Bayesian modelling of social factors in
small scale fisheries
Ingrid van Putten, Eriko Hoshino, Helven Naranjo-Madrigal, Mariska Weijerman
CSIRO Hobart
CSIRO OCEANS & ATMOSPHERE, AUSTRALIA
MSEAS, Brest, 2016
Marine & Coastal
Science, National
University (Costa Rica)
NOAA Hawaii
Why Small Scale Fisheries?
Big component of global catch (70%)
Important for food security
Important for livelihoods (90% of
worlds’ fishers)
Important for employment & women
Tight links between coastal
communities and small-scale fisheries
Fishing households [not commercial companies],
Small amount of capital and energy,
Small fishing vessels [if any],
Short fishing trips, close to shore
Important links between local social and cultural values
and governance outcomes
Also important links between local social and cultural values
and environmental / sustainable outcomes
Kolding, Béné and Bavinck (2014) Small-scale fisheries: Importance, vulnerability and deficient knowledge
2 | Social factors in small scale fisheries| Ingrid van Putten
Why model Small Scale Fisheries?
Fisheries models (single-, multi species, or
ecosystems) can help to achieve sustainable /
environmental outcomes
Fisheries models can help evaluate management
approaches & outcomes
We use one (of many) quantitative approaches that
can be useful in incorporating SSFs social and
cultural factors in SES models
3 | Social factors in small scale fisheries| Ingrid van Putten
Aim
Quantitatively model social and cultural drivers for SSFs
and to dynamically link these models
with fisheries or ecosystem models
To ultimately help achieve sustainable /
environmental outcomes for SSFs
4 | Social factors in small scale fisheries| Ingrid van Putten
What is a Bayesian model?
.
Directed graph (one thing influences another)
A node represents a random variable
Fish abundance
High
Medium
Low
75
20
5
Relationship between parent node and child node
Parent nodes represent variables (B1, B2,..., Bn)
Child node (A) is assigned a conditional probability table
P(A|B1,....,Bn)
5 | Social factors in small scale fisheries| Ingrid van Putten
Price of fish
High
13.3
Low
86.7
What question can you ask a BN?
Predictive reasoning
Fish abundance
Community role model
High
Medium
Low
Present 91.0
Absent 9.03
Social capital
High
Low
Price of fish
High
13.3
Low
86.7
58.7
41.3
Catch of fish
High
Low
CSIRO.
75.0
21.8
3.25
75.2
24.8
Season (time of the year)
Peak (china) 58.7
41.3
No peak
Weather conditions
yes
77.2
no
22.8
Given evidence about a cause, what are the
predicted effects (e.g. you know fish abundance
is high what is the probability that the price of
fish is low)
What question can you ask a BN?
Diagnostic reasoning
Fish abundance
Community role model
High
Medium
Low
Present 91.0
Absent 9.03
Social capital
High
Low
Price of fish
High
13.3
Low
86.7
58.7
41.3
Catch of fish
High
Low
CSIRO.
75.0
21.8
3.25
75.2
24.8
Season (time of the year)
Peak (china) 58.7
41.3
No peak
Weather conditions
yes
77.2
no
22.8
Given evidence about an effect (symptom) how
does this change our beliefs in the causes? (e.g.
I observe low catches – how does that the
affect the probability of low fish abundance)
What question can you ask a BN?
Inter-causal
Fish abundance
Community role model
High
Medium
Low
Present 91.0
Absent 9.03
Social capital
High
Low
Price of fish
High
13.3
Low
86.7
58.7
41.3
Catch of fish
High
Low
CSIRO.
75.0
21.8
3.25
75.2
24.8
Season (time of the year)
Peak (china) 58.7
41.3
No peak
Weather conditions
yes
77.2
no
22.8
Given evidence about a cause and about its
effect, how does this change our belief in other
causes? (e.g. fish abundance is high but there
are low catches what is the probability of a lack
of social capital)
Why are BNs useful for social data?
Can combine information from different domains (i.e. natural – social science)
Fish abundance
Community role model
Natural
High
75.0
(fisheries)
Medium
21.8
science
Low
3.25
Present 91.0
Anthropology
Absent
9.03
Social capital
Price of fish
High
13.3
Low Economics
86.7
/
High Sociology
58.7
Low psychology
41.3
Catch of fish
High
Low
75.2
Economics
24.8
Social factors in small scale fisheries| Ingrid van Putten
Season (time of the year)
Peak (china) 58.7
41.3
No peak
Weather conditions
yes
77.2
no
22.8
Example of data types?
