Conservation genetics

Transcription

1 Conservation genetics Franky ossuyt Conservation genetics Franky ossuyt im To be able to evaluate evolutionary history as one of the parameters that can be used to estimate the value of fauna and flora in a certain region (<--> populations)

2 Hotspots and endemism 5

3 utterfly sp. Treefrog sp. utterfly sp. Treefrog sp. utterfly sp. utterfly sp. Treefrog sp. Treefrog sp. utterfly sp. Treefrog sp. utterfly sp. Treefrog sp.

4 (Phylo)genetic Relationships

5 The principle of parsimony 5 C C D D Finding optimal trees - exact solutions xact solutions can only be used for small numbers of taxa xhaustive search examines all possible trees Typically used for problems with less than 0 taxa

6 Finding optimal trees - exhaustive search C Starting tree, any taxa dd fourth taxon (D) in each of three possible positions -> three trees a D C D b C c dd fifth taxon () in each of the five possible positions on each of the three trees -> 5 trees, and so on... C D Finding optimal trees - exact solutions ranch and bound saves time by discarding families of trees during tree construction that cannot be shorter than the shortest tree found so far Can be enhanced by specifying an initial upper bound for tree length Typically used only for problems with less than 8 taxa

7 Finding optimal trees - heuristics The number of possible trees increases exponentially with the number of taxa making exhaustive searches impractical for many data sets (an NP complete problem) Heuristic methods are used to search tree space for most parsimonious trees by building or selecting an initial tree and swapping branches to search for better ones The trees found are not guaranteed to be the most parsimonious - they are best guesses Finding most parsimonious trees - heuristics ranch Swapping: Nearest neighbor interchange (NNI) Subtree pruning and regrafting (SPR) Tree bisection and reconnection (TR) Other methods...

8 Finding optimal trees - heuristics Tree bisection and reconnection (TR) C D F G G F C D F C D G Tree space may be populated by local minima and islands of optimal trees RNDOM DDITION SQUNC RPLICTS FILUR SUCCSS FILUR ranch Swapping ranch Swapping ranch Swapping Tree Length Local Minimum GLOL MINIMUM Local Minima

9 Searching with topological constraints Topological constraints are user-defined phylogenetic hypotheses Can be used to find optimal trees that either:. include a specified clade or set of relationships. exclude a specified clade or set of relationships (reverse constraint) Searching with topological constraints C D F G CD FG CONSTRINT TR ((,,C,D)(,F,G)) C D F G C D F G CD FG Incompatible with constraint tree Compatible with constraint tree Incompatible with reverse constraint tree

10 l Searching with topological constraints backbone constraints ackbone constraints specify relationships among a subset of the taxa D CKON CONSTRINT ((,)(D,)) relationships of taxon C are not specified D D possible positions of taxon C Compatible with backbone constraint Incompatible with reverse constraint Incompatible with backbone constraint Compatible with reverse constraint Parsimonious Character Optimization C D OR parallelism separate origins 0 => (DLTRN) => 0 origin and reversal (CCTRN) * = 0 => * = Homoplastic characters often have alternative equally parsimonious optimizations Commonly used varieties are: CCTRN - accelerated transformation DLTRN - delayed transformation Consequently, branch lengths are not always fully determined PUP reports minimum and maximum branch lengths

11 Support ootstrapping remer support (Decay index) ootstrapping Sp GGCTCC Sp GGTTCG Sp GCCCCG Sp TTTCCGC Re 0000 Re Sp GGGTTTC Sp GGGTTTG Sp GCCCCCG Sp TTTCCCGC Sp TTCCCC Sp TTCCGG Sp CCCCGG Sp CCCCGGCCC

12 ootstrapping Sp GGCTCC Sp GGTTCG Sp GCCCCG Sp TTTCCGC Re 000 Sp GGGTTTC Sp GGGTTTG Sp GCCCCCG Sp TTTCCCGC remer support (Decay index) Sp GGCT...CC Sp GGTT...CG Sp GCCC...CG Sp TTTC...CGC TL=50 TL=5 TL=5 TL=5 TL=5 Question: How can we calculate this (without calculating all these trees)?

