AN ABSTRACT OF THE THESIS OF
Katlyn M. M. Haven for the degree of Master of Science in Marine Resource Management presented on March 22, 2019.
Title: Pacific Coast Groundfish Fishery Assessment and Management: Incorporating Nearshore Surveys into the Fishery Management Framework
Abstract approved: ______Dr. Lorenzo Ciannelli
The Pacific coast groundfish fishery is a diverse, important and lucrative commercial and recreational fishery. Part of this fishery’s monitoring process includes regular fishery-independent surveys for stock assessment. Although these fishery- independent surveys are cost-effective, they are susceptible to scientific uncertainty, and they do not currently sample in nearshore (water depth < 55 meters) soft- sediment habitats. The NOAA Fisheries Northwest Fisheries Science Center, in collaboration with the Pacific States Marine Fisheries Commission and Oregon State
University, is currently sampling young-of-the-year (YOY) groundfishes and other small demersal fishes, along the Newport Hydrographic (NH) Line off the central
Oregon coast. A potential use of this survey is to complement the current fishery- independent survey used to inform stock assessments. First, two questions must be addressed: 1) what can nearshore sampling provide to groundfish science and management, and 2) is it practical to incorporate additional nearshore sampling into
the current management framework. Answering these questions would help determine whether adding nearshore sampling would improve fishery management. For the purposes of this thesis, “nearshore” is defined as the marine habitats along the continental shelf with water depths shallower than 55 meters. In this analysis, data from bottom trawl and beam trawl surveys were compared within an overlapping nearshore region in order to characterize the juvenile groundfish populations along the continental shelf of Oregon. Examining fish assemblages and environmental variables from both surveys, I found significant differences in fish assemblages, oxygen, temperature, and salinity based on depth, season, and year. Exploring the regulatory framework of the Pacific coast groundfish fishery showed that adding nearshore monitoring to fishery-independent survey designs would be difficult, but it is possible. I also found that the addition of nearshore monitoring would benefit the future designations of groundfish Essential Fish Habitat by providing environmental and biological monitoring of sensitive nursery habitats. By comparing fish community composition and habitat between these surveys and examining current management frameworks, I addressed the overall question of whether monitoring of nearshore habitat would provide a more representative sample of groundfish communities and ecosystem indicators and if it is feasible to do so.
©Copyright by Katlyn M. M. Haven March 22, 2019 All Rights Reserved
Pacific Coast Groundfish Fishery Assessment and Management: Incorporating Nearshore Surveys into the Fishery Management Framework
by Katlyn M. M. Haven
A THESIS
submitted to
Oregon State University
in partial fulfillment of the requirements for the degree of
Master of Science
Presented March 22, 2019 Commencement June 2019
Master of Science thesis of Katlyn M. M. Haven presented on March 22, 2019
APPROVED:
Major Professor, representing Marine Resource Management
Dean of the College of Earth, Ocean, and Atmospheric Sciences
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.
Katlyn M. M. Haven, Author
ACKNOWLEDGEMENTS
I would like to extend my sincerest appreciation and gratitude to all that made my research possible. I could not have done this without the advice, contributions, suggestions, and funding from so many individuals. This thesis would not be possible without the funding and support of the OSU Laurel Scholarship, the MRM Geoffrey
Dimmick Memorial Scholarship, Teaching Assistantships through the Geography,
Geology, and Integrative Biology departments, the National Science Foundation’s
National Research Traineeship (NRT) program at OSU, Tedd Strub and Flaxen Conway from the SeaCast project at OSU, NOAA’s Fisheries Cooperative Research Program, and
NOAA’s Northwest Fisheries Science Center (NWFSC).
Thank you to Lorenzo Ciannelli, Waldo Wakefield, Jason Phillips, Toby Auth,
Matthew Yergey, and the crews of the Elakha, Miss Yvonne, Lady Law, and Michele
Ann for all the help collecting and sorting fish on our cruises. Thank you for Jason
Phillips for countless hours in the laboratory identifying fish, entering data, and running
QA/QC on the beam trawl data from 2012-2015. Thank you to the researchers on the
Northwest Fisheries Science Center’s West Coast Groundfish Bottom Trawl Survey
(WCGBTS) team. Without their hard work, I would not have had the opportunity to compare the WCGBTS to the beam trawl survey. Thank you to Chantel Wetzel, especially, for helping me extract the WCGBTS data from the National Marine Fisheries
Service data warehouse, and for troubleshooting some of my R code.
Thank you to Kristin Marshall and the NWFSC’s Pacific Whiting Management
Strategy Evaluation (MSE) group for allowing me to work with you for my NRT
internship. Being able to see the MSE process first-hand and going to an international meeting of fisheries scientists, fishermen, and industry was a great professional development opportunity for me. I am sincerely grateful for the entire experience in
Seattle and Victoria.
Thank you to all the other NOAA and OSU researchers that helped guide, advise, and inform me about different aspects of fishery management. Thank you to Stacey
Miller (NWFSC) and Kerry Griffin (PFMC) for informing me about the Pacific Fishery
Management Council (PFMC) and stock assessment procedures. Thank you to Yvonne deReyner (NMFS) for her guidance and counsel on U.S. federal fisheries management and Ecosystem-Based Management. Thank you to Dave Sampson for expanding my understanding of stock assessment models and the PFMC’s Scientific and Statistical
Committee process. Thank you to Bruce McCune for the wisdom, help, and advice on the multivariate community analysis portion of my second chapter.
Thank you to Flaxen Conway, Lori Hartline, Robert Allen, and other CEOAS faculty who have guided, assisted, and advised me throughout this process; you have all made this journey possible, and I appreciate all the time and effort you have devoted to helping me along the way. Thank you to all the professors and groups that helped me pursue a Master’s degree by helping me gain experience and funding during my undergraduate coursework and research. A special thanks to the graduate student community here in MRM and CEOAS, both past and present. You have made this such an enjoyable experience, and I am so glad I was able to be surrounded by an extraordinary group of supporting and caring individuals. Last, but certainly not least, thank you to my family, friends, roommates, and boyfriend; I could not have gotten this
far without your support and encouragement throughout the whole process. I am so blessed to have such an amazing support team in all areas of my life, and I want to thank you all for everything you have done for me.
To everyone else who has helped me get this far, thank you! This truly has been a journey, and I am so glad that I have had the pleasure of being influenced and encouraged by all of you.
CONTRIBUTION OF AUTHORS
Alrik Firl and Samm Newton contributed to Chapter 3 in partial fulfillment of the NRT
Interdisciplinary Chapter. Drs. Lorenzo Ciannelli and Waldo Wakefield provided guidance during the study design and preliminary data analyses of Chapter 2. Drs.
Lorenzo Ciannelli, Waldo Wakefield, Ana Spalding, and Yvonne deReynier provided guidance on the design and subject matter of Chapter 3. Drs. Lorenzo Ciannelli, Waldo
Wakefield, and Ana Spalding provided feedback, including suggested organization, on all chapters throughout the writing process.
TABLE OF CONTENTS
Page
CHAPTER 1: GENERAL INTRODUCTION ……………………………………… 1
1.1 Background .………………………………………………………………… 1
1.1.1 Groundfish management and policy framework …….………… 1
1.1.2 Coping with uncertainty in fisheries management ……..……… 3
1.1.3 Nearshore nursery habitats …………….……….……………… 5
1.2 Thesis overview and research objectives .….………………………………... 6
1.3 References .…………………………………………………………………… 8
CHAPTER 2: AN ANALYSIS OF ABUNDANCE AND DISTRIBUTION OF YOUNG-OF-THE-YEAR GROUNDFISHES IN SOFT-SEDIMENT HABITATS OFF OF THE CENTRAL OREGON COAST …………………………………….. 13
2.1 Introduction ………………………………………………………………… 13
2.2 Methods ..…………………………………………………………………… 17
2.2.1 Sampling procedure – Newport Hydrographic Line …….…… 17
2.2.2 Sampling procedure – West Coast Groundfish Bottom Trawl Survey ……………………………………………………………… 19
2.2.3 Data preparation ……………………………………………… 21
2.2.4 Data analysis – Multivariate analysis on Beam Trawl Survey ……………………………………………………………… 23
2.2.5 Data analysis – Survey comparison …………………….……. 26
2.3 Results .……………………………………………………………….……. 28
2.3.1 Descriptive summaries of beam trawl and bottom trawl surveys ……………………………………………………………... 28
2.3.2 Multivariate community analysis of beam trawl data ………... 29
TABLE OF CONTENTS (Continued)
Page 2.3.3 Comparisons of beam trawl and bottom trawl (WCGBTS) data …………………………………………………………………. 31
2.4 Discussion ….……………………………………………………………... 35
2.5 Conclusion ….…………………………………………………………….. 41
2.6 References ………………………………………………………………… 43
2.7 Figures and Tables ………………………………………………………... 49
CHAPTER 3: EMERGING TECHNOLOGIES IN FISHERIES STOCK ASSESSMENT AND ECOSYSTEM SCIENCES – AN EXAMINATION OF HOW NEW METHODOLOGIES ARE INCORPORATED INTO FISHERIES MANAGEMENT …………………………………………………………………... 71
3.1 Chapter objective and structure …..………………………………………. 71
3.2 Methods ….……………………………………………………………….. 72
3.3 Science in the U.S. fisheries management process .………………………. 74
3.3.1 U.S. marine fishery management ..……………………….……74
3.3.2 The call for Ecosystem-Based Management ….……………… 75
3.3.3 Role of science in fisheries management…..…….…………… 76
3.3.4 Role of stock assessment models within fisheries Management ………………………………………………………... 79
3.4 Management of Pacific coast groundfishes ..…………………………….. 81
3.4.1 Pacific coast groundfish fishery …………………..………….. 81
3.4.2 The Pacific Fishery Management Council.…………….……... 82
3.4.3 Data used to develop stock assessment models in the Pacific coast groundfish fishery ……………………………………………. 85
TABLE OF CONTENTS (Continued)
Page
3.4.4 Scientific uncertainty within fisheries management…………… 86
3.5 Nearshore data collection in support of stock assessments .……………….. 88
3.5.1 Oregon’s nearshore survey ……...…..…………………………. 88
3.5.2 Advances toward Ecosystem-Based Management …………….. 90
3.5.3 Pre-recruit and ichthyoplankton surveys ………………………. 93
3.6 Discussion ……………………....…………………………………………. 96
3.6.1 Roadblocks and moving forward ………………………………. 96
3.6.2 Suggestions for nearshore survey development ………………... 99
3.7 Conclusion ……………………....………………………………………… 99
3.8 References ……………………....……………………………………….. 101
3.9 Figures ….……………………....………………………………………... 110
CHAPTER 4: GENERAL CONCLUSION ….…………………………………… 111
4.1 Summary and relevance of findings ……………………………………. 111
4.2 Recommendations and future work …....….……………………………. 114
4.3 References ………………………………………………………………. 115
BIBLIOGRAPHY …………………………...……………………………………. 117
APPENDIX …..…………………………………………………………………… 130
APPENDIX A: ADDITIONAL FIGURES AND TABLES ……………….. 131
LIST OF FIGURES
Figure Page
1. Sampling locations along the central Oregon coast ...………………………….. 49
2. Sampling period for both the WCGBTS and the Beam Trawl Survey from 2012- 2015 .……………………………………………………………………………. 50
3. Depth distribution of West Coast Groundfish Bottom Trawl Survey Stations comparable to the Newport Hydrographic Line Beam Trawl Survey …………. 51
4. Nonmetric Multidimensional Scaling (NMS) ordination of sample units points in species space showing Axis 1 and Axis 2 of a 2-dimensional solution using a Sorenson distance measure …………………………………………………….. 52
5. Average sample diversity for the beam trawl survey (left) and West Coast Groundfish Bottom Trawl Survey (right) based on Shannon-Wiener, Simpson, and species richness indices per year (2012-2015) .…………………………… 62
6. Average sample diversity for the beam trawl survey (left) and West Coast Groundfish Bottom Trawl Survey (right) based on Shannon-Wiener, Simpson, and species richness indices per depth strata …………………………………... 63
7. Comparison of Frequency of Occurrence for species caught in the beam trawl and bottom trawl surveys between 2012-2015 ……………………………………... 64
8. Average standardized relative species abundance of fish caught in the beam trawl (left) and bottom trawl (right) surveys based on sample year (2012-2015) …… 65
9. Average standardized relative species abundance of fish caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15) ……...…… 66
10. Average standardized relative species abundance of fish caught in the beam trawl and bottom trawl surveys based on non-comparable depth strata (no analogs in other study) ……………………………………………………………………. 67
11. Size distribution of specimens caught in the beam trawl and bottom trawl surveys between 2012-2015 …………………………………………………………… 68
12. Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys between 2012-2015 …………………………………………………………………….. 69
LIST OF FIGURES (Continued)
Figure Page
13. Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys between 2012- 2015……………………………………………………………………………. 70
14. Theoretical relationship between the fishing effort and the average yield of a fishery………………………………………………………………………… 110
LIST OF TABLES
Table Page
1. Species Summary Table for Newport Beam Trawl Survey…………………….. 53
2. Species Summary Table for West Coast Groundfish Bottom Trawl Survey…… 57
LIST OF APPENDIX FIGURES
Figure Page
A.1 Average environmental data for each year (2012-2015) along the NH Line compared between surveys …………………………………………………... 139
A.2 Average environmental data (dissolved oxygen, salinity, and temperature) at each depth strata along or near the NH Line compared between surveys .………... 140
A.3 Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on sample year (2012 -2015) .…………………………………………………… 141
A.4 Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15) ……………………………………………… 142
A.5 Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on non-comparable depth strata (no analogs to other survey) …………………... 143
A.6 Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on sample year (2012-2015) .……………………………………………………………...….. 144
A.7 Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15) ………………………………………………………… 145
A.8 Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on non- comparable depth strata (no analogs to other survey) ……………………… 146
LIST OF APPENDIX TABLES
Table Page
A.1 Species Summary Table for Newport Beam Trawl Survey ………………….. 131
A.2 Summary Table of Environmental Data for PC-ORD Environmental Matrix ………………………………………………………………………... 134
A.3 Data Adjustments and their effects on Whittaker’s Beta Diversity, Beta Diversity in half-changes, coefficient of variation (CV) for row sums, CV for column sums, and average column skewness of the community abundance data ……. 135
A.4 Indicator Species Analysis Summary Table …………………………………. 136
1
CHAPTER 1: GENERAL INTRODUCTION
1.1 Background
The Pacific coast groundfish fishery near the continental United States is important for commercial, recreational, tribal, and ecological purposes (The Research
Group, LLC 2015, 2017; Rodomsky et al. 2018). The groundfish fishery management plan currently includes over 90 commercially important fishes, including rockfishes, flatfishes, elasmobranch species (sharks, skates, and chimaeras), and various other groundfishes. Two groundfish species – Sablefish (Anoplopoma fimbria), worth
$15,100,00 in 2016, and Pacific hake (Merluccius productus), averaging $16,100,000 annually between 2010-2015 – are some of the most economically valuable species within the Oregon commercial fishing industry alone (The Research Group, LLC 2017).
However, this fishery does not function in a vacuum. These fish live in an open ocean, and fish do not stay within human-constructed jurisdictional boundaries. Additionally, there are many different stakeholder groups that are a part of the fishery. Therefore, competing goals, values, and perspectives must be considered in fisheries management.
These facts alone cause the management of this fishery to be highly complex.
1.1.1 Groundfish management and policy framework
The National Oceanic and Atmospheric Association’s (NOAA) National Marine
Fishery Service (NMFS) is the federal agency that partners with the Pacific Fishery
Management Council (PFMC) to manage the Pacific coast groundfish fishery (Eagle et al. 2015). The Magnuson-Stevens Fishery Conservation and Management Act (MSA) is
2 the primary legislation governing all marine fisheries in the United States. This landmark legislation was passed in 1976 and most recently reauthorized in 2007 (16 U.S.C. §§
1801 et seq.; Eagle et al. 2015). The primary goal of the MSA has been to promote sustainable fishery conservation and management (Eagle et al. 2015). The main way it does this is through the ten national standards that mandate different aspects of sustainable practices including the following: 1) ending overfishing, 2) using “best scientific information available” to inform management decisions, 3) managing an entire fish stock as a unit, 4) allocating fishing privileges fairly, 5) using resources efficiently,
6) accounting for variability, 7) minimizing costs, 8) accounting for the impacts on fishing communities, 9) minimizing bycatch, and 10) promoting fishermen’s safety at sea
(16 U.S.C. §§ 1801 et seq.; Eagle et al. 2015). This mandated use of “best scientific information available” or “best available science” (BAS) has proven difficult due to the vagueness of the term and the evolving nature of science, but it is still the standard the regional fishery councils and the NMFS aim to achieve (National Research Council et al.
2004; Sullivan et al. 2006; Kuhn 2016). One of the goals of this thesis is to evaluate the effectiveness of currently available science about nearshore soft-sediment demersal fish communities. This evaluation can then lead to considerations about supplementing the current fishery-independent survey with the additional sampling of nearshore soft- sediment demersal fish communities.
In order to carry out the primary goal discussed above, the MSA established the eight United States’ regional fishery councils (16 U.S.C. §§ 1801 et seq.; Eagle et al.
2015). The PFMC is one of these councils, and it consists of various state, federal, tribal,
3 and at-large voting representatives from Washington, Oregon, California, and Idaho, as well as non-voting representatives from Alaska and various other commissions and government entities (“Council Staff | Pacific Fishery Management Council” n.d.; Eagle et al. 2015). The PFMC and NMFS develop and implement, respectively, Fishery
Management Plans (FMP) such as the Pacific Coast Groundfish Fishery Management
Plan (PFMC 2016). Scientists from the NMFS and state agencies develop stock assessments from fishery-dependent and fishery-independent data, and these stock assessments are then used by the PFMC to make recommendations for management actions such as gear restrictions, fishing season, and harvest limits (Eagle et al. 2015;
PFMC 2016; Keller et al. 2017). The recommendations are compiled into an FMP, which is then sent to the NMFS for approval (it is only denied if the NMFS finds it inconsistent with the MSA or other relevant law) and implementation (Eagle et al. 2015).
1.1.2 Coping with uncertainty in fisheries management
To compound the complexity that government agencies and fishery management councils such as NOAA and the PMFC must cope with when managing a fishery, fisheries must also be managed under inevitable scientific uncertainty. Uncertainty in fisheries management includes insufficient or lack of information about the following:
1) details about population dynamics, 2) impacts of management decisions, and 3) future conditions and implications due to changing oceans (Charles 1998; Rosenberg and
Sandifer 2009; Polasky et al. 2011). Specific to population dynamics uncertainties, the following ecological concepts can only be understood to a limited extent: 1) how fish react to current and changing future oceanographic conditions, 2) where the fish stocks
4 are spatially located, and 3) how the species of interest interact with other fish species
(Charles, 1998). However, as fisheries represent a coupled social-ecological system, the social aspect must also be considered (Rosenberg and Sandifer 2009; Berkes 2012).
Specific uncertainties in fisheries management from a social standpoint include: 1) how specific human actions will affect the fishery, and thus, 2) to what extent the changes in a fishery would impact the human social system.
One strategy for coping with the complexity of this coupled social-ecological system and the necessity of making management decisions under uncertainty is to sustainably manage a fishery through ecosystem-based management (EBM) and adaptive management (Berkes, 2012; Charles, 1998). Adaptive management allows a plan to be flexible and adjusted when new information is available, whereas EBM considers the system being managed holistically, instead of individual independent sectors. Although
EBM is an adaptive management strategy to cope with uncertainty, it is not directly mandated by the MSA. However, management agencies such as NMFS have voluntarily begun to shift toward EBM by developing of Fishery Ecosystem Plans (FEP) and Marine
Fisheries Habitat Assessment Improvement Plans, and by considering ecological information and analyses in implementing FMPs (PFMC 2013, 2016; Sigler et al. 2017;
Levin et al. 2018; Peters et al. 2018). Monitoring nearshore habitats could help improve these various ecosystem-based plans by adding habitat and biological information that is currently missing.
5 1.1.3 Nearshore nursery habitats
Estuaries and nearshore coastal habitats are sensitive areas because they have a greater potential to be impacted by human activities and pollution, changing ocean conditions, interannual variation, and seasonal variation (Edgar et al. 2000; Asch 2015;
Hughes et al. 2015; Li et al. 2016; Ruzicka et al. 2016). This is especially true for Eastern
Boundary Current Upwelling ecosystems such at the Northern California Current (NCC) shelf ecosystem, since seasonal upwelling and oxygen depletion (i.e., hypoxia) are impacted by variations in oceanographic conditions (Huyer 1983; Chan et al. 2008;
Rykaczewski and Checkley 2008; Pierce et al. 2012; Peterson et al. 2013; Ruzicka et al.
2016). Additionally, estuaries and nearshore coastal regions exist on the human-marine interface, which means they are areas of high human use including renewable energy
(e.g., wave energy testing sites and wind farms), recreation, fishing, and shipping, and these activities impact marine resources such as fisheries (Boehlert et al. 2008; Halpern et al. 2008; Brekken et al. 2009; ten Brink and Dalton 2018).
