Search for most parsimonious trees using the parsimony ratchet and TBR rearrangements, treating inapplicable data as such using the algorithm of Brazeau et al. (2019) .

Tree search will be conducted from a specified or automatically-generated starting tree in order to find a tree with an optimal parsimony score, under implied or equal weights, treating inapplicable characters as such in order to avoid the artefacts of the standard Fitch algorithm (see Maddison 1993; Brazeau et al. 2019) . Tree length is calculated using the MorphyLib C library (Brazeau et al. 2017) .

  ratchIter = 6L,
  tbrIter = 2L,
  startIter = 2L,
  finalIter = 1L,
  maxHits = NTip(dataset) * 1.8,
  maxTime = 60,
  quickHits = 1/3,
  concavity = Inf,
  tolerance = sqrt(.Machine$double.eps),
  verbosity = 3L

  method = "jack",
  proportion = 2/3,
  ratchIter = 1L,
  tbrIter = 8L,
  finalIter = 3L,
  maxHits = 12L,
  concavity = Inf,
  tolerance = sqrt(.Machine$double.eps),
  verbosity = 2L,





A phylogenetic data matrix of phangorn class phyDat, whose names correspond to the labels of any accompanying tree.


(optional) A bifurcating tree of class phylo, containing only the tips listed in dataset, from which the search should begin. If unspecified, an addition tree will be generated from dataset, respecting any supplied constraint. Edge lengths are not supported and will be deleted.


Numeric specifying number of iterations of the parsimony ratchet (Nixon 1999) to conduct.


Numeric specifying the maximum number of TBR break points to evaluate before concluding each search. The counter is reset to zero each time tree score improves. The counter is reset to zero each time tree score improves. One 'iteration' comprises breaking a single branch and evaluating all possible reconnections.


Numeric: an initial round of tree search with startIter × tbrIter TBR break points is conducted in order to locate a local optimum before beginning ratchet searches.


Numeric: a final round of tree search will evaluate finalIter × tbrIter TBR break points, in order to sample the final optimal neighbourhood more intensely.


Numeric specifying the maximum times that an optimal parsimony score may be hit before concluding a ratchet iteration or final search concluded.


Numeric: after maxTime minutes, stop tree search at the next opportunity.


Numeric: iterations on subsampled datasets will retain quickHits × maxHits trees with the best score.


Numeric specifying concavity constant for implied step weighting. The most appropriate value will depend on the dataset, but values around 10--15 often perform well (Goloboff et al. 2018; Smith 2019) . The character string "profile" employs an approximation of profile parsimony (Faith and Trueman 2001) . Set as Inf for equal step weights, which underperforms step weighting approaches (Goloboff et al. 2008; Goloboff et al. 2018; Goloboff and Arias 2019; Smith 2019) .


Numeric specifying degree of suboptimality to tolerate before rejecting a tree. The default, sqrt(.Machine$double.eps), retains trees that may be equally parsimonious but for rounding errors. Setting to larger values will include trees suboptimal by up to tolerance in search results, which may improve the accuracy of the consensus tree (at the expense of resolution) (Smith 2019) .


An object of class phyDat; returned trees will be perfectly compatible with each character in constraint. See ImposeConstraint() and vignette for further examples.


Integer specifying level of messaging; higher values give more detailed commentary on search progress. Set to 0 to run silently.


Unambiguous abbreviation of jackknife or bootstrap specifying how to resample characters. Note that jackknife is considered to give more meaningful results.


Numeric between 0 and 1 specifying what proportion of characters to retain under jackknife resampling.


Additional parameters to MaximizeParsimony().


MaximizeParsimony() returns a list of trees with class multiPhylo. This lists all trees found during each search step that are within tolerance of the optimal score, listed in the sequence that they were first visited, and named according to the step in which they were first found; it may contain more than maxHits elements. Note that the default search parameters may need to be increased in order for these trees to be the globally optimal trees; examine the messages printed during tree search to evaluate whether the optimal score has stabilized. The return value has the attribute firstHit, a named integer vector listing the number of optimal trees visited for the first time in each stage of the tree search. Stages are named:

  • seed: starting trees;

  • start: Initial TBR search;

  • ratchN: Ratchet iteration N;

  • final: Final TBR search. The first tree hit for the first time in ratchet iteration three is named ratch3_1.

Resample() returns a multiPhylo object containing a list of trees obtained by tree search using a resampled version of dataset.


Tree search commences with ratchIter iterations of the parsimony ratchet (Nixon 1999) , which bootstraps the input dataset in order to escape local optima. A final round of tree bisection and reconnection (TBR) is conducted to broaden the sampling of trees.

This function can be called using the R command line / terminal, or through the 'shiny' graphical user interface app (type EasyTrees() to launch).

For detailed documentation of the 'TreeSearch' package, including full instructions for loading phylogenetic data into R and initiating and configuring tree search, see the package documentation.


Note that bootstrap support is a measure of the amount of data supporting a split, rather than the amount of confidence that should be afforded the grouping. "Bootstrap support of 100% is not enough, the tree must also be correct" (Phillips et al. 2004) . See discussion in Egan (2006) ; Wagele et al. (2009) ; (Simmons and Freudenstein 2011) ; Kumar et al. (2012) .

For a discussion of suitable search parameters in resampling estimates, see Muller (2005) . The user should decide whether to start each resampling from the optimal tree (which may be quicker, but result in overestimated support values as searches get stuck in local optima close to the optimal tree) or a random tree (which may take longer as more rearrangements are necessary to find an optimal tree on each iteration).

For other ways to estimate clade concordance, see SiteConcordance().


Brazeau MD, Guillerme T, Smith MR (2019). “An algorithm for morphological phylogenetic analysis with inapplicable data.” Systematic Biology, 68(4), 619--631. doi:10.1093/sysbio/syy083 .

