Do social networks influence small-scale fishermen's enforcement of sea tenure?


Stevens, K.; Frank, K.A.; Kramer, D.B.

Plos One 10(3): E0121431-E0121431

2016


Resource systems with enforced rules and strong monitoring systems typically have more predictable resource abundance, which can confer economic and social benefits to local communities. Co-management regimes demonstrate better social and ecological outcomes, but require an active role by community members in management activities, such as monitoring and enforcement. Previous work has emphasized understanding what makes fishermen comply with rules. This research takes a different approach to understand what influences an individual to enforce rules, particularly sea tenure. We conducted interviews and used multiple regression and Akaike's Information Criteria model selection to evaluate the effect of social networks, food security, recent catch success, fisherman's age and personal gear investment on individual's enforcement of sea tenure. We found that fishermen's enforcement of sea tenure declined between the two time periods measured and that social networks, age, food security, and changes in gear investment explained enforcement behavior across three different communities on Nicaragua's Atlantic Coast, an area undergoing rapid globalization.

PLOS
ONE
(
11
)
CrossMark
dick
for
updates
RESEARCH
ARTICLE
Do
Social
Networks
Influence
Small-Scale
Fishermen's
Enforcement
of
Sea
Tenure?
Kara
Stevens
l
*,
Kenneth
A.
Frank
l
'
2
,
Daniel
B.
Kramer
l
'
3
1
Michigan
State
University,
Department
of
Fisheries
and
Wildlife,
East
Lansing,
Michigan,
United
States
of
America,
2
Michigan
State
University,
Department
of
Counseling,
Educational
Psychology
and
Special
Education,
East
Lansing,
Michigan,
United
States
of
America,
3
Michigan
State
University,
James
Madison
College,
East
Lansing,
Michigan,
United
States
of
America
*
stevenskara@yahoo.com
Abstract
Resource
systems
with
enforced
rules
and
strong
monitoring
systems
typically
have
more
predictable
resource
abundance,
which
can
confer
economic
and
social
benefits
to
local
communities.
Co-management
regimes
demonstrate
better
social
and
ecological
out-
comes,
but
require
an
active
role
by
community
members
in
management
activities,
such
as
monitoring
and
enforcement.
Previous
work
has
emphasized
understanding
what
makes
fishermen
comply
with
rules.
This
research
takes
a
different
approach
to
understand
what
influences
an
individual
to
enforce
rules,
particularly
sea
tenure.
We
conducted
interviews
and
used
multiple
regression
and
Akaike's
Information
Criteria
model
selection
to
evaluate
the
effect
of
social
networks,
food
security,
recent
catch
success,
fisherman's
age
and
per-
sonal
gear
investment
on
individual's
enforcement
of
sea
tenure.
We
found
that
fishermen's
enforcement
of
sea
tenure
declined
between
the
two
time
periods
measured
and
that
social
networks,
age,
food
security,
and
changes
in
gear
investment
explained
enforcement
be-
havior
across
three
different
communities
on
Nicaragua's
Atlantic
Coast,
an
area
undergo-
ing
rapid
globalization.
Introduction
Fisheries
are
a
challenging
system
to
manage
due
to
the
mobility
and
nonexcludability
of
the
resource.
Evidence
from
around
the
world
has
demonstrated
the
effectiveness
and
conservation
benefits
of
a
variety
of
sea
tenure
regimes
[1],[2].
Not
surprisingly,
fisheries
with
enforced
rules
result
in
greater
fish
biomass
and
abundance,
improved
habitat
and
increased
fishermen's
in-
comes
[3],[4].
Evidence
suggests
that
fisheries
have
better
ecological,
social
and
economic
out-
comes
under
a
co-management
regime
in
which
responsibility
for
management
is
shared
between
resource
users
and
typically,
a
government
agency
[5],[6],[7],[8].
In
addition,
co-man-
agement
implies
local
involvement
in
rule-making
and
enforcement,
which
reduces
transaction
costs
for
enforcement
[9],[10].
Small-scale
fisheries
in
developing
countries
are
often
remote
and
outside
the
reach
and
in-
fluence
of
central
governments,
but
this
does
not
presume
a
lack
of
governance.
Community
I6
OPEN
ACCESS
Citation:
Stevens
K,
Frank
KA,
Kramer
DB
(2015)
Do
Social
Networks
Influence
Small-Scale
Fishermen's
Enforcement
of
Sea
Tenure?.
PLoS
ONE
10(3):
e0121431.
doi:10.1371/joumal.
pone.0121431
Academic
Editor:
Sebastian
C.
A.
Ferse,
Leibniz
Center
for
Tropical
Marine
Ecology,
GERMANY
Received:
May
14,
2014
Accepted:
February
16,
2015
Published:
March
30,
2015
Copyright:
©
2015
Stevens
et
al.
This
is
an
open
access
article
distributed
under
the
terms
of
the
CrAativA
Commor-
Al+.41
"
4".
"
which
permits
unrestricted
use,
distribution,
and
reproduction
in
any
medium,
provided
the
original
author
and
source
are
credited.
Data
Availability
Statement:
Data
are
from
the
project
titled,
"Globalization
and
the
Connection
of
Remote
Communities"
funded
by
the
National
Science
Foundation
(#0815966).
Even
with
de-
identified
codes
in
the
database,
the
data
cannot
be
made
publicly
available
due
to
the
ethical
restrictions
imposed
by
our
IRB.
The
principal
investigator
for
the
project
is
Dan
Kramer
at
Michigan
State
University.
Researchers
can
send
requests
for
access
to
the
data
by
contacting
him
at
Funding:
This
research
was
funded
by
the
National
Science
Foundation
(NSF)
Coupled
Human
and
Natural
Systems
Program
(#0815966).
In
addition,
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ONE
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2015
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(0)
PLOS
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Social
Networks
and
Small-Scale
Fishermen's
Behavior
the
authors
thank
the
U.S.
Student
Fulbright
program
for
providing
support
for
fieldwork.
The
funders
had
no
role
in
study
design,
data
collection
and
analysis,
decision
to
publish,
or
preparation
of
the
manuscript.
Competing
Interests:
The
authors
have
declared
that
no
competing
interests
exist.
institutions,
be
they
formal
or
informal,
hold
the
responsibility
and
burden
to
ensure
rule
com-
pliance
and
handle
conflict
[11],
[12].
Successfully
managed
common
pool
resources
depend
on
the
ability
of
users
to
undertake
enforcement
themselves
[13],[12].
This
reliance
on
enforce-
ment
from
resource users
is
especially
true
in
small-scale
fisheries
of
the
remote
developing
world.
The
commons
literature
provides
rich
evidence
of
self-enforcing
communities.
A
classic
example
from
Maine
demonstrates
a
strong
social
norm
of
territoriality,
enforced
by
fishermen
themselves
with
the
ability
to
organize
and
enforce
in
the
absence
of
a
government-recognized
legal
framework
[14].
Other
studies
suggest
that
community
characteristics
such
as
high
de-
grees
of
social
capital,
clear
rules
and
sanctions,
the
involvement
of
resource
users
in
establish-
ing
regulations
and
cross-scale
linkages
between
communities
and
higher
levels
of
governance
improve
enforcement
at
the
community
level
[15],[16],[17].
