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Reef Fish Connectivity in the Hawaiian Archipelago: A Biophysical Modeling Approach
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|Title:||Reef Fish Connectivity in the Hawaiian Archipelago: A Biophysical Modeling Approach|
|Issue Date:||Aug 2016|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [August 2016]|
|Abstract:||Despite decades of research, factors that drive population patterns and connectivity in the ocean are hotly debated and largely unknown. With a changing climate and an ever increasing anthropogenic strain, protecting our oceans for future generations is vital. Coral reefs are some of the most productive ecosystems on earth, and in order to protect them we need to gain a deeper understanding of the biological and physical dynamics that govern species distributions and survival.|
This dissertation aims to explore larval dispersal and population connectivity in the Hawaiian Archipelago. To effectively manage coral reef ecosystems, it is imperative to understand where the new generation comes from. To gain insight into the drivers behind observed larval distribution patterns I ground-truthed a biophysical model with in situ larval distributions obtained during midwater trawling off the coast of West Hawai‘i Island. I was able to show that a connectivity model explained observed larval abundances and distributions of the yellow tang, Zebrasoma flavescens, to a significant degree. The dispersal model also showed that successful larvae most likely inhabit the deeper waters around 100 m for optimum settlement success and that larvae can travel from one end of the Main Hawaiian Islands to the other in 45 days.
The groundtruthed model allowed me to explore modeled potential connectivity in the Hawaiian Archipelago and generate a comprehensive estimate of connectivity of passive particles for the region. Genetic population connectivity has been studied extensively in the Hawaiian Archipelago, but to date no study has looked at large scale modeled larval connectivity patterns. By comparing genetic population connectivity patterns with modeled larval connectivity patterns driven by the physical environment we can begin to understand drivers of population connectivity. I found that modeled self-recruitment was high throughout the archipelago. This is important because being able to provide your own young makes a population less reliant on outside sources of genes and larvae. Results from the biophysical model indicate that connectivity in the NWHI is predominantly driven by physical factors e.g. ocean currents. Connectivity patterns in the Main Hawaiian Islands are not explained by the physical oceanographic environment, rather, biological and anthropogenic factors are likely important for dispersal. The biophysical model identified distinct breaks in the archipelago where larval exchange is limited, and I was able to describe the directionality and relative size of dispersal between the MHI and the NWHI. Understanding larval exchange between the MHI and NWHI is important because the MHI are heavily fished while the NWHI are protected as part of one of the largest marine protected areas in the world, Papahānaumokuākea Marine National Monument.
In the final part of this work I investigate how El Nino Southern Oscillation (ENSO) change connectivity patterns in the Hawaiian Archipelago. Having a deeper understanding of changes in connectivity during relatively extreme events such as ENSO allows us to better plan for management in a changing climate. The study showed that unique connectivity pathways open up between the Hawaiian Archipelago and Johnston Atoll during El Niño events providing a pathway for larval exchange between the Hawaiian Archipelago and other islands in the Pacific Ocean. During La Niña years Johnston Atoll acts as an outpost, with modeled connectivity pathways opening up from Hawai‘i towards Johnston Atoll. El Niño years had longer mean dispersal distances, and more larvae that traveled further, compared with normal and La Niña years. These periodic long distance dispersal events may contribute to the exchange of genes between distant populations, and allowing greater genetic diversity and potentially building resilience towards changing environments.
|Description:||Ph.D. University of Hawaii at Manoa 2016.|
Includes bibliographical references.
|Appears in Collections:||Ph.D. - Oceanography|
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