Patrick et al Use of productivity and susceptibility indices to assess vulnerability of fish stocks to overfishing 
315 
Table 4 
Summary of the productivity and susceptibility scoring frequencies and correlations to the overall index or category score. Cor- 
relations were based on stock attributes scores (1—3) (see Tables 1 and 2) that were compared to a modified categorical score for 
the stock, the latter of which did not include the related attribute score. 
Frequency Pearson correlation 
Category No. scored scored coefficient P-value 
Productivity 
r 
Maximum age 
Maximum size 
von Bertalanffy growth coefficient ( k ) 
Estimated natural mortality (M) 
Measured fecundity 
Breeding strategy 
Recruitment pattern 
Age at maturity 
Mean trophic level 
Susceptibility 
Catchability 
Areal overlap 
Geographic concentration 
Vertical overlap 
Seasonal migrations 
Schooling, aggregation, and other behavioral responses 
Morphology affecting capture 
Desirability or value of the fishery 
Management 
Management strategy 
Fishing rate in relation to M 
Biomass of spawners (SSB) or other proxies 
Survival after capture and release 
Fishery impact to essential fish habitat (EFH) or habitat 
in general for nontargeted fish 
128 
96% 
0.596 
<0.001 
126 
95% 
0.674 
<0.001 
128 
96% 
0.592 
<0.001 
129 
97% 
0.656 
<0.001 
127 
95% 
0.785 
<0.001 
126 
95% 
0.509 
<0.001 
133 
100% 
0.568 
<0.001 
84 
63% 
-0.211 
0.054 
125 
94% 
0.802 
<0.001 
132 
99% 
0.439 
<0.001 
123 
92% 
0.333 
<0.001 
133 
100% 
0.345 
<0.001 
133 
100% 
0.772 
<0.001 
49 
37% 
0.058 
0.692 
87 
65% 
0.340 
0.001 
132 
99% 
0.319 
<0.001 
133 
100% 
0.504 
<0.001 
133 
100% 
0.154 
0.077 
79 
59% 
0.510 
<0.001 
78 
59% 
0.389 
<0.001 
126 
95% 
0.201 
0.024 
133 
100% 
0.286 
0.001 
tibility scores. The swordfish sector overall exhibited 
a slightly reduced susceptibility when compared to the 
tuna sector, probably due to the higher level of tar- 
geting in the tuna sector of the fishery (Fig. 1). The 
restricted range in some of the example applications 
may reflect the species chosen for these examples, and 
a more expanded range may be observed if the PSA 
were applied to all species in a fishery management 
plan (FMP). For example, BSAI skate complexes are 
managed as bycatch within the BSAI Groundfish FMP, 
which includes a range of life-history types, including 
rockfish and flatfish, and the productivity and suscepti- 
bility scores for these species would likely contrast with 
those obtained for skates. 
A restricted range of scores from a PSA may moti- 
vate some to modify the attribute scoring thresholds 
to produce greater contrast. But because the overall 
goal of the present PSA is to estimate vulnerability 
in relation to an overall standard appropriate for the 
range of managed species, a lack of contrast in vulner- 
ability scores may simply reflect a limited breadth of 
species diversity. It may be advantageous in some cases 
to modify the attribute scoring thresholds to increase 
the contrast within a given region or FMP (see Field et 
al., in press), while recognizing that the vulnerability 
scores for that particular fishery no longer represent 
the risk of overfishing based on the original scoring 
criteria described here. 
Data availability and data quality 
From our example applications, data availability was 
relatively high for the majority of the attributes evalu- 
ated, averaging 88% and ranging from 37% to 100% 
in scoring frequency (Table 4). However, the quality of 
these data was considered moderate (i.e., medium data 
quality scores of 2—3), except for the Northeast multi- 
species groundfish fishery (Fig. 1). The high degree 
of data quality for those targeted stocks reflects the 
relatively long time series of fishery and survey data. 
In general, a relationship between susceptibility and 
data quality is intuitive (i.e., valuable stocks are likely 
