social and ecological dimensions of the striped bass ...cj82rb038/fulltext.pdfnumerous samples...

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Social and ecological dimensions of the Striped Bass (Morone saxatilis) fisheries in southern

New England

by Robert D. Murphy Jr.

B.S., Northeastern University, 2012

A dissertation submitted to

The Faculty of

the College of Science of

Northeastern University

in partial fulfillment of the requirements

for the degree of Doctor of Philosophy

April 10th, 2018

Dissertation directed by

Jonathan H. Grabowski

Professor of Marine and Environmental Sciences

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Acknowledgements

The dissertation presented herein is as much a result of the collective support of my

mentors, colleagues, friends, and family as it is my own efforts. The last five years have been

among the most challenging, educative, and rewarding, and I can confidently say that none of my

achievements would have been possible without such an amazing group of people.

At the forefront of this journey has been my academic advisor, Dr. Jonathan Grabowski,

who has been a wonderful role model, mentor, and friend. Jon hired me as a lab technician back

in 2011 and introduced me to the fascinating and complex world of fisheries ecology. He

provided me with direction when I was a young undergraduate and has since been at the

cornerstone of my development as a scientist. The entirety of my future scientific career will be

tied to the experiences I had during my time in Jon’s lab and to his academic guidance. I am also

deeply appreciative of the support provided by all of my dissertation committee members: Dr.

Geoffrey Trussell, Dr. Randall Hughes, Dr. Steven Scyphers, and Dr. Gary Nelson. My decision

to pursue a doctorate can be, in large part, attributed to conversations I had with Geoff during my

undergraduate schooling, while his academic and professional advice has been vital to my

growth over the last eight years. Randall provided invaluable insight throughout my doctorate

and entrusted in me to give multiple lectures in her Conservation Biology course, which proved

to be a critical step in my development as a science communicator. Gary’s research on Striped

Bass was the foundation for much of the work presented in this dissertation, specifically the third

and fourth chapters, which would not have been possible without his instruction. Both as a friend

and mentor, Steven was an important part of my doctorate. His door was always open and his

enthusiasm and guidance were key reasons I have decided to pursue a career in the human-

dimensions of natural resource management.

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I also owe a lot of my success to the Marine Science Center community and staff,

including Heather Sears, Ryan Hill, Sonya Simpson, Roberto Valdez, David Dawson, Liz

Bentley Magee, and Kelsey Tuminelli. They have all made the MSC a fantastic place to work

and made for a seamless graduate school experience. To all the members of the Grabowski lab, I

am deeply grateful for your support. This dissertation truly was a ‘team effort’ and would not

have been possible without your help. Fellow PhD students, Chris Baillie, Chris Conroy, Marissa

McMahan, Micah Dean, Theresa Davenport, and Louise Cameron – I can’t thank you enough for

your camaraderie and help over the last five years. From stats questions to picking through fish

guts, you guys were there for it all. Kelsey Schultz was an integral part of the last two years as

the Grabowski Lab Technician and made our lives as graduate students much easier. Steve Heck,

Joe Caracappa, Suzanne Kent, Lucy Harrington, Rami Maalouf, Sandi Scripa and to all the

Grabowski lab Masters students and technicians over the years that helped with lab work, sample

collections, and diving – this dissertation was built on your backs and for that I am eternally

gratefully.

I would also like to thank all of the friends I have made at the MSC over the years. Chris

Marks, our time together in Panama, Washington, and, of course, Nahant will be some of my

favorite memories of graduate school and I am very appreciative of all your support. Sarah,

thank you for your friendship and for all of your invaluable advice on how to navigate graduate

school. Ryan, my time at the MSC would not have been the same without you and I will

definitely look back fondly at our year in Lynn – I can’t think of a better way to spend my free

time during my first year as a graduate student. Chris B., from practicing talks, reviewing my

papers, helping with field work on countless occasions, to early mornings in goose blinds and

fishing out on the rocks, I am deeply grateful for your support and friendship.

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Many of the Striped Bass collected for this research came from two amazing fishers,

Randy Sigler and Greg Veprek. Randy runs a local youth fishing program which donated

numerous samples during the first couple years of my doctorate. Randy and the participants of

his fishing camp were instrumental in getting my work off the ground. I would like to thank Greg

Veprek for his extreme generosity and for imparting on me his Striped Bass fishing wisdom and

knowledge. I would not be the scientist nor fisherman I am today without his help. Thanks also

to the folks at the Massachusetts Division of Marine Fisheries that provided me with data for the

fourth chapter of my dissertation, boat time, and guidance on my acoustic study and otolith

processing: Micah Dean, Gary Nelson, Nick Buchans, Scott Elzey, and Bill Hoffman. My

second chapter benefited greatly from Dr. Steven Gray from Michigan State University, who

helped with the design of our Striped Bass fishing survey and offered important suggestions that

significantly improved the finished product.

Much of this dissertation was made possible by multiple sources of funding including the

National Oceanic and Atmospheric Administration’s Saltonstall-Kennedy Grant Program which

provided me with resources to complete my second chapter. Generous donors from

experiment.com, including many family members and friends, were a critical source a funding

for my fourth chapter. Funds to process stable isotope samples in my third chapter were provided

by the Northeastern University Dissertation Research Grant. Additionally, I was able to attend

and present at career-building conferences thanks to funding made available by the Marine

Science Center Travel Award and the Northeastern University College of Science Travel Grant.

Ultimately, my family has been the foundation to my success throughout my life and

during my doctorate. The time I spent hiking and fishing for trout with my Grandfather and uncle

Karl was the root of my passion for the natural world. I am extremely grateful for these

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experiences and for all that you taught me, Karl. My siblings, Erin, Christopher, Heather, Megan,

Ashley, and Laura, I cannot thank you enough for your support and encouragement. Erin, Steve,

and Christopher, you have always been there as a source moral support and positivity, and I

attribute much of who I am today to you. Mom and Dad, words cannot truly express how

grateful I am for your selflessness and for all that you have done. You gave me every possible

opportunity to succeed and were never wavering in your encouragement. You both represent all

that I hope to become as a person and I owe everything to you.

To my fiancée, Lauren; the last 10 years have been quite a ride and I am forever grateful

that I have had you by my side. Through all the trials and tribulations of graduate school, you

have been there – from your emotional support and unconditional love, to helping me study for

qualifying exams and prepping hundreds of feet of rope on the fourth of July. You motivate me

every day to push myself and get out of my comfort zone, but importantly, you have taught me to

find the beauty in all the little things in life. It is because of your support that I have made it this

far and I look forward to what the rest of our lives will bring.

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Abstract of Dissertation

People cannot be considered separate from ecosystems, as we operate as important

predators, contribute to species distributions, rely on the environment for food production, and

derive significant cultural and recreation value from them. The way in which we manage these

natural resource systems should be guided by their internal structure and interactions between

both their social and ecological domains. Historically, however, we have managed fisheries as if

species are isolated, which can lead to unintended spillover effects into other fisheries, fishery

failures, species collapses, declines in resource-dependent community well-being, and the loss of

culture. To begin moving towards a more holistic approach to management, we must develop

research frameworks that explore the underlying characteristics of both social and ecological

domains, and the ways in which they interact to mediate the delivery of ecosystem services.

In the western Atlantic, the Striped Bass (Morone saxatilis), as part of a dynamic social-

ecological system, is targeted by both commercial and recreational fishers, and as such,

contributes substantially to the coastal economy via its consumptive value and through fishing-

related expenditures. In New England, Striped Bass are one of only a few large-bodied fish that

often swim along the shore, providing access for a diversity of anglers to target this highly

sought-after species. Striped Bass also are an important predator in coastal ecosystems as they

consume numerous prey species during their summer residency in New England, such as the

American Lobster (Homarus americanus) and Menhaden (Brevoortia tyrannus). While Striped

Bass completely recovered from a population collapse in the late twentieth century, the coastal

population has recently declined again, leading to management changes aimed at preventing

another collapse. Importantly, the degree to which future regulations and fluctuations in the size

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and structure of the Striped Bass population will impact resource users and other fisheries is

unclear.

This dissertation applied an integrated approach using the Striped Bass fishery as a model

to increase our understanding of social-ecological systems. (1) I first explore whether disparate

groups of stakeholders would be in favor of policy changes aimed at enhancing the sustainability

of the Striped Bass fishery, and if there are user attributes that correlate with perceptions. (2) I

then assess if alternative policies would change the fishing effort of fishers, both within the

Striped Bass fishery and into other fisheries, and if we can predict their behavioral responses

based upon underlying motivations and attitudes. (3) The role of ontogeny in the diet of Striped

Bass is explored, along with the potential top-down effect of Striped Bass on local prey

communities and whether prey choice contributes to Striped Bass condition. (4) Finally, the

degree to which Striped Bass exhibit ontogenetic changes in their summer residence in northern

Massachusetts is examined using an acoustic study to assess whether differences in behavior

could affect the ability of Striped Bass to exert top-down pressure on localized populations of

prey.

Collectively, the work presented in this dissertation highlights the interconnectedness

between social and ecological domains within a natural resource system and reveals the ways in

which separate fisheries interact via social dynamics and predator-prey relationships. Chapter 1

identifies unique user groups that hold disparate viewpoints on how we should manage the

Striped Bass fishery, which could undermine the success of management if cheating ensues, or if

fishers lose trust in the management process. Chapter 2 reveals that the fishing effort and

behavior of recreational anglers within the Striped Bass fishery and into other fisheries is, in part,

contingent upon the structure of harvest-control rules and the underlying attitudes of anglers. By

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changing harvest size limits and the behavior of anglers, alternative regulations may influence

fishing mortality on different size classes of Striped Bass; Chapter 3 shows that different sizes of

Striped Bass have disparate impacts on prey species. Specifically, I found that diet is driven

partly by ontogenetic processes, such that large Striped Bass may benefit energetically from the

consumption of crustaceans over forage fish prey. The final chapter did not find differences in

habitat use across a range of Striped Bass sizes. However, my results do suggest that residency

time, in an important summer feeding area, increases with Striped Bass length, potentially

heightening the ability of large individuals to impact local crustacean populations. These

research findings emphasize the importance of understanding how diverse user groups and

fluctuating fish populations interact with each other and the broader ecosystem, which will

enhance our ability to achieve both social and biological management objectives, and

consequently help operationalize ecosystem-based fisheries management efforts.

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Table of Contents

Acknowledgements 2

Abstract of Dissertation 6

Table of Contents 9

List of Tables 12

List of Figures 13

List of Supplementary Figures 15

Introduction: People and ecosystems in the fisheries management of the future 16

Literature Cited 27

Figures 33

Chapter 1: Assessing fishers' support of Striped Bass management strategies

Abstract 34

Introduction 35

Materials and Methods 38

Results 41

Discussion 46

Literature Cited 54

Tables 57

Figures 60

Chapter 2: The disparate behavioral effects of fishery regulations can be explained by

angler attitudes

Abstract 68

Introduction 69


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Results 78

Discussion 84

Literature Cited 90

Tables 93

Figures 95

Supplementary Materials 102

Chapter 3: The feeding ecology of Striped Bass and the role of ontogeny

Abstract 106

Introduction 107


Results 117

Discussion 120

Literature Cited 128

Tables 133

Figures 136

Supplementary Materials 142

Chapter 4: Ontogenetic shifts in movement behavior of an anadromous predatory fish

Abstract 144

Introduction 145


Results 153

Discussion 154

Literature Cited 159

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Tables 163

Figures 163

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List of Tables


1.1 Summary of survey questions 57

1.2 Investigated questions and statistics used 58

1.3 Summary of demographic and other fishing variables by state 59


angler attitudes

2.1 Regulations scenarios 93

2.2 Percentage of anglers that either increased, decreased, or remained 94

constant in their effort towards a number of activity options


3.1 Size categories of Striped Bass 133

3.2 Summary of stomach contents by prey taxon 134

3.3 Bioenergetic model results 135


4.1 Summary detection statistics for both study years 163

4.2 Summary of residency metrics by categories of fish size 164

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List of Figures

Introduction People and ecosystems in the fisheries management of the future

i.1 A social-ecological research framework adapted from 33

Collins et al. (2011)


1.1 Classification tree of fishers’ perceptions of management 60

1.2 Percent of total response for participants that are supportive/neutral 61

towards four management changes

1.3 Effectiveness of hypothetical regulations 62

1.4 Slot limit analysis 63

1.5 Circle hook analysis 64

1.6 Classification tree of circle hook analysis 65

1.7 Reduction in recreational daily bag limit analysis 66

1.8 Classification tree analysis depicting the percent of fishers who are 67

supportive/neutral or opposed to a reduction in the commercial

yearly quota


angler attitudes

2.1 Example experimental scenario 95

2.2 Shift in effort upon the implementation of a new regulation 96

2.3 Shift in the frequency of which anglers would aim to keep 97

Striped Bass grouped by the direction of effort change

2.4 Hypothetical Striped Bass fishing days in MA under 98

status-quo and new regulations

2.5 Box-and-whisker plots for activity preferences and each of the four 99

consumptive orientation subdimensions

2.6 Classification tree analysis for each activity preference metric 100

2.7 Angler behavior compared to their consumptive attitude 101

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3.1 Study area with inset map of Massachusetts 136

3.2 Plot of most important prey taxon for all Striped Bass 137

3.3 Fisher observations of Striped Bass diets 138

3.4 Diet ontogeny by time period according to stable isotope 139

samples from Striped Bass white muscle and liver

3.5 Prey and Striped Bass stable isotopic values (from muscle samples) 140

3.6 Linear regression comparisons of Striped Bass condition indices 141

versus δ13C’ (white muscle samples) for the four size categories

of Striped Bass


4.1 Study area with inset map showing the broader region 165

within New England

4.2 Receiver locations for both study years with substrate classification 166

based on Pendleton et al. (2015)

4.3 Proportion of soft-bottom substrate for receivers in both study years 167

4.4 Detections by fish for 2015 and 2008 168

4.5 Acoustic receivers that detected Striped Bass tagged during 2015 169

4.6 Plots of fish total length by residency metrics 170

4.7 Date of last detection by Striped Bass total length 171

4.8 Percent of detections for each fish in receivers defined 172

by proportion of soft-bottom habitat

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List of Supplementary Material


angler attitudes

2.1 Internal reliability tests for activity preferences 102

2.2 Internal reliability tests for consumptive orientation subdimensions 103

2.3 Examination of attitudinal and behavioral differences 104

between online survey respondents in MA and mail

survey respondents that did not initially receive an online survey

2.4 Examination of attitudinal and behavioral differences 105

between online survey respondents in MA and mail

survey respondents that initially received an online survey


3.1 Prey energy densities and the literature source of energy estimate 142

and length-weight relationships

3.2 ANOVA tests of significance and Tukey post-hoc tests between 143

Striped Bass size classes

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Introduction

People and ecosystems in the fisheries management of the future

Population growth projections suggest that global populations are unlikely to stabilize in

the near future, growing from 7.2 billion to over 9.6 billion people by 2100 (Gerland et al. 2014).

The need to generate sustainable and predictable sources of food is becoming increasingly

important, even in the United States where the population is expected to rise by nearly 100

million people by 2060 (Godfray et al. 2010, Colby and Ortman 2017). Wild capture and

aquaculture seafood already represent a considerable portion of the world’s protein intake (Food

and Agriculture Organization of the United Nations 2009), but in order to effectively and

efficiently utilize food from our oceans, we must adopt management strategies that consider

multiple uses, promote flexibility, and consider the interconnectedness between species,

ecosystems, and people (Swan and Gréboval 2004, Hilborn 2007a, Marshall et al. 2017).

Fisheries management objectives have evolved considerably from more traditional

approaches that focused on yield-based metrics towards methods that incorporate both ecological

and social objectives (Hilborn 2007b). Often considered the pinnacle of a truly holistic approach

to managing our oceans is ecosystem-based management (EBM) which aims to provide and

sustain key ecosystem services for coastal communities (Rosenberg and McLeod 2005).

Grounded in a triple bottom line approach (i.e., spanning economic, cultural, and ecological

dimensions), EBM increases the complexity of management in general (Anderson et al. 2015)

and should account for the connections between systems, for the cumulative impacts of multiple

human activities, and for multiple objectives and potential tradeoffs between objectives (Halpern

et al. 2008a, Halpern et al. 2008b, McLeod and Leslie 2009). As an example, in order to

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maintain the production of salmon for food as an ecosystem service, we must not only manage

for a healthy population of fish, but also for suitable habitat, prey and nursery habitats, water

quality, local fleet access, and for local markets and restaurants (Halpern et al. 2008a).

Moving towards this all-inclusive form of management will necessitate small,

incremental steps that may involve significant challenges. Rather than exclusively utilizing

biological indicators as in single species management, an ecosystem approach to fisheries

management should attempt to account for other factors including habitat, water quality and

temperature, and predator-prey interactions. However, fisheries do not occur in isolation and

often interact spatially, through behavioral spillover effects, or via consumption of one fishery

species by another (Nelson et al. 2006, Chan and Pan 2016, Cunningham et al. 2016).

Ecosystem-based fisheries management (EBFM) tries to account for these fishery-to-fishery

interactions, as well as the relationships between target stock biomass and environmental, social,

and biotic variables (Patrick and Link 2015). Moreover, successful implementation of EBFM

will require integration of multiple disciplines, from biology and ecology to sociology (McLeod

and Leslie 2012).

Humans and ecosystems, represented collectively as a social-ecological system, are

inextricably linked, and are made of a number of interacting components including the resource

system, resource units, resource users, and a governance system [e.g., a fishery, the fish, the

people catching the fish, and the regulatory structure, respectively (Ostrom 2009, Shackeroff et

al. 2009)]. The ways in which these complex systems are managed should be guided by the

characteristics of and interactions between their components. Traditionally, research has been

confined to either the social or biological disciplines, referred to as the social template and

biophysical template by Collins and colleagues (2011), but it is becoming increasingly clear that

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these domains interact across time and space, and are often mediated by external disturbances.

These disturbances can be either “press” or “pulse” events that impact social-ecological systems

via long-term, slow processes or more immediate, intense events, respectively (see Figure i.1 for

an example as adapted from Collins et al. 2011). As a hypothetical example, shellfish

populations may be affected by sea level rise or nutrient runoff (both press disturbances) or

extreme storm events (a pulse disturbance). Importantly, shellfish provide ecosystem services

back to people, via their consumptive value and nitrogen removal, thus linking the biophysical

and social templates. Of course, the characteristics and behavior of people can change the

magnitude of disturbances, such as through increased coastal development or increased

consumptive demand, thereby creating a feedback loop between both disciplines.

Applying this integrated research approach will require cross-disciplinary collaboration

and interdisciplinary work that assesses how template attributes affect the internal structure of

each template, and importantly, how these attributes interact to regulate the delivery of

ecosystem services to resource users. Traditional fisheries management already considers some

biophysical template attributes that influence the quantity and quality of these services, namely

the capacity of a fish population to reproduce (i.e., spawning stock biomass) and the link

between population size and future production (i.e., stock-recruit relationship). However,

incorporating the dynamic trophic relationships between the target fish population and its

predators and prey will allow for more informed management strategies, especially in the wake

of a changing climate and shifting species distributions. For one, predators can have a broad

array of evolutionary and ecological impacts on prey populations (Connell 1961, Paine 1974,

Carpenter et al. 1985, Trussell et al. 2006, Denno and Lewis 2009), which may ultimately change

the delivery of ecosystem services back to people (Figure i.1). For example, as summarized by

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Holmlund and Hammer (1999), Atlantic Cod declined precipitously in the 1980’s and 90’s

(MacKenzie et al. 1996) which fundamentally altered the Baltic Sea social-ecological system.

Atlantic Cod (Gadus morhua) were thought to be important predators of multiple forage fish

species, like Atlantic Herring, such that after the Cod decline, forage fish saw a boost in

abundance and fishing communities that largely focused on Cod were now forced to shift to

lower trophic-level food sources (Rudstam et al. 1994, Sparholt 1994). Additionally, South

African sport fisheries suffered when target finfish populations declined (van der Elst 1979).

Worried about attacks on people, large sharks were selectively harvested, freeing smaller sharks

from predation, which created a trophic cascade resulting in a reduction of finfish. Alternatively,

prey may impact their predators, such as along the coast of Canada where declining prey

availability, specifically the Capelin (Mallotus villosus), may have contributed to reduced lipid

storage and spawning potential in Atlantic Cod (Sherwood et al. 2007). To complicate matters,

however, there is growing evidence that the reliance of a predator population on forage fish

abundance is largely context dependent. Hilborn and colleagues (2017) argued that very few

predator-prey systems (from U.S. fisheries) indicate that forage fish abundance had any

noticeable impact on predator abundance over time. They go on to suggest that predators may

exhibit significant behavioral plasticity and can capitalize on the natural variability of prey

populations, thus decoupling many predator-prey relationships. An ecosystem approach to

management should consider these predator-prey interactions and their context dependency as to

account for potential unintended indirect effects of harvest control rules.