Estuaries and nearshore coastal regions along the U.S. West Coast are highly productive, nutrient-rich ecosystems that are important nursery habitats for many marine species (Pauly and Christensen 1995; Beck et al. 2001, 2003; Hughes et al. 2014;
Sheaves et al. 2015). Early life history stages, such as settling or newly-settled juveniles
(i.e., young-of-the-year (YOY) fishes), of many flatfish and rockfish species are known to use estuaries and open coastal habitats for growth before moving further offshore or to other habitat types (such as rocky reefs) to live as adults (Laroche and Holton 1979;
Rosenberg 1982; Krygier and Pearcy 1986; Boehlert and Mundy 1987; Gunderson et al.
6 1990; Gallagher and Heppell 2010; Dauble et al. 2012; Hughes et al. 2014). These early life history stages are more sensitive to environmental stressors, and they are a critical life stage for the development of future adult populations (Fuiman and Werner 2002;
Fiksen et al. 2007). Therefore, stresses due to changing oceanographic conditions could be detrimental for the growth and survival of these fishes, thus impacting future fisheries.
Despite the importance of this habitat and these early life history stages, there is currently no monitoring survey of YOY within nearshore soft-sediment habitats along the U.S.
West Coast.
1.2 Thesis overview and research objectives
This project aims to provide additional information about the nearshore and continental shelf nursery grounds in Oregon in order to evaluate the current monitoring surveys and management framework. For the purpose of this thesis “nearshore” will be defined as the marine ecosystem (habitat and biological communities) along the continental shelf with a water depth of 55 meters or less, and “coastal species” will specifically refer to fishes that reside in the nearshore area. The nearshore information will be compared to NOAA’s current groundfish bottom trawl survey, the U.S. West
Coast Groundfish Bottom Trawl Survey (WCGBTS), to determine if adding shallower sampling areas would improve this important fishery-independent survey. The objectives of this thesis are the following: 1) characterize the YOY groundfish communities along the central Oregon coast, 2) evaluate the representativeness of nearshore fish communities in the WCGBTS to assess if additional nearshore sampling could be used to
7 enhance the WCGBTS, and 3) examine the practicality of adding nearshore sampling to the WCGBTS.
In Chapter 2, I examine data from bottom trawl (WCGBTS) and beam trawl
(nearshore and continental shelf) surveys in an overlapping region in order to characterize the juvenile groundfish populations along the continental shelf of Oregon. This nearshore and continental shelf investigation will include a comparison of groundfish surveys by gear type, species composition, abundance, and specimen size all based on spatial and temporal environmental factors. This new ecological information about the socio- ecological groundfish fishery system will inform how the National Marine Fishery
Service’s WCGBTS could be modified to provide a more holistic representation of the fish populations, and thus support the adaptive management of the groundfish fishery.
In Chapter 3, I examine the policy and regulatory framework currently in place to include new data into the groundfish fishery management framework. In this context, I also consider and discuss the concepts of best available science and the monitoring practices that could be put in place in order to expand monitoring in nearshore habitats.
Specifically, I assess the practicability of incorporating nearshore sampling into a fishery- independent survey by 1) reviewing examples of new methodologies that have been included in groundfish management, 2) describing the avenues for communication and cooperation between federal and state agencies, and 3) discussing practical procedures for sampling nearshore trawlable habitat. By adding to the best available science about groundfish populations and examining the practicality of this innovation, this thesis supports the use and implementation of an adaptive management framework.
8 1.3 References
Asch, R. G. 2015. Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem. Proceedings of the National Academy of Sciences of the United States of America 112(30):4065-74. Beck, M. W., K. L. Heck, K. W. Able, D. L. Childers, D. B. Eggleston, B. M. Gillanders, B. Halpern, C. G. Hays, K. Hoshino, T. J. Minello, R. J. Orth, P. F. Sheridan, and M. P. Weinstein. 2003. The role of nearshore ecosystems as fish and shellfish nurseries. Issues in Ecology 11:1-12. Beck, M. W., K. L. Heck, K. W. Able, D. L. Childers, D. B. Eggleston, B. M. Gillanders, B. Halpern, C. G. Hays, K. Hoshino, T. J. Minello, R. J. Orth, P. F. Sheridan, and M. P. Weinstein. 2001. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. BioScience 51(8):633– 641. Berkes, F. 2012. Implementing ecosystem-based management: Evolution or revolution? Fish & Fisheries 13(4):465–476. Boehlert, G. W., G. R. McMurray, and C. E. Tortorici, eds. 2008. Ecological effects of wave energy development in the Pacific Northwest: A scientific workshop, October 11-12, 2007. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Seattle, WA. Boehlert, G. W., and B. C. Mundy. 1987. Recruitment dynamics of metamorphosing English sole, Parophrys vetulus, to Yaquina Bay, Oregon. Estuarine, Coastal and Shelf Science 25(3):261–281. Brekken, T. K. A., A. Von Jouanne, and H. Y. Han. 2009. Ocean wave energy overview and research at Oregon State University. Pages 1–7. Power Electronics and Machines in Wind Applications. IEEE Publishing. ten Brink, T., and T. Dalton. 2018. Perceptions of commercial and recreational fishers on the potential ecological impacts of the Block Island Wind Farm (US). Frontiers in Marine Science 5(439):1-13. Chan, F., J. A. Barth, J. Lubchenco, A. Kirincich, H. Weeks, W. T. Peterson, and B. A. Menge. 2008. Emergence of anoxia in the California current large marine ecosystem. Science 319(5865):920. Charles, A. 1998. Living with uncertainty in fisheries: Analytical methods, management priorities and the Canadian groundfishery experience. Fisheries Research 37(1– 3):37–50. Council Staff | Pacific Fishery Management Council. (n.d.). https://www.pcouncil.org/council-operations/meet-the-council-staff/.
9 Dauble, A. D., S. A. Heppell, and M. L. Johansson. 2012. Settlement patterns of young- of-the-year rockfish among six Oregon estuaries experiencing different levels of human development. Marine Ecology Progress Series 448:143–154. Eagle, J., M. Goldberg, and J. Sterne. 2015. Domestic Fishery Management. Pages 305– 329 in Ocean and Coastal Law and Policy. Second edition. American Bar Association, Section of Environment, Energy, and Resources, Chicago, Illinois. Edgar, G. J., N. S. Barrett, D. J. Graddon, and P. R. Last. 2000. The conservation significance of estuaries: A classification of Tasmanian estuaries using ecological, physical and demographic attributes as a case study. Biological Conservation 92(3):383–397. Fiksen, O., C. Jorgensen, T. Kristiansen, F. Vikebo, and G. Huse. 2007. Linking behavioural ecology and oceanography: Larval behaviour determines growth, mortality and dispersal. Marine Ecology Progress Series 347:195–205. Fuiman, L. A., and R. G. Werner. 2002. Fishery science: The unique contributions of early life stages. Blackwell Science, Malden, MA. Gallagher, M. B., and S. S. Heppell. 2010. Essential habitat identification for age-0 rockfish along the central Oregon coast. Marine and Coastal Fisheries 2(1):60–72. Gunderson, D. R., D. A. Armstrong, Y.-B. Shi, and R. A. McConnaughey. 1990. Patterns of estuarine use by juvenile English sole (Parophrys vetulus) and Dungeness crab (Cancer magister). Estuaries 13(1):59. Halpern, B. S., S. Walbridge, K. A. Selkoe, C. V. Kappel, F. Micheli, C. D’Agrosa, J. F. Bruno, K. S. Casey, C. Ebert, H. E. Fox, R. Fujita, D. Heinemann, H. S. Lenihan, E. M. P. Madin, M. T. Perry, E. R. Selig, M. Spalding, R. Steneck, and R. Watson. 2008. A global map of human impact on marine ecosystems. Science 319(5865):948–952. Hughes, B. B., M. D. Levey, J. A. Brown, M. C. Fountain, A. B. Carlisle, S. Y. Litvin, C. M. Greene, W. N. Heady, and M. G. Gleason. 2014. Nursery functions of US West Coast Estuaries: The state of knowledge for juveniles of focal invertebrate and fish species. The Nature Conservancy, Arlington, Virginia. Hughes, B. B., M. D. Levey, M. C. Fountain, A. B. Carlisle, F. P. Chavez, and M. G. Gleason. 2015. Climate mediates hypoxic stress on fish diversity and nursery function at the land-sea interface. Proceedings of the National Academy of Sciences of the United States of America 112(26):8025–30. Huyer, A. 1983. Coastal upwelling in the California current system. Progress in Oceanography 12(3):259–284. Keller, A. A., J. Wallace, and R. Methot. 2017. The Northwest Fisheries Science Center’s West Coast Groundfish Bottom Trawl Survey: History, design, and description. U.S. Department of Commerce, NOAA Technical Memorandum 136.
10 Krygier, E. E., and W. Pearcy. 1986. The role of estuarine and offshore nursery areas for young English sole, Parophrys vetulus Girard, of Oregon. Fishery Bulletin 84(1):119–132. Kuhn, E. 2016, October 20. Science and deference: The “Best Available Science” mandate is a fiction in the ninth circuit. Environmental Law Review - Lewis & Clack Law. Laroche, W., and R. Holton. 1979. Occurrence of 0-age English sole, Parophrys vetulus, along the Oregon coast: An open coast nursery area? Northwest Science 53(2):94–96. Levin, P. S., T. E. Essington, K. N. Marshall, L. E. Koehn, L. G. Anderson, A. Bundy, C. Carothers, F. Coleman, L. R. Gerber, J. H. Grabowski, E. Houde, O. P. Jensen, C. Möllmann, K. Rose, J. N. Sanchirico, and A. D. M. Smith. 2018. Building effective fishery ecosystem plans. Marine Policy 92:48–57. Li, M., Y. J. Lee, J. M. Testa, Y. Li, W. Ni, W. M. Kemp, and D. M. Di Toro. 2016. What drives interannual variability of hypoxia in Chesapeake Bay: Climate forcing versus nutrient loading? Geophysical Research Letters 43(5):2015GL067334. National Research Council, Division on Earth and Life Studies, Ocean Studies Board, and Committee on Defining Best Scientific Information Available for Fisheries Management. 2004. Improving the use of the “Best Scientific Information Available” standard in fisheries management. The National Academies Press, Washington, DC. Pauly, D., and V. Christensen. 1995. Primary production required to sustain global fisheries. Nature 374(6519):255–257. Peters, R., A. R. Marshak, M. M. Brady, S. K. Brown, K. Osgood, C. Greene, V. Guida, M. Johnson, T. Kellison, R. McConnaughey, T. Noji, M. Parke, C. Rooper, W. Wakefield, and M. Yoklavich. 2018. Habitat Science is a Fundamental in an Ecosystem-Based Fisheries Management Framework: An Update to the Marine Fisheries Habitat Assessment Improvement Plan. U.S. Dept. of Commerce, NOAA. NOAA Technical Memorandum NMFS-F/SPO-181 29 p. Peterson, J. O., C. A. Morgan, W. T. Peterson, and E. D. Lorenzo. 2013. Seasonal and interannual variation in the extent of hypoxia in the northern California Current from 1998–2012. Limnology and Oceanography 58(6):2279–2292. PFMC. 2013. Pacific Coast Fishery Ecosystem Plan for the U.S. Portion of the California Current Large Marine Ecosystem – Public Review Draft, February 2013. Pacific Fishery Management Council, Portland, OR. PFMC. 2016. Pacific Coast Groundfish Fishery Management Plan for the California, Oregon, and Washington Groundfish Fishery. Pages 1–160. Pacific Fishery Management Council, Portland, OR.
11 Pierce, S. D., J. A. Barth, R. K. Shearman, and A. Y. Erofeev. 2012. Declining oxygen in the Northeast Pacific. Journal of Physical Oceanography 42(3):495–501. Polasky, S., S. R. Carpenter, C. Folke, and B. Keeler. 2011. Decision-making under great uncertainty: Environmental management in an era of global change. Trends in Ecology & Evolution 26(8):398–404. Rodomsky, B. T., T. R. Calavan, and K. C. Lomeli. 2018. The Oregon Commercial Nearshore Fishery Data Update: 2017. Pages 1–53. Oregon Department of Fish and Wildlife, Marine Resources Program, Newport, OR. Rosenberg, A. A. 1982. Growth of juvenile English sole, Parophrys vetulus, in estuarine and open coastal nursery grounds. Fishery bulletin - United States, National Marine Fisheries Service 80(2):245–252. Rosenberg, A., and P. Sandifer. 2009. What do managers need? Pages 13–28 in Ecosystem-based management for the oceans. Island Press, Washington, DC. Ruzicka, J. J., K. H. Brink, D. J. Gifford, and F. Bahr. 2016. A physically coupled end-to- end model platform for coastal ecosystems: Simulating the effects of climate change and changing upwelling characteristics on the Northern California Current ecosystem. Ecological Modelling 331:86–99. Rykaczewski, R. R., and D. M. Checkley. 2008. Influence of ocean winds on the pelagic ecosystem in upwelling regions. Proceedings of the National Academy of Sciences of the United States of America 105(6):1965–1970. Sheaves, M., R. Baker, I. Nagelkerken, and R. M. Connolly. 2015. True value of estuarine and coastal nurseries for fish: Incorporating complexity and dynamics. Estuaries and Coasts 38(2):401–414. Sigler, M. F., M. P. Eagleton, T. E. Helser, J. V. Olson, J. L. Pirtle, C. N. Rooper, S. C. Simpson, and R. P. Stone. 2017. Alaska Essential Fish Habitat Research Plan: A Research Plan for the National Marine Fisheries Service’s Alaska Fisheries Science Center and Alaska Regional Office. AFSC Processed Rep. 2015-05, 22 p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv. Seattle, WA. Sullivan, P., J. Acheson, P. Angermeier, T. Faast, J. Flemma, C. Jones, and E. E. Knudsen. 2006. Defining and implementing Best Available Science for fisheries and environmental science, policy, and management. American Fisheries Society 31(9):460–460. The Research Group, LLC. 2015. Oregon Marine Recreational Fisheries Economic Contributions in 2013 and 2014. Pages 1–70. Oregon Department of Fish & Wildlife, Prepared for Oregon Department of Fish and Wildlife and Oregon Coastal Zone Management Association, Corvallis, OR. The Research Group, LLC. 2017. Oregon Commercial Fishing Industry Year 2016 Economic Activity Summary. Pages 1–13. Oregon Department of Fish & Wildlife, Marine Resource Program, Economic Impact 1.5, Corvallis, OR.
12 U.S. Department of Commerce, National Oceanic and Atmospheric Administration, and National Marine Fisheries Service. 1976. Magnuson-Stevens Fishery Conservation and Management Act. Pages 1-170. 16 U.S.C. §§ 1801 et seq.
13 CHAPTER 2: AN ANALYSIS OF ABUNDANCE AND DISTRIBUTION OF YOUNG-OF-THE-YEAR GROUNDFISHES IN SOFT-SEDIMENT HABITATS OFF THE CENTRAL OREGON COAST
2.1 Introduction
Pacific coast groundfishes are important for commercial, tribal, and recreational fisheries, as well as for ecological purposes (NMFS 2015; The Research Group, LLC
2015, 2017; PFMC 2018). In Oregon alone, the groundfish commercial fishery is one of the most lucrative local commercial fisheries, only exceeded by the Dungeness crab fishery (The Research Group, LLC 2017). Some of the most economically valuable species within the Oregon groundfish fishery are sablefish (Anoplopoma fimbria), worth
$15,100,00 in 2016, and Pacific hake (Merluccius productus), averaging $16,100,000 annually between 2010-2015 (The Research Group, LLC 2017). Many other ecologically and commercially important fish stocks within this fishery are supported by Oregon’s coastal waters including: butter sole (Isopsetta isolepis), Dover sole (Microstomus pacificus), English sole (Parophrys vetulus), Pacific sanddab (Citharichthys sordidus), rex sole (Glyptocephalus zachirus), sand sole (Psettichthys melanostictus), and starry flounder (Platichthys stellatus) (Pearcy 1978; Krygier and Pearcy 1986; Toole et al.
1997; ODFW 2016; PFMC 2018). In order to make sound fisheries management decisions, the abundance and distribution of these fishes, their habitat, and the environmental conditions that affect them, need to be well understood (ODFW 2016;
PFMC 2016a, 2018).
14 Habitat plays a large role in regulating the diversity, abundance, and distribution of fish communities (Pearcy 1978; Auster et al. 1995; NMFS 2010, 2015). Nearshore habitats, specifically, are important nurseries for settling or newly-settled juvenile flatfishes, also known as young-of-the-year (YOY) flatfishes (Krygier and Pearcy 1986;
Hughes et al. 2015). Many of these commercially important species settle in estuaries or the nearshore open coast regions before entering the estuary at later stages (Krygier and
Pearcy 1986; Boehlert and Mundy 1987). However, estuaries are not the only nursery habitats since some species, such as Dover sole, complete their entire life cycle in coastal habitats (Toole et al. 1997). Species that do have an estuarine-dependent life cycle, such as English Sole, are also found along the coast as juveniles (Laroche and Holton 1979;
Rosenberg 1982; Krygier and Pearcy 1986). There is great interest in examining these coastal nurseries because some of these areas have been selected for wind and wave energy testing sites, and it is unclear at the present time what effects these testing sites could have on juvenile survival (Boehlert et al. 2008; Brekken et al. 2009).
Together the National Oceanic and Atmospheric Association’s (NOAA) National
Marine Fisheries Service (NMFS) and the Pacific Fisheries Management Council
(PFMC) oversee the development of policy and the implementation of management of over 90 groundfish species, which are harvested by commercial, recreational, and tribal fisheries off the Washington, Oregon, and California coasts (NOAA Fisheries West Coast
Region n.d.; Baur et al. 2015). The Magnuson–Stevens Fishery Conservation and
Management Act (MSA) requires that Fishery Management Plans (FMPs) “assess and specify the present and probable future conditions of, and the maximum sustainable yield
15 and optimum yield from, the fishery, and include a summary of the information utilized in making such specification. . .” (16 U.S.C. 1853(a)(3)). The NMFS create these models using fish abundance and distribution data from multiple sources including landing records and fishery-independent data from hook-and-line and bottom trawl surveys
(Stauffer 2004; PFMC 2016b; Keller et al. 2017b). One of these fishery-independent surveys is the annual standardized bottom trawl survey known as the West Coast
Groundfish Bottom Trawl Survey (WCGBTS), which is conducted by NMFS’ Northwest
Fisheries Science Center’s (NWFSC) Fishery Resource Analysis and Monitoring
(FRAM) division (Keller et al. 2017b). This survey is adapted from the NMFS’ Alaskan
Fisheries Science Center’s (AFSC) Triennial Survey that started in 1977 and AFSC’s
Slope Survey that began in 1984 (Keller et al. 2017b). Currently, the WCGBTS samples from Cape Flattery, Washington (U.S.-Canada border) to the southern tip of California
(U.S.-Mexico border) between depths of 55 meters to 1280 meters (Keller et al. 2017b).
This current design may not be giving an accurate representation of juvenile groundfishes abundance since it does not capture smaller individuals and because it does not sample nearshore nursery habitats shallower than 55 meters. The design may also miss juvenile groundfishes because the survey is only conducted between May and October, thus missing half of the year including all winter months when several juvenile fish are known to settle in the nearshore (Krygier and Pearcy 1986; Toole et al. 1997; Gibson et al. 2002;
Keller et al. 2017b).
Young-of-the-year (YOY) fishes are a critical life stage toward the development of adult populations (Fuiman and Werner 2002). These juveniles are more sensitive to
16 environmental stressors than their adult counterparts (Fuiman and Werner 2002; Fiksen et al. 2007). Therefore, potential stresses from the changing physical environment and habitat could be detrimental for the growth of these developing adult populations. A major stressor that could affect the YOY fishes are hypoxic events – times of low dissolved oxygen. These events vary in severity, but they normally occur seasonally with upwelling events. Upwelling is a wind-driven phenomenon where cold, nutrient-rich water that may have low levels of dissolved oxygen is brought up to the continental shelf from the deep ocean floor (Chan et al. 2008). This nutrient-rich water is further depleted of oxygen once respiration starts taking place in the productive continental shelf area
(Johnson 2012). These low oxygen events are predicted to worsen due to global climate change (Pierce et al. 2012). Another environmental stressor that is predicted to worsen within the California Current System with global climate change is ocean acidification
(Somero et al. 2016; Klinger et al. 2017). Changing ocean conditions mean that early life history stages could be exposed to even more environmental stressors (Somero et al.
2016). Thus, further understanding of their distribution, abundance, and habitat use is needed in order to manage for healthy and abundant fish stocks in the future.
In order to better understand demersal fish assemblages and how they are affected by changing oceanographic conditions, especially in relation to dissolved oxygen concentrations, the NMFS Northwest Fisheries Science Center, in collaboration with the
Pacific States Marine Fisheries Commission and Oregon State University, started a monthly nearshore beam trawl survey (Johnson 2012; Stinton et al. 2014; Sobocinski et al. 2018). The beam trawl survey has been sampling young-of-the-year groundfishes and
17 other small demersal fishes along the Newport Hydrographic (NH) Line off the central
Oregon coast regularly since 2012. The beam trawl survey has the potential to be repurposed and used as a complement to the WCGBTS given the design of the survey’s methodology and location, as well as the fine-scale time series it provides.
The goal of this project is to compare catches of nearshore fish assemblages from two surveys with different methodologies – the WCGBTS, and the beam trawl survey along the NH Line. The WCGBTS is designed to sample adult groundfishes, whereas the beam trawl is designed to sample YOY assemblages. Comparing the two surveys provides insight on a) characteristics of coastal demersal fish assemblages, and b) whether the two surveys sample the same assemblage. The results of this comparison provide insight into whether a coastal nearshore fish sampling program could be used to enhance the WCGBTS.
