Phylogenetic analyses will be carried out using parsimony (see Farris, 1983; Huelsenbeck and Hillis, 1993), as implimented in programs such as PAUP (Swofford, 1993), and studies of character evolution will make use of MacClade (Maddison and Maddison, 1992) and related tools. Maximum likelihood methods will also be utilized in the case of molecular datasets (e.g., Hibbett and Donoghue, in review), and we will explore the influence of character and character-state weighting schemes (e.g., Albert and Mishler, Goloboff, 1994; Wheeler, 1990), including a priori approaches based, for example, on transition/transversion ratios, and iterative approaches such as succesive approximations weighting (Farris, 1969; Carpenter, 1988). The main intention of such weighting studies is to explore the sensitivity of the results to a variety of assumptions, in the hope of identifying especially robust conclusions, which hold over a wide range of circumstances.
Analyses involving a relatively small number of taxa will be conducted using exhaustive or branch-and-bound searches, whereas larger matrices will be analyzed using heuristic search strategies (Swofford and Olsen, 1990; Maddison et al., 1992; Olmstead et al, 1993) . In general, such strategies involve multiple searches from different starting topologies, which increases the chance of locating alternative tree islands (Maddison, 1991) . The robustness of phylogenetic results will be explored in several ways, including bootstrap analysis (Felsenstein, 1985; Hillis and Bull, 1993; Sanderson, 1989) , "decay" or "Bremer support" analysis (Bremer, 1988; Donoghue et al., 1992) , and various permutation tests (such as the T-PTP test described by Faith, 1991; Faith and Cranston, 1991; but see Kallersjo et al., 1992). Although none of these measures is perhaps ideal, it is hoped that through a combination of approaches we will be able to provide an indication of the level of support for particular hypothesized relationships. Such testing is highly desirable when phylogenetic results are used as a basis for formal taxonomic changes, or in studies of character evolution or biogeography.
Comparing the results of separate analyses of data from different sources may be helpful in identifying areas of conflict (Hillis, 1987) . Strongly supported conflicts may indicate non-independence of sets of characters within one or more of the datasets (Shaffer et al., 1991) or different underlying histories for different genes (Doyle, 1992) , resulting perhaps from hybridization (Rieseberg and Brunsfeld, 1992; Rieseberg and Soltis, 1991) , lineage sorting (Maddison, 1995; Pamilo and Nei, 1988) , or lateral transfer (de Queiroz, 1993) . In the face of such conflict, or significant heterogeneity in rates of evolution (tested, e.g., using the technique of Rodrigo et al., 1994), combined analyses should be interpreted cautiously, and characters should perhaps be weighted to reflect their likelihood of change (Barrett et al., 1991; Bull et al., 1993; Chippendale and Wiens, 1994; Huelsenbeck et al., 1994) or non- independence (Doyle, 1992) . If strong conflicts are identified, involving the position of one or a few taxa, experiments can be conducted in which suspect taxa are removed and the data are then combined for the remaining taxa (de Queiroz, Donoghue, and Kim, in review).
Combined analyses allow characters from different
datasets to interact with one another in estimating phylogenetic
relationships, which may reveal complimentary signals present
in different datasets that may not have been seen in separate
analyses (Barrett et al., 1991; Chippendale and Wiens, 1994) .
This kind of complimentarity has been observed in a number of
combined analyses; e.g., cpDNA datasets in Solanaceae (Olmsetad
and Sweere, 1994) , and rDNA and morphology in angiosperms (Doyle
et al., 1994) . When datasets appear to be more or less homogenous
or to show only weak conflicts the combined analysis should provide
the best estimate of the phylogeny (Miyamoto, 1985; Kluge, 1989;
Barrett et al., 1991; Larson, 1994);, and it is this estimate
that is best used in studies of character evolution, etc. (Donoghue,
1989; Maddison, 1990; Brooks and McLennan, 1991; Harvey and Pagel,