Patrick et al.: Use of productivity and susceptibility indices to assess vulnerability of fish stocks to overfishing 
311 
Management strategy The susceptibility of a stock to 
overfishing may largely depend on the effectiveness of 
fishery management procedures used to control catch 
(Roughgarden and Smith, 1996; Sethi et al., 2005; 
Dankel et al., 2008). Stocks managed by using catch 
limits that allow for fishery closure before the catch limit 
is exceeded (i.e., in-season or proactive accountability 
measures) are considered to have a low susceptibility 
to overfishing. Stocks managed by using catch limits 
and reactive accountability measures (e.g., catch levels 
determined after the fishing season) are considered to 
be moderately susceptible to overfishing or to becoming 
overfished. Lastly, stocks that have neither catch limits 
nor accountability measures are considered to be highly 
susceptible to overfishing. 
Fishing mortality rate (in relation to M) This attribute 
is applicable to stocks for which estimates of both fish- 
ing and natural mortality rates ( F and M) are available. 
Because sustainable fisheries management typically 
involves conserving the reproductive potential of a stock, 
it is recommended that the average F on mature fish be 
used where possible, as opposed to the fully selected or 
“peak” F. We base our thresholds on the conservative 
rule of thumb that the M should be an upper limit of F 
(Thompson, 1993), and thus F/M should not exceed 1. 
For this attribute, we define intermediate F/M values 
as those between 0.5 and 1.0; values above 1.0 and 
below 0.5 are defined as high and low susceptibility, 
respectively. 
Biomass of spawners Analogous to fishing mortality 
rate, a comparison of the current stock biomass (B CUR 
rent/ expected unfished levels (B 0 ) offers information 
on the extent to which fishing has potentially depleted 
the stock and the stock’s realized susceptibility to over- 
fishing. If B 0 is not available, one could compare B CUR 
rent against the maximum observed biomass from a time 
series of population size estimates (e.g., from a research 
survey). If a time series is used, it should be of adequate 
length, and it should be recognized that the maximum 
observed survey estimates may not correspond to the 
true maximum biomass and that substantial observation 
errors in estimates may be present. Additionally, stocks 
may decline in abundance because of environmental fac- 
tors unrelated to their susceptibility to the fishery, and 
therefore this situation should be considered by scientists 
when evaluating depletion estimates. Notwithstanding 
these issues, which can be addressed with the data 
quality score described below, some measure of cur- 
rent stock abundance was viewed as a useful attribute. 
Survival after capture and release Fish survival after 
capture and release varies by species, region, depth, gear 
type, and even market conditions, and thus can affect 
the susceptibility of the stock (Davis, 2002). Consider- 
ations of barotraumatic effects, discarding methods, and 
gear invasiveness (e.g., gears with hooks or nets would 
likely be more invasive than traps) are particularly 
relevant. 
Fishery impact on habitat A fishery may have an indi- 
rect effect on a species through adverse impacts on habi- 
tat (Benaka, 1999; Barnes and Thomas, 2005). Within 
the United States, a definition of the level of impact 
is the focus of environmental impact statements and 
essential fish habitat evaluations (see Rosenberg et al., 
2000). To align with NMFS evaluations of impact, the 
scoring thresholds for this attribute were categorized as 
minimal, temporary, or mitigated. 
Defining attribute scores and weights 
Depending on the specific stock being evaluated, not all 
of the productivity and susceptibility attributes listed in 
Tables 1 and 2 will be equally useful in determining the 
vulnerability of a stock. In previous versions of the PSA, 
an attribute weighting scheme was used in which higher 
weights were applied to the more important attributes 
(Stobutzki et al., 2001b; Hobday et al. 1 ; Rosenberg et 
al. 3 ). We used a default weight of 2 for the productivity 
and susceptibility attributes, where attribute weights 
can be adjusted within a scale from 0 to 4 to customize 
the application to each fishery. In determining the proper 
weighting of each attribute, users should consider the 
relevance of the attribute for describing productivity or 
susceptibility rather than the availability of data for 
that attribute (e.g., data-poor attributes should not auto- 
matically receive low weightings). In some rare cases, 
it is also anticipated that some attributes will receive 
a weighting of zero, which cause them to be removed 
from the analysis, because the attribute has no rela- 
tion to the fishery and its stocks. Some attributes (e.g., 
management strategy, fishing mortality rate, biomass of 
spawners, etc.) may also be removed from the analysis 
to avoid double-counting if they are considered in a more 
overarching risk analysis, for which the results of the 
PSA are only one component. 
Like Milton (2001) and Stobutzki et al. (2001b), we 
defined the criteria for a score of 1, 2, or 3 to a produc- 
tivity or susceptibility attribute (see Table 1). However, 
our approach provides users the flexibility to apply in- 
termediate scores (e.g., 1.5 or 2.5) when the attribute 
value spans two categories. Owing to the subjective 
nature of semiquantitative analyses, scores should be 
applied in a consistent manner to reduce scoring bias 
(Lichtensten and Newman, 1967; Janis, 1983; Von Win- 
terfeldt and Edwards, 1986; Bell et al., 1988), such as 
by employing the Delphi method (see Okoli and Paw- 
lowski, 2004 and Landeta, 2006). 
Data-quality index 
As a precautionary measure for risk assessment scor- 
ing, the highest-level risk score can be used when data 
are missing to account for uncertainty and to avoid 
identifying a high-risk stock as low risk (Hardwood, 
2000; Milton, 2001; Stobutzki et al., 2001b; Astles et 
al., 2006). Although precautionary, that approach also 
confounds the issues of data quality with risk assess- 
ment. For example, a data-poor stock may receive 