An understanding of the abundances of predator and prey populations and the

directionality of these trophic relationships is not enough, however, to fully capture the degree of

influence one population may have on the other. For example, ontogenetic shifts may change

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community structure and dynamics, which will depend on the traits of each size class (Werner

and Gilliam 1984). Ontogenetic shifts in aquatic systems are common and can take many forms,

such as habitat, diet, and behavioral shifts (Werner and Gilliam 1984, Sherwood et al. 2002, Carr

et al. 2003, Hultgren and Stachowicz 2010). For example, a species of Sunfish makes size-

specific shifts in habitat to avoid predation from Largemouth Bass (Werner and Hall 1988).

Sunfish move into the more profitable, pelagic zone of a lake ecosystem once they have grown to

a size in which they are unlikely to be consumed by Largemouth Bass. Additionally, energetic

demands and tradeoffs change over the course of a predator’s life that often result in shifts in

prey consumption (Townsend and Winfield 1985), as is the case for Yellow Perch, which switch

from consuming zooplankton, to benthic invertebrates, to forage fish (Sherwood et al. 2002).

Failure to perform an ontogenetic diet shift can result in consequences for the predator; Lake

Trout that do not consume fish exhibit reduced growth rates and are smaller than their

piscivorous counterparts (Pazzia et al. 2002). Clearly, blueprint approaches to management will

not work, given the nuances and complexities of coastal ecosystems, and because the stock

structure and abundance of fish populations are not static in place nor time (Fogarty et al. 2012).

Fish deliver a variety of ecosystem services (Holmlund and Hammer 1999), thereby

linking the biophysical and social templates as described in the framework by Collins et al.

(2011) (Figure i.1). These can range from supporting ecosystem services, such as nutrient

cycling and the transport of key nutrients, to provisioning services, like food production, and

cultural services, such as the cultural value of a particular species (Holmlund and Hammer 1999,

Millennium Ecosystem Assessment 2005). Importantly, managing for the sustainable delivery of

ecosystems services should consider how they are utilized and viewed by people, along with the

users’ perspectives, motivations, and well-being (Ecosystem Principles Advisory Panel 1999,

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Charles 2012, Long et al. 2015). For example, different cultures can maintain alternative

worldviews of their relationship with the ocean that can fundamentally change how they interact

with natural resources (Shackeroff et al. 2009). Additionally, enacting policy without a priori

information on stakeholder values or behavior can lead to unintended consequences and

counterproductive results (e.g., Pierce and Tomcko 1998). This was potentially the case in the

commercial groundfish fishery in the Atlantic, where the implementation of a sector-based

management system likely caused fishing effort to shift from New England to the Mid-Atlantic

region (Cunningham et al. 2016). Moreover, finding solutions to complex management problems

will require research programs to evaluate the factors that contribute to stakeholder perceptions

and behavior in the face of variable environmental and regulatory conditions (Pinsky and

Fogarty 2012).

The directionality and magnitude of stakeholder-to-resource interactions are mediated by

a plethora of factors, particularly in fishery systems. As is often the case, conflicts among

commercial, subsistence, and recreational fisheries are highly contentious when allocating

resources (Kearney 2001). Fundamental differences between these groups and numerous within-

group characteristics likely drive their behavior, which should ultimately help guide regulatory

decision-making (Branch et al. 2006, Cooke and Cowx 2006). For example, California

commercial sea urchin divers must weigh multiple types of risk before deciding whether they

should fish on a given day, including bad weather or the potential presence of White Sharks

(Smith and Wilen 2005). Additionally, the behavior of subsistence fishers in response to policy,

which by definition fish for consumptive purposes, may be influenced by social and economic

variables, such as their perceptions of the environment and job diversification (Gelcich et al.

2005). Behavioral, attitudinal, and motivational diversity also exist within and between

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recreational fishery user groups, making blueprint management strategies quite difficult (e.g.,

Fedler and Ditton 1994, Fisher 1997, Dorow and Arlinghaus 2012). For instance, anglers in

North Carolina who valued fishing as an important part of their life displayed an increased

affinity for catch-and-release of Bluefin Tuna (Sutton and Ditton 2001). In a separate system on

the other hand, the specialization of saltwater anglers in the northeastern U.S. did not correlate

with their beliefs about marine protected areas (Salz and Loomis 2005). Alternative policies can

produce disparate responses and non-compliance rates from anglers (Beardmore et al. 2011,

Caroffino 2013), such that an understanding of their perspectives and the context dependency of

their behavior will be necessary if we hope to progress towards a truly integrated approach to

management.

To begin bridging the gap between the social and biophysical domains of research

(Figure i.1), this dissertation applies an integrated framework for assessing how the Striped Bass,

embedded within a complex social-ecological system, interacts with resource users, the

ecosystem, and management. This system was chosen as a case study because of the inherent

importance of Striped Bass as a marine predator in coastal ecosystems and because of its cultural

and economic significance in New England (Nelson et al. 2006, National Marine Fisheries

Service 2014). Striped Bass in New England are highly migratory, voracious predators that

typically spawn in mid-Atlantic estuaries and brackish habitats and migrate north during the

spring and summer (Bigelow et al. 1953, Boreman et al. 1987). Prior to the collapse of prominent

forage fish populations in New England, Striped Bass were thought to feed heavily on fish prey

such as the Blueback Herring (Greene et al. 2009). As opportunistic predators, Striped Bass diets

may have shifted from the late 1990’s, towards a focus on other Clupeid prey like the Atlantic

Menhaden, plus some crustaceans (Nelson et al. 2003).

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Striped Bass undergo potential ontogenetic shifts in diet that could influence its impacts

on benthic communities in coastal New England. While small Striped Bass consume a high

proportion of forage fish, large Striped Bass feed more heavily on benthic organisms and may

exert considerable top-down pressure on prey populations such as the American Lobster, which

do not appear to respond behaviorally to Striped Bass (Wilkinson et al. 2015); compared to the

harvest from fisheries, Striped Bass were predicted to eat three times as many lobsters (Nelson et

al. 2006). These effects may also depend on the stock structure and size classes of Striped Bass

given that their diet may be influenced by ontogenetic processes. Clearly, we cannot disregard

the importance of Striped Bass in future discussions of an ecosystem approach to fisheries

management. But given the opportunistic foraging behavior of Striped Bass and the natural

variability of prey populations, the degree to which Striped Bass are still affecting prey

communities or are affected by fluctuations in prey abundance is unclear.

Historically, resident populations of Striped Bass existed in the rivers and coastal

estuaries of New England (Little 1995). They were considered a staple food for the early

European colonizers of America (Cole 1989), but they suffered from precipitous declines in the

late twentieth century because of intense fishing pressure, loss of habitat (Hill et al. 1989), and

poor environmental conditions (ASMFC 2014). Restrictive management changes outlined in the

Striped Bass Conservation Act of 1984, enabled the Striped Bass population to climb to a

sustainable size prompting the resurgence of targeted fishing pressure (United States Congress

1994). Despite these efforts, populations may be decreasing again (ASMFC 2014, 2016), causing

managers to implement new policies in both commercial and recreational sectors in a number of

coastal states. Within the recreational fishery, many states have elected to reduce bag limits from

two to one fish per day, while commercial fisheries have also seen reductions in quotas. These

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new policies must meet standards within Amendment 6 of the Fishery Management Plan which

attempts to reduce fishing mortality (F) by 25% (ASMFC 2014). Aligning management

decisions with the social dynamics and perceptions of stakeholders will become increasingly

important in the Striped Bass fisheries given the current conflict that already exists; recreational

fishing special-interest groups blame commercial fishing for declining populations and are

pushing to eliminate the harvest of Striped Bass for profit altogether.

Considered within the context of the social-ecological research framework presented in

Figure i.1, it will be critically important to understand how these alternative regulations can

impact both the social and biophysical templates. Depending upon user perceptions of different

regulations, we may expect to see variable compliance rates or large effects on the overall fishing

behavior of users. Within the biophysical template, fluctuations in fishing mortality, especially

on different sizes of Striped Bass, may fundamentally change trophic interactions and thus the

delivery of ecosystem services. These changes may manifest within the Striped Bass system or

possibly other social-ecological systems (like the American Lobster fishery) that are affected

indirectly by Striped Bass. Collectively, this dissertation informs our understanding of these

dynamic and evolving relationships through the implementation a comprehensive social

assessment (Chapters 1 & 2) that aims to determine 1) the perspectives of recreational and

commercial fishers in response to regulations to reveal factors that influence stakeholders’

support for management strategies, and 2) the behavioral impacts of policy implementation and

whether we can predict responses according to the underlying motivations and attitudes of

fishers. Chapter 3 quantifies the consumptive effect of Striped Bass on prey, some of which are

important fisheries, and the possible implications of prey selection on predator condition. Lastly,

Striped Bass habitat use and the duration of their summer residence in a critical feeding area is

25

explored in Chapter 4 to further assess the ability of different sizes of Striped Bass to impact

local prey populations.

The findings of this dissertation emphasize the need to manage fisheries in light of their

social and ecological interactions. Specifically, the delivery of ecosystem services within the

coastal New England social-ecological system will depend on the dynamics of resource users

and the connections between Striped Bass and other important fishery species. The work

presented here revealed a number of conclusions. While stakeholders generally perceive

management positively, they disagree on strategies aimed at enhancing the sustainability of the

Striped Bass fishery, which may undermine the success of management if policies lead to non-

compliance within some user groups or impact some stakeholders more than others. Different

policy strategies can fundamentally change how resources users behave within the system and

contribute to fishing mortality, which may depend, in part, on their underlying attitudes about

fishing. The ability of Striped Bass to exert top-down pressure on other important fisheries, such

as the American Lobster, is contingent upon the stock structure and age-class strength of the

Striped Bass population given that diet is linked to ontogeny (i.e., larger fish consume more

decapod crustaceans). Importantly, this ontogenetic shift to crustaceans corresponds with an

increase in Striped Bass condition, suggesting that the selection of crustacean prey is beneficial.

These top-down effects on local decapod crustacean communities may be heightened by large

Striped Bass that spend a significant amount of time in small summer feeding areas. Importantly,

this works suggests that policies can change the amount of fishing pressure users exert on

different size classes of Striped Bass, directly through changes in harvest size limits, but also

indirectly through changes in fishing effort when policies misalign with user goals and attitudes.

These disparate effects on different size classes have the potential to change the ability of the

26

Striped Bass population to impact local prey populations, which often constitute important

fisheries themselves. Collectively, we have demonstrated that the internal structure of both social

and biophysical templates, plus their interconnectedness, should dictate how we approach the

management of complex social-ecological systems, especially in the wake of changing

stakeholder groups and predator and prey population dynamics.

27

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33

Figures

Figure i.1. A social-ecological research framework adapted from Collins et al. (2011). The social

and biophysical template are linked via “pulse” and “press” disturbances, mitigating factors, and

ecosystem services, while a variety of external drivers can exert top-down influence on the entire

system.

34

Chapter 1

Assessing fishers' support of striped bass management strategies

The content of this chapter is published in the journal PLoS ONE

Citation: Murphy RD, Jr., Scyphers SB, Grabowski JH (2015) Assessing Fishers' Support of

Striped Bass Management Strategies. PLoS ONE 10(8): e0136412.

doi:10.1371/journal.pone.0136412

Abstract

Incorporating the perspectives and insights of stakeholders is an essential component of

ecosystem-based fisheries management, such that policy strategies should account for the diverse

interests of various groups of anglers to enhance their efficacy. Here we assessed fishing

stakeholders’ perceptions on the management of Atlantic striped bass (Morone saxatilis) and

receptiveness to potential future regulations using an online survey of recreational and

commercial fishers in Massachusetts and Connecticut (USA). Our results indicate that most

fishers harbored adequate to positive perceptions of current striped bass management policies

when asked to grade their state’s management regime. Yet, subtle differences in perceptions

existed between recreational and commercial fishers, as well as across individuals with differing

levels of fishing experience, resource dependency, and tournament participation. Recreational

fishers in both states were generally supportive or neutral towards potential management actions

including slot limits (71%) and mandated circle hooks to reduce mortality of released fish (74%),

but less supportive of reduced recreational bag limits (51%). Although commercial anglers were

typically less supportive of management changes than their recreational counterparts, the

majority were still supportive of slot limits (54%) and mandated use of circle hooks (56%). Our

study suggests that both recreational and commercial fishers are generally supportive of

35

additional management strategies aimed at sustaining healthy striped bass populations and agree

on a variety of strategies. However, both stakeholder groups were less supportive of harvest

reductions, which is the most direct measure of reducing mortality available to fisheries

managers. By revealing factors that influence stakeholders’ support or willingness to comply

with management strategies, studies such as ours can help managers identify potential

stakeholder support for or conflicts that may result from regulation changes.

Introduction

Successful management of marine fisheries hinges upon understanding and promoting

rule compliance and sustainable fishing behaviors across diverse stakeholder groups often with

competing interests (Mikalsen and Jentoft 2001, Hilborn 2007). Developing and implementing

well-supported management strategies that account for these interests can prove to be difficult as

commercial anglers, recreational anglers, and charter boat captains often compete to maintain

their share of catch within a fishery. Even within stakeholder groups, fisher behavior and thus,

fishing pressure, can be influenced by a wide range of social and economic factors including

perceptions, motivations, social norms, and resource dependency (Cinner and McClanahan 2006,

Gelcich et al. 2008, Ostrom 2009). Therefore, effectively managing fish populations requires

implementing management strategies that promote biological productivity and also account for

these dynamic relationships between the fishery and stakeholders.

While the impacts of commercial fishing on fish population dynamics has received

substantial scientific and public attention, recreational and subsistence fishing has been

increasingly recognized to also strongly influence fish populations (Coleman et al. 2004, Cooke

and Cowx 2006). Recreational fishers represent a highly diverse group of stakeholders and

recreational fishing can significantly influence the welfare of fishing communities as well as

36

contribute substantially to local and national economies (Storey and Allen 1993, Shrestha et al.

2002). For example, the direct expenditures from the striped bass recreational fishery in

Massachusetts alone have been estimated at over US$600 million (Storey and Allen 1993).

Additionally, recreational fishing often has strong cultural significance, such as in the tribal

Pacific lamprey fishery (Close et al. 2002) and Pacific salmon fishery (Quinn 2011). Thus, the

value of both recreational and commercial fishing is substantial, such that the interests of both

stakeholder groups should be considered in the management process. Successful management

strategies hinge upon stakeholder support and compliance, and for many fisheries this must

involve both recreational and commercial fishery participants. Our study focuses on an iconic

and controversial fishery in the northeast U.S. and aims to understand the perspectives of

recreational and commercial fishers on the effectiveness of current management efforts and

predict the degree to which they support different proposed management strategies.

Striped bass (Morone saxatilis) are of high economic value in the United States and are

targeted heavily throughout New England and the Mid-Atlantic (National Marine Fisheries

Service 2014). Vulnerable to heavy fishing pressure because of their close proximity to

shorelines, striped bass catches along the U.S. Atlantic coast reached historical highs in the early

1970’s, but soon after collapsed (Richards and Rago 1999). Upon establishment of the Striped

Bass Conservation Act in 1984, coastal states began implementing moratoriums (United States

Congress 1994), which lasted until the mid-1990’s when stocks were deemed fully recovered

(46th Northeast Regional Stock Assessment Workshop 2008).

Currently, the recreational fishery alone is comprised of more than 3 million anglers and

accounts for landings estimated at roughly 1.5 million fish per year (Atlantic States Marine

Fisheries Commission 2012, 2013). While recreational harvest occurs in all states throughout

37

their range, only seven states currently permit commercial harvest (Massachusetts, Delaware,

Rhode Island, Maryland, New York, North Carolina, and Virginia), which accounted for

approximately 840 thousand fish in 2012 (Atlantic States Marine Fisheries Commission 2013).

Striped bass commercial and recreational fisheries along the Atlantic Coast are currently

regulated by a complex of management regimes. An interstate management body, the Atlantic

States Marine Fisheries Commission (ASMFC), decides upon management strategies using

guidelines outlined in Amendment 6 of the Interstate Fishery Management Plan for Atlantic

Striped Bass (Atlantic States Marine Fisheries Commission 2003). Through this plan, specific

emphasis is given to the status of the female spawning stock biomass (i.e., % of SSBMSY), fishing

mortality (F), and striped bass age structure. Each coastal state must enforce the required

regulations set by the ASMFC or implement alternatives with equivalent standards and

biological reference points. This management structure is composed of a variety of layers, one of

which includes an advisory panel consisting of commercial and recreational fishery stakeholders.

While this is certainly beneficial, our study would potentially allow for a larger, representative

population of anglers to be considered in the management process.

Our study explores the perspectives of striped bass recreational anglers, commercial

anglers, and charter boat captains/guides across two contrasting states: Massachusetts (MA),

where both recreational and commercial harvesting occur, and Connecticut (CT), where only

recreational fishing is permitted. While CT maintains no commercial fishery, MA commercially

harvested roughly 66 thousand fish in 2012, or 8% of the national harvest (ASMFC 2013). CT

and MA recreationally harvested 65 and 378 thousand striped bass in 2012, respectively. We

conducted an online survey of licensed MA and CT anglers and assessed: 1) fisher perceptions of

current management regimes 2) fisher receptiveness towards policy changes and 3) the perceived

38

effectiveness of these potential policy changes for the health of both striped bass populations and

the fisheries. For the purposes of our study, health is defined as the status (i.e. abundance and

condition) of the striped bass stock, while the fishery encompasses both the stock and

stakeholders involved in harvest. The concept of ‘health’ was chosen because it is a central tenet

of the Magnuson-Stevens Fisheries Conservation and Management Act (National Oceanic and

Atmospheric Administration 2007). Our survey identified management strategies that anglers

from both states perceive as effective and would be most receptive towards. Additionally, our

analyses revealed several key predictors of fishers’ perceptions of fisheries management.

Materials and Methods

To compare the perspectives of striped bass anglers from contrasting management

regimes, fishers were surveyed from MA and CT. While both states contain substantial

recreational fisheries, only MA permits commercial harvest. At the time of the survey, both

states limited recreational fishers to two fish per day that can be no shorter than 28” (total

length). MA commercial anglers were permitted to fish four days of the week during the striped

bass season, in which they could harvest 30 fish per day (34” minimum size limit), with the

exception of Sunday, where a 5 fish per day maximum was enforced.

Fishing licensee information was obtained from the MA Division of Marine Fisheries and

the CT Marine Fisheries Division and consisted of commercial and recreational saltwater fishing

license holders from 2013. In total, we compiled roughly 3,900 commercial fishers plus 155,000

and 35,000 recreational fishers from MA and CT, respectively. We randomly sub-sampled a total

of 2,000 recreational fishers from each state and 1,000 commercial fishers. Sampling rates were

chosen to achieve a representative sample of the population of each type of fisher in

Massachusetts and Connecticut (Agresti and Barbara 1997). We assumed that response rates for

39

recreational fishers would likely be ~10-20% (Scyphers et al. 2013), which would provide us

with an adequate sample size to test whether the attitudes and perceptions of these fishers differ

between these two states. Given that we expected potentially higher response rates of greater

than 25% for commercial stakeholders (Crosson 2009), a lower sample size was chosen.

Participants were sent emails and asked to participate in an online survey approximately 15

minutes in length using Qualtrics Survey Software Research Suite. All survey methods,

including written consent statements, were approved by Northeastern University’s Institutional

Review Board (IRB #13-11-25). Ten $25 gift certificates towards one of two outdoor stores were

raffled as an incentive. The online survey was open for one month from February 7th until March

7th, 2014, and throughout its duration, brief reminder emails were sent weekly to promote

responses.

The survey can be parsed into three categories based on question type: Fisher

classification, Management perceptions, and Demographic questions (Table 1.1). The fisher

classification section of the survey documented fisher type (i.e., commercial, recreational, charter

boat captains/guides), fisher state of residence, primary fishing location (i.e., state), effort

allocated towards striped bass, percent of fishing effort from shore, fishing experience, fishing

club membership, and tournament participation, and screened out anglers that do not target

striped bass. For commercial fishers, this section also measured percent contribution of striped

bass harvest towards personal and household income. The management perceptions section of

the survey consisted of questions measuring fishers’ perspectives and receptiveness towards

several hypothetical management changes including: reduced recreational daily bag limit from

two fish per day down to one fish per day (this question was only given to recreational anglers),

mandated use of circle hooks, a slot limit for the release of fish larger than a maximum length

40

(example; 40” maximum size limit), and reduction in commercial yearly quota (only displayed to

commercial anglers). These hypothetical policies were chosen for this study because they have

either been utilized in other marine fisheries (Vaughan and Carmichael 2002) and / or have been

repeatedly identified as points of interest (either negative or positive) by recreational and

commercial anglers with which we have had personal communications. Among the four potential

management changes, fishers ranked their support on a scale from “strongly support” to

“strongly oppose.” Supportive and neutral responses were grouped together as to identify fishers

who would potentially exhibit no resistance (i.e., high compliance) to the proposed management

alterations. We used a split-sample design that asked participants to consider each of the four

management changes and provide their perceptions on how beneficial each would be for either

the health of striped bass populations or the sustainability of the fishery. A split-sample design

was used to determine if anglers perceive a disconnect between the health of the fish population

and fishery. This design was chosen to examine angler perceptions of the health of the fish

population versus the fishery independently of one another as to remove potential biases

associated with answering both questions in a particular order (i.e., order bias) (Ferber 1952).

