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From Beach to Bisque: an analysis of lobster fisheries management

Alison Dyer

June 3, 2016

Advisor: Michael Cox

Bachelor of the Arts Honors Thesis

Environmental Studies Department

Dartmouth College

Hanover, New Hampshire

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Special thanks to:

Professor Cox for all his assistance and mentorship throughout this process. Without his

assistance this thesis would not have happened. Additional thanks to Professor Howarth for his

commentary and econometric assistance, Stephen Long for his advice and guidance on the

ground in Madagascar, to Lindsay Keare and Nicolas Guitierrez for their assistance in translating

studies and to all friends and family for their unwavering support.

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Table of Contents CHAPTER ONE ..................................................................................... 4 

I. Introduction ....................................................................................................... 4 II. Background ...................................................................................................... 7 

i. Determination of Stock Status ........................................................................................... 9 

ii. Biological Factors ............................................................................................................ 14 

iii. Anthropocentric Factors................................................................................................ 15 CHAPTER TWO: METHODS ........................................................... 21 

I. Sampling ........................................................................................................... 21 II. Measurement .................................................................................................. 23 

i. Determination of Outcome Variables ............................................................................. 25 

ii. Definition of Independent Factors ................................................................................. 27 

iii. Controls ........................................................................................................................... 29 II. Statistical Methods ......................................................................................... 32 

CHAPTER THREE: FINDINGS AND DISCUSSION .................... 33 

I. Descriptive Statistics ....................................................................................... 33 

i. Dependent Variables ........................................................................................................ 33 

ii. Independent Variables .................................................................................................... 35 

iii. Summary of descriptive statistics ................................................................................. 39 II. Main Results ................................................................................................... 41 

i. Analysis .............................................................................................................................. 42 ii. Discussion ................................................................................................................................... 47 

CHAPTER FOUR: LIMITATIONS .................................................................... 54 I. Weakness of Meta-analysis ............................................................................. 54 II. Percentage Change in CPUE. ....................................................................... 55 III. Omitted Variables Bias ................................................................................ 55 IV. Endogeneity ................................................................................................... 56 

CHAPTER 5: Application and Further Study .................................. 59 I. Case Study: Sainte Luce, Madagascar .......................................................... 59 II. Further Analysis ............................................................................................ 64 

APPENDICES ...................................................................................... 65 

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ABSTRACT

Lobster fisheries represent lucrative fishing ventures around the globe. Fisheries like the

Gulf of Maine have been the subject of numerous case studies and analyses yet there are no

studies which examine the aggregate data to determine which regulatory methods are on average

the most effective.

The purpose of this thesis is to examine the impact of various regulatory measures on the

exploitation levels of lobster fisheries through a meta-analysis. Variables were coded from peer-

reviewed journals, government records, and NGO reports. Dependent variables are whether or

not catch is declining, coded on a binary, level of exploitation, coded categorically from 1

(underexploited) to 4 (collapsed), and percentage annual change in CPUE per year over the time

surveyed. Primary independent variables coded on a binary are: minimum landing size, ban on

berried females, no take zone, closed season, and access variables stating if the fishery requires

registration, limits access, or has an individual transferable quota or total allowable catch system

in place.

The data demonstrates that total allowable catch limits and registration requirements

being the most effective, while fisheries with ITQ systems are more likely to be overexploited.

Minimum landing sizes, closed seasons and bans on berried females are effective in reducing

overexploitation while no take zones dramatically increase the likelihood of overexploitation,

significant at the 5% level.

Overall, these findings demonstrate that proper regulatory techniques have a positive

effect on sustainable exploitation, however when applied to a case study it becomes apparent this

is a simplistic model which does not take all necessary factors into account.

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CHAPTER ONE

I. Introduction

As of 2009, roughly 25% of global fish stocks were overexploited or fully depleted

(Hauge et al. 2009). Declining fisheries represent the classic “tragedy of the commons” scenario

in which common-pool resources are depleted because there is a lack of long-term individual

incentive to conserve them. They are delicate systems which are prone to overfishing and

collapse unless effective management systems are implemented. This is especially problematic

in the developing world, where fish account for over 20% of animal protein consumed by 2.6

billion people (Hauge et al. 2009). As outlined by the International Risk Governance Council,

the primary risks related to overfishing include threats to global food security, economic

security, collapse of coastal settlements dependent on the fishing industry, and erosion of

biological diversity and ecosystem stability (Hauge et al. 2009). These negative impacts are

amplified in the developing world as it is more difficult for communities to diversify, and thus

they are left at the mercy of supply shocks.

Crustacean fisheries represent a unique case as they are rarely relied on for subsistence-

level consumption yet represent some of the most lucrative fishing ventures in the world. In

2001, crustacean fisheries accounted for 7% of all landings by weight, but 28% by market value

(Smith et al. 2003). Lobster is one of the most valuable marine products selling for $20/kg on the

global market, double the value of shrimp and four times that of finfish (Globefish 2015).

As of June 2015, the United States and European Union were the largest markets for live

lobster (Globefish 2015) though demand for lobster has increased in developing countries with

rising incomes. With the growth of China’s middle class comes a spike in demand for live

lobster as the crustaceans serve as a symbol of good luck and prosperity in addition to being a

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delicacy (Whittle 2015). The Chinese live lobster import market grew 400% from 2008 to 2010,

from $74, 651 of US lobster sold to $1,308,401 in 2010 and approaching $3 million in 2011

(Whittle 2015). As economies continue to grow it is projected that demand will continue to

increase exponentially.

Lobster fisheries represent difficult cases from a management perspective. Spiny lobsters,

served in restaurants typically as tails as opposed to the whole clawed lobsters, represented 44%

of global production in 2006 and are captured primarily in developing countries (Tselikis 2007).

Given that these are export-based systems, the fishers are left at the mercy of global supply and

demand shocks. Export dependent systems may be particularly prone to overexploitation as

lucrative prices encourage users to take advantage of the resource now at the cost of preserving it

for later use. Extractive systems require up-front capital costs, but after the initial investment

marginal cost of each unit of output is negligible. This motivates continued extraction to the

point of overexploitation (Kosamu et al. 2015).

A system’s resilience is crucial in its exploitation potential. Resilience is defined as “[a

system’s] capacity to absorb recurrent stochastic events…and it continue to function without

changing fundamentally” (Steneck et al. 2011, 2). Human interventions in ecological systems

have the ability to change the system to the point that the resilience is undermined and the

system begins to collapse. Steneck et al. (2011) coined the term “gilded trap” to describe this

feedback loop in which social and human pressures drive an ecosystem closer to its ecological

tipping point. A gilded trap forms when lucrative economic benefits encourage the simplification

of the ecosystem such that extraction is less costly and thus greater profits can be made.

However this simplification of the system, which often occurs through the elimination of natural

predators, renders it less resistant to exogenous shocks such as storms or disease. They suggest

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that increasing fishing pressure and intensity has made fisheries more vulnerable to collapse as

they are simplified into artificial monocultures.

Numerous efforts have been made to determine how to effectively manage small scale

fisheries and balance the various stakeholders involved. Fisheries are common-pool resources:

they are non-excludable but rival in the sense that it is impossible to prevent someone from

accessing the resource, yet one’s use of the resource is detrimental to others’ ability to utilize it.

If initial capital costs can be met an individual can gain access to the resource and disregard any

legislation which may be in place to limit entry if they so desire.

Common-pool resources are primed for the application of game theory models to

understand actors’ motivations. Their nonexcludable but rival nature renders them vulnerable to

prisoner’s dilemma and lose-lose outcomes. Acting for his or herself, each actor is motivated to

exploit the resource as quickly as possible under the assumption that others are doing the same,

thus the resource will be depleted whether or not the original actor is exploiting it. Taylor and

Ward (1982) make note that game theory is difficult to apply to ecological systems as they can

continue operating at roughly the same economic output until a critical level is reached, at which

their resilience erodes and the system collapses. In order to prevent this collapse, access to the

system must be restricted before prisoner’s dilemma mentality can take over.

The lucrative nature of lobster and the concentration of spiny lobster populations in

developing countries makes proper management particularly critical to the economic stability of

the communities which depend on them. Fisheries as a whole represent a collective action

problem. Without proper management methods in place, resource scarcity and the higher prices

it generates makes fishing a more lucrative venture. In subsistence fisheries, pressure from

increasing human populations can rapidly deplete stocks in spite of seemingly accurate

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management diagnoses. Due to the complex nature of these systems, it can be difficult to discern

the true impact of any management measures.

Given the nature of common-pool resources and fisheries, the research question at hand is

what management techniques are most effective when managing lobster fisheries in particular,

and under what circumstances is the fishery most prone to collapse?

II. Background

One of the dominant theories behind fisheries management is based in the concept of

achieving maximum sustainable yield (MSY). The OECD defines MSY as “the largest long-term

average catch or yield that can be taken from a stock…under prevailing ecological and

environmental conditions” (2001). MSY is unique in that it seeks to maximize the longevity of a

resource as opposed to maximizing short-run economic profit. It is a theory which is applicable

solely to renewable resources like forests or fisheries. For a nonrenewable resource such as oil,

there is no MSY because the extraction of this resource is inherently unsustainable as it will not

be replenished.

Fisheries management is often understood through the lens of the Gordon-Schaefer

model which combines maximum sustainable yield and economic output models to derive the

equilibrium at which long-term biomass output and economic returns are maximized (Grafton et

al. 2004). In a system seeking to maximize long-run returns, this equilibrium will be roughly

equivalent to the maximum sustainable yield of the fishery if cost per unit effort is low. In an

open access or monopoly system which seeks to maximize short-run profits, maximizing

economic returns will often result in the overexploitation and eventual collapse of the fishery. If

there is a high discount rate the value of long-run resource rents approaches zero, thus rendering

rapid exploitation the optimal course of action. One of the primary weaknesses of the model, as

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noted by Gordon, is that it can assist in predicting fishers’ behavior and where the equilibrium

output lies, but can do little to suggest what the best course of action in getting there (2004). This

is where natural resource management comes in.

While management is theoretically forward-looking in reality it is inherently reactive,

making it difficult to empirically study and draw causal relationships. Webster (2015) outlines

the cyclical nature of ineffective and effective environmental governance. These cycles work to

signal whether or not environmental policy is working and can be indicative of problems in the

future (Figure 1).

Figure 1: Responsive governance cycles (Webster, 2015)

In the case of a lobster fishery, the environmental problem is decline in recruitment and

erosion of the stock. This results in increasing marginal cost for each lobster caught, signaled by

a decline in catch per unit effort employed. Increasing costs result in calls for evaluation and

regulation, leading to increasing catches and decreasing marginal costs if proper management

techniques were implemented.