It
is
important
to
distinguish
between
compliance
and
enforcement,
particularly
in
manage-
ment
of
the
commons.
Enforcement
is
one
aspect
that
contributes
to
overall
compliance,
yet
when
we
think
of
individual
behavior,
the
act
of
enforcing
rules
requires
an
elevated
sense
of
commitment
to
community-based
governance
compared
to
simply
complying
with
rules.
In
the
case
of
illegal
fishing,
while
a
fisherman
may
limit
his
fishing
to
the
legal
grounds
(compli-
ance),
he
may
not
go
so
far
as
to
prevent
and
confront
others
from
fishing
in
restricted
areas
(enforcement).
Enforcement
has
been
examined
from
the
top-down
by
exploring
the
question
of
what
makes
people
comply
with
rules
[18],[19],[20],[21],[22],[23]
or
the
effectiveness
of
government
agencies
in
using
enforcement
to
achieve
compliance
[24].
Peer
group
solidarity,
moral
motives
and
the
legislator's
authority
have
been
found
to
explain
fishermen's
compliance
[25],[26],[27],[28].
Studies
have
also
found
a
fisherman's
motivation
to
comply
varies
depend-
ing
on
the
degree
to
which
he
violates
rules
[25];
similarly
there
is
variation
in
fishermen's
will-
ingness
to
denounce
others
for
illegal
fishing
[29].
Yet
there
have
been
few
empirical
studies
exploring
the
motivation
of
individual
resource
users
to
participate
in
enforcing
restrictions
on
illegal
fishing
activities
amongst
themselves,
a
fundamental
component
of
co-management
regimes.
One
pathway
by
which
social
pressure
is
applied
is
the
social
network,
in
other
words,
the
relationships
amongst
individuals
in
a
defined
population
such
as
a
community
or
group
of
re-
source
users.
Networks
have
been
analyzed
at
multiple
scales
to
understand
behavioral
out-
comes
in
health,
governance,
education
and
business
[30],[31],[32],
and
more
recently
social
network
analysis
has
been
applied
to
explain
dynamics
in
agriculture
and
fisheries
manage-
ment.
Social
network
characteristics
such
as
the
level
of
network
cohesion
or
the
existence
of
bridging
ties
can
affect
cooperation
on
resource
management
issues,
conflict
resolution,
influ-
ence
on
decision-making
processes,
information
sharing
and
community-based
monitoring
and
enforcement
of
rules
[33],[34],[35],[36].
Social
network
structure
can
also
change
as
a
re-
sult
of
changes
in
resource
condition
[37].
Other
network
studies
identify
links
between
indi-
vidual
network
position,
such
as
centrality,
or
personal
network
size,
and
farmers'
ability
to
accept
agricultural
extension
information
or
plant
diverse
species
[38],[39].
In
this
study,
rath-
er
than
trying
to
understand
how
an
individual's
structural
position
in
a
network
affects
deci-
sion-making,
we
study
explicitly
how
his
ties
affect
his
behavior.
That
is,
we
examine
how
the
behavioral
characteristics
of
a
fisherman's
social
network
affect
his
own
behavior,
particularly
his
enforcement
of
sea
tenure
(i.e.
enforcement
behavior).
While
Kuperan
&
Sutinen
[18]
found
that
amongst
other
factors,
social
influence
was
important
in
determining
fishermen's
compliance
with
rules,
the
question
here
is
whether
social
ties
influence
a
fisherman's
enforce-
ment
of
rules.
We
build
on
the
framework
of
communal
management
of
the
commons
to
examine
how
in-
dividuals
within
a
community
experience
norms
in
different
ways,
depending
on
the
networks
in
which
they
are
embedded
[40].
Enforcement
of
social
norms
is
a
second-order
free-rider
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Social
Networks
and
Small-Scale
Fishermen's
Behavior
problem
in
which
the
cost
of
punishing
others
is
borne
by
a
few,
but
benefits
of
the
punishment
are
reaped
by
many
[41].
This
voluntary
engagement
in
enforcement
is
supported
by
both
the-
oretical
and
empirical
studies
[42],
[43],
[44].
Of
particular
relevance
are
studies
of
altruistic
punishment,
which
find
that
group
selection
can
play
an
important
role
in
the
motivation
of
an
individual
to
expend
costly
resources
to
punish
violators
of
social
norms
[45].
Social
net-
works
can
provide
the
support
for
norm
enforcement
that
is
beneficial
at
the
community
level
and
contributes
to
fisheries
sustainability.
This
study
contributes
to
our
understanding
of
social
networks
by
examining
how
peer
groups
influence
individual
behavior
while
controlling
for
other
factors
such
as
prior
behavior,
food
security,
age,
and
investment
in
the
fishery.
We
explored
this
question
in
Nicaragua's
rapidly
transforming
Atlantic
Coast
region,
which
was
connected
via
road
to
the
country's
interior
for
the
first
time
in
2007.
Road
connection
has
resulted
in
a
host
of
direct
and
indirect
changes
to
coastal
communities,
including
expansion
of
markets
for
fisheries
products,
facilitated
migration
and
the
introduction
of
new
technologies
[46],[47].
Increased
market
access
in
these
communities
has
resulted
in
a
40-65%
increase
in
fish
price
(paid
to
fishermen)
from
2010-2012
(pers.
obs.).
With
widespread
perceptions
from
local
fishermen
of
declines
in
lagoon
fisheries
coupled
with
increased
market
value
of
fisheries
products,
we
might
expect
enforcement
of
sea
tenure
to
increase.
In
contrast,
rapid
large-scale
changes
and
declining
fish
stocks
may
induce
despair
that
the
state
of
the
resource
is
such
that
individual
actions
have
no
meaningful
impact.
In
this
study,
we
examined
1)
whether
enforcement
behavior
amongst
individual
fishermen
changed
over
time,
2)
if
social
networks
had
an
influence
on
that
change
and
3)
other
factors
that
explain
changes
in
fishermen's
behavior.
Study
Site
Pearl
Lagoon
refers
to
a
municipality,
a
community
and
an
estuary.
For
the
purposes
of
clarity
and
to
be
consistent
with
previous
work,
we
refer
to
the
estuary
as
the
Lagoon,
the
municipality
as
the
Municipality
and
the
community
as
Pearl
Lagoon.
Nicaragua's
Southern
Autonomous
Region
(RAAS)
is
divided
into
thirteen
municipalities.
The
Pearl
Lagoon
municipality
prohib-
its
artisanal
fishermen
and
semi-industrial
boats
from
other
municipalities
from
fishing
in
its
waters
[
].
The
four
communities
studied
in
this
research,
Raitipura,
Awas,
Brown
Bank
and
Orinoco,
vary
in
distance
from
the
main town
of
Pearl
Lagoon,
which
is
one
of
the
closest
points
of
entry
from
the
municipality
to
the
south,
Bluefields
(Fig
1).