Additionally, we quantified percent circle hook usage among striped bass anglers. Respondents

were also asked about their supportiveness for a maximum size limit. To identify if a threshold in

support for a maximum size limit exists, respondents were presented a randomly assigned length

between 36” and 44”. Another question asked fishers to grade their state’s management regime

on an “A+ to F” scale. Lastly, the survey included basic demographic questions to record age,

gender, ZIP code, occupation, education, and income.

Statistical analyses

41

Pearson chi-squared tests were used to evaluate categorical variables (Table 1.2). Thus,

Pearson chi-squared tests examined potential differences in receptiveness towards policy changes

between “fisher type” and “state,” and the perceived effectiveness of various slot limit lengths.

Statistical comparisons of circle hook usage by “fisher type” were completed using Kruskal-

Wallis tests. Kruskal-Wallis tests were also used to evaluate fisher perceptions on the

effectiveness of management changes towards the health of striped bass populations versus the

sustainability of the fishery (α < 0.05) (Table 1.2). Kruskal-Wallis tests were used for the above

analyses due to non-normal distributions. To identify predictors of fisher receptiveness towards

the four potential management changes and fisher management grades, we applied the partition

method from JMP 10.0.2. The partition method allows for the construction of classification trees

that evaluate the explanatory power of assigned variables. Using LogWorth values, this method

hierarchically identifies the strongest predictor at the top of the classification tree, while

subsequent splits explain variation in the preceding variable. Only significant splits were shown

in our classification trees (P ≤ 0.05). For all classification trees, the following factors were

included in the analysis when applicable; “fisher type”, “state”, “percent effort dedicated to

striped bass fishing”, “striped bass fishing experience”, “salary”, “percent personal income from

the commercial harvest of striped bass”, “participation in at least one striped bass tournament per

year” (binary), “membership in a fishing club or organization” (binary) and “gender.” Lastly,

median grades were calculated for the fisher management grade question.

Results

Descriptives and Demographics

A total of 1,025 anglers completed our online survey (overall response rate: 20.5%) with

835 participants who fish in MA and 190 from CT (Table 1.3). Response rates provide

42

confidence intervals between ±4 -7% for all groups surveyed at a confidence level of 95% when

extrapolating our results to the entire group of license holders in each state. Only 23 participants

did not fish for striped bass and were consequently eliminated from the survey. Also, any

comparison between MA and CT excluded commercial anglers as only the former state permits

commercial harvesting.

Management Grade Analysis

Participants were asked to grade their state’s current management of the striped bass

fishery on a typical A+ to F scale. Classification tree analysis revealed that “striped bass fishing

experience” was the strongest predictor of angler management grade (Figure 1.1): those that have

been fishing for fewer than 13 years assigned a median grade of a B, while those with 13 or more

years of experience were slightly more critical and assigned a median score of a B-. For more

experienced anglers, “fisher type” was the strongest explanatory variable. Commercial fishers

and charter boat captains/guides were statistically non-distinct and gave management a B- grade,

while recreational fishers assigned it a B. Commercial anglers and charter boat captains/guides

could be further classified by fishing experience. Anglers with 49 years of experience or more

had the lowest opinion of striped bass management with a median score of a C, compared with a

median score of B- from those with less than 49 years. Lastly, recreational anglers’ degree of

participation in tournaments was a predictor of their perceptions of the effectiveness of striped

bass management efforts in their fishery: anglers that participated in a tournament were slightly

less positive of management and assigned a median grade of a B-, compared to a B from the non-

tournament anglers.

Overall receptiveness and perceived effectiveness of regulations

43

Both recreational and commercial anglers were generally amenable to most of the

different management strategies that were offered. The management alternatives with greatest

support included mandating circle hook usage and implementing slot limit regulation changes,

with 68% (n = 900) and 66% (n = 893) of participants selecting supportive/neutral options for

each alternative, respectively. Opinions on the reduction of recreational bag limits were

reasonably split down the middle (52% supportive/neutral, n = 780). Additionally, 35% (n = 266)

of commercial anglers were supportive or indifferent towards a reduction in the commercial

industry’s yearly quota (Figure 1.2).

All stakeholder groups in our survey believe regulation changes will have similar

impacts, respectively, on the health the fish population and fishery. Both recreational and

commercial anglers perceive the implementation of a slot limit to be equally effective at

promoting the health of striped bass populations and promoting the sustainability of the fishery

(recreational; P = 0.1177, commercial; P = 0.3025, charter boat captains/guides; P = 0.9813,

Figure 1.3a). Participants from both fisheries perceived the effectiveness of circle hooks to be

equivalent for both categories as well (recreational; P = 0.8916, commercial; P = 0.3060, charter

boat captains/guides; P = 0.3858, Figure 1.3b). Recreational anglers responded similarly to the

effectiveness of a reduced recreational daily bag limit (P = 0.6816, Figure 1.3c), as did

commercial anglers to the effectiveness of a reduced commercial yearly quota (P = 0.6058,

Figure 1.3d).

Implementing a Slot Limit

As a whole, recreational fishers were very supportive (71%; n = 594) of implementing a

slot limit, as were charter boat captains/guides (77%; n = 30, P < 0.001, Figure 1.4a). Least

supportive were the commercial anglers, but the majority (54%; n = 263) of these participants

44

still selected supportive or neutral responses. When grouped by state, CT recreational anglers

and charter boat captains/guides had a non-negative response rate of 81% (n = 149), and were

more receptive than their MA analogues (66%; n = 400; P < 0.001, Figure 1.4a). While the

following results are not statistically significant, analysis of randomly assigned upper size limits

identified a slight trend of peak support at 40”, where the majority of participants displayed

positive or neutral opinions (P = 0.18, Figure 1.4b). Support decreased slightly for shorter

maximum-lengths, whereas there was a sharp decline for limits of 42” and 44”. Classification

tree analysis generated only one strong predictor variable capable of explaining variation in

support for a slot limit regulation change: “State.”

Mandating Circle Hook Usage

Similar to their perception of implementing a slot limit, commercial anglers were

indifferent or supportive of mandating circle hooks slightly more than half of the time (56%; n =

262). Recreational anglers were highly supportive with a 74% (n = 598) non-negative response

rate. Charter boat captains/guides remained intermediary at 69% (n = 38). All fisher types were

significantly different from one another (P < 0.001, Figure 1.5a). Perceptions of mandating circle

hook usage among recreational anglers and charter boat captains/guides from each state were

largely similar with non-negative response rates at 74% (n = 401) in MA and 75% (n = 149) in

CT (P = 0.825, Figure 1.5a). “Fisher type” was a strong predictor of circle hook usage, as

recreational anglers used circles hooks significantly more than commercial anglers (recreational

anglers; 52%, commercial anglers; 45%, P = 0.0181, Figure 1.5b). There was a trend of slightly

less circle hook usage by charter boat captains/guides (41%, Tukey’s post-hoc test, Figure 1.5b).

Results of classification tree analysis produced two explanatory variables of participant

receptiveness to mandating circle hook usage: “fisher type” and “percent personal income from

45

the commercial harvest of striped bass” for commercial anglers (Figure 1.6). The former is the

strongest predictor, as commercial fishers were supportive or neutral 56% percent of the time (n

= 262). Recreational fishers and charter boat captains/guides were considered statistically non-

distinct and, as a whole, displayed a 74% non-negative response rate (n = 636). Within

commercial anglers, those that rely on striped bass harvest for 1% or more of their annual

income were the most opposed to mandating circle hook usage, although roughly 52% of

respondents were still supportive or neutral towards this regulation change (n = 203).

Reduced Recreational Daily Bag Limit

In MA, 47% (n = 371) of recreational anglers were in favor of or indifferent to reducing

the recreational daily bag limit from two down to one fish per day. These results were not

significantly different from CT, where 51% of recreational anglers were supportive or neutral (n

= 143; P = 0.4303, Figure 1.7a). Classification tree analysis revealed that tournament

participation was the strongest predictor of support for bag limit reductions. In particular, anglers

that participate in tournaments were less supportive (34% non-negative response rate, n = 62,

Figure 1.7b) than non-tournament anglers (50%, n = 452).

Reduced Commercial Yearly Quota

Analysis of a reduced commercial yearly quota was not possible by either “state” or

“fisher type” since only commercial anglers were included and there is no commercial harvest in

CT. Classification tree analysis revealed “percent personal income from the commercial harvest

of striped bass” as the most powerful predictor of support (Figure 1.8). Anglers that derived less

than 10% of their income from striped bass fisheries displayed a non-negative response rate of

41% (n = 182), versus 18% for their counterparts (n = 74).

46

Discussion

Incorporating social dynamics into fisheries management is necessary for a holistic

approach to ecosystem-based management (Halpern and Agardy 2014). Engaging stakeholders

in the management process is also central to the development of effective governance structure

(Jentoft and McCay 1995, Coffey 2005) because it likely will increase fisher compliance to

regulations (Hatcher et al. 2000). For instance, understanding the perceptions of these

stakeholders can help identify policy changes that anglers would be highly amenable to. Our

survey revealed that New England striped bass fishers have positive perceptions of both

mandating circle hook usage and implementing a slot limit regulation, the former of which has

been proposed to benefit striped bass by reducing post-release mortality (Cooke and Suski 2004).

Fishers’ compliance and awareness of new regulations will likely mediate whether these

regulations are successfully implemented. For instance, a study in Minnesota on the northern

pike freshwater recreational fishery revealed low compliance and a lack of awareness of slot

limit regulations, such that over 10% of fish harvested were of illegal sizes (Pierce and Tomcko

1998). Fisher compliance to policy changes would in part depend on their perceptions of the

efficacy of these proposed management policies. Furthermore, adopting policies that anglers are

amenable to could reduce illegal activities and enhance their overall trust in fisheries

management (Mackinson et al. 2011). Considering that policy enforcement is dependent on

limited federal and state budgets, a self-regulating system of compliant stakeholders could lead

to more effective long-term management.

More experienced anglers comprised a large subset of our sample and held mixed

attitudes towards management. Angler dissatisfaction with management may be typical among

this group or could possibly be associated with historical striped bass population trends or with

47

changes in policy. In addition to experience level, financial reliance on the commercial fishery

seemingly influences the degree to which they are supportive of how striped bass is being

managed. On the other hand, while recreational anglers may not be economically-dependent on

the fishery, the cultural significance of the recreational fishery is substantial, as striped bass are

one of the primary inshore fish species targeted in New England and are caught by tens of

thousands of anglers annually. However, recreational anglers maintained generally positive

viewpoints towards striped bass management and potential regulation changes.

While the effectiveness of either a slot limit or mandating circle hooks for sustaining

striped bass populations involves scientific uncertainty, our work demonstrates that overall many

fishers would be supportive of such management changes. Additionally, almost all fisher types in

our survey, but particularly among recreational anglers, seem to support the implementation of a

slot limit and mandating circle hooks, since they believe it will aid in both the proliferation of

striped bass and the success of the fishery. These results suggest that participants perceive a

strong connection between the health of the ecosystem and the striped bass fishery. Resource

systems where the participants understand the connection among the ecosystem, fish populations

and the fishery may enhance angler compliance with regulations (McClanahan et al. 2006).

Conversely, future assessments could use similar survey techniques to identify resource systems

where there is a perceptional disconnect between the resource and industry. In these instances,

education and outreach efforts would be aimed at minimizing gaps in understanding.

To elaborate on fisher perceptions of slot limit regulations, we asked participants to

express viewpoints of randomly assigned maximum harvest lengths. Despite the absence of

significant differences between proposed slot maximums, anglers seemed to identify 40” as their

preferred limit. This potential threshold may reflect a tradeoff between reducing harvest of large

48

female striped bass and fisher satisfaction. Specifically, maximum harvest lengths of 36” and 38”

may result in the release of more fish than many anglers prefer. Meanwhile, the lack of support

for higher limits may indicate that anglers believe that longer maximum catch sizes would not

have significant, positive impacts on striped bass abundance. Future research should investigate

why anglers are in favor or against specific optimal size minimums and maximums to better

gauge potential compliance of alternate options within one regulation category. It is plausible

that high compliance may occur at one maximum size limit that is well supported, but at another

that is not, poaching may increase to a point such that the regulation’s costs are greater than its

benefits. However, angler education could help push opinions in favor of scientifically sound

regulations, thus increasing support and possibly compliance.

Limiting unnecessary mortality is a high management priority, especially for highly

valuable game fish species where recreational anglers may release fish in an unsustainable

manner (Scyphers et al. 2013). From personal communication with both recreational and

commercial anglers, many individuals already use circle hooks due to the perceived reduction in

release mortality, which may be as high as 70% for striped bass (Muoneke and Childress 1994).

This perception is in agreement with research on the use of circle hooks; they have been shown

to reduce post-release mortality and injury for striped bass by 12.5% (Caruso 2000, Cooke and

Suski 2004). Our results suggest that a policy mandating circle hook usage would be widely

supported likely due to the perceived increases in striped bass survival post catch-and-release.

Recreational fishers already use circle hooks more than half of the time while fishing for striped

bass, and adopting this policy would likely shift circle hook usage closer to full compliance.

While we are not advocating for or against this regulation (or any of the included for that matter),

49

we simply highlight the potential sources of and reasoning behind angler perceptions of each

management strategy.

There is considerable support for the implementation of a slot limit and mandating circle

hooks, but support for other management alternatives such as a reduced recreational daily bag

limit is lacking. Among other recreational regulations in our survey, this could potentially have

the largest impact on fishing mortality, yet angler support is low in comparison. With a current

two fish per day regulation, anglers are seemingly opposed to further decreases in harvest rates,

which seems to be a consistent attitude across states. Most extreme among this participatory

group was tournament anglers. The competitive nature of tournaments may influence why these

anglers are less supportive, or perhaps tournament anglers are more dependent upon the

recreational fishery. Targeted outreach initiatives and assessments could occur at tournaments to

evaluate fisher behavioral responses to regulation changes and could potentially aim to mitigate

social and cultural impacts (e.g., stakeholder conflict) of policy.

There was even less support for reducing the commercial quota, but still a third of

commercial anglers were neutral or supportive of this change. This can be attributed to the

relatively low financial reliance of striped bass anglers on the fishery for income, or perhaps

signifies that many anglers perceive long-term benefits for striped bass populations, and hence

the sustainability of the fishery, from a reduction in harvest levels. Our results suggest, however,

that minimal reliance (≥10% of annual income) corresponds with largely reduced support for this

regulation change. These commercial anglers are overwhelmingly against quota cuts and

consequently should be included in the previously mentioned outreach initiatives targeting

heavily impacted stakeholder groups. Making these results even more pertinent, recent

restrictions limit commercial fishing to Mondays and Thursdays with a 15 fish per day bag limit.

50

The public announcement of these regulation changes occurred two months after the release of

our survey. Including this type of social analysis into management decisions could give

managers insight into non-compliant stakeholder groups and may inform decisions among

multiple regulation options.

To note, our results may be subject to response bias such that responses could be skewed

towards experienced and specialized anglers. Responses were solicited using an email that

specifically indicated that we were conducting a survey of striped bass anglers, potentially

increasing the response rate in favor of anglers who place higher importance on striped bass or

those with increased recreation specialization (Oh and Ditton 2006). However, the comments

that we received and the demographic information that we collected as part of the survey

indicated broad representation of recreational and commercial striped bass anglers, and

consequently suggests that this bias was likely modest and did not significantly influence the

presented results. Furthermore, while data for ‘How many years have you been fishing for

striped bass?’ is of a non-normal distribution, the results suggest that respondents span a breadth

of fishing experience levels including a large number of extremely new anglers (<5 years fishing

experience). Additionally, the monetary incentive placed on the completion of the survey likely

reduced non-response bias. Disparate response rates from MA and CT anglers also suggests a

higher level of interest among MA anglers, since our email correspondence specifically listed

that we were conducting a survey of striped bass anglers.

Online surveys inherently exclude a portion of anglers without computer access or email

addresses, potentially resulting in coverage error. Despite this bias, computer use is becoming

universal, making it more efficient for researchers to utilize online-based surveys, while also

providing them with representative sample responses. As an example, more than 70% of

51

commercial anglers listed their email address in the database provided to us by MA DMF

highlighting the near ubiquity of computer use in our sample population of anglers.

Results from this study must be conscientiously applied to other systems. For example,

commercial striped bass anglers in our survey derive on average 10% of their personal income

from the harvest of striped bass. It is not uncommon for commercial striped bass anglers to have

occupations outside of fishing, thus potentially increasing the likelihood that they would support

management changes in general. Additionally, the mode of the total household income for

respondents is between $100,000 and $150,000 suggesting that our results may not be

generalizable to other less financially stable fishing communities in other fisheries. As a whole,

recreational anglers indicated that roughly half of total striped bass fishing effort is strictly shore-

based and does not involve the use of a boat. As a shore-bound angler in New England, large

bodied gamefish seldomly can be easily accessed. This may influence the perceptions of anglers

due to a potentially larger proportional investment in striped bass fishing as compared to other

geographic regions that may harbor a higher diversity of shore-based fishing options. Future

assessments should aim to capture responses from a broader array of socioeconomic

backgrounds and recreational settings in order to make generalizations across regions and

fisheries.

Our study revealed that the perceptions and responses of key stakeholders to existing and

proposed fishery regulations can be assessed with online surveys, which should aid decision

making by managers. To select strategies that will garner higher relative compliance rates,

management agencies could utilize similar survey techniques to assess stakeholder viewpoints

prior to the implementation of a policy or the restructuring of existing regulations. To note,

recent stock assessments have resulted in proposed new regulation requirements for coastal states

52

(Atlantic States Marine Fisheries Commission 2014), and will likely involve one or more of the

regulations in this survey. Therefore, future assessments should examine potential differences

between hypothetical and realized support for management changes to determine the degree to

which surveys of fisher perceptions of management can be used effectively to guide management

decision making. It is quite possible that responses will vary and will show decreased support

after the enactment of a regulation.

While anglers within the striped bass fishery generally perceive management as adequate

or better, perspectives differ by state and group membership. Differing perspectives may also be

present within regulations, such as slot limit maximums, and could potentially influence

compliance post-regulation implementation. Additionally, increased integration of fishing into an

individual’s hobbies or livelihood, here in the form of tournament participation and financial

reliance, seem to negatively influence the magnitude of their support. By identifying groups that

are less receptive to proposed regulation changes, managers can develop strategies to minimize

stakeholders’ financial losses or target outreach efforts at these groups to educate them on the

benefits of a proposed management alternative. Ideally, this approach helps increase trust and

compliance and thus, reduces conflict and illegal harvest. Used in conjunction with population

dynamics and ecosystem-based modeling, data on fisher perceptions derived by surveys such as

ours can be used to weigh the benefits and costs of each potential regulation alternative.

Acknowledgements:

We thank the Massachusetts Division of Marine Fisheries and the Connecticut Marine

Fisheries Division for supplying us with their fishing license databases. We would also like to

thank G. Nelson for his perspective on a variety of our results and two anonymous reviewers for

53

their helpful comments. This is contribution number 326 of the Marine Science Center of

Northeastern University.

54

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57

Tables

Table 1.1. Summary of survey questions

Question categories

Fisher classification

Fisher type

State of residence

Fishing location (state)

Percent effort towards striped bass

Years fishing for striped bass

Percent of striped bass fishing from shore

Fishing club membership

Striped bass tournament participation

Income from commercial harvest of striped bass

Management

perceptions

Effectiveness of current management

Effectiveness of policy strategies

Receptiveness to policy strategies

Current circle hook usage

Opinion of an upper size limit for recreational striped bass harvest

Demographics

Year of birth

Gender

ZIP code

Primary occupation

Highest level of education

Total household income

58

Table 1.2. Investigated questions and statistics used

Question Statistical Test

Do fishers’ perceptions of current management

regimes vary according to some underlying

variable(s)?

Classification tree

analysis

Does fisher receptiveness vary among different types

of fishers and among fishers in different states? Pearson chi-squared test

Does fisher receptiveness vary according to some

underlying variable(s)?

Classification tree

analysis

Do fishers perceive that different slot limit maximum

lengths have altered levels of effectiveness? Pearson chi-squared test

Does circle hook usage vary among fisher types? Kruskal-Wallis test

Do anglers perceive that policy changes will be

similarly effective at promoting the health of striped

bass populations and the sustainability of the striped

bass fishery?

Kruskal-Wallis test

59

Table 1.3. Summary of demographics and other fishing variables by state.

Massachusetts Connecticut

Sample Size 835 190

Gender Male 97% 96%

Female 3% 4%

Age – Mode 1955-1959 1955-1959

Annual income Under $40k 14% 8%

$40k-$60k 12% 12%

$60k-$80k 14% 19%

$80k-$100k 16% 14%

$100k-$150k 23% 23%

$150k-$200k 10% 13%

$200k-$250k 3% 4%

Over $250k 8% 7%

Type of fisher Recreational 59% 97%

Commercial 38% n/a

Charter/Guide 4% 3%

Effort allocated towards striped bass fishing (%) – Mean 64% 54%

Fishing experience (years) – Mean 26.1 20.8

Effort from shore (%) – Mean 42% 49%

Member of fishing club Yes 24% 18%

No 76% 82%

Striped bass tournament participation Yes 25% 6%

No 75% 94%

Annual income from commercial striped bass harvest – Mean 10% n/a

60

Figures

Figure 1.1. Classification tree of fishers’ perceptions of management. Letters in each bubble

correspond to the median grade for each group, while numbers represent the sample size.

