vertical behaviour. The maximum dive depths per day and night, as a proxy for such
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changes and the species vulnerability to longline gear, showed no relation to local bathymetry or diel vertical patterns (Figures S4.2-4.6).
Cluster analysis of the hourly depth averages of the different C. galapagensis revealed two distinct vertical behaviour patterns (Figure 4.6; Table S4.3). Cluster 1 indicated a continuous descent of sharks from twilight until the end of the subsequent day. This cluster was infrequent, accounting for 16.5% of the data, but was limited to the vicinity of Salas y Gómez Island (Figures 4.6 and
S4.11). Cluster 2 showed periodical descents to deeper depths both during the day and night- time, with an occupation of shallower water during the twilight periods. This behaviour pattern occurred at higher frequencies (83.5%) both in coastal and offshore areas.
Figure 4.5. Diel vertical movement patterns of the five fish tagged with MiniPat tags. Average night and twilight periods are indicated in dark and light grey, respectively.
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Figure 4.6. Behaviour patterns (upper panel) for the vertical behaviour clusters and their spatial distribution (lower panel) in relation to the bathymetry of the study area. The EEZ and MMHMP borders are shown by white and violet lines, respectively. Clusters are based on the hourly depth averages of all available deployment days from the Galapagos sharks.
4.4.3 Fishing activities within the Easter Island Ecoregion
Analysis of fishing effort from AIS data showed a total of 194,541 detections in the area, of which
118,086 (60.7%) corresponded to active fishing activities. Virtually no fishing was detected within the EEZ around Rapa Nui and Salas y Gómez Island (only 17 detection; 0.014%) (Figure 4.7).
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However, high fishing pressure occurred in the surrounding waters outside of the EEZ, which consisted of longliners from China, Vanuatu, and Spain (60.3%, 28.3%, and 11.2%, respectively).
China and Spain fishing effort were mainly recorded in the northern and southern limits of the
EEZ, meanwhile Vanuatu fishing effort was concentrated towards the south-western limit of the
MPA.
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Figure 4.7. Commercial fishing effort by all as well as by each of the three major fishing nations (representing together 99.9% of all fishing detections in the area) between 2012-01-03 and 2016-12-31 in the study region as well as the borders of the EEZ (orange) and the MMHMP (violet). Fishing locations are based on daily AIS derived vessel positions with fishing hours > 0 at a 10th degree resolution that were gridded at a 5th degree resolution to illustrate effort densities (https://globalfishingwatch.org).
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4.5 DISCUSSION
The current study is the first archival tagging study on C. galapagensis and S. lalandi in the Easter
Island Ecoregion. The insights gained on the habitat use and migratory behaviour of these top predators will help to address the potential vulnerability of these species in the MMHMP.
4.5.1 Horizontal and vertical migratory behaviour
The geolocation estimates from the archival tagging data indicated that C. galapagensis and S. lalandi remained largely in the waters around Salas y Gómez Island, even considering the uncertainty in the estimated positions. This is consistent with earlier findings from the Hawaiian
Archipelago where top predators, such as jacks and sharks, were site-attached to islands or atolls
(Dale, Meyer, & Clark, 2011). However, all fish left the MMHMP at least once over the tracking periods, travelling into unprotected waters. Although such excursions were not frequent and many fish returned later to the island, we cannot exclude the possibility that this “homing behaviour” is weaker during other seasons due to the limited time period of the current study (November-
February). In fact, one C. galapagensis (ID: 173480) travelled up to 139 km away from Salas y
Gómez Island, and 235.8 km between distant geolocations points. While sharks in this study were only juveniles (< 200 cm TL; Wetherbee et al., 1996), such a behaviour has previously been observed to be common in adults that are known to migrate into deeper oceanic waters
(Compagno, 1984; Wetherbee et al., 1996; Kohler et al., 1998; Lizardi et al. 2020). In this context, the seamount chain along the Salas y Gómez Ridge may serve as stepping-stones for these mobile predators as well as for other species (Friedlander et al., 2013). Significant genetic differences have been detected for Galapagos sharks at both small and large scales (Pazmiño et al., 2017; Pazmiño et al., 2018). Rapa Nui and Salas y Gómez Island are the southernmost remote coral reef in the southern hemisphere, so further studies using genetic tools should be employed
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to address the potential connectivity between Rapa Nui, Salas y Gómez Island and other islands in the Pacific Ocean where these species are distributed.
Vertical behaviour data revealed that C. galapagensis and S. lalandi remained within the epipelagic layer (0-200 m) but showed distinct diel vertical migration patterns during day and night-time. These patterns were particularly consistent in the case of the yellowtail amberjack.
The one S. lalandi tagged during this study remained close to the surface during the daytime but descended to deeper waters during night. Carcharhinus galapagensis showed a similar behaviour as S. lalandi during night-time, but then either returned to the surface waters and then descended during daytime or descended immediately at dawn to even deeper waters until twilight.
It is interesting to note that similar behaviour patterns have been found for other epipelagic species such as oceanic whitetip sharks (Carcharhinus longimanus; Tolotti et al., 2017). This similarity might be related to comparable feeding habits of pelagic species that are foraging on epi- and mesopelagic prey. Although the type of habitat use remains unclear due the low sample size and short duration of the study, these findings contribute to the development of management strategies to minimize the fishing mortality of these species (e.g., by altering the timing and depth of longline sets). For instance, hook depth of commercial tuna fishing fleets can be adjusted to reduce the bycatch of sharks and other predators critical to local ecosystems (Beverly et al., 2008;
Zhu et al., 2012). Based on the vertical movements of C. galapagensis, longlines should be set at depth deeper than 200 m to reduce incidental mortality outside the MMHMP and other oceanic areas.
4.5.2 Fishing activities around Rapa Nui and Salas y Gómez
These results support previous findings that seamounts and oceanic islands attract and aggregate highly migratory pelagic species (Holland et al., 1999; Worm, Lotze, & Myers, 2003; Garrigue et al., 2015; Morato et al., 2010). Therefore, these areas are frequently exploited by commercial
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fishing fleets (Pitcher et al., 2008; Morato et al., 2010). Analysis of fishing effort from AIS data showed that fishing activities (longliners) concentrates in the surrounding waters outside of
Chile´s EEZ. However, it is important to note that AIS does not include data from the “dark fleet”, small artisanal vessels, and other vessels that are not equipped with AIS technology (FAO, 2016;
Kroodsma et al., 2018). In addition, vessels may intentionally turn off their transponders, falsifying position data or transmitting inadequate identification data in order to escape detection (McCauley et al., 2016). While there are no means to verify the correct functioning of the vessel transponders that fish around Chile’s EEZ at Rapa Nui and Salas y Gómez Island, from our AIS analysis it is evident that they precisely identify the EEZ borders and with only a few exceptions do not enter it. However, fishing effort is concentrated along the EEZ border, and among the highest fishing hours registered are located south-east and south-west of MMHMP, with some Chinese vessels apparently entering the EEZ to a limited extent (Figure 4.7). China and Spain are the most prominent fishing nations in this area. These two countries have been recently characterized as poor performing countries based on traceable fishing scores (Macfadyen et al., 2019). These poor performances together with the limited surveillance in the area, and anecdotal observations by locals regarding industrial vessels nearby the islands, make it questionable to what extent the AIS data reflects the true fishing positions of these industrial fleets. The Easter Island Ecoregion also has been described as an area where trans-shipment activities may be occurring (Boerder, Miller,
& Worm, 2018; Miller et al., 2018). Trans-shipment vessels are known to be associated with longliners fishing on highly migratory species, such as, tunas, billfishes, and sharks in the high seas (Miller et al., 2018). These practices increase the efficiency of fishing, especially on the high seas; however, they can also contribute to illegal fishing and other criminal activities (Miller et al.,
2018; Rezac, 2018).