Bluefields
is
also
situated
on
an
estuary
and
is
located
approximately
40
km
from
Pearl
Lagoon,
which
is
reached
through
freshwater
canals
and
rivers.
While
both
municipal
centers
lie
on
a
lagoon,
the
Bluefields
Bay
is
less
than
half
the
surface
area
of
the
lagoon
and
supports
a
human
population
10
times
the
size.
Most
of
the
illegal
fishing
in
the
lagoon
can
be
attributed
to
fishermen
from
Bluefields,
who
have
been
coming
into
the
lagoon
at
least
since
1997
[49],
and
possibly
before,
the
cause
of
which
may
be
linked
to
an
exhaustion
of
commercially
viable
fishery
resources
in
Bluefields
Bay.
Lagoon
fishermen
report
that
Bluefields
fishermen
violate
several
local
social
norms
(K.
Stevens,
qualitative
interview
data, pers.
obs.).
In
addition
to
reports
of
illegal
fishing,
Bluefields
interlopers
are
reported
to
use
gill
nets
with
a
mesh
size
smaller
(3-inch)
than
the
minimum
ac-
ceptable
size
(4-inch)
and
use
3-4
times
the
number
of
gill
nets
typically
used
by
most
local
fishermen
to
ensure
profitability
(X.
Gordon,
pers.
comm.
K.Stevens,
qualitative
interview
data).
Additionally,
they
carry
multiple
iceboxes
capable
of
carrying
several
hundred
pounds
each,
often
attempt
to
enter
and
exit
the
lagoon
under
cover
of
night,
and
do
not
sell
their
product
in
the
municipality,
generating
no
tax
revenue
for
the
local
government
nor
income
for
local
fish-buying
middlemen
(X.
Gordon,
pers.
comm.).
However,
they
do
sometimes
invite
local
fishermen
to
work
on
their
boats
in
an
attempt
to
legitimize
their
activities
(K.Stevens,
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Honduras
Nicaragua
osta
Ric
Orinoco
Brown
Ban
Bank
PEARL
LAGOON
Pearl
Lagoo
Raitipura/
it
ii
ii
it
r1
ii
=======
New
Road
Community
NV3ggleIVO
0
3.75
7.5
15
I I I I I I I I
I
Kilometers
Bluefiel
(1
PLOS
ONE
Social
Networks
and
Small-Scale
Fishermen's
Behavior
Fig
1.
Nicaragua's
Atlantic
Coast.
The
capital
of
Nicaragua's
Southern
Autonomous
Region
is
Bluefields.
The
study
took
place
in
the
municipal
capital
north
of
Bluefields,
Pearl
Lagoon.
doi:10.1371/joumal.pone.0121431.g001
qualitative
interview
data).
Based
on
interviews,
fishermen
may
report
the
outsiders
whose
ac-
tivities
do
not
align
with
established
norms
to
their
communal
board
or
the
municipal
authori-
ties
(K.Stevens,
qualitative
interview
data,
pers.
obs,
X.
Gordon,
pers.
comm.).
Lacking
resources
to
make
regular
patrols,
the
municipal
authority
only
expends
resources
for
on-water
patrol
if
they
hear
complaints
from
fishermen
about
illegal
fishing
(X.
Gordon,
pers.
comm.).
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Social
Networks
and
Small-Scale
Fishermen's
Behavior
Methods
Interviews
were
conducted
with
fishermen
in
four
communities
in
February
of
2011
and
2012
(n
=
285
in
each
year).
The
same
fishermen
were
interviewed
in
both
years.
They
ranged
in
age
from
18-71.
A
core
group
of
fishermen
was
identified
based
on
pilot
interviews
in
2010.
There-
after,
a
snowball
interview
method
was
used
to
define
the
population
of
fishermen
based
on
each
person's
responses
to
the
following
questions
regarding
their
social
networks:
1)
with
whom
do
you
fish
and
2)
with
whom
are
you
friends?
Interviews
were
conducted
with
all
fish-
ermen
mentioned
in
response
to
these
questions
until
saturation
was
reached.
Fishermen
were
also
asked
whether
they
had
seen
or
verbally
confronted
a
fisherman violating
sea
tenure
in
the
past
year,
the
frequency
of
confrontation(s),
whether
it
led
to
physical
violence
and
whether
they
requested
action
or
support
from
government
entities,
such
as
the
communal
board,
com-
munal
leader
or
local
municipality
(S4
Table).
This
is
considered
an
exhaustive
list
of
likely
en-
forcement
actions.
An
enforcement
score
was
tabulated
based
on
individual
responses
to
these
questions,
such
that
one
point
was
given
for
each
enforcement
action
taken.
The
lower
the
score,
the
fewer
actions
the
fisherman
undertook
in
confronting
or
reporting
outsiders
fishing
in
the
lagoon.
The
responses
were
standardized
between
years
to
only
compare
enforcement
of
sea
tenure
between
those
who
had/had
not
encountered
outsiders
in
both
years
measured.
Trained
community
data
collectors
interviewed
fishermen
in
each
community
on
a
weekly
basis
about
their
fishing
trips
from
March
2010—June
2012.
They
collected
data
on
type,
size
and
number
of
gear
used,
fishing
location,
duration
of
fishing
trip,
number
and
total
pounds
of
each
species
captured
and
the
price
per
pound
for
species
sold.
These
data
were
used
to
calcu-
late
the
covariate,
income,
described
in
the
model
statements
below.
The
Institutional
Review
Board
of
Michigan
State
University
approved
these
studies.
Writ-
ten
consent
was
obtained
from
participants
prior
to
the
start
of
the
survey.
The
consent
form,
which
included
the
purpose
of
the
study,
who
would
have
access
to
the
data,
how
the
data
would
be
used
and
the
right
to
refuse
was
explained
to
participants.
Thereafter
participants
signed
the
consent
form.
Data
Analysis
To
examine
whether
individual
enforcement
behavior
changed
between
the
two
years,
we
ana-
lyzed
individual
differences
in
enforcement
action
in
four
communities
using
a
paired
t-test
(R
v.
2.15.1,
2012).
Two
social
network
measures
were
used
in
our
analysis.
First,
we
used
KliqueFinder
to
ex-
amine
social
network
data
and
identify
possible
subgroups
in
each
community
[50],
[51].
Kli-
queFinder
is
one
of
several
programs
capable
of
identifying
subgroups
in
network
data.
It
does
so
by
identifying
which
individuals
are
more
likely
to
interact
with
each
other
than
with
others
in
the
community
by
iteratively
maximizing
the
odds
ratio
of
ties
between
fishermen
and
their
subgroup
membership
[50].
Subgroups
have
been
shown
to
have
a
strong
influence
on
behav-
ior
in
other
contexts
[52]
and
are
important
in
assessing
outcomes
in
resource
governance
[35].
Second,
we
determined
each
fisherman's
egocentric
network,
those
individuals
directly
identified
by
the
fisherman
as
fishing
partners
or
friends.
Awas
is
a
small
community
that
was
established
due
to
land
shortages
in
Raitipura
and
the
two
are
in
close
proximity
and
linked
by
family
relations
and
intermarriage.