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Fisheries management is dependent on several factors: the species being managed, the

methods of stock assessment used to determine which regulations will be successful and the

capitalization of the fishery. Each of these come together to inform the type of management

which will be most successful in the environment. These will be discussed in the context of the

lobster fisheries being examined in this study.

i. Determination of Stock Status

For the purpose of this study, the overarching outcome variable is stock status and its

changes over time. The status of the stock must be determined prior to the implementation of a

management plan. Oceanic fisheries like tuna are inherently fluid, as fish can enter or exit the

fishery’s boundaries at any time. This fluidity means that there is always a degree of uncertainty

in the reliability of stock assessment models. Benthic fisheries experience less migration and

thus this is less of a problem when estimating stocks.

Smith and Addison (2003) outline the numerous methods of stock assessment used in

analysis: depletion, delay-difference, equilibrium yield and egg per recruit, dynamic size-

structured, and biomass dynamics models used to estimate stock stability and abundance. This

study relies on the use of biomass models.

Biomass dynamic models are the most simplistic and the most widely used. They are

developed using catch and effort data in tandem with biomass and recruitment models to paint a

picture of the stock status of the fishery. Essentially, they seek to estimate the amount of biomass

present in the fishery in a given year as compared to previous years to discern trends, ie if the

fishery is expanding or contracting. While they are widely used because there are minimal data

and assumptions requirements, these models are unable to address any issues with age or size

dynamics (Smith et al. 2003). Another weakness lies in the inability to account for changes in the

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size to the exploited area or the density of fish populations. Geographic size of the fishery will

change population dynamics and thus the impact fishing has on the stock, however this

component is typically not included in standardization measures. Similarly due to lack of

information and technical constraints, it is impossible to accurately determine the population

density across the whole region and thus impossible to state with complete certainty how any

fishing activity will affect the stock. This weakness is amplified as biomass estimates are derived

from catch and effort data and thus only examines the portion of the stock which is exploited.

Subsections of the stock which lie outside of the exploitation zone are left unaccounted for, thus

undermining the reliability of the estimate (Zhou et al. 2011).

There are two primary types of biomass models: equilibrium and non-equilibrium

models. Equilibrium models are age-structured and assume that the population fits a standard age

distribution. However, there is the danger of assuming a population has a normal age

distribution. Non-equilibrium models do not standardize based on age and have been

demonstrated to be equally effective at a fraction of the cost, making them appealing for

management (Hoggarth 2006).

Additionally, it should be noted biomass models can assist in determining the overall

status of the stock and in setting total allowable catch estimates and quotas. However, when an

age-structured model is not used it is not a viable technique for determining proper closed

seasons or minimum landing sizes as it fails to take into account the life-cycle components of the

stock (Hoggarth 2006). It is legitimate to use biomass models as the basis for this study because

they are employed to determine the status of the stock, not to set specific regulations.

For the purpose of this meta-analysis, only studies using the basic biomass model dependent on

catch and effort data were included.

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a. Catch and Effort Data

Historical catch data is key to assessing the stock and status of a fishery. As Saenz-

Arroyo et al. (2005) found in their interviews with fishermen in the Baja California, people

assume that whatever the status of the resource was in their youth is “natural.” This assumption

can mask the true rate of decline and create a false sense of security regarding the status of the

resource. However, intergenerational interviews can be used to reconstruct the status of the

historical fishery in the absence of reliable catch and effort data to show trends in abundance.

The idea behind the collection of effort data is that this is assumed to be more accurate in

determining fishery trends. Using only catch data fails to capture changes in input or economic

trends. For example, a large decline in catch could be due to a younger generation pursuing other

livelihoods and thus there being fewer fishermen exploiting the resource. Failure to account for

this change in input could result in the diagnosis that the fishery is collapsing when in fact it is

underexploited.

When reliable catch and effort data is available, a common method is the calculation of

catch per unit effort (CPUE) and its changes over time. For benthic fisheries which follow a

more simplistic model, CPUE is defined by the equation:

CPUE = catch/effort = q*Density = q*(Number of fish/spatial area of fishery) where q is a fixed proportionality known as the catchability coefficient of a specific gear

q is assumed to be a constant in this equation such that C/E = q*D, thus a drop in CPUE

indicates a drop in population density and abundance. q is dependent on gear type and efficiency

and thus technological advances result in rising q, meaning fishermen can catch more fish with

less effort. If this is not corrected for, rising q can mask falling population density and create a

situation of hyperstability. Hyperstability is the occurrence of a “false negative” in that the

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situation appears to be stable according to the model when it is in fact deteriorating, leading to

collapse without warning (Smith and Addison 2003). In the case of CPUE, hyperstability occurs

when q rises and catch rates remain constant, thus masking the drop in population density. As q

is the catchability coefficient, it can unexpectedly rise with the addition of new and more

technologies whose increased efficiency allows for the maintenance of catch rates despite

declining population.

CPUE is standardized through the development of a model of variables influencing

fishing power and efficiency which is compared among other vessels in the fleet, as described in

Salthaug and Godo’s 2001 paper, “Standardization of commercial CPUE”. Their study uses the

example of the Norwegian bottom trawl fleet. Trawlers fishing in the same area on the same day

were assumed to be targeting the same density of fish under similar environmental conditions. A

ratio is taken between the CPUEs of the trawlers, and the standard vessel is “set to be the vessel

having the highest number of comparisons with other vessels during the analyzed time period”

(Salthaug et al. 2001, 274). There is generally a linear model between standardized CPUE and

engine size, in that boats with larger engines have higher CPUEs. However, this was disproven

in the Norwegian trawl fleet in a study by Eggert and Ulmestrand (1999) finding no significant

correlation between engine size and catch rate. Standardization of CPUE is designed to account

for changes in q which could otherwise bias CPUE calculations. Once CPUE is standardized, it

is deemed ready to use as a measurement of stock status.

Several problems arise with the use of CPUE as a metric for biomass. As noted by Smith

and Addison (2003), CPUE is a relatively simple assessment which does not take age or

population structure into account, which can lead to hyperstability. Similar inter-temporal issues

arise when regulations or spatial area of the fishery have changed. CPUE only accounts for

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specimens which are vulnerable to a specific gear type and regulation. Thus, if a new regulation

is passed requiring traps to have a vent through which undersized lobsters can escape, or the

minimum landing size is changed, CPUE data prior to the regulation cannot be compared to that

post regulation because a different segment of the population is now being targeted (Maunder et

al. 2006).

Current literature suggests the implementation of an integrated stock assessment, which

incorporates CPUE, fishing mortality and recruitment data to develop a more complete picture.

However, in the developing world these data are rarely available and reliable. When other data

are lacking standardized CPUE can be used as a proxy for abundance.

CPUE fails to take into account ecosystem and other endogenous effects which may not

be related to management. The Maine lobster fishery serves as a clear example of the importance

of interspecies interactions. The dramatic increase in landings and effort in the Gulf of Maine

lobster fishery has been attributed to an explosion in lobster populations caused by changing

relationship with codfish populations. As cod populations declined due to overfishing in the

1990s and early 2000s, there was a predator-prey reversal as codfish populations shrunk and

reduced predation on lobster populations. Mature lobsters prey on larvae juvenile codfish,

furthering exacerbating the trophic cascade and preventing cod populations from recovering

(Zhang et al. 2012). This resulted in an explosion of lobster biomass, rapidly increasing CPUE in

the fishery with no changes in management.

Another problem with CPUE is that it can be difficult to determine which unit of effort is

targeting which species in multi-species fisheries. This problem is negligible in lobster fisheries

as pots used in multi-species fisheries exclusively target lobster. While other species may be

targeted using different units of effort, these will not be factored into lobster CPUE calculations.

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It is important to recognize these shortcomings when using CPUE. However, CPUE is

the most widely used particularly among the developing world and these uncertainties can be

accounted for in the use of standardized data.

ii. Biological Factors

The predominant biological difference between fisheries is the lobster species which is being

exploited. For the purpose of this study, two primary types of lobster are being considered.

First is the Panulirus genus, encompassing spiny and rock lobster, which is indigenous to

warm-water tropical regions such as the Caribbean, Sub-Saharan Africa, and parts of the Indo-

Pacific. Spiny lobsters are commercially valued for their tails in the West but demand for live

spiny lobster is on the rise in Asia (Whittle 2015). Their lifecycle begins when the female

releases fertilized eggs into open-ocean and the planktonic larval drift for six to twelve months

(precise timing being dependent on species) before reaching the puerulus stage and migrating to

coastal habitat (Lipcius et al. 1994). This extended larval stage creates a quandary for

management as lobsters bred in one fishery will be caught in another, creating an inter-dependent

system. Some scientists have gone so far as to hypothesize that the entire Caribbean lobster

population is in the same gene pool and that no truly closed systems exist within this area

(Lipcius et al. 1994). As they age, the lobsters migrate further out to sea. Some populations, like

that within the Torres Strait between Australia and Papua New Guinea, migrate throughout their

life (Brown et al. 1994). When mature, spiny and rock lobsters inhabit rocky shallow-marine

areas and are communal by nature. They are typically exploited by divers, nets or traps (Brown

et al. 1994).

The most economically valuable lobster species is the Nephropidae family, or the clawed

lobster, found predominantly in the cold waters off North America and Europe. Clawed lobsters

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are known as “true lobsters” and are typically sold whole in restaurants (Museum of Victoria

2000). They are more solitary creatures than their warm-water counterparts and follow a similar

pattern of post-larval juveniles settling inshore to mature before migrating to sea as adults. There

are three commercially viable sub-species of clawed lobster: Nephrops and Homorus gammarus,

found in Europe, and Homorus americanus found off the East Coast of the United States and

Canada. Nephrops and H. gammarus prefer to settle in burrows dug in fine mud, making them

vulnerable to exploitation in trawl fisheries. H. americanus has been observed to seek preexisting

shelter in rocks or crevices. This location selection makes the species more susceptible to trap

fisheries (Cobb et al. 1994).

iii. Anthropocentric Factors

In addition to the biological and ecological factors of the fishery, capitalization and

institutions present play a role in determining the sustainability of the system. These “human”

factors represent man’s intervention into the system and can be examined to aid in determining

its long-term viability for resource extraction.

a. Capitalization

Capitalization of a fishery occurs when fishermen invest in new equipment, or capital.

This increases the exploitation of the fishery and creates problems in terms of long-term

sustainability of the system. As Eisenack et al. (2006) noted in their analysis on capital

accumulation in unregulated fisheries, capital evolves to become more efficient over time

meaning that exerting the same quantity of effort now versus a decade ago will result in far more

output using today’s equipment. The financial burden for capital investment in equipment occurs

predominantly at the time of purchase while the marginal cost per use, generally maintenance or

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fuel, is negligible by comparison. This dynamic allows for overexploitation to occur without

much notice.