Illegal, unreported, and unregulated (IUU) fishing (which includes catches taken within an EEZ and unreported catches) have a strong effect on stocks and their dependent ecosystem (Agnew
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et al., 2010). Illegal fishing, vessels in violation of national laws or international obligations (FAO,
2001), usually occur along the borders of established MPAs (known as the “border effect”; Gribble
& Robertson, 1998) or at locations with weak fisheries management and control, where a small number of patrol vessels are available (Agnew et al., 2010; Petrossian, 2015). In this regard, the rapanui people regularly mention seeing the lights of industrial vessels at night close to Rapa Nui and report longline fishing gear frequently washing up on the shores of Rapa Nui (Yáñez et al.,
2007; Hernández Mares, 2016; Thiel et al., 2018). A few incidents of illegal fishing within the
MMHMP have been documented (Muñoz, 2011; Friedlander et al., 2013). While there is a limited capacity for naval patrol vessels and planes to enforce the MMHMP, it is clear that the protection of this important refuge for oceanic species requires an effective ecosystem-based management plan that implements strict controls on IUU fishing (Agnew et al., 2010). Countries with less effective fisheries management measures and those with poor patrol capacity are more likely to suffer from IUU fishing (Petrossian, 2015). Even though IUU fishing is a complex matter because it involves international waters, several international measures have been adopted, such as The
International Plan of Action on Illegal, Unreported and Unregulated Fishing (Edeson, 2001), the
Agreement on Port State Measures (Flothmann et al., 2010), and The Project Catch (Detsis et al., 2012), among others (see Lindley & Techera, 2017). Nevertheless, the distance of Salas y
Gómez Island from mainland Chile (~3600 km), and the virtual absence of Chilean industrial fishing vessels inside the EEZ of Rapa Nui, limits the amount of IUU fishing from smaller continental fishing boats. For example, the Juan Fernandez MPA is located only ~700 km from mainland Chile and has significant legal and illegal fishing pressure inside the EEZ and around the MPAs borders, much of it coming from Chilean vessels (Friedlander et al., 2017). Therefore, while isolated areas seem more protected from IUU fishing because of their remoteness, they also create the opportunity for larger fishing fleets to go undetected when entering MPAs, because of the absence or limited surveillance and enforcement at these distant areas. This paradox has recently been debated, pointing out the political advantages of closing isolated marine areas, 68
where little participation of local communities is anticipated and whose commercial interest is outnumbered by foreign fishing vessels (Devillers et al., 2015; Jones & De Santo, 2016). Despite being less susceptible to IUU fisheries than nearshore MPAs, isolated areas also account for much higher biomass of sharks and jacks (Edgar et al., 2014), highlighting the need for accurate ecological evaluations of the effectiveness of these remote MPAs.
4.5.3 Future Perspectives of the MMHMP
The implementation of management actions, such as MPAs, that do not reflect ecological patterns and processes are destined to fail (Crowder & Norse, 2008). To achieve a realistic approach, scientists and resource managers need to have a broad understanding of the movement dynamics and habitat requirements when planning the spatial protection of a given species (Dale et al., 2010, Carlisle et al. 2018). This study provides the first insights into the habitat use and migratory behaviour of C. galapagensis and S. lalandi in one of the most isolated and understudied areas of the Southeast Pacific Ocean. However, the small sample size, the inclusion of only juvenile sharks, and the short duration of this study limits the ability to make broader inferences about the application of this work. These limitations are common in electronic tagging studies due to premature release and transmission failures (Williams, Nicol & Leroy, 2010;
Domingo et al., 2018; Hagihara et al. 2018). The high cost of satellite archival tags, which is reflected in small sample sizes, and the logistical challenges associated with expeditions to remote locations further constrain the number of animals that can be tagged and tracked (Carlisle et al. 2018). Hence, future studies should attempt to obtain larger sample sizes, which include individuals of different sizes and sexes to define the complete extent of species home ranges.
Additionally, sampling through the year should also be performed to avoid seasonal bias. For instance, due to the premature release of the tags it is not possible to assure that the patterns found here (horizontal and vertical movements) are repeated throughout the year or if they only occur during the austral summer, as observed here. This is an important issue in determining their
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entire home ranges since sharks shift their movement patterns seasonally in response to current exposure, storms, and temperatures throughout the year (Lizardi et al. 2020), or for biological reasons such as feeding and reproduction (e.g., Bessudo et al. 2011; Acuna et al. 2017). In this sense, the intraspecific variability found in habitat use also requires larger sample sizes (Carlisle et al., 2018).
Despite the limitations described above, the information generated by this study is essential for the management of these species. Movement dynamics should be considered not only for MPA implementation, but also for planning and designation of other MPAs, in order to define proper limits and buffer zones to ensure effective protection of large mobile predators. The results presented here show that top predators at Salas y Gómez have a strong fidelity to the island and the surrounding seamounts. However, the home range of the juveniles C. galapagensis tagged during this study, was larger than previously reported (up to 235.8 km vs. < 50 km, Wetherbee et al., 1996), and extended beyond the boundaries of the MMHMP. The only S. lalandi tagged here also extended beyond these borders over the tracking period. Based on these findings, the mayor deficiency of the existing MMHMP is not the size of the protected area (150.000 km2), but the location of the western border due to its close proximity to Salas y Gómez Island. This island has been described as the biological hotspot in the area and the core of the MMHMP (Friedlander et al. 2013). MPAs boundaries should be designed to reduce exposure of conservation targeted species (edge porosity; Roberts, 2000). By knowing the movement patterns of these species, the limits can easily be adapted (Kramer & Chapman, 1999).
Originally, the MMHMP borders were established to avoid conflict with the Rapa Nui EEZ boundaries (SUBPESCA, 2010), and therefore with the local artisanal fishing activities coming from Rapa Nui. In 2012, an initiative to expand the MMHMP from 150,000 km2 to 411,000 km2 to the west was intended to provide better protection for Salas y Gómez Island and the surrounding seamounts. The enlarged no-take zone would have encompassed most of the EEZ 70
around Salas y Gómez Island and part of the EEZ of Rapa Nui. However, this initiative never happened due to negative reactions from the local fishing community (Gaymer et al., 2013).
Finally, in 2017 an unprecedented participatory initiative conducted by the Chilean Government and the Rapa Nui Development Commission, promoted the creation of a Multiple Uses Coastal
Marine Protected Area (MUMPA) around the entire EEZ of the Easter Island Ecoregion, expanding the protection initially provided by the MMHMP. However, this new MPA did not change the existing boundaries of the MMHMP; and while the MUMPA regulations permit artisanal fishing, it strictly prohibits industrial fishing activities from both foreign and Chilean vessels within its boundaries. It is hoped that the zoning process for the MUMPA that is currently underway will adopt a precautionary approach, which accounts for the movement patterns of the top predators identified here. A no-take zone west of the MMHMP is a possible way forward in this regard.
The Salas y Gómez Ridge connects the fully protected MMHMP with unprotected Rapa Nui. At
Rapa Nui, individuals of C. galapagensis are caught periodically by fishermen who still use coastal nets during night-time. Sharks are caught as bycatch and usually used as baits for economically important species such as the endemic lobster Panulirus pascuensis. However, the last large shark catch event that we know of occurred in early 2020, where more than 30 juvenile sharks were caught and killed because, according to the fishermen, they represented a risk to tourists.
Even though the sharks tagged during this study did not leave the Salas y Gómez area, we believe adults are capable of reaching Rapa Nui and near seamounts, probably using the ridge as stepping-stones (Friedlander et al. 2013). Therefore, it is imperative that fishing regulations are implemented at Rapa Nui to ensure the survival of this key species within the entire area.
Moreover, Lizardi et al. (2020) suggested the need for cooperation between countries due to the occurrence of movement corridors (swimways) that connect the different populations of C. galapagensis. Future studies should therefore focus on the current swimways that connect this
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southeast hotspot with other islands (countries) in the Pacific Ocean, especially in light of the recent arrival of new pelagic species to the area such as Triaenodon obesus (Morales et al.
2019b), Seriola rivoliana and Rhincodon typus (N. Morales unpublished data).