Because
fishermen
from
Awas
have
fishing
partners
and
friends
from
Raitipura
and
vice
versa,
the
two
communities
were
analyzed
separately
and
together.
We
used
Akaike's
Information
Criteria
corrected
for
small
sample
size
(AICc)
to
evaluate
twelve
candidate
models'
ability
to
explain
fishermen's
enforcement
behavior
[53].
These
candidate
models
were
selected
a
priori
based
on
the
inclusion
of
economic
and
ecological
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Fishermen's
Behavior
explanatory
variables,
social
network
influence
variables,
a
combination
of
the
two
and
the
global
model.
We
included
fishermen's
income
as
a
variable
given
the
positive
correlation
be-
tween
resource
abundance
and
enforcement,
as
well
as
other
evidence
indicating
stock
crashes
induce
a
stronger
conservation
ethic
and
support
for
established
tenure
rights
[54],[3],[55].
Age
has
been
shown
to
be
positively
associated
with
rule
compliance
and
we
expect
that
it
could
also
be
a
factor
affecting
enforcement
behavior
[56],[57].
While
studies
have
examined
how
fisheries
abundance
and
diversity
affect
food
security
of
coastal
communities
[58],[59],
the
reverse
pathway,
how
food
security
affects
a
fisherman's
harvest
practices,
has
been
rarely
studied.
Yet
we
know
from
other
disciplines
that
food
insecurity
has
profound
effects
on
health
and
physiology,
which
can
influence
behavior
[60],[61].
We
included
gear
investment
as
a
proxy
for
a
fishermen's
investment
in
the
fishery
based
on
the
logic
that
those
with
greater
in-
vestment
have
greater
incentives
to
engage
in
management
of
the
fishery
[52].
The
model
with
the
lowest
AICc
score
was
considered
the
most
likely
[53].
In
addition,
we
report
the
difference
in
AICc
scores
between
the
best-fit
model
and
other
candidate
models
(AAICc).
Delta
AICc
values
of
0
indicate
the
model
with
the
most
explanatory
power;
and
models
with
AAICc
values
less
than
two
are
considered
equally
likely
to
be
the
best
model
[53].
The
weights
(w
i
)
sum
to
one
and
indicate
how
much
support
that
model
has
amongst
candidate
models
in
explaining
the
outcome.
The
fishermen's
enforcement
score
in
the
second
year
was
the
dependent
variable,
and
the
effect
of
the
egocentric
network,
the
subgroup,
the
fishermen's
prior
behavior
and
other
eco-
nomic
and
ecological
factors
were
explanatory
variables
(
).
Y,
=
*
v
/
,
t_l,
p2prior
behavior,„
+
p3age,
+p4food
security,
+
p5gear
i
+
pflincome,
+
p7group,„
Eq.1
In
this
model,
Y
it
isthe
enforcement
behavior
of
fisherman
i
at
timet.
The
relationship
be-
tween
fisherman
i
and
his
friends/fishing
partners
i'
is
described
as
x
ie
.
y
et
_
i
is
the
prior
behav-
ior
of
fisherman
Thus
the
egocentric
network
exposure
term
(referred
to
as
ego
in
model
statements)
E
x
if
*
'Cie
represents
the
exposure
to
the
practices
in
one's
network,
in
other
words,
the
mean
enforcement
score
in
year
one
amongst
the
fishermen's
selected
friends
and
fishing
partners
[63].
The
mean
exposure
to
members
of
the
fishermen's
friends
or
fishing
partners
subgroup
as
identified
by
KliqueFinder
is
encompassed
in
the
term
group.
The
first
year
enforcement
measure
in
the
model
statement
is
described
as
the
prior
behavior
of
fisher-
man
i.
Age
of
the
fishermen
was
measured
in
year
two
(age).
Food
security
was
measured
di-
rectly
in
one
community,
Orinoco.
It
was
based
on
interviewee's
responses
to
four
questions
about
food
scarcity,
affordability
and
uncertainty
based
on
an
adaptation
of
the
United
States
Department
of
Agriculture
Short
Form
Food
Security
Survey
[64]
(S2
Table).
Without
direct
measures
of
food
security
in
Raitipura
and
Awas,
we
divided
household
wealth
by
household
size
to
generate
a
metric
of
wealth
per
household
member,
which
serves
as
a
proxy
for
food
se-
curity
(food
in
model
statements).
We
have
both
of
these
metrics
in
11
households
in
Orinoco.
The
two
measures
are
significantly
correlated
based
on
Pearson's
correlation
test
(r
=
.63,
p-
value
=
.04).
Principal
components
analysis
(R
v.2.15.1,
2002,
princomp
package)
was
used
to
generate
a
household
wealth
measure
from
binary
responses
to
ownership
of
household
goods
[
].
Fishermen's
personal
gear
investment
was
estimated
based
on
the
difference
in
the
summed
monetary
value
of
all
fishing
gear
owned
between
the
first
and
second
year
including
boat,
motor,
dugout
canoe
and
various
types
of
gear
like
gill
net,
cast
net
and
trawl
net
(gear)
(S3
Table).
The
term,
income,
is
a
measure
of
the
average
income
per
trip
earned
by
the
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Fishermen's
Behavior
individual
fishermen
in
the
two
months
prior
to
the
year
two
behavior
measure.
It
includes
shrimp
and
fish
harvest
with
any
gear
type
including
handline,
cast
net
and
gill
net.
In
order
to
approximate
the
near-term
individual
economic
status
of
fishermen
the
measure
is
not
species
or
gear
specific.
Data
on
price
per
pound
was
recorded
for
each
species
and
total
income
per
trip
was
summed
across
all
species.
Most
commonly
captured
fish
species
in
this
metric
include
mojarra
Eugerres
plumieri,
catfish
Bagre
marinus,
snook
Centropomus
spp.,
and
croaker
Micro-
pogonias
furnieri.
This
paper
includes
explanatory
variables
that
are
contextually
important
and
highly
variable
in
small-scale
fisheries.
In
addition
to
the
influence
of
their
social
network,
we
might
expect
that
fishermen
with
a
greater
investment
in
the
fishery
(gear),
whose
recent
in-
come
was
low
(income),
who
have
more
experience
(age),
and/or
who
are
food
insecure
(food)
to
be
more
likely
to
enforce
sea
tenure
to
remove
illegal
fishermen.
We
tested
for
multicollinearity
by
evaluating
variance
inflation
factors
of
explanatory
vari-
ables;
all
had
values
between
1
and
2
(R
v.
2.15.1,
2012).
Raitipura
and
Raitipura/Awas
friends
and
partners
models
met
the
assumptions
of
regression
without
transformation.
The
depen-
dent
variable
in
the
Orinoco
friends
and
partners
models
was
square-root
transformed
to
meet
assumptions
of
multiple
linear
regression.
There
are
concerns
about
potential
dependencies
in
estimating
any
social
network
model
(e.g.,
[66],
[67]).
Estimated
influence
is
biased
if
the
errors
are
not
independent
of
the
network
exposure
term
(see
[68],
equations
1.2-1.4).
The
estimate
of
influence
will
be
positively
biased
if
there
is
some
unexplained
aspect
of
enforcement
behavior
that
is
related
to
the
network
exposure.
