Complementary analyses
Catalano SA, Ercoli M, Prevosti F. “The More, the Better: The Use of Multiple Landmark
Configurations to Solve the Phylogenetic Relationships in Musteloids”. Systematic Biology.
We present here a series of complementary analyses conducted in order to evaluate if
different methodological decisions made during the analyses (method of superimposition,
standardization procedure, presence of missing data, phylogeny used as reference) affect the
main conclusions obtained in the present study. Most of these analyses were suggested during
the revision of the manuscript by the editors and/or the reviewers.
Different superimposition strategies
The analyses included in the main body of the paper were derived from a dataset
where the superimposition strategy was the following: (1) superimposing all the configurations
for each species by means of a Generalized Procrustes Analysis (PCA), (2) calculating the
consensus for each species 3 ) performing a second GPA to superimpose the consensus
configurations. In the complementary analysis presented here, and following the suggestion of
one of the reviewers, we repeated the analysis considering the following strategy: (1)
superimposing the specimens of all the species by means of a GPA, (2) calculating the
consensus for each species. The superimpositions were performed using tpsrelW (Rohlf 2008).
Search settings were the same than those considered in the analysis presented in the main
body of the paper. The resulting tree for the complete dataset was the same than that
presented in the paper.
The complementary analysis presented above as well as the analyses presented in the
main body of the paper start from a static superimposition that remains unaltered during the
tree search. We present here an alternative procedure to analyze this dataset under a dynamic
approach where the score for each tree evaluated during searches is calculated considering
the best (heuristic) superimposition. This was done following the approach proposed by
Catalano & Goloboff (2012), where the tree score is calculated considering the multiple
superimposition and the ancestral assignments (i.e. ancestral shapes) such that the sum of the
landmark displacements between the corresponding landmarks along tree nodes is minimized.
That approach allows us to obtain improvements in tree score by rotating and translating
configurations but not by resizing. As the score of a multiple alignment depends on the size of
the configurations, the score will always decrease if all the configurations are shrunk (Catalano
& Goloboff 2012). ). Hence size of all configurations remained unmodified during the dynamic
approach. Search settings were the same as those considered in the static approach described
in the main body of the paper. The best tree obtained in this analysis was very similar to that
obtained in the static approach, only differing in the position of Martes (Figure 1).
Standardizing for the differences in size among structure
As indicated in Supplementary Material 3, the standardization among configurations
can be based on different characteristics of the configurations: size, number of landmarks,
contribution of each configuration to the total score, etc. Our choice was to standardize the
configurations trying to obtain a similar a priori contribution of each configuration for the
election of the phylogenetic hypothesis. One of the reviewers suggested us to repeat the
analysis standardizing by size but not by the number of landmarks (i.e. allowing configurations
with more landmarks to have more influence). Following the reviewer´s suggestion we
standardize the configurations using a normalized centroid size (Dryden & Mardia 1998, see
Supplementary Material 3). Search settings were the same than those considered in the static
approach described in the main body of the paper. The obtained tree (Figure 2) presented less
agreement with the reference tree than that obtained in the original analysis.
Using morphological tree as reference
In the analyses presented in the paper, the trees obtained with variable number of
configurations were compared with a reference molecular tree. We repeated these analyses
by comparing the resulting trees with a tree derived from morphological evidence (Bryant et
al. 1993). Given the difference in taxon sampling between the present dataset and previous
morphological analyses, the comparisons were done excluding 7 species (Galictis cuja, Lontra
canadensis, Lontra longicaudis, Lontra provocax, Martes americana, Mustela vison and
Procyon cancrivorus). The results obtained showed the same as that obtained when the
reference tree was molecular: increasing congruence when more landmark configurations
were included. Figure 3 illustrate the results.
Missing Data
Some of the species included in the analysis presented in the main body of the paper
presented missing data for different structures. This may have potentially affected the results
of correlation between number of structures and congruence with the reference topology.
Consequently, we generated a dataset without missing data, trying to exclude as few
species/configurations as possible. In this new analysis three species (Martes americana,
Lontra canadensis and Galictis vitata) and two configurations (sixth cervical vertebra and
pelvis) were excluded from the analysis. The analyses were run considering the same settings
as those used for the whole dataset. The results of these new analyses are presented in Figure
4. The same trend of increasing congruence with the reference tree was obtained.
Inclusion / exclusion of semi-landmarks
Two configurations (axis and sixth cervical vertebra) were digitalized including semilandmarks: 14 semi-landmarks for the axis and seven for the cervical vertebra. In order to
evaluate whether the results were affected by the inclusion of the semi-landmarks, we
repeated the analysis for the complete dataset but excluding the semi-landmarks. The results
did not differ when the semi-landmarks were excluded from the configurations.
REFERENCES
Bryant H.N., Russell A.P., Fitch W.D. 1993. Zool. Journal of the Linnean Society 108: 301–334
Catalano SA, Goloboff PA. 2012. Simultaneously mapping and superimposing landmark
configurations with parsimony as optimality criterion. Syst. Biol. 61:392–400.
Dryden IL, Mardia KV. 1998. Statistical shape analysis. John Wiley & Sons, New York
Rohlf FJ. 2008. TPSrelw: relative warps, version 1.46. N. Y. State University at Stony Brook.
Figure 1. Optimal tree obtained in the combined analysis of nine different skeletal structures,
under a dynamic approach (Catalano & Goloboff 2012). Except for the position of Meles, the
rest of the relationships were identical to those present in the tree derived from the static
approach.
Figure 2. Optimal tree obtained in the combined analysis of nine different skeletal structures,
standardizing the configurations only by differences in size.
Figure 3. Correspondence between the trees obtained considering a variable number of
landmark configurations and a morphological reference tree. (a) Number of nodes shared
between the obtained trees and the morphological reference tree. (b) SPR similarity between
the obtained trees and the morphological reference tree. The mean values (±SD) for the trees
derived from all possible combinations of configurations (for a given number of configurations)
are shown.
Figure 4. Correspondence between the trees obtained considering a variable number of
landmark configurations and previous phylogenetic and taxonomic schemes when only species
without missing data were included in the analysis. (a) Number of clades in agreement with
accepted taxonomy. (b) Number of nodes shared between the obtained trees and the
molecular reference tree. (c) SPR similarity between the obtained trees and the molecular
reference tree. The mean values (±SD) for the trees derived from all possible combinations of
configurations (for a given number of configurations) are shown.