Variables predict grades based on their relative placement on the tree, where the highest variable

explains the maximum variation. All splits shown are significant at P < 0.05 and were predicted

according to LogWorth values.

61

Figure 1.2. Percent of total response for participants that are supportive/neutral towards four

management changes. Numbers in each bar represent the number of participants with

supportive/neutral responses. *Reduced recreational daily bag limit includes responses from only

recreational anglers. **Reduced commercial yearly quota includes responses from only

commercial fishers.

62

Figure 1.3. Effectiveness of hypothetical regulations. Mean ranking +1SE of the effectiveness of

proposed regulations by “fisher type,” where a score of 10 correlates to maximum effectiveness.

Proposed regulations are as follows: a) Slot limit, b) Circle hook mandate, c) Reduced

recreational daily bag limit, d) Reduced commercial yearly quota. *Reduced recreational daily

bag limit includes responses from only recreational anglers. **Reduced commercial yearly quota

includes responses from only commercial fishers.

63

Fig 1.4. Slot limit analysis. a) Percent of total response for participants by “fisher type” and

“state” that are supportive/neutral to the implementation of a slot limit. Numbers in each bar

represent the number of participants with supportive/neutral responses. *Respondents did not

include commercial anglers. b) Percent of total response for recreational anglers that agree with

or are neutral towards a randomly assigned maximum allowable size for recreational striped bass

harvest.

64

Fig 1.5. Circle hook analysis. a) Percent of total response for participants by “fisher type” and

“state” that are supportive/neutral to mandated circle hook usage. Numbers in each bar represent

the number of participants with supportive/neutral responses. *Respondents did not include

commercial anglers. b) Mean ± 1SE of the percent of time participants use circle hooks when

fishing for striped bass by “fisher type.” Letters below error bars are the results of a Tukey’s

post-hoc test.

65

Fig 1.6. Classification tree of circle hook analysis. Variables predict support based on their

relative placement on the tree, where the highest variable explains the maximum variation. All

splits shown are significant at P < 0.05 and were predicted according to LogWorth values.

Numbers in each bubble correspond to the percent response for each category.

66

Fig 1.7. Reduction in recreational daily bag limit analysis. a) Percent of total response for

participants by “state” that are supportive/neutral to reducing the recreational daily bag limit.

Only recreational anglers were asked this question. Numbers in each bar represent the number of

participants with supportive/neutral responses. b) Classification tree analysis depicting the

percent of fishers who are supportive/neutral or opposed to reducing the recreational daily bag

limit. Variables predict support based on their relative placement on the tree, where the highest

variable explains the maximum variation. All splits shown are significant at P < 0.05 and were

predicted according to LogWorth values. Numbers in each bubble correspond to the percent

response for each category.

67

Fig 1.8. Classification tree analysis depicting the percent of fishers who are supportive/neutral or

opposed to a reduction in the commercial yearly quota. Only commercial anglers were asked this

question. Variables predict support based on their relative placement on the tree, where the

highest variable explains the maximum variation. All splits shown are significant at P < 0.05 and

were predicted according to LogWorth values. Numbers in each bubble correspond to the percent

response for each category.

68

Chapter 2

The disparate behavioral effects of fishery regulations can be explained by angler attitudes

Abstract

The management of recreational fisheries poses many unique challenges, as diverse user

groups can maintain disparate beliefs and behaviors, limiting our ability to predict how fishing

mortality will change under future environmental and regulatory conditions. The structure of

harvest-control rules may significantly alter the fishing effort of anglers and cause spillover

effects into other fisheries, especially if policies misalign with angler goals or motivations. We

surveyed Striped Bass anglers from multiple coastal Atlantic states to 1) explore how the

implementation of new policies may change fishing behavior, 2) examine the underlying

motivations and catch-related attitudes of anglers, and 3) assess whether angler attitudes

correlate with their responses to alternative regulations. We employed a basic experimental

approach where participants were presented with two regulations and asked to allocate days to a

variety of recreation options (i.e., fishing for Striped Bass versus another species) under each

regulation. Results revealed that the behavior of Striped Bass anglers may depend upon

regulatory conditions. Specifically, modest rule changes did not dramatically alter total fishing

effort, whereas more aggressive strategies fundamentally changed angler allocation of effort.

Participants often traded fishing effort for other saltwater species, such as Bluefish or Black Sea

Bass in Massachusetts, illuminating the possibility of spillover effects into other fisheries.

Importantly, variability in angler responses moderated the overall effect of some policies; many

anglers increased effort when a new policy was implemented, whereas others decreased effort.

Differences in participant attitudes about fishing for Striped Bass, such as how much they value

keeping fish, was an important predictor for they respond to various proposed harvest-control

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rules. Overall, our study illustrates that an understanding of the characteristics and attributes of

recreational fishing populations will help predict how behavior, and thus fishing mortality, may

shift under future management scenarios.

Introduction

Despite efforts to integrate social dynamics and human decision-making into fisheries

management, utilization of social assessments in policy has proven challenging (An and López-

Carr 2012, Gray et al. 2012). While management agencies often employ public hearings or

advisory panels composed of multiple stakeholder groups (Mikalsen and Jentoft 2001), there

remains substantial barriers to incorporating the attitudes of stakeholders into management (Gray

et al. 2013). Attending public hearings and management meetings can also involve high costs

(e.g., travel costs to far away meetings) for stakeholders hoping to participate (Lynham et al.

2017). As a result, we have struggled to predict how resource users will behave in response to

new or revised policies. Understanding the perceptions and behavior of diverse stakeholder

groups, and how future environmental or regulatory conditions may fundamentally change

behavior, will help bridge the gap between human-dimensions research and natural resource

decision-making (Gentner and Sutton 2008). Given that fisheries managers ultimately manage

people and not fish, an understanding of the context-dependency of stakeholder habits will foster

the creation of more informed regulations and harvest-control rules (Johnston et al. 2010).

The behavior of recreational anglers is influenced by numerous factors including, but not

limited to, individual-level variables, population norms, fishing attributes, and fishing-site

characteristics (Hunt et al. 2002, Oh and Ditton 2008). For example, the previous experience of

trout anglers in South Carolina appeared to correspond with their potential actions, whereby

more experienced anglers are highly connected to particular places (i.e., place bonding) and are

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more likely to display fishing-site substitution behavior (Hammitt et al. 2004). However, angler

behavior is not fixed and may be largely contingent on the structure of fisheries policy

(Beardmore et al. 2011). Beardmore et al. (2011) found that the effort of eel anglers in Germany

can vary from inelastic to extremely elastic in response to changes in fishing regulations. Modest

regulation changes were not found to affect behavior, while extremely restrictive policies

dramatically reduced angling effort, which could ultimately have cascading effects on reducing

fishing mortality.

Policy changes can directly alter effort within a fishery (e.g., through effort reductions

(Beard Jr et al. 2003)), but they may also indirectly affect other fisheries when anglers have

substitutable fishing opportunities (Gentner 2004). There is ample evidence of such ‘spillover

effects’ in commercial fishing and in natural resource systems more generally (Böhringer and

Rutherford 2002, Chan and Pan 2016, Cunningham et al. 2016). For example, Chan and Pan

(2016) found that foreign swordfish fleets increased effort in response to decreases in the

Hawaiian swordfish fishery, which caused heightened bycatch on sea turtle populations due to

less-restrictive regulations imposed on the fishing activities of foreign fleets. Additionally, along

the Atlantic Coast, altered regulations may have shifted the distribution of some groundfish

fishing from New England into fishing grounds further south (Cunningham et al. 2016). It is

plausible that spillover effects also occur within recreational fisheries when regulations are

modified, since anglers may have other potential target species or may be able to participate in

other outdoor recreation activities (Gentner 2004, Gentner and Sutton 2008).

Proactively accounting for the variable and cascading effects of policy will allow

managers to structure more effective policy. Therefore, our study explored the presence of

spillover effects, and behavior change more generally, in the recreational Striped Bass fishery

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along the Atlantic Coast into other fishing and non-fishing activities. We also assessed the

underlying motivations and attitudes of anglers to evaluate the extent to which the heterogeneity

of angling populations may interact with recreation behavior. Specifically, we addressed three

primary questions; (1) How might the implementation of new recreational fishing policies alter

angler effort and behavior? (2) What are the underlying motivations and catch-related

preferences of Striped Bass anglers? (3) Can the attitudes of Striped Bass anglers predict their

responses to alternative regulations? We focused on the Striped Bass recreational fishery because

of its importance as a recreational fishery along much of the U.S. Atlantic Coast. It has also

recently undergone major regulatory changes, including a reduced recreational daily bag limit,

which received mixed supported by fishery participants (Murphy Jr et al. 2015). Moreover, the

degree to which policies may impact the satisfaction and behavior of Striped Bass anglers

remains uncertain.

Using a modified discrete choice experiment, we compared the potential behavior of

anglers to a number of possible regulatory options. The potential behavior of anglers in our study

is analogous to an individual’s intended behavior (Ajzen 1991), which may ultimately lead to

observed (i.e., actual) behavior depending on other external factors and individual perceptions.

Importantly, human intentions are mediated by individual beliefs and attitudes coupled with

social pressures (i.e., norms) (Fishbein and Ajzen 1977). As such, our study utilized a multi-

dimensional approach to examine the attitudes and motivations of anglers to explore the degree

to which they align with intended behavior. Specifically, we assessed the fishing experience

preferences and consumptive orientation of Striped Bass anglers, drawing from previously

established indices (Driver and Knopf 1976, Fedler and Ditton 1986, 1994, Anderson et al. 2007,

Oh et al. 2013). The experience preferences of outdoor recreationists are synonymous with their

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internalized motivations, and they can vary from general preferences to specific preferences (Oh

et al. 2013). For example, whitetail deer hunters may partake in hunting because it provides them

an opportunity to escape the stressors of work. This would be considered an activity general

preference, that is, this motivation could be satisfied by some other outdoor recreation activity.

On the other hand, using hunting as a source of sustainable protein would be akin to an activity

specific preference. An alternative construct, consumptive orientation (generally considered an

attitudinal dimension), explores the importance anglers place on particular aspects of fishing

such as the act of catching fish and keeping fish (Graefe 1981, Sutton and Ditton 2001). Each of

these constructs was investigated since fishing motivations and attitudes can be very diverse and

often difficult to apply to other fisheries (Fedler and Ditton 1994).


Email and mailing addresses for licensed recreational anglers in 2016 were obtained from

the MA Division of Marine Fisheries, CT Department of Energy and Environmental Protection,

NC Division of Marine Fisheries, and the VA Marine Resources Commission. Prior to launch,

our survey was approved by Northeastern University’s Institutional Review Board (Project #13-

11-25). An online version of the survey was sent to a random subsample of 3,000 anglers per

state that supplied email addresses (12,000 total). Administered via Qualtrics Survey Software

Research Suite, the online survey was launched in May 2017 and ran for one month. Emails were

sent weekly (modified Dillman Method) and gift cards were offered as a raffle prize and

incentive for participation (Dillman 1978). Upon conclusion of the online survey, a printed

version was sent to 1,000 recreational anglers from MA. Half of the surveys were sent to

individuals who did not provide email addresses to the MA licensing system and the other half

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were sent to non-respondents of the online version. Our goal was to examine under-coverage

selection bias and non-response bias, respectively.

(1) How might the implementation of new recreational fishing policies alter angler effort and

behavior?

To examine the potential behavior of Striped Bass anglers under different regulatory

conditions, we employed a basic experimental approach in which each participant was given one

of five possible experimental scenarios. Under each scenario, two regulations were displayed, the

first of which was held constant for all participants. This first regulation (Option A for all

experimental scenarios) involved a minimum size limit of 28 inches, no maximum size limit, and

a daily bag limit of one fish. This regulatory scenario was considered the status-quo since MA,

CT, NC, VA operated under this structure in 2016. To note, there were some exceptions in NC

and VA depending on time of year and management area. Therefore, the implications from the

results of this portion of our survey must be carefully considered, as not all participating anglers

would have necessarily operated under the same regulations in 2016. The second regulation

included one of five potential regulations (Option B, Table 2.1), and was randomly assigned to

participants.

An increased daily bag limit was chosen since a large portion of the Atlantic coast

operated under this policy strategy up until recent changes. Previous survey efforts (Murphy Jr et

al. 2015) revealed that a slot limit (preference towards a 40” maximum size limit) would be

highly supported by many anglers and, as such, was included in this survey. A more restrictive

slot limit was also included as a comparison to the moderate approach, but also because there

have been efforts to implement this regulation by a recreational Striped Bass special-interest

group. The remaining regulations (catch-and-release fishing only and an unrestrictive regulation

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where anglers can keep four fish per day of any size) were chosen as extreme scenarios to

compare against less aggressive strategies.

For each of the experimental scenarios, participants were asked to allocate effort to four

activities; days fishing for Striped Bass, days fishing for another specific species, days fishing for

any species (indiscriminately fishing), and days participating in some other outdoor recreation

activity. For both Option A and Option B, participants were given 10 non-working days to

allocate to these activities (Figure 2.1). To examine differences in effort between regulations, the

mean number of days allocated to each activity was compared under the status-quo (Option A)

and Option B (i.e., paired samples were compared) using Wilcoxon Signed Rank tests. To note,

the mean number of days allocated to Striped Bass fishing under the status-quo scenario (Option

A) did not differ among all experimental scenarios (Kruskal-Wallis test p-value = 0.50). The

total percentage of individuals that either increased, decreased, or remained constant for each

activity was examined separately for all experimental scenarios. A final component of this

section of the survey measured whether anglers keep fish more or less frequently under

alternative regulations. Participants were presented with six options for how often they would

aim to keep fish under each regulation option (in descending order): on every trip, every other

trip, every few trips, every ten trips, once a season, and never.

Two additional analyses were conducted for MA anglers only (i.e., anglers that selected

that they primarily fish for Striped Bass in MA), since we can be confident that all individuals

fished under the status-quo regulations for 2016, and importantly, so that any conclusions drawn

from these analyses would have direct implications at the state level. First, anglers that selected

they would fish one or more days for another specific species under either regulation option were

shuttled to another question in which they listed their target species. We tallied the number of

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times each species was entered to establish the degree to which fisheries would be impacted by a

change in the recreational Striped Bass regulations within MA. Second, we calculated the

hypothetical total number of days fished for Striped Bass under the status quo and under each

regulation change. To quantify the number of days fished under the status-quo, we applied the

following formula; (a/100) * b, where a was equivalent to the percent of effort participants

allocate to Striped Bass (as indicated by the survey question: Roughly, what percentage of your

fishing effort is targeted towards catching Striped Bass as opposed to other saltwater fish

species?) and b was equivalent to the number of days they spent saltwater fishing in 2016 (as

indicated by the survey question: In each of the last three years, about how many days did you

go saltwater fishing?). To calculate the number of days fished under the new regulation, we

adjusted the previous formula as follows: (a/100) * b * c where c is equivalent to the fractional

effort change from Option A to Option B in each experimental scenario. For example, if a

participant increased their effort allocated to Striped Bass from 3 to 6 days from Option A to

Option B, c would equal 2 and, as such, would equate to a doubling of hypothetical effort under

the new regulation.

(2) What are the underlying motivations and catch-related preferences of Striped Bass anglers?

Two approaches were taken to describe the motivations and attitudes of Striped Bass

anglers. First, we examined the motivations anglers hold for fishing through an assessment of

their activity general and activity specific preferences. We modified the approach by Oh et al.

(2013) and Fedler and Ditton (1994), originally created by Driver and Knopf (1976), to include

six questions each to measure the activity specific preferences and activity general preferences of

anglers. Participants were queried on the importance of these fishing attributes on a Likert-scale

from not at all important (1) to extremely important (5). The internal reliability of each

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preference type was validated using Cronbach’s alpha (), and each metric was considered

reliable if > 0.7 (Hammitt et al. 2006). The second approach, adapted from a number of

studies, asked participants to select how they feel about several statements regarding Striped

Bass fishing (Likert-scale from strongly disagree (1) to strongly agree (5)) (Graefe 1981, Fedler

and Ditton 1986, Sutton and Ditton 2001, Anderson et al. 2007). These statements encompass

four subdimensions of consumptive orientation; attitudes towards catching Striped Bass, keeping

Striped Bass, the number of Striped Bass caught, and catching trophy Striped Bass. Each

subdimension had two or three questions, a number of which were reverse coded to account for

positive versus negative terminology. Again, Cronbach’s alpha was used to test each latent

variable’s internal reliability, and it was deemed acceptable at > 0.7 (Hammitt et al. 2006).

Once it was determined that the two metrics of activity preference and the four

subdimensions of consumptive orientation had high item reliability, a series of analyses were

conducted. First, a total activity general score and a total activity specific score were created by

summing scores (1 to 5) for all questions in each preference metric. Because consumptive

orientation subdimensions consisted of a different number of questions, the mean scores were

calculated instead (values attributed to responses were based on strongly disagree = 1 to strongly

agree = 5). Classification tree analysis (using the partition method in JMP version 13.0.0) was

conducted to explore the characteristics of anglers that explain variation in activity general and

activity specific preferences, and to assess whether unique groups of anglers separate according

to their underlying motivations for fishing. The following variables were included as possible

predictors: the effort allocated from shore versus from a boat (%), number of years Striped Bass

fishing experience, effort allocated to Striped Bass versus other saltwater species (%), number of

days saltwater fishing in 2016, number of Striped Bass caught in 2016, percent of Striped Bass

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typically released (%), birth year, how often Striped Bass is consumed during the fishing season

(ordinal), ethnicity (white versus non-white), education (ordinal), income (ordinal based on the

minimum from selected income range), gender, and mean consumptive orientation score for each

subdimension. A 33% validation data set and a minimum split size of 30 was used to ensure the

best fit model and to eliminate the potential for meaningless groupings, respectively.

(3) Can the attitudes of Striped Bass anglers predict their responses to alternative regulations?

Little variation existed in the activity preferences of survey participants, with most

individuals displaying both high activity specific and general preferences. Instead, there was

large variability in the consumptive orientation of anglers, suggesting that the fishing community

could be more appropriately described and grouped according to their attitudes regarding

catching fish, keeping fish, catching large numbers of fish, and catching trophy fish. Therefore,

we attempted to explain variation in behavior according to the four subdimensions of

consumptive orientation. For all five experimental scenarios and for each subdimension of

consumptive orientation, Kruskal-Wallis tests were used to compare the consumptive orientation

scores for anglers that either increased, decreased, or remained constant in their effort towards

Striped Bass fishing upon shifting from the status-quo regulation to an alternative regulation.

Results were deemed significant at p < 0.05. Post-hoc, multiple comparisons between groups

were conducted using the Steel-Dwass method (Neuhäuser and Bretz 2001).

Additional analyses were completed to examine potential biases associated with survey

methodology (see supplementary materials). Survey responses were compared between online

survey respondents in MA and mail survey respondents that did not initially receive an online

survey and between online survey respondents in MA and mail survey respondents that did

receive an online survey. Only anglers that primarily fished for Striped Bass in MA and were

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included in the MA license database were included in this analysis, since the mail survey was

restricted to MA. Angler motivations and consumptive orientation were compared using

Kruskal-Wallis tests for significance. The number of individuals that displayed constant effort,

decreased effort, and increased effort towards Striped Bass under each hypothetical management

change were compared using Fisher’s Exact tests due to low sample sizes within experimental

scenarios. Because there were no significant attitudinal or behavioral differences between

groups, mail and online survey respondents were included in the final analysis. We also include

mail respondents from MA because our study was not focused on understanding regional

differences between Striped Bass anglers.

Results

Upon conclusion of both online and mail surveys, we had received a total of 1,463

responses, resulting in an overall 11.3% response rate, with 11.1% and 13.4% response rates

from the online and mail survey, respectively. Of these respondents, 191 individuals selected that

they did not fish for Striped Bass recreationally, and they were excluded from our analyses. An

additional 3 participants were excluded that did not fish for Striped Bass in a coastal Atlantic

state (i.e., these participants likely misunderstood our request for information about fishing for

non-landlocked Striped Bass). Of the remaining 1,154 recreational Striped Bass anglers that

responded to the online survey, roughly 43% had purchased a license from MA, 25% from VA,

23% from CT, and 10% from NC. There were 115 responses from the mail survey, while 59

surveys were returned to the sender. Within the mail survey, 49 individuals responded that were

only sent the mail survey since they did not include an email address on the MA license

database. The remaining 66 mail survey participants did not respond to the online survey

originally (Note, that two additional mail survey respondents were excluded from analyses

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because the number of days in these participants’ experimental regulation scenarios did not sum

to 10). Collectively, the median birth year for participants was 1963, while nearly 94% of

participants were male. Roughly 91% of anglers considered themselves White / Caucasian. The

plurality of participants possessed a four-year college degree and had a total household income

between $100,000 and $150,000

(1) How might the implementation of new recreational fishing policies alter angler effort and

behavior?