The
most
compelling
source
of
such
dependencies
would
be
if
people
choose
to
interact
with
others
whose
behaviors
are
similar
to
their
own,
known
as
selection
in
the
network
literature
[69].
Those
who
engaged
in
enforcement
in
the
first
year
might
have
chosen
to
interact
with
similar
others be-
tween
the
first
and
second
year,
and
also
would
have
been
inclined
to
engage
in
enforcement
be-
haviors
in
the
second
year.
Because
the
network
exposure
term
is
likely
confounded
with
prior
enforcement
behavior,
the
model
used
here
includes
a
control
for
prior
enforcement
behavior.
A
second
concern
would
arise
if
the
model
of
a
fisherman's
behavior
was
a
function
of
the
contemporaneous
behavior
of
those
in
his
network.
This
would
essentially
put
the
outcome
on
both
sides
of
the
model
in
which
case
the
errors
would
be
directly
related
to
the
exposure
term.
It
is
for
this
reason
that
we
model
enforcement
behavior
as
a
function
of
the
previous
behaviors
of
others
in
one's
network.
This
avoids
creating
dependencies
between
the
errors
and
predic-
tors
by
putting
the
same
variables
on
both
sides
of
the
model.
Results
Behavior
change
Brown
Bank
was
the
only
community
in
which
enforcement
scores
between
the
two
years
were
not
significantly
different
(Table
1).
In
all
communities,
the
enforcement
score
decreased
in
the
second
time
period,
indicating
fewer
mean
enforcement
actions.
Raitipura
had
the
highest
en-
forcement
score
of
all
communities.
Enforcement
scores
decreased
with
increasing
distance
from
the
municipal
center
and
the
newly
constructed
road.
When
we
compare
specific
enforcement
actions
of
fishermen
who
observed
outsiders
in
both
years,
we
see
fewer
direct
confrontations
with
outsiders
amongst
Orinoco
and
Brown
Bank
fishermen,
but
more
in
Raitipura
and
Awas
(Table
2).
From
2011
to
2012,
across
all
com-
munities
there
were
fewer
(or
no
change
in)
requests
to
the
community
leader
and
community
board
for
assistance
in
preventing
illegal
fishing,
and
fewer
requests
to
the
municipality.
Of
96
direct
confrontations
in
2011,
one
resulted
in
physical
attack
or
threat
of
attack.
Of
65
direct
confrontations
in
2012,
zero
resulted
in
physical
attack.
Across
all
fishermen
interviewed
in
2011,
25%
of
fishermen
who
observed
illegal
fishing
by
those
from
outside
the
municipality
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Behavior
Table
1.
Results
of
t-test
of
change
in
individual
enforcement
actions
between
2011
and
2012
in
four
communities
of
the
Pearl
Lagoon
basin.
2011
2012
df
p
Mean
SE
Mean
SE
Brown
Bank
3.09
.60
2.53
.61
16
.31
Orinoco
1.72
.19
.79
.12
102
0.00*
Raitipura
3.47
.33
2.58
.28
73
.002*
Raitipura/Awas
3.22
.28
2.49
.24
90
.005*
*p<.01.
doi:10.1371/joumal.pone.0121431.t001
took
no
enforcement
action.
In
2012,
this
had
increased
to
42%.
We
used
Cronbach
alpha
as
a
measure
of
internal
consistency
to
assess
the
degree
to
which
the
enforcement
questions
mea-
sured
enforcement.
In
2011,
the
Cronbach
alpha
was.
73
and
in
2012
it
was.
69,
which
was
cal-
culated
from
questions
about
reporting
to
the
communal
leader,
communal
board
and
municipal
staff
since
those
questions
were
asked
consistently
to
all
respondents
regardless
of
observation
of
illegal
activity
(Table
2).
Cronbach's
alpha
coefficients
showed
good
internal
consistency
in
both
years.
Social
Network
Based
on
KliqueFinder
results,
we
determined
that
between
4
and
24
distinct
subgroups
of
friends
and
fishing
partners
exist
within
the
communities,
depending
on
the
size
of
the
fisher-
men
population
(Table
3).
When
analyzed
independently,
Awas
did
not
show
evidence
of
dis-
tinct
subgroups.
Across
all
communities
and
both
networks,
the
mean
size
of
the
subgroup
was
4.7
individuals;
the
mean
size
of
the
egocentric
network
was
3.0.
Factors
influencing
behavior
change
With
year
two
enforcement
behavior
as
the
dependent
variable,
12
candidate
models
were
eval-
uated.
We
first
ran
these
models
for
the
friends
network
(Table
4).
In
Orinoco,
models
H,
I,
J
and
K
were
considered
highly
likely
and
differed
in
the
inclusion
of
age
and
egocentric
or
sub-
group
network
terms.
Gear
investment
and
income
were
also
included
in
the
best-fit
models
in
Orinoco.
In
both
Raitipura
and
Raitipura/Awas,
the
global
model
was
highly
supported
(w
i
=
1.0).
Sample
size
in
Brown
Bank
was
too
small
to
effectively
evaluate
the
models.
Next,
we
ran
the
same
twelve
candidate
models
to
examine
the
effects
of
the
fishing
partners
social
network
(Table
5,
See
S1
Table
for
global
model
fit
characteristics).
In
Orinoco,
Raitipura
and
Raitipura/Awas
the
same
4
models
selected
in
the
previous
analysis
were
again
selected.
Table
2.
Percent
change
in
specific
individual
enforcement
actions
from
2011-2012.
Observation
of
illegal
fishers
Confrontation
with
illegal
fishers
Report
to
communal
leader
Report
to
communal
board
Report
to
municipality
Brown
Bank
-9% -4%
0
-50%
-17%
Orinoco
-15% -56%
-37%
-47%
-63%
Raitipura
-15%
10%
-11%
-17%
-3%
Raitipura/
-20%
22%
-5%
-12%
-9%
Awas
Cronbach
alpha
2011
=
.73;
Cronbach
alpha2012
=
.69.
doi:10.1371/joumal.pone.0121431.t002
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and
Small-Scale
Fishermen's
Behavior
Table
3.
Results
of
KliqueFinder
analysis
of
social
network
data
showing
evidence
of
distinct
sub-
groups
in
friends
and
fishing
partners
networks
in
four
communities
of
the
Pearl
Lagoon
basin.
Community
Friends
subgroups
Fishing
partners
subgroups
Awas
(n
=
23)
None
None
Brown
Bank
(n
=
31)
4
*
4
*
Orinoco
(n
=
130)
24
*
20
*
Raitipura
(n
=
92)
18
*
16
*
Raitipura/Awas
(n
=
115)
22
*
22
*
*p<.01.
doi:10.1371/joumal.pone.0121431.t003
We
calculated
the
percent
deviance
contributed
by
each
variable
in
the
model
of
best
fit
for
each
community
(Fig
2).
If
the
parameter
estimate
plus
or
minus
standard
error
did
not
include
zero,
the
sign
is
given
for
the
explanatory
variable.
In
both
friends
and
fishing
partners
network
models,
in
all
communities,
prior
behavior
explained
most
of
the
deviance.