13 Origin and early diversification of frogs GYMNOPHION a) Neobatrachia monophyletic b) sister relationship of scaphidiae and Leiopelmatidae ombina variegata (OMINTORID) ombina orientalis lytes 00(DISCOGLOSSID) 96 Discoglossus Rhinophrynus (RHINOPHRYNID) 00Crown-group Pipa frogs 00- / Hymenochirus (PIPID) Xenopus 00- / Silurana Scaphiopus c) a basal position for mphicoela c Spea Pelodytes / subst./site (PLOTID) Leptobrachium Leptolalax rachytarsophrys (MGOPHRYID) (MYOTRCHID) 9-8 e (PLODYTID) Pelobates d (SCPHIOPODID) PLOTOID PIPOID a DISCOGLOSSOID CUDT Hynobius urycea scaphus montanus (SCPHID) scaphus truei Leiopelma hochstetteri (LIOPLMTID) Leiopelma archeyi (HYLID) (CRTOPHRYIN) (RHINDRMTID) (MICROHYLID) (RNID) (LPTOPLIN) NOTRCHI b Which value is support for: MPHICOL Geotrypetes Gegeneophis Typhlonectes Pleurodeles (STYLOSTRNID) Questions Homo sapiens Pan paniscus Which value is support for: Gorilla gorilla a) a (homo, Pan) sister relationship Pongo pygmaeus b) a basal position for Pongo Hylobates OUTGROUP

14 Missing data l Missing data is ignored in tree building but can lead to alternative equally parsimonious optimizations in the absence of homoplasy?? 0 0 C D single origin 0 => * on any one of branches * * bundant missing data can lead to multiple equally parsimonious trees. This can be a serious problem with morphological data but is less likely to arise with molecular data Consensus methods l consensus tree is a summary of the agreement among a set of fundamental trees l There are many consensus methods that differ in:. the kind of agreement. the level of agreement l Consensus methods can be used with multiple trees from a single analysis or from multiple analyses

15 Strict consensus methods l Strict consensus methods require agreement across all the fundamental trees l They show only those relationships that are unambiguously supported by the parsimonious interpretation of the data l The commonest method (strict component consensus) focuses on clades/components/full splits l This method produces a consensus tree that includes all and only those full splits found in all the fundamental trees l Other relationships (those in which the fundamental trees disagree) are shown as unresolved polytomies Strict consensus methods TWO FUNDMNTL TRS C D F G C D F G C D F G STRICT COMPONNT CONSNSUS TR

16 Majority-rule consensus methods Majority-rule consensus methods require agreement across a majority of the fundamental trees May include relationships that are not supported by the most parsimonious interpretation of the data The commonest method focuses on clades/components/full splits This method produces a consensus tree that includes all and only those full splits found in a majority (>50%) of the fundamental trees Other relationships are shown as unresolved polytomies Of particular use in bootstrapping Majority rule consensus THR FUNDMNTL TRS C D F G C F D G C D F G C D F G Numbers indicate frequency of clades in the fundamental trees MJORITY-RUL COMPONNT CONSNSUS TR

17 Reduced consensus methods l l l l l Focuses upon any relationships (not just full splits) Reduced consensus methods occur in strict and majority-rule varieties Other relationships are shown as unresolved polytomies May be more sensitive than methods focusing only on clades/components/full splits Strict reduced consensus methods are implemented in RadCon Reduced consensus methods TWO FUNDMNTL TRS C D F G G C D F C D F C D F G Strict component consensus completely unresolved STRICT RDUCD CONSNSUS TR Taxon G is excluded

18 Consensus methods l Use strict methods to identify those relationships unambiguously supported by parsimonious interpretation of the data l Use reduced methods where consensus trees are poorly resolved l Use majority-rule methods in bootstrapping l void other methods which have ambiguous interpretations