Experimental scenarios testing different Striped Bass harvest control rules compared to

the status quo revealed differential impacts of regulations on angler intended behavior (Table

2.2, Figure 2.2). Upon increasing the daily limit from one to two fish per day, a large contingent

of anglers (19%) increased their relative effort towards Striped Bass, coupled with a smaller

decrease (10% of anglers) resulting in a net increase of effort (Table 2.2). Effort was reallocated

from other particular fisheries, indicating that anglers would likely redirect a portion their

saltwater fishing specifically towards Striped Bass. The enactment of a moderate slot limit

instead resulted in a net decrease in effort towards Striped Bass, which was not accompanied

with a large increase in effort towards the other three recreation activities. Alternatively, the

implementation of a restrictive slot limit, appeared to result in large effort swings, both negative

(24% of anglers) and positive (15%) towards Striped Bass. Disparate responses from anglers

reduced the overall impact of a restrictive slot limit, such that we found only a small (yet

significant) net decrease in total effort towards Striped Bass, whereas no significant differences

were revealed for the other three activity options. The catch-and-release fishing scenario was the

most dramatic example of effort reduction in the Striped Bass fishery (45% of anglers), while

only a small percentage of anglers increased their effort (7%). Many anglers shifted effort to

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other fisheries (29% of anglers) in response to the catch-and-release scenario, but a large

contingent of anglers (23%) also allocated more days to other types of outdoor recreation all

together. Significant differences in effort upon the implementation of catch-and-release fishing

were found for all activities except fishing for any species. Lastly, the unrestrictive regulation

scenario (no size restrictions, four fish per day), resulted in the largest positive effort shift in the

Striped Bass fishery (25% of anglers), coupled with a minor decrease in indiscriminate fishing

effort.

Anglers were also queried on the frequency with which they would aim to keep Striped

Bass under the two regulations presented to them. For each regulation (with the exception of the

catch-and-release option, since anglers would not be allowed to keep fish), we examined if

participants would try to keep fish more or less frequently, depending on whether they increased,

decreased, or remained constant in their Striped Bass fishing effort in response to a new

regulation (Figure 2.3). Results here are highly variable but revealed a number of trends. Across

the four regulations, the majority of anglers that decreased effort within the Striped Bass fishery

also decreased the frequency with which they would aim to keep fish. The converse is less

apparent and appears to be context dependent. For example, under a restrictive slot limit, anglers

that increased effort would also try to keep fish more often, but under a moderate slot limit,

anglers that increased effort were split on whether they would keep fish more or less often.

Participants that remained constant in their effort towards Striped Bass, which is a vast majority

of individuals for all experimental scenarios (except catch-and-release fishing which was not

included in this analysis), often changed the frequency of which they would keep fish. For

example, 72% of anglers didn’t change their Striped Bass effort after the daily bag limit was

increased, but 7% of them would aim to keep a Striped Bass on fewer fishing trips. On the other

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hand, of the anglers that didn’t change effort after the implementation of a restrictive slot limit,

19% of participants increased their keeping frequency.

Next, we independently examined anglers that primarily fished in MA to determine the

relative amount of effort that could shift in a single state’s Striped Bass fishery and to which

other fisheries that effort would be redirected. There were only modest increases in the number

of days allocated to Striped Bass after the implementation of an increased daily bag limit (+2%,

Figure 2.4). Conversely, 6% of fishing days were lost under a moderate slot limit and 6% were

gained under the unrestrictive regulatory scenario. Larger decreases in total Striped Bass fishing

days were found under the restrictive slot limit (-10%) and catch-and-release fishing (-41%).

Participants that allocated effort to another specific species, were asked to list these other target

saltwater species. Bluefish (Pomatomus saltatrix), Black Sea Bass (Centropristis striata),

Summer Flounder (Paralichthys dentatus), and Cod (Gadus morhua) were found to be the most

popular alternative species for anglers that increased or decreased effort towards other fisheries

in response to regulations.

(2) What are the underlying motivations and catch-related preferences of Striped Bass anglers?

Tests of all latent variables (activity general and specific preferences and consumptive

orientation subdimensions) revealed Cronbach’s alpha values above 0.7, indicating high internal

reliability (Supplementary Table 2.1, Supplementary Table 2.2). Anglers generally ranked both

activity general preferences and activity specific preferences as being highly important (median

score, respectively: 26, 24 out of a possible 30) with scores ranging from 6 to 30 for both metrics

(Figure 2.5). Large variability existed within all four consumptive orientation subdimensions,

and the angling population generally ranked catching trophy fish as their highest motivation

(median score = 3.66), followed by the amount of fish caught (median score = 3.5), catching fish

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(median score = 3), and lastly, keeping fish (median score = 2.66) (Figure 2.5). Classification

tree analysis revealed that only the consumptive orientation subdimension, ‘trophy fishing,’

predicted activity specific preferences; anglers that cared more about trophy fishing displayed

higher activity specific preferences (Figure 2.6). Activity general preferences were more

complex and were explained by two variables (Figure 2.6). Anglers that cared most about

catching fish displayed the lowest activity general preferences. Participants that felt less strongly

about catching Striped Bass were further subdivided by birth year; younger anglers cared more

about activity general fishing motivations. Within these younger participants, the consumptive

subdimension, ‘catching Striped Bass,’ followed the same prior pattern, and split the remaining

individuals into two groups.

(3) Can the attitudes of Striped Bass anglers predict their responses to alternative regulations?

A number of consumptive orientation subdimensions explained variation in behavior.

Although, this appears to be largely context dependent, as alternative attitudes aligned with

behavior for some regulations and not others, while the relationship’s directionality appeared to

also depend on which regulation was implemented. When moving from the status-quo regulation

to an increased daily bag limit, anglers that increased their fishing effort towards Striped Bass

cared more about keeping fish than anglers that maintained constant effort (Figure 2.7, Kruskal-

Wallis test - p-value = 0.03). Under this first experimental scenario, no other consumptive

orientation subdimensions predicted behavior (Kruskal-Wallis tests - catching fish: p-value =

0.14, number of fish: p-value = 0.61, catching trophy fish: p-value = 0.27). Alternatively, anglers

that decreased their effort upon the implementation of a moderate slot limit appeared to care

more about catching trophy fish (Kruskal-Wallis test - p-value = 0.04), while other

subdimensions did not significantly correlate with angler responses to this policy change

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(Kruskal-Wallis tests - catching fish: p-value = 0.08, keeping fish: p-value = 0.43, number of

fish: p-value = 0.07). Post-hoc, multiple comparison tests revealed a p-value of 0.055 and 0.078

when anglers that decreased effort were compared to those that remained constant or increased

effort, respectively. This result suggests only modest differences in consumptive orientation

between groups. Behavioral variation after the implementation of a restrictive slot limit was

explained by two consumptive attitudes: anglers that decreased their effort placed more value in

the catching of fish compared to anglers that increased effort (note, multiple comparison tests

revealed a marginal p-value of 0.059 between these two groups), while anglers that increased and

decreased effort valued the keeping of fish more than anglers that did not change behavior

(Kruskal-Wallis tests - catching fish: p-value = 0.04, keeping fish: p-value < 0.01, number of

fish: p-value = 0.89, catching trophy fish: p-value = 0.11). Under a catch-and-release fishing

scenario, anglers that decreased effort cared more about catching fish than anglers that

maintained constant effort, while anglers that decreased effort valued keeping fish more than

both other groups (Kruskal-Wallis tests - catching fish: p-value = 0.03, keeping fish: p-value <

0.01, number of fish: p-value = 0.46, catching trophy fish: p-value = 0.56). Similar to an

increased daily bag limit, changing to unrestrictive regulations revealed that anglers that

increased effort were more likely to value harvesting Striped Bass compared to anglers that did

not change behavior (Kruskal-Wallis tests - p-value < 0.01). Meanwhile, other consumptive

orientation subdimensions did not significantly explain variation in behavior (Kruskal-Wallis

tests - catching fish: p-value = 0.06, number of fish: p-value = 0.36, catching trophy fish: p-value

= 0.54).

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Discussion

The success of management is ultimately contingent upon the behavior of resource users.

However, fishing populations can be quite diverse, maintaining alternative motivations, fishing

methodologies, and perceptions, such that blueprint approaches to decision-making are difficult,

if not impossible (Fedler and Ditton 1994, Aas et al. 2000, Murphy Jr et al. 2015). Through an

examination of Striped Bass recreational fishery participants along the Atlantic coast, our study

found that the intended behavior of anglers is highly variable in response to new regulations.

However, the aggregate number of days anglers elected to fish for Striped Bass, and the total

number of predicted fishing days in Massachusetts, did not dramatically change after the

implementation of an alternative regulation. A notable exception was mandating catch-and-

release fishing only, whereby fishing effort plummeted. Compared to the status quo, the

implementation of more restrictive management measures caused only a modest reduction in

effort within the Striped Bass fishery, while the converse was also true; more relaxed regulations

caused a minor, aggregate increase in effort. Importantly, however, the aggregate behavior of

anglers is less informative, as extremely disparate responses within some regulations moderated

overall behavior (i.e., some people increased effort while others decreased effort). Surprisingly,

implementing a narrow slot limit, where anglers would only be able to harvest small fish, caused

a similar aggregate response as compared to a moderate slot limit because of the disparate

intended behavior of participants. Under the moderate slot limit ~20% of anglers changed their

effort such that roughly twice as many people decreased versus increased their effort. Nearly

40% of participants changed behavior under a restrictive slot limit, but the ratio of people that

decreased versus increased effort was similar, thereby moderating the overall effect of enacting a

more aggressive strategy.

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Our results demonstrate that spillover effects are likely to occur in response to new

recreational regulations, potentially because anglers perceive that they have alternative recreation

options. If anglers find a new regulation favorable, they may pull fishing effort away from other

species depending upon how they prioritize the harvest of one species over the other. These types

of species substitutions may be common in saltwater systems (e.g., Ditton and Sutton 2004,

Sutton and Ditton 2005). For example, saltwater anglers in the southeastern United States

commonly substitute target species when regulations are changed, which has a cascading effect

on economic expenditures (Gentner 2004). In Massachusetts, spillover effects are most likely to

occur between Striped Bass and other nearshore species such as Bluefish and Black Sea Bass.

While largely speculative, these alternatives may satisfy comparable experience preferences or

may be popular alternatives because of similarities in gear types used. If so, this finding would

agree with results from Sutton and Ditton (2005), which found that anglers would be more

willing to substitute species if the new species is perceived as a good food source or if they could

use the same type of fishing equipment. Spillover effects in our system are not marginal,

especially after the implementation of aggressive policy changes. For example, when we

proposed catch-and-release fishing, nearly one-third of the participants intended to increase

effort towards another specific saltwater species. Spillover effects also do not appear to be

limited to recreational fishing as nearly 30% and 20% of participants changed the total amount of

effort they would allocate to some other outdoor recreation activity upon the implementation of

catch-and-release fishing and the least restrictive regulation scenarios, respectively.

As we have demonstrated, a sizeable contingent of anglers altered the number of days

they allocated to Striped Bass fishing when policies were changed. However, regulations may

also impact other aspects of recreation behavior such as site choice and harvest decisions

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(Scrogin et al. 2004). In our study, participants often changed the frequency of which they would

aim to keep fish when we implemented a new hypothetical policy. Foremost, there appeared to

be a compounding effect of new policies, such that anglers that decreased the number of days

allocated to Striped Bass were also much more likely to reduce their harvest frequency. This

suggests that the enactment of an unfavorable policy could have a larger effect on fishing

mortality than expected. Additionally, anglers that did not change how much they would fish for

Striped Bass did sometimes change how often they would attempt to harvest a fish. Given that

the majority of anglers did not change total effort upon the implementation of a new regulation,

the catch-and-release behavior of this group can have significant implications if many of them

alter harvest behavior. This appeared to be the case under the restrictive slot limit, where nearly

one-fifth of anglers (who did not change total effort) increased the frequency of which they

would try to harvest a Striped Bass. By decreasing the harvestable size of Striped Bass, anglers

appeared to be capitalizing on the common belief that smaller Striped Bass have a more pleasant

flavor (personal communication with anglers). Especially given the relative ease of which

smaller fish can be caught, it is possible that fishing morality could increase significantly on

younger age-classes.

In our study system, unique groups of recreationists differed in their underlying

motivations for fishing. Variation in angler activity specific preferences appeared to be partly

explained by aspects of consumptive orientation, specifically their desire to catch large Striped

Bass. This result is intuitive given that activity specific preferences are often related to

components of consumptive orientation (Fedler and Ditton 1986, Oh et al. 2013) and since one

question within our activity specific construct was related to catching trophy fish. However, this

finding identifies trophy fishers as a potential unique user group, or “sub-world” of anglers;

87

groups of recreationists may separate from another to create sub-worlds based upon their

ideologies, skill, or attention they place towards certain objects within their activity of choice

(Strauss 1984, Ditton et al. 1992). Analysis of experimental regulation scenarios corroborates

this result and suggests that trophy anglers may be disparately impacted by the implementation

of some slot limit regulations. Alternatively, the relationship between activity general

motivations and angler age, plus the degree to which anglers value catching Striped Bass were

negatively correlated. Fedler and Ditton (1986) suggest that low-consumptive fishers may find

satisfaction more often on fishing trips since they appeared to be motivated principally by

general preferences that can be fulfilled in the absence of catching fish. They go on to purport

that these anglers would be more resilient to policy changes. One might then expect the opposite

to be true; high-consumptive anglers would be less resilient to policy changes. In our study,

anglers that decreased effort after the implementation of a restrictive slot limit and catch-and-

release fishing valued catching fish more than some other groups, which aligns with the

prediction from Fedler and Ditton. Younger anglers seemed to value the psychological aspects of

fishing more than older anglers. Given that fishing experience did not correlate with activity

preferences, our finding suggests that generational social norms may be acting independently on

different ages of anglers. This finding agrees with the general theory of planned behavior, in that

human behavior is guided by both individual attitudes and beliefs, but also by social norms

(Ajzen 1991). This finding could have important implications for how managers approach

stakeholder engagement since different generations of anglers may hold unique motivations for

fishing, thus changing the social dynamics of the fishery as participants age and leave the

fishery.

88

We found that the intended behavior of Striped Bass anglers in response to alternative

management strategies is correlated with their underlying attitudes about fishing. However,

regulations did not impact anglers equally, as different segments of the fishing population altered

their behavior depending on which regulation was implemented. In some cases, aspects of

consumptive orientation aligned unidirectionally with behavior. For example, participants that

decreased effort under catch-and-release fishing valued keeping fish more than both other groups

of anglers. Alternatively, when a restrictive slot limit was implemented, anglers that increased

and decreased effort cared most about keeping fish. It is possible that that anglers who increased

effort may care about keeping small fish for consumption, versus those that decreased effort may

value keeping large fish.

Collectively, our study demonstrates that the behavior of fishery participants is partly

contingent upon policy and the underlying attitudes of anglers. A diverse assemblage of anglers

present within the Striped Bass recreational fishery appeared to moderate the overall effect of the

implementation of some hypothetical regulations (Murphy Jr et al. 2015). However, changes to

the structure of regulations has the potential to significantly decrease fishing effort if the new

policy misaligns with angler goals and attitudes. The findings herein also suggest that the

implementation of a new regulation may not only alter effort, but also the frequency of which

anglers would aim to harvest fish. Managers should consider if new policies would cause anglers

to change their catch-and-release behavior, since this could have significant implications for

fishing mortality. Spillover effects into, or out of, other fisheries or other forms of outdoor

recreation will likely occur if anglers perceive that they have other recreation options available

and are subjected to more extreme policy changes. Moreover, characterization of the social sub-

89

worlds that exist within recreational fisheries will be important if we hope to predict the direct

and indirect effects of different potential policy regulations.

90

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Tables

Table 2.1. Regulation scenarios. Status-quo regulation was shown to all participants in addition

to one of the remaining five hypothetical regulations.

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Table 2.2 Percentage of anglers that either increased, decreased, or remained constant in their

effort towards a number of activity options (effort under status quo regulation compared to

alternatives regulations).

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Figures

Figure 2.1. Example experimental scenario. Participants were also asked to allocate 10 days

under Option B.

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Figure 2.2. Shift in effort upon the implementation of a new regulation. Points on the left

represent the number of days allocated under the status quo, while points on the rights represent

number of days allocated under the new, hypothetical regulation. * significant difference

between the same activity upon changing regulation.

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Figure 2.3. Shift in the frequency of which anglers would aim to keep Striped Bass grouped

by the direction of effort change. Within each regulation, this plot shows the percentage of

anglers that would aim to keep more versus less fish (note, people that did not change keeping

frequency are not shown below) and whether they increased their effort in the Striped Bass

fishery, decreased their effort, or remained constant from the status-quo regulation to the

experimental regulation. Note, the catch-and-release scenario is not shown since anglers would

not be able to keep fish under this regulation.

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Figure 2.4. Hypothetical Striped Bass fishing days in MA under status-quo and new

regulations. Days calculated according to participant's Striped Bass specialization, days fished

in saltwater in 2016, and the degree to which they change effort under the experimental scenario.

The y-axis represents the number of Striped Bass fishing days predicted for 100 anglers

(extrapolated based on the amount of effort shifted for an average angler under each

experimental scenario).

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Figure 2.5. Box-and-whisker plots for activity preferences and each of the four

consumptive orientation subdimensions.

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Figure 2.6. Classification tree analysis for each activity preference metric. The following

variables were included as possible predictors: the effort allocated from shore versus from a boat

(%), number of years Striped Bass fishing experience, effort allocated to Striped Bass versus

other saltwater species (%), number of days saltwater fishing in 2016, number of Striped Bass

caught in 2016, percent of Striped Bass typically released (%), birth year, how often Striped Bass

is consumed during the fishing season (ordinal), ethnicity (white versus non-white), education

(ordinal), income (minimum from selected income range), gender, and mean consumptive

orientation score for each subdimension. A 33% validation data set and a minimum split size of

30 was used to ensure the best fit model and to eliminate the potential for meaningless

groupings, respectively.

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Figure 2.7. Angler behavior compared to their consumptive attitudes. Plot depicts the mean

consumptive orientation subdimension score for participants that increased their effort within the

Striped Bass fishery, decreased their effort, or remained constant from the status-quo to one of

five regulations. * indicates a significant difference between groups. Relationships were deemed

significant at p<0.05.

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Supplementary Materials

Supplementary Table 2.1. Internal reliability tests for activity preferences.

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Supplementary Table 2.2. Internal reliability tests for consumptive orientation subdimensions. *

indicates item was reverse coded.

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Supplementary Table 2.3. Examination of attitudinal and behavioral differences between online

survey respondents in MA and mail survey respondents that did not initially receive an online

survey. Experience preferences and consumptive orientation scores were compared using

Kruskal-Wallis tests for significance. Behavior was compared using Fisher’s Exact tests for

significance. Comparisons were made according to the number of individuals that displayed

constant effort, decreased effort, or increased effort towards Striped Bass upon the

implementation of one of five hypothetical regulations.

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Supplementary Table 2.4. Examination of attitudinal and behavioral differences between online

survey respondents in MA and mail survey respondents that initially received an online survey.

Experience preferences and consumptive orientation scores were compared using Kruskal-Wallis

tests for significance. Behavior was compared using Fisher’s Exact tests for significance.

Comparisons were made according to the number of individuals that displayed constant effort,

decreased effort, or increased effort towards Striped Bass upon the implementation of one of five

hypothetical regulations.

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Chapter 3

The feeding ecology of Striped Bass and the role of ontogeny

Abstract

Striped Bass are a prominent marine predator in coastal Massachusetts that feed on a

variety of prey species and impose top-down pressure on other important fishery species, such as

the American Lobster. This study assessed the diet of Striped Bass using diet analyses, the

observations of fishers from an online survey, and a bioenergetic model. The role of ontogeny

was explored using stable isotope analysis, while Striped Bass that feed on benthic versus

pelagic prey were compared using multiple condition indices. Empirical results revealed that

Striped Bass in northern Massachusetts may have shifted from feeding predominantly on

Atlantic Menhaden in the late 1990’s and early 2000’s to Atlantic Mackerel in this study. This

finding was corroborated by the observations of fishers, suggesting potential value in the

consideration of stakeholder knowledge. However, diet analysis identified the American Lobster

as the second most important prey species, whereas fishers recognized other forage fish prey,

illuminating potential biases of fishers or spatial differences in the diets of Striped Bass. Stable

isotope analysis suggested that the diet of Striped Bass is largely driven by ontogeny; larger fish

feed more heavily on benthic prey, particularly in the latter half of their migration into

Massachusetts. It appears that large Striped Bass gain an energetic advantage to feeding on

benthic prey, possibly due to decreased foraging costs. Collectively, this study illustrates the

ability of predatory fish to capitalize on the variability of forage fish populations and proposes

energetic-based mechanisms for an ontogenetic diet switch from piscivory to benthivory.

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Introduction

Predators can have strong top-down effects on prey populations and fundamentally alter

ecosystems (Denno and Lewis 2009). Predators may not only control the distribution of species

within an ecosystem (Connell 1961), but they can also contribute to trophic cascades (Carpenter

et al. 1985), control the flow of nutrients within food-webs (Trussell et al. 2006, Hawlena and

Schmitz 2010), and operate as a keystone species (Paine 1974). These important predator species

often are harvested by humans, such that how they are managed can have broad implications for

community structure and ecosystem processes.