Thereafter,
we
find
fishermen's
age,
food
security,
recent
catch
(income),
and
social
network
measures
to
explain
deviance
in
enforcement
of
sea
tenure.
The
relative
importance
of
these
factors
varies
by
community.
Discussion
This
study
explores
why
fishermen
enforce
restrictions
on
illegal
activity,
particularly
violations
of
sea
tenure,
and
fmds
that
accounting
for
a
fisherman's
social
network,
food
security,
age,
gear
investment
and
income
is
important
in
predicting
enforcement
actions.
Social
network
terms
were
included
in
models
of
best-fit
across
all
communities,
consistent
with
the
normative
Table
4.
Model
selection
results
of
the
influence
of
a
fisherman's
friends
network
on
enforcement
behavior.
1
Models
k
AlCc
score
Orinoco
AAICc
w,
Raitipura
w,
Raitipura/Awas
AlCc
score
AAICc
w,
AlCc
score
AAICc
Ecological
and
Economic
Models
A)
age
+
gear
+
income
+
food
4
187.1
26.9
0
312.8
36.2
0
325.2
41.1
0
B)
age
+
gear
+
income
3
204.7
44.5
0
333.9
57.3
0
337.8
53.7
0
c)
gear
+
income
2
204.3
44.1
0
344.6
68.0
0
343.9
59.8
0
Network
Influence
Models
G)
ego
+
prior
behavior
2
202.3
42.1
0
309.1
32.5
0
371.1
87
0
E)
ego
+
group
+
prior
behavior
3
203.6
43.4
0
310.6
34.0
0
354.8
70.7
0
9
group
+
prior
behavior
2
201.4
41.2
0
308.6
32.0
0
352.6
68.5
0
Prior
Behavior
G)
prior
behavior
1
200.2
40.0
0
307.9
31.3
0
369.1
85
0
Combined
Models
FI)
gear
+
income
+
ego
+
prior
behavior
4
160.5
0.3
.23
304.2
27.6
0
305.3
21.2
0
I)
gear
+
income
+
group
+
prior
behavior
4
160.7
0.5
.21
303.9
27.3
0
303.7
19.6
0
J)
age
+
gear
+
income
+
ego
+
prior
behavior
5
160.2
0
.27
294.3
17.7
0
297.9
13.8
0
1
`
)
age
+
gear
+
income
+
group
+
prior
behavior
5
160.7
0.5
.21
293.9
17.3
0
296.4
12.3
0
Global
Model
14
ego
+
group
+
prior
behavior
+
income
+
age
+
gear
+
food
7
162.9
2.7
.07
276.6
0
1
284.1
0
1
1
See
S1
Table
for
parameter
estimates
and
adjusted
r-squared
for
models
of
best
fit.
doi:10.1371/joumal.pone.0121431.t004
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ONE
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30,
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9
/
17
PLOS
ONE
Social
Networks
and
Small-Scale
Fishermen's
Behavior
Table
5.
Model
selection
results
of
the
influence
of
a
fisherman's
fishing
partners
network
on
enforcement
behavior.'
Models
k
Orinoco
Raitipura
Raitipura/Awas
AlCc
Score
AAICc
W,
AlCc
Score
AAICc
W,
AlCc
Score
AAICc
W,
Ecological
and
Economic
Models
A)
age
+
gear
+
income
+
food
4
187.1
26.6
0
312.8
37.2
0
325.2
39.4
0
B)
age
+
gear
+
income
3
204.7
44.2
0
333.9
58.3
0
337.8
52.0
0
c)
gear
+
income
2
204.3
43.8
0
344.6
69.0
0
343.9
58.1
0
Social
Network
Models
G)
ego
+
prior
behavior
2
202.4
41.9
0
310.1
34.5
0
371.3
85.5
0
E)
ego
+
group
+
prior
behavior
3
204.3
43.8
0
312.0
36.4
0
371.3
85.5
0
9
group
+
prior
behavior
2
202.1
41.6
0
309.7
34.1
0
369.5
83.7
0
Prior
Behavior
G)
prior
behavior
1
200.2
39.7
0
307.9
32.3
0
369.1
83.3
0
Combined
Models
H)
gear
+
income
+
ego
+
prior
behavior
4
160.6
0.1
.24
304.6
29.0
0
306.1
20.3
0
I)
gear
+
income
+
group
+
prior
behavior
4
160.8
0.3
.22
304.5
28.9
0
303.9
18.1
0
J)
age
+
gear
+
income
+
ego
+
prior
behavior
5
160.5
0
.25
294.3
18.7
0
297.9
12.1
0
K)
age
+
gear
+
income
+
group
+
prior
behavior
5
160.7
0.2
.23
294.2
18.6
0
297.1
11.3
0
Global
Model
L)
ego
+
group
+
prior
behavior
+
income
+
age
+
gear
+
food
7
163.1
2.6
.07
275.6
0
1
285.8
0
1
1
See
S1
Table
for
parameter
estimates
and
adjusted
r-squared
for
models
of
best
fit.
doi:10.1371/joumal.pone.0121431.t005
model
of
behavior
that
fishermen
are
influenced
by
social
norms
and
their
peer
group,
though
networks
appear
to
play
a
minor
role
relative
to
other
factors
in
the
model
[
].
We
compared
the
influence
of
two
types
of
networks-friends
and
fishing
partners-on
en-
forcement
actions
and
found
little
difference
between
the
two.
The
questions
we
used
to
mea-
sure
enforcement
asked
about
both
on-water
and
on-land
enforcement
actions.
It
is
logical
that
both
social
networks
that
were
measured
influence
this
behavior
since
on-water
confronta-
tion
with
an
illegal
fisherman
would
likely
be
accomplished
with
a
fishing
partner,
while
fol-
low-up
with
local
or
municipal
government
would
potentially
be
accomplished
with
friends.
Based
on
qualitative
responses
during
interviews,
there
is
community
variation
in
how
to
han-
dle
outsiders
fishing
in
Pearl
Lagoon.
While
some
fishermen
expressed
staunch
opposition
to
outsiders
fishing
in
the
lagoon
on
any
terms,
some
allow
it
depending
on
the
methods
being
used
or
based
on
the
belief
that
all
people
have
a
right
to
make
a
living,
and
in
some
cases
local
fishermen
join
the
illegal
fishing
group.
Few
fishermen
joined
illegal
fishermen,
depending
on
the
community,
and
for
various
reasons
these
individuals
were
not
included
in
the
models.
The
lack
of
a
widely
held
and
agreed
upon
social
norm
related
to
sea
tenure
within
the
communities
may
affect
the
influence
of
a
fisherman's
social
network.
Sanctioning
those
who
violate
norms
can
be
a
socially
costly
action,
though
less
so
when
the
violation
is
commonly
practiced
within
a
community
[70].
Social
networks
can
have
both
a
positive
and
negative
effect
on
individual
enforcement.
A
positive
effect
can
be
interpreted
as
the
more
an
individual's
network
members
engage
in
enforcement
behavior,
the
more
likely
that
individual
is
to
engage
in
the
behavior.