The global trends indicate a continual increase in fleet size, particularly in artisan

fisheries (Defeo et al. 2003). Given that many fisheries, particularly in developing countries, are

open access or managed by licenses limiting inputs, increasing capitalization and the increasing

efficiency poses a threat to long-term viability. A method to combat increasing capitalization

could be the monitoring of output through catch quotas as is done in the Torres Strait and Maine

fisheries.

b. Institutions

The quandary of artisanal fishery management lies in the creation of an effective

management and enforcement plan by the community itself to ensure local buy-in. Without

community consultation, there is the potential for a principle-agent problem: if users are unable

to see how abiding by regulations will benefit them, they will simply carry on as though the laws

were not put in place. The dependency of many communities on the fishing sector as a source of

livelihood creates additional management and enforcement problems. On one hand, there are

basic needs which must be met in the present. On the other, there is the desire to conserve the

fishery for future use and avoid depletion of the resource for the sake of the community in the

long-run.

Regarding implementation and enforcement, Kosamu (2014) conducted a study on

general artisanal fisheries management which concluded that co-management policies are the

best to tackle these principle-agent problems. Top-down solutions create unequal power

dynamics which undermine the sustainability of the intervention while disenfranchising the local

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population. Increased co-management gives fishermen a sense of ownership over the resource

and encourages them to preserve it. Gutierrez, Hilborn and Defeo (2011) supported these

findings in their analysis which identified strong leadership, individual and community quotas,

social cohesion and protected areas, from most to least valuable, as the key factors pertaining to

the success of a program.

Once proper institutions are in place an intervention plan can be formulated. Within the

management framework there are three families of regulations: those controlling lobsters which

are landed, those controlling how fishing is conducted and those controlling access to the fishery.

i. Landings and Fishing Regulations

Extraction is controlled through various management techniques. Lobster fisheries are

typically managed with one or more of the following: a closed season, “undersize” regulations

specifying a minimum landing size generally denoted in carapace length, a ban on berried

lobsters, or females with eggs, and no take zones. All regulatory variables which have been

coded for are formal rules.

This includes minimum landing size, bans on berried females, closed seasons and no take

zones. Minimum landing size (MLS) is the smallest size at which a lobster can be legally landed.

The internationally recognized measurement is that of carapace length, however some artisanal

fisheries prefer to measure total length. Lobsters take several years to reach sexual maturity, thus

the purpose of MLS is to ensure there is a healthy breeding population within the stock. Ideally,

MLS will be set at the size at which 50% of the population has reached sexual maturity (Froese

et al. 2007). In more heavily regulated fisheries this means that male and female lobsters will

have different MLS. Determination of an appropriate MLS requires the local population to be

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studied as there is no universal size of sexual maturity. There have been cases of variance of

population size within a single fishery where the MLS was appropriate in one region and in

another was too low and allowed for the landing of immature lobsters.

The landing of berried females, or females with eggs, is banned in most lobster fisheries.

Theoretically this protects the breeding stock and ensures that populations will continue to grow.

However, lobster eggs are carried long distances by the current once fertilized and thus eggs

released in one fishery will not necessarily mature there. Entire fishery systems needs to enforce

the ban on landing berried females in order for it to be effective.

Closed seasons are typically structured around the breeding season and are designed to

protect berried females. No take zones (NTZs) are sections of reef in which fishing is not

permitted, thus not singling out any particular group for protection but rather the stock as a

whole. NTZs have been associated with larger lobsters but also with higher disease and injury

prevalence due to increasing population density (Wootton 2012).

While these laws may be in place, enforcement is a major obstacle in the developing

world. Berried lobsters, or females with eggs, are heavier and thus fetch higher profits on the

beach, incentivizing fishermen to catch them. Undersize regulations are often disregarded by

both the fishermen and purchasing companies due to a lack of repercussions. In Madagascar, the

saying was that if it fit in companies’ machines, it sold (Dyer 2015). This can be combatted

through institutionalized local co-management of the resource.

ii. Access Variables

One of the primary techniques of management is limiting access to and extraction of the

resource as methods for preventing overfishing. There are two primary categories of access

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regulations: those which control capital and labor inputs into the system and those which control

biomass output.

Input regulations seek to regulate how many fishermen, boats, or fishing equipment is

allowed in the fishery at a given time. This is typically done through the licensing of boats in the

fishery, in which a certain number of boats are permitted, or the limiting of trap licenses allowed.

Input regulations are effective in preventing overfishing due to augmented effort and

overcrowding. Traps and trawls can damage reefs and seafloors, thus limiting their use can have

a positive effect on the ecological health of the system.

However, these policies are more difficult to adapt to exogenous shocks. If a hurricane

suddenly and unexpectedly reduces the biomass in the stock, it is difficult to adjust the amount of

licenses available to prevent overfishing. License distribution typically occurs prior to season

opening and are often renewed or “grandfathered in” at the end of the season, making it difficult

to contract the amount of effort. Even with input regulations in place at a federal level, local

management and cooperation is needed to ensure success. Basurto et al. (2012) examines two

similar Mexican communities seeking to manage their benthic resources through access controls

where one has local management in place via cooperatives and one does not. They find that

while both have access controls in place, these controls are ineffective unless local enforcement

is also present.

Output regulations seek to regulate how much biomass is landed and ultimately exits the

fishery. This may be seen in developed countries where fisheries open for a few days at a time

before closing again, like the salmon run in Alaska. The two most common output regulations

present in lobster fisheries management are total allowable catch limits (TACs) and individual

transferable quotas (ITQs). TACs regulate the total biomass exiting the fishery while ITQs allot a

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set amount of the total catch to each fisherman (Gibbs, 2010). ITQs, while seemingly controlling

both inputs and outputs, are classified as output-based because they are derived from TACs and

are focused on maintaining a biomass rather than effort quota. The most studied example of this

system in a lobster fishery is in the Torres Strait between Papua New Guinea and Australia.

Systems limiting output are more adaptable to sudden shocks than those limiting input. If

there is an exogenous shock to the system or threat of population collapse, managers can close

the fishery more easily and without generating conflicts regarding fairness. The threat with

output-based regulation lies in the potential for temporally concentrated exploitation. As with

prisoner’s dilemma present in open-access systems, there is a chance that fishermen will rapidly

deplete the resource in the first few days of the season so as to catch as much as possible before

total quota is reached. This is less of a threat in ITQs as each fishermen is given their allotted

amount and thus extraction is not rushed.

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CHAPTER TWO: METHODS

I. Sampling

For this thesis, I conducted a meta-analysis of 62 case studies. Thomas Rudel’s (2008)

paper, “Meta-analyses of case studies: A method for studying regional and global environmental

change” served as the guideline for data collection and dataset development. Data were collected

from peer-reviewed publications, government records, and nongovernmental organizations’

reports. Individual fisheries describes in these studies are the units of analysis. The 62 fisheries

selected, shown in Figure 2, span six continents and varying political, economic and

developmental statuses. Case studies were found using JStor, ProQuest, ScienceDirect, and

United Nations databases. The keywords “lobster,” “fishery,” “CPUE,” “effort,” “stock status,”

and “assessment” were used in searching. A majority of fisheries were found via snowball

sampling through the sources of previously read papers. In numerous cases, data were compiled

from multiple studies for a single fishery to find all the necessary variables and determine

changes in stock status over time. This was most often used in the coding of regulatory measures

on the national scale using government records and the combination of ethnographies with

government production records. The 62 fisheries coded for in this dataset were compiled from 49

publications. Several fisheries were coded from government-sponsored publications which

examined all fisheries in one region, such as the Donohue et al. (2000) study of Australian, New

Zealand and Tasmanian fisheries and the Glass (2014) study of the Tristan da Cunha Islands

group. The unit of analysis is an individual fishery and each individual fishery represents an

observation in this sample. Fisheries are defined according to the regulatory area they cover. In

developed countries, such as the three zones along the Western Coast of Australia

22  

Figure 2

: Map of fish

ery locatio

ns. B

lue in

dica

tes clawed

 lobster fish

eries while red

 are sp

iny o

r rock lo

bster fish

eries.  

 

(Reid et al. 2013),

this is area is

defined with

geographic

coordinates. In

developing

23  

countries, such the Drini fishery in Java, Indonesia (Milton et al. 2014), a fishery is defined as

the port to which fishermen return to sell or process their catch.

II. Measurement

17 variables were coded from the selected cases. Variables were selected in part using

Kosamu’s (2015) meta-analysis of seventeen third-world fisheries and Basurto’s (2013) paper on

important variables in benthic small-scale fishery management. The goal of the analysis is to

determine the impact of regulation on the long-term success or failure of a fishery.

Kosamu (2015) focuses on the importance of local management institutions to prevent

the overexploitation of resources. This analysis includes variables for co-management, degrees to

which the government supports local management and sustainability outcome. For my study,

local management, sustainability outcomes and corruption as a proxy for compliance variables

were coded for.

Basurto (2013) discusses how it is difficult to attribute collapse of small-scale fisheries to

a single factor, rather it is dependent on the connectivity of the system as a whole with particular

focus on systems of government and actors’ motivations. This lead to the inclusion of

socioeconomic attributes of actors, location, fishing technologies available, governance system

variables, access variables and catch shares. Additional variables were added based on what was

commonly repeated in case studies, such as lobster-specific regulatory variables.

An explanation of all variables and coding is found in Table 1. Extended definitions of selected

variables follow.

Table 1: Variable and Coding Description

Variable Type Coded as Definition

24  

Dependent Variables

Decline Categorical 1 (yes) 0 (no)

Whether or not the fishery is seen as in a state of decline. Used in cases in which interviews are reconstructing historical catch data so trends are known but specific quantities are not.

% change CPUE/year

Quantitative %/year Percent change in CPUE/year. Serves as a method of standardization between the fisheries.

Level of exploitation

Ordinal 1, 2, 3, 4 1: underexploited. 2: fully exploited. 3: overexploited. 4: collapsed. Elaborated on in Chapter 2.ii.c.

Regulatory Variables

Registration (input) Categorical 1 (yes) 0 (no) Whether or not the government requires some form of registration but has no cap on the amount available

Limited access (input)

Categorical 1 (yes) 0 (no) Whether or not the government has limited access to the fishery, typically by number of licenses.

TAC (output) Categorical 1 (yes) 0 (no) Whether or not there is a total limit for biomass exiting the fishery.

ITC (output) Categorical 1 (yes) 0 (no) Whether or not there is an individual transferable quota system in place which limits output in the fishery.

NTZ Categorical 1 (yes) 0 (no) Whether or not there is a no-take zone or reserve has been established in part of the fishery.

Closed Categorical 1 (yes) 0 (no) Whether or not the fishery has a closed season.

Berried Categorical 1 (yes) 0 (no)

Whether or not the landing of egg-bearing females is banned. Egg-bearing females are called “berried” and it is illegal to land them or to remove their eggs.

MLS Categorical 1 (yes) 0 (no) Whether or not there is a minimum legal landing size.

Variable Type Coded as Definition

25  

Control Variables

Trap Categorical 1 (yes) 0 (no)

Whether or not lobster pots or traps are used in the fishery. Traps can damage reefs when they are dropped, as they are often weighted with rocks.