Despite their isolation, remote islands such as Salas y Gómez are becoming increasingly exposed to a variety of anthropogenic stressors, such as overfishing and IUU fishing. This remoteness also poses governance and enforcement challenges, especially for countries with limited resources
(Game et al., 2009). Despite these challenges, the MMHMP remains a healthy ecosystem with an abundance of top predators that deserves maximum protection so as to preserve what is one of the last relatively pristine areas in the world.
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CHAPTER 5. GENETIC CONNECTIVITY OF THE GALAPAGOS SHARK,
CARCHARHINUS GALAPAGENSIS, IN THE EASTER ISLAND ECOREGION.
5.1 ABSTRACT
The Galapagos shark Carcharhinus galapagensis is the only resident species of shark inhabiting
Rapa Nui and Salas y Gómez Island, and therefore should be a priority for conservation. Recent studies have suggested a genetic relationship between both islands. Determining the degree of genetic connectivity between locations is important for developing effective conservation and management strategies that aims to ensure gene flow, and thus prevent local extinctions. We determined the degree of population connectivity of this species between Rapa Nui and Salas y
Gómez islands using variability of both genome-wide neutral Single Nucleotide Polymorphism
(13496 neutral SNP), and a section of the mitochondrial DNA (636 pb). The results showed no evidence for genetic structure, thus suggesting only one genetic population occurring within the
Easter Island Ecoregion. Our findings were consistent with previous tagging data in the area where individuals seemed capable of migrating between both islands. The results of the mtDNA showed also a low genetic diversity in the Easter Island Ecoregion population when compared to others in the Indo-Pacific Ocean, which may be a result of only a few colonization events due to the isolation of this area. Ongoing studies in a global context will identify structure and global patterns of colonization in the central-south Pacific. Our results also highlight the importance of the Motu Motiro Hiva Marine Park (MMHMP) within the ecosystem, thus, future studies should also be conducted on more species to determine the genetic relationship between these two very isolated islands.
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5.2 Introduction
Oceanic islands and seamounts are usually used as resting and/or feeding areas for mobile species (Rogers 1994; Holland et al., 1999; Worm et al., 2003; Morato et al., 2010; Garrigue et al., 2015), and thus facilitating the dispersion of organisms and connecting communities between distant areas (Wilson & Kaufman 1987; Friedlander et al., 2013).
The Salas y Gómez Ridge extends 2232 km eastwards to the Nazca Seamount, where it merges with the Nazca Ridge (Gálvez-Larach, 2009). Rapa Nui (Easter Island) and Salas y Gómez Island are the only two places where this seamount chain rises above sea level (Ray et al., 2012). These two islands are connected by several dozen seamounts, which can act as stepping-stones
(Newman & Foster 1983; Friedlander et al., 2013). Rapa Nui is one of the most isolated islands in the Pacific Ocean (Fig. 5.1). Its historical overfishing, together with geographic characteristics such as limited habitats, small size, and sub-tropical location has resulted in a low number of shore species compared to other islands in the Pacific Ocean (Randall & Cea, 2011). In contrast,
Salas y Gómez Island is a small rocky island (0.15 km2) located 400 km east of Rapa Nui. The waters of Salas y Gómez Island together with dozens of seamounts are currently part of the Motu
Motiro Hiva Marine Park (MMHMP), a 150,000 km2 no-take area that is characterized by a healthy ecosystem dominated by top predator species (Friedlander et al., 2013).
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Figure 5.1. Location of Rapa Nui and Salas y Gómez Island in relation to other islands in the Pacific Ocean. The pink polygon represents the Motu Motiro Hiva Marine Park (MMHMP) borders. Black circles represent the EEZ.
The Galapagos shark, Carcharhinus galapagensis, is one of the most common top predatory species and the only resident shark species in both Rapa Nui and Salas y Gómez Island (Morales et al., 2019a). It has a circumglobal but patchy distribution associated with warm and temperate water and oceanic islands (Duffy, 2016; Kohler et al., 1998; Meyer et al., 2010; Wetherbee et al.,
1996). This species was recently assessed as a Least Concern species on the IUCN Red List, since its population is suspected to be stable in large part of its distribution in the Pacific Ocean.
However, the habitat specificity of this species and their limited biological productivity make it susceptible to local population reduction, especially at places where it has been under fishing pressure (Kyne et al., 2020)
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Studies on the genetic structure along its distribution suggest that this widely distributed species shows at least two genetically discrete geographic groups (subpopulations): the east central pacific (Mexico, and the east and west Galapagos Islands), and the west central pacific (Lord
Howe Island, Middleton Reef, Norfolk Island, Elizabeth Reef, Kermadec, Hawaii, and Southern
Africa) (Pazmiño et al., 2018). The study did not include samples from what is thought to be a third subpopulation from the Atlantic Ocean (Kyne et al. 2020), and neither from the South-eastern
Pacific islands (Pazmiño et al., 2018). Using Baited Remote Underwater Videos System
(BRUVS), Morales et al. (2019a) identified a resident population inhabiting the Easter Island
Ecoregion, which is likely to be at risk due to direct and indirect fishing pressure (Friedlander et al., 2013; Zylich et al., 2014). According to the rapanui, sharks used to be abundant in the past, while they are hardly seen today. In comparison, at Salas y Gómez Island, C. galapagensis accounts for 26% of the total biomass (Friedlander et al., 2013). Recent studies at Salas y Gómez
Island showed that even though this species is usually classified as reef associated (Compagno
1984; Kohler et al., 1988), juveniles are capable of traveling longer distances (up to 236 km,
Morales et al., in press; Chapter 4). This new record together with the recent finding of Lizardi et al. (2020) that described a 3,000 km migration through the eastern tropical Pacific raises the question about the genetic connectivity between Rapa Nui and Salas y Gómez Island.
Connectivity between these two islands has been poorly studied. Using genetic analysis and oceanographic modelling, Meerhoff et al. (2018) and Valencia et al. (in press) found a lack of genetic population structure but low connectivity in two benthic species, the endemic rapanui lobster Panulirus pascuensis and the rudderfish Kyphosus sandwicensis, respectively. The low levels of genetic connectivity were attributed to the low dispersal capacity of both adults and larvae. However, further studies using more species are needed in order to reveal the connection between these two isolated islands.
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Therefore, the aim of this study was to determine the degree of population connectivity of C. galapagensis between Rapa Nui and Salas y Gómez Island using two different genetic markers, mitochondrial DNA (mtDNA) and Single Nucleotide Polymorphism (SNPs). Mitochondrial genomes are based on maternity inheritance, which tells us about the historic population structure of the species (Heuter et al., 2005). SNPs, on the other hand, provides both historic and contemporary evidence including bi-parental inheritance (Allendorf et al., 2010). Determining the degree of population connectivity among geographic areas, with the estimated location of genetic breaks, allows determining an appropriate scale at which conservation and management strategies should be applied to continue demographic exchange and prevent local extinctions
(Crowder & Norse 2008; Toonen et al., 2011). Additionally, estimating connectivity at these spatial scales will allow us to determine the relevance of current marine protected areas like MMHMP, and to recommend conservation strategies to manage important components of the marine ecosystem in a sustainable way.
5.3 METHODS
5.3.1 Sample collection
Samples from Galapagos sharks were collected between 2015 and 2017 at Rapa Nui (muscle) and Salas y Gómez (fin clip). Samples from Salas y Gómez were collected during two research cruises in November 2015 (CIMAR 21) and 2017 (ESMOI-Save Our Seas Foundation). Samples were labelled and stored in 95% ethanol.
5.3.2 DNA extraction and sequencing for SNP
Samples from Rapa Nui (n=24) and Salas y Gómez (n=29) were sent to the Diversity Arrays
Technology Pty Ltd (DArT, Canberra-Australia) (https://www.diversityarrays.com/) for DNA extraction and sequencing using protocols available in Ren et al., (2015) and Marie et al., (2019). 77
5.3.3 SNPs filtering and Outlier detection
The genotype dataset was filtered to retain only the highly informative SNPs. We filtered data according to the following criteria: Minor Allele Frequencies (MAF) > 1%, Hardy–Weinberg
Equilibrium (HWE), Linkage disequilibrium (LD), discarding monomorphic markers, and a call rate threshold for both loci and individual of 90% using the dartR package in R software v0.98.977 (R
Core Team 2019; Jombart & Collins 2015).