2.2 Methods
2.2.1 Sampling procedure – Newport Hydrographic Line
Sampling of the Newport Hydrographic (NH) Line was conducted at six stations categorized by depth. These stations included two off of Moolack Beach at target depths of 30 m and 40 m (MB-30 and MB-40), three along the NH Line at NH-03 (~50 m), NH-
05 (~60 m), NH-10 (~80 m), and a station (~100 m) north of Stonewall Bank used as a replacement for NH-15 which is too rocky to trawl (Figure 1). The Moolack Beach stations were used instead of NH-01 due to rocky outcrops. Moolack Beach is also a historical study area for juvenile flatfishes (e.g., Rosenberg 1982; Hogue and Carey
1982). Sampling was conducted monthly between July 2012 and October 2018, unless
18 poor conditions made sampling unfeasible. However, for the purposes of this study, only samples taken during the years 2012-2015 were used. Four cruises conducted between
2012-2015 (October 2013, November 2013, May 2015, and September 2015) were not available for analysis, and therefore, these cruises were not included in this thesis chapter.
At each station sampled, a beam trawl tow of approximately ten minutes duration was conducted (time from when the trawl winch brake was set to the beginning of trawl haul- back). On occasion when high volumes of ctenophores or sand dollars were encountered, tow duration was shortened to five minutes. A conductivity, temperature, and depth profile (CTD; Seabird SBE 19 CTD with flow-through dissolved oxygen sensor) was also taken that included sensors for dissolved oxygen, light scattering transmission, and chlorophyll fluorescence.
The beam trawl was comprised of a galvanized steel frame with two 0.5-meter- tall sled-like runners equipped with a paired odometer wheel system for measuring the distance sampled, a 2-meter-long beam separating the two runners, a net attached to the two trailing edges of the runners, and a tickler chain attached to the front of the runner
(Carey and Heyamoto 1972; Carney and Carey 1980; Sobocinski et al. 2018). The net of the beam trawl was composed of shrimp trawl webbing, which was lined throughout with a 2.5 X 3 mm mesh liner. This beam trawl gear was constructed in such a way as to target small demersal fishes. The beam trawl was also equipped with a high-definition video camera system with scaling lasers that provided a vertical downward image of the seafloor in the area between the runners.
19 The catch from each beam trawl tow was sorted at sea, with fishes 151 mm or more in length being identified to species, measured to the nearest mm in length
(standard length, SL) with this length recorded, and discarded. All other fishes less than
151 mm SL were flash frozen at sea using dry ice and stored in the lab at -80°C until processed. These fishes were identified in the lab to the lowest taxonomic level (species in most cases), and their standard length (in mm) and wet weight (in g) were recorded.
Fishes were preserved in 10% formalin and archived.
2.2.2 Sampling procedure – West Coast Groundfish Bottom Trawl Survey
The West Coast Groundfish Bottom Trawl Survey (WCGBTS) has been ongoing since 2003 after it was adapted from the AFSC’s Triennial and Slope surveys (Keller et al. 2017b). For the purposes of this study, only samples collected during 2012-2015 were included. Sampling was conducted by randomly selecting locations within a grid with depth and geographical strata restrictions, making this effectively a stratified random survey. The annual survey encompassed the area from the U.S.–Canada border (~ lat.
48°20'N) to the U.S.–Mexico border (~ lat. 32°40'N) between depths of 55 m to 1,280 m.
Sampling took place from late spring to early autumn (May – October) with two coast- wide north-south passes made each year. This survey was a cooperative research effort using West Coast chartered fishing vessels (four for each year). Each of the chartered commercial fishing vessels were equipped with standardized sampling gear comprised of standard four-panel, single-bridle, Aberdeen-type trawl spread by 1.5 × 2.1-m steel V doors with headrope and footropes lengths of 85 m, 25.9 m, and 31.7 m, respectively.
The trawl net was lined with a 3.81-cm (stretch measure) mesh liner to retain smaller
20 fishes and invertebrates. A standardized tow of 15-minutes of on-bottom time with a target towing speed of 2.2 ± 0.5 knots (nm/hr) over ground was made at each station deemed to have trawlable habitat. If the primary station was deemed untrawlable, a secondary or tertiary sampling site was used. As thoroughly described in Keller et al.
(2015, 2017a), oceanographic data including near-bottom dissolved oxygen concentration
(ml/L), salinity (ppt), temperature (°C), and depth (m) were also collected at each satisfactory station using a CTD (factory-calibrated Sea-bird SBE 39 and Sea-bird SBE
19plus, both equipped with an SBE 43 polarographic membrane-type oxygen sensor).
The CTDs were attached to the bottom trawl about 2.8 m behind the headrope. Mean depth, temperature, salinity and dissolved oxygen per tow were averaged over the center 80% of the on-bottom tow duration.
Biological catch was sorted on deck to species (or lowest taxonomic level). Each species (or taxonomic group) was weighed using an electronic, motion-compensated scale (total weight per species). Predetermined species with management relevance were subsampled for individual characteristics necessary for determining population dynamics and biological information. Although not every subsampled individual had all characteristics recorded, the characteristics included: length, weight, age (otoliths, fin rays, or vertebrae – depending on the species), sex, maturity, and stomach content.
Individuals were measured to the nearest cm using total length or fork length unless these were unfeasible for that species (e.g., Hydrolagus colliei and Coryphaenoides acrolepis were measured using anal length). For a full description of methods refer to Stauffer
(2004) and Keller et al. (2017b).
21 2.2.3 Data preparation
Biological and environmental data from the NWFSC’s U.S. West Coast
Groundfish Bottom Trawl Survey (WCGBTS) was extracted from FRAM’s open access
Data Warehouse (https://www.nwfsc.noaa.gov/data/map). Data were subset taxonomically, temporally, and spatially to allow for comparison between the two surveys. Specifically, the subset only included vertebrates from satisfactory (met
FRAM’s rigorous standards) stations that were both sampled between the years 2012-
2015 and within 30 km of any of the six stations from the NH beam trawl survey (Figure
1). There were 65 sample units (combination of sample date and location) from the
WCGBTS that met the geographical restriction of within 30 km from a beam trawl sampling station. This subsetting was meant to match the limited geographical and temporal scope of the NH beam trawl survey, since only vertebrates were recorded and only beam trawl samples collected between 2012-2015 were used. The beam trawl survey continues to this day (as does the WCGBTS), but quality assurance and quality control had only been performed on data up to 2015.
For the survey comparison portion of this study, the NH beam trawl data were subset to include available samples from only early summer to early autumn (May –
October) to directly compare samples from the two surveys (Figure 2). A total of 162 sample units (combination of sample date and station) collected with the beam trawl survey were included in this study from the 2012-2015 time period. Four cruises (October
2013, November 2013, May 2015, and September 2015) were not available for use in this thesis chapter. A total of 78 fish species were caught in beam trawl samples within these
22 162 sample units (Table A1). After subsetting the data set to only include available data collected between April and October (for a meaningful comparison to the WCGBTS), there were a total of 108 sample units, and there was still a total of 78 unique fish species.
The data sets of both surveys were further subset to exclude rare species. For the purposes of this study, rare species were considered those with a frequency of occurrence less than 10%.
A multivariate analysis was done to characterize the main spatial and temporal characteristics of the beam trawl fish assemblages. For this portion of the study, the NH beam trawl survey alone was used. Thus, the data were subset differently. All available
NH beam trawl samples from between the years 2012-2015 were used (Figure 2). Rare species were also removed for this analysis. However, species were considered rare if they were found in less than 5% of samples units. This designation has been found to reduce noise and increase the ability to detect community structure in ecological data without losing much information (McCune and Grace 2002).
The WCGBTS data were subdivided geographically and temporally for comparison purposes. Specifically, sample units were grouped by depth and by year since these are two factors known to be associated with differences in fish distributions
(Tolimieri and Levin 2006; Juan‐Jordá et al. 2009; Sobocinski et al. 2018). Four a priori groups based on depth, termed “depth bins”, were formed to most closely match the depths of the stations sampled with the beam trawl along the NH Line. These depth bins were: 1) > 63 m & ≤ 70 m, compared to NH-05, 2) > 70 m & ≤ 90 m, compared to NH-
10, 3) > 90 m & ≤ 140 m, compared to NH-15, and 4) > 140 m, did not have a NH Line
23 analog (Figure 3). The WCGBTS did not sample any sites shallower than 63 m within the geographical and temporal constraints of this study, thus no analog for MB-30 or MB-40 was available. Four a priori groups based on year (2012-2015) were also formed to account for interannual variability. For the NH beam trawl survey, catch per unit effort
(CPUE) was calculated as the number of fishes caught per 1000 m2 (for readability of output). The catch from the WCGBTS was standardized to the number of fishes caught per hectare (10,000 m2) swept due to the commercial-sized net used.
2.2.4 Data analysis – Multivariate analysis on Beam Trawl Survey
I conducted a multivariate community analysis using PC-ORD (Grace et al. 2016) only on the beam trawl data, which were chosen due to their more expansive temporal coverage. The data were imported into PC-ORD with a species and environmental matrix. Samples missing environmental or catch data were excluded from this analysis.
Environmental variables used in this analysis included: 1) sample date, 2) sample month,
3) sample year, 4) station, 5) depth towed, 6) time towed, 7) distance towed, 8) season, 9) temperature, 10) dissolved oxygen, and 11) salinity (Table A2). The relative species abundance (CPUE) was transformed using a variation of a natural logarithmic transformation with the formula 푏푖푗 = log(푥푖푗 + 푑) − 푐, where xij is the original value in row i and column j in a data matrix, bij is the adjusted value, c is an order of magnitude constant equal to log(minimum value of x) rounded to the nearest integer (subtracting c shifts the values to make the lowest value in the data set equal zero), and d is log-1(c)
(McCune and Grace 2002). This was done to adjust for the wide range of species’ abundance observed (Table A1). The species abundance data were further relativized by
24 species maximum (b = xij/xmaxj) in order to reduce the weight that dominant species were given in the analysis. Since not much information can be gained from a species that occurs once or twice in the community matrix, 46 species that occurred in less than 7 sample units (less than 5% of sample units) were removed from the data set for further analysis (Table A1).
The species abundances were determined to not be multivariate normal due to zero truncation (“dusty bunny” distributions) seen in a scatterplot matrix from PC-ORD
(McCune and Grace 2002). Nonmetric Multidimensional Scaling (NMS) was used as an ordination method in order to account for the non-normality of the data (McCune and
Grace 2002). Random starting configurations were used with 40 runs of the original data and 250 iterations. The Bray-Curtis (Sorenson) measure of similarities was used during the procedure. This distance measure was chosen for the following two reasons: 1) it is a city-block (as opposed to a straight-line, Euclidean) distance measure that only measures distances in species-habitable space (instead of measuring through uninhabitable space), and 2) it deals well with the large variation in species abundance seen in the data (Table
A1) (Krebs 1999; McCune and Grace 2002).
Due to the data departing from multivariate normal, the non-parametric Multi-
Response Permutation Procedure (MRPP) was used to test for differences in the fish communities and abundances between a-prori groupings. Multiple MRPP tests were run, each with a different set of a-priori groupings. These a-priori groupings were based on
1) month sampled, 2) year sampled, 3) season sampled (either “winter” or “summer” with summer being defined as between the spring and fall transitions, which typically occur in
25 May and October), and 4) depth sampled (six depth bins: 30 m, 40 m, 50 m, 60 m, 80 m, and 100 m), which corresponded to different locations along the continental shelf
(McCune and Grace 2002; Brodeur et al. 2008). The Bray-Curtis (Sorenson) distance measure was used for the previously stated reason. Also, an Indicator Species Analysis
(ISA) was run in order to identify species that were statistically driving the differences between groupings by using a Monte Carlo test with 4999 permutations with an alpha level of 0.05 (Brodeur et al. 2008; Toole et al. 2011).
To investigate differences in fish communities based on depth within each season, the data set was further truncated into two subsets, “winter” (November-April) and
“summer” (May-October). An MRPP test was run on each seasonal subset to test for differences in fish communities based on depth sampled (six depth bins: 30 m, 40 m, 50 m, 60 m, 80 m, and 100 m). An ISA was then run to identify the species driving the differences between groupings following the same methods described previously.
In order to see the effect of the different data transformations and relativizations performed on this data, the PC-ORD data advisor was used to show the current profile of the data. Data Adjustments were evaluated based on Whittaker’s Beta Diversity, Beta
Diversity in half-changes, coefficient of variation (CV) for row sums, CV for column sums, and average column skewness (Table A2). An outlier analysis was also run to identify any sample units with an average Bray-Curtis (Sorenson) distance more than two standard deviations away from the mean average Bray-Curtis (Sorenson) distance among sample units.
26 2.2.5 Data analysis – Survey comparison
To quantify some of the geographical, seasonal, and interannual variation in oceanographic conditions that could influence the biological communities, I analyzed the environmental data collected from CTD profilers. For each survey, near-bottom oxygen, temperature, and salinity readings were taken for all sample units used within this study.
These environmental data were averaged based on depth strata (bin or target depth; survey-dependent) and by sample year.
Once environmental data were averaged spatially and temporally, biological catch data were analyzed and compared between surveys in the following three ways: 1) average sample biodiversity, 2) catch composition, and 3) size composition. Biodiversity metrics of the fish assemblages sampled in both surveys were quantified using three different diversity indices: 1) species richness, 2) Shannon-Wiener, and 3) Simpson.
These indices were calculated using the vegan package within R (Oksanen et al. 2018; R
Core Team 2018). Species richness was defined as the number of species within the sample (McCune and Grace 2002). The Shannon-Wiener index measured species
′ 푆 diversity (H’) defined as: 퐻 = − ∑푖=1 푝푖 ln 푝푖 (Whittaker 1972; Hill 1973; McCune and
Grace 2002). The Simpson diversity measured species’ “dominance concentration” using
푠 2 the formula: 퐷1 = 1 − ∑푖=1 푝푖 (Simpson 1949; McCune and Grace 2002). For both formulas, 푝푖 is the proportional abundance of species i and S is the number of species within the sample (or species richness).
In addition to the comparison of diversity, two more approaches were used to evaluate the similarities and differences between the two surveys. First, the catch
27 compositions of species that occurred in > 10% of each survey were compared. To make this a meaningful comparison, the NH beam trawl survey data were restricted to only include samples from April through October. This was due to the short temporal window when the WCGBTS samples near Newport, Oregon (May/June and August/September;
Figure 2). The catch composition of each survey was averaged based on mean CPUE of species caught per depth bin or target depth (survey-dependent) and per sample year. In addition to the CPUE, the frequencies of occurrence (FO) for species that were present in
>10% of sample units in either survey were also compared between surveys. The FO comparisons used aggregated data, while the CPUE comparisons used data partitioned by year and depth bin (or station) sampled.
Second, the size distribution of fishes caught in each survey was compared. The purpose of this size comparison was to investigate how gear type and study design influenced catch. All species with a frequency of occurrence >10% were included in a course summarization. A more in-depth analysis was run on the top two species that occurred in both surveys, namely English sole and Pacific sanddab. Due to differences in sampling methods, all fish lengths for this in-depth analysis were converted to total length and rounded to the nearest centimeter. Fish lengths were converted using simple linear regression models based on a sample of English sole (n = 236) and Pacific sanddab
(n = 491) that were measured using both standard length (SL) and total length (TL). The simple linear regression models used for English sole and Pacific sanddab, respectively, were 푇퐿 = −0.028510 + 1.170211 ∗ 푆퐿 (adjusted R-squared = 0.9973, p-value <<
0.001) and 푇퐿 = 4.758499 + 1.109125 ∗ 푆퐿 (adjusted R-squared = 0.9892, p-value <<
28 0.001). The distribution of specimen lengths was shown using kernel density estimates of individual specimen lengths (i.e., smoothed histogram), which is a smoothed estimate of the relative abundance of individuals within a certain size range as a proportion of the total abundance of individuals caught within each survey (Wickham 2016). All data analysis – with the exception of the multivariate community analysis – was conducted in
R (R Core Team 2018).
2.3 Results
2.3.1 Descriptive summaries of beam trawl and bottom trawl surveys
A total of 11,904 fish of 78 unique species (or taxon) were caught in the beam trawl survey within the temporally-restricted data set of sampled units between April and
October from 2012-2015 (Table 1). A total of 10,046 fish of 85 unique species (or taxon) were caught in the WCGBTS within the spatially and temporally-restricted data set of sample units within 30 km of an NH beam trawl sampling station and from the sample years 2012-2015 (Table 2). The exclusion of species with a FO <10% brought the beam trawl survey down from 78 to 19 unique species (or taxon). The WCGBTS was reduced from 85 to 40 unique species (or taxon).
A summary of the geographical, seasonal, and interannual variation in oceanographic conditions that could influence the biological communities is shown in
Appendix A (Figure A1 and Figure A2). Average dissolved oxygen (DO) concentration, temperature, and salinity showed higher variability (based on the size of the standard error bars) by depth and by year in the beam trawl survey than in the bottom trawl survey
(Figure A1 and Figure A2). Annual averages and depth strata averages for DO and
29 temperature were higher in the beam trawl survey than the bottom trawl survey (Figure
A1 and Figure A2). Salinity showed the opposite trend with higher averages in the bottom trawl survey than the beam trawl survey (Figure A1 and Figure A2). Average temperature decreased with depth in both surveys, whereas only the bottom trawl survey showed an inverse relationship between DO and depth (Figure A2). Salinity increased with depth in both surveys (Figure A2).
2.3.2 Multivariate community analysis of beam trawl data
A total of 149 sample units (SUs) from the beam trawl survey were used for the multivariate community analysis after excluding samples missing environmental or catch data. A total of 27 fish species were included in this analysis after 51 rare species
(defined as occurring in less than 5% (7) of the 149 SUs) were excluded (Table A1). By evaluating data adjustments based on Whittaker’s Beta Diversity, Beta Diversity in half- changes, coefficient of variation (CV) for row sums, CV for column sums, and average column skewness, it was determined that deleting rare species, transforming using a generalized logarithmic technique, and relativizing by species maximum improved the overall performance of the community data for analysis (Table A3).
Running a Nonmetric Multidimensional Scaling (NMS) ordination with 250 runs of species data resulted in an NMS scree plot (a line segment plot that shows the stress of the model as a function of dimensionality) that showed an “elbow”, or a substantial reduction in stress, at two dimensions. Thus, a two-dimensional solution was chosen. The final stress of this solution was 19.81. This final stress is between 10-20, which according to Clarke’s (1993) rule of thumb, is a stress level that corresponds to a useable
30 representation of the data but has the potential to be misleading (McCune and Grace
2002). A Monte Carlo test with 250 runs of randomized data resulted in a p-value of
0.0159. The instability of the solution was <<0.001 with 52 iterations. The proportion of variance in the original dissimilarities represented by Axes 1 and 2 were r2 = 0.566 and
0.199, respectively. The sample units in species space showed a gradient in depth correlating with Axis 1 and dissolved oxygen and salinity correlating with both Axis 1 and Axis 2 (Figure 4). This gradient in depth along Axis 1 corresponded to a gradient in sample units based on station due to the orientation of stations along a nearshore continental shelf transect (Figure 1; Figure 4).
Juvenile groundfish community compositions differed by depth (MRPP, A =
0.154, p << 0.001). These communities also differed by month (MRPP, A = 0.027, p =
0.00035) and by year (MRPP, A = 0.026, p < 0.001). Indicator Species Analysis (ISA) showed that 15 species were significantly driving the difference between the 6 depth groups (30 m, 40 m, 50 m, 60 m, 80 m, and 100 m), from a Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.4 (Table A4). Indicator Species Analysis (ISA) showed that 4 species were significantly driving the difference between the 11 sample date groupings based on month of the year (January, February, March, April, May, June, July, August, September,
October, November), from a Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.8 (Table A4). Indicator
Species Analysis (ISA) showed that 6 species were significantly driving the difference between the sampled date groupings based on year (2012, 2013, 2014, and 2015), from a
31 Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.4 (Table A4).
Juvenile groundfish community compositions also differed based on season, where season was defined as “winter” or “summer” based on the average spring and fall transitions (MRPP, A = 0.014, p < 0.001). Indicator Species Analysis (ISA) showed that
7 species were significantly driving the difference between the seasons based on winter and summer (November-March and April-October, respectively), from a Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.4 (Table A4). Within these individual winter and summer subsets, juvenile groundfish communities differed by depth (winter MRPP, A = 0.208, p
<< 0.001; summer MRPP, A = 0.157, p << 0.001). Indicator Species Analysis (ISA) showed that for winter months (November-March) 10 species were significantly driving the difference between the 6 depth groups (30 m, 40 m, 50 m, 60 m, 80 m, and 100 m), from a Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.2 (Table A4). Indicator Species Analysis
(ISA) showed that for summer months (April-October) 11 species were significantly driving the difference between the 6 depth groups (30 m, 40 m, 50 m, 60 m, 80 m, and
100 m), from a Monte Carlo test with 4999 permutations with an alpha level of 0.05, where the number of expected significant values was 1.4 (Table A4).