Trawl Categorical 1 (yes) 0 (no)

Whether or not trawls are used in the fishery. Trawls are defined by the FAO as “a cone or funnel-shaped net that is towed through the water by one or more vessels.”

Dive Categorical 1 (yes) 0 (no) Whether or not any means of diving is used as a form of extraction in the fishery.

Commercial Categorical 1 (yes) 0 (no)

Whether or not the fishery is classified as “industrial” or “commercial.” An industrial fishery is a capital intensive large-scale operation.

Local Management Categorical 1 (yes) 0 (no) Whether or not the fishermen have a local management system in place.

Development Level Ordinal 1, 2, 3, 4 Coded from the World Bank. Categories: 1: low 2: low-middle 3: upper-middle 4: high

Regulatory Quality Quantitative Percentiles Measure of perceived government regulation efficacy generated by the World Bank.

i. Determination of Outcome Variables

CPUE, fishery declining and level of exploitation are used as the primary outcome

variables in this study. While all are related in some way as they are seeking to measure the

status of the fishery, each is coded based on slightly different information and thus the

combination allows for a more accurate estimation of stock status. The coefficients across these

three regressions are then compared in an attempt to gather a more complete picture of what is

happening in the fishery. Relying on one outcome variable allows for bias if the CPUE

calculation or exploitation classification is faulty in some way, thus comparing between three

different outcomes helps increase accuracy.

26  

a. Change in CPUE

CPUE measurements are pulled from case studies and coded as their raw values. As

discussed and outlined in Salthaug (2001), CPUE standardization standardizes catch rates within

individual fisheries. As standardization occurs within an individual fishery, raw standardized

CPUE cannot be compared to other fisheries. To combat this, our data are coded such that each

fishery is only compared to itself. The variable “change in CPUE” compares standardized CPUE

data from different time periods in the fishery’s history. This accounts for differences in

standardization.

The percentage change in CPUE relative to the beginning of the surveyed period is taken.

To make these comparable between fisheries, the rate of decline was divided by the number of

years surveyed to produce a rate of annual decline.

b. Decline

The second outcome variable is decline, which is coded on a 1-0 binary. A value of 1

indicates that the stock is declining while a value of 0 means that it is not. Similar to the idea of

change in CPUE, decline allows for the use of cases which do not have reliable CPUE data

available. For cases where CPUE data was available, “decline” was determined using both

CPUE data and the authors’ commentary based on interviews. The link between these two

outcomes helps to explain the high degree of correlation between their results.

“Decline” is useful in cases where intergenerational interviews were used to determine

whether the fishery was stable or declining. This data is typically pulled from ethnographical

studies such as that describing the Kuma Indians in Panama.

27  

c. Level of Exploitation

The state of exploitation of the fishery is described by a categorical variable ranging from

1 to 4. 1 indicates that the fishery is currently underexploited and fishing is below the maximum

sustainable yield. Effort can be increased without long-term damage to the stock. 2 is defined as

a fishery which is fully exploited and operating at maximum sustainable yield. The goal of

management is to set quotas and regulations such that a fishery is operating at this level. 3

represents a fishery which is overexploited and effort is too high. CPUE is declining but the

stock has not yet reached the point of collapse. 4 is a fishery which has collapsed, defined by the

FAO as when landings have declined 90% from maximum sustainable yield (FAO). The

Namibian lobster fishery prior to 1992 (Grobler et al. 1997) and Kanyakujmari coast fishery in

India (Radhakrishnan et al. 2005) are examples of those which have completely collapsed and

are now closed to fishing.

Level of exploitation is coded based on authors’ commentary and the examination of

CPUE and total average catch trends. Typically, if CPUE and total average catch are increasing,

the fishery is underexploited. If both are relatively stable or CPUE is stable while average catch

is increasing, the fishery is fully exploited. In a case where CPUE is declining while average

catch remains constant or increases slightly, the fishery is overexploited. Fisheries in which both

are declining or fishing has stopped completely are considered to be collapsed.

ii. Definition of Independent Factors

All independent variables were coded from case studies and government publications

describing the fishery in question. They consist of descriptive variables regarding trap type,

commercial vs recreational fisheries, and regulatory variables denoting which regulation

28  

methods are in place. All are coded on a 1-0 binary, with a 1 indicating that the regulation is

present in the fishery as a formal rule and 0 if it is not present.

The presence of MLS, NTZ, closed seasons, and bans on berried females were initially

coded for on a 1-0 binary. Based on theoretical understanding of fishery regulation, these were

further classified into two categories: regulations which control which lobsters are landed (MLS

and berried females) and those which control how fishing is conducted (NTZ and closed season).

Table 2: correlation between exploitation and regulatory variables

MLS NTZ Closed Berried Development Exploitation

MLS 1 NTZ 0.3162 1

Closed Season 0.4082 0.2582 1 Berried 0.606 0.2283 0.3053 1

Development 0.3592 0.3692 -0.0183 0.374 1 Exploitation -0.4893 -0.1138 -0.3114 -0.4401 -0.2514 1

Within these sub-categories, it was found that MLS regulation and bans on berried

females have a moderate correlation (.606) while NTZ and closed seasons have a weak

correlation of .2582 (Table 2). Neither was deemed strong enough to merit the coding of

aggregate regulation variables.

Regulations controlling for access to the fishery were also initially coded on a 1-0 binary.

Within the two overarching categories of access controls, those regulating inputs to the fishery

and those regulating biomass output, four individual variables were coded. Input controls are

whether or not registration is required in the fishery and whether or not access is limited in some

way. Output controls are whether or not the fishery has a TAC system in place and whether or

not it has an ITQ system in place. All were coded as “1” if the regulation was present and “0” if

it was not. Fisheries which are all access regulations are considered open access. 16 fisheries are

29  

classified as open access, 46 are regulated via some form of input and 21 via some form of

output. 20 fisheries have both input and output regulations present.

In the cases of all regulatory variables, it is possible for a fishery to fall into multiple

categories. For example, a fishery can have landings regulation and a NTZ, or both input and

output regulation. Of the 28 fisheries with output regulation (ITQ or TAC), only 1 does not have

registration requirements alongside.

iii. Controls

Controls were generated at the country level using data from the time frame of the fishery

being studied. Data was gathered from the World Bank World Development Indicators and

Population and Health Statistics, and Worldwide Governance Indicators databases.

Selected controls are whether or not the fishery is classified as commercial, perceived

regulatory quality of the country, whether or not local management institutions are in place, and

the country’s development level.

a. Country-level Characteristics

Some controls were coded using data available at the country level to capture overarching

characteristics which were not available at the level of the individual fishery, or were used as

proxies to capture cultural and economic differences between fishing communities.

Development level is a categorical variable generated from the World Bank Development

Indices. “Low” development is coded as 1, “lower middle” as 2, “upper middle” as 3, and “high”

as 4. All are coded based on stated development level as determined by the World Bank.

One of the largest problems in examining the efficacy of environmental regulation is the

question of enforcement and compliance. As data on illegal landings was only available for a

30  

select number of fisheries in the sample, proxies for noncompliance were utilized instead.

Fisman’s (2006) paper “Cultures of Corruption: Evidence from Diplomatic Parking Tickets”

found that diplomats from countries with higher corruption indices had more parking tickets,

suggesting a cultural link between high corruption and general disrespect for the rule of law.

Following this, rule of law and regulatory quality were selected as proxies for noncompliance.

Rule of law is defined as capturing “perceptions of the extent to which agents have confidence in

and abide by the rules of society” (World Bank) and is constructed examining property rights,

crime, contract protection and the judicial system, among other factors. Regulatory quality is

stated to capture “perceptions of the ability of the government to formulate and implement sound

policies and regulation” (World Bank) captured through price controls, tariffs, taxes, financial

freedom, and regulatory burden among other factors. Both are calculated based on percentiles.

b. Fishery-level Characteristics

When available, controls at the level of the individual fishery were included. These are

fishery classification and local management methods.

i. Fishery Classification

Fisheries are divided into artisanal and commercial fisheries based on what elements are

present. All were coded using the terminology found in the case studies.

Recreational fisheries often exist alongside commercial operations. Due to their small

size and low economic value they were not included in this study. Recreational fisheries are

defined as those which are not a source of livelihood for the population extracting resources.

Examples of this include the Biscayne Bay fishery in the Florida Keys which has a recreational

mini-season. Commercial fisheries are those which do represent a source of livelihood and

31  

operate using industrialized mechanisms. A majority of lobster captured in commercial fisheries

is destined for export. The Small-Scale and Artisanal Fisheries Research Network at the

University of California San Diego defines artisanal fisheries as “small-scale fisheries for

subsistence or local, small markets, generally using traditional fishing techniques and small

boats” (SAFRN). Globally, artisan and commercial fisheries produce the same amount of fish for

human consumption yet artisanal fisheries employ 25 times the number of fishers and use an

eighth of the fuel used by industrial fisheries (Jacquet and Pauly, 2008).

ii. Local Management

The final control variable, local management, is coded on a 1-0 binary. As previously

stated, local management and engagement has been demonstrated as critical to the long-term

sustainability of the fishery. In this study, local management is coded based on authors’ language

in the case studies. In cases where it states that local actors were engaged or had formed

cooperatives, local management takes the value of 1. This coding is limited by the quality and

depth of the cases studies. Not all authors give the same level of attention to fisheries

descriptions and the surrounding communities, thus local management methods may go

unreported.

iii. Fishery Size

One of the primary concerns was the large variability in size between cases, as problems

would arise when comparing a large commercial fishery like the Gulf of Maine to the Parque

Nacional Archipelago Los Roques fishery which is a third of its size. However, these differing

sizes are not a source of bias because CPUE, the only direct comparison variable, is generated by

comparing the fishery at two different points of time to itself, thus standardizing the

32  

measurements and making them comparable globally. Level of exploitation and decline have no

relationship with the fishery’s size.

II. Statistical Methods

All statistical analysis was done with STATA software and the dataset was constructed with

Microsoft Excel.

Categorical variables include gear type, development status of the country measured on

the World Development Index, regulations governing the fishery, and basic metrics like artisanal

vs industrial production. The dependent variables are annual percentage change in CPUE, level

of exploitation, and whether or not the fishery is in a state of decline. Other variables include

fishery and country-level control variables.

Regressions were run on three dependent variables: annual change in CPUE, whether or

not the fishery is in a state of decline, and exploitation level. An ordinary least squares estimator

was used for annual change in CPUE while decline and exploitation level employed a logistic

estimator and ordinal logistic estimator, respectively. Odds ratios are reported for all logistic

regressions.

Independent variables are whether or not the following regulations are present: access

limits, registration, ITQs, TACs, MLS, bans on berried females, NTZ and closed seasons.

Included controls are a measure of regulatory quality, if there is a structure for local

management, national development level and whether or not the fishery is classified as

commercial.