To identify putative loci under selection and removal for demographic connectivity analyses, two genome scan analysis based on the distribution of FST were run: OUTFLANK (Whitlock &
Lotterhos, 2015) and BayeScan v.2.1 (Foll & Gaggiotti, 2008) software using a false discovery rate (FDR) of 0.05.
5.3.4 Genetic structure analysis
Pairwise FST was calculated using the package adegenet implemented in R software v0.98.977
(Jombart & Collins 2015) on neutral SNP loci to determine the level of gene flow and subdivision between populations (Wright, 1965; Schneider et al., 2000). A Discriminant Analysis of Principal
Component (DAPC) was used to estimate the number of cluaters k with high probability in the data pool. The function find.cluster on the adegent library was used to estimate the probable number of clusters (k) within the samples. The calculation of k optimizes the variability between groups and minimizes the variability within groups, thus exacerbating the occurrence of different groups. The calculated different clustering solutions were then compared using Bayesian
Information Criterion (BIC) and the best solutions correspond to the lowest BICs. All analyses were run using the adegenet package.
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5.3.5 Mitocondrial (mtDNA) extraction, amplification, sequencing, and alignment
Samples from Rapa Nui (n=28) and Salas y Gómez (n=46) were analysed. DNA was extracted with the Wizard Genomics DNA Purification Kit (PROMEGA®, Promega Inc., Madison, WI). The control region (CR) was amplified through polymerase chain reaction (PCR), using species- specific primers designed for Carcharhinidae using Primer3 software (Rozen & Skalersky, 2000) based on the reference sequences of Carcharhinus leucas (NC_023522; Chen et al.,
2015) and Galeocerdo cuvier (KX858828; Bustamante et al, unpublished data). The CR for Carcharhinus galapagensis was amplified with the primers: 41F 5’-ATT CTG CCT AAA CTG
CCC CC-3’ and 1190R 5’-AGC ATC TTC AGT GCC ATG CT-3’. An initial denaturing step was carried out at 94°C for 2 minutes, followed by 30 cycles with an optimized profile of 30 seconds at 94°C, 30 seconds at 59°C, and 1 minute at 72°C, followed by a final extension step of 2 minutes at 72°C. The PCR products were sent to the Molecular Cloning Laboratory (MCLAB) in the USA for Sanger 3730 XL sequencing with both the forward and reverse primers. The sequences were edited and aligned using the GENEIOUS v10.0.8 software. The BLASTn tool was used in
GenBank to confirm the correct genetic identification of each species.
5.3.6 Haplotype & nucleotide diversities
Haplotype (h) and nucleotide (π) diversities were estimated as an indicator of genetic diversity of the mitochondrial DNA. Both indices (h and π) and pairwise FST were calculated in Arlequin v3.5
(Excoffier et al., 2010). h and π where calculated following Nei (1989) and Nei & Li (1979) equations, respectively.
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5.4 RESULTS
5.4.1 SNPs analysis
A total of 32,660 SNPs were genotyped from the 53 individuals. After filtering, a total of 13,496
SNPs and 52 individuals were retained for analyses. Candidate loci under selection were not detected with the software used and none of the loci showed significant departures from HWE.
Population genetic structure assessed by the FST index showed statistical significances (FST =
0.006; p < 0.0001). The DAPC analysis showed k= 1 as with the lowest BIC value, suggesting the occurrence of only one population (Figure S5).
5.4.2 Mitochondrial DNA analysis
Mitochondrial control region sequences (636 pb) were analysed and four mtDNA haplotypes were found in total. Haplotype 1 (H1) was the dominant in both locations and occurred in 64 individuals.
H3 was also shared between locations but it only occurred in 7 individuals. H2 and H4 were unique to Salas y Gómez Island and Rapa Nui, respectively (Table 5.1; Fig. 5.2). Pairwise FST values showed no evidence of population differences (FST = 0.00909; p-value = 0.29) suggesting the presence of one population inhabiting both islands. Haplotype (h) and nucleotide (π) diversities values are showed in Table 5.1.
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Figure 5.2. Haplotype network based on the informative mtDNA control region. Each circle represents a different haplotype. Haplotype frequencies are relative to the size of the circles. The number of branches reflects the mutations between haplotypes. Red correspond to Rapa Nui and blue to Salas y Gómez.
Table 5.1. Genetic diversity parameters determined by mtDNA Control Region for Rapa Nui and Salas y Gómez Island.
Rapa Nui Salas y Gómez
Sample size (n) 24 29
Number of Haplotypes (H) 3 3
Polymorphic sites 4 3
Nucleotide diversity (π) 0.468 0.295
Haplotypes diversity (h) 0.315 0.204
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5.5 DISCUSSION
The current study is the first use of genome-wide technology to investigate the genetic connectivity of C. galapagensis within the Easter Island Ecoregion. The results suggest a high genetic flow between islands, resulting in low genetic differentiation. These findings are also consistent with the migratory capacity of C. galapagensis in the area (Morales et al., in press;
Chapter 4). The occurrence of only one population within the ecoregion has conservation and management significance. Additionally, the absence of outlier loci in our samples could be the result of the homogeneous conditions within the ecoregion, since outliers usually reflect local adaptation (Candy et al., 2015).
5.5.1 Connectivity within the Easter Island Ecoregion
The main results obtained from SNPs suggest that individuals sampled at both islands belong to the same population. This result is surprising and unexpected considering previous studies on this species at smaller scales. Using SNPs, Pazmiño et al. (2017) found clear population structure for Galapagos sharks within the Galapagos Archipelago. They suggested that the genetic structure found there was the result of oceanic currents that created five bioregions within the archipelago. Glynn et al. (2007) suggested that lower levels of differentiation between distant locations was attributed to large dispersal capacities together with a lack of physical barriers.
Unlike the Galapagos Archipelago, Rapa Nui and Salas y Gómez Island share similar oceanographic conditions, and therefore are part of the same ecoregion (Friedlander et al., 2013;
Andrade et al., 2014). With no mayor oceanic currents or any other oceanographic condition that could divide the Easter Island Ecoregion, the only evident biogeographical barrier between both islands is distance (~400 km). However, geographical distance by its own is a poor predictor of
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genetic breaks (Tonnen et al. 2011). Moreover, the seamount chain between Rapa Nui and Salas y Gómez Island, as part of the Salas y Gómez Ridge, might be efficiently acting as a corridor for mobile species as previously suggested by Newman & Foster (1983) and Friedlander et al.,
(2013). Stepping-stone dispersal on this species has been suggested by Green et al. (2014).
Based on mtDNA, the authors analysed four locations within the south-west Pacific and found an increase in the genetic differentiation over geographic distance.
The lack of genetic structure using mtDNA was also found for C. galapagensis in the southwest
Pacific (Green et al. 2014) and the Galapagos Archipelago (Pazmiño et al. 2017). These results were attributed mainly to a low mutation rate in elasmobranch mtDNA (Martin et al. 1992), together with a short distance between sampling sites (250 km max.) relative to the dispersion capacity of this species (Kohler 1998). Usually, female Galapagos sharks display higher levels of site-fidelity to shallower water, while males are less resident to reef systems (Kohler et al 1998;
Meyer et al 2010). However, in a recent study (Chapter 4) a juvenile female C. galapagensis was documented travelling a lineal distance of 236 km, which greatly exceeded the previous estimate of the movement for juveniles of this species (~50 km; Kohler et al 1998). In this sense, the low genetic differentiation found here using both SNPs and mtDNA analysis might suggest a sufficient rate of gene flow coming from both sexes, supporting the idea of a female exchange between sites. Also, the low FST found in the mtDNA analyses, a valid indicator for female philopatry when high (Heuter et al., 2005), supports the lack of genetic structure.