2.3.3 Comparisons of beam trawl and bottom trawl (WCGBTS) data
Shannon-Wiener, Simpson, and species richness biodiversity metrics showed fluctuations in sample diversity based on sample year for both the beam trawl survey and
32 the WCGBTS (Figure 5). All three metrics showed the most sample diversity in 2013 for the beam trawl survey, and 2015 for the WCGBTS (Figure 5). Both surveys had the lowest sample diversity in 2014 (Figure 5). All biodiversity metrics showed fluctuations in sample diversity based on depth strata for both surveys (Figure 6). For the beam trawl survey, station MB40 with a target depth of 40 m showed the highest sample diversity, and station NH10 with a target depth of 80 m showed the lowest (Figure 6). For the
WCGBTS, depth bin 4 with a depth range of 140-500 m showed the highest sample diversity, and depth bin 1 with a depth range of 63-70 m showed the lowest (Figure 6).
The top five most frequently occurring species in the beam trawl survey were
Pacific sanddab, English sole, speckled sanddab (Citharichthys stigmaeus), butter sole, and sand sole with FOs of 90%, 81%, 69%, 60%, and 42%, respectively (Table 1 and
Figure 7). Pacific sanddab also had the highest average CPUE per 1000 m2 and the largest observed depth range with specimens caught from 24.4 -123.8 m (Table 1). The top five most frequently occurring species in the WCGBTS were rex sole, Dover sole, spotted ratfish (Hydrolagus colliei), petrale sole (Eopsetta jordani), and English sole with
FOs of 92%, 86%, 77%, 74%, and 70%, respectively (Table 2 and Figure 7). Sharpchin rockfish had the highest average CPUE per hectare overall, but only a frequency of occurrence of 20% (Table 2 and Figure 7). Of the top five most frequently occurring fish species in the WCGBTS, rex sole had the highest average CPUE per hectare (Table 2).
Rex sole also had the largest observed depth range with specimens caught from 64.3 -
498.9 m (Table 2). Comparing the FO of fish species caught in each survey showed that
English sole and Pacific sanddab frequently occurred in both surveys (Figure 7).
33 Although speckled sanddab was the third most frequently occurring species in the beam trawl survey, they were not observed in the WCGBTS (Figure 7).
Average CPUE showed differences in the rank order of species both per year and per depth strata in the beam trawl and bottom trawl surveys (Figure 8 and Figure 9). In the beam trawl survey, Pacific sanddab, English sole, and butter sole had the highest average CPUE in 2014 and 2015, 2013, and 2012, respectively (Figure 8). By depth strata, Pacific sanddab, slender sole, and English sole were the top three most abundant species caught in the beam trawl survey between 60-120 m (NH05, NH10, and NH15) based on average CPUE (Figure 9). Butter sole, Pacific sanddab, English sole, and speckled sanddab were the top four most abundant species caught in the beam trawl survey between 30-50 m based on average CPUE (Figure 10).
The bottom trawl survey showed an average CPUE for sharpchin rockfish two to three times higher than any other species in all years except 2013 (Figure 8). In 2013,
Pacific sanddab exceeded sharpchin rockfish in mean CPUE (Figure 8). However, average CPUE based on depth strata for the bottom trawl survey showed that Pacific sanddab was caught two to four times more than any other species between 63-140 m
(depth bins 1-3; Figure 9). English sole, rex sole, and slender sole had the next three highest rank abundances in the bottom trawl survey within depth bins 1-3 (comparable to the beam trawl stations: NH05, NH10, & NH15; Figure 9). Sharpchin rockfish was the dominant species in the deepest depth strata (bin 4; 140-500 m) with a relative abundance
5 times the second highest species, rex sole (Figure 10). Pacific sanddab and English sole
34 were among species with the lowest relative abundance within this deep depth strata
(Figure 10).
The comparison between specimen lengths caught in each survey showed an overall trend of smaller individuals caught in the beam trawl survey, but higher density
(density throughout refers to the relative abundance of individuals within a certain size range as a proportion of the total abundance of individuals) of individuals caught in the
WCGBTS (Figure 11). The size distribution of specimens caught in the beam trawl survey showed a peak in density of individuals with a standard length (SL) of ~3 cm
(Figure 11). The size distribution of specimens caught in the WCGBTS showed a peak in density of individuals with a total length of ~25 cm (Figure 11). The overall density of individuals in the beam trawl survey was dominated by smaller specimens (< 25 cm), whereas the specimens caught in the WCGBTS had a broader range with most individuals being between the lengths of 3-100 cm (Figure 11).
A fine-scale comparison of specimen size for Pacific sanddab and English sole caught in each survey showed that the beam trawl survey caught more small individuals
(0-35 cm; Figure 12 and Figure 13). However, the bottom trawl survey (WCGBTS) had a peak in density of individuals of a smaller length than the beam trawl survey for both species (Figure 12 and Figure 13). For Pacific sanddab, the bottom trawl survey had a peak in density of individuals ~13 cm compared to the beam trawl survey’s peak at 25 cm
(Figure 12). For English sole, the bottom trawl survey had a peak in density of individuals ~8 cm compared to the beam trawl survey’s 11 cm (Figure 13). For the spatial
35 and interannual variation of these size composition broken up by survey, see Appendix A
(Figures A3-A8).
2.4 Discussion
Using a multivariate community analysis and a comprehensive survey comparison, I observed a difference in demersal fish assemblages based on depth, oceanographic conditions, and temporal variation. The comparison between surveys showed fish species known to reside in shallower, nearshore habitats dominated the beam trawl survey, while a wider variety of species were observed in the bottom trawl survey.
The beam trawl subset showed greater variability in dissolved oxygen concentration, near-bottom temperature, and salinity than the bottom trawl survey due to the higher temporal resolution in the beam trawl survey’s sampling design. The comparison between surveys also showed the beam trawl survey captured an abundance of smaller specimens
(< 25 cm SL), which the bottom trawl survey did not catch (Figure 11). However, a size comparison focused on two coastal species abundant in both surveys showed the bottom trawl did capture small specimens (< 20 cm TL), although not to the high level of abundance seen in the beam trawl survey (Figure 12 and Figure 13).
The higher variability in oceanographic conditions, especially dissolved oxygen concentration and near-bottom temperature (Figure A1 and Figure A2), seen in the beam trawl survey is a product of monthly sampling instead of the broad coast-wide swath provided by the bottom trawl survey. Although the bottom trawl survey has large spatial coverage (55-1,280 m depths along the coasts of Washington to California), it lacks fine- scale temporal coverage that the beam trawl survey provides. Eastern boundary currents
36 such as the California Current System experience summer upwelling and winter downwelling that can both change drastically within a short timeframe (only days during the spring and fall transitions) and vary in intensity with interannual variation of ocean conditions (Huyer 1983; Reid 1986; Lynn et al. 2003; Huyer et al. 2007; Marshall and
Plumb 2008). Intensified upwelling and resulting hypoxia have already been observed in the California Current System, and they are predicted to continue with global climate change (Chan et al. 2008; García-Reyes and Largier 2010). Therefore, fine-scale ecosystem monitoring, such as the beam trawl survey, is important for detecting ephemeral periods of hypoxia along the continental shelf that are predicted to increase in frequency and intensity with global climate change.
From a biological standpoint, the fish assemblages caught in the beam trawl survey had spatial variation in that correlated with depth strata and other environmental factors such as dissolved oxygen and salinity (Figure 4). This result follows what other studies have examined in the distribution of juvenile demersal fish assemblages.
Tolimieri and Levin (2006), using the WCGBTS, found strong correlations between adult
Pacific coast groundfishes and depth when examining depths between 200-1,200 m.
Gabriel (1982) found distinct assemblages of demersal fish species along the U.S. West
Coast based on depth contours. Keller et al. (2015, 2017b) found a correlation between the distribution of demersal fishes and environmental factors such as salinity, temperature, dissolved oxygen, and depth. Sobocinski et al. (2018) found a similar relationship between juvenile demersal fishes and summer dissolved oxygen conditions and other environmental factors.
37 The beam trawl survey also detected temporal variation within juvenile demersal fish assemblages (Figure 4). Johnson (2012) found that season and location explained the majority of variation within nearshore larval and juvenile demersal community assemblages, which agrees with the results of this survey. Interannual variability in fish assemblages also aligns with the findings of other studies. Summer upwelling intensity, nutrient availability, and seasonal hypoxia severity varies interannually within the
California current, which influences the demersal fish assemblages (Huyer 1983; Huyer et al. 2007; Juan‐Jordá et al. 2009; Peterson et al. 2013; Keller et al. 2015, 2017a).
The differences in gear type and sampling designs were demonstrated in multiple ways, including in the distribution of specimen lengths. The small mesh size of the beam trawl and the shallower sampling locations had the ability to catch smaller juvenile fish, whereas the bottom trawl survey caught larger specimens on average. This size selectivity agrees with what other studies have found with trawl gears (Cadrin et al. 2016;
Stepputtis et al. 2016) and should be considered in stock assessments and fisheries management decisions.
Another difference between the surveys was demonstrated in the diversity patterns and the species caught in each survey. Overall, the bottom trawl survey caught a higher number of species than the beam trawl survey including multiple rockfish species that the beam trawl survey is not designed to sample (Table 1, Table 2, and Figure 5). For instance, sharpchin rockfish (Sebastes zacentrus) were absent in the beam trawl survey, but they were the dominant species in the bottom trawl survey, although, only at stations deeper than 140 m (Bin 4; Table 2, Figure 9 and Figure 10). These trends in sharpchin
38 rockfish align with previous studies, and its patchiness (seen in the large error bars;
Figure 10) is likely due to its preference for rocky reefs that cannot be sampled with trawl gear (Love 2011). Rex sole (Glyptocephalus zachirus) shows a similar trend, although these fish are still found in the beam trawl survey, the frequency of occurrence is 7-8 times as high in the bottom trawl survey (Figure 7). This discrepancy between the surveys could be because rex soles are typically found deeper than 100 m, thus, their habitat is not highly sampled in the beam trawl survey. Conversely, speckled sanddabs are completely absent from the bottom trawl survey, but among the most abundant in the beam trawl survey (Table 1, Table 2, Figure 7, Figure 8, Figure 9, and Figure 10). The lack of speckled sanddabs within the bottom trawl survey could be because this species is found in shallow waters ≤ 60 m (Love 2011), and therefore, its habitat range is not sampled in the bottom trawl survey that has a boundary of 55 m (63 m within this subset).
Although the bottom trawl survey caught a higher number of species from a more diverse group of family, the beam trawl showed a broad representation of flatfishes and was dominated by nearshore species due to the nature of the sampling design (Figure 7 and Figure 8). Additionally, the mean sample diversity from the beam trawl survey showed an inverse relationship with depth, while the bottom trawl survey showed an increasing trend with depth. These differences could be due to the differences in depth ranges sampled by each survey. The beam trawl survey sampled depths between 30-120 m, as opposed to the 63-500 m depths sampled in the bottom trawl survey subset.
This difference in sampling location between the two surveys drove a shift in observed community assemblages. As demonstrated by the multivariate community
39 analysis of the beam trawl data, there are two distinct community assemblages based on depth along the NH Line. These assemblages consist of a shallower community sampled between depths of 30-60 m (MB 30, MB 40, NH 03, and NH 05) and a deeper community sampled between depths of 60-120 m (NH05, NH 10, and NH15; Figure 4).
The beam trawl’s sampling station with a target depth of 60 m (NH 05) overlaps the two distinct assemblages (Figure 4). The shallowest depth sampled by the WCGBTS survey is 55 m (63 m within the geographical area examined in this study), so the current design is not fully representing the shallower community assemblages (Keller et al. 2017b).
The shift in community assemblages based on depth aligns with the findings from other studies. Specifically, English sole, butter sole, Pacific sanddab, speckled sanddab, and sand sole are known to use nearshore coastal waters and estuaries off Oregon as a nursery ground (Pearcy 1978; Laroche and Holton 1979; Hogue and Carey 1982; Krygier and Pearcy 1986; Boehlert and Mundy 1987; Gunderson et al. 1990; Rooper et al. 2006).
This life history trait is exemplified in the beam trawl sampling, but this is also observed in the shallower bottom trawl survey samples.
Juvenile groundfish community compositions were shown to vary seasonally by the MRPP on the beam trawl data based on the summer (May-October) and winter
(November-April) months. Therefore, from a temporal context, the WCGBTS is not accurately representing the seasonal settling patterns of juvenile flatfishes by sampling between late spring and early fall, since some flatfish species, such as English sole, settle in late winter and early spring (Rosenberg 1982; Krygier and Pearcy 1986; Boehlert and
Mundy 1987). Settlement patterns are also misrepresented interannually by the bottom
40 trawl survey since the beam trawl survey shows much higher interannual variation
(Figure 8). Settlement patterns vary interannually due to fluctuations in oceanographic conditions and other factors (Bradford 1992; Sakuma et al. 2006; Field et al. 2010; Toole et al. 2011; Ralston et al. 2013; Stige et al. 2013; Sobocinski et al. 2018). Thus, monitoring shallower habitats is important for an accurate prediction of year class strength.
Overall, the beam trawl provides a representative sample of juvenile flatfish communities within nearshore soft-sediment habitats off the central Oregon coast. The fine-scale temporal coverage allows the beam trawl survey to better capture ephemeral environmental conditions than the bottom trawl survey. Conversely, the bottom trawl survey provides a representation of groundfish species all along the U.S. West coast, instead of the small spatial extent of the beam trawl survey. The gear type and sampling location make this survey better suited for sampling smaller-sized individuals and species that live at shallower depths. However, the beam trawl is not well-suited for sampling rockfish species, unlike the bottom trawl survey. The two surveys overlap in some capabilities, but this thesis chapter shows that the beam trawl survey or a similar nearshore survey could complement the bottom trawl survey. Based on the size comparisons of Pacific sanddab and English sole caught in each survey, extending the current bottom trawl survey into shallower waters could also be sufficient to provide a more holistic representation of groundfish populations and various life stages
41 2.5 Conclusion
Nearshore coastal waters and estuaries are important ecosystems serving as nursery habitats for developing early life history stages of many ecologically and economically important fish species (Pearcy and Myers 1974; Beck et al. 2001, 2003;
Hughes et al. 2014). These early life history stages will later recruit into the adult population, and juvenile abundance can be a strong predictor of future recruitment
(Bradford 1992; Fuiman and Werner 2002; Stige et al. 2013). Therefore, with the known value of nursery habitats and their contribution to future fish populations (Beck et al.
2001, 2003; Chittaro et al. 2009; Hughes et al. 2014; Sheaves et al. 2015), it is necessary to monitor the fish assemblages in these nearshore areas. This nearshore monitoring could improve stock assessments by providing recruitment indices that will help predict strong year-classes and allow for a better understanding of population dynamics
(Bradford 1992; Field et al. 2010; Stige et al. 2013). Additionally, these nearshore continental shelf regions are exposed to higher variability in ocean conditions such as seasonal upwelling with associated hypoxia and ocean acidification, which is predicted to intensify and increase in frequency with global climate change (Huyer 1983; Chan et al.
2008; García-Reyes and Largier 2010; Klinger et al., 2017). Thus, monitoring nearshore habitats would provide a more holistic representation of both the groundfish populations and the California Current Ecosystem, which is especially important in a time of uncertain future environmental conditions. In order to implement nearshore habitat monitoring into a fishery-independent survey further work is required to understand the
42 feasibility and practicality of incorporating nearshore sampling into the current regulatory management framework.
43 2.6 References
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47 PFMC. 2016a. Pacific Coast Groundfish Fishery Management Plan for the California, Oregon, and Washington Groundfish Fishery. Pacific Fishery Management Council. PFMC. 2016b. Status of the Pacific Coast Groundfish Fishery. Pages 1–309. Pacific Fishery Management Council, Description of the Fishery, Portland, OR. PFMC. 2018. Status of the Pacific Coast Groundfish Fishery: Stock Assessment and Fishery Evaluation - Description of the Fishery. Pages 1–323. The Pacific Fishery Management Council, Description of the Fishery, Portland, OR. Pierce, S. D., J. A. Barth, R. K. Shearman, and A. Y. Erofeev. 2012. Declining oxygen in the Northeast Pacific. Journal of Physical Oceanography 42(3):495–501. R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Ralston, S., K. M. Sakuma, and J. C. Field. 2013. Interannual variation in pelagic juvenile rockfish (Sebastes spp.) abundance – going with the flow. Fisheries Oceanography 22(4):288–308. Reid, B. J. 1986. The fall transition of Oregon shelf waters. M.S. Thesis, Oregon State University, Corvallis, OR. Rooper, C. N., D. R. Gunderson, and D. A. Armstrong. 2006. Evidence for resource partitioning and competition in nursery estuaries by juvenile flatfish in Oregon and Washington. Fishery Bulletin 104(4):616–622. Rosenberg, A. A. 1982. Growth of juvenile English sole, Parophrys vetulus, in estuarine and open coastal nursery grounds. Fishery bulletin - United States, National Marine Fisheries Service 80(2):245–252. Sakuma, K. M., S. R. Ralston, and V. G. Wespestad. 2006. Interannual and spatial variation in the distribution of young-of-the-year rockfish (Sebastes spp): Expanding and coordinating a survey sampling frame. California Cooperative Oceanic Fisheries Investigations Reports 47:127–139. Sheaves, M., R. Baker, I. Nagelkerken, and R. M. Connolly. 2015. True value of estuarine and coastal nurseries for fish: Incorporating complexity and dynamics. Estuaries and Coasts 38(2):401–414. Simpson, E. H. 1949. Measurement of Diversity. Nature 163(4148):688–688. Sobocinski, K. L., L. Ciannelli, W. W. Wakefield, M. E. Yergey, and A. Johnson- Colegrove. 2018. Distribution and abundance of juvenile demersal fishes in relation to summer hypoxia and other environmental variables in coastal Oregon, USA. Estuarine, Coastal and Shelf Science 205:75–90.
48 Somero, G. N., J. M. Beers, F. Chan, T. M. Hill, T. Klinger, and S. Y. Litvin. 2016. What changes in the carbonate system, oxygen, and temperature portend for the Northeastern Pacific Ocean: A physiological perspective. BioScience 66(1):14– 26. Stauffer, G. (compiler). 2004. NOAA Protocols for Groundfish Bottom Trawl Surveys of the Nation’s Fishery Resources. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-65, 205 p. Stepputtis, D., J. Santos, B. Herrmann, and B. Mieske. 2016. Broadening the horizon of size selectivity in trawl gears. Fisheries Research 184:18–25. Stige, L. C., M. E. Hunsicker, K. M. Bailey, N. A. Yaragina, and G. L. H. Jr. 2013. Predicting fish recruitment from juvenile abundance and environmental indices. Marine Ecology Progress Series 480:245–261. Stinton, A., L. Ciannelli, D. C. Reese, and W. Wakefield. 2014. Using in situ video analysis to access juvenile flatfish behavior along the Oregon central coast. California Cooperative Oceanic Fisheries Investigations Reports 55:158–168. The Research Group, LLC. 2015. Oregon Marine Recreational Fisheries Economic Contributions in 2013 and 2014. Pages 1–70. Oregon Department of Fish & Wildlife, Prepared for Oregon Department of Fish and Wildlife and Oregon Coastal Zone Management Association, Corvallis, OR. The Research Group, LLC. 2017. Oregon Commercial Fishing Industry Year 2016 Economic Activity Summary. Pages 1–13. Oregon Department of Fish & Wildlife, Marine Resource Program, Economic Impact 1.5, Corvallis, OR. Tolimieri, N., and P. S. Levin. 2006. Assemblage structure of Eastern Pacific Groundfishes on the U.S. continental slope in relation to physical and environmental variables. Transactions of the American Fisheries Society 135(2):317–332. Toole, C., R. D. Brodeur, C. J. Donohoe, and D. Markle. 2011. Seasonal and interannual variability in the community structure of small demersal fishes off the central Oregon coast. Marine Ecology Progress Series 428:201–217. Toole, C., D. Markle, and C. Donohoe. 1997. Settlement timing, distribution, and abundance of Dover sole (Microstomus pacificus) on an outer continental shelf nursery area. Canadian Journal of Fisheries and Aquatic Sciences 54(3):531–542. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, and National Marine Fisheries Service. 1976. Magnuson-Stevens Fishery Conservation and Management Act. Pages 1-170. 16 U.S.C. §§ 1801 et seq. Whittaker, R. H. 1972. Evolution and measurement of species diversity. Taxon 21(2/3):213–251. Wickham, H. 2016. ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York.
49 2.7 Figures and Tables
Figure 1. Sampling locations along the central Oregon coast. The Newport Hydrographic (NH) Line (blue dashed line), stations from the beam trawl survey (blue circles), stations from the West Coast Groundfish Bottom Trawl Survey (WCGBTS) within 30 km of the NH Line used in this study (red circles), and stations from the WCGBTS not used in this study (black diamonds) are represented.
50
Figure 2. Sampling period for both the WCGBTS and the Beam Trawl Survey from 2012-2015.
51
Figure 3. Depth distribution of West Coast Groundfish Bottom Trawl Survey Stations comparable to the Newport Hydrographic line Beam Trawl Survey. Red lines denote depth bins: 1) > 63 m & ≤ 70 m, compared to NH-05, 2) > 70 m & ≤ 90 m, compared to NH-10, 3) > 90 m & ≤ 140 m, compared to NH-15, and 4) > 140 m, no NH Line analog.