33  

CHAPTER THREE: FINDINGS AND DISCUSSION I. Descriptive Statistics

62 fisheries were coded for in total, encompassing commercial and artisan fisheries

around the globe. 9.5% of cases lie in Africa, 13% in Asia, 20% in Australia, 15% in Europe and

31% and 11% lie in North and South America, respectively. Roughly 20% of fisheries surveyed

lie in low to low-middle developed countries while the remaining 80% lie in high or high-middle

developed countries.

i. Dependent Variables

The primary dependent variables are annual change in CPUE, exploitation level and whether or

not the fishery is in a state of decline.

The mean of change CPUE is 0.042 and the standard deviation is 0.1723, implying that

typical changes are small while there remains large variance in the data. Average annual change

in CPUE is clustered around zero, which is to be expected if data is normal or close to normally

distributed (Figure 3). The variable is close to normally distributed but slightly right skewed.

Figure 3: Histogram of annual change in CPUE

34  

This is due in part to the nature of how average annual change in CPUE is constructed.

Given that it is an average, it encounters the same problem as with average catch-fisheries which

undergo rapid change will be outliers in the model.

Decline is coded on a binary for whether or not the fishery is in a state of decline

according to CPUE and biomass output estimates. This is used in conjunction with exploitation

level to assess the sensitivity of the results and for fisheries in which stock assessments are

constructed primarily through intergenerational interviewing and observation as opposed to

CPUE measurements. The cases are split 46% to 54%, declining versus not.

The third primary dependent variable is level of exploitation which is coded as a

categorical variable from 1 to 4. Level of exploitation is slightly right-skewed (Figure 4). A

majority of the cases are concentrated in the 2 and 3 range, with 50.1% of cases falling into the

“fully exploited” category and 30.2% categorized as “overexploited.” Collectively the tails,

categories 1 and 4, account for roughly 19% of cases.

 

 

 

Figure 4: Histogram of exploitation level

35  

ii. Independent Variables Primary independent variables being tested are varying forms of regulation and control

variables including gear type and country-level controls. In order to properly understand and

interpret the regressional relationships between these and various outcome variables the

preexisting correlations within the case studies must be understood.

a. Access Variables

Access to the fishery is crucial to its management success and strategy. There are four

primary means by which access is regulated: individual transferable quotas, total allowable catch

limits, registration, and limited access.

To further break down the relationship between access and exploitation, access variables

were regrouped into “input” and “output” categories according to whether they regulated inputs

or outputs of the fishery. ITC and TAC are categorized as output variables and have a correlation

of .4236, while limited access and registration have a correlation of .5096 and are categorized as

input variables limiting effort in the fishery. As Table 3 shows, input and output controls have

roughly the same relationship with exploitation, -.4614 and -.403, respectfully.

Table 3: Correlation of exploitation and access type

Exploitation Input Output

Exploitation 1 Input -0.4614 1

Output -0.403 0.354 1

There are some fisheries which have both input and output regulations, typically seen

with the combination of registration and total allowable catch limits. 73% of fisheries in the

dataset have some form of input regulation while 33% have some form of output regulation.

Fisheries without either form of regulation are classified as open access. Interestingly, 37.5% of

36  

open access fisheries are in countries with high levels of development and 50% are based in

those with development levels of low to low middle.

To determine the most effective management technique, these aggregate categories were

split into their respective components. 14.3% of fisheries in the dataset have an ITQ system,

30.2% have a TAC system, 41.3% limit access and 73% have registration requirements. 88% of

fisheries in the “fully exploited” category, optimal for MSY, mandate registration and of those

with registration controls 93% lie in high-middle or highly developed countries.

There is the question of endogeneity given that different access regulations require

different levels of enforcement. 100% of countries employing ITQs, 74% using TACs and 62%

of those limiting access lie in highly developed countries, as these forms of management require

greater infrastructure and enforcement to manage properly. Access regulations of all types are

positively correlated with development level, making fishing and landing regulations more likely

solutions for artisan fisheries.

b. Regulatory Variables

There are four primary regulations put in place in lobster fisheries: minimum landing

size, no take zones, closed seasons, and bans on the landing of berried females. These regulations

can be divided into two primary categories of regulations: regulations on landings (berried

females and minimum landing size) and regulations on fishing activity (no take zones and closed

seasons). There are positive correlations between each of the regulatory variables suggesting that

if a fishery has one regulation in place, they are likely to have multiple.

It is important to explore the relationship between each level of exploitation and

regulation type (Appendix A, Table 9). 75% of fisheries in the dataset regulated landings while

37  

only 32% regulate fishing activity. This can help to explain the stronger correlation between

lower exploitation levels and landing regulations. 60% of fisheries regulating landings and 60%

of fisheries regulating fishing activity fall in the fully exploited category, suggesting there is a

positive relationship between the regulation and having a healthy exploitation level.

As with access regulation, it is important to break down these aggregate categories into

their individual components to determine the most effective form of management. Minimum

landing sizes, bans on berried females, and closed seasons have the strongest negative

correlations with exploitation level (Table 4). All have a positive relationship with level of

development, suggesting that more developed countries are more likely to put these regulations

in place as well as have lower exploitation levels overall.

Table 4: Correlations between regulation types

Exploitation DVL MLS Berried NTZ Closed ITQ TAC Access limit Register

Exploitation 1 DVL -0.373 1 MLS -0.489 0.529 1

Berried -0.440 0.423 0.606 1 NTZ -0.114 0.275 0.316 0.228 1

Closed -0.311 0.36 0.408 0.305 0.258 1 ITQ -0.008 0.306 0.144 0.238 0.274 -0.196 1

TAC -0.383 0.22 0.232 0.145 0.387 0.15 0.424 1 Access Limit -0.251 0.229 0.296 0.415 0.159 0.335 -0.342 -0.340 1

Register -0.461 0.468 0.582 0.631 0.472 0.341 0.248 0.322 0.51 1

It is important to analyze the relationship between different types of regulation. Table 4

demonstrates that there is a positive correlation between all regulation types, suggesting that

having one form of regulation in place increases the likelihood that a fishery will have multiple

kinds in effect. All regulations have negative correlations with exploitation level demonstrating

that regulations are effective in some way. NTZ and ITQs have the smallest relationships with

38  

exploitation while registration has the highest correlation with other regulatory variables, likely

because it is relatively easy to implement.

c. Gear type

Multiple gear types may be used in the same fishery. The most frequently represented in

this study are trap, trawl and dive fisheries. Traps are used in 48 fisheries, trawls are used in 10,

and 20 are dive fisheries. There is a slight negative correlation between dive, trap and trawl

fisheries. The strongest negative correlation is between trap and trawl fisheries (Appendix A,

Table 1). This is to be expected given the literature which states that trawling destroys traps

causing fishermen to lose valuable capital.

Exploitation rates differ based upon what gear type is being used in the fishery. As shown

in Table 4, there is a negative correlation between exploitation level and dive and trap fisheries,

meaning these fisheries are less likely to be overexploited. Trawl fisheries have a positive

relationship with exploitation.

When broken down by level of exploitation, the differences between the gear types

become more apparent. Of the 32 fisheries falling in the category 2 (full exploited), 72% employ

traps, 28% are dive-based, and 12.5% trawl. 60% of trawl fisheries fall in category 3

(overexploited) or 4 (collapsed), while 37.5% and 35% of trap and dive fisheries, respectively,

do (Appendix A, Tables 2-4). Dive fisheries are most likely to be underexploited, possibly due to

the lack of capitalization present in these fisheries.

d. Controls

Included controls are regulatory quality, local management, development level and

whether the fishery is a commercial fishery. These are designed to control for basic

characteristics which may account for underlying differences between the fisheries.

39  

There is the possibility that development level is helping drive these relationships as

opposed to the gear itself as developed countries typically have better regulations, enforcement

and general management. There is a strong correlation between increased development and

lower rates of exploitation (Table 4).

Again, this could be due to the country’s development level. There is a strong positive

correlation between development level and access regulations. This suggests that more

developed countries simply have better access management.

Examining the controls, it was found that the corruption variables are highly correlated

(Table 5). Given this, rule of law was selected to be included given it better represented the

characteristics concerning compliance and enforcement.

Table 5: Corruption Indices Correlation

Rule of Law Regulatory Quality

Rule of Law 1Regulatory Quality 0.9495 1

iii. Summary of descriptive statistics

Overall findings indicate that there is a strong correlation between the country’s

development level and the general stability of the fishery. Fisheries in developed countries tend

to be larger, more commercialized, trap fisheries and have lower levels of exploitation. They

typically have some regulation on access to the fishery as well as minimum landing sizes, no

take zones, and bans on the landing of berried females. All of these factors have negative

relationships with level of exploitation.

Dive fisheries are the least likely to be overexploited, followed by trap, while trawl

fisheries have a positive relationship with exploitation level. Trap fisheries are most common in

40  

developed countries while dive fisheries have a strong negative correlation with development.

Dive fisheries are also more likely to be open access.

Table 4 demonstrates that there is a weakly positive relationship between regulation

types, suggesting that fisheries with one type of regulation are more likely to have others as well.

This further complicates the endogeneity problem as it is difficult to determine the individual

impact of one type of regulation.

41  

II. Main Results Table 6: Master Results

(1) (2) (3) VARIABLES Change in CPUE Exploitation Level Decline OLS Ordered Logit Logistic

Limited Access -0.0151 0.179 13.02* (-0.292 - 0.262) (0.014 - 2.31) (0.820 - 206.8)Registration 0.0513 1.308 0.0390 (-0.236 - 0.339) (0.068 - 25.02) (0.001 - 2.226)ITQ -0.112 3.231 6.039 (-0.318 - 0.094) (0.122 - 85.28) (0.313 - 116.3)TAC 0.136 0.0236** 0.565 (-0.072 - 0.343) (0.001 - 0.944) (0.082 - 3.906)MLS -0.0936 0.208 55.97 (-0.298 - 0.111) (0.009 - 4.845) (0.254 - 12,313)Berried Ban 0.0227 0.324 0.0779 (-0.137 - 0.182) (0.041 - 2.557) (0.003 - 2.170)No Take Zone -0.0366 8.261** 12.58** (-0.148 - 0.075) (1.203 - 56.73) (1.436 - 110.2)Closed season -0.0563 0.947 0.516 (-0.175 - 0.062) (0.136 - 6.592) (0.071 - 3.770)Traps used 0.0185 6.660* 9.283* (-0.119 - 0.156) (0.781 - 56.80) (0.699 - 123.2)Diving 0.194** 0.376 0.202* (0.027 - 0.360) (0.047 - 2.974) (0.032 - 1.258)Trawl used 0.0378 1.764 7.430 (-0.110 - 0.185) (0.112 - 27.88) (0.127 - 436.1)Regulatory quality 0.00198** 0.958** 0.948** (0.001 - 0.004) (0.923 - 0.994) (0.908 - 0.989)Local management -0.0263 0.503 0.389 (-0.138 - 0.086) (0.109 - 2.315) (0.039 - 3.920)Development level 0.0212 0.983 0.358 (-0.045 - 0.088) (0.276 - 3.501) (0.081 - 1.575)Commercial fishery -0.0253 1.641 1.194 (-0.181 - 0.130) (0.245 - 10.98) (0.083 - 17.15)Constant -0.168* 0.000332*** 96.33** (-0.363 - 0.027) (0.001 - 0.011) (2.927 - 3,170)

Observations 61 62 62

R-squared 0.369 0.336 0.3825

Robust ci in parentheses *** p<0.01, ** p<0.05, * p<0.1 * pseudo R-squared  reported for logistic regressions (2-3)

42  

i. Analysis

The primary findings are described in Table 6. Coefficients are interpreted based on 95%

confidence intervals. For ease of interpretation, discussion is divided based on the dependent

variable being analyzed. There are three primary groups of independent variables being

examined: access variables (ITQs, TACs, access limits and registration), fishing regulation

variables (presence of minimum landing sizes, bans on berried females, no take zones, and

closed seasons) and gear variables (trap, dive and trawl). High R-squared and proxy R-squared

values (for logistic regressions) suggest that the model explains the data given the circumstances

surrounding a meta-analysis.

i. Annual percentage change in CPUE

Coefficients on annual percentage change in CPUE is interpreted as if a fishery has the

regulation, on average there is an “x” percentage change in annual change in CPUE. An R-

squared value of 0.369 suggests that this model explains much of what is driving the results.