Genetic and spatial approaches are complementary in the study of population connectivity of mobile species (Boulet et al., 2007). Genetic connectivity is defined as the degree to which gene flow affect evolutionary processes within population (Lowe & Allendorf, 2010), and where only a few migrants per generation are required to maintain apparent panmixia (Planes & Fauvelot,
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2002). Demographic connectivity, on the other hand, is when intrinsic parameter of a single population, such as population growth rates, are affected by migration (Lowe & Allendorf, 2010).
However, directionally biased movement can produce asymmetrical rates of interpopulation dispersal (source vs. sink). According to Kawecki & Holt (2002), “sources” are those locations that generate migrants, and “sinks” are the recipients of those migrants. The demographic contribution of immigrates will depend among others on local density and competition for recruitment. When these components are high in a population, we could expect a low contribution from immigrants, and vice versa (Lowe & Allendorf, 2010). Salas y Gómez Island is a healthy ecosystem were the
Galapagos sharks and other top predator species are abundant. On the contrary, Rapa Nui has experienced historical overfishing that has depleted top predator populations, including the
Galapagos shark (Friedlander et al., 2013). Due to the higher population density of Galapagos sharks at Salas y Gómez Island, we could expect a higher dispersal rate going from there to Rapa
Nui. Meerhoff et al. (2018) and Valencia et al. (in review) identified a higher genetic flow rate for the endemic rapanui lobster Panulirus pascuensis and the rudderfish Kyphosus sandwicensis, from Salas y Gómez Island to Rapa Nui, caused mainly by currents direction. These findings corresponded to the first real evidence of the MMHMP seeding Rapa Nui’s unprotected waters.
A similar patterns of gene flow from east to west was also described in the Hawaiian Archipelago
(Rivera et al. 2011). However, a broader and longer study is needed to address connectivity rates properly. For instance, using satellite and telemetry tags placed at Rapa Nui and Salas y Gómez
Island we could determine if immigrants are moving between islands and define the direction of its movement. Using a similar approach Lizardi et al. (2020) studied the dispersal of the
Galapagos shark between the oceanic islands in the central east Pacific. Additionally, studies that include other species, should be conducted to determinate the genetic relationship of species
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inhabiting the Easter Island Ecoregion and the importance of the MMHMP in the health of the entire ecosystem.
5.5.2. Genetic diversity within the Easter Island Ecoregion
In terms of genetic diversity parameters, Galapagos sharks display high overall genetic diversity
(Pazmiño et al., 2018). Rapa Nui and Salas y Gómez Island have a genetic diversity similar to west Galapagos and Mexico in terms of number of haplotypes. However, the haplotype (h) and nucleotide (π) diversity are much lower than the ones recorded along its distribution in the Indo-
Pacific Ocean (see Pazmiño et al., 2018). The low genetic diversity found in this study might be the result of only a few colonization events due to the isolation of this region (Fig. 5.1). The opposite scenario occurred in the Hawaiian Archipelago where several probable events have occurred from neighbouring locations, providing the Hawaiian population a higher genetic diversity (Pazmiño et al., 2018). Therefore, future studies should examine the relationship of the single population inhabiting the Easter Island Ecoregion with other locations in the Pacific Ocean where Galapagos sharks occur. Such studies could also shed lights into the more probable route from where Galapagos sharks and other mobile species colonized this area. For example, using neutral and outlier SNPs, Pazmiño et al. (2018) suggested that the central-west Pacific population of the Galapagos shark was colonized westward via Mexico. This route (from the Eastern Pacific) is feasible due to the relatively shallow seamounts along the Nazca and Salas y Gómez Ridges
(Glynn et al., 2007). Oceanic islands and seamounts are important in connecting distant locations for the Galapagos shark (Pazmiño et al., 2018). Additionally, the low genetic diversity found here, when compared to other locations (see Pazmiño et al., 2018), implies that the Rapa Nui population is younger than the others. Grant & Bowen (1998) suggest that populations with low h (< 0.5) and low π (<0.5) might correspond to recent founder events by a single or a few mtDNA lineages.
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Finally, genetic diversity can also be considered an indicator of population resilience. Low genetic diversity in small populations is expected to increase extinction risk since populations drastically reduce their abilities to cope with new conditions (Frankham, 2005). The low values observed in this study are disturbing due to the extreme isolation of the region (and the low probability of new colonization events), and the historical (un)regulated fishing pressure in the area.
5.5.3 Implication for the conservation of the species
The results presented here suggest a level of historical migration, where males and females can travel between Rapa Nui and Salas y Gómez Island. For conservation purposes, this mean that the Easter Island Ecoregion should be considered as a single conservation unit. A conservation unit is defined as a population unit considered different for purposes of conservation (Funk et al.,
2012). They are usually used by managers and policy makers to identify the boundaries of population requiring management and conservation actions (Funk et al., 2012), since each unit might need different strategies (Ward, 2000). The identification of one large genetic stock (or conservation unit) in the entire ecoregion provides important ecological information that should be used by the Chilean authorities to enhance protection of the unique population inhabiting the area. For example, this information could be used to support expanding the current MMHMP borders, where the Galapagos shark is most abundant, to the west. It can also be used to protect the seamount ridge that connects the MMHMP and Rapa Nui since it is highly likely that it is acting as a swimway or biological corridor between both areas. Seamounts often aggregate large amounts of biomass and therefore concentrate fishery attention (Pitcher et al., 2008; Morato et al., 2010). In this sense, if sharks are caught at these seamounts it could decrease the dispersal rates of these species, which would directly affect population viability in the entire ecosystem
(Lowe & Allendorf, 2010). Additionally, this information should be used to zone the new Rapa Nui multiple uses coastal marine protected areas (MUMPA) and protect important areas where this 86
species occurs (e.g., south coast; Morales et al. 2019a). Due to the unlikeliness of establishing large no-take areas at Rapa Nui, we believe that the best approach for the conservation of the
Galapagos shark should be a mix of small no-take areas (e.g., nursery areas; Bonfil, 1997) combined with conventional management strategies (e.g., fishing regulation) where the catch of juveniles, and ideally adult sharks (Kinney & Simpfendorfer, 2008), is strictly prohibited. Similar actions were taken in the Galapagos Archipelagos after a severe population reduction of sharks
(Wolff et al., 2012), which has recently led to one of the largest recorded shark biomasses estimates anywhere (Salinas-de-León et al., 2016).
Sharks usually exert a top-down regulation in the ecosystem where they occur by controlling the demography and behaviour of lower trophic levels (Roff et al., 2016). Even though there are several shark species found around the Easter Island Ecoregion (Randal & Cea, 2011), the
Galapagos shark is the only resident species recorded in the area (Morales et al. 2019a).
Considering the occurrence of only one population within the Easter Island Ecoregion, and the limited reproductive capacity of this species (Kyne et al. 2020), its rapid decline at Rapa Nui is extremely disturbing and could have catastrophic consequences for the health of the entire ecosystem if conservation actions are not taken in the near future.
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CHAPTER 6. CONCLUSIONS
6.1 MAIN FINDINGS AND CONSERVATION IMPLICATIONS
The development of effective strategies for the management and conservation of key species requires a broad knowledge of their ecology. This thesis represents the first investigation focused on top predator species inhabiting the Easter Island Ecoregion. The use of BRUVS in Chapter 2 allowed me to determine which species inhabit Rapa Nui, what is their relative abundance, and their general distribution. In this chapter I found that the south coast of the island is the main habitat for top predators, where species such as jacks, tunas, and the Galapagos shark are abundant year-round, and a possible nursery area for the Galapagos shark. I hypothesized that the main reason for these differences is the exposure to high swells and winds coming from
Antarctica, and as a result, lower fishing pressure in this area. BRUVS also confirmed the occurrence of a resident population of Galapagos sharks around Rapa Nui, which was thought to be missing due to historical overfishing. This information is crucial for the establishment of the new Rapa Nui MUMPA and the identification of priority areas for conservation (e.g., no-take areas).