Quantifying the impacts of predators on local prey species requires an understanding of

their prey selection, which can be driven by a plethora of factors (Juanes et al. 1994). Optimal

Foraging Theory (OFT) suggests that a predator should select prey items by balancing the costs

of consumption relative to the intake of energy. More specifically, prey selection should be in

accordance with the energetic value of the prey minus the energetic cost of pursuing, attacking,

and handling the prey (Pyke et al. 1977), which can affect predator growth rates (Hart and

Hamrin 1990). However, predators often do not conform to OFT due to a variety of factors,

including the presence of competitors (intra and interspecific competition), avoidance of their

own predators, and morphology (Hughes 1990, Hambright 1991, Einfalt and Wahl 1997). For

instance, Milinksi (1982) found that sticklebacks consumed less optimal prey items in the

presence of superior intraspecific competitors. Additionally, as predators grow, they may

“switch” to consuming a completely new, often larger, prey type (i.e., an ontogenetic diet shift)

to overcome the aerobic and anaerobic costs of prey consumption (Townsend and Winfield 1985,

Sherwood et al. 2002). Fluctuations in the abundance of prey populations may also drive

predators to consume unfavorable prey. Work by Sherwood et al. (2007) suggests that Atlantic

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Cod that feed on benthic prey as a likely result of declines in Capelin populations have reduced

liver sizes, indicating poor energy reserves. An understanding of the potential energetic

consequences of prey selection is important, particularly if the stock structure of the predator or

prey fluctuates over time as a consequence of natural variation or size-selective harvesting

pressure.

Our study explores the feeding ecology and energetics of Striped Bass (Morone

saxatilis), which have the potential to exert top-down pressure on coastal prey communities

(Nelson et al. 2006). Striped Bass are highly mobile, generalist predators, consuming a variety of

prey items from zooplankton and fish to large invertebrates, such as the American Lobster

(Homarus americanus) and Green Crab (Carcinus maenas) (Chapoton and Sykes 1961,

Manooch 1973, Nelson et al. 2003). Because they spawn and predominately reside in the Mid-

Atlantic, the vast majority of studies on Striped Bass feeding ecology have been conducted in the

southern extent of their range, such as the Chesapeake Bay (Dovel 1968, Gardinier and Hoff

1982, Dunning et al. 1997, Griffin and Margraf 2003). By and large, these studies suggest that

juveniles feed on zooplankton and small crustaceans, while adult Striped Bass are predominately

piscivorous, but may also consume a small proportion of invertebrate prey (Manooch 1973,

Gardinier and Hoff 1982, Griffin and Margraf 2003, Overton et al. 2009). In contrast to these

Mid-Atlantic studies, Nelson et al. (2003, 2006) conducted an extensive diet study on Striped

Bass collected between 1997-2000, whereby half of the collected fish were from the North Shore

region of coastal MA. Their results suggested that as Striped Bass grow they rely more heavily

on decapod crustaceans, while smaller adults feed more on forage fish. This apparent ontogenetic

diet shift may have other consequences on Striped Bass since crustaceans, such as lobsters, may

generate proportionally less energy per gram wet weight as compared to forage fish such as

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Atlantic Herring (Nelson et al. 2006). Crustaceans also require more time to digest (Langton and

Center 1982) and, as such, may represent a suboptimal prey choice. The mechanisms for this

potential ontogenetic prey shift are unclear, along with the degree to which Striped Bass are

affected by the consumption of a prey species which may be a suboptimal choice.

Striped Bass are abundant during the spring and summer months in MA and, as such,

may impact local populations of prey via their consumptive effects. Nelson et al. (2006)

incorporated previous diet information into a bioenergetic model (Hanson 1997) to estimate

Striped Bass consumption of a variety of prey species. Model results suggested that adult Striped

Bass consume 3 times more lobster (numerical abundance) than the commercial fishery harvests

annually, and 965 times the number of Menhaden. These staggering values elucidate the

potential for considerable top-down forcing of Striped Bass on lobsters and other economically

important prey items even though they are a transient, highly migratory species that is only

present in the Gulf of Maine for a few months of the year. Declines in Menhaden may also point

to Striped Bass food limitation (Nelson et al. 2006). However, given that a recent stock

assessment for Striped Bass has suggested a decrease in Striped Bass spawning stock biomass, it

is unclear if Striped Bass are currently food limited, and whether they are still exerting strong

top-down pressure on lobsters or other prey items (Atlantic States Marine Fisheries Commission

2016).

To assess the recent diet of Striped Bass in northern MA, we conducted stomach content

analysis, stable isotope analysis, and an online survey of Striped Bass fishers. Traditional

stomach content analysis can result in precise identification of prey species, but potentially offers

only a snap shot of what an individual has been consuming. An alternative approach, utilizing

stable isotopic ratios in predator tissue, provides a coarser, more holistic metric of consumption.

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The stable isotope ratios of nitrogen (𝛿14N / 𝛿15N) are indicators of trophic position due to the

predictable enrichment of nitrogen for predators relative to their prey (Fry 1988, Post 2002).

Conversely, the stable isotope ratios of carbon (𝛿13C / 𝛿12C) do not fractionate as much between

trophic levels, but instead indicate benthic versus pelagic feeding due to carbon isotope

enrichment for benthic prey at the base of the food web (Post 2002). Both approaches, however,

are limited by the ability of researchers to exhaustively sample the species spatially and

temporally.

Alternatively, the ecological knowledge and observations of fishers are increasingly

being used to supplement empirical data as a low-cost alternative to more intensive sampling

methodologies (Johannes et al. 2000, Bergmann et al. 2004). It has also been suggested that

fisher knowledge of fish ecology, such as feeding behavior, could be used to validate traditional

sampling approaches or as a first step in scientific hypothesis formulation (Silvano and Valbo-

Jørgensen 2008). For example, as discussed in Silvano and Valbo-Jørgensen (2008), fishers in

Brazil accurately identified that an invasive species of Croaker consumes the native Twospot

Astyanax, thus threatening it (Braga 1995). In our system, the Striped Bass is a prime candidate

to test the knowledge of fishers given its prominence in recreational fishing culture in MA.

Regularly sampling Striped Bass using traditional approaches poses logistical and financial

challenges since these predators typically inhabit rocky shorelines inaccessible to the MA bi-

annual trawl survey. Fishers, on the other hand, are broadly distributed and have access to a

range of Striped Bass habitats. If deemed reliable, the ecological knowledge of fishery

participants in MA could be used to examine predator-prey interactions across broad spatial and

temporal gradients.

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For the reasons described above, our study used stomach content analysis and the

observations of fishers to explore the diet of Striped Bass and to identify important prey taxon. A

bioenergetic model was then fit to Striped Bass sampled in this study as to predict the potential

impact of individual age classes of Striped Bass on prey communities. Finally, the stable isotopes

of carbon and nitrogen were then used to evaluate the effects of predator ontogeny on diet

preferences, and whether diet effects condition.


Sampling

From 2012 to 2016, Striped Bass were collected via rod-and-reel from the North Shore

region of MA between Nahant and Gloucester, centralized around Salem Sound (Figure 3.1)

(n=164, total length range = 41.3cm – 114.1cm, mean = 77.5 cm). Note, this protocol was

approved by Northeastern University’s Institutional Animal Care and Use Committee. Harvested

fish were placed on a measuring board and the following measurements were recorded; fork

length (cm), total length (cm), gape width (cm), and gape height (cm). A small muscle plug was

extracted from an area 1-3 cm below the first dorsal fin for stable isotope analysis. The muscle

plug was immediately placed in foil and frozen. Fish were sexed and each individual’s liver and

stomach were extracted, weighed, and frozen. Sagittal otoliths were removed and cleaned to

remove any organic matter. Sampled fish were aged based on methodology from Secor et al.

(1990), and adapted according to protocol at the MA Division of Marine Fisheries otolith

laboratory (personal communication). Once ready for aging, otoliths were mounted to individual

slides using heat-activated CrystalBond. Mounted otoliths were sectioned using a low-speed

Isomet Saw and were polished using fine, 600 grit sandpaper. Sections were mounted using

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CrystalBond and aged under a compound microscope. A second age reader validated ages, and

any discrepancies were discussed until an agreed upon conclusion was reached.

Stomach contents

In the lab, stomach contents were extracted, prey items were identified to the lowest

taxon possible, and the following data were recorded: number of individuals by species, weight

of each species, and length of all individuals (carapace width for crabs, carapace length for

lobsters, total length for fish). In rare circumstances where many individuals of the same species

were found, a subset of 10 individuals were measured for length (only occurred for amphipod

and bivalve prey). Prey specimens in good condition (i.e., very minimally digested) were saved

and frozen for stable isotope analysis. Multiple metrics were used to examine the importance of

prey taxon for Striped Bass. First however, empty stomachs were removed from further analysis.

Percent weight (%W) is a useful metric for comparing the relative energetic value of prey taxon

especially when individuals from each taxon are different in size (Zale et al. 2012). Percent

weight was calculated as the fraction of the total weight of an individual taxon across all sampled

fish by the total weight of stomach contents for all sampled fish. To determine how often Striped

Bass consumed particular prey, we calculated the frequency of occurrence (F) for each prey

item: the fraction of stomachs with an individual taxon by the total number of non-empty

stomachs.

Fisher observations

In 2013, 2,000 recreational fishers each from MA and CT and 1,000 commercial fishers

from MA were sent an online survey through Qualtrics Survey Software Research Suite. The

bulk of this survey was aimed at understanding the perceptions of fishers in regards to alternative

113

management strategies and was discussed in Murphy Jr et al. (2015). However, a subset of this

survey queried participants on ecological concepts. Specifically, participants were asked to select

the three species from a list of thirteen that they believed to most important to the diet of Striped

Bass. This question was asked separately for both large (28” TL) and small (<28” TL) Striped

Bass. An additional question asked participants to click on a map where they would fish (up to

three locations) for Striped Bass. Since Striped Bass collections occurred within the North Shore

of MA, analysis of fisher observations was restricted to individuals who selected that they would

fish in the North Shore (n=185).

Bioenergetics Model

The bioenergetics model was executed in the R package shiny, using the Fish

Bioenergetics 4.0 platform (Hanson 1997, Deslauriers et al. 2017). This model is grounded by a

balanced energetics equation as developed by Kitchell et al. (1977), such that the energy

consumed by an individual fish equals respiration plus waste and growth. Our goal was to

estimate the total consumption of each prey taxon, for an average Striped Bass in each age class

(ages 5-10 chosen due to adequate sample sizes) across a range of days during their migration

through MA. Using adult Striped Bass-specific physiological parameters (Hartman and Brandt

1995a), daily estimates of prey consumption were calculated based upon fish metabolic costs,

environmental temperature, predator/prey energy densities, and diet as assessed in the stomach

content analysis. The proportion of individual prey taxon by weight in the diet of each age class

was calculated on a daily basis (for days where diet data was available). To more accurately

characterize the diet of Striped Bass, we used length-to-weight relationships from the literature to

back-calculate the predicted original weights of fish and decapod prey, which were often heavily

digested (Sawyer 1967, Krouse 1973, Murawski and Cole 1978, Lange and Johnson 1981,

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Richards 1982, Campbell and Eagles 1983, Hartman and Brandt 1995b, McDermott 1998,

Wigley et al. 2003, Audet et al. 2008) (Supplementary Table 3.1). When prey items were too

broken or digested to generate reliable estimates of total length or carapace width/length, they

were assigned the average reconstructed weight of all other prey items in that taxon.

Temperatures experienced by Striped Bass were informed by temperature loggers

monitored by the MA Division of Marine Fisheries. Temperatures from 2012 to 2015 were based

on data from Beverly Harbor. This logger was lost prior to the 2016 sampling season, so

temperatures for 2016 were generated based on the relationship between the Beverly Harbor

logger and a logger off of Gloucester, MA (data from the previous four years was used to create

a linear relationship between the two data loggers).

The energy density of Striped Bass was assumed to be 6395 joules per gram wet weight,

as determined by Nelson et al. (2006), while prey energy densities were gathered from Steimle

and Terranova (1985). Direct energy estimates for individual taxon were available for most

prominent prey of Striped Bass including the American Lobster, Atlantic Herring, Atlantic

Mackerel, Menhaden, and Rock Crab. In some cases, the energy density of a closely related prey

item or the average value of related taxa was used for a prey taxon lacking a specific estimate

(Supplementary Table 3.1).

Models for each individual age class were fit according to the initial and final weight of

an average Striped Bass across a range of migration days. The first day we caught a fish of a

particular age class represented the first day of the model, while the last date of capture

represented the final day. Growth, used to estimate initial and final Striped Bass weight, was

based upon fish collected in this study and was calculated from the relationship between fish

body weight (g) and age. A weight-based growth curve was created using a power regression

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model that accurately represented the weights of fish in our study (weight (g) = 142.37 *

age1.6167). Fish age was indicated in years plus a decimal extension signifying the day on which

the individual fish was captured (Nelson et al. 2006).

Stable Isotopes

As a third approach to explore the diet of Striped Bass, and particularly the potential role

of ontogeny, we used stable isotope analysis, which is a longer-term approximation of predator

diet because it measures prey that have been assimilated into muscle and other tissues (Post

2002). Stable isotope analysis was conducted on white muscle and liver samples due to disparate

tissue turnover rates between these two tissue types. Laboratory estimates of tissue turnover rates

in Summer Flounder for example, suggest carbon and nitrogen half-lives of between 10 and 20

days for liver and 49 to 107 days for muscle (Trudel et al. 2010).

Using sterile techniques, a small internal plug from each frozen sample (muscle and liver)

was collected internally as to avoid contamination. Each sample was then dried for 48 hours at

45°C and subsequently ground to a homogenous powder using a sterilized mortar and pestle.

Samples were then weighed, placed in tin caps, and packed for shipment. All samples, including

10% duplicates (to examine variation between replicate pairs), were sent to the Colorado Plateau

Laboratories to be analyzed. Since lipid content can influence isotopic carbon signatures,

predator samples were lipid-corrected according to methods suggested by Skinner et al. (2016).

As such, lipid percentages were generated based on a formula from Post et al. (2007), which was

used as an input in another formula from Kiljunen et al. (2006) to correct δ13C values (hereby

called δ13C’).

Striped Bass condition

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To estimate predator condition, two metrics were utilized. A liver somatic index (LSI)

was calculated for each fish as follows: LSI = w / W * 100 where w = wet liver weight and W =

wet body weight (Adams and McLean 1985). An individual fish’s LSI should be closely

correlated with health since excess energy is stored as glycogen in the liver, typically after

periods of high consumption (Hoque et al. 1998). Thus, higher relative LSI values should

indicate a healthier individual. To explore the effects of diet on Striped Bass relative body size,

the Relative Condition Factor (Kn) was used, which accounts for allometric growth (Le Cren

1951). Here, individual fish weight (W) was divided by the length specific mean weight (W’) of

Striped Bass in Massachusetts such that Kn = W / W’. Length specific mean weight was

calculated according to the Massachusetts Striped Bass Monitoring Report for 2014.

Statistical Analysis

For the online survey, the number of times each species was ranked among the top three

prey species was tallied to calculate perceived prey importance (i.e., the percentage of fishers

that ranked each species among the top three most important species). The relative rankings of

prey taxa were then compared to both prey importance metrics (percent weight and frequency of

occurrence) generated from stomach content analysis. Comparisons were restricted to fisher

observations and stomach content data from Striped Bass over 28” TL only ( 28” TL), as

fishers would not have been able to observe stomach contents from small fish (recreational

minimum size limit = 28”, commercial minimum size limit = 34”).

Striped Bass stable isotopic values for δ13C’ and δ15N were regressed against Striped

Bass TL by time of year, whereby three time-period categories were included: June and prior,

July, and August and later. These time periods were chosen in order to capture the leading and

falling edges of the Striped Bass migration through MA, and since the bulk of Striped Bass were

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collected in June, July, and August. For all linear regressions, the normality of residuals

assumption was validated using normal quantiles plots, while homoscedasticity was inspected

using residuals versus fitted values plots. Extreme outliers were removed based on examination

of Cook’s D influence (Cook 1977). Regressions were deemed significant at p ≤ 0.05. For the

remaining stable isotope analyses, Striped Bass were separated into four size categories (Table

3.1). These groups were based on recreational and commercial Striped Bass fishing regulations

but they also provided four relatively equivalent sample sizes. Analysis of Variance (ANOVA)

coupled with Tukey post-hoc tests were used to compare the δ13C’ and δ15N values of each

Striped Bass size category across the three time periods. Two extreme outliers were removed that

were roughly 3 or more standard deviations away from the mean and these points were also

identified as obvious outliers from the previous regression analysis using Cook’s-D (Osborne

and Overbay 2004). LSI and Kn values were adjusted to account for seasonal variation

(Sherwood et al. 2007) and were regressed against δ13C’ by fish size class to examine the

potential impact of benthic versus pelagic feeding on fish condition.

Results

Atlantic Mackerel was the most important prey item by weight (%W = 43.1), followed by

American Lobster (%W = 20.0), and Menhaden (%W = 7.1), while a number of other species

were of much lower importance (Table 3.2, Figure 3.2). Frequency of occurrence of Atlantic

Mackerel (F = 17.5), American Lobster (F = 16.5), and Rock Crabs (F = 16.5) were higher than

other prey taxon consumed by Striped Bass (Table 3.2, Figure 3.2). Collectively, fish (%W =

66.2, F = 61.2) were much more important than decapods (%W = 29.3, F = 43.7) to the diet of

Striped Bass in our study (Figure 3.2).

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Recreationally-legal Striped Bass (i.e., 28” or greater) consumed Atlantic Mackerel more

than other individual prey species, while American Lobster was second most important by

weight and by frequency of occurrence (Figure 3.3). Similar to our empirical results, participants

from our online survey believed that Atlantic Mackerel was the most important prey item for

recreationally-legal sized Striped Bass. However, there was moderate disagreement on the

relevancy of other prey items. Participants deemed that Atlantic Herring was second most

important, followed by Menhaden, American Eels, and American Lobster (Figure 3.3).

The results of the bioenergetics model for Striped Bass revealed that on a daily basis

Striped Bass ages 5 – 10 (roughly 20” to 35” TL) can consume between 30.8 and 56.7 grams of

prey, and consumption generally increased as fish grew older (Table 3.3). In addition, older

Striped Bass generally consumed more decapod prey and fish per day on average than younger

individuals. Most strikingly, Striped Bass ages 9 and 10 consumed on average 870g and 721g of

American Lobster, respectively, during the days captured in our model (mid-June to mid-

August). These older individuals primarily consumed lobsters compared to other decapod prey.

Younger Striped Bass also consumed decapod prey (e.g., age 5 fish consumed 11.4 g/day), but

the composition of the decapod diet was much different from that of larger striped bass. For

example, age 5 fish consumed on average per day 0.8 grams of Asian Shore Crabs, 5.2 grams of

Green Crabs, 4.6 grams Rock Crabs, and only 0.7 grams of American Lobsters. Fish prey were

important for all ages of Striped Bass analyzed, whereby daily consumption was estimated

between 11.9 and 35.1 grams per day. Atlantic Mackerel represented a significant portion of

daily diet for all age classes with the exception of age 5 fish. Up to 24.2 grams of Mackerel were

consumed daily.

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Stable isotope analysis revealed little variation between replicate pairs for muscle

samples (δ13C = 1.0%, δ15N = 1.4%), liver samples (δ13C = 0.4%, δ15N = 1.0%), and prey

samples (δ13C = 1.0%, δ15N = 0.9%). One extreme outlier was removed each from the liver

samples and prey samples. Linear regression of stable isotopes from muscle samples revealed

that Striped Bass diet was correlated with length (Figure 3.4). Larger fish apparently relied more

prey items at a higher relative trophic level during the first part of the migration, as indicated by

a significant relationship between length and δ15N (Figure 3.4). No trends were observed in July,

while a positive relationship between both δ13C’ and δ15N and length was observed in the final

time period of the migration (Figure 3.4). Samples from Striped Bass livers, which have a faster

tissue turnover rates, corroborate results observed in August from muscle samples, but reveal a

positive relationship between δ13C’ and length in June as well (Figure 3.4).

Comparison of muscle stable isotope values between Striped Bass size categories

validated results from linear regressions (Supplementary Table 3.2). During June and prior, there

were no differences between size categories (Tukey post hoc tests, p > 0,05), but there was a

significant overall relationship between both carbon and nitrogen stable isotopic values and

Striped Bass size class (likely because of the conservative nature of the Tukey follow-up test).