Annual percentage change in CPUE finds stark differences in the efficacy of input versus

output regulation. Fisheries which limit access and have ITQ systems are associated with 0.015%

and 0.112% declines in CPUE, respectively. The coefficient on ITQ is especially impactful given

that the average change in the sample is 0.042%. Both coefficients have comparatively large

confidence intervals and thus the estimates are not reliable, however the consistency in direction

of relationship across all outcome variables indicates that there is some merit to this analysis.

Registration and TAC limits both have positive relationships with change in CPUE of 0.051%

and 0.14%, respectively. Given the mean change of 0.042%, this demonstrates TAC limits have

a significant impact on change in CPUE. Although the confidence interval crosses zero, it is

43  

skewed towards the positive range (-0.072-0.343), thus increasing the likelihood of a positive

relationship.

MLS, no take zones, and closed seasons all have negative relationships with change in

CPUE. While none are statistically significant, the confidence intervals are skewed towards a

negative relationship on NTZs and closed seasons, implying that it is likely the negative

correlation is accurate. This finding is interesting because typically landing and fishing

regulations are implemented before access regulations so it would assume that they would be

effective. MLS is found to have an ambiguous relationship as the confidence interval crosses

zero and is too broad to make a claim either way. The ban on berried females is found to have a

slightly positive relationship with change in CPUE, though the confidence interval is too large to

draw a definitive conclusion.

It appears as though the preexisting cultural characteristics of a fishery have little impact

with respect to change CPUE. Local management and regulatory quality have a negligible

impact, though in the opposite directions. Development level and whether or not the fishery is

commercial have more substantive impacts, however the confidence interval for development

level and commercialization both cross zero. This suggests that preexisting characteristics

unrelated to management have a larger impact than the culture surrounding corruption and local

involvement.

ii. Level of Exploitation

Coefficients on level of exploitation are odds ratios and are interpreted as the likelihood

of moving from one category to another, ie from underexploited to fully exploited, if a particular

regulation is present in the fishery.

44  

Access regulations vary widely in their efficacy. Regarding input controls, fisheries with

access limits are 82% less likely to reach increasing levels of exploitation while those with

registration requirements are 1.3 times more likely to reach increasing levels. However, in both

cases these confidence intervals cross one and thus are unreliable. Regarding output regulations,

having an ITQ system in place renders a fishery three times more likely to be increasingly

exploited while fisheries with a TAC limit are 97% less likely to reach increasing exploitation

levels, significant at the 5% level. The coefficient on TAC is the only access coefficient which

can be considered reliable as all others have confidence intervals which are too large to draw

conclusions, suggesting the model does not accurately portray the relationship between

exploitation level and access variables.

MLS and bans on berried females both have negative relationships with exploitation level

however neither confidence interval is small enough to draw any strong conclusions. No take

zones increase the likelihood of increased exploitation by a factor of 8.3, significant at the 5%

level. This is likely because while the other regulations seek to protect breeding females and

juvenile lobsters, no take zones do not seek to protect these vulnerable groups. In developing

countries, no take zones can be difficult to enforce as they rely heavily on honor code and

community monitoring. Regulatory quality has a statistically significant impact of essentially

zero, which may in turn factor into the inefficacy of the no take zone. It is difficult to determine

the efficacy of closed seasons given the large confidence interval, though it appears to have close

to no impact.

Regarding gear types, trawl fisheries are associated with roughly twice the likelihood of

increased exploitation while dive-based fisheries are 82.6% less likely to have increasing

exploitation levels. It should be noted that the confidence interval on the trawl coefficient is

45  

incredibly large, meaning that this relationship is highly varied and unreliable, however it leans

towards an increasing likelihood. Trap fisheries are 6 times more likely to be increasingly

exploited as non-trap fisheries, significant at the 10% level. While this coefficient has a very

large confidence interval, it is skewed towards a positive relationship.

The impact of regulatory quality is essentially zero, significant at the 5% level.

Additionally, local management is significant at the 5% level and demonstrates that fisheries

with local management measures in place are roughly 50% less likely to reach higher levels of

exploitation. However, the large confidence interval renders it difficult to draw a definitive

conclusion.

iii. State of Decline

Findings are reported as odds ratios which represent as the likelihood of a fishery moving

from one category to the next, in this case from a designation of “0” or not in a state of decline to

a “1” or in a state of decline. For example, a coefficient of .4 means that the likelihood of a

fishery with that regulation in place declining is 40% greater than a fishery which does not have

that regulation in place.

Fisheries with any form of input regulations and with output regulations are 87.8% and

89.3%, respectively, more likely to not be in a state of decline relative to those without these

regulations. This implies that access regulations in general are effective in preventing

unsustainable harvesting.

When disaggregated, the relationships between decline and various regulatory variables

are predominantly inconclusive. Fisheries with access limits are 13 times more likely to be in a

state of decline, significant at the 10% level. Registration requirements and TACs are associated

46  

with a 96% and 44%, respectively, lower likelihood of being in a state of decline. These

relationships are consistent across all findings. Fisheries with minimum landing sizes are 55

times more likely to be in a state of decline, however the confidence interval is so large that there

is essentially no relationship between the two variables. Fisheries with bans on berried females

are 93% less likely to be in a state of decline, however the confidence interval is too large to

draw a solid conclusion from this. Fisheries with no take zones are 12.6 times more likely to be

in a state of decline, significant at the 5% level. This positive relationship is likely because NTZs

do little to protect berried females and undersized lobsters, the groups which are most critical to

the long-term stability of the stock. With the exception of the NTZ coefficient, relationships

between regulatory variables and decline are inconclusive.

iv. Gear Variables

An additional factor influencing the outcomes could be that different gear types have

significantly different relationships with regulations. To test for this, regressions were rerun

while controlling for gear type. It was found that dive fisheries are associated with lower

exploitation levels while traps dramatically increase the likelihood of heavy exploitation and

decline. This contradicts the literature which states that trawls are the most damaging to the

overall sustainability of the fishery while trap use is widespread. The confidence interval on

trawl is too large to draw any meaningful conclusions.

While gear types are included, it is difficult to contribute any causal relationship to gear

type because gear used is dependent on numerous characteristics of the region unrelated to the

fishery. Strong correlations between development level and classification indicate that gear type

is more indicative of the characteristics of the fishery as opposed to its level of exploitation. The

exception to this rule is trap fisheries. 44% of trap fisheries are located in developing countries,

47  

while 56% are in developed. 21% are classified as artisanal fisheries, which still demonstrates

the most diversity of range of any gear type in the data set. Trawl fisheries are associated with

more sustainable outcomes than dive fisheries, but this could also be a factor of the

commercialization of fisheries. 75% of dive fisheries are located in developing countries and

60% are classified as artisanal. Conversely, 50% of trawl fisheries are located in developing

countries and 100% of trawl fisheries in this dataset are classified as commercial fisheries. Given

the negative relationship between exploitation level and commercialization of the fishery, there

is a possibility that these characteristics are driving the relationship as opposed to the gear type

itself.

ii. Discussion

Overall, findings indicate that regulation plays an important role in maintaining stock

health and sustainable harvesting. TAC and registration requirements are the most effective and

fisheries with no take zones are more likely to be overexploited. The most interesting aspect of

the findings described above is the difference in results between percentage change in CPUE and

the other outcome variables.

i. Discrepancies in relationships across outcomes

When examining the relationship between regressions run with and without gear type

variables, magnitudes of coefficients were dramatically different. Despite the statistical

significance of some results, a majority of confidence intervals are too large to draw any reliable

conclusions, particularly with respect to gear variables. This suggests that gear type is not a

deciding factor in the sustainability of a fishery and perhaps any fishery, regardless of gear type,

can be exploited sustainability if the proper management techniques are put in place. Shifting the

focus away from gear changes towards regulatory changes renders fisheries management more

48  

adaptable. It is difficult to convince fishermen to switch gear types when they have made the

initial capital investment while it is comparatively simpler to institute local management policies

which will protect the viability of the stock in the long-run.

Regarding access regulations, there are discrepancies in the direction of the relationship

across access variables seeking to regulate input. Output-based regulation (TAC and ITQ) are

consistent while access limits are found to result in declining CPUE and increase likelihood of

decline while also reducing the likelihood of increased exploitation by 72%. Registration

requirements have a positive relationship with CPUE, lower likelihood of decline, but a higher

change of increased exploitation. The outcome “decline” is partially derived from change in

CPUE thus the similar patterns between the two are to be expected. In the case of all coefficients,

the confidence intervals are too large to draw a strong conclusion as to the direction of the

relationship. These discrepancies then may be due to measurement error and inconsistency in the

case studies.

The largest discrepancy in the data is between coefficients on minimum landing size.

MLS has a slightly negative relationship with change in CPUE yet fisheries with MLS in place

are 80% less likely to reach increased exploitation levels. One possible explanation for this

discrepancy is that having a minimum landing size in place shrinks the target population for the

fishery, so for the same unit of effort they may be catching the same quantity as a fishery without

MLS in place but have to release the undersized catch. This would become apparent in CPUE

calculations but as level of exploitation presents a more holistic view of the fishery, this change

in target population would be accounted for. The large confidence interval in the decline

outcome, ranging from 0.254 to 12, 313, indicates that this model is not an accurate predictor of

the relationship between MLS regulation and decline.

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Regarding gear variables, the coefficient on diving is 0.194, which is statistically

significant at the 5% level, and five times the value of the mean of the change in CPUE variable.