Pre-season survey (CSIRO)
Present
95.0
Absent
5.00
infrastructure
Privately run freezer
Present 29.0
Absent
71.0
hookah ownership
OwnOrBorrow 70.0
Hire
30.0
Functional freezer on island
Present
38.9
Absent
61.1
Torres Strait Region Authority
Funding
50.0
Nofunding
50.0
Papua New Guinea
highcatch 50.0
lowcatch
50.0
Season (time of year)
PeakChineseFestivals 60.0
NonPeak
40.0
Cost of Fuel
Aboveagerage 55.0
Belowaverage
45.0
Externalities (exchange rate)
favourable
40.0
unfavourable
60.0
Other (southern) lobster availability
HighRLavail
50.0
LowRLAvail
50.0
Economic
Cost of fishing
High 53.2
Low
46.8
SEC fishery
highcatch 47.7
lowcatch
52.3
Non-indigenous TVH catch
highTVHcatch 55.7
lowTVHcatch
44.3
TRL abundance
HighAbundance
32.8
MediumAbundance 38.0
LowAbundance
29.2
high
low
Price of live
54.5
45.5
Weather conditions
Favourable
50.0
unfavorable 50.0
Ease of cathing TRL by TIB
easy
49.9
difficult
50.1
Price of tails
high 47.6
low
52.4
Community business knowledge
present
50.0
absent
50.0
Social
Community role models
present
50.0
absent
50.0
Social capital
Present 48.7
absent
51.2
Crew availability
Present 50.0
Absent
50.0
Returns from fishing
High
48.6
Low
51.4
Soc/economic
Part time employment (CDEP scheme)
Available
90.0
NotAvailable
10.0
Young working age men
availabel
25.0
notavailable 75.0
Full time alternative income sources
Available
30.0
NotAvailable
70.0
Community Tradition & cultural events
strong
50.0
weak
50.0
Incidental household payments
high
60.0
Low
40.0
Bottom line
10 | Social factors in small scale fisheries| Ingrid van Putten
Catch / effort
Full Time fishing activity
High
41.1
Low
58.9
Half time fishing (week about)
High
76.5
Low
23.5
Weekend (casual) fishing
High
45.6
Low
54.4
Why are BNs useful for social data?
Can combine qualitative and quantitative information
Different ways to get the information to populate a BN
Use existing data (proxies)
High
Low
Social capital
75
25
Census data
(e.g. female workforce participation, education level, participation
in volunteering activities as proxy for social capital)
Do a survey
Survey people to measure the components of social capital
(e.g. level of trust – reciprocity, norms, rules of law)
Ask experts
Ask experts to give their assessment
(e.g. their expert insight on the presence / level of social capital)
Same goes for establishing conditional probabilities (although a bit more complex)
Social factors in small scale fisheries| Ingrid van Putten
Four Bayesian models for case study locations
Guam
Playa
Lagarto
(Costa Rica)
Kei Island
(Indonesia)
12 | Social factors in small scale fisheries| Ingrid van Putten
Torres Strait
(Australia)
Costa Rica
Dive based fishery
(hookah & free-diving)
Six commercial species (Octopus, lobster,
parrotfish, snail, oyster, other species)
Semi-structured interviews & landing site
observations majority of fishers
Only fisheries considered (no other marine sectors)
Model bottom line (key variable link to biology):
Probability distribution for location specific CPUE
Biology:
Linking to ecosystem* or production
model is a work in progress
Pre-season survey (CSIRO)
Present
95.0
Absent
5.00
Privately run freezer
Present 29.0
Absent
71.0
Functional freezer on island
Present
38.9
Absent
61.1
Papua New Guinea
highcatch 50.0
lowcatch
50.0
Season (time of year)
PeakChineseFestivals 60.0
NonPeak
40.0
Cost of Fuel
Aboveagerage 55.0
Belowaverage
45.0
SEC fishery
highcatch 47.7
lowcatch
52.3
Non-indigenous TVH catch
highTVHcatch 55.7
lowTVHcatch
44.3
TRL abundance
HighAbundance
32.8
MediumAbundance 38.0
LowAbundance
29.2
hookah ownership
OwnOrBorrow 70.0
Hire
30.0
Externalities (exchange rate)
favourable
40.0
unfavourable
60.0
Other (southern) lobster availability
HighRLavail
50.0
LowRLAvail
50.0
Weather conditions
Favourable
