Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology
-
Lazarus K. Mramba
,
Stuart Barber
Abstract
There is a need for a reliable statistical test which is appropriate for assessing cospeciation of more than two phylogenies. We have developed an algorithm using a permutation method that can be used to test for and infer tri-trophic evolutionary relationships of organisms given both their phylogenies and pairwise interactions. An overall statistic has been developed based on the dominant eigenvalue of a covariance matrix, and compared to values of the statistic computed when tree labels are permuted. The resulting overall p-value is used to test for the presence or absence of cospeciation in a tri-trophic system. If cospeciation is detected, we propose new test statistics based on partial correlations to uncover more details about the relationships between multiple phylogenies. One of the strengths of our method is that it allows more parasites than hosts or more hosts than parasites, with multiple associations and more than one parasite attached to a host (or one parasite attached to multiple hosts). The new method does not require any parametric assumptions of the distribution of the data, and unlike the old methods, which utilize several pairwise steps, the overall statistic used is obtained in one step. We have applied our method to two published datasets where we obtained detailed information about the strength of associations among species with calculated partial p-values and one overall p-value from the dominant eigenvalue test statistic. Our permutation method produces reliable results with a clear procedure and statistics applied in an intuitive manner. Our algorithm is useful in testing evidence for three-way cospeciation in multiple phylogenies with tri-trophic associations and determining which phylogenies are involved in cospeciation.
The authors would like to thank the editor and referees whose comments have substantially improved this paper. LKM gratefully acknowledges Kenya Medical Research Institute (KEMRI) Kilifi, Kenya, for funding his masters research studies. The Dyer and Forister labs acknowledge support from the United States National Science Foundation, awards DEB 1020509 and DEB 1145609.
Appendix A: Example datasets
The phylogenetic trees simulated for Sections 3.1–3.3 are shown in Figure A1(A–C) respectively and their corresponding triangular interaction matrices T(a)–T(c) are given in (2). Here, (A) refers to the example with no cospeciation; (B) refers to strong cospeciation betweeen X and Y while Z is independent of both; and (C) refers to the example where all three trees are strongly cospeciated.
Trees generated with varying degrees of cospeciation.
Appendix B: Labels for the termite-bacteria-protist dataset
Termite labels, X.
| 1 | Rhinotermes_marginalis |
| 2 | Rhinotermes_hispidus |
| 3 | Schedorhinotermes_sp_Australia |
| 4 | Parrhinotermes_sp |
| 5 | Schedorhinotermes_sp_Laos |
| 6 | Termitogeton_planus |
| 7 | Psammotermes_allocerus |
| 8 | Heterotermes_longiceps |
| 9 | Heterotermes_tenuis |
| 10 | Coptotermes_formosanus_japan |
| 11 | Coptotermes_formosanus_china |
| 12 | Coptotermes_sp_Malaysia |
| 13 | Coptotermes_sp_Laos |
| 14 | Coptotermes_testaceus |
Protist labels, Y.
| 1 | AB262494_Psudotrichonympha_sp |
| 2 | AB262495_Psudotrichonympha_sp |
| 3 | AB262496_Psudotrichonympha_sp |
| 4 | AB262497_Psudotrichonympha_sp |
| 5 | AB262498_Psudotrichonympha_sp |
| 6 | AB032211_Psudotrichonympha_sp |
| 7 | AB262486_Psudotrichonympha_sp |
| 8 | AB262487_Psudotrichonympha_sp |
| 9 | AB262488_Psudotrichonympha_sp |
| 10 | AB262489_Psudotrichonympha_sp |
| 11 | AB262490_Psudotrichonympha_sp |
| 12 | AB262491_Psudotrichonympha_sp |
| 13 | AB262492_Psudotrichonympha_sp |
| 14 | AB262493_Psudotrichonympha_sp |
Bacteria labels, Z.
| 1 | AB262559_Br02Htl_S4 |
| 2 | AB262560_Br78HtT_S1 |
| 3 | AB218918_CfPt1_2 |
| 4 | AB262555_CNCpF_S1 |
| 5 | AB262556_Ma79Cp_S1 |
| 6 | AB262557_La10Cp_S3 |
| 7 | AB262558_Br75CpT_S1 |
| 8 | AB218919_TpPtN_4 |
| 9 | AB262562_Br84RhM_S5 |
| 10 | AB262563_Br76RhH_S1 |
| 11 | AB262564_La19Sc_S1 |
| 12 | AB262566_My26Pa_S1 |
| 13 | AB262565_Au05Sc_S1 |
| 14 | AB262561_SA16PsA_S4 |
Appendix C: Labels and interaction matrix for the tree-moth-wasp dataset
Tree labels, X.
| 1 | Viburnum |
| 2 | Acer |
| 3 | Salix |
| 4 | Trifolium |
| 5 | Medicago |
| 6 | Ulmus |
| 7 | Prunus |
| 8 | Crataegus |
| 9 | Malus |
| 10 | Sorbus |
| 11 | Fagus |
| 12 | Quercus_robur |
| 13 | Alnus |
| 14 | Betula |
| 15 | Corylus |
| 16 | Carpinus |
Moth labels, Y.
| 1 | Paronix_carpinella |
| 2 | P_schreberella |
| 3 | P_harrisella |
| 4 | P_nicellii |
| 5 | P_cavella |
| 6 | P_froelichiella |
| 7 | P_lautella |
| 8 | P_insignitella |
| 9 | P_roboris |
| 10 | P_spinicolella |
| 11 | P_viminiella |
| 12 | P_salicicolella |
| 13 | P_rajella |
| 14 | P_ulmifoliella |
| 15 | P_geniculella |
| 16 | P_platanoidella |
| 17 | P_sylvella |
| 18 | P_quercifoliella |
| 19 | P_lantanella |
| 20 | P_maestingella |
| 21 | P_sorbi |
| 22 | P_corylifoliella |
| 23 | P_coryli |
| 24 | P_esperella |
| 25 | P_cydoniella |
| 26 | P_oxyacanthae |
| 27 | P_mespilella |
| 28 | P_blancardella |
Wasp labels Z.
| 1 | insignitellae |
| 2 | carpini |
| 3 | zwoelferi |
| 4 | niveipes |
| 5 | atys |
| 6 | suprafolius |
| 7 | Cila_ex_Quercus |
| 8 | splendens |
| 9 | cila_ex_Viburnum |
| 10 | cila_ex_Corylus |
| 11 | buekkensis |
| 12 | pseudoplatanus |
| 13 | acerianus |
| 14 | latreillii |
| 15 | butus |
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Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- A new variance stabilizing transformation for gene expression data analysis
- Kernel approximate Bayesian computation in population genetic inferences
- Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology
- Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
- Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments
- Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data
- Random forests on distance matrices for imaging genetics studies
Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- A new variance stabilizing transformation for gene expression data analysis
- Kernel approximate Bayesian computation in population genetic inferences
- Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology
- Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
- Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments
- Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data
- Random forests on distance matrices for imaging genetics studies