This suggests that dive fisheries have a very large impact relative to other gear types whose

coefficients are roughly 0.02. However, this strong and significant relationship is opposite the

relationship with the other outcome variables in both magnitude and direction. Diving has

positive relationships with level of exploitation and decline which are not statistically significant

but whose relatively small confidence interval suggests that the true value is close to the

estimate. Both confidence intervals cross one, which could account for the inverse relationship

found between these two outcomes and change in CPUE.

Regulatory quality was found to have little impact across all outcomes, suggesting that

country-level corruption data does not have an impact on the success of the fishery. This could

be due to two reasons: that country-level data does not accurately capture the likelihood of

individual fishermen to respect regulations or that disregarding regulations is not a large problem

in fisheries’ management. It is likely the former given that literature states that fishermen in rural

areas often disregard national regulations, namely by landing berried females and undersized

lobsters.

Interestingly, other controls vary across outcome variables. Local management has a

small but negative relationship with change in CPUE yet fisheries with these institutions are 50%

less likely to be overexploited. Local management has a small but negative relationship with

change in CPUE which is 63% of the mean, suggesting that local management has essentially no

impact on change in CPUE. This relationship helps to explain the insignificant relationship with

decline which implies that fisheries with local management are less likely to be in a state of

decline. Decline is based on the change in CPUE variable, thus it is reasonable that they would

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have similar magnitudes and directions. The exploitation variable seems to better capture the

larger trends in the fishery because it is based on other factors besides change in CPUE. Local

management then has a statistically insignificant relationship with exploitation which states that

fisheries with local management are 50% less likely to reach increasing levels of exploitation,

however the confidence interval crosses 1 and thus there is the possibility that no relationship

truly exists. Given the literature stating that local management is critically important to the

success of the fishery, there is the likelihood that the coding methodology did not accurately

capture this variable. Meta-analyses are limited by the information available and it is possible

some authors did not see the relevance of local management structures and thus did not discuss it

in their papers, thus it did not come through in the coding.

ii. Reliability of Outcome Variables

Discrepancies in relationships between independent and dependent variables across

outcomes call into question the reliability of outcome variables as predictors of the overall

sustainability of the fishery. As discussed previously, change in CPUE fails to capture all factors

pertaining to a fishery’s status. This failure to encompass the whole picture is carried over into

the decline variable, which is coded as a 1-0 binary describing whether or not CPUE is declining,

or for fisheries where CPUE data is unavailable if fishermen have stated that it is becoming more

difficult to find lobsters with the same effort. Thus, any fishery whose decline may be driven by

something other than regulation or other factors which are not captured by change in CPUE is

subject to omitted variable bias.

To remedy this, exploitation was developed as a secondary outcome variable.

Exploitation seeks to capture various other factors which are neglected by change in CPUE and,

by association, decline. As this outcome is relatively unrelated to change in CPUE and decline,

51  

any variables which have discrepancies across outcomes could signify that the changing

classification of the fishery is driven by something which is not captured by CPUE. In these

cases, the exploitation coefficient’s magnitude and direction should be considered the best

estimation.

iii. Implications for Regulation

Overall, these results demonstrate the importance of regulation. The most effective form of

regulation is access controls, with specific emphasis on registration requirements and TAC

limits.

Access regulations are key because they ensure that there remains a viable breeding

stock in the fishery at the end of each season. Without access controls, landing regulations are

relatively useless because they protect only berried females and juvenile lobsters while failing to

ensure that a healthy breeding population remains intact. It is likely that TACs and registration

requirements are more effective than limiting access and ITQs because they are more malleable.

Registration requirements enable the overseeing body to more adequately control and enforce

regulations. Total allowable catch limits are set at the beginning of each season based on the

stock status at the end of the previous season. They are carefully allocated and can be raised or

lowered seasonally based on what stock status reports deem is necessary. Additionally, whenever

limits are reached fishing simply stops. Thus, in the event of an exogenous factor changing stock

status it is possible to adjust mid-season in a manner which is impossible with ITQs and access

limits. Unlike total output, which is adjusted seasonally, fishing licenses are often passed down

for generations and it is assumed that once one has a fishing license, it will not be taken away.

This limits the adaptability of management. In years where the stock is particularly low, it is

difficult to control the amount of people fishing when all have licenses. A solution would be to

52  

require the renewal of licenses at the beginning of each season to limit the amount of fishers

exploiting the resource. However, this brings into question the issue of fairness and who will be

allocated said license and why. In areas where lobster fishing is a primary source of livelihood, it

is unrealistic to take away fishing licenses at the beginning of each season. It is difficult to get

the necessary buy-in for policies which threaten people’s livelihoods and difficult to sell a long-

term solution to populations which are much more concerned about meeting basic needs in the

short-term.

According to these results, no take zones are highly ineffective management techniques.

No take zones have the most significant relationship, increasing the factor of increased

exploitation by a factor of eight. These are likely ineffective because they are difficult to enforce,

rendering them relatively useless. Regarding general structure, unlike minimum landing sizes

and bans on berried females, no take zones do not single out the most vulnerable groups for

protection. An additional problem which has surfaced particularly in the Lundy Marine Reserve

near Ireland is disease (Wooton 2012). Large populations in close quarters have higher risk and

prevalence of disease outbreaks. Shell disease outbreaks have been recorded in the marine

reserve, leading researchers to question the value of no take zones. Given the economic

importance of lobster, disease and injury due to proximity are considered huge threats to the

long-term viability of the stock.

Landing regulations seek to protect the groups which are most critical to the long-term

viability of the fishery: berried females and undersized juveniles. While landings have a negative

relationship with change in CPUE, they have also have a negative relationship with exploitation

level. Given that landings regulations are present in 88.9% of fisheries in this dataset, it is clear

that landings regulations are considered necessary for the overall success of the fishery.

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iv. Ideal Fishery

According to these results, the gold standard for lobster fishery management is a fishery

which regulates access through mandatory registration and a TAC limit while also implementing

landing controls including a minimum landing size and a ban on berried females. Not

surprisingly, these are the management techniques employed in the Maine and Canadian lobster

fisheries which are widely regarded as the best managed lobster fisheries in the world.

Judging only from the data, the ideal demographics of a fishery would be an artisan

fishery located in a developed country in which diving is the primary method of extraction. If

global demand for lobster remains at present levels or continues to grow, there will be little

incentive for fishermen to scale back or forgo further capital investment when there is a profit to

be made. Given how few dive fisheries are currently located in developed countries it is unlikely

that this number will increase.

54  

CHAPTER FOUR: LIMITATIONS

This analysis is limited by the consistency of the case descriptions. When possible,

multiple sources were compiled for each fishery to ensure some reliability within the data,

however this was not possible for all cases. The most predominant is the possibility of

measurement error in the outcome variable. Omitted variables bias is a source of concern in any

statistical analysis, as is the endogeneity problem produced with retrospective studies.

I. Weakness of Meta-analysis

Meta-analyses are inherently weakened by the reliability and consistency of case

descriptions. As they are completely dependent on prior research, it is impossible to guarantee

that the same sets of methods were used in measuring each variable in each case. This can be

somewhat remedied through assuring consistency in coding and selectivity as to which cases to

include. If there is data which the authors have little confidence in, it is best not to use the case to

avoid an unreliable outlier driving the results.

As Poteete et al. (2011) cites, another key challenge is that the cases used in meta-

analyses are usually not a representative sample of the universe of cases. In the case of lobster

fisheries, those studied in academic literature are those which are either particularly well-

managed or not. A majority are either success stories or failures-there is little need to examine a

fishery which is middle of the road. Those coded from scientific papers are particularly biased in

this sense as many of the analyses are attempting to determine what failed in management plans,

meaning the fisheries are overexploited or collapsed. Well-managed stocks were coded

predominantly from government records, of which reliable ones are found almost exclusively in

developed countries, further augmenting the relationship between development and exploitation

level.

55  

II. Percentage Change in CPUE.

In some cases CPUE data may be less reliable than others. To remedy this, level of

exploitation and decline are used as a secondary dependent variables. In the analysis and

comparing the results of different outcomes, it appears as though CPUE may not be an accurate

measure of a fishery’s status. Exploitation level and decline in the fishery take into account many

different parameters within the fishery while CPUE is comparatively narrow. It is also vulnerable

to measurement error. Measurement error in either the dependent or independent variables

increases margins of error and renders results less reliable.

Coefficients are essentially the opposite for change in CPUE than is seen in decline or

exploitation, despite the positive correlations among all three. If illegal fishing is commonplace

in the fishery or illegal landings (undersized, berried females) are not reported the reported

CPUE of the fishery will be inaccurate.

Additionally, this is a meta-analysis. While measurements and controls were added to

account for variability in the reliability of CPUE data it is impossible to ensure that all data were

measured accurately using similar methods in each case.

III. Omitted Variables Bias

The model is further limited by the question of omitted variables. As is seen with the

Grand Bank lobster fishery’s boom following the collapse of the cod fishery, it is possible there

are other factors driving the outcome variables besides management methods. It is important to

take into consideration how each change in management will lead to a change in outputs. These

are somewhat captured through the use of level of exploitation and declining as secondary

outcome variables.

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In order for omitted variable bias to be present, the omitted variable must be correlated

with the independent variable. This could be present in the coding of gear variables. It is highly

improbable that fishermen randomly select which gear type to use when exploiting the resource,

thus there is some missing factor driving their selection. One reason could be the question of

capitalization, in that fishermen who depend on diving tend to have less access to capital than

those using traps or trawls to exploit lobster. Another could be that different species of lobster

are exploited differently. Nephrops, found in Europe, are highly vulnerable to trawls as their

primary habitat is the muddy sea floor. Homorus americanus are most often exploited by trap as

their rocky habitat discourages the use of trawls and their large claws discourage divers. The

Panulirus genus, spiny or rock lobsters, are the only example which is exploited by all three gear

variables. In order to mitigate this problem the analysis could be redone using only spiny or rock

lobster fisheries.

IV. Endogeneity

The primary weakness in this analysis is that it is retrospective, meaning there is a high

probability of endogeneity between the treatment and outcome variables. Endogeneity is

primarily a problem in the comparison of fisheries in developed versus developing countries as

developed countries are much more likely to be under or fully exploited and have more thorough

management policies and more consistent gear types. Thus, it is impossible to determine the

direction of a causal relationship.

Due to the retrospective nature of this report, it can be difficult to determine the true

driver of these relationships between independent and outcome variables. While landing

regulations are typically implemented when a fishery is founded, access controls, closed seasons,

and no take zones are often not implemented until they are deemed necessary and the fishery is

57  

in an unstable state. Thus there is no random selection as to which fisheries are receiving the

“treatment,” regulation, which can assist in the determination of its true impact. Absent a

randomized control trial, this endogeneity problem may be magnified in the regressions and

hinder the ability to understand what is actually driving these results.