A
negative
effect
means
that
the
more
an
individual's
network
members
engage
in
enforcement,
the
less
likely
that
individual
is
to
engage
in
the
behavior.
This
situation
can
arise
when
the
be-
havior
is
regarded
as
less
desirable
or
in
cases
of
prevalent
free-riding.
For
example,
in
commu-
nities
where
network
influence
of
enforcement
is
negative,
there
may
be
an
over-reliance
on
certain
individuals
to
enforce
while
others
reap
the
benefits.
PLOS
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(a)
Raitipura
0.25
-
Raitipura/Awas
0.2
-
Orinoco
0.15
-
0.1
-
0.05
-
Percen
t
Dev
iance
Exp
la
ine
d
0.3
-
Prior
Food
Beh.
+
+
Age
Pounds
Ego
Gear
_i
F
Group
0
Prior
Beh.
Pe
rce
n
t
Dev
ia
nce
Exp
la
ine
d
0.35
0.3
-
I
0.25
0.2
-
0.15
-
0.1
-
0.05
(b)
Raitipura
Raitipura/Awas
Orinoco
Age
Food
Pounds
Ego
Gear
Group
I
+
PLOS
ONE
Social
Networks
and
Small-Scale
Fishermen's
Behavior
Fig
2.
Percent
deviance
of
each
parameter.
Percent
deviance
explained
by
each
parameter
in
the
best-fit
model
explaining
fishermen's
enforcement
behavior
for
the
(a)
friends
network
and
the
(b)
fishing
partners
network.
If
the
parameter
estimate
plus/minus
the
standard
error
does
not
include
zero,
the
direction
of
the
parameter
estimate
is
indicated
above
the
column.
doi:10.1371/joumal.pone.0121431.g002
In
all
models
with
network
influence
terms
we
controlled
for
prior
behavior,
yet
there
was
no
evidence
to
support
the
model
that
only
included
prior
behavior,
indicating
that
behavior
had
changed
and
other
factors
explain
the
change.
Further,
the
models
that
only
included
eco-
logical
and
economic
variables,
such
as
food
security,
age,
gear
investment
and
income
received
no
support
in
explaining
enforcement
behavior.
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Social
Networks
and
Small-Scale
Fishermen's
Behavior
The
lack
of
enforcement
of
sea
tenure
by
the
majority
of
fishermen
in
the
lagoon
coupled
with
the
variation
in
response
to
illegal
fishers
may
explain
why
network
terms
did
not
explain
most
of
the
deviance
in
the
outcome
variable
relative
to
other
variables
in
the
model.
In
addi-
tion
to
the
influence
terms,
this
study
found
that
other
factors
also
affect
fishermen's
enforce-
ment
behavior.
In
all
communities,
gear
investment,
income
and
age
explained
deviation
in
enforcement
of
sea
tenure.
Gear
investment
was
negatively
related
to
enforcement
in
Orinoco,
a
counterintuitive
result
as
it
would
be
expected
that
fishermen
heavily
invested
in
the
fishery
would
also
be
invested
in
protecting
it.
Individuals
with
lots
of
gear
often
rent
it
out
and
thus
may
be
a
step
removed
from
management
of
the
fishery
and
lack
knowledge
about
threats
to
the
resource.
Gear
lenders
may
also
demonstrate
little
knowledge
of
declining
fisheries
[71].
Age
had
a
positive
effect
on
enforcement
actions
in
Raitipura
and
Awas,
implying
the
relevance
of
engagement
with
communal
elders
to
increase
community
involvement
in
enforcement.
This
is
consistent
with
the
compliance
literature
that
finds
rule
compliance
is
positively
related
to
age
[56],[57].
In
Raitipura
and
Awas,
food
security
also
explained
fishermen's
enforcement
actions,
but
in
Orinoco
this
factor
was
not
included
in
models
of
best
fit.
Understanding
the
contribution
of
small-scale
fisheries
to
food
security
is
of
increasing
importance
[72],[73],
par-
ticularly
if
food
security
affects
fishermen's
management
behavior
as
suggested
in
this
study.
Decreases
in
food
security
as
a
result
of
declining
fisheries
have
the
potential
for
a
negative
or
positive
feedback
to
the
fishery
depending
on
fishermen's
response
to
these
changes.
If
food-in-
secure
fishermen
choose
not
to
expend
resources
to
prevent
illegal
fishing,
the
long-term
result
may
be
further
deterioration
of
food
security.
In
Raitipura/Awas
we
see
a
positive
relationship
between
food
security
and
enforcement
suggesting
that
as
fishermen
are
more
food
secure
they
are
likely
to
enforce.
One
data
limitation
is
that
we
do
not
know
precisely
the
contribution
of
fish
to
food
security,
though
it
is
a
substantial
portion
of
the
diet
(pers.
obs.).
Enforcement
of
sea
tenure
by
fishermen
decreased
amongst
all
communities
from
2011-
2012.
While
at
least
80%
of
fishermen
in
each
community
had
witnessed
outsiders
fishing
in
the
lagoon
in
the
year
preceding
2011,
the
following
year
this
percentage
had
decreased
to
67-
75%,
depending
on
the
community.
This
does
not
explain
the
decline
in
enforcement
score
be-
tween
years
since
we
standardized
data
to
only
compare
enforcement
of
sea
tenure
between
those
who
had
or
had
not
encountered
outsiders
in
both
years
measured.
Physical
violence
did
not
appear
to
be
a
relevant
factor
preventing
fishermen
from
enforcing
sea
tenure.
There
was
one
incident
reported
in
2011
from
Raitipura
in
which
an
illegal
fisher
threatened
a
local
fisher-
man
with
a
harpoon.
In
other
communities
there
were
zero
reports
in
both
years.
The
lack
of
evidence
for
confrontations
that
end
in
violence
suggests
that
the
risk
of
physical
harm
does
not
play
a
role
in
enforcement
behavior.
An
abundance
of
Bluefields
fishermen
in
the
first
year
coupled
with
active
enforcement
via
direct
confrontation
and
regular
reports
to
the
municipal
fisheries
inspector
may
have
resulted
in
a
'cooling
effect'
in
which
fewer
Bluefields
fishermen
came
to
the
region
in
the
second
year.
In
addition,
there
was
a
significant
seizure
of
Bluefields
fishermen's
gear,
thermos
and
fish
by
the
Pearl
Lagoon
municipal
fisheries
inspector
in
the
sec-
ond
year,
an
enforcement
measure
widely
noticed
locally
for
its
severity
since
previous
enforce-
ment
measures
by
the
municipality
only
consisted
of
verbal
warnings
(pers.
obs.,
X.
Gordon,
pers.
comm.).
The
unintended
side
effect
of
municipal
enforcement
of
sea
tenure
in
the
lagoon
may
have
been
a
resulting
reliance
on
the
municipality
to
handle
illegal
fishermen
[
].
In
addi-
tion,
anecdotal
reports
from
interviews
conducted
as
part
of
this
survey
suggest
that
the
harvest
practices
of
the
Bluefields
fishermen
in
the
lagoon
were
generally
less
offensive
to
local
fisher-
men
in
the
second
year.