50.0
unfavorable 50.0
Ease of cathing TRL by TIB
easy
49.9
difficult
50.1
Torres Strait Region Authority
Funding
50.0
Nofunding
50.0
Cost of fishing
High 53.2
Low
46.8
high
low
Price of live
54.5
45.5
Price of tails
high 47.6
low
52.4
Community business knowledge
present
50.0
absent
50.0
Community role models
present
50.0
absent
50.0
Social capital
Present 48.7
absent
51.2
Crew availability
Present 50.0
Absent
50.0
Returns from fishing
High
48.6
Low
51.4
Part time employment (CDEP scheme)
Available
90.0
NotAvailable
10.0
Young working age men
availabel
25.0
notavailable 75.0
Full time alternative income sources
Available
30.0
NotAvailable
70.0
Community Tradition & cultural events
strong
50.0
weak
50.0
Incidental household payments
high
60.0
Low
40.0
Full Time fishing activity
High
41.1
Low
58.9
Half time fishing (week about)
High
76.5
Low
23.5
* Ecosystem model for a region that includes trophic links with other species – such as Atlantis, MICE
13 | Social factors in small scale fisheries| Ingrid van Putten
Weekend (casual) fishing
High
45.6
Low
54.4
Indonesia
Small boat based fishery
(variety of fishing gears)
Coastal and pelagic species (Bluefin trevally, snapper spp,
grouper, parrot fish, small reef fishes)
Structured interviews & landing site observations
majority of fishers
Only fisheries considered (no other marine sectors)
Model bottom line (key variable link to biology):
Probability distribution for location specific CPUE
Biology:
Not currently dynamically linked to
population dynamics or ecosystem model
Pre-season survey (CSIRO)
Present
95.0
Absent
5.00
Privately run freezer
Present 29.0
Absent
71.0
hookah ownership
OwnOrBorrow 70.0
Hire
30.0
Functional freezer on island
Present
38.9
Absent
61.1
Papua New Guinea
highcatch 50.0
lowcatch
50.0
Season (time of year)
PeakChineseFestivals 60.0
NonPeak
40.0
Cost of Fuel
Aboveagerage 55.0
Belowaverage
45.0
SEC fishery
highcatch 47.7
lowcatch
52.3
Non-indigenous TVH catch
highTVHcatch 55.7
lowTVHcatch
44.3
TRL abundance
HighAbundance
32.8
MediumAbundance 38.0
LowAbundance
29.2
Externalities (exchange rate)
favourable
40.0
unfavourable
60.0
Other (southern) lobster availability
HighRLavail
50.0
LowRLAvail
50.0
Weather conditions
Favourable
50.0
unfavorable 50.0
Ease of cathing TRL by TIB
easy
49.9
difficult
50.1
Torres Strait Region Authority
Funding
50.0
Nofunding
50.0
Cost of fishing
High 53.2
Low
46.8
Price of live
high 54.5
low
45.5
Price of tails
high 47.6
low
52.4
Community business knowledge
present
50.0
absent
50.0
Community role models
present
50.0
absent
50.0
Social capital
Present 48.7
absent
51.2
Crew availability
Present 50.0
Absent
50.0
Returns from fishing
High
48.6
Low
51.4
Part time employment (CDEP scheme)
Available
90.0
NotAvailable
10.0
Young working age men
availabel
25.0
notavailable 75.0
Full time alternative income sources
Available
30.0
NotAvailable
70.0
Community Tradition & cultural events
strong
50.0
weak
50.0
Incidental household payments
high
60.0
Low
40.0
Full Time fishing activity
High
41.1
Low
58.9
14 | Social factors in small scale fisheries| Ingrid van Putten
Half time fishing (week about)
High
76.5
Low
23.5
Weekend (casual) fishing
High
45.6
Low
54.4
Australia
Dive based fishery
(hookah & free-diving)
One commercial species
(Tropical rock lobster)
Community consultation meetings
Only fisheries considered (no other marine sectors)
Model bottom line (key variable link to biology):
Probability distribution of the number of fisher that
participate in the fishery (full-time, part-time and casually)
Location specific catches estimated based on
fisher participation
Biology:
Bayesian model linked to spatial lobster
population dynamics model (Plaganyi et al
2009)
Pre-season survey (CSIRO)
Present
95.0
Absent
5.00
Privately run freezer