There is always the chance that differences in outcomes across fisheries are due to

preexisting conditions rather than management regulations. As noted earlier, development has a

negative relationship with exploitation and a positive relationship with all regulatory variables,

meaning that increasing levels of development lead to higher probabilities of regulation and

lower likelihood of overexploitation. 93.5% of fisheries with any form of input controls and

90.5% of fisheries with some form of output controls are found in upper middle or highly

developed countries, demonstrating that these treatments are not randomly assigned and further

magnifying the endogeneity problem. Landings regulations displayed slightly more variability,

with 89.3% of fisheries located in upper middle and highly developed countries. However, these

values demonstrate that it is highly improbable that the treatment and control groups are

fundamentally similar.

In the case of gear types, dive fisheries are more common in undeveloped countries while

trap fisheries are positively correlated with development. There is a strong possibility that this

relationship is driven primarily by the capitalization of the fishery itself as opposed to the

development level of the country. In this dataset, 89% of artisanal fisheries are located in

undeveloped countries while 60% of commercial fisheries lie in developed areas. Artisanal

fisheries are associated with higher levels of exploitation than commercial. Given the negative

correlation between development level and exploitation and the high percentage of artisanal

58  

fisheries found in developing countries, it is likely that these characteristics are driving the

relationship rather than the gear type.

This is one of the roots of the endogeneity problems in retrospective analysis: that the

two groups being compared differ fundamentally in a manner which is unrelated to the treatment

variable.

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CHAPTER 5: Application and Further Study

The application of generalized scientific findings to real-world situations is difficult

because each solution must be tailored to the individual fishery in question. These results

represent the average impact of regulations over a wide range of cases. Implementing them in a

large commercial fishery like the Gulf of Maine is dramatically different than the small-scale

artisanal fisheries found in Colombia and Bermuda.

The motivation for this thesis was my experience working on a research team for Project

Oratsimba with NGO Azafady (now SEED Madagascar) to determine the exploitation status of a

lobster fishery in Sainte Luce, Madagascar and develop a management plan for long-term

sustainable harvesting. I assisted in the diagnostic phase owhich involved gathering CPUE data

from fishermen as they came ashore, sampling catch for sex and size ratios, and conducting

informal interviews with fishermen to understand the regulations currently in place and their

efficacy. Through conversations with the research team and these interviews, I began to question

what methods were proven to be the most effective globally when it came to lobster fishery

management and how these could be applied to Sainte Luce. While there are many case studies

on individual fisheries there are no overarching meta-analyses seeking to determine what truly

works. This project was conducted to develop a scientific basis for management and to determine

what factors have the greatest influence over a fishery’s long-term sustainable exploitation.

I. Case Study: Sainte Luce, Madagascar

Sainte Luce is a community of three coastal villages located 50 kilometers from Fort

Dauphin in southeastern Madagascar, one of the most underdeveloped areas of one of the least

developed countries in the world. The community lacks basic sanitation, healthcare, education

and food security. Primary industries of the area are subsistence based, i.e. woodcutting, fishing,

60  

and farming, though Azafady has founded an embroidery studio for sale abroad and aims to

cultivate a tourism industry in the community (Dyer 2015).

 

Figure 5: Map of Madagascar with Sainte Luce. Copyright: Google Earth 

Lobster fishing serves as a primary source of livelihood for 80% of households in Sainte

Luce (SEED 2016). Fishing is conducted with traps from dugout canoes holding between two

and four fishermen who depart before dawn and return late afternoon daily to sell their catch to

middlemen from Fort Dauphin, who in turn sell to one of three export-based companies. Sainte

Luce’s isolation and lack of access to market puts fishermen at the mercy of prices set by

middlemen from Fort Dauphin or Mahatalaky: 1,200 Ariary ($0.50) per kilogram of fish and

8,000 Ariary ($3.64) per kilogram of lobster (Ott 2015). Project Oratsimba was implemented by

Azafady in partnership with SmartFish, the FAO and the EU after years of illegal practices,

intense exploitation and unstable weather patterns began to undermine the resilience of the

fishery.

Phase one of Project Oratsimba began in early 2015 with the daily collection of CPUE

data, establish a “Riaky Committee” composed of fishermen and local leaders to manage the

61  

fishery and enforce regulation, and the creation of a voluntary no take zone which is closed for

10 months of the year (SEED 2016). To date, the NTZ has been formally established and the

Riaky Committee has been upholding the MLS and ban on berried females with the support of

the local fishermen.

i. Application of Findings

According to the findings of this study, the implementation of a no take zone in Sainte

Luce is predicted to increase its likelihood of becoming increasingly exploited by 8 times. As I

found that closed seasons are largely ineffective, there is no need to implement one in a

community like Sainte Luce where daily fishing is crucial for economic livelihoods. For access

regulations, it is ill-advised to limit effort access to the fishery given that it is the source of 80%

of livelihoods in the community. Implementing mandatory registration would be beneficial for

accountability of individual fishermen to one another and the regulations set forth by the Riaky

Committee. According to the results, TAC limits are considerably more effective than ITQs. The

question in practice is how to objectively set a TAC limit when community leaders have a

personal stake in setting as high a limit as possible.

Within the regulatory framework, the institutions present must be examined to determine

the efficacy of the solutions. Institutions are critically important in determining to what extent

formal rules will be enforced and in what manner.

i. Local Management

I believe this model underestimates the importance of local management in artisanal

fisheries and developing countries. Top-down command and control style solutions are

notoriously ineffective in dealing with the issues on the ground. Prior to the creation of the Riaky

62  

Committee in Sainte Luce, formal rules (dinas) regarding the landing of berried females and

undersized lobsters had been passed in the capital of Antananarivo but federal enforcement is

non-existent in isolated communities. In January of 2015, 18% of total lobsters landed were

berried females and the second most frequently landed class size was 191-200 mm, which is

below the MLS of 200mm (Long 2015) . Fishermen stated that they were aware of the dina

banning these landings however berried lobsters are heavier and thus fetch higher prices (Dyer

2015). This creates a perverse incentive to land berried lobsters.

The creation of the Riaky Committee and the fishing contract restating the rules creates

an enforcement mechanism at the local level which increases accountability for individual

fishers. Giving fishermen ownership over the resource increases their likelihood to conserve it.

While the Riaky Committee represents a bottom-up method of regulation and thus assists with

enforcement, it has little control over the incentives driving exploitation of the resource.

ii. Incentives

The simplest way to encourage preservation of the resource is to make exploitation less

attractive. For the community of Sainte Luce, lobster fishing serves as a way to meet basic needs.

This renders it extremely difficult and controversial to create an output limit for families living at

or below the poverty line. Thus, one way to eliminate this problem is to command higher prices

per lobster and thus make it economically viable for fishermen to catch fewer individuals. Under

the current system the three primary buyers have largely colluded to charge the same price and

thus eliminate the fishermen’s bargaining power. They have, in a sense, created an artificial

monopsony as these three buyers represent the only market which Sainte Luce has access to,

forcing fishermen to accept lower prices than they would otherwise. A way to combat this would

be for the fishermen to engage in collective action create an artificial monopoly themselves,

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Figure 6: Graph of price determination in monopolistic scenarios

cooperating to demand higher prices. Countering the monopoly with a monopoly levels the

bargaining power of the supply and demand sides of the equation, resulting in a price equilibrium

which is lower than that of a true monopoly and higher than that of a true monopsony. Game

theory is applicable to this scenario as if neither party chooses to negotiate, the prisoner’s

dilemma solution is reached in which buyers receive no lobster and sellers receive no payment.

Under circumstances when negotiation is possible a mutually beneficial outcome will be reached

resulting in a final price which is closer to competitive equilibrium than either of the extremes.

As is demonstrated in Figure 6, the price after negotiations will fall between PMS (the

price determined by a monopoly selling market, in this case the fishermen) and PMB (the price

determined by a monpoly buying market, in this case the lobster companies). In perfect

competition prices are determined by the supply and demand. In monopolistic cases, prices are

determined by demand and the marginal cost to the monopolistic party to maximize profits. In a

case like this with two monopolistic parties, the final price will be somewhere between the two

extremes.

Disenfranchisement of local fishermen, particularly in small-scale artisanal fisheries, is a

one of the greatest threats in preserving long-term sustainability of the resource. Low prices

64  

given to fishermen for their catch encourage increased exploitation while middlemen and

processing companies are able to make substantial profits. If these profits were able to trickle-

down to the fishermen themselves, it would be simpler to implement regulatory methods and

manage fisheries sustainably. Until this happens, implementing regulatory methods which limit

access or output are unrealistic in subsistence fisheries.

II. Further Analysis

These findings will not be generalizable until a larger sample size is obtained and the

endogeneity problem is dealt with. Endogeneity is a threat to internal validity which may in turn

lead to incorrect assumptions regarding the rest of the population. This could be done through

randomized controls trials using difference in differences structures which track previously

unregulated fisheries for many years to track which interventions are most effective, however the

long time scale and confounding biological factors such as climate change can muddle the

reliability of the treatment.

Another method could be the employment of regression discontinuity analysis through

the construction of a dataset examining fisheries with reliable data before and after regulatory

interventions. However, this also requires a long period of observation as fisheries in a state of

collapse may take decades to fully recover.

While this study fills the preexisting gap in the literature regarding meta-analyses of

lobster fisheries management to inform better management in the future, more research is needed

before definitive conclusions can be drawn. This study represents a first step at a holistic

examination of lobster fisheries globally and its findings could be applied to other benthic

fisheries worldwide.

65  

APPENDICES

APPENDIX A: SUPPLEMENTARY TABLES AND FIGURES

Table 1: Gear type correlations

Dive trap trawlDive 1Trap -0.1792 1Trawl -0.2962 -0.4711 1

Table 2: tabulate exploitation level and trap

ExploitationTrap 1 2 3 4 Total

0 0 9 5 1 151 7 23 14 4 48

Total 7 32 19 5 63

Table 3: tabulate exploitation level and dive

ExploitationDive 1 2 3 4 Total

0 3 23 12 5 431 4 9 7 0 20

Total 7 32 19 5 63

Table 4: tabulate exploitation level and trawl

ExploitationTrawl 1 2 3 4 Total

0 7 28 15 3 531 0 4 4 2 10

Total 7 32 19 5 63

Table 5: tabulation of artisan and development level

DevelopedArtisan 0 1 Total

0 15 30 451 16 2 18

Total 31 32 63

66  

Table 6: Tabulation of development and commercial

DevelopedCommercial 0 1 Total

0 10 1 111 21 31 52

Total 31 32 63

Table 7: Correlation of development and access

Developed Effort OutputDeveloped 1

Effort 0.4031 1Output 0.3592 0.354 1

Table 8: Classification and exploitation correlation

artisan Commercial Exploitation Artisan 1

Commercial -0.7272 1Exploitation 0.0772 -0.0621 1

Table 9: tabulation of exploitation level and landing

regulations

Exploitation LevelLandings Regulation 1 2 3 4 Total

0 1 4 6 5 161 6 28 13 0 47

Total 7 32 19 5 63

67  

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