No significant differences were found in July. Nitrogen stable isotopes were not different among

size classes in August, but ANOVA, followed by Tukey post-hoc tests, revealed that extra-large

Striped Bass relied more heavily on benthic prey items as compared to the smallest and third

smallest size categories of Striped Bass. For plotting purposes and to visually compare Striped

Bass isotopic values to prey items, predator values were adjusted to account for trophic

fractionation between predator and prey (δ13C= +0.8‰, δ15N = +3.4‰, Figure 3.5) (Zanden and

Rasmussen 2001). Both species of prey fish (Atlantic Herring and Atlantic Mackerel) had the

120

highest δ15N and lowest δ13C values among Striped Bass prey, indicating that they were a higher

trophic level and a more pelagic food source than crustacean prey. Meanwhile, the two crab

species (Green Crab and Rock Crab) were lower on the food chain, while the δ13C’ values of the

American Lobster were highly enriched, indicating that they are among the most benthic prey of

the taxa consumed by Striped Bass (Figure 3.5).

Examination of condition indices revealed significant interactions between carbon

isotopic values and both LSI and Kn (Figure 3.6). Specifically, there was a positive relationship

between δ13C’ and LSI for extra-large striped bass (p = 0.02, r2 = 0.21), while there was a

negative, yet non-significant, trend for the smallest fish (p = 0.13, r2 = 0.07). Within the medium

fish category, fish with less negative δ13C’ values appeared to be relatively smaller by weight, as

indicated by lower Kn values, than their counterparts (p = 0.03, r2 = 0.09). Conversely, fish that

were one size category up displayed a positive relationship between δ13C’ and Kn (p = 0.04, r2 =

0.11).

Discussion

Our results indicate that Striped Bass (~over 40cm in length) in the North Shore region of

MA have transitioned from a diet dominated by Atlantic Menhaden (Nelson et al. 2003) two

decades ago to targeting Atlantic Mackerel. The occurrence of Mackerel in Striped Bass diets

increased over 10-fold, potentially indicating a major shift in local availability of historic forage

fish such as Menhaden. As opportunistic predators, Striped Bass appear to have capitalized on

the local variability of forage fish populations (Hilborn et al. 2017). Additionally, Nelson et al.

(2003) found Striped Bass diets (of fish of similar size to those sampled in this study) to be

dominated by Menhaden in the later summer months, during the time at which this forage fish

typically migrates into nearshore communities along coastal MA. Our field results suggest that

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Menhaden abundances in Salem Sound were very low over consecutive years, while there was a

considerable uptick in Atlantic Mackerel spawning-stock-biomass and total biomass following

the Nelson et al (2003) diet study (42nd Northeast Regional Stock Assessment Workshop (42nd

SAW) 2006).

The importance of Mackerel was supported by recreational and commercial fishers who

also believe Mackerel to be of prime importance to large Striped Bass. The accuracy of fisher

observations here is not surprising given their cumulative experience and on-the-water time, but

also because of their reliance on baitfish for rod-and-reel fishing. Fishers in MA often spend the

early morning in search of baitfish, which they will either catch via rod-and-reel, gillnets, or cast-

nets, or will purchase live fish at local bait shops. The use of artificial lures that mimic prey fish,

which is another technique used regularly by recreational fishers, requires constant adaptation to

match lure style and color to current Striped Bass prey. These techniques ensure that they are

using (or mimicking) baitfish that are currently most abundant, providing fishers with real-time

information of what forage fish species Striped Bass may be consuming given that these

predators are highly opportunistic.

Stomach content analysis revealed that the American Lobster may also be a critical prey

item, and was the most important invertebrate taxon, highlighting an interaction with another

vital New England fishery. Catch of American Lobster in MA was valued at over $82 million in

2016, second only to Sea Scallops (MA Division of Marine Fisheries 2016 Annual Report). Rock

Crabs were consumed at similar rates but are much smaller and thus represent a lesser energy

source. This finding is in agreement with Nelson et al. (2003) in their study of adult Striped Bass

throughout MA from 1997-2000, such that crustaceans were found to represent ~45% of Striped

Bass diet by weight within the North Shore region. While fish in our study consumed a slightly

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smaller proportion of crustaceans, the overall consumption of juvenile American Lobster

remained high.

Unlike Atlantic Mackerel, fishers did not rank American Lobsters among the top prey

items as suggested by our empirical results and instead believed other fish prey were more

important (i.e., Atlantic Herring, Atlantic Menhaden, American Eel). As previously discussed,

fishing methodology norms likely predispose fishers to detect changes in the importance of

forage fish. In turn, fishers appeared to over-emphasize the relevancy of forage fish prey and

may be missing a critical interaction between Striped Bass and crustacean prey. The

misalignment between our empirical results and fisher observations, does not, however,

necessarily insinuate that fishers are incorrect (Silvano and Valbo-Jørgensen 2008). Recreational

and commercial fishing effort is broadly distributed across a range of Striped Bass habitats,

including rocky shorelines, sandy beaches, estuaries and brackish habitats, river-mouths, and

open-water environments. Fishers may, therefore, possess knowledge unavailable to scientists

using trawl-surveys or other traditional methods. Additionally, there are a diversity of angling

methodologies used by fishers within MA, which likely reduces some of the sampling biases that

possibly confounds more limited sampling approaches.

Results from the bioenergetic model highlight the potentially large top-down effect of

Striped Bass on a number of prey species, in turn emphasizing the value of considering predator-

prey interactions in fisheries management, especially when between two important fisheries

species. For example, we found that an individual Striped Bass (age 9) would, on average,

consume 870 grams of American Lobsters during its summer residence in northern MA, which is

likely an underestimate since we only estimated consumption over the time period during which

we captured fish of each specific age class. Given that the average lobster weighed 77 grams in

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the diet analysis (reconstructed weight), a typical 9-year-old Striped Bass would need to

consume more than 11 juvenile lobsters to satisfy its energetic demands between June and

August. The potential top-down effect of Striped Bass here is profound, since there were nearly

43,000 fish caught in the commercial fishery in MA alone during 2015 – the vast majority of

which were over 8 years of age (ASMFC 2016).

Striped Bass appear to exert considerable predation pressure on a number of other prey

taxa. For one, they may have a large effect on invasive populations of Green Crabs since even

young individuals are predicted to consume over 65 Green Crabs on average in MA. Contrary to

previous findings, however (Nelson et al. 2006), Atlantic Menhaden have declined in importance

and were only consumed by 6-year-old Striped Bass (of fish analyzed in the bioenergetic model)

at a rate of 1.8 grams per day. They appear to have been replaced by Atlantic Mackerel, which

represented a large portion of the diet of fish sampled in this study, whereby over 1kg of prey

would be consumed by multiples ages of Striped Bass during their summer residency.

Analysis of stable isotopes offers a more holistic examination of diet ontogeny as we are

able to sample all fish (including empty stomachs) and because isotopic signatures integrate

across longer time periods (Post 2002). As indicated by carbon and nitrogen stable isotopes from

white muscle samples, relatively smaller Striped Bass apparently consumed benthic organisms at

low trophic levels initially, while their larger counterparts were likely feeding on higher trophic

level, pelagic prey. Based on Striped Bass samples collected during August and later, this

benthic/pelagic relationship flips over time such that fish size became positively correlated with

benthic feeding. Relatively larger fish, however, still appeared to be feeding at higher trophic

levels than smaller fish, which was likely related to larger Striped Bass feeding on American

Lobsters versus smaller fish that feed on small invertebrates like the Sand Shrimp, Crangon

124

septemspinosa (Nelson et al. 2003), Green Crabs, and Rock Crabs (Cancer irroratus).

Importantly, carbon values were drastically disparate, whereas variation in nitrogen isotopes

among size classes was less substantial, indicating that diet ontogeny was likely more closely

related to benthic versus pelagic feeding than trophic position.

Due to the significant time lag between prey consumption and assimilation into muscle

tissue (Trudel et al. 2010), examination of tissue with faster turnover rates was important. White

muscle samples analyzed from Striped Bass caught during the first couple of months of the

migration may have indicated what they were eating as they were still migrating north and had

yet to reach MA. Carbon isotopic signatures from liver samples (i.e., with much faster turnover

rates) supported the notion that predator size was positively correlated with benthic feeding even

during the first part of the migration into northern MA. This relationship was also true in August

and suggests that larger fish feed more heavily on benthic prey sources throughout much of their

residency time to northern MA. Muscle and liver results collectively indicated that relatively

larger Striped Bass may feed primarily on pelagic food sources prior to their immediate arrival

into MA, followed by a switch to benthic prey in MA where there is higher availability of

crustaceans such as American Lobsters (Thunberg 2007).

This ontogenetic diet switch is somewhat counterintuitive given that fish prey offer more

energy per gram wet weight (Steimle and Terranova 1985) and since crustaceans, like the

American Lobster, are partly composed of chitin (Boßelmann et al. 2007) – an indigestible

organic material. Analysis of the liver somatic index and the relative condition factor provide

insight into possible explanations. Feeding amongst the benthos appears to have negative

repercussions for smaller Striped Bass, as these fish weigh less, relative to length-specific-mean-

weight, than their pelagic-feeding counterparts. Conversely, benthic feeding seems to favor

125

larger Striped Bass such that fish are heavier if they consumed a diet comprised largely of

benthic organisms. Additionally, extra-large Striped Bass that feed on benthic prey items have

larger livers, indicating that benthic feeding may allow these predators to build up better energy

reserves. Given that Striped Bass experience decelerating growth by length, but their weight

increases exponentially with age, large Striped Bass must propel a relatively heavier body

through the water to capture prey. Chasing after fast-moving forage fish is thus likely associated

with high attack and pursuit costs for larger Striped Bass, while smaller, more streamlined

individuals may be more capable of efficiently searching for and capturing forage fish. This

finding is supported by work from a lake ecosystem, where pelagic Eurasian Perch were more

streamlined than Perch feeding in the littoral zone (Quevedo et al. 2009).

By consuming benthic crustaceans that are slower than fish and thus potentially easier to

capture, large Striped Bass may be able to reduce the energetic costs associated with capturing

prey. This approach would allow Striped Bass to allocate more energy to grow and build up

excess energy reserves, which are suggested by our condition factor and liver somatic index

results, respectively. Similarly, work by Sherwood et al. (2002) suggests that the burst speed

required to capture prey is an important component of foraging activity costs. In a lake

ecosystem, the authors measured the lactate dehydrogenase (LDH) levels in the white muscle of

Yellow Perch, which is a proxy for anaerobic metabolism and burst swimming activity.

Predatory Yellow Perch that exhibited ontogenetic variation in diet and shifted from consuming

zooplankton to benthic invertebrates and then large prey fish, were able to reduce their anaerobic

activity costs in a step-wise fashion with each diet switch. By resetting their activity costs after

each ontogenetic prey switch, these fish were able to maintain growth and prevent a bioenergetic

126

bottleneck. It is plausible that large Striped Bass switch to feeding more heavily on lobsters to

reduce the metabolic costs of foraging.

As evidenced by a bioenergetic model, this migratory predator likely exerts extreme top-

down pressure on multiple prey populations, including species representing large fisheries in the

Gulf of Maine, such as the American Lobster, Atlantic Herring, and Atlantic Mackerel. While

the Atlantic Mackerel appears to be the result a relatively recent shift in the diet of Striped Bass

in northern MA, Striped Bass predation pressure on the American Lobster has remained

consistently high since the late 1990’s (Nelson et al. 2006). As such, the health of the Striped

Bass population likely has direct implications for the American Lobster fishery, given that

Striped Bass consume lobsters before they have recruited into the fishery. Importantly however,

the impact of Striped Bass on prey communities will largely depend on cohort size, given the

significant role of ontogeny in prey selection. We have proposed potential mechanisms for an

ontogenetic shift from piscivory to benthivory; smaller, more streamlined Striped Bass likely

benefit from the consumption of energetically-rich forage fish. Conversely, large Striped Bass

may suffer from increased attack or searching costs associated with pelagic feeding and, as such,

likely switch to benthic feeding, which apparently enhances their growth and condition.

Collectively, our study illustrates the plasticity of predatory fish to capitalize on alternative

forage fish populations and provides important insight into the energetic basis for diet ontogeny.

Acknowledgements

We would like to thank Kelsey Schultz who was our second age reader and Joe

Carracappa, Suzanne Kent, and Christopher Baillie for helping with Striped Bass collections.

Randy Sigler and the members of his fishing camp provided numerous Striped Bass samples.

127

Greg Veprek always went out of his way to bring us on his boat to collect samples and we are

extremely grateful for his generosity.

128

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Tables

Table 3.1. Size categories of Striped Bass.

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Table 3.2. Summary of stomach contents by prey taxon.

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Table 3.3. Bioenergetic model results. Consumptions estimates for major prey taxon and

categories for Striped Bass ages 5 to 10. Average consumption per day was calculated by

dividing the total estimated consumption of each prey taxon by the number of days captured in

the model for each age class.

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Figures

Figure 3.1. Study area with inset map of Massachusetts.

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Figure 3.2. Plot of most important prey taxon for all Striped Bass.

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Figure 3.3. Fisher observations of Striped Bass diets compared to empirical results for fish over

28” TL. Left axis represents the percentage of fishers that ranked each species among the top

three most important prey species. Right axis represents percent weight and frequency of

occurrence for each prey species.

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Figure 3.4. Diet ontogeny by time period according to stable isotope samples from Striped Bass

white muscle and liver.

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Figure 3.5. Prey and Striped Bass stable isotopic values (from muscle samples). Striped Bass

values were corrected to account for trophic fractionation and were lipid corrected. Prey isotopic

values represent results from all time periods.

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Figure 3.6. Linear regression comparisons of Striped Bass condition indices versus δ13C’ (white

muscle samples) for the four size categories of Striped Bass.

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Supplementary Materials

Supplementary Table 3.1. Prey energy densities and the literature source of energy estimate and

length-weight relationships. * from Steimle and Terranova 1985

Prey Category

Joules

per gram

wet

weight Prey used for energy estimate

Source of length-weight

relationship

American Lobster 4800 American Lobster * Krouse 1973

Annelid Worms 4580 Polychaetes average * n/a

Asian Shore Crab 3700 Rock Crab * McDermott 1998

Atlantic Herring 10600 Atlantic Herring * Wigley et al. 2003

Atlantic Mackerel 6000 Atlantic Mackerel * Wigley et al. 2003

Bivalve 1540 Bivalves average * n/a

Crabs Unidentified 3700 Rock Crab *

Rock crab equation from

Cambell and Eagles 1983

Cunner 6600 Cunner * Wigley et al. 2003

Euphausiacea 3400 Euphausiacea * n/a

Fish Unidentified 6508 average of all fish taxa from this study Hartman and Brandt 1995b

Gammaridae 1700 Gammarus annulatus * n/a

Gastropoda 2280 Gastropods average * n/a

Green Crab 3700 Rock Crab * Audet et al. 2008

Gunnels 4770 dermersal fishes * Sawyer 1967

Haddock 4500 Haddock * Wigley et al. 2003

Idoteidae 3400 Euphausiacea * n/a

Jonah Crab 3700 Rock Crab *

Rock crab equation from

Cambell and Eagles 1983

Menhaden 7500 Menhaden * Hartman and Brandt 1995b

Other Amphipods 5440 Amphipods average * n/a

Rock Crab 3700 Rock Crab * Cambell and Eagles 1983

Sand lance 6800 Sand Lance * Richards 1982

Sand Shrimp 3700 Sand Shrimp * n/a

Squids 5600 Loligo pealei *

Lange and Johnson 1981:

average weight for Loligo

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Supplementary Table 3.2. ANOVA tests of significance and Tukey post-hoc tests between

Striped Bass size classes.

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Chapter 4

Ontogenetic shifts in movement behavior of an anadromous predatory fish

Abstract

Predator-prey interactions are mediated by numerous factors, including the size and

structure of predator and prey populations, their spatial and temporal overlap, and habitat

characteristics. In Massachusetts, migratory Striped Bass are only present for part of the year, but

they are an abundant predator that may significantly affect local prey populations. Striped Bass

prey selection is partly driven by ontogenetic processes, such that their top-down forcing on prey

may depend on the residency behavior of different sizes of Striped Bass. This study assessed the

residency duration and habitat use of Striped Bass in an important summer feeding area (Salem

Sound) in northern Massachusetts using acoustic arrays to track individual movement behavior.

The number of days Striped Bass spent in Salem Sound was positively correlated with their body

size. Larger individuals were more consistently detected during their residency in our study area

as well, suggesting that large Striped Bass use Salem Sound as a foraging site more extensively

during their summer residency. This finding aligns with our understanding of Striped Bass

feeding ecology, since large adults consume a high proportion of decapod crustaceans, which are

less mobile than fish prey and are abundant in Salem Sound. Importantly, this also suggests that

large Striped Bass may be predisposed to localized depletion if fishers can key in on hotspots.

We did not find a relationship between habitat use and fish length; however, Striped Bass of all

sizes were detected more often in soft-bottom habitats. It is possible that Striped Bass are able to

forage for both fish and crustacean prey in soft-bottom habitats, or that foraging represents only a

fraction of daily activity, such that fine-scale telemetry studies would be needed to elucidate

size-specific differences. We found size-specific variation in the residency behavior of a

migratory predator, which has important implications for how interactions between fisheries and

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community structure may change as the stock structure of this predator fluctuates over time due

to natural and anthropogenic causes.

Introduction

The ability of predators to impact prey populations and community structure is

contingent, in part, upon their distribution and spatial overlap with their prey. For example,

resident fishes may have an increased ability to control local prey communities compared to

more transient predators given the continual presence of residents (e.g., Hixon and Beets 1993).

Additionally, highly mobile predators may elicit reduced behavioral responses in prey compared

to stationary, resident predators potentially because predatory risk cues (i.e., olfactory cues) from

the former would not be as reliable (Schmitz et al. 2004, Wilkinson et al. 2015). Migratory fishes

can, however, contribute to important ecological processes, such as the transport of nutrients and

trophic dynamics (Chapman et al. 2012). For example, the migration behavior of a freshwater

cyprinid fish impacted the population of its zooplankton prey, which indirectly affected

phytoplankton (Brodersen et al. 2011). Because the presence of highly transient or migratory

predators may vary temporally (Mather et al. 2010), their top-down effects on local communities

will depend in part on their movement behavior, and in particular the duration of their residency

(Chapman et al. 2012).

Predator-prey interactions are also mediated by spatial processes such as habitat

complexity, patch size, and the spatial configuration of habitat patches (Crowder and Cooper

1982, Micheli and Peterson 1999, Grabowski 2004, Grabowski et al. 2005). Predators may

utilize habitats as corridors to target localized prey populations; for example, blue crabs utilize

seagrass habitat to avoid predators while accessing juvenile hard clams on oyster reefs (Micheli

and Peterson 1999). Additionally, prey often seek refuge in complex habitats, whereby predators

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may attempt to capitalize on an increased abundance of prey items in these areas, despite

decreased overall foraging success rates (Savino and Stein 1989). Predator foraging-site selection

and habitat use will thus have important implications for trophic dynamics.

In addition to the roles of movement behavior and habitat characteristics, life-history

variation can profoundly influence predator-prey interactions and community structure. For

instance, ontogenetic shifts and the presence of life history variants can diversify the behavioral

traits and feeding ecology of fish populations (Werner and Gilliam 1984, Conroy et al. 2017),

such as seen in beaked redfish, which have separated into distinct behavioral groups that occupy

separate habitats as adults (Cadrin et al. 2010). Additionally, bluegills in a lake ecosystem

undergo multiple ontogenetic habitat shifts between littoral and pelagic zones that are associated

with changes in feeding behavior and diet (Werner and Hall 1988). These forms of intraspecific

variation can then have consequences for community dynamics, and are quite common among

fish populations (Werner and Gilliam 1984).

Our study explores the movements of Striped Bass (Morone saxatilis) along the North

Shore of Massachusetts and the potential relationship between their behavior and size. Striped

Bass found in Massachusetts are generally considered highly migratory, and typically spawn in

estuaries in the Mid-Atlantic (Setzler et al. 1980, Waldman et al. 1990, Wingate et al. 2011,

Kneebone et al. 2014b). Striped Bass are heavily targeted by recreational anglers, and to a lesser

degree, by commercial fishers. Throughout their migration, which begins in MA in late spring

and early summer and concludes in fall, Striped Bass consume a diversity of prey species

including American Lobster, Rock Crabs, Atlantic Menhaden, and Atlantic Herring (Nelson et

al. 2003). Importantly, Striped Bass undergo an ontogenetic diet shift as adults in northern

Massachusetts, such that small Striped Bass primarily consume forage fish prey and small crabs,

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while larger individuals eat a larger proportion of crustaceans, including the economically

important American Lobster (Chapter 3, Nelson et al. 2003).

As Striped Bass populations have recovered from a collapse in the late twentieth century,

they have exerted greater top-down pressure on prey populations (Nelson et al. 2006). For

example, in southern Massachusetts, Striped Bass, along with other predators, may help prevent

salt-marsh die-off by consuming herbivorous crabs (Altieri et al. 2012). Additionally, in northern

Massachusetts, Striped Bass are estimated to consume over three times as many lobsters as

harvested by fishers (Nelson et al. 2006). The ability of Striped Bass to shape local prey

communities via consumptive and indirect effects will depend on their residency behavior and

habitat use. But, while previous studies have examined the broad seasonal movements of

migratory Striped Bass in New England (Mather et al. 2010, Kneebone et al. 2014a, Kneebone et

al. 2014b), little is known about their foraging arenas and summer residency behavior while in

coastal Massachusetts. However, Pautzke and colleagues (2010) identified three separate groups

of small Striped Bass that used a Massachusetts estuary differently, but these groups did not

differ in size. Importantly however, these fish were much smaller than individuals which

undergo an ontogenetic diet shift to crustaceans, such that their study would have missed

important differences between small and large adults. There is some evidence, that Striped Bass

size may correlate with habitat use; in a Virginian estuary, large Striped Bass were more

common in sites with complex, oyster reef habitats than sandy habitats compared to smaller

individuals (Harding and Mann 2003). Additionally, migratory Striped Bass will reside in

confined areas throughout the summer months in Massachusetts and can display high interannual

site fidelity (Mather et al. 2009), suggesting that an examination of their summer residency

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behavior is critical if we hope to understand where and when Striped Bass may shape localized

prey communities via top-down forcing and / or trait-mediated indirect effects.