52
Figure 4. Nonmetric Multidimensional Scaling (NMS) ordination of sample units points in species space showing Axis 1 and Axis 2 of a 2-dimensional solution using a Sorenson distance measure. Proportion of variance represented by Axis 1 and Axis 2 is 0.566 and 0.199, respectively. Environmental variables with a combined linear correlation (r2 for each axis) > 0.2 have been overlayed. The length of the lines overlayed with the environmental variables represent the strength of the environmental variable’s correlation with Axis 1 in the horizontal direction and Axis 2 in the vertical direction. Stations have been overlayed to group sample units by depth strata.
53 Table 1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between April and October from 2012 until 2015. Frequency of Occurrence (FO) refers to the percent of tows (out of a total of 108 tows) in which a given species was present. Average catch per unit effort (CPUE) is the standardized relative abundance calculated by dividing the catch by the area swept (tow distance multiplied by beam trawl width). Minimum and maximum depths where species was observed are also shown. *Denotes a target depth instead of an observed depth since this species was only observed at a station that did not have a depth recorded. Frequency Average Min. Max. Common of CPUE Depth Depth Scientific Name Name Occurrence per 1000 Observed Observed (FO %) m2 Citharichthys Pacific 89.81 24.461 24.4 123.8 sordidus sanddab Parophrys vetulus English sole 80.56 14.268 24.4 108.4 Citharichthys speckled 69.44 13.312 24.4 99.9 stigmaeus sanddab Isopsetta isolepis butter sole 60.19 19.196 24.4 102.1 Psettichthys sand sole 41.67 0.843 24.4 100.0 melanostictus Lyopsetta exilis slender sole 35.19 8.517 29.9 123.8 Microgadus Pacific 28.70 3.164 24.4 57.2 proximus tomcod Liparidae snailfishes 25.93 0.965 28.3 102.0 Microstomus Dover sole 23.15 0.314 33.8 123.8 pacificus Ammodytes Pacific 22.22 0.616 24.4 81.8 hexapterus sandlance Leptocottus armatus Pacific 20.37 0.377 25.6 59.1 staghorn sculpin Chesnonia warty 19.44 0.387 25.6 123.8 verrucosa poacher Pleuronectidae righteye 15.74 0.935 24.4 82.0 flounders Radulinus asprellus slim sculpin 15.74 0.813 78.6 123.8 Eopsetta jordani petrale sole 12.96 0.137 29.4 108.4 Chitonotus roughback 12.04 0.168 28.3 57.6 pugetensis sculpin Glyptocephalus rex sole 11.11 0.338 57.6 123.8 zachirus Raja binoculata big skate 11.11 0.154 29.4 84.6
54 Table 1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between April and October from 2012 until 2015. (Continued)
Pallasina barbata tubenose 10.19 0.171 28.9 47.0 poacher Citharichthys spp. sanddabs 9.26 0.791 25.6 80.0 Osmeridae smelts 9.26 0.185 25.6 80.4 Allosmerus whitebait 8.33 0.497 31.8 78.0 elongatus smelt Ophiodon elongatus lingcod 6.48 0.070 24.4 41.8 Spirinchus starksi night smelt 6.48 0.307 25.6 47.5 Agonopsis vulsa northern 5.56 0.045 31.0 106.0 spearnose poacher Cottidae sculpins 5.56 0.099 28.3 56.1 Odontopyxis pygmy 5.56 0.041 32.0 100.0 trispinosa poacher Atheresthes stomias arrowtooth 4.63 0.093 44.8 106.6 flounder Sebastes spp. rockfishes 4.63 0.078 31.0 48.0 Hydrolagus colliei spotted 3.70 0.023 46.5 94.0 ratfish Pleuronichthys curlfin sole 3.70 0.030 29.9 80.4 decurrens Ronquilus jordani northern 3.70 0.088 24.4 47.7 ronquil Cymatogaster shiner 2.78 0.057 31.3 47.5 aggregata surfperch Gadidae gadids 2.78 0.501 24.4 32.9 Hemilepidotus red Irish lord 2.78 0.024 25.6 32.9 hemilepidotus Hemilepidotus brown Irish 2.78 0.069 32.9 41.4 spinosus lord Plectobranchus bluebarred 2.78 0.072 29.4 57.2 evides prickleback Sebastes elongatus greenstriped 2.78 0.078 44.0 102.0 rockfish Sebastes pinniger canary 2.78 0.037 29.9 47.5 rockfish Stellerina xyosterna pricklebreast 2.78 0.026 25.6 42.9 poacher Unknown unknown 2.78 0.025 47.5 57.0 Xeneretmus bluespotted 2.78 0.017 81.0 106.0 triacanthus poacher
55 Table 1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between April and October from 2012 until 2015. (Continued)
Bathyagonus gray 1.85 0.012 82.0 93.0 alascanus starsnout Cololabis saira Pacific saury 1.85 0.026 57.6 106.6 Lepidogobius bay goby 1.85 0.024 31.3 42.9 lepidus Xeneretmus latifrons blacktip 1.85 0.081 93.0 93.0 poacher Anoplagonus smooth 0.93 0.014 41.9 41.9 inermis aligatorfish Anoplopoma fimbria sablefish 0.93 0.007 38.0 38.0 Artedius sculpins 0.93 0.007 41.4 41.4 Bathymasteridae ronquils 0.93 0.016 57.9 57.9 Chilara taylori spotted cusk- 0.93 0.007 47.5 47.5 eel Cryptacanthodes dwarf 0.93 0.031 100* 100* aleutensis wrymouth Enophrys bison buffalo 0.93 0.017 25.6 25.6 scuplin Eptatretus stoutii Pacific 0.93 0.007 106.8 106.8 hagfish Gasterosteus three-spined 0.93 0.010 28.9 28.9 aculeatus stickleback Hippoglossoides flathead sole 0.93 0.014 78.6 78.6 elassodon Icelinus burchami dusty sculpin 0.93 0.006 106.6 106.6 Lepidopsetta northern rock 0.93 0.012 46.7 46.7 polyxystra sole Lycodes cortezianus bigfin 0.93 0.065 93.0 93.0 eelpout Lycodes pacificus blackbelly 0.93 0.062 100* 100* eelpout Lycodes palearis wattled 0.93 0.229 123.8 123.8 eelpout Pholidae gunnels 0.93 0.006 47.5 47.5 Poroclinus rothrocki whitebarred 0.93 0.034 123.8 123.8 prickleback Raja rhina longnose 0.93 0.008 100* 100* skate Sebastes diploproa splitnose 0.93 0.006 44.0 44.0 rockfish
56 Table 1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between April and October from 2012 until 2015. (Continued)
Sebastes entomelas starry 0.93 0.013 44.8 44.8 rockfish Sebastes flavidus yellowtail 0.93 0.007 38.0 38.0 rockfish Sebastes mystinus blue rockfish 0.93 0.008 32.9 32.9 Spirinchus longfin smelt 0.93 0.061 32.9 32.9 thaleichthys Syngnathus bay Pipefish 0.93 0.011 47.0 47.0 leptorhynchus
57 Table 2. Fish species sampled within 30 km of the Newport Hydrographic Line with the U.S. West Coast Groundfish Bottom Trawl Survey. Frequency of Occurrence (FO) refers to the percent of tows (out of a total of 65 tows) in which a given species was present. Average catch per unit effort (CPUE) is the standardized relative abundance calculated by dividing the catch by the area swept. Minimum and maximum depths where this species was observed are also shown. Frequency Average Min. Max. Common of CPUE Depth Depth Scientific Name Name Occurrence per Observed Observed (FO %) Hectare Glyptocephalus zachirus rex sole 92.31 88.842 64.3 498.9 Microstomus pacificus Dover sole 86.15 58.574 64.3 498.9 spotted Hydrolagus colliei ratfish 76.92 9.282 63.1 388.9 Eopsetta jordani petrale sole 73.85 18.918 63.1 286.5 Parophrys vetulus English sole 70.77 32.022 63.1 363.4 Atheresthes arrowtooth stomias flounder 64.62 26.388 63.1 498.9 Lyopsetta exilis slender sole 63.08 92.004 64.3 498.9 longnose Raja rhina skate 63.08 3.936 64.3 498.9 Citharichthys Pacific sordidus sanddab 53.85 290.687 63.1 186.8 Ophiodon elongatus lingcod 49.23 5.120 70.0 238.8 Anoplopoma fimbria sablefish 47.69 7.194 70.0 498.9 Raja binoculata big skate 40.00 2.566 63.1 199.3 sandpaper Bathyraja kincaidii skate 38.46 3.826 64.3 498.9 Icelinus threadfin filamentosus sculpin 35.38 6.858 81.8 388.9 Merluccius productus Pacific hake 33.85 11.737 109.6 498.9 Lycodes cortezianus bigfin eelpout 32.31 13.697 138.6 498.9 greenstriped Sebastes elongatus rockfish 32.31 42.797 85.6 247.5 Pleuronichthys decurrens curlfin sole 30.77 2.716 64.3 103.2
58 Table 2. Fish species sampled within 30 km of the Newport Hydrographic Line with the U.S. West Coast Groundfish Bottom Trawl Survey. (Continued)
splitnose Sebastes diploproa rockfish 30.77 93.543 179.4 426.7 Sebastolobus shortspine alascanus thornyhead 29.23 118.537 186.8 498.9 Lepidopsetta southern rock bilineata sole 26.15 4.719 64.3 103.2 Pacific spiny Squalus suckleyi dogfish 26.15 4.966 70.0 349.5 Hippoglossus Pacific stenolepis halibut 24.62 1.812 70.8 247.5 Pacific ocean Sebastes alutus perch 24.62 4.548 138.6 381.7 darkblotched Sebastes crameri rockfish 24.62 19.566 143.9 363.4 Thaleichthys pacificus eulachon 24.62 11.501 72.0 238.8 Isopsetta isolepis butter sole 20.00 3.171 63.1 103.2 sharpchin Sebastes zacentrus rockfish 20.00 2016.631 138.6 286.5 blackbelly Lycodes pacificus eelpout 16.92 3.644 80.7 498.9 stripetail Sebastes saxicola rockfish 16.92 65.886 179.9 247.5 Psettichthys melanostictus sand sole 15.38 4.527 63.1 103.2 redbanded Sebastes babcocki rockfish 15.38 2.925 205.9 349.5 Sebastes rosethorn helvomaculatus rockfish 15.38 195.721 89.0 247.5 canary Sebastes pinniger rockfish 15.38 6.108 138.6 238.8 widow Sebastes entomelas rockfish 13.85 59.818 117.5 363.4 redstripe Sebastes proriger rockfish 13.85 48.334 89.0 349.5 Pacific Leptocottus staghorn armatus sculpin 12.31 1.244 63.1 103.2 Careproctus blacktail melanurus snailfish 10.77 1.160 293.7 498.9
59 Table 2. Fish species sampled within 30 km of the Newport Hydrographic Line with the U.S. West Coast Groundfish Bottom Trawl Survey. (Continued)
skates and Rajiformes rays 10.77 8.089 71.4 317.0 Sebastes silvergray brevispinis rockfish 10.77 15.464 138.6 227.6 Lycodes diapterus black eelpout 9.23 6.132 293.7 426.7 shortbelly Sebastes jordani rockfish 9.23 20.077 158.6 317.0 Pacific Clupea pallasii herring 7.69 0.993 64.3 238.8 Microgadus Pacific proximus tomcod 7.69 4.459 63.1 95.0 Platichthys starry stellatus flounder 7.69 0.824 65.0 83.0 Sebastes yelloweye ruberrimus rockfish 7.69 5.046 160.8 227.6 Sebastes spp. rougheye and (aleutianus / blackspotted melanostictus) rockfish 7.69 0.745 179.4 426.7 bigeye Bathyagonus starsnout pentacanthus poacher 6.15 0.474 224.6 363.4 Gadus macrocephalus Pacific cod 6.15 1.635 158.6 199.3 Hippoglossoides elassodon flathead sole 6.15 3.849 179.4 286.5 Pacific pompano Peprilus simillimus butterfish 6.15 7.849 65.3 72.9 Raja stellulata starry skate 6.15 0.967 66.3 89.0 aurora Sebastes aurora rockfish 6.15 1.029 186.8 498.9 American Alosa sapidissima shad 4.62 0.680 70.7 238.8 Myctophidae lanternfishes 4.62 0.669 190.3 498.9 Sebastes goodei chilipepper 4.62 1.369 179.9 199.3 yellowmouth Sebastes reedi rockfish 4.62 6.049 183.6 227.1 Sebastolobus longspine altivelis thornyhead 4.62 1.385 349.5 498.9
60 Table 2. Fish species sampled within 30 km of the Newport Hydrographic Line with the U.S. West Coast Groundfish Bottom Trawl Survey. (Continued)
northern spearnose Agonopsis vulsa poacher 3.08 2.828 103.2 183.6 Apristurus brown cat brunneus shark 3.08 4.799 317.0 498.9 Chitonotus roughback pugetensis sculpin 3.08 0.660 66.6 89.0 Cymatogaster aggregata shiner perch 3.08 2.383 64.3 65.3 Hexagrammos kelp decagrammus greenling 3.08 5.306 79.8 89.0 Oncorhynchus Chinook tshawytscha salmon 3.08 NA 66.6 109.6 quillback Sebastes maliger rockfish 3.08 0.624 79.8 89.0 Sebastes paucispinis bocaccio 3.08 0.764 206.7 227.6 pygmy Sebastes wilsoni rockfish 3.08 8.803 89.0 183.6 Allosmerus whitebait elongatus smelt 1.54 6.259 64.3 64.3 Bathyagonus blackfin nigripinnis poacher 1.54 0.531 349.5 349.5 Chauliodus Pacific macouni viperfish 1.54 0.624 426.7 426.7 Chesnonia warty verrucosa poacher 1.54 0.629 65.0 65.0 Coryphaenoides Pacific acrolepis grenadier 1.54 1.200 498.9 498.9 Pacific Eptatretus stoutii hagfish 1.54 0.575 286.5 286.5 Gadus walleye chalcogrammus pollock 1.54 0.695 64.3 64.3 Hemilepidotus spp. Irish lords 1.54 0.639 81.8 81.8 Myxinidae hagfishes 1.54 0.832 293.7 293.7 Nautichthys sailfin oculofasciatus sculpin 1.54 0.669 160.8 160.8 Radulinus asprellus slim sculpin 1.54 5.037 183.6 183.6 Rajidae skates 1.54 1.240 103.2 103.2 Sebastes spp. rockfishes 1.54 1.336 143.9 143.9
61 Table 2. Fish species sampled within 30 km of the Newport Hydrographic Line with the U.S. West Coast Groundfish Bottom Trawl Survey. (Continued)
Sebastes greenspotted chlorostictus rockfish 1.54 0.616 138.6 138.6 yellowtail Sebastes flavidus rockfish 1.54 206.818 138.6 138.6 Sebastes rufus bank rockfish 1.54 17.438 227.6 227.6 Spirinchus starksi night smelt 1.54 3.137 109.6 109.6 Tactostoma longfin macropus dragonfish 1.54 0.600 498.9 498.9
62
Figure 5. Average sample diversity for the beam trawl survey (left) and West Coast Groundfish Bottom Trawl Survey (right) based on Shannon-Wiener, Simpson, and species richness indices per year (2012-2015). Note that the y-axis scales are different between surveys for ease of visualization.
63
Figure 6. Average sample diversity for the beam trawl survey (left) and West Coast Groundfish Bottom Trawl Survey (right) based on Shannon-Wiener, Simpson, and species richness indices per depth strata. Beam trawl stations MB30, MB40, and NH03 have a target depth of 30 m, 40 m, and 50 m respectively. These stations have no analog in the bottom trawl survey. The bottom trawl depth bins encompass: 1) > 63 m & ≤ 70 m, compared to NH-05, 2) > 70 m & ≤ 90 m, compared to NH-10, 3) > 90 m & ≤ 140 m, compared to NH-15, and 4) > 140 m, no beam trawl analog. Note that the y-axis scales are different between surveys for ease of visualization.
64
Figure 7. Comparison of frequency of occurrence for fishes caught in the beam trawl and bottom trawl surveys between 2012-2015.
65
Figure 8. Average standardized relative abundance of fishes caught in the beam trawl (left) and bottom trawl (right) surveys based on sample year (2012-2015). Note the beam trawl mean CPUE is per 1000 m2, whereas the bottom trawl mean CPUE is per hectare.
66
Figure 9. Average standardized relative abundance of fishes caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15). Note the beam trawl mean CPUE is per 1000 m2, whereas the bottom trawl mean CPUE is per hectare. Note also the standard error bars for Pacific sanddab (Citharichthys sordidus) exceed the y-axis limits for ease of visualization.
67
Figure 10. Average standardized relative abundance of fishes caught in the beam trawl and bottom trawl surveys based on non-comparable depth strata (no analogs in other study). Note that the standard error bars for sharpchin rockfish (Sebastes zacentrus) exceed the y-axis limits for ease of visualization.
68
Figure 11. Size distribution of fishes caught in the beam trawl and bottom trawl surveys between 2012-2015. Fishes were measured to the nearest mm standard length (SL) in the beam trawl survey and the nearest cm total length (TL) in the bottom trawl survey.
69
Figure 12. Size distribution of Citharichthys sordidus (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys between 2012-2015. Count of individuals based on size and survey is shown (top). Density of individuals based on size and survey is also shown (bottom).
70
Figure 13. Size distribution of Parophrys vetulus (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys between 2012-2015. Count of individuals based on size and survey is shown (top). Density of individuals based on size and survey is also shown (bottom).
71 CHAPTER 3: EMERGING TECHNOLOGIES IN FISHERIES STOCK ASSESSMENT AND ECOSYSTEM SCIENCES – AN EXAMINATION OF HOW NEW METHODOLOGIES ARE INCORPORATED INTO FISHERIES MANAGEMENT
3.1 Chapter objective and structure
Building on a collaborative transdisciplinary research project and other previous research (Stinton et al. 2014; Firl et al. 2018; Sobocinski et al. 2018; Haven 2019), this chapter investigated the following questions: 1) what are the assessment and regulatory frameworks to incorporate new data into current fisheries management, and 2) what innovations have been incorporated in the past? The potential new methodology focused on in this chapter is nearshore sampling of juvenile groundfish soft-sediment habitat. The specific goal of this chapter is to explore the feasibility of incorporating additional nearshore soft-sediment sampling into the current groundfish fishery management and policy framework. I am proposing to provide a new source of fishery-independent information in the form of an index of juvenile groundfish abundance by sampling nearshore soft-sediment habitats.
To address the feasibility of including nearshore sampling into the current management framework, this chapter will be composed of three parts. First, an overview of the topic including a description of the current state of management (including overarching policy framework, science as a tool, stock assessments, and Ecosystem- based Management recommendations), a description of the fishery, previous projects studying this fishery, and examples of previous scientific innovations that are now included in fishery monitoring. Second, a discussion about how new scientific
72 methodologies become incorporated into management (incorporating innovation, and challenges and opportunities for inclusion of information). As a conclusion, this chapter will provide suggestions for practically developing nearshore surveys in trawlable soft- sediment habitat.
3.2 Methods
This chapter is an expansion of collaborative research completed as part of an
NSF-funded research program at Oregon State University (OSU), “Risk and Uncertainty
Quantification in Marine Science - A National Science Foundation Research
Traineeship” (abbreviated to National Research Traineeship; NRT). The collaborative project was completed by an interdisciplinary team of three graduate students – pursuing degrees in Computer Science and Engineering, Environmental Arts and Humanities, and
Marine Resource Management – who sought to address ecological and social questions using the National Marine Fisheries Service’s (NMFS) in-situ videos (Firl et al. 2018).
Specifically, the research question addressed by the interdisciplinary team was: “To what extent are large ecological datasets informing the production and application of emerging technologies in fisheries science, and how are these new technologies and sampling methods being integrated into fisheries management frameworks?” (Firl et al. 2018) To address that question, the Pacific coast groundfish fishery was used as a case study (Firl et al. 2018). The new technology to be incorporated was an automated program to analyze in-situ video of benthic habitats and fish behavior (Firl et al. 2018).
The NSF-funded NRT program was structured around three interwoven core concepts: 1) Big Data (BD), 2) Coupled Natural-Human (CNH) systems, and 3) Risk and
73 Uncertainty (R&U) analysis and communication. Big Data refers to any high volume of data with high throughput. Coupled Natural-Human systems are the biological and human worlds, as well as their overlap and feedback loops. Risk is defined as the potential and likelihood of an unfavorable event, and uncertainty refers to the unknowns of a likelihood, process, or analysis. The interdisciplinary team project investigated these three concepts within the framework of emerging technologies and fisheries science. Emerging technologies are those dealing with BD, since this is a relatively new area of study, and the team project specifically focused on computer vision within machine learning. This technology was applied to the realm of fisheries science and management, understood as a coupled natural-human system. Global climate change means that groundfishes are at risk and their future is uncertain. Therefore, the interdisciplinary team project investigated how big data, machine learning, ecological inference, and environmental decision-making overlap (Firl et al. 2018).
To assess the feasibility of incorporating nearshore soft-sediment sampling, this chapter examined the existing scientific research and regulatory framework for monitoring and managing the Pacific coast groundfish fishery. Data collection included an extensive literature review using the Oregon State University library archives and web-based search engines, such as Google Scholar. The literature review focused on the history of the Pacific coast groundfish fishery, the primary law governing this fishery (the
Magnuson-Stevens Fishery Conservation and Management Act), the current management framework, the use and history of the term “Best Scientific Information Available”, and previous scientific studies that have incorporated the sampling of juvenile or larval fish
74 into stock assessments. Data collection was also informed by collaboration with other graduate students through the NRT program at Oregon State University, and personal communication with experts in the field of fisheries stock assessment, habitat, and ecosystem sciences, as well as federal fisheries managers.
