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Table 1
Fisheries vulnerability assessment matrix (scoring guide).
Components
Sensitivity
Exposure
Score
Compare catch 20 years ago
(Catch rate = number of kgs.
added/deducted from the catch
20 yrs ago compared today)
Average length at first sexual
maturity (for validation)
Dependence on resource
Household age structure
(15–70 y/o)
Health conditions
Fishing ground (annually)
Household (annually)
Adaptive Capacity
Frequency of storms/typhoons,
records of landslides and/or
flash floods (annually)
What is the annual income
from fishing?
Awareness
Access to information
Adaptive strategy
Literacy
Gear modification/replacement
for the past 10 years
Climate change
support/organization
1 or 2
3 or 4
5
Increasing catch rate over the
years; Most catches are large,
mature fishes
Normal catch rate: Mix of
small and large fishes
Decreasing catch rate; Small,
immature fishes are abundant
≥ 5 cm;Greater than 2 cm but
less than 5 cm
Household income from
resource-dependent is less
than 20%; 21–40%
26–40; 41–55
±2 cm; Greater than 2 cm but
less than 5 cm
41–60%; 61–30%
≤5 cm
No special health needs
Rare occurrence of typhoons,
storms, weather disturbances
(0–1); 2
(0–1); 2
Symptomatic diseases
Intermediate incidence of
typhoons, storms, weather
disturbances (3); 4
3; 4
No climate events
experienced; 1–2
3–4; 5–6
More than 80% of annual
income is generated from
fishing: 60–79%
No to little idea about the topic
No sources of information; 1–2
40–59%; 20–39%
Less than 20% came from
fishing
Moderately knows the topic
3–4; 5–6
No to insignificant
precautionary measures
Out of school youth or
elementary student/graduate
No modification/No
knowledge/No resources
(budget)
No climate change
support/organization
Less but significant
precautionary measures
High school
graduate/student/skilled
With some modification/With
knowledge/With limited
resources/Unsuccessful
2–3 climate change
support/organization
Knows the topic very well
More than 6 sources of
information
More and significant
precautionary measures
College or vocational
student/graduate
With full modification,
knowledge, and re-sources
Successful
More than 4 climate change
support/organization
15–25; 56–65
This study developed a tool that will complement VA-TURF
by providing effective data collection for assessing the potential impacts of climate change to commercial fishery sectors or
commodities. The tool enables the identification of fisheries commodities and areas that are highly vulnerable to climate change,
which is important for the formulation of measures that will
address the impacts of climate change on fisheries and ensure
sustainable livelihood and food security (Food and Agriculture
Organization (FAO), 2008).
Table 2
Potential impact scoring.
2. Methodology
Table 3
Overall vulnerability index scoring.
2.1. Development of Fish Vool and vulnerability map
The fisheries vulnerability assessment tool (Fish Vool) was
developed following the key goal of VA (e.g., Allison et al., 2009;
IPCC, 2001; Mamauag et al., 2013) to determine the vulnerabilities of fishery commodities or sectors of local municipal or
commercial landing sites. The tool was refined using input and
validation from fisheries and VA specialists of the University of
the Philippines—Marine Science Institute, National Agricultural and
Fishery Council, and Bureau of Fisheries and Aquatic Resources
(BFAR) regional representatives to eliminate unnecessary parameters and incorporate relevant components.
Key informants from General Santos and Zamboanga City were
interviewed and assessed in November 2012 and August 2013,
respectively, using a vulnerability matrix (Table 1). Data were analyzed using a simple semi-quantitative approach (Johnson and
Welch, 2009; Mamauag et al., 2013), and scores calculated using
the cross-tabulation (Punnett square) method by Mamauag et al.
Household income from
resource-dependent is greater
than 80%
above 65 years old
With special health needs
Frequented by typhoons,
storms, weather disturbances
(5 above)
More than 5 weather
disturbances
More than 6 climate events
experienced
Potential
impact
Sensitivity
L
M
H
Exposure
L
M
H
L
L
M
L
M
H
Adaptive capacity
Vulnerability
Potential
impact
M
H
H
L
M
H
L
M
H
M
H
H
L
M
H
L
L
M
(2013) (Tables 2 and 3). An open source geographic information
system software (Quantum GIS v1.8 Lisboa) was used to produce vulnerability maps of the target commodities. Scores of 1
to 2 correspond to low vulnerability, and scores of 3 to 4 correspond to medium vulnerability; a score of 5 corresponds to high
vulnerability (Table 4). The numerical values of each component
were summed and translated using a rank system, in which point
class intervals correspond to low, medium, and high vulnerability
(Mamauag et al., 2013) (Table 5).