Our study assessed whether Striped Bass display ontogenetic changes in movement

behavior during their summer residency. In particular, we examined how ontogeny affects

Striped Bass residency duration and proportional habitat use. We predicted that larger

individuals would spend more time in a summer feeding area given that they may rely more

heavily on relatively stationary prey (i.e., benthic crustaceans). Additionally, since large Striped

Bass feed on American Lobsters and other crustaceans that depend on structured bottom for

shelter, we hypothesized that Striped Bass size is inversely related to soft-bottom habitat use.


Study Site and Acoustic Array

During the spring and summer of 2008 and 2015, Striped Bass were surgically implanted

with acoustic transmitters to track their residency behavior within Salem Sound in the North

Shore region of Massachusetts (Figure 4.1). Two separate acoustic receiver arrays were set up

during 2008 and 2015 by researchers at the MA Division of Marine Fisheries and Northeastern

University, respectively. Salem Sound was chosen as a study site since Striped Bass commonly

utilize this semi-enclosed embayment during summer (Chase et al. 2002, Kneebone et al. 2014b).

In addition, there is anecdotal evidence that Salem Sound represents an important feeding ground

for Striped Bass and a fishing hotspot for local recreational and commercial fishers (Chase et al.

2002). Stomach content and stable isotope analyses revealed that Striped Bass feed on a number

of prey species within Salem Sound, including the economically important American Lobster,

Rock Crab, and Atlantic Mackerel (Chapter 3). Thus, Striped Bass likely exert considerable top-

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down pressure on prey communities locally. Geographically delineated by Marblehead, MA to

the south and Manchester-by-the-Sea, MA to the north, Salem Sound has a diverse assemblage

of substrate types, including rocky bottom, mud, sand, and cobble (Pendleton et al. 2015).

During the 2008 study period, 22 acoustic receivers (VR2-W receivers from VEMCO

operating at a frequency of 69 kHz) were deployed during June-November. Receivers were

deployed in a diffuse haphazard grid to maximize coverage of Salem Sound and cover a variety

of bottom habitats (Figure 4.2). Meanwhile, seven receivers (fourteen receivers were deployed,

but seven were lost) were deployed for 2015 study (also VR2-W receivers), and locations were

selected based upon known hotspots from the 2008 study period and the local ecological

knowledge of fishers (Figure 4.2). All receivers were affixed to a dual-anchor mooring system,

whereby the receiver was attached to a second line and a mooring ball roughly 2-4 meters above

the substrate.

Tagging

Striped Bass were caught within Salem Sound via rod-and-reel, and were reeled in

efficiently to reduce the likelihood of injury to the fish. All fish were immediately placed in a

large tank with fresh seawater and were anesthetized (<5 minutes). Anesthesia was deemed

sufficient once caudal fin activity ceased and fish no longer responded to stimuli (Neiffer and

Stamper 2009). Striped Bass tagged by the MA DMF were not anesthetized. Fish were placed

inverted on a V-shaped table for surgery while fresh seawater was run through their gills. Using

aseptic techniques, a small incision was made in the abdominal cavity posterior to the pectoral

fins. After an acoustic tag was inserted, the cut was closed using a series of surgeon’s square

knots and was covered in antibiotic ointment. All acoustic tags were sterilized and covered in

antibiotic ointment to prevent infection. Fish were also weighed and measured (total length), and

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affixed with external tags for identification in case of recapture (Floy anchor tags were inserted

into the incision prior to suturing for the 2008 study and into white muscle roughly 2-4cm below

the dorsal fin in the 2015 study). Fish were carefully placed back into the water and were held

upright to promote recovery. Tagged Striped Bass were released once they were deemed fully

recovered and were vigorously attempting to escape. (Protocol for fish tagging by researchers

from Northeastern University (NU) during 2015 was approved by the NU Institutional Animal

Care and Use Committee)

Striped Bass were tagged with V13 and V16 acoustic transmitters during 2008 and 2015,

respectively. A total of 26 fish were tagged during 2008, while 12 were tagged during 2015.

Transmitters from both years emitted a signal every 60 seconds, resulting in an estimated battery

life of 196 days during 2008 and 850 days during the 2015 study (note that average signal delay

increased to 150 seconds after the first 30 days during the 2015 study to increase battery life).

During 2008, batteries would have expired well after the conclusion of the monitoring period

(late December and early January), and thus likely did not affect our estimates of residency times

using acoustic receiver data.

Analysis

Prior to analysis, data were examined for potential false detections using criteria in

VEMCO’s VUE software manual (VEMCO 2015). A number of metrics were utilized to

examine the potential relationship between Striped Bass size and residency while in Salem

Sound, including the number of calendar days that each fish was detected and the duration of

residency, as indicated by the number of days between the first and last date of detection.

Additionally, the number of days detected was divided by residency duration for each fish (i.e.,

values between 0 and 1) to explore the consistency with which Striped Bass were detected within

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Salem Sound during the total time they were potentially present in the general area. To examine

the effect of Striped Bass total length on residency behavior, we employed a GLMM with fish

length as a possible explanatory variable and study year as a random effect. Fish that were

recaptured by fishers during the same year that they were tagged were not included in this

analysis, since they would bias residency estimates. Study year was considered a random effect

because we were not inherently interested in the differences between fish behavior by year; there

were dissimilarities in study design between study years, such as the number of receivers, that

would have prevented any meaningful conclusions if study year was considered a fixed effect.

Assumptions of normality and homoscedasticity were validated upon inspection of residual plots.

Models with fish length were compared to the corresponding null model using ANOVA test to

examine whether the model with fish length was considered significantly different from the null

model.

To explore the relative habitat use of Striped Bass of different sizes, we restricted

analysis to the 2008 dataset since receivers were well distributed throughout Salem Sound and

represented a broad distribution of habitat types (Figure 4.2). Substrate classification was based

on a classification system by Barnhardt et al. (1998), whereby four geological substrates were

considered: rock, gravel, sand, and mud. For the purposes of this analysis, we classified bottom-

type as either soft-bottom (sand or mud dominated) or hard-bottom (rock or gravel dominated).

Data for Salem Sound were gathered from sediment and geophysical data generated by the U.S.

Geological Survey (Pendleton et al. 2015). Sediment data layers were downloaded into Esri’s

Geographic Information Systems (ArcGIS 10.4.1) and the proportion of soft-bottom versus hard-

bottom was calculated for each receiver based upon a 325 meter detection radius. This minimum

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working detection radius was determined during range testing and is considered a conservative

estimate.

Receivers from 2008 had an average of 60% soft-bottom coverage (Figure 4.3).

Receivers were further classified as either high (>75% soft-bottom coverage), medium, or low

soft-bottom (<25% soft-bottom coverage). Given that the fine-scale movement behavior of

tagged fish is not possible with presence-absence acoustic data, we decided to create a broad

substrate classification system to analyze movement based upon the dominant habitat type at

each receiver. Since five receivers were lost during September of 2008, analysis was restricted to

data collected from June through August. For each fish by month, the number of detections in

high, medium, and low soft-bottom substrate receivers was divided by the total number of

detections as a proxy for proportion of time spent in each habitat type. Analysis was restricted to

time spent in high soft-bottom receivers since this was the dominant habitat type selected by

Striped Bass (months were eliminated for each fish if there were less than 100 detections to

prevent non-representative values from biasing the results). Using a beta regression GLMM

(glmmTMB package in R Version 3.3.3), the proportion of time spent in high soft-bottom

receivers was analyzed with month and length as explanatory variables, and individual tagged

fish as a random effect. When proportion of time equaled 1, a nominal value (10-7) was

subtracted since beta regression models require values to be between 0 and 1. An interactive

model (fish length * month), additive model (fish length + month), and null model (random

effect only) were compared. An additional analysis using data summarized by fish only (i.e.,

month not included) was conducted, such that fish did not need to be included as a random effect

(fish with less than 100 detections were still excluded). A beta regression GLMM (betareg

package in RStudio) was conducted with fish length as a possible explanatory variable. This

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model was compared to a null model using ANOVA. Assumptions of normality and

homoscedasticity for all models were validated upon inspection of residual plots.

Results

All 26 Striped Bass were detected during the 2008 study year within Salem Sound, while

11 out of the 12 fish that were tagged in 2015 were detected (Figure 4.4). No fish were

recaptured during 2015, and only 5 fish were recaptured in 2008. Fish that were not recaptured

during 2008 ranged in length from 64 – 101cm, and they were detected on an average of 19 days

(± 4.0) with an average residency duration of 48 days (± 8.5) (Table 4.1). During 2015, fish

length ranged from 64 – 98cm, and they were detected roughly 24 days (± 5.8) across an average

residency duration of 37 days (± 7.9) (Table 4.1). Using detection data provided by researchers

within the Acoustic Telemetry Network (ACT), we found that fish tagged during 2015 were also

detected along their migratory path at 118 acoustic receivers outside of Salem Sound during

2015, 2016, and 2017, ranging as far south as the Chesapeake Bay (Figure 4.5).

Examination of fish from both studies (excluding recaptured individuals) revealed a

positive correlation between fish size and important residency metrics. For instance, the number

of days that a fish was detected significantly increased with fish length (p = 0.006, t-value =

2.97; Figure 4.6). Although total residency duration was not related to fish length (p = 0.33, t-

value = 1.00), when duration was normalized by the number of detection days, fish length was

again positively correlated with residency (p = 0.036, t-value = 2.19).

Further exploration of Striped Bass behavior suggested that a small subset of tagged

individuals departed Salem Sound much earlier in the calendar year than other similar-sized

individuals (Figure 4.7). Striped Bass are thought to display high site fidelity, returning to the

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same embayment or estuary year after year (Ng et al. 2007, Mather et al. 2009). Therefore, we

hypothesized that fish leaving Salem Sound earlier than their counterparts may have still been

searching for their annual summer-residency-site. While this assumption is largely speculative,

we wanted to explore the potential relationship between fish size and residency behavior when

these ‘early-departing’ fish (n = 7) were not included in the analyses. The same series of analyses

were conducted as before after excluding them. Results of the GLMM once again revealed a

strong positive correlation between fish size and the number of detection days (p < 0.001, t-value

= 4.29). However, unlike our previous analysis, larger Striped Bass were also detected within

Salem Sound across a longer time period (p = 0.03, t-value = 2.39). Lastly, there was once again

a positive relationship between fish size and the ratio of detection days to total residency

duration (p = 0.01, t-value = 2.68).

During 2008, nearly all tagged Striped Bass were primarily detected in receivers

classified as being composed of a majority of soft-bottom habitat (Figure 4.8). Visual inspection

of the data (i.e., Figure 4.8) did not suggest any clear pattern between fish size and broad habitat

use. Neither month, fish length, or any interaction significantly affected Striped Bass use of

habitat (p > 0.05 for all main effects and the interaction term). When the data were completely

summarized by fish (i.e., month not included and without fish as a random effect in model), the

results again supported the notion that fish size was not related to habitat use (p > 0.05).

Discussion

Our study revealed that Salem Sound, a semi-enclosed embayment in northern

Massachusetts, is an important summer residency site for Striped Bass. As evidenced by fish

tagged in 2015, Striped Bass present in Salem Sound are likely part of the larger migratory stock

of Striped Bass given that fish were detected at receivers in the Mid-Atlantic. These fish

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potentially originated from the three primary spawning stocks located within the Chesapeake

Bay, Delaware River, and Hudson River (Mather et al. 2010, Kneebone et al. 2014b). Salem

Sound is home to a diversity and abundance of fish and invertebrate species, which likely

contributes substantially to the annual occurrence of Striped Bass (Chase et al. 2002).

Historically, the Gulf of Maine was home to a number of putative resident spawning populations

of Striped Bass that are currently either non-existent or persist at extremely low, nearly

undetectable levels (Little 1995). Yet coastal migratory populations likely utilized New England

rivers and estuaries as important feeding grounds, where Striped Bass foraged on river-run

Blueback Herring and American Shad prior to their collapse (Greene et al. 2009). Precipitous

declines in many coastal and riverine populations of these forage fish heightens the importance

of coastal embayments like Salem Sound, where Striped Bass are consuming large populations

of crustaceans (Nelson et al. 2011).

The residency behavior of Striped Bass appears partly driven by fish length, such that

larger fish remain present for a longer period of time (among fish that did not depart Salem

Sound early in our study) and are present more consistently during this period. This latter finding

is in agreement with our diet results, which suggests that larger Striped Bass in Salem Sound

consume a diet consisting largely of decapod crustaceans – a more stationary prey item (Chapter

3, Nelson et al. 2003). In addition, it supports the broader theoretical framework that purports

that fish size is generally negatively correlated with movement rates (Wardle 1975, Videler and

Wardle 1991). Here, larger, more robust Striped Bass may be able to reduce energy costs by

foraging on decapod crustaceans within a smaller summer feeding range. Smaller individuals

instead use Salem Sound to a lesser extent and may make excursions to other locations along the

Massachusetts coastline, returning periodically, potentially chasing schools of forage fish such at

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Atlantic Mackerel which form roving schools in nearshore waters. Given that smaller Striped

Bass consume a higher proportion of fish prey (Chapter 3), which likely require heightened

searching and pursuit costs but are also rich in lipids, these individuals may spend a significantly

greater portion of time searching for highly mobile forage fish prey outside of the confines of our

acoustic array in Salem Sound.

Variation in residency time among size classes has important implications for prey

populations since the diet composition of Striped Bass is also size-dependent, as evidenced by

previous and recent diet and bioenergetic analyses (Chapter 3, Nelson et al. 2006). For example,

bioenergetic modeling results from Chapter 3 estimated that an average 9-year-old Striped Bass,

which is typically ~80cm+ total length, consumes 14.5 grams of American Lobster daily. The

average 80cm+ fish in our study was detected in Salem Sound for 45 days (excluding early

departing fish, Table 4.2), such that a typical Striped Bass of this size would need to eat over 8

juvenile American Lobsters within Salem Sound during the summer to satisfy its energetic

demands. Conversely, a 5-year-old Striped Bass (likely <70cm TL), would only need to consume

roughly 120 grams of fish prey during its 10 days of residency (average number of days detected

for small fish, Table 4.2). Consequently, these results refine our understanding of the variable

effects of Striped Bass on local prey populations in confined areas, and suggest that larger

individuals exert much stronger top down forcing on benthic prey such as lobsters and crabs.

Counter to our hypothesis, there was no relationship between habitat use and fish length.

Instead, tagged Striped Bass were detected more often in soft-bottom habitat throughout their

residency in Salem Sound. We initially hypothesized that larger Striped Bass would select hard-

bottom substrate more often than small fish as larger fish are known to feed on benthic

crustaceans, which rely on rocky and cobble habitats (Wahle and Steneck 1991, Nelson et al.

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2003, Nelson et al. 2006). It is possible that feeding represents a small portion of their daily

activities, while there is no ontogenetic selection of habitat during non-feeding events (i.e., when

fish are at rest). Alternatively, large Striped Bass may not feed on crustaceans in hard-bottom

habitats, and instead capitalize on vulnerable individuals as they make excursions into soft-

bottom habitat away from the safety of shelter, which is common in warmer months (personal

observation). Specifically, lobsters have often been observed in sandy habitats throughout

summer on the outskirts of Salem Sound and in other studies (Golet et al. 2006). Meanwhile,

stomach content and stable isotope analyses revealed that Striped Bass of all sizes consume at

least some fish prey (Chapter 3). This may partly explain why tagged individuals in our study

spent a majority of their time in soft-bottom dominated areas, if Striped Bass target schooling

fish prey in soft-bottom, open habitats.

In addition to these ecological explanations, our study design may have prevented us

from detecting a clear ontogenetic pattern in habitat use. It is possible that Striped Bass did

associate with hard-bottom habitat, but typically remained on the outskirts of structure (i.e., an

‘edge-effect’). Striped Bass may also target lobsters in and around smaller patches of hard

substrate, such that lobsters must cross soft bottom to move among patches. Similarly, Ng and

colleagues (2007) found Striped Bass in a New Jersey estuary aggregated near structure even in

areas where complex substrate was not the dominant habitat. The nature of the passive acoustic

system used in this study also precluded a fine-scale assessment of habitat use. Future studies

could utilize alternative methodologies, such as using satellite tags or overlapping receivers to

create an acoustic telemetry array, to examine the fine-scale movement patterns and habitat

associations of Striped Bass, which may reveal more nuanced ontogenetic behaviors and other

drivers of variation in movement behavior and habitat usage.

158

Collectively, the results from our study revealed that Striped Bass use of Salem Sound in

Massachusetts is size-dependent. Large Striped Bass spend significantly more time within the

study area, suggesting that these individuals have a heightened ability to exert top-down pressure

on local populations of decapod crustaceans, like the American Lobster, which is an important

component of their diet. A better understanding of how fisheries species interact will facilitate

efforts to move toward ecosystem-based fisheries management (EBFM). Additionally, the

relative stationarity of large Striped Bass coupled with high fishing pressure may predispose

large individuals to localized depletion. Recreational fishing pressure is intense in the North

Shore region of Massachusetts, and a sizeable contingent of anglers specifically target “trophy”

Striped Bass (i.e., generally fish that are over 40” TL, Chapter 2). Given that real-time fishing

information is often exchanged using social media or through online sources (i.e., the location of

fishing hotspots), large Striped Bass may be subject to heavy fish pressure if they remain in a

small area for too long. As such, efforts to move towards more holistic management approaches

(i.e., EBFM) will need to also consider this human component since angler behavior may drive

local depletion of their target species, resulting in consequences for other species and the broader

ecosystem (Jackson et al. 2001).

159

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Tables

Table 4.1. Summary detection statistics for both study years.

Study

Year Mean SD SE Min Max Range

2008

Fish Length (cm) 77 10.5 2.3 64 101 37

Number of Days Detected 19 18.3 4.0 1 64 63

Residency Duration (days) 48 38.8 8.5 1 127 126

Days Detected / Residency Duration 0.50 0.27 0.06 0.07 1.00 0.93

2015

Fish Length (cm) 80 10.6 3.2 64 98 34

Number of Days Detected 24 19.1 5.8 2 51 49

Residency Duration (days) 37 26.1 7.9 3 65 62

Days Detected / Residency Duration 0.64 0.15 0.05 0.36 0.88 0.52

164

Table 4.2. Summary of residency metrics by categories of fish size. Small fish were up to 70cm

in length, medium fish were up to 80cm, and large fish were over 80cm.

Fish Size

Residency Metric Mean LARGE MEDIUM SMALL

All Fish

Number of Days Detected 31 24 8

Residency Duration 46 56 23

Days Detected / Residency Duration 0.73 0.45 0.53

Excluding 'early-

departing' fish

Number of Days Detected 45 25 10

Residency Duration 68 59 33

Days Detected / Residency Duration 0.70 0.47 0.41

165

Figures

Figure 4.1. Study area with inset map showing the broader region within New England.

166

Figure 4.2. Receiver locations for both study years with substrate classification based on

Pendleton et al. (2015). DMF; MA Division of Marine Fisheries. NU; Northeastern University

167

Figure 4.3. Proportion of soft-bottom substrate for receivers in both study years (study in

parentheses, DMF = 2008, NU = 2015). Two receivers from 2008 are not shown since they were

lost soon after the start of the study and did not detect any tagged Striped Bass, while one

receiver was not shown from 2015 since it did not detect any tagged Striped Bass. Dashed

horizontal line represents the average percentage of soft-bottom for all receivers (if years are

considered separately; 60% for 2008, and 63% for 2015).

168

Figure 4.4. Detections by fish for 2015 (left) and 2008 (right).

169

Figure 4.5. Acoustic receivers that detected Striped Bass tagged during 2015. Letters in each

smaller inset map correspond with the appropriate region along the Atlantic Coast.

170

Figure 4.6. Plots of fish total length by residency metrics. The left column represents the analysis

with all fish included, while the right column represents the analysis that excluded fish that left

Salem Sound prior to July 11th. Trend-lines only shown for significant relationships and are

based on GLMM analysis.

171

Figure 4.7. Date of last detection by Striped Bass total length. The box represents fish that were

excluded from a secondary residency analysis.

172

Figure 4.8. Percent of detections (relative size of gray circles) for each fish in receivers defined

by proportion of soft-bottom habitat. Only fish from 2008 are included and are sorted by total

length (cm). Fish with less than 100 detections were excluded to prevent the influence of non-

representative values.

social and ecological dimensions of the striped bass ...cj82rb038/fulltext.pdfnumerous samples...

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