Present 29.0
Absent
71.0
Papua New Guinea
highcatch 50.0
lowcatch
50.0
Season (time of year)
PeakChineseFestivals 60.0
NonPeak
40.0
Cost of Fuel
Aboveagerage 55.0
Belowaverage
45.0
Externalities (exchange rate)
favourable
40.0
unfavourable
60.0
Other (southern) lobster availability
HighRLavail
50.0
LowRLAvail
50.0
Weather conditions
Favourable
50.0
unfavorable 50.0
Ease of cathing TRL by TIB
easy
49.9
difficult
50.1
Torres Strait Region Authority
Funding
50.0
Nofunding
50.0
Cost of fishing
High 53.2
Low
46.8
high
low
Price of live
54.5
45.5
Price of tails
high 47.6
low
52.4
Community business knowledge
present
50.0
absent
50.0
Community role models
present
50.0
absent
50.0
Social capital
Present 48.7
absent
51.2
Crew availability
Present 50.0
Absent
50.0
Returns from fishing
High
48.6
Low
51.4
Part time employment (CDEP scheme)
Available
90.0
NotAvailable
10.0
Young working age men
availabel
25.0
notavailable 75.0
Full time alternative income sources
Available
30.0
NotAvailable
70.0
Community Tradition & cultural events
strong
50.0
weak
50.0
Incidental household payments
high
60.0
Low
40.0
Full Time fishing activity
High
41.1
Low
58.9
Plagányi et al (2009) TAC estimation & relative lobster abundance surveys 2008/09 CSIRO Marine and
Atmospheric Research and AFMA
15 | Social factors in small scale fisheries| Ingrid van Putten
SEC fishery
highcatch 47.7
lowcatch
52.3
Non-indigenous TVH catch
highTVHcatch 55.7
lowTVHcatch
44.3
TRL abundance
HighAbundance
32.8
MediumAbundance 38.0
LowAbundance
29.2
hookah ownership
OwnOrBorrow 70.0
Hire
30.0
Functional freezer on island
Present
38.9
Absent
61.1
Half time fishing (week about)
High
76.5
Low
23.5
Weekend (casual) fishing
High
45.6
Low
54.4
Guam
Fishing from shore and small boats,
and spearfishing (snorkel and scuba).
Inshore reef species
(mostly non-commercial)
Expert opinion and literature
Fishing, tourism, and recreation sectors considered
Model bottom line (key variable link to biology):
Participation in reef fishing and participation in tourism diving *
Location specific catches estimated based on fisher participation
Biology:
Ecological ‘Atlantis’ model
(Weijerman et al 2015, PlosOne; Weijerman et al
2016 PlosOne)
Pre-season survey (CSIRO)
Present
95.0
Absent
5.00
Privately run freezer
Present 29.0
Absent
71.0
Functional freezer on island
Present
38.9
Absent
61.1
Papua New Guinea
highcatch 50.0
lowcatch
50.0
Season (time of year)
PeakChineseFestivals 60.0
NonPeak
40.0
Cost of Fuel
Aboveagerage 55.0
Belowaverage
45.0
Weather conditions
Favourable
50.0
unfavorable 50.0
Ease of cathing TRL by TIB
easy
49.9
difficult
50.1
Torres Strait Region Authority
Funding
50.0
Nofunding
50.0
Cost of fishing
High 53.2
Low
46.8
high
low
Price of live
54.5
45.5
Price of tails
high 47.6
low
52.4
Community business knowledge
present
50.0
absent
50.0
Community role models
present
50.0
absent
50.0
16 | Social factors in small scale fisheries| Ingrid van Putten
Externalities (exchange rate)
favourable
40.0
unfavourable
60.0
Other (southern) lobster availability
HighRLavail
50.0
LowRLAvail
50.0
Social capital
Present 48.7
absent
51.2
Crew availability
Present 50.0
Absent
50.0
Returns from fishing
High
48.6
Low
51.4
Part time employment (CDEP scheme)
Available
90.0
NotAvailable
10.0
Young working age men
availabel
25.0
notavailable 75.0
* Full Bayesian model currently being planned (depending on
funding) by M. Weijerman NOAA and K. Oleson Hawaii University
SEC fishery
highcatch 47.7
lowcatch
52.3
Non-indigenous TVH catch
highTVHcatch 55.7
lowTVHcatch
44.3
TRL abundance
HighAbundance
32.8
MediumAbundance 38.0
LowAbundance
29.2
hookah ownership
OwnOrBorrow 70.0
Hire
30.0
Full time alternative income sources
Available
30.0
NotAvailable
70.0
Community Tradition & cultural events
