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1996 McGUIRE-SYSTEMATICS OF CROTAPHYTID LIZARDS 7
was not evident in the characters in question. Unless
otherwise stated, scale terminology follows Smith
(1946), skull terminology follows Oelrich (1 956),
and postcranial skeletal terminology follows Eth-
eridge (1964, 1965, 1967), Hofstetter and Gasc
(1969), and de Queiroz (1987). Museum numbers
of crotaphytid specimens examined and their lo-
calities are listed in Appendix 1, along with museum
numbers of iguanian outgroup taxa examined.
Hypotheses of phylogenetic relationships were es-
timated using cladistic analysis (e.g., Hennig, 1966;
Wiley, 198 1). Character states were polarized using
outgroup analysis (Watrous and Wheeler, 198 1 ;
Maddison et al., 1984), a procedure that was com-
plicated by the lack ofinterfamilial resolution within
Iguania (see discussion of outgoup taxa below).
Many characters could not be polarized unequivo-
cally and these were described as "unpolarized" or
"not polarized" in the character descriptions. Once
character polarities were obtained, a hypothetical
ancestor was constructed summarizing the hypoth-
esized ancestral states for each character. The hy-
pothetical ancestor was included in the analysis in
order to root the tree. The phylogenetic software
employed here was a test version of PAUP (version
4.0.0d26, Swofford, 1995). Because the number of
taxa is relatively small, the branch-and-bound al-
gorithm of Hendy and Penny (1 982) was employed,
guaranteeing that all most parsimonious trees would
be discovered. Logical incongruencies (e.g., trans-
formations of the collar pattern in species that have
no collar) were coded as missing or unknown data
("?"). Following the recovery of the most parsi-
monious tree, tree stability and phylogenetic infor-
mation content were tested using the nonparametric
bootstrap (Felsenstein, 1985; 2000 bootstrap rep-
licates), as well as analyses of tree length distribution
skewness (g, statistic; Hillis, 199 1 ; Huelsenbeck,
199 1; Hillis and Huelsenbeck, 1992) and the decay
index (Donoghue et al., 1992). Simulations indicate
that a strongly lefi-skewed distribution of tree lengths
(described by a negative g, value) is an indicator of
phylogenetic information content of the data (Hillis,
199 1 ; Huelsenbeck, 199 1 ; Hillis and Huelsenbeck,
1992). Hillis and Huelsenbeck (1 992) provided crit-
ical g, values for data matrices composed of various
numbers of binary and four state characters. Because
this data set differs from the simulated data sets
generated by Hillis and Huelsenbeck (1 992) both in
number of characters and in the numbers of states
per character, new g, critical values were calculated
that are specific to this data set using a computer
Table I .- Recalculared g, critical values expected for randont data
for rhe nrorpho1og)t-onlj~, a1lo:j~me-onlji, and morphology + allozyme
(alloz~~~nes coded using Manhattan distance frequency approach)
data sers and the obsen?ed g, value for each.
Nwnber.of
informauve Number
characten of P - 0.05 P - 0.01 Obrmd
Morphology only: 88 13 -0.15 -0.16 - 1.49
Allozymes: 10 7 -0.43 -0.45 -0.50
Morphology -t
Allozymes: 98 13 -0.15 -0.15 -1.45
program written by J. Huelsenbeck (Table 1 ). These
values were generated by randomly reshuffling char-
acter states among taxa in the original data set 100
times and recalculating the g, for each reshuffled
matrix. Critical values at both 95 percent and 99
percent confidence intervals were then calculated
from the distribution of g, values generated.
FREQUENCY
CODING
The character coding scheme applied to morpho-
logical data in this analysis is a frequency approach
developed by Wiens (19936, 1995). An approach
wherein polymorphic characters are excluded from
the analysis is rejected because it is clear that many
characters will be found to be polymorphic given a
sufficient sample site. This was especially evident
in this analysis as large sample sizes were available
for both preserved (up to 87 specimens per taxon)
and osteological (as many as 55 specimens per tax-
on) material. Under the frequency approach, each
binary character is partitioned into 25 bins (a-y),
each representing 4 percent of the total range of
possible frequencies that may be observed in a poly-
morphic or monomorphic character (i.e., bin a =
0-3%, bin b = 4-7%, and so on; Table 2). Note that
it is necessary for one of the bins to have a range of
5 percent rather than 4 percent in order to encom-
pass the entire range of possible frequencies (O-
100%); this bin was arbitrarily chosen as bin y (96-
100%). Twenty-five frequency bins were used be-
cause this was the maximum number of whole num-
ber bins (i.e., 4 percent vs. 3.26 percent per bin, etc.)
that PAUP is able to include (although PAUP will
allow up to 3 1 bins; Swofford, 1995). Those char-
acters that were analyzed using frequency coding
were treated as ordered, following the assumption
that any character state transformation must pass
through a polymorphic state, no matter how tran-
sitory, before reaching fixation (Wiens, 1993b, 1995).
Frequency coding was not applied to the three mul-