In Chapter 3, I determined the trophic role and defined the isotopic niche of predator species observed in Chapter 2 and other species described in the area (Acanthocybium solandri,
Aulostomus chinensis, Carcharhinus galapagensis, Coryphaena hippurus, Katsuwonus pelamis,
Pseudocaranx dentex, Seriola lalandi, Kajikia audax, Thunnus albacares, Thyrsites atun). Using
δ13C and δ15N isotopic signatures, I discovered that not all large fishes should be classified as top predators. The correct identification of top predator species contributes to the understanding of
Rapa Nui ecosystem dynamics and the identification of important conservation areas (e.g., within the Rapa Nui MUMPA). My results also showed a high isotopic overlap between four species
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(Acanthocybium solandri, Katsuwonus pelamis, Pseudocaranx dentex, and Thunnus albacares), suggesting potential for interspecific competition of trophic resources. This chapter highlights the need for multispecies studies to elucidate the tropho-dynamics of this isolated and largely understudied ecosystem.
Identifying movement patterns of fish populations is essential for interpreting results of catch data and underwater visual surveys, and to develop effective managements plans. These may be used to infer changes in populations of reef fishes. In Chapter 4 I studied the movement patterns of the most abundant top predators within the MMHMP, the Galapagos shark and the yellowtail amberjack. Results showed that even though all individuals spent most of the time within the limits of the marine park, all of them crossed the borders during portions of the tracking period. My results also indicated that the travel distance of juvenile Galapagos sharks in open waters is much higher than noted in previous studies. This means that the Galapagos shark may not be a strictly reef-associated species as previously thought, which could also explain its wide distribution.
Additionally, I used data from the Automatic Identification System (https://globalfishingwatch.org) to study fishing activities within Chile’s Exclusive Economic Zone (EEZ) and to put into perspective the potential threat that mobile species are exposed to from these fishing activities.
No fishing activities were recorded inside the Chilean EEZ from 2012 to 2016. However, it is evident that fishing vessels accurately identified the EEZ border, but it is unclear to what extent they adhered to these boundaries. Summarizing, these results supports the idea of expanding the current borders of the MMHMP to the west in order to improve protection of highly mobile species. I also suggest the implementation of a better fisheries monitoring program within the
MMHMP, since the remoteness of this area increases the opportunity for illegal fishing without punishment. Currently, only one local patrol boat “Tokerau” is available in the area. Unfortunately, this patrol boat does not have the capacity to intercept vessels engaged in suspicious activities 89
or to reach, and therefore protect, the MMHMP. Further, even though the Chilean navy runs two oceanic fishing enforcement operations twice a year, including an offshore patrol vessel and a radar equipped plane, this is not enough to have a dissuasive presence in the region.
It is well known that connectivity studies should include both tracking and genetic studies. While tracking studies provide information on where species move, genetic studies determine if those species are reproducing (gene flow) within those areas. In Chapter 5 I studied the genetic connectivity of the Galapagos shark between Rapa Nui and Salas y Gómez islands. Genetic markers (SNPs and mtDNA) suggest that individuals at both islands are part of the same population. These results are unexpected since genetic structure has been demonstrated in smaller scales (e.g., within the Galapagos Archipelago; Pazmiño et al. 2017) for this species.
However, the lack of geographic barriers and the occurrence of a seamount chain between islands, which may serve as stepping-stones, could help to explain my results. Additionally, results from Chapter 5 were consistent with the findings of Chapter 4, suggesting that both female and male Galapagos sharks can travel between islands. My results highlight the important role of the MMHMP in keeping the entire ecosystem healthy.
Three additional papers have been published (Easton et al., 2016; Thiel et al., 2018; and Morales et al., 2019b), and two more are being prepared regarding large fishes of Rapa Nui. In Easton et al. (2016), we described the biodiversity of deeper areas, including the top predator species around Rapa Nui. In Thiel et al. (2018), we showed how marine species interact with marine plastic pollution and demonstrated how plastic pollution threatens top predator species. Finally, in Morales et al. (2019b), we recorded the first observation of the whitetip reef shark Triaenodon obesus at Rapa Nui. Additionally, two other publication are being prepared on the first records of
Seriola rivoliana, and Rhincodon typus on Rapa Nui. The increasing new records in the area
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highlights the scarce knowledge of the local biodiversity and the need for longer monitoring programs in the entire region.
6.2 LIMITATION OF THE STUDY AND SUGGESTIONS FOR FUTURE RESEARCH
DIRECTIONS
The most important limitation of working at the Easter Island Ecoregion is the isolation of the area.
Its remoteness from the mainland increases the general costs of research, which at the same time is reflected in a limited sampling effort. For example, in Chapter 3 a more constant sampling effort would have increased the low n of some species and allowed me to examine more specific patterns such as seasonal differences in trophic dynamics. However, the chapter most affected by economic constrains was Chapter 4. The extreme remoteness of Salas y Gómez Island (~ 400 km from Rapa Nui) limited the field investigation to only two short campaigns, which allowed for the sampling of only a few individuals (see discussion of Chapter 4 for more details). Another reason for the limited sampling effort is the exposure of the study area to unfavorable weather conditions. This resulted in a constant uncertainty in field work and in the ability of meeting my objectives. For example, in Chapter 2, the sampling effort was affected by the weather, as certain areas could not be sampled during winter months due to strong wind and sea conditions.
Despite the limitations described above, this thesis represents a baseline for the development of more effective management and conservation strategies for the protection of top predator species in the Easter Island Ecoregion. However, it is worth emphasizing that this baseline does not represent the pristine state of the Rapa Nui ecosystem. On the contrary, what we see now corresponds to a highly impacted state or a “shifted baseline” as defined by Pauly (1995) and
Pinnegar & Engelh (2008). In this sense, if the Rapa Nui ecosystem has shifted due to historical overfishing, then perhaps we need to focus our attention on a less impacted “control” system such
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as the Salas y Gómez ecosystem as a reference for a past ecological state. “There (Salas y
Gómez Island) you will see how Rapa Nui used to be before the first settlement arrived here
(Rapa Nui) … You will see how the world was 2,000 years ago” (Edmundo Edwards from The
Lost Sharks of Rapa Nui National Geographic documentary). That said and based on the results and the conclusion described above, I propose:
• A long-term monitoring programs should occur at Rapa Nui and Salas y Gómez Island. In
Chapter 2 BRUVS are shown as a reliable technique to study the marine biodiversity in
the area. In this regard, longer-term temporal and spatial surveys should include
mesophotic zones and seamounts to understand the patchy occurrence and distribution
of some of the species described in Chapter 2.
• A tropho-dynamic comparison between Rapa Nui and Salas y Gómez should be
conducted. Due to the large differences in the fish assemblages (e.g., top predator
abundance), considerable variations in how these ecosystems are structured might be
found.
• An extensive tagging (satellite and telemetry tags) program that includes adults of the
Galapagos shark and other mobile species at both islands should be implemented. The
use of a mix of tagging studies together with a monitoring program would provide important
short- and long-term information on the habitat use of key species. That information could
be then used to design more effective conservation measures on the MPAs.
• The expansion westward of the current borders of the MMHMP to better protect the mobile
species of Salas y Gómez Island. In this regard, a more constant and efficient surveillance
of these distant areas is imperative to prevent illegal fishing within the EEZ and the
MMHMP. Chapter 4 shows that even though no fishing activity inside the MMHMP or the
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EEZ was recorded, fishing vessels could go undetected since the legal borders are an
obstacle easy to circumvent and vessel monitoring systems can be turned off.
A broad genetic study to compare present results with other locations where Galapagos sharks occurs is needed. This will allow for a better understanding of how colonization processes occurred in this isolated area. Easter Island ecoregion can be considered as an oasis of life in the middle of the Pacific Ocean. Most probably due to its isolated location, this area is widely understudied, when compared to other Polynesian islands. Overall, this thesis lays the foundation for top predator species research, development of science-based management approaches and effective conservation strategies in the Easter Island ecoregion. Knowing the species composition of this ecologically important trophic group, together with the understanding of how these species use the few available habitats in the ecoregion, is essential for identifying important areas for conservation. This research also provides scientific evidence for the existence of a single widespread population of the
Galapagos shark within the area, which would result in a single conservation unit for management purposes. I hope that these findings encourage other researchers to explore some of the several related topics that remain unsolved in the area.