3.3 Science in the U.S. fisheries management process
3.3.1 U.S. marine fishery management
The primary law governing the management of marine fisheries in the United
States (U.S.) is the Magnuson-Stevens Fishery Conservation and Management Act
(MSA), which was first passed in 1976 (16 U.S.C. §§ 1801 et seq.; Fisheries 2018). The
MSA covers many different fishery topics including fishing authority, international fishing agreements, fishery management, and fishery monitoring and research (16 U.S.C.
§§ 1801 et seq.). One of the main goals of this act is to establish U.S. fishing authority within the Exclusive Economic Zone (EEZ), which consists of the waters from the 3-mile boundary for state waters to 200 nautical miles offshore (16 U.S.C. §§ 1801-1883).
Another main goal of the MSA is to “conserve and manage the fishery resources found off the coasts of the United States, and the anadromous species and Continental Shelf fishery resources of the United States” (16 U.S.C. §§ 1801(2)(b)(1)). One way the MSA promoted the achievement of this second goal is that this act established a network of eight regional fisheries management councils across the United States (16 U.S.C. §§
1801(2)(b)(5); Eagle et al. 2015). These councils are tasked with the preparation and implementation of Fishery Management Plans (FMP), “which will achieve and maintain, on a continuing basis, the optimum yield from each fishery” (16 U.S.C. §§ 801(2)(b)(4)).
75 3.3.2 The call for Ecosystem-Based Management
Marine fishes do not function in a vacuum, and they do not stay within human- constructed jurisdictional boundaries. There are many different stakeholder groups that influence a fishery or are influenced by a fishery, so competing goals, values, and perspectives must be considered in fisheries management. Competing stakeholder views and variability in fish abundances and distributions, combined with changing ocean conditions and inevitable uncertainty, cause fishery management to be highly complex.
To reduce uncertainty and address a fishery’s complexity in order to manage it well, management must go beyond “sector-based fishery management” and consider other human activities or interests that might interact with the fishery (NMFS 2009; Rosenberg and Sandifer 2009; Levin et al. 2018). For the scope of this chapter, I am focusing on reducing uncertainty about nearshore soft-sediment areas. These nearshore areas are sensitive to seasonal variations in environmental conditions as well as climate change.
Additionally, these areas are where many early life history stages of fishes reside before they recruit to the adult fishery. Therefore, by monitoring these areas, scientists can have a better understanding of the entire life cycle of these fishes and the habitat within these nearshore areas as part of the transition beyond “sector management”.
Moving past “sector management” requires one to take on a new perspective about fisheries resources and their management. This new perspective includes humans as part of the ecosystem, instead of excluded or separate, and understands how dependent fisheries are on other ocean resource management (Douvere and Ehler 2009; Berkes
2012). This connection between people and place is what some call “Coupled Natural-
76 Human” (CNH) systems (Liu et al. 2007). In fisheries alone, the connection between people and fishes can easily be seen. The fishermen go out onto the ocean to catch the fish; thus, they influence the natural system. The fishes are brought back to land where they are sold to a consumer who eats it or uses it for some other purpose; thus, the fishes impact the human system by providing food and resources for the economy. Managing the ecosystem holistically is known as “Ecosystem-Based Management” (EBM), or in the case of marine fisheries, “Ecosystem-based Fisheries Management” (EBFM). Although the EBM framework is an idealist way to manage an ecosystem holistically, making
EBM achievable requires a great deal of communication and cooperation between governing agencies with different jurisdictions and interest groups to consider (Tanz
2016).
3.3.3 Role of science in fisheries management
The term “best available science” or “best scientific information available” has come up in multiple pieces of United States’ legislation within the last 50 years. It first appeared in the Endangered Species Conservation Act of 1969, and it was brought over to the current Endangered Species Act of 1973 (Endangered Species Conservation Act of
1969 1969; Kuhn 2016). Since then, it has been seen in the Environmental Protection
Agency’s Clean Water Act of 1997, and in the MSA of 1976 (16 U.S.C. §§ 1801 et seq.;
Sullivan et al. 2006; Schwaab 2012). Although this term has been in use since 1969, it is still difficult to determine what is considered “best” (Sullivan et al. 2006; Schwaab
2012). The 1996 MSA reauthorization called for the use of “best scientific information available” (BSIA), specifically to better inform the setting and enforcing of maximum
77 sustainable yield (MSY), as well as to focus on essential fish habitat (EFH), and cooperative research (Baur et al. 2015). The 1996 reauthorization – known as the
Sustainable Fisheries Act – shifted the focus of the MSA from claiming national fishing grounds for the United States to conserving and sustainably managing the nation’s marine fisheries (16 U.S.C. ch. 38 § 1812 et seq.; Baur et al. 2015). In 2006 and 2007 reauthorizations and amendments were again made to the MSA, and over time there became a set list of ten “National Standards” (Baur et al. 2015) that also specified the type of info that could inform management.
“National Standard 2”, for instance, states “[fishery] conservation and management measures shall be based upon the best scientific information available”
(National standards for fishery conservation and management 2007). In 2004, a national report on what “best scientific information available” meant was published by the
National Academy of Sciences (NAS) (National Research Council et al. 2004). It stressed the need to improve scientific information and reduce uncertainty, but ultimately recommended that a universal definition or application of best available scientific information was impossible and problematic (Committee on Defining the Best Scientific
Information Available for Fisheries Management et al. 2004). Despite the ambiguity of the term, the 2006 MSA reauthorization still calls for the use of “best scientific information available” with the purpose of mandating the SSC to constantly evaluate the science used, knowing that there is always room for improvement (Magnuson-Stevens
Fishery Conservation and Management Reauthorization Act of 2006 2007).
78 The task of determining what “best scientific information available” meant was undertaken once more in 2013 when NMFS revised its guidelines on National Standard 2 in order to better define what “best scientific information available” (BSIA) means in the context of conservation and management of federal fisheries and Fishery Management
Plans (FMPs) (NMFS 2013). Due to the dynamic nature of science and the varying amount or quality of data available, the revised National Standard 2 did not give a prescriptive definition of “best scientific information available (National Research
Council et al. 2004; NMFS 2013).” Instead, the revised National Standard 2 stated that the BSIA should follow a credible scientific method process and be evaluated according to relevance, inclusiveness, objectivity, transparency and openness, timeliness, verification and validation, and peer review (National Research Council et al. 2004;
NMFS 2013). Each of these terms was further defined within the revision. It was also stated that the role of peer review is to evaluate the quality and credibility of the scientific information and not to provide advice to the Regional Fisheries Councils since that is the role of the Scientific and Statistical Committee (NMFS 2013). In the end, “best scientific information available” was difficult to define, but it is still a necessary term for making management and policy decisions.
Ultimately, the fishery management councils must decide what to do with this scientific information, and they make the final recommendations for fishery management
(such as harvest limits, season time period, etc.) within the fishery management plan
(PFMC 2016b). Science can be used as a tool, but it can only take the decision-maker so far, and the council must set annual catch limits for all federally-managed fisheries, even
79 those with incomplete or poor-quality data (NMFS 2013; Eagle et al. 2015). Fishery management decisions that the council must make are known as “decisions made under uncertainty” because there are many unknowns associated with them (Sullivan et al.
2006). Making decisions in spite of these uncertainties is one main source of risk in fisheries management (Sullivan et al. 2006; NMFS 2013). However, it is an unavoidable risk because—as previously stated—regional fisheries councils are mandated to set an annual catch limit for all federally-managed fisheries regardless of limitations in scientific information (NMFS 2013). These uncertainties and their associated risk can be mediated by through an adaptive, ecosystem-based management framework where the science being used is constantly evaluated and continuously improved.
3.3.4 Role of stock assessment models within fisheries management
With the goal of sustainable fisheries management, and thus sustainable fishing practices, stock assessments are a quantification tool used to predict how fish populations will react to different management decisions (Hilborn and Walters 1992). Fisheries stock assessments are mathematical models that are used to simulate the population dynamics of a fishery, make predictions about trends in the population through time based on policy decisions and expected environmental or biological changes, and take into account the fishermen’s and industries’ response to management decisions (Hilborn and Walters
1992). When these models, and fisheries science as a whole, were in their infancy, stock assessments usually focused on two questions: 1) “what is the optimum [fishing] effort?”
(Hilborn and Walters 1992), and 2) “what is the maximum sustainable yield (MSY)?”
(Hilborn and Walters 1992). However as Hilborn and Walters (1992) suggest, there are
80 two main issues with using stock assessments to only address the questions of so-called
“best” fishing effort and “maximum sustainable yield (MSY)”. The first is that the MSY can only be determined after it has been exceeded because it is the apex of an inverted U- curve, and therefore, one can only know that the maximum has been reached once the yield has begun to decrease (Figure 14) (Hilborn and Walters 1992). The second issue is that reducing fishing effort is one of the most difficult tasks to accomplish in fisheries management (Hilborn and Walters 1992). According to Hilborn and Walters (1992), there are only two ways to reduce fishing effort: 1) decrease fishermen’s catches, or 2) reduce the number of fisheries participants. This translates to either the reduction of income or the number of jobs available. Although these two questions are still the main focus of some stock assessments, ideally a stock assessment would instead be used as a tool to focus on designing a fisheries management system that can adapt to changing ocean conditions, natural variability, and inherent uncertainty associated with fisheries and marine systems (Hilborn and Walters 1992).
Stock assessment models aim to provide estimates of fish populations and trends in future fish abundances with the main goals of avoiding overfishing or a fishery collapse and managing for a healthy fishery (which includes both the fish stocks and the fishing industry) over time (Hilborn and Walters 1992; National Research Council et al.
1998; Haddon 2011). These models use available data on population dynamics, fishing effort, and catch to predict future fish populations based on the fisheries response to natural dynamic processes as well as to fishing pressure (Haddon 2011). However, these models are only simulations, so they will always have some level of inaccuracy and
81 uncertainty associated with them (Hilborn and Walters 1992; Haddon 2011).
Additionally, they can only predict future possibilities based on the data available, so they do not perform well for new, unseen management strategies or unprecedented natural variations (Hilborn and Walters 1992).
Therefore, stock assessments are only a tool and they have limitations (Hilborn and Walters 1992; Haddon 2011). They should not be used as a basis for policy and management decisions such as catch limits directly, but instead to make predictions about future fish populations and their response to management choices and natural variation
(Hilborn and Walters 1992). These predictions can then be used as one piece to consider in the policy decision-making process, along with other important factors affecting the final decision, such as stakeholder values (Hilborn and Walters 1992).
To summarize, stock assessment models are a necessary fishery management tool in order to set sustainable fishing regulations, and they have been described extensively elsewhere (Hilborn and Walters 1992; Haddon 2011). However, in the context of this chapter, it is important to highlight that these models are only simulations; therefore, they should not be used in the absence of other information.
3.4 Management of Pacific coast groundfishes
3.4.1 Pacific coast groundfish fishery
The Pacific coast groundfish fishery is composed of a diverse and large grouping of fishes, and it is one of the largest and most lucrative fisheries in Oregon—with a current harvest value of approximately $48 million per year, which is exceeded only by the Dungeness crab fishery valued at approximately $51.3 million per year (The Research
82 Group, LLC 2017). The groundfish fishery is also important for recreational and tribal purposes, although their scale is not comparable to the commercial industry (PFMC
2016b, 2018a; “ODFW, Economic Impact” 2018). With over 90 different species to consider, this fishery is complex and unique on the ecological side. On the social side, there are many different stakeholder groups involved including commercial fishermen, tribal members, industry members, scientists, and conservation groups, each with their own goals, values, and perspectives (Baur et al. 2015; PFMC 2016b, 2018a).
3.4.2 The Pacific Fishery Management Council
The PFMC and NMFS manage the groundfish fishery by developing and implementing (respectively) the rules and regulations laid out in the Pacific Coast
Groundfish Fishery Management Plan (FMP) (16 U.S.C. §§ 1801 et seq.; Eagle et al.
2015; PFMC 2016b). Specifically, the NMFS provides scientific information that the
PFMC uses to develop the FMPs, and NMFS ultimately reviews, approves, or disapproves the FMPs and implementing regulations by ensuring they align with the
MSA and other relevant laws (Eagle et al. 2015; PFMC 2016b).
To understand the feasibility of adding new technologies and methodologies (such as nearshore sampling of juvenile groundfish habitat) to sources of fishery-independent data critical to improving stock assessments, one must first understand the current framework of the management system. As previously stated in the introductory chapter of this thesis, the Pacific coast groundfish fishery is managed by the NMFS and the Pacific
Fisheries Management Council (PFMC). The Pacific coast groundfish fishery is federally managed due to the fishery extending well beyond the state waters limit of three nautical
83 miles offshore (Eagle et al. 2015). The PFMC is one of eight regional fishery management councils established by the MSA in 1977 (Eagle et al. 2015). The region that the PFMC oversees includes the waters within the U.S. exclusive economic zone
(EEZ) of Washington, Oregon, and California, which spans from 3-200 nautical miles
(nm) offshore. Idaho is also included within the region covered by the PMFC due to the inclusion of anadromous salmon which migrate into Idaho’s rivers, lakes, and streams.
There are 19 people on the council: 14 are voting members, and the remaining 5 are non-voting members. Voting members include state representatives, state obligatory members, at-large members, a tribal representative, and a NMFS representative (“Council
Staff | Pacific Fishery Management Council” n.d.; Eagle et al. 2015). The obligatory, at- large, and tribal members are appointed by the U.S. Secretary of Commerce, while the remaining members are appointed by their respective agencies. The non-voting members represent the state of Alaska, the Pacific States Marine Fisheries Commission, the U.S.
State Department, the U.S. Fish and Wildlife Service, and the U.S. Coast Guard
(“Council Staff | Pacific Fishery Management Council” n.d.; Eagle et al. 2015).
The voting members are tasked with developing four fisheries management plans, or updating old ones, that encompass over 120 different fish species plus 13 fish families and 8 krill species that are under their management, and they make decisions based on majority vote (PFMC 2016a, 2016b, 2018b, 2018c). These fisheries management plans are then sent to the NMFS Regional Administrator and NMFS Headquarters who must approve, partially approve, or disapprove them (Eagle et al. 2015). If approved, the
Secretary of Commerce promulgates the fishery management plan or the amendment, and
84 then NMFS is responsible for implementing, administering, and enforcing these newly established plans (Baur et al. 2015; Eagle et al. 2015; “Regional Operating Agreement”
2017).
In addition to the voting and non-voting members on the council, there are also a series of advisory bodies including Advisory Subpanels, Enforcement Consultants, a
Habitat Committee, the Scientific and Statistical Committee, as well as various Plan
Development, Technical, and Management Teams. The Advisory Subpanels are made up of stakeholder representatives such as commercial and recreational fishing industry members, tribal members, conservation groups, and the public. The Enforcement
Consultants represent the Coast Guard, state fish and wildlife agencies, state police agencies, and the National Marine Fisheries Service. The Habitat Committee is composed of representatives from state and federal management agencies, tribes, conservation groups, and fishing industries. This committee collaborates with other advisory bodies on issues related to the habitat of council-managed fish species. The PFMC is also provided legal advice by the National Oceanic and Atmospheric Administration’s (NOAA)
General Counsel since it oversees allocating groundfish harvestable surplus among the fishing industry. Finally, the Scientific and Statistical Committee (SSC) is composed of agency and academic scientists, and they are tasked with reviewing the scientific content of the council and other advisory bodies to confirm that management decisions are using the best scientific information available as according to the MSA. Any new scientific methodologies, technology, or general innovation must be approved by the SSC to maintain the robustness of the Fisheries Management Plans. The constant evaluation by
85 the SSC of the science used in managing this fishery is directly addressing the MSA’s call for the use of BSIA.
3.4.3 Data used to develop stock assessment models in the Pacific coast groundfish fishery
Part of the current fishery management plan includes using stock assessment models to help inform fishery regulations, such as determining optimum yield (PFMC
2016b; Keller et al. 2017). Stock assessment models are made using information from multiple sources including fisheries dependent (i.e., landings records), and fisheries independent (i.e., hook-and-line surveys, and bottom trawl surveys) data (PFMC 2016b,
2018a; Keller et al. 2017). The Pacific coast groundfish fishery is currently monitored by
NMFS with fishery-independent (hook-and-line and bottom trawl) surveys and fishery- dependent data (landings records) (Stauffer 2004; PFMC 2016c; Keller et al. 2017). The fishery-independent survey that this report will focus on is the “West Coast Groundfish
Bottom Trawl Survey” (Keller et al. 2017). NMFS uses these different sources of data to develop stock assessments for multiple groundfish species or groups of species (PFMC
2016c; Keller et al. 2017). Stock assessments are peer-reviewed for their scientific integrity and potential utility in management by independent experts, including the SSC
(16 U.S.C. §§ 1801 et seq.). Results of stock assessments are then evaluated by the
Pacific Fisheries Management Council (PMFC), which then recommends regulations such as Overfishing Limit (OFL), Allowable Biological Catch (ABC), Annual Catch
Limit (ACL), and Harvest Guidelines (HL) (16 U.S.C. §§ 1801 et seq.; PFMC 2016b;
Keller et al. 2017). These suggested regulations are then sent back to NOAA for review and approval or disapproval.
86 Currently, Pacific coast groundfish stock assessments lack information about the nearshore areas (less than 55 m, or 30 fathoms) of the U.S. West coast’s continental shelf
(Keller et al. 2017). This area is an important nursery habitat for juvenile age classes, and understanding juvenile abundances and distributions can help predict future recruitment into the adult population (Bradford 1992; Beck et al. 2003; Chittaro et al. 2009; Field et al. 2010; Stige et al. 2013; Hughes et al. 2014). Incorporating new methodologies to capture nearshore information could improve knowledge about these early life history stages. These methodologies, along with incorporating further collaboration and communication between federal, state, and university fishery scientists, have the potential to improve groundfish fishery management.
3.4.4 Scientific uncertainty within fisheries management
There is inherent scientific uncertainty associated with these stock assessments since they are mathematical models. If the fish population’s abundance is underestimated, then this would cause fishing regulations to be stricter than necessary, which would potentially harm the fishing industry and seafood consumers. However, if the fish population is overestimated, then the fishing regulations would be too lenient, which could result in overfishing or even stock or fishery collapse, such as the collapse of the
Pacific coast groundfish fishery in the late 1990s, which caused NMFS to declare a national disaster in 2000 due to a “sustained decline in recruitment” (Gorman and Fergus
2000; Fisheries 2019). Therefore, local stakeholders are dependent on accurate stock assessment surveys and models so that the fishing regulations are appropriate. Some stakeholders believe that regulations tend to be overly cautious to compensate for the
87 large amount of uncertainty involved with managing a fishery and estimating a fish population (Charles 1998; “House Natural Resources Subcommittee Holds Hearing on
Magnuson-Stevens Act” 2017).
There are four main types of scientific uncertainty associated with fisheries, some of these types are due to lack of data (such as those associated with “data-poor” fisheries), and other types are related to the estimation of fish populations that are unavoidable and will never be known (Sullivan et al. 2006). These four sources of uncertainty in fisheries science include: 1) information about the biology of the fish stock, 2) information about how the fish stock interacts with other biological populations or with environmental factors, 3) unpredictable events such as natural disasters, and 4) variability in parameter estimates that are necessary to determine the size of the current or past stock and predict the future stock (Sullivan et al. 2006).
To reduce uncertainty and the associated tendency to err heavily on the side of caution, fisheries management aims to be adaptive by constantly evaluating the available science and using new data and methodologies (such as new or expanded fishery- independent surveys) in stock assessment surveys when they become considered “best available science” (Moser et al. 2001; Sakuma et al. 2006; Berkes 2012; Ralston et al.
2013; McClatchie 2014; Dick et al. 2017). Examples of early life stages sampling methodologies that have already been incorporated into stock assessments are the NMFS pelagic juvenile rockfish survey and the California Cooperative Oceanic and Fisheries
Investigations (CalCOFI) ichthyoplankton survey, which I will discuss in greater detail later (Moser et al. 2001; Sakuma et al. 2006; Charter et al. 2011; Ralston et al. 2013). A
88 proposed methodology – and one that is already considered a data-need in some stock assessments (Dick et al. 2017) – that this report will use as a case study for improving fisheries management is the inclusion of nearshore sampling of juvenile groundfish habitat.
3.5 Nearshore data collection in support of stock assessments
3.5.1 Oregon’s nearshore survey
Previous research has analyzed the communities of juvenile flatfishes and other small demersal fishes in Oregon estuaries and off the central Oregon coast (Pearcy 1978;
Krygier and Pearcy 1986; Toole et al. 2011). However, the distribution, abundance, and temporal variation of nearshore juvenile demersal fish assemblages are still not fully understood, and there is a knowledge gap between the larval and adult stages of these commercially-important fish (Chittaro et al. 2009; Keller et al. 2012; McClatchie 2014;
Sobocinski et al. 2018). In order to better understand these fish communities, the NMFS’
Northwest Fisheries Science Center and their collaborators, the Pacific States Marine
Fisheries Commission and Oregon State University, have been conducting an ongoing study since the summer of 2008 (Johnson 2012; Stinton et al. 2014; Sobocinski et al.
