243 
Abstract— Improved methods for 
estimating saltwater recreational 
fishing catch and effort have been 
developed by the NOAA National 
Marine Fisheries Service. Sampling 
weights that account for a complex 
sample design in surveys of anglers 
are now available with NMFS catch 
and effort estimates. Previously, es- 
timates of the economic value to an- 
glers (known as the “willingness to 
pay”) for additional fish caught that 
were based on angler surveys did 
not typically account for the under- 
lying complex sample design. In this 
study, a recreational-demand model 
was used for analysis of fishing site 
choices in the Gulf of Mexico in 2009 
among private-boat anglers who 
target groupers ( Epinephelus spp., 
Hyporthodus spp., or Mycteroper- 
ca spp.) or red snapper (Lutjanus 
campechanus). Different versions of 
the model were developed with and 
without accounting for the complex 
sample design. Results between the 
unweighted version and weighted 
versions of the model varied in esti- 
mates of catch between sites and the 
value anglers place on being able to 
catch and keep additional fish. 
Manuscript submitted 2 May 2013. 
Manuscript accepted 13 June 2014. 
Fish. Bull. 112:243-252 (2014). 
doi:10.7755/FB. 112.4.1 
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. 
The use of sampling weights in regression 
models of recreational fishing-site choices 
Sabrina J. Lovell (contact author ) 1 
David W. Carter 2 
Email address for contact author: sabrina.lovell@noaa.gov 
1 Economics and Social Analysis Division 
Office of Science and Technology 
National Marine Fisheries Service, NOAA 
1315 East-West Highway 
Silver Spring, Maryland 20910-3282 
2 Southeast Fisheries Science Center 
National Marine Fisheries Service, NOAA 
75 Virginia Beach Drive 
Miami, Florida, 33149-1003 
In 2012, the NOAA National Marine 
Fisheries Service (NMFS) released 
a new method for estimation of rec- 
reational fishing catch and effort 
based on data obtained from its Ac- 
cess Point Angler Intercept Survey 
(APAIS) of saltwater anglers. Previ- 
ous methods of estimation of catch 
and fishing effort from this intercept 
[interview] survey were subject to a 
number of different potential biases 
as pointed out by the National Re- 
search Council of the National Acad- 
emies (NRC, 2006). In particular, the 
earlier estimation methods did not 
account for the complex sample de- 
sign of the intercept survey and in- 
stead simple random sampling was 
assumed. The new method of esti- 
mating catch and effort uses special- 
ly calculated weights and variance 
adjustments (Breidt et al. 1 ). 
The APAIS sampling weights in- 
corporate information from a sepa- 
1 Breidt, F. J., H.-L. Lai., J. D. Opsomer, 
and D. A. Van Voorhees. 2012. A 
report of the MRIP sampling and 
estimation project: improved estimation 
methods for the Access Point Angler 
Intercept Survey component of the 
Marine Recreational Fishery Statistics 
Survey. [Available from Fisheries 
Statistics Division, Natl. Mar. Fish. 
Serv., NOAA, Silver Spring, MD, and 
from http://www.countmyfish.noaa.gov/ 
projects/downloads/Final%20Report%20 
of%20New%20Estimation_Method_for_ 
MRFSS_Data-01242012.pdf.] 
rate survey, the NMFS Coastal 
Household Telephone Survey (CHTS), 
that is used to estimate fishing effort 
by coastal residents by state, wave 
(defined as a consecutive 2-month pe- 
riod), and fishing mode (private boat 
and shore). Data from the APAIS on 
the proportion of angler effort from 
coastal residents to angler effort 
from noncoastal and out-of-state res- 
idents are used to scale the level of 
angler effort from coastal residents 
up to an unbiased estimate of total 
effort for all anglers, both coastal 
and noncoastal. For example, 85% of 
private boat trips that targeted grou- 
pers and red snapper in the Gulf of 
Mexico in 2009 were taken by resi- 
dents of coastal counties. Inclusion 
of the APAIS sampling weights in 
recreational site-choice demand mod- 
els will ensure that results correctly 
reflect the true proportion of trips 
that come from coastal residents 
compared with trips from noncoastal 
residents. This inclusion is important 
because the costs associated with 
traveling between an angler’s home 
and different fishing sites used in 
the demand models will vary on the 
basis of proximity to the coast. 
A number of recreational site- 
choice demand models have been 
developed with the APAIS data (e.g., 
Whitehead and Haab, 2000; Gentner, 
2007; Haab et al., 2012). These mod- 