Brazeau MD, Smith MR, Guillerme T (2017). “MorphyLib: a library for phylogenetic analysis of categorical trait data with inapplicability.” doi:10.5281/zenodo.815372 .

Egan MG (2006). “Support versus corroboration.” Journal of Biomedical Informatics, 39(1), 72--85. doi:10.1016/j.jbi.2005.11.007 .

Faith DP, Trueman JWH (2001). “Towards an inclusive philosophy for phylogenetic inference.” Systematic Biology, 50(3), 331--350. doi:10.1080/10635150118627 .

Goloboff PA, Arias JS (2019). “Likelihood approximations of implied weights parsimony can be selected over the Mk model by the Akaike information criterion.” Cladistics, 35(6), 695--716. doi:10.1111/cla.12380 .

Goloboff PA, Carpenter JM, Arias JS, Esquivel DRM (2008). “Weighting against homoplasy improves phylogenetic analysis of morphological data sets.” Cladistics, 24(5), 758--773. doi:10.1111/j.1096-0031.2008.00209.x .

Goloboff PA, Torres A, Arias JS (2018). “Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology.” Cladistics, 34(4), 407--437. doi:10.1111/cla.12205 .

Kumar S, Filipski AJ, Battistuzzi FU, Kosakovsky Pond SL, Tamura K (2012). “Statistics and truth in phylogenomics.” Molecular Biology and Evolution, 29(2), 457--472. doi:10.1093/molbev/msr202 .

Maddison WP (1993). “Missing data versus missing characters in phylogenetic analysis.” Systematic Biology, 42(4), 576--581. doi:10.1093/sysbio/42.4.576 .

Muller KF (2005). “The efficiency of different search strategies in estimating parsimony jackknife, bootstrap, and Bremer support.” BMC Evolutionary Biology, 5(1), 58. doi:10.1186/1471-2148-5-58 .

Nixon KC (1999). “The Parsimony Ratchet, a new method for rapid parsimony analysis.” Cladistics, 15(4), 407--414. ISSN 0748-3007, doi:10.1111/j.1096-0031.1999.tb00277.x .

Phillips MJ, Delsuc F, Penny D (2004). “Genome-scale phylogeny and the detection of systematic biases.” Molecular biology and evolution, 21(7), 1455--8. doi:10.1093/molbev/msh137 .

Simmons MP, Freudenstein JV (2011). “Spurious 99% bootstrap and jackknife support for unsupported clades.” Molecular Phylogenetics and Evolution, 61(1), 177--191. doi:10.1016/j.ympev.2011.06.003 .

Smith MR (2019). “Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets.” Biology Letters, 15(2), 20180632. doi:10.1098/rsbl.2018.0632 .

Wagele JW, Letsch H, Klussmann-Kolb A, Mayer C, Misof B, Wagele H (2009). “Phylogenetic support values are not necessarily informative: the case of the Serialia hypothesis (a mollusk phylogeny).” Frontiers in Zoology, 6(1), 12--29. doi:10.1186/1742-9994-6-12 .

See also

Tree search via graphical user interface: EasyTrees()

Other split support functions: JackLabels(), Jackknife(), SiteConcordance


## Only run examples in interactive R sessions
if (interactive()) {
  # launch 'shiny' point-and-click interface
  # Here too, use the "continue search" function to ensure that tree score
  # has stabilized and a global optimum has been found

# Load data for analysis in R
data('congreveLamsdellMatrices', package = 'TreeSearch')
dataset <- congreveLamsdellMatrices[[42]]

# A very quick run for demonstration purposes
trees <- MaximizeParsimony(dataset, ratchIter = 0, startIter = 0,
                           tbrIter = 1, maxHits = 4, maxTime = 1/100,
                           concavity = 10, verbosity = 4)
#> ── Sample local optimum ────────────────────────────────────────────────────────
#> → TBR depth 1; keeping 4 trees; k = 10
#>  2022-05-11 07:32:46: Score: 12.4347
#>  2022-05-11 07:32:46: Tree search terminated with score 12.112
#> [1] "final_1" "final_2" "final_3"

# In actual use, be sure to check that the score has converged on a global
# optimum, conducting additional iterations and runs as necessary.
if (interactive()) {
# Jackknife resampling
nReplicates <- 10
jackTrees <- replicate(nReplicates,
  #c() ensures that each replicate returns a list of trees
  c(Resample(dataset, trees, ratchIter = 0, tbrIter = 2, startIter = 1,
             maxHits = 5, maxTime = 1 / 10,
             concavity = 10, verbosity = 0))

# In a serious analysis, more replicates would be conducted, and each
# search would undergo more iterations.

# Now we must decide what to do with the multiple optimal trees from
# each replicate.

# Treat each tree equally
JackLabels(ape::consensus(trees), unlist(jackTrees, recursive = FALSE))

# Take the strict consensus of all trees for each replicate
JackLabels(ape::consensus(trees), lapply(jackTrees, ape::consensus))

# Take a single tree from each replicate (the first; order's irrelevant)
JackLabels(ape::consensus(trees), lapply(jackTrees, `[[`, 1))

# Tree search with a constraint
constraint <- MatrixToPhyDat(c(a = 1, b = 1, c = 0, d = 0, e = 0, f = 0))
characters <- MatrixToPhyDat(matrix(
  c(0, 1, 1, 1, 0, 0,
    1, 1, 1, 0, 0, 0), ncol = 2,
  dimnames = list(letters[1:6], NULL)))
MaximizeParsimony(characters, constraint = constraint, verbosity = 0)
#>  Initialized 1 distinct constraints.
#> → 2022-05-11 07:32:46: Score to beat: 3
#> 1 phylogenetic tree