National Marine 
Fisheries Service 
NOAA 
Fishery Bulletin 
@& established in 1881 =< 
Spencer F. Baird ( mal 
First U.S. Commissioner he ’ 
of Fisheries and founder Sa 
of Fishery Bulletin 
Abstract—The limited resources and 
high species diversity associated with 
coastal fisheries present challenges 
to their effective management. Data- 
limited approaches to assessment of 
stocks are often used in these situa- 
tions, but most assessments require 
basic life history information that is 
often unavailable. We expanded a step- 
wise meta-analytical approach in which 
statistical relationships between key 
life history traits are used to estimate 
parameters related to growth, maturity, 
and longevity. This approach was origi- 
nally devised for 6 fish families and has 
been successfully implemented in the 
assessments of data-poor reef fish spe- 
cies in Hawaii and Guam. We expanded 
this approach to groupers, wrasses, 
grunts, and sharks and, here, present 
an R package that greatly simplifies its 
implementation. Further, we tested this 
expansion by selecting a species from 
each of these taxa and compared results 
from use of the stepwise approach to 
results from life history studies. Our 
results indicate agreement between the 
probability distributions from our step- 
wise approach and those from previous 
studies. Distributions from the stepwise 
simulation had higher variability but 
reasonable accuracy in estimating miss- 
ing values of life history parameters. 
We also tested our approach against 
another meta-analytical life history 
approach that was recently published 
(and made available as the R pack- 
age FishLife) and found our stepwise 
approach to be generally more precise 
and accurate. 
Manuscript submitted 5 June 2020. 
Manuscript accepted 30 April 2021. 
Fish. Bull. 119:77—-92 (2021). 
Online publication date: 4 June 2021. 
doi: 10.7755/FB.119.1.9 
The views and opinions expressed or 
implied in this article are those of the 
author (or authors) and do not necessarily 
reflect the position of the National 
Marine Fisheries Service, NOAA. 
An extension of the stepwise stochastic simulation 
approach for estimating distributions of missing 
life history parameter values for sharks, groupers, 
and other taxa 
Kenneth A. Erickson (contact author)'? 
Marc O. Nadon?? 
Email address for contact author: kerick6@lsu.edu 
" Department of Oceanography and Coastal Sciences 
Louisiana State University 
Energy Coast and Environment Building 
93 South Quad Drive, Room 2263 
Baton Rouge, Louisiana 70803 
? Joint Institute for Marine and Atmospheric Research 
School of Ocean and Earth Science and Technology 
University of Hawaii at Manoa 
1000 Pope Road 
Honolulu, Hawaii 96822 
3 Pacific Islands Fisheries Science Center 
National Marine Fisheries Service, NOAA 
1845 Wasp Boulevard, Building 176 
Honolulu, Hawaii 96818 
Coastal fisheries provide sustenance 
and a source of income for millions of 
people across the globe (Kronen et al., 
2010; Symes et al., 2015; FAO, 2016). 
These fisheries typically target hun- 
dreds of species from different taxa, 
and this high species diversity makes 
the management of these fisheries chal- 
lenging (Ault et al., 2014). In addition, 
resources for fisheries research and 
management are often lacking, result- 
ing in a paucity of data that limits the 
development of well-informed man- 
agement plans (Fenner, 2012; Gilman 
et al., 2014; Hilborn and Ovando, 2014; 
Berkson and Thorson, 2015). Assessing 
these fisheries is further limited by a 
lack of long-term catch and fishing 
effort records needed to implement cer- 
tain stock assessment models. 
To compensate for shortcomings, 
recent stock assessment methods have 
been focused on the use of cost-efficient 
length data (Ault et al., 1998, 2008; 
Gedamke and Hoenig, 2006; Nadon 
et al., 2015; Hordyk et al., 2016; Rudd 
and Thorson, 2018; Nadon, 2019). These 
length-based methods require life his- 
tory information related to growth: 
parameters of the von Bertalanffy 
growth function (von Bertalanffy, 1938), 
the asymptotic length (Z..) and growth 
coefficient (K); natural mortality (M); 
and length at maturity (L,,,;, the length 
at which 50% of individuals are mature). 
However, species-specific information 
on life history traits is missing for as 
many as 83% of exploited stocks glob- 
ally (Froese and Binohlan, 2000). To 
address this issue, Nadon and Ault 
(2016) developed a stepwise stochastic 
simulation approach in which a local 
estimate of maximum length (Z,,,,) and 
statistical relationships between life 
history parameters are used to esti- 
mate this missing information. 
