Consensus approach
where they all agree - meta analyses
topology composed of different formal analyses with or without some sort of formal analysis.
allow us to combine results from incompatible data sets
supertree methods are all consensus tree methods
Total evidence approach
take all the data and make a tree
Why did people first start creating super trees?
1 - the unwieldiness of analyses, gets harder to work with bigger data sets
2- like to summarize what has already been done, more formal way to summarize analyses
Supertree approach
divide and conquer
priori super tree approach
...
Posteriori -ad hoc approach
after the fact
-
representative of major groups
supertrees
Tree we put together with seperate analyses and reach a consensus, what is the most likely arrangement of species.
Informal supertree
"cut and paste" kind of know from other studies how some species are related, paste it with what is know from other species and groups. no objective analysis that occurs
can be an issue if conflicting subtrees
Formal supetree
secondary analysis to make connected tree. allow us to combine results from incompatible data sets
meta analysis - take raw data and synthesize into distance data, analysis done on secondary data set. rather than relying on primary data set, uses secondary data.
confounded (?)
Two processes used to make formal supertrees
AGREEMENT AND OPTIMIZATION via matrix representation
Agreement
making a consensus tree, put a number on what % of trees define those relationships
Optimization
make secondary matrix, separate analyses that uses the data in a secondary matrix that summarizes all of the trees (1)
matrix representation - make a second data set that synthesizes our - one.
Criticisms of supertree methods
no primary data,
no signal enhancement - reducing noise and enhancing signal
prescende of clades (novel clades) in a super tree that weren't present in previous. sticks two things together that weren't related before
inadvertent replication of source data. its a meta analysis- an analysis of previous analyses without collecting data. (reusing our data, replicate source data, giving extra weight to relationship ?)
Two methodologies taken with this newer approach
Disk covering method
biclique method
Disk covering method
use an estimate of rough relationships to create separate analyses and use a supertree to put it together. collect new data.
if we have a rough estimate of relationships, we come up with areas that have some overlap, better fit when putting back together.
priori supertree approach (?)
Biclique method
find large data sets and put them together in sequence analyses that is based on the data. "divide and conquer" approach. uses old data
put together a matrix that represents different groups, identify areas with large overlaps, represents one analysis we could do, separate analyses can be done, allow us to identify good data sets that could be put together and ones that identify diversity (?)
posteriori (ad hoc) (?)