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APPENDIXES
Figure S4.1. Length frequency distribution per species of all fish caught during the tagging cruises CIMAR 21 in 2015 and the SOSF 380 campaign of 2017.
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Figure S4.2. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154061 (amberjack) in relation to bathymetry (Deployment duration: 148 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.
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Figure S4.3. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154062 (Galapagos shark) in relation to bathymetry (Deployment duration: 35 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.
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Figure S4.4. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154064 (Galapagos shark) in relation to bathymetry (Deployment duration: 28 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.
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Figure S4.5. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154065 (Galapagos shark) in relation to bathymetry (Deployment duration: 18 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.
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Figure S4.6. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 173480 (Galapagos shark) in relation to bathymetry (Deployment duration: 95 days. Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.
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Figure S4.7. 50% likelihood areas of each tag. Different colour represents different days. 123
Figure S4.8. 95% likelihood areas of each tag. Different colour represents different days. 124
Figure S4.9. 99% likelihood areas of each tag. Different colour represents different days. 125
Figure S4.10. Time at different depths during night and daytime periods per individual. Female (F) and male (M).
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Figure S4.11. Distance of daily geolocations to Salas y Gómez islet per vertical behaviour cluster of all archivally-tagged Galapagos sharks.
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Figure S5.1. Number of cluster (k) suggested by BIC values.
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Table S2.1. Mean wave energy values (kW/m) and percentage of occurrence from every (360° degree) direction. Autumn (March- June); winter (June-September); spring (September-December); summer (December -March).
Direction Mean Percentage Mean Percentage Mean Percentage Mean Percentage Power occurrence Power occurrence Power occurrence Power occurrence (degree) Long-term Wave Energy (2005-2015) Autumn Winter Spring Summer 0 45.996 0.1 49.817 0.08 29.23 0.08 15.169 0.03 22.5 0 0.01 47.896 0.18 51.586 0.16 0 0 45 0 0 30.633 0.87 32.205 0.87 20.171 0.15 67.5 22.668 0.3 38.359 2.08 29.567 2.34 18.31 1.4 90 41.308 1.34 64.37 1.8 29.915 1.68 19.312 0.61 112.5 41.924 0.78 59.407 2.02 51.097 0.6 26.563 0.55 135 80.406 1 48.923 2.2 38.107 0.34 26.459 0.44 157.5 68.59 1.15 60.981 5.15 39.376 1.24 60.248 0.19 180 68.195 18.65 68.696 14.95 53.093 9.01 37.128 4.34 202.5 61.698 53.63 77.077 44.38 54.84 52.7 36.6 41.38 225 59.686 15.53 70.942 23.24 47.086 22.07 32.513 20.93 247.5 38.733 3.16 56.676 1.55 31.134 2.9 30.431 6.21 270 36.067 1.55 44.103 0.69 32.888 1.21 32.583 5 292.5 43.165 2.07 52.508 0.54 26.892 2.34 34.588 12.62 315 42.979 0.73 54.927 0.28 35.519 2.46 38.798 6.14 337.5 0 0 0 0 0 0 0 0
Recent Wave Energy (2016-2017) Autumn Winter Spring Summer 0 0 0 0 0.42 0 0 0 0 22.5 0 0 15.471 2.51 0 0 0 0 45 0 0 14.976 1.26 0 0 0 0 67.5 18.433 15.83 0 0 0 0 0 0 90 0 0 0 0 0 0 0 0 112.5 0 0.42 0 0 0 0 0 0 135 67.318 9.17 14.243 3.35 0 0 0 0 157.5 70.302 7.92 0 0.42 0 0 0 0 180 58.7 12.92 28.605 10.04 15.983 6.05 26.789 7.66 202.5 40.651 45.42 48.94 76.99 28.868 51.21 32.747 72.18 225 32.686 8.33 50.62 3.77 29.566 16.13 31.654 16.53 247.5 0 0 0 0 24.626 7.66 26.55 1.21 270 0 0 0 0 24.761 6.45 22.706 0.81 292.5 0 0 20.776 0.84 19.917 7.66 25.284 1.61 315 0 0 0 0.42 31.161 4.84 0 0 337.5 0 0 0 0 0 0 0 0
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Table S2.2. Environmental variables used in the DistLM analysis for every site and season. Sample sites are showed in Figure 2.1.
Season/ Site Temperature Historical Specific wave Distance from Shelf width (°C) wave energy energy (kW/m) shore (m) (m) (kW/m) Winter Ana hukahu - - - - - Ovahe 20.669 30.633 14.976 392.875 250 Omohi - - - - - Kari Kari 20.69 56.676 0 324.25 250 Motu tautara 19.285 44.103 20.776 202.5 0 Poike 22.668 18.433 395.5 250 Vaihu 20 60.981 28.605 463.75 1000 Vinapu 20 77.077 48.94 311.75 750
Spring Ana hukahu - - - - - Ovahe 23.746 32.205 0 392.875 250 Omohi - - - - - Kari Kari 23.463 31.134 24.626 324.25 250 Motu tautara 23.149 32.888 19.917 202.5 0 Poike - - - - - Vaihu 22 39.376 15.963 463.75 1000 Vinapu 22 54.84 28.868 311.75 750
Summer Ana hukahu 26 26.563 0 386.5 1000 Ovahe 26.758 20.171 0 392.875 250 Omohi 26.247 38.798 0 255.25 0 Kari Kari 26.59 30.431 26.55 324.25 250 Motu tautara 26.38 32.583 25.284 202.5 0 Poike 26.43 18.31 0 395.5 250 Vaihu 26 60.248 26.789 463.75 1000 Vinapu 26 36.6 32.747 311.75 750
Autumn Ana hukahu 22.683 0 0 392.875 250 Ovahe 22.708 42.979 0 255.25 0 Omohi 22.84 38.733 0 324.25 250 Kari Kari 22.773 36.067 0 202.5 0 Motu tautara 22 22.668 18.433 395.5 250 Poike 22 68.59 58.7 463.75 1000 Vaihu 22 61.698 40.651 311.75 750 Vinapu 22 41.924 67.318 386.5 1000
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Table S2.3. PERMANOVA test for all the pelagic fish species. Figures in bold indicate significant results.
Level Type Pseudo-F P(perm) Unique perms MAIN TEST Site 5 Fixed 4.9648 0.0001 9943 Season 4 Fixed 8.274 0.0001 9924 Season x Site 1.3362 0.0881 9887
PAIR-WISE TEST Sites Ovahe. Kari Kari 0.1441 9964 Ovahe. Motu Tautara 0.0978 9977 Ovahe. Vaihu 0.0001 9951 Ovahe. Vinapu 0.0158 9956 Kari Kari. Motu Tautara 0.2019 9947 Kari Kari. Vaihu 0.0001 9948 Kari Kari. Vinapu 0.0047 9956 Motu Tautara. Vaihu 0.0001 9956 Motu Tautara. Vinapu 0.0005 9954 Vaihu. Vinapu 0.001 9943
Season Autumn. Spring 0.4036 9960 Autumn. Summer 0.1654 9954 Autumn. Winter 0.0001 9956 Spring. Summer 0.1402 9952 Spring. Winter 0.0001 9945 Summer. Winter 0.0001 9965
Table S2.4. DistLM test for all the pelagic fish species. Figures in bold indicate significant results.