strong
50.0
weak
50.0
Incidental household payments
high
60.0
Low
40.0
Full Time fishing activity
High
41.1
Low
58.9
Half time fishing (week about)
High
76.5
Low
23.5
Weekend (casual) fishing
High
45.6
Low
54.4
Key ecological
drivers from
biological model
(feedback)
BN variables that
link to biology
Species abundance
Visibility
location specific CPUE
17 | Social factors in small scale fisheries| Ingrid van Putten
Species abundance
Habitat quality
location specific CPUE
Species abundance
Fisher participation
Species abundance
Habitat quality
Species diversity
Iconic species
Participation in reef
fishing & tourism diving
Key social
(demographic)
drivers
Cultural assets
Strength of local tradition Fishing tradition
Cooperation
Village leader relation
Social capital
Information sharing
Inter village conflict
Role models
Head of household
Intra village conflict
Young men fishing
Fishing tradition
Ethnicity
Gender
Age
Access to dive locations
Previous dive experience
Size of the dive group
Variables that
link to biology
Location specific CPUE
18 | Social factors in small scale fisheries| Ingrid van Putten
Location specific CPUE
Fisher participation
Participation in reef
fishing & tourism diving
Key economic
drivers
Land transportation
Rent
Employment
opportunities
Employment
opportunities
Employment
opportunities
Cost of fishing
Price of fish
Cost of fuel
Price of fish
Government
unemployment
scheme
Variables that
link to biology
Location specific CPUE
19 | Social factors in small scale fisheries| Ingrid van Putten
Location specific CPUE
Fisher participation
Cost of fuel
Cost of dive charter
Accommodation / price
Participation in reef
fishing & tourism diving
Findings
Social and cultural drivers are important in SSF (which make up a
large component of global catches)
Bayesian approach is one way of modelling the impact of social and
economic drivers on catches (CPUE or participation in fishing)
Social interactions like cooperation, close friendships, kinships, and information
sharing (i.e traditional ecological knowledge) influence local catches
But also …. Inter- and intra-village conflict and the strength of customary
tenure (Sasi) are important
As well as …. Social capital and capacity, infrastructure availability, and
availability of a government employment program
These Bayesian models (with social and economic variables) can be dynamically
linked to key environmental variables in ecosystem or single species models
Can ‘ask’ Bayesian models different kinds of questions
Modelling may help achieve sustainable / environmental outcomes
20 | Social factors in small scale fisheries| Ingrid van Putten
Refs:
Thank you
Ingrid van Putten
Research Scientist
Hobart, Australia
t +61 7 6232 5048
e [email protected]
w www.csiro.au
OCEANS & ATMOSPHERE FLAGSHIP
Eriko Hoshino, Ingrid van Putten, Wardis Girsang, Budy P. Resosudarmo, Satoshi Yamazaki
(in press) A Bayesian Belief Network Model for Community-based Coastal Resource
Management in the Kei Islands, Indonesia, Ecology and Society.
Weijerman, M. Grace-McCaskey, C., Grafeld, S.L., Kotowicz, D.M, Oleson, K.L.L., van Putten,
E.I. (2016) Towards an ecosystem-based approach of Guam’s coral reefs: The human
dimension, Marine Policy, 63, 8-17.
Helven Naranjo, Ana Norman-Lopez, Ingrid van Putten (2015) Understanding socioecological drivers of spatial allocation choice in a multi-species artisanal fishery: a Bayesian
network modelling approach. Marine Policy 62, 102-115.
Putten, van E.I., Lalancette, A, Bayliss, P., Dennis, D., Hutton, T., Norman-López, A., Pascoe,
S., Plagányi, E., Skewes. T. (2012) A Bayesian model of factors influencing indigenous
participation in the Torres Strait tropical rock lobster fishery Marine Policy Special Issue.
DOI 10.1016/j.marpol.2012.04.001