Fishermen,
particularly
in
Raitipura
and
Awas,
indicated
that
some
Bluefields
fishermen
were
fishing
in
alignment
with
local
norms
and
several
fishermen
re-
ported
that
they
do
not
confront
outsiders
as
long
as
their
methods
are
consistent
with
how
lo-
cals
fish.
This
is
contrasted
with
first
year
anecdotal
reports
of
many
Bluefields
fishermen
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Social
Networks
and
Small-Scale
Fishermen's
Behavior
"abusing"
the
lagoon
by
using
small-mesh
gill
nets.
These
two
distinct
approaches
to
illegal
fishers
are
essentially
based
on
whether
the
fisherman
is
fishing
to
eat
or
fishing
for
substantial
profit,
and
the
fishermen
of
the
lagoon
are
more
sympathetic
to
the
former,
a
distinction
also
observed
in
a
Norwegian
fishery
[
].
In
any
case,
the
apparent
reduction
in
fishermen's
en-
forcement
of
sea
tenure
is
not
an
encouraging
indicator
of
sustainable
fisheries
management,
which
is
difficult
to
achieve
in
a
de
facto
open
access
rights
regime
[74].
It
is
unclear
if
the
re-
duction
in
enforcement
extends
beyond
sea
tenure
to
other
social
norms
designed
for
sustain-
able
resource
management.
Of
all
the
sanctions
fishermen
may
choose
to
enforce,
it
is
perhaps
less
risky
and
least
costly
to
confront
and
sanction
outsiders
compared
to
fellow
community
members.
Mean
community
enforcement
scores
decreased
between
the
two
years
with
increasing
dis-
tance
from
the
municipal
center
and
road
terminus
at
the
town
of
Pearl
Lagoon.
This
could
be
explained
by
several
factors.
First,
Raitipura
and
Awas
have
logistically
easier
and
more
fre-
quent
access
to
municipal
staff,
including
the
fisheries
inspector.
Second,
the
illegal
fishermen
concentrate
their
activities
in
parts
of
the
lagoon
more
frequented
by
fishermen
from
Raiti-
pura/Awas.
With
a
higher
likelihood
of
Raitipura
fishermen
encountering
illegal
fishermen
and
more
competition
for
the
resource,
it
is
reasonable
that
they
are
more
likely
to
enforce
sea
tenure.
Finally,
Raitipura
and
Awas
are
the
only
two
Miskitu
communities
of
the
four
studied
in
this
research.
Brown
Bank
is
a
predominantly
Creole
community,
and
Orinoco
predomi-
nantly
Garifuna.
A
recent
study
of
Hawaii's
longline
fishery
found
that
ethnicity
influences
net-
work
structure,
which
may
affect
information
flow,
collaboration
and
overall
management
[75].
Given
the
history
of
the
Atlantic
Coast,
Miskitu
communities
are
in
a
better
position
to
claim
and
assert
their
territorial
rights
to
resources
compared
to
other
ethnic
groups
[76]
(Nic-
araguan
Law
445).
There
are
multiple
threats
to
the
sustainability
of
the
Pearl
Lagoon
fishery.
Perhaps
the
reg-
ulation
with
the
least
local
resistance
to
implementation
is
the
prevention
of
illegal
fishers
from
other
municipalities
from
fishing
in
the
lagoon.
That
it
continues
to
be
a
problem
after
15
years
indicates
that
enforcement
could
be
improved.
While
fishermen
cannot
be
expected
to
stop
all
illegal
fishing
without
support
from
the
government,
strengthening
existing
communal
social
norms
of
sea
tenure
and
empowering
fishermen
to
enforce
their
own
rules
and
increase
coordi-
nation
with
communal
and
municipal
authorities
can
result
in
improved
compliance
and
re-
duced
costs
for
management
[8].
Systems
with
monitors
that
are
appointed
by,
accountable
to,
or
are
the
resource
users
themselves
is
one
of
the
eight
design
principles
identified
for
successful
institutions
and
empiri-
cally
demonstrated
to
result
in
better
management
outcomes
[77],[78].
Here
we
explore
fisher-
men's
enforcement
of
sea
tenure,
yet
understanding
individual
enforcement
through
the
lens
of
social
networks
is
relevant
to
the
enforcement
of
any
communal
social
norm.
Fishermen
are
less
likely
to
sanction
others
if
there
is
no
support
amongst
their
social
network
for
doing
so.
Just
as
public
health
officials
use
social
networks
to
identify
opinion
leaders
or
"champions"
in
the
community
who
are
instrumental
in
driving
behavior
change
related
to
certain
health
prac-
tices
[79],
fisheries
managers
could
use
knowledge
of
social
networks
to
improve
stakeholder
communication,
identify
key
people
to
engage
in
policy
reform,
and
promote
adoption
of
cer-
tain
harvest
practices.
These
types
of
'network
interventions'
have
been
so
far
little
used
in
nat-
ural
resource
management
[
].
Additionally,
if
fishermen's
behaviors
are
affected
by
their
network,
analysis
can
uncover clustering
of
subgroups
within
communities
each
with
distinctly
evolving
normative
pressures,
which
can
create
challenges
for
management
[35].
Using
knowl-
edge
of
social
networks
to
strengthen
traditional
norms
of
resource
use
may
result
in
improved
effectiveness
of
co-management
regimes,
particularly
during
periods
of
social
and
ecological
change
that
may
be
driven
by
rapid
globalization.
PLOS
ONE
1D01:10.1371/journal.pone.0121431
March
30,
2015
13
/
17
PLOS
ONE
Social
Networks
and
Small-Scale
Fishermen's
Behavior
Supporting
Information
Si
Table.
Parameter
estimates
(standard
error)
and
model
fit
characteristics
for
the
model
of
best
fit
in
each
community
for
both
the
friends
and
fishing
partners'
social
networks.
(DOCX)
S2
Table.
Survey
questions
used
to
assess
food
security
for
fishermen
adapted
from
USDA's
Six
Item
Short
Form
of
the
Food
Security
Survey
(Interviewees
responded
with
agree/disagree/neither
agree
nor
disagree).
(DOCX)
S3
Table.
Survey
question
used
to
assess
gear
ownership
amongst
fishermen
in
four
com-
munities
around
Pearl
Lagoon,
Nicaragua.
(DOCX)
S4
Table.
Survey
questions
used
to
assess
enforcement
behavior
amongst
fishermen
in
four
communities
around
Pearl
Lagoon,
Nicaragua.
(DOCX)
Acknowledgments
We
would
like
to
thank
the
fishermen
of
the
Atlantic
Coast
of
Nicaragua
and
Xenia
Gordon
for
their
invaluable
participation
in
this
research.
We
thank
several
anonymous
reviewers
whose
comments
substantially
improved
earlier
versions
of
this
manuscript.
Author
Contributions
Conceived
and
designed
the
experiments:
KS
KAF
DBK.
Performed
the
experiments:
KS.
Ana-
lyzed
the
data:
KS.
Contributed
reagents/materials/analysis
tools:
KAF
DBK
KS.
Wrote
the
paper:
KS
DBK
KAF.
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