2018). This collaborative study has included sampling and analyzing newly-settled young-of-the-year groundfishes and other small demersal fishes in the nearshore nursery habitat along the central Oregon coast, specifically along the Newport Hydrographic
(NH) line. This sampling area was chosen because nearshore habitats are known to be important nursery grounds for settling larval fish. Some fish species, such as English sole
(Parophrys vetulus), use nearshore coastal regions and estuaries as nursery habitats
89 during larval pelagic and settling juvenile stages before migrating to their adult continental shelf habitats (Laroche and Holton 1979; Rosenberg 1982; Krygier and
Pearcy 1986; Boehlert and Mundy 1987; Hughes et al. 2015). By sampling juvenile demersal fish communities, their recruitment patterns can be better understood (Field et al. 2010; Stige et al. 2013; Dick et al. 2017). These recruitment patterns can help with the estimation of year-class strength, which is important for fisheries stock assessments and management decisions (Fuiman and Werner 2002; Thanassekos et al. 2016; Sobocinski et al. 2018).
In addition to sampling these young-of-the-year groundfishes, researchers from the NMFS collaborative study have attached a high-definition underwater video camera to the sampling gear in order to supplement the catch data with in-situ habitat and behavioral data (Mueter and Norcross 1999; Stinton et al. 2014). These videos provide a method of addressing ecological questions about newly-settled groundfishes and other small demersal fishes, such as microhabitat usage, catchability (i.e., net avoidance), and fish behavior, which are hard to address with conventional net sampling (Auster et al.
1995; Cailliet et al. 1999; Mueter and Norcross 1999; Stoner et al. 2008; Hannah et al.
2011; Stinton et al. 2014). Understanding microhabitat usage is important since microhabitats such as biogenic depressions, sand ripple/wave crests, and shell debris have been found to provide locations for demersal fishes to avoid predators, capture prey, and forage (Auster et al. 1995). Evaluating net avoidance is important for bycatch reduction and for evaluating the catchability of some fish species, thus increase accuracy or reduce bias of fish surveys (Hannah et al. 2011). Examining fish behavior is important for other
90 reasons such as understanding how fishes are impacted physiologically, and how their distributions will shift, due to changing environmental conditions (Chittaro et al. 2009;
Stinton et al. 2014).
To incorporate nearshore sampling of juvenile groundfish habitats into the current management framework, this new methodology would first need to be considered the best scientific information available. For new methodologies such as additional nearshore sampling of juvenile groundfish habitat to be considered as BSIA, they must be evaluated based on the criteria laid out in the newly-revised National Standard 2. However, even if these new methodologies were considered BSIA and were used to supplement fisheries stock assessments, there are additional sources of information that the regional fisheries councils use to set annual catch limits (Lackey 2006; Eagle et al. 2015). Even in the best- case scenario, with the BSIA, science is only one piece in the decision-making process
(Hilborn and Walters 1992; Lackey 2006; Sullivan et al. 2006). Policy decisions are innately laden with human values and perspectives (Hilborn and Walters 1992; Lackey
2006, 2016; Sullivan et al. 2006). Decisions such as determining an annual catch limit for a fishery are mandated to be driven by the BSIA, but science can only tell the decision makers information about the fish stocks (Hilborn and Walters 1992; Lackey 2006; Eagle et al. 2015).
3.5.2 Advances toward Ecosystem-Based Management
One tactic for coping with the inherent uncertainty and system complexity of a fishery is to build adaptive management into an EBM plan (Charles 1998; Berkes 2012).
This allows the plan to be frequently evaluated for efficiency and to be flexible and
91 adjusted when new information is available. Thus, incorporating new information on the juvenile groundfish assemblages along the nearshore continental shelf region of Oregon could be a form of new information that is incorporated into the adaptive management framework leading to a more robust management system.
Although EBM seems to be an ideal way of managing marine systems, it has been difficult to implement. As part of a two-decade-long effort, the current tools that NMFS and the fisheries councils are using to transition to EBM are Marine Fisheries Habitat
Assessment Improvement Plans, Essential Fish Habitat designations that are incorporated into FMPs, and a new tool called a Fishery Ecosystem Plan (FEP) (NMFS 2009, 2015;
NMFS 2010; PFMC 2013, 2016b; Sigler et al. 2017; Levin et al. 2018; Peters et al.
2018). These FEPs are not mandated by the MSA; thus, the creation of these documents and how they are used in fisheries management is voluntary instead of mandated like the more-specific FMPs (16 U.S.C. § 1851; PFMC 2013). Despite not being mandated by the
MSA, they have great potential to improve management and conservation through improved understanding of the connections within the complex marine system, and by implementing overarching themes common among multiple sectors (PFMC 2013; Levin et al. 2018).
The PFMC has had an FEP since 2013 (PFMC 2013). The purpose of this plan is to promote ecosystem-based management within the current framework of the council by incorporating more ecosystem science and considering the U.S. portion of the California
Current Large Marine Ecosystem holistically when managing fisheries within this region with species-specific FMPs (PFMC 2013). One of the many ecosystem initiatives within
92 this FEP is cross-FMP Essential Fish Habitat (EFH) designations (PFMC 2017). EFH is defined by the MSA as “those waters and substrate necessary to fish for spawning, breeding, feeding or growth to maturity” (16 U.S.C. §§ 1802(3)(10)), which goes beyond the scope of a single species or a single life history stage.
Therefore, with the FEP and EFH in mind, even if it is decided that sampling these shallower areas cannot feasibly be implemented for the direct inclusion in stock assessments, sampling nearshore (< 55 m or 30 fathoms) nursery habitats can be incorporated into the Marine Fisheries Habitat Assessment Improvement Plans, Essential
Fish Habitat designations within FMPs, and the cross-FMP EFH designation within the
FEP (NMFS 2009, 2015; NMFS 2010; PFMC 2013, 2016b; Sigler et al. 2017; Levin et al. 2018; Peters et al. 2018). This nearshore sampling could also be used as a potential indicator of the fishery system’s status and future direction by allowing for a better understanding of the entire life history cycle of coastal marine species. Again, this area is an important nursery habitat for many marine species, such as English sole (Parophyrs vetulus), that are well-known for using estuaries and nearshore coastal regions as areas for settlement and development (Pearcy and Myers 1974; Laroche and Holton 1979;
Krygier and Pearcy 1986; Boehlert and Mundy 1987; Hughes et al. 2014). Therefore, this sampling could provide a better understanding of the overall ecosystem and life history cycles of many species that dwell within the U.S. portion of the California Current Large
Marine Ecosystem.
93 3.5.3 Pre-recruit and ichthyoplankton surveys
Sampling nearshore soft-sediment habitats regularly to monitor juvenile groundfish assemblages and develop indices of abundance does appear to be feasible if funding allows because similar surveys have been incorporated into management in the past. An example of a fishery-independent survey that has been used in stock assessments to provide indices of juvenile abundance is the NMFS Southwest Fisheries Science
Center’s (SWFSC) pelagic juvenile rockfish survey (Ralston et al. 2013). This midwater trawl survey has been conducted annually off the coast of central California since 1983
(Ralston et al. 2013). A collaboration between the NMFS Northwest Fisheries Science
Center (NWFSC) and the Pacific Whiting Conservation Cooperation (PWCC) extended the survey to encompass the entirety of the California and Oregon coasts in 2004 and the
Washington coast in 2005 (Sakuma et al. 2006).
This combination of surveys allows the abundance of juvenile rockfishes to be sampled on a larger scale than one survey could cover alone (Sakuma et al. 2006). This design is important because the two surveys can be running simultaneously in two areas, which allows the northern and southern portions of the U.S. West Coast to be sampled during the months of May-June when juvenile rockfishes are pelagic and have not yet settled (Sakuma et al. 2006). The combination of surveys formed out of convenience since the PWCC was collaborating with the NWFSC to develop a juvenile abundance survey specifically targeting Pacific hake (whiting; Merluccius productus) (Sakuma et al.
2006). This new survey was intended to provide an index of juvenile abundance to be used in Pacific hake stock assessments, but since it was patterned after the SWFSC’s
94 juvenile rockfish survey, it also doubled as an extension of this parent survey (Sakuma et al. 2006). Additionally, this PWCC-NWFSC survey is a great example of industry- federal agency collaboration.
This coast-wide (Northern Washington to Southern California) juvenile rockfish survey commonly collects ten species of rockfishes including: brown rockfish (Sebastes auriculatus), widow rockfish (Sebastes entomelas), yellowtail rockfish (Sebastes flavidus), chilipepper (Sebastes goodei), squarespot rockfish (Sebastes hopkinsi), shortbelly rockfish (Sebastes jordani), blue rockfish (Sebastes mystinus), bocaccio
(Sebastes paucispinis), canary rockfish (Sebastes pinniger), and stripetail rockfish
(Sebastes Saxicola) (Ralston et al. 2013). Of those ten, six rockfish species’ stock assessments currently utilize this juvenile abundance survey. These six species are: chilipepper (Field et al. 2015), shortbelly rockfish (Field et al. 2007), blue rockfish (Dick et al. 2017), bocaccio (He and Field 2017), canary rockfish (Thorson and Wetzel 2016), and widow rockfish (Hicks and Wetzel 2015). Of these six, three of them blue rockfish
(Dick et al. 2017), bocaccio (He and Field 2017), and shortbelly rockfish (Field et al.
2007) use another fishery-independent survey conducted by California Cooperative
Oceanic and Fisheries Investigations (CalCOFI), which samples pelagic eggs and larvae
(ichthyoplankton) along the coast of California (Charter et al. 2011). A more in-depth description of how these indices are used and the index model structure is described in the Blue-Deacon Assessment of 2017 and is beyond the purview of this study (Dick et al.
2017). Instead, I wish to highlight how the use of abundance indices for juvenile or larval
95 rockfish in a stock assessment improves the ability of the model to estimate future stock abundance trends (Field et al. 2010).
If one must decide on one stage in the early life history to sample to develop an index of abundance that will inform predictions of trends in future stock abundance, then juveniles are likely to be the better choice than larval individuals – unless measurement error is high (Stige et al. 2013). Juveniles are closer to the recruitment period; thus, this life stage provides a more accurate prediction of recruitment (Bradford 1992).
Additionally, juveniles are more fit for survival since they can actively swim to capture prey, avoid predators, and search out favorable environment conditions (Houde 1994;
Sogard 1997; Fiksen et al. 2007). Thus, juvenile abundances are less impacted by stochastic density-independent mortality rates and can provide a more robust prediction of recruitment (Field et al. 2010). However, as previously stated, only six rockfish stock assessments use a juvenile abundance index to predict recruitment, and only three use both the juvenile and larval abundance indices.
Although the incorporation of these juvenile rockfish abundance indices into stock assessments of a select number of species is encouraging, there are many species that do not have an index of juvenile abundance. Rockfish species (Sebastes) and several other related scorpaenids (Scorpaena and Sebastolobus) constitute 65 of the 87 fish species managed (not including 25 ecosystem component species) within the Pacific
Coast Groundfish Fishery Management Plan (FMP) (PFMC 2016b). Fifty-five species of rockfish and 21 flatfishes, roundfishes (excluding Pacific hake), or elasmobranches do not benefit from this survey since there is no analog survey sampling these 76 fish
96 species (PFMC 2016b). Pacific hake (whiting; Merluccius productus) is the only non- rockfish groundfish species where stock assessment modelers use a fishery-independent survey to develop juvenile abundance indices (Edwards et al. 2018). Therefore, stock assessments of nearshore species, such as some of the rockfishes and flatfishes (e.g.,
Sebastes pinniger, Microstomus pacificus, or Eopsetta jordani), could benefit from a fishery-independent survey monitoring nearshore soft-sediment habitats.
3.6 Discussion
3.6.1 Roadblocks and moving forward
The lack of nearshore sampling along Oregon’s coast has been recognized as a data-need in fisheries’ management since the commercial West Coast groundfish fishery collapsed in 2000 (Fox et al. 2000). However, most attempts to better sample nearshore areas have been focused on non-trawlable rocky reefs, where many rockfish species and other commercially-important species are known to live (Fox et al. 2000). Despite research projects having attempted to fill this knowledge gap since 1995, there is still not a consistent fishery-independent nearshore survey to estimate fish populations in this area
(Fox et al. 2000; Amend et al. 2001; Merems 2003; Weeks and Merems 2004). In fact, this lack of a nearshore fishery-independent survey is still considered a data-need in stock assessments today (Dick et al. 2017).
Over two decades have passed since nearshore sampling has been considered a data-need, and due to the challenges of sampling a rocky habitat, the potential methodologies for surveying nearshore rocky reefs – such as Remote Operated Vehicles
(ROVs), hydroacoustics, or stationary video landers – are still a hot topic of discussion
97 for fisheries management (Fox et al. 2000; Hannah and Blume 2012; Huntington et al.
2015; Watson and Huntington 2016; “Methodology Review Meeting to be held February
12-14, 2019 in Santa Cruz, CA” 2019). In fact, current PMFC meetings might lay the foundation for future rocky reef surveys in Oregon and California conducted by state fish and wildlife agencies (“Methodology Review Meeting to be held February 12-14, 2019 in
Santa Cruz, CA” 2019). However, there has been no discussion of expanding the trawlable habitat surveys into nearshore waters.
If it was decided that nearshore soft-sediment sampling of juvenile groundfish habitat would be a helpful supplement to fisheries stock assessments, there are still roadblocks for new methodologies to be implemented into the fishery management framework. One major roadblock is a lack of funding. For instance, NOAA’s science budget for the 2019 fiscal year is being reduced, and the costs for running research surveys continues to increase (Daniels 2018). This means that the current bottom trawl survey needs to be reduced in some fashion; thus, new surveys or expansions to surveys are currently not feasible (Daniels 2018). The second major roadblock is the difficulty of facilitating interagency collaboration.
These two issues are related because one potential solution to funding shortages is to share the burden between agencies. For example, state agencies, such as Oregon
Department of Fish and Wildlife (ODFW), California Department of Fish and Wildlife
(CDFW), or Washington Department of Fish and Wildlife (WDFW), could partner with
NOAA and contribute to the stock assessments with nearshore soft-sediment monitoring data. State agency involvement in monitoring the Pacific coast groundfish fishery a clear
98 choice because, although federally managed, these nearshore habitats are in state waters, thus fall within state jurisdiction (43 U.S.C. §§ 1301 et seq., 16 U.S.C. §§ 1801 (101)).
Interagency collaboration and cooperation has already been suggested as a benefit to both science and the agencies themselves for the two following reasons: 1) it allows for multiple perspectives on one common problem, resulting in a more robust solution, and
2) it could reduce individual agency costs by allowing multiple agencies to share the financial burden (Lowell and Kelly 2016). Additionally, interagency collaboration is already occurring for rocky reef areas and marine protected areas (Weeks and Merems
2004; Huntington et al. 2015; ODFW 2015, 2016), so soft-sediment habitat monitoring is a logical next step following the collaborative precedent already set by these other research initiatives.
However, if state agencies conducted fishery-independent surveys in nearshore trawlable soft-sediment habitats and non-trawlable rocky reefs, the collaboration between state and federal agencies would require extensive communication and partnership. A potential solution is that this collaboration could be facilitated through the Pacific
Fisheries Management Council process, which includes representatives from all three coastal states as described earlier. Additionally, this collaborative project could be modeled after other collaborative state-federal agency or industry-federal agency projects such as ODFW’s Marine Reserves’ Ecological Monitoring Program or the PWCC-
NWFSC fishery-independent survey discussed earlier (Sakuma et al. 2006; Huntington et al. 2015; ODFW 2015).
99 3.6.2 Suggestions for nearshore survey development
If fisheries managers decide to implement nearshore sampling within soft- sediment habitats along the U.S. west coast, there are a few methodological technicalities to consider. In chapter 2 of this thesis, I compared sampling of juvenile demersal fish assemblages with a beam trawl net along the central Oregon coast to sampling from the
WCGBTS within the same area (Haven 2019). The findings showed a gradient in fish assemblages based on depth, month, and year caught, as well as gear type used.
Specifically, I found for the most common species (English sole and Pacific sanddab) the specimens caught within the two surveys overlapped. This would suggest that a simple expansion of the groundfish survey closer to shore (e.g., 30 m) would already provide much greater insight into juvenile coastal fish assemblages. Additionally, I found there was a break in community composition at approximately 60 m (i.e, NH 05). This would suggest that to monitor coastal assemblages the survey needs to be expanded to depths that are shallower than the 60 m cut off. In order to best understand population trends, one must sample systematically to account for spatial and temporal variation, as well as gear selectivity.
3.7 Conclusion
Accurate stock assessments are necessary for the development of appropriate fishing regulations for the Pacific coast groundfish fishery. When stock assessments are inaccurate the future fish population may be inaccurately estimated, resulting in potentially inappropriate fishing regulations that could harm many different stakeholder groups. Efforts to improve stock assessment accuracy have been made in the past in the
100 form of juvenile rockfish and ichthyoplankton surveys providing indices of early life history abundance. I propose a similar survey be implemented to provide an index of abundance for juvenile flatfish by sampling nearshore nursery soft-sediment habitats.
This would add an extra layer to an already complicated adaptive management system.
Adjusting the current management system might be difficult and arduous. It could, however, lead to better-informed fishing regulations thereby, allowing for healthy, productive, resilient, and long-lasting fisheries and fishing industries along the U.S. West
Coast.
101 3.8 References
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Sakuma, K. M., S. R. Ralston, and V. G. Wespestad. 2006. Interannual and spatial variation in the distribution of young-of-the-year rockfish (Sebastes spp.): Expanding and coordinating a survey sampling frame. California Cooperative Oceanic Fisheries Investigations Reports 47:127–139. Schwaab, E. 2012. Taking stock: The Magnuson-Stevens Act revisited: The Magnuson Act thirty-five years later. Roger Williams University Law Review 17(1):14–20. Sigler, M. F., M. P. Eagleton, T. E. Helser, J. V. Olson, J. L. Pirtle, C. N. Rooper, S. C. Simpson, and R. P. Stone. 2017. Alaska Essential Fish Habitat Research Plan: A Research Plan for the National Marine Fisheries Service’s Alaska Fisheries Science Center and Alaska Regional Office. AFSC Processed Rep. 2015-05, 22 p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv. Seattle, WA. Sobocinski, K. L., L. Ciannelli, W. W. Wakefield, M. E. Yergey, and A. Johnson- Colegrove. 2018. Distribution and abundance of juvenile demersal fishes in relation to summer hypoxia and other environmental variables in coastal Oregon, USA. Estuarine, Coastal and Shelf Science 205:75–90. Sogard, S. M. 1997. Size-selective mortality in the juvenile stage of teleost fishes: A review. Bulletin Of Marine Science 60(3):1129–1157. Stauffer, G. (compiler). 2004. NOAA Protocols for Groundfish Bottom Trawl Surveys of the Nation’s Fishery Resources. U.S. Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-65, 205 p. Stige, L. C., M. E. Hunsicker, K. M. Bailey, N. A. Yaragina, and G. L. H. Jr. 2013. Predicting fish recruitment from juvenile abundance and environmental indices. Marine Ecology Progress Series 480:245–261. Stinton, A., L. Ciannelli, D. C. Reese, and W. Wakefield. 2014. Using in situ video analysis to access juvenile flatfish behavior along the Oregon central coast. California Cooperative Oceanic Fisheries Investigations Reports 55:158–168. Stoner, A.W., C.H. Ryer, S.J. Parker, P.J. Auster, and W.W. Wakefield. 2008. Evaluating the role of fish behavior in surveys conducted with underwater vehicles. Canadian Journal of Fisheries and Aquatic Sciences 65(6):1230-1243. Submerged Lands Act. 1953. 43 U.S.C. §§1301 et seq. Sullivan, P., J. Acheson, P. Angermeier, T. Faast, J. Flemma, C. Jones, and E. E. Knudsen. 2006. Defining and implementing Best Available Science for fisheries and environmental science, policy, and management. American Fisheries Society 31(9):460–460. Sustainable Fisheries Act. 1996. 16 U.S.C. ch. 38 § 1812 et seq.
109 Tanz, A. 2016. How have institutional barriers impacted implementation of ecosystem based fishery management in the US. Master of Marine Affairs, University of Washington, Seattle, Washington. Thanassekos, S., R. J. Latour, and M. C. Fabrizio. 2016. An individual-based approach to year-class strength estimation. ICES Journal of Marine Science 73(9):2252–2266. The Research Group, LLC. 2017. Oregon Commercial Fishing Industry Year 2016 Economic Activity Summary. Pages 1–13. Oregon Department of Fish & Wildlife, Marine Resource Program, Economic Impact, Corvallis, OR. Thorson, J. T, and C. Wetzel. 2016. The status of canary rockfish (Sebastes pinniger) in the California Current in 2015. Pages 1–678. Pacific Fishery Management Council, Stock Assessment and Fishery Evaluation, Portland, OR. Toole, C., R. D. Brodeur, C. J. Donohoe, and D. Markle. 2011. Seasonal and interannual variability in the community structure of small demersal fishes off the central Oregon coast. Marine Ecology Progress Series 428:201–217. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, and National Marine Fisheries Service. 1976. Magnuson-Stevens Fishery Conservation and Management Act. Page 170. 16 U.S.C. §§ 1801 et seq. Watson, J. L., and B. E. Huntington. 2016. Assessing the performance of a cost-effective video lander for estimating relative abundance and diversity of nearshore fish assemblages. Journal of Experimental Marine Biology and Ecology 483:104–111. Weeks, H., and A. Merems. 2004. 2003 Nearshore rocky reef habitat and fish survey, and multi-year summary. Pages 1–10. Oregon Department of Fish and Wildlife, Marine Habitat Project, Marine Resources Program, Final Report for 2003-04 Grant Cooperative Agreement No. 001-3176C-Fish.