Variable SS(trace) Pseudo-F P Prop. Site Temperature (ºC) 913.69 1.9302 0.085 0.12117 Historical WE (kW/m) 1008.9 2.1624 0.052 0.13379 Specific WE (kW/m) 1162.3 2.5512 0.032 0.15414 Distance from shore (m) 1093.5 2.3746 0.043 0.14502 Shelf width (m) 2004.5 5.0691 0.001 0.26583
Season Temperature (ºC) 639.58 1.1143 0.3476 0.058295 Historical WE (kW/m) 1887 3.7986 0.0308 0.17199 Specific WE (kW/m) 462.36 0.92675 0.437 0.042142
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Table S3.1. PERMANOVA test for all the large fish species. Numbers in bold indicate significant results. AC, Aulostomus chinensis; AS, Acanthocybium solandri; CG, Carcharhinus galapagensis; CH, Coryphaena hippurus; KP, Katsuwonus pelamis; PD, Pseudocaranx dentex; SL, Seriola lalandi; TeA, Kajikia audax; KA, Thunnus albacares; TyA, Thyrsites atun.
AC AS CG CH KP PD SL TeA KA AS 0.124 CG 0.238 0.08 CH 0.197 0.08 0.039 KP 0.175 0.297 0.039 0.08 PC 0.383 0.011 0.083 0.011 0.011 SL 0.857 0.08 0.174 0.049 0.23 0.615 TeA 0.242 0.565 0.449 0.08 0.369 0.126 0.329 ThA 0.08 0.238 0.045 0.08 0.982 0.011 0.127 0.304 TyA 0.236 0.795 0.037 0.08 0.886 0.036 0.174 0.314 0.886
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Table S4.1. Complete data of the entire catch during both expeditions 2015 and 2017. Time of deployment; No data (ND); Conv. (Conventional) Tag ID (identification number); MiniPAT (satellite tag) ID (identification number).
Gear Conv. Total MiniPAT Species Date Time Expedition Sex type Tag ID length (cm) ID Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 01V 75 ND Carcharhinus 2015-11-02 16:50 CIMAR 21 Longline 02V 76 F galapagensis Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 03V 75 ND Carcharhinus 2015-11-02 16:50 CIMAR 21 Longline 04V 160 F 154062 galapagensis Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 1 ND ND Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 06V 77 ND Carcharhinus 2015-11-02 16:50 CIMAR 21 Longline 05VB 145 M 154066 galapagensis Carcharhinus 2015-11-02 16:50 CIMAR 21 Longline 07V 160 M 154064 galapagensis Carcharhinus 2015-11-02 16:50 CIMAR 21 Longline 08V 114 F galapagensis Seriola lalandi 2015-11-03 10:10 CIMAR 21 Longline 09V 75 ND Carcharhinus 2015-11-03 10:10 CIMAR 21 Longline 10V 87 F galapagensis Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 65A 86 ND Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 62A 81.5 ND Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 68A 81.5 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 66A 105 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 63A 108 F galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 64A 90.1 M galapagensis Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 61A 102 ND Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 69A 93 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 70A 86 F galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 2 ND ND galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 67A 83 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 3 ND ND galapagensis Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 39N 124 ND 154061 Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 32N 104 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 34N 86 M galapagensis Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 4 84 M galapagensis
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Carcharhinus 2015-11-03 17.35 CIMAR 21 Longline 31N 120 F galapagensis Seriola lalandi 2015-11-04 9.05 CIMAR 21 Handline 37N 76.5 ND Seriola lalandi 2015-11-04 9.20 CIMAR 21 Handline 33N 94.5 ND Seriola lalandi 2015-11-04 9.50 CIMAR 21 Handline 36N 93 ND Carcharhinus 2015-11-04 10.25 CIMAR 21 Handline 35N 91.5 F galapagensis Carcharhinus 2015-11-04 10.30 CIMAR 21 Handline 40N 108 F galapagensis Carcharhinus 2015-11-04 10.40 CIMAR 21 Handline 30B 110 M galapagensis Seriola lalandi 2015-11-04 17.40 CIMAR 21 Handline 23B 78 ND Seriola lalandi 2015-11-04 17.46 CIMAR 21 Handline 21B 71 ND Seriola lalandi 2015-11-04 17.51 CIMAR 21 Handline 25B 76.5 ND Carcharhinus 2015-11-04 17.52 CIMAR 21 Handline 26B 97 M galapagensis Carcharhinus 2015-11-04 17.56 CIMAR 21 Handline 29B 79 M galapagensis Seriola lalandi 2015-11-04 18.05 CIMAR 21 Handline 22B 75.5 ND Carcharhinus 2015-11-04 18.10 CIMAR 21 Handline 24B 100 F galapagensis Carcharhinus 2015-11-04 18.14 CIMAR 21 Handline 27B 96.5 F galapagensis Carcharhinus 2015-11-04 18.20 CIMAR 21 Handline 28B 93.5 M galapagensis Carcharhinus 2015-11-04 18.25 CIMAR 21 Handline 46AZ 101 F galapagensis Seriola lalandi 2015-11-04 18.33 CIMAR 21 Handline 47AZ 82 ND Carcharhinus 2015-11-04 18.37 CIMAR 21 Handline 50AZ 93 F galapagensis Carcharhinus 2015-11-04 18.45 CIMAR 21 Handline 44AZ 82.5 F galapagensis Seriola lalandi 2015-11-04 18.50 CIMAR 21 Handline 43AZ 69 ND Carcharhinus 2015-11-04 18.55 CIMAR 21 Handline 41AZ 85 F galapagensis Carcharhinus 2015-11-04 17.00 CIMAR 21 Handline s/n 170 F 154065 galapagensis Carcharhinus 2015-11-04 17.10 CIMAR 21 Handline 48AZ 80 F galapagensis Seriola lalandi 2015-11-04 17.15 CIMAR 21 Handline 49AZ 74 ND Carcharhinus 2017-11-20 18.30 SOSF 380 Handline ND 130 F galapagensis Carcharhinus 2017-11-20 18.35 SOSF 380 Handline ND 110 ND galapagensis Carcharhinus 2017-11-20 18.40 SOSF 380 Handline 40R 100 F galapagensis Seriola lalandi 2017-11-20 18.45 SOSF 380 Handline 12 83 ND Carcharhinus 2017-11-20 18.50 SOSF 380 Handline 13 128 ND galapagensis Carcharhinus 2017-11-21 17.43 SOSF 380 Handline 8 119 F 173480 galapagensis
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Carcharhinus 2017-11-21 17.59 SOSF 380 Handline 11 110 M galapagensis Carcharhinus 2017-11-21 18.07 SOSF 380 Handline 15 128 F galapagensis Carcharhinus 2017-11-21 18.20 SOSF 380 Handline 3 90 F galapagensis Carcharhinus 2017-11-21 18.30 SOSF 380 Handline 14 129 M galapagensis Carcharhinus 2017-11-21 18.35 SOSF 380 Handline 27 124 M galapagensis Carcharhinus 2017-11-21 18.40 SOSF 380 Handline 20 103 ND galapagensis Carcharhinus 2017-11-21 18.47 SOSF 380 Handline 22 110 M galapagensis Carcharhinus 2017-11-21 18.55 SOSF 380 Handline ND 129 F galapagensis Carcharhinus 2017-11-21 19.03 SOSF 380 Handline ND 98 F galapagensis Carcharhinus 2017-11-21 19.20 SOSF 380 Handline ND 85 M galapagensis
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Table S4.2. Minimum, maximum, and average depth of night and daytime periods from all tags as well as its standard deviation (SD).
Depth (m) Tag ID daytime n (days of data) Minimum Maximum Average SD
154061 Day 59 9.5 75 19 7.8
154061 Night 58 5.5 100.5 26.4 12.8
154062 Day 14 2 114 24.7 30.8
154062 Night 13 0.5 131.5 25.6 22.4
154064 Day 11 0.5 195.5 58.1 46.1
154064 Night 11 0.5 165.5 37.5 34.7
154065 Day 18 1.5 109 40.4 30.9
154065 Night 18 1 100.5 40.3 22.8
173480 Day 38 0.5 96 26.6 16.2
173480 Night 38 0.5 63 11.4 8.3
Table S4.3. Number of data points per vertical behaviour cluster and Galapagos shark tagged.
Data points (n) Tag ID Cluster 1 Cluster 2
154062 2 11
154064 5 6
154065 6 12
173480 0 37
Sum 13 66
Percentage 16.5 83.5
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