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110 3.9 Figures
Average Yield Average
Fishing Effort Figure 14. Theoretical relationship between the fishing effort and the average yield of a fishery. Adapted from Hilborn, R. & Walters, C. J. Role of Stock Assessment in Fisheries Management. in Quantitative fisheries stock assessment: choice, dynamics and uncertainty 3–21 (Kluwer Academic Publishers, 2001).
111 CHAPTER 4: GENERAL CONCLUSION
4.1 Summary and relevance of findings
Pacific coast groundfishes along the U.S. West coast are important economically and ecologically (PFMC 2016, 2018; NOAA Fisheries 2017; The Research Group, LLC
2017). The Pacific coast groundfish fishery is managed by the National Marine Fisheries
Service (NMFS) and the Pacific Fishery Management Council (PFMC) as mandated by the Magnuson-Stevens Fishery Conservation and Management Act (MSA) of 1976 (16
U.S.C. 1801 et seq; Baur et al. 2015). The PFMC develops Fishery Management Plans
(FMP) that are informed by the scientific research and subsequent stock assessments developed by the NMFS (Baur et al. 2015). These FMPs consist of harvest regulations for the fishery such as gear restrictions, annual harvest limits, and season duration, and the
FMP is ultimately approved by the NMFS as long as it is in accordance with the MSA and other relevant laws (16 U.S.C. 1801 et seq; Baur et al. 2015; PFMC 2016).
Nearshore habitats within the California Current Ecosystem are important nursery grounds for the early life history stages of Pacific coast groundfish species such as rockfishes and flatfishes (Pearcy and Myers 1974; Rosenberg 1982; Krygier and Pearcy
1986; Dauble et al. 2012; Hughes et al. 2014; Sheaves et al. 2015). Nearshore open coastal regions and estuaries are also areas of greater risk of global climate change perturbations and human impacts since they are areas of high environmental variability
(e.g., upwelling intensity, upwelling frequency, and related hypoxia) and human use (e.g., recreation, transportation, shipping, and renewable energy) (Huyer 1983; Boehlert et al.
2008; Halpern et al. 2008; Brekken et al. 2009; García-Reyes and Largier 2010; Peterson
112 et al. 2013; Li et al. 2016). Therefore, in the context of global climate change, the future of Pacific coast groundfish fishery and the California Current Ecosystem are uncertain, and they both could be at risk of poor management if not monitored well. Additionally, an Ecosystem-based approach to fisheries management can improve the adaptive capacity of regulatory frameworks and reduce the risk associated with making decisions under uncertainty (McLeod and Leslie 2009; Rosenberg and Sandifer 2009; Berkes 2012).
A comparison of fishery-independent surveys showed that a nearshore monitoring survey would complement the West Coast Groundfish Bottom Trawl Survey (WCGBTS or bottom trawl survey) to provide a more representative sample of Pacific coast groundfish populations and the environmental conditions (e.g., dissolved oxygen concentration, temperature, and salinity) that these fishes are exposed to within the
California Current Ecosystem. Within the designated geographical and temporal subset established within this survey comparison, the catch diversity of the WCGBTS was higher than the beam trawl survey, including 85 species of rockfish, flatfish, and other demersal species. Conversely, the beam trawl survey did not catch many rockfishes and was instead dominated by flatfish species, including some coastal species not found in the
WCGBTS, such as speckled sanddab (Citharichys stigmaeus). The survey comparison also showed that the beam trawl caught a higher abundance of smaller individuals (< 30 cm standard length) than the bottom trawl survey, but there was overlap in the density of individuals caught at smaller lengths when examining coastal species (i.e., English sole and Pacific sanddab) within both surveys. The environmental variables observed also showed a difference between surveys. The beam trawl survey had higher variability
113 within dissolved oxygen concentration, temperature, and salinity compared to the bottom trawl survey due to the higher temporal resolution of the survey design. Overall, the design of the bottom trawl survey has a larger spatial extent (i.e., entire U.S. West coast between 55-1,280 m), but the beam trawl survey has a greater temporal resolution within a much smaller area (i.e., Newport Hydrographic Line).
The relevance of this survey comparison is that a fishery-independent survey such as the beam trawl survey could provide two pieces of information currently lacking in the
WCGBTS. First, the beam trawl survey could provide a more accurate environmental indicator of changing oceans on an ecosystem level due to fine-scale temporal sampling.
Second, the beam trawl survey could offer an improved estimate of settlement patterns
(or possibly recruitment and year-class strength) within the Pacific coast groundfish populations by including an index of early life history abundance within nearshore nursery habitats. Therefore, the next step is to understand the feasibility of incorporating a fishery-independent survey of nearshore habitats.
Examining the regulatory frameworks of the Pacific coast groundfish fishery showed that previous efforts to improve stock assessment accuracy have been made in the form of juvenile rockfish and ichthyoplankton surveys providing indices of early life history abundance. This examination also exposed three current barriers to incorporating nearshore sampling into fishery monitoring methods, which are the following: 1) lack of funding, 2) communication difficulties associated with interagency collaboration, and
3) technical, or methodological, difficulties with establishing a nearshore fishery- independent survey. The potential solutions to these roadblocks are as follows:
114 1) collaborate between agencies to share the funding burden, 2) use previous interagency studies and the PFMC system to model and facilitate effective collaborative techniques, and 3) follow the example of the beam trawl survey by sampling in waters shallower than
55 m and sample on a fine-scale temporal scope.
4.2 Recommendations and future work
I propose a fishery-independent survey be implemented along the U.S. West coast to provide an index of abundance for juvenile flatfish and ecosystem indicator of ocean conditions by sampling nearshore nursery soft-sediment habitats. This would add an extra layer to an already complicated adaptive management system. Adjusting the current management system might be difficult and arduous. However, it could lead to better- informed fishing regulations thereby, allowing for healthy, productive, resilient, and long-lasting fisheries and fishing industries along the U.S. West coast. In order to incorporate nearshore sampling into stock assessments or a Fishery Ecosystem Plan, future work would need to determine the optimal survey design for this fishery- independent survey. This thesis shows that extending the current WCGBTS might be enough to provide a more representative estimate of the nearshore communities.
However, further research needs to examine this in greater detail to address the question of whether to extend the current survey or establish an additional complementary survey.
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APPENDIX
131 APPENDIX A: ADDITIONAL FIGURES AND TABLES
Table A1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between from 2012 until 2015. Frequency of Occurrence (FO) refers to the percent of tows (out of a total of 162 tows) in which this species was present. Average catch per unit effort (CPUE) is the standardized relative species abundance calculated by dividing the catch by the distance swept. Minimum and maximum depths where this species was observed are also shown. *Denotes a target depth instead of an observed depth since this species was only observed at a station that did not have a depth recorded. Frequency Average Sample Minimum Maximum of CPUE Scientific Name Common Name Units Depth Depth Occurrence per 1000 Present Observed Observed (FO %) m2 Parophrys vetulus English sole 84.57 22.914 137 24.4 112.2 Citharichthys Pacific sanddab 80.86 19.703 131 24.4 123.8 sordidus Citharichthys speckled 70.99 12.485 115 24.4 99.9 stigmaeus sanddab Isopsetta isolepis butter sole 60.49 13.769 98 24.4 102.1 Psettichthys sand sole 43.21 1.558 70 24.4 100 melanostictus Leptocottus armatus Pacific staghorn 26.54 0.457 43 25.6 82 sculpin Lyopsetta exilis slender sole 26.54 5.838 43 29.9 123.8 Microgadus Pacific tomcod 24.69 2.301 40 24.4 57.2 proximus Ammodytes Pacific 22.84 0.838 37 24.4 81.8 hexapterus sandlance Liparidae spp. snailfishes 22.22 0.726 36 28.3 102 Chesnonia warty poacher 16.67 0.302 27 25.6 123.8 verrucosa Microstomus Dover sole 16.05 0.214 26 33.8 123.8 pacificus Radulinus asprellus slim sculpin 12.96 0.579 21 78.6 123.8 Raja binoculata big skate 12.96 0.157 21 28.2 84.6 Pleuronectidae righteye 12.35 0.638 20 24.4 82 flounders Glyptocephalus rex sole 9.88 0.261 16 57.6 123.8 zachirus Osmeridae smelts 9.88 0.184 16 25.6 80.4 Eopsetta jordani petrale sole 9.26 0.108 15 29.4 108.4 Pallasina barbata tubenose 8.64 0.129 14 28.9 57.7 poacher Chitonotus roughback 8.02 0.112 13 28.3 57.6 pugetensis sculpin Citharichthys spp. sanddabs 8.02 0.551 13 25.6 80 Allosmerus whitebait smelt 6.79 0.363 11 29 78 elongatus
132 Table A1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between from 2012 until 2015 continued. (Continued)
Spirinchus starksi night smelt 6.79 0.262 11 25.6 47.5 Odontopyxis pygmy poacher 5.56 0.049 9 32 100 trispinosa Cottidae spp. sculpins 4.94 0.077 8 28.3 78.5 Ophiodon elongatus lingcod 4.32 0.046 7 24.4 41.8 Sebastes elongatus greenstriped 4.32 0.289 7 29.1 102 rockfish Agonopsis vulsa northern 3.70 0.030 6 31 106 spearnose poacher Sebastes spp. rockfishes 3.70 0.061 6 31 48 Syngnathus bay pipefish 3.70 0.082 6 32 58 leptorhynchus Atheresthes stomias arrowtooth 3.09 0.062 5 44.8 106.6 flounder Cymatogaster shiner surfperch 3.09 0.058 5 31.3 49 aggregata Hemilepidotus red Irish lord 3.09 0.028 5 25.6 63 hemilepidotus Lepidogobius bay goby 3.09 0.280 5 29 56.9 lepidus Pleuronichthys curlfin sole 3.09 0.028 5 29 80.4 decurrens Stellerina xyosterna pricklebreast 3.09 0.037 5 25.6 42.9 poacher Gadidae spp. gadids 2.47 0.339 4 24.4 42.8 Hemilepidotus brown Irish lord 2.47 0.051 4 32.9 41.4 spinosus Hydrolagus colliei spotted ratfish 2.47 0.015 4 46.5 94 Ronquilus jordani northern ronquil 2.47 0.059 4 24.4 47.7 Unknown unknown 2.47 0.021 4 47.5 63.9 Xeneretmus bluespotted 2.47 0.018 4 81 106 triacanthus poacher Enophrys bison buffalo scuplin 1.85 0.023 3 25.6 43 Plectobranchus bluebarred 1.85 0.048 3 29.4 57.2 evides prickleback Sebastes pinniger canary rockfish 1.85 0.025 3 29.9 47.5 Bathyagonus gray starsnout 1.23 0.008 2 82 93 alascanus Cololabis saira Pacific saury 1.23 0.017 2 57.6 106.6 Lycodes pacificus blackbelly 1.23 0.047 2 109.7 109.7 eelpout Poroclinus rothrocki whitebarred 1.23 0.026 2 82 123.8 prickleback Raja rhina longnose skate 1.23 0.010 2 57.7 57.7
133 Table A1. Fish species sampled with the Newport Hydrographic Line beam trawl survey between from 2012 until 2015 continued. (Continued)
Spirinchus longfin smelt 1.23 0.046 2 32.9 43.8 thaleichthys Xeneretmus latifrons blacktip 1.23 0.054 2 93 93 poacher Anoplagonus smooth 0.62 0.009 1 41.9 41.9 inermis aligatorfish Anoplopoma fimbria sablefish 0.62 0.005 1 38 38 Artedius harringtoni scalyhead 0.62 0.005 1 29.4 29.4 sculpin Artedius spp. sculpins 0.62 0.005 1 41.4 41.4 Bathyagonus bigeye poacher 0.62 0.006 1 109.7 109.7 pentacanthus Bathymasteridae ronquils 0.62 0.010 1 57.9 57.9 spp. Chilara taylori spotted cusk-eel 0.62 0.005 1 47.5 47.5 Cryptacanthodes dwarf 0.62 0.021 1 100* 100* aleutensis wrymouth Engraulis mordax northern 0.62 0.008 1 29 29 anchovy Eptatretus stoutii Pacific hagfish 0.62 0.004 1 106.8 106.8 Gasterosteus three-spined 0.62 0.007 1 28.9 28.9 aculeatus stickleback Hexagrammos kelp greenling 0.62 0.006 1 80* 80* decagrammus Hippoglossoides flathead sole 0.62 0.010 1 78.6 78.6 elassodon Icelinus burchami dusty sculpin 0.62 0.004 1 106.6 106.6 Icichthys lockingtoni Medusafish 0.62 0.005 1 43.3 43.3 Lepidopsetta northern rock 0.62 0.008 1 46.7 46.7 polyxystra sole Lycodes cortezianus bigfin eelpout 0.62 0.043 1 93 93 Lycodes palearis wattled eelpout 0.62 0.153 1 123.8 123.8 Peprilus simillimus butterfish 0.62 0.004 1 46.9 46.9 Pholidae spp. gunnels 0.62 0.004 1 47.5 47.5 Rhinogobiops blackeye goby 0.62 0.007 1 40 40 nicholsii Sebastes diploproa splitnose 0.62 0.004 1 44 44 rockfish Sebastes entomelas starry rockfish 0.62 0.008 1 44.8 44.8 Sebastes flavidus yellowtail 0.62 0.005 1 38 38 rockfish Sebastes mystinus blue rockfish 0.62 0.005 1 32.9 32.9 Symphurus California 0.62 0.008 1 29 29 atricaudus tonguefish
134 Table A2. Summary Table of Environmental Data for PC-ORD Environmental Matrix. Environmental Code Minumum Maximum Variable Date-Station Sample Unit SU 7612MB30 102115NH15 Sample Date sampleDate 7/6/2012 10/21/2015 Sample Month month January November Sample Year year 2012 2015 Station station MB30 NH15 Depth towed (m) depth.tow 24.4 125.326 Time Towed (minutes) time_towed 5 10 Distance Towed (m) maxTowDist 294 1244 Season (Winter/Summer) comparison 0 1 Temperature (°C) temp 6.9984 14.7399 Oxygen (ml/l) DO 0.93426 6.87805 Salinity (PSU) sal 31.9029 34.757
135 Table A3. Data Adjustments and their effects on Whittaker’s Beta Diversity, Beta Diversity in half-changes, coefficient of variation (CV) for row sums, CV for column sums, and average column skewness of the community abundance data. Rare species were defined as those that occurred in <7 sample units (~ 5 % of total). Adjustments Parameter Before i – Empty 1 – Delete 2 – Delete 3 – Gen. 4 – Rel. columns single rare Log by species (species) occurrences species Transform. maximum removed Beta diversity 10.3 10.0 6.7 3.4 3.4 3.4 (Whittakers, βW)
Beta diversity 1.9 1.9 1.9 1.9 1.3 1.5 (half changes, βD)
ROWS CV of row sums 130.93 130.93 131.18 131.12 49.47 57.64 COLUMNS Average skewness 9.0 9.2 7.8 6.4 3.0 3.0 CV of column 351.60 346.75 283.45 197.52 140.42 100.41 sums
136 Table A4. Indicator Species Analysis from beam trawl multivariate community analysis showing only significant driving species. Group identifier is the group with maximum observed indicator value (IV). Species are sorted by mean IV from randomized groups. *Proportion of randomized trials with indicator value equal to or exceeding the observed indicator value p = (1 + number of runs ≥ observed)/(1 + number of randomized runs). Grouping Based on Target Depth Species Group Observed IV from IV from P-value Identifier Indicator randomized randomized * Value groups groups (St. (IV) (mean) Dev.) Parophrys vetulus 30 m 20.8 17.8 1.56 0.0464 Citharichthys 80 m 22.3 17.2 1.45 0.0018 sordidus Isopsetta isolepis 40 m 30.4 14.9 2.32 0.0002 Citharichthys 50 m 26.8 16.0 1.89 0.0002 stigmaeus Lyopsetta exilis 100 m 39.9 9.8 2.60 0.0002 Microgadus 30 m 27.3 9.1 2.62 0.0004 proximus Psettichthys 40 m 21.2 12.4 2.46 0.0058 melanostictus Ammodytes 50 m 32.9 9.0 2.48 0.0002 hexapterus Liparidae spp. 40 m 18.2 8.7 2.52 0.0052 Radulinus asprellus 100 m 51.4 6.6 2.50 0.0002 Leptocottus armatus 40 m 19.5 9.0 2.47 0.0032 Chesnonia verrucosa 30 m 15.4 7.3 2.51 0.0112 Glyptocephalus 100 m 33.0 6.2 2.57 0.0002 zachirus Pallasina barbata 30 m 11.6 5.7 2.44 0.0280 Chitonotus 40 m 11.1 5.3 2.42 0.0314 pugetensis Grouping Based on Target Depth – Comparison
Subset Only Isopsetta isolepis 40 m 30.6 16.1 2.82 0.0004 Citharichthys 50 m 28.9 16.8 2.38 0.0002 stigmaeus Lyopsetta exilis 100 m 36.3 12.9 3.50 0.0002 Microgadus 30 m 32.7 11.5 3.40 0.0002 proximus Ammodytes 50 m 31.6 10.2 3.34 0.0004 hexapterus
137 Table A4. Indicator Species Analysis from beam trawl multivariate community analysis showing only significant driving species. (Continued)
Radulinus asprellus 100 m 52.0 8.6 3.57 0.0002 Leptocottus armatus 40 m 19.0 9.3 3.37 0.0152 Glyptocephalus 100 m 32.7 8.0 3.56 0.0004 zachirus Osmeridae 40 m 14.0 6.8 3.41 0.0392 Raja binoculata 40 m 15.1 7.5 3.39 0.0360 Chitonotus 40 m 15.7 7.7 3.28 0.0280 pugetensis Grouping Based on Target Depth – Non-Comparison Subset Only Citharichthys 80 m 32.5 19.4 3.55 0.002 sordidus Isopsetta isolepis 40 m 29.5 19.1 4.10 0.0194 Citharichthys 60 m 26.9 20.3 3.26 0.0408 stigmaeus Lyopsetta exilis 100 m 71.4 11.9 6.39 0.0002 Ammodytes 50 m 34.4 15.0 5.75 0.0086 hexapterus Liparidae 40 m 33.5 12.7 6.22 0.0112 Radulinus asprellus 100 m 57.1 11.3 6.60 0.0004 Glyptocephalus 100 m 35.4 11.3 6.61 0.0108 zachirus Osmeridae 30 m 25.1 12.0 6.09 0.0360 Raja binoculata 30 m 26.9 11.9 6.00 0.0266 Grouping based on Sampling Month Parophrys vetulus March 14.4 11.6 1.31 0.0284 Citharichthys May 13.8 11.1 1.09 0.0074 sordidus Ammodytes May 15.3 8.5 3.26 0.0442 hexapterus Sebastes elongatus October 24.3 6.4 3.91 0.0030 Grouping based on Season – “Winter” or “Summer” Parophrys vetulus Winter 52.3 45.0 2.49 0.0094 Citharichthys Summer 53.1 43.6 2.40 0.0012 sordidus Lyopsetta exilis Summer 29.1 17.3 2.91 0.0046 Microgadus Summer 22.0 15.4 2.83 0.0296 proximus Leptocottus armatus Winter 24.3 15.8 2.83 0.0136
138 Table A4. Indicator Species Analysis from beam trawl multivariate community analysis showing only significant driving species. (Continued)
Microstomus Summer 22.5 11.3 2.45 0.0010 pacificus Chitonotus Summer 12.0 6.3 1.95 0.0190 pugetensis Grouping based on Sampling Year Lyopsetta exilis 2012 23.1 11.6 2.64 0.0016 Ammodytes 2013 16.3 10.6 2.56 0.0318 hexapterus Pleuronectidae 2012 16.2 7.4 2.44 0.0076 Microstomus 2012 16.3 8.3 2.44 0.0116 pacificus Odontopyxis 2014 10.4 4.4 1.99 0.0154 trispinosa Ophiodon elongatus 2013 8.7 4.1 1.93 0.0260
139
Figure A1. Average environmental data (dissolved oxygen, salinity, and temperature) for each year (2012-2015) along the NH Line compared between the beam trawl survey (left half) and the bottom trawl survey (WCGBTS; right half). The red line on the dissolved oxygen concentration plots denotes the designated hypoxia threshold.
140
Figure A2. Average environmental data (dissolved oxygen, salinity, and temperature) at each depth strata along or near the NH Line compared between the beam trawl survey (left half) and the bottom trawl survey (WCGBTS; right half). The red line on the dissolved oxygen concentration plots denotes the designated hypoxia threshold.
141
Figure A3. Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on sample year (2012-2015).
142
Figure A4. Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15)
143
Figure A5. Size distribution of Citharichthys sordidus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on non-comparable depth strata (no analogs to other survey).
144
Figure A6. Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on sample year (2012-2015).
145
Figure A7. Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on comparable depth strata (Bin 1 compared to NH-05, Bin 2 compared to NH-10, and Bin 3 compared to NH-15.
146
Figure A8. Size distribution of Parophrys vetulus specimens (measured using total length in cm; TL) caught in the beam trawl and bottom trawl surveys based on non-comparable depth strata (no analogs to other survey).