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Supplement_forPNAS.Rmd
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Supplement_forPNAS.Rmd
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---
bibliography: manuscript.bib
csl: pnas.csl
site: "bookdown::bookdown_site"
output:
bookdown::pdf_document2:
toc: FALSE
indent: TRUE
monofont: "Times New Roman"
fontsize: 12pt
language:
label:
fig: "**Figure S**"
tab: "**Table S**"
header-includes:
- \usepackage[left]{lineno}
- \linenumbers
- \usepackage{setspace}
- \doublespacing
- \usepackage{placeins}
---
```{r setup, include=FALSE}
# Load packages
## General
library(knitr)
library(tidyverse)
library(stringr)
library(reshape2)
## Biological
library(Rphylip)
library(arbutus)
library(vegan)
library(ape)
library(phangorn)
library(phytools)
library(OUwie)
library(picante)
library(geiger)
library(phylobase)
library(fields)
library(phylosignal)
library(geomorph)
library(phylopath)
library(phylolm)
## Graphics
library(FactoMineR)
library(factoextra)
library(corrplot)
library(BAMMtools)
library(gridExtra)
library(xtable)
library(colorRamps)
# Configure knitr, see http://yihui.name/knitr/options
opts_knit$set(
progress=TRUE,
verbose=TRUE)
opts_chunk$set(
# include=FALSE,
cache=TRUE,
echo=FALSE,
message=FALSE
)
knitr::opts_chunk$set(echo = TRUE)
```
# Supplementary Materials File {-}
## Supplementary Methods {-}
Phylogenetic inference: We aligned the sequences using MAFFT [@katoh2002mafft] (alignments available in Dryad). We inferred a Maximum Likelihood (ML) phylogeny (S2) from 16S and 18S ribosomal rRNA genes using IQTree [@nguyen2014iq] with 1000 bootstrap replicates (iqtree -s alignment.fa -nt AUTO -bb 1000). We used ModelFinder [@kalyaanamoorthy2017modelfinder] implemented in IQTree v1.5.5. to assess the relative model fit. ModelFinder selected GTR+R4 for having the lowest Bayesian Information Criterion score. Additionally, we inferred a Bayesian tree with each gene as an independent partition in RevBayes [@hohna2016revbayes] (S5), which was topologically congruent with the unconstrained ML tree. The *alpha* priors were selected to minimize prior load in site variation.
Diet data curation: We removed the gelatinous prey observations for *Praya dubia* eating a ctenophore and a hydromedusa, and for *Nanomia* sp. eating *Aegina* since we believe these are rare events that have a much larger probability of being detected by ROV methods than their usual prey, and it is not clear whether the medusae were attempting to prey upon the siphonophores. Other published observations on feeding were also included for *Resomia ornicephala* [@pugh2010three], *Erenna richardi*, *Erenna laciniata*, *Erenna sirena* [@pugh2016description], *Lychnagalma utricularia*, *Bargmannia amoena*, and *Apolemia rubriversa* [@choy2017deep]. The feeding guilds declared in this study are: small-crustacean specialist (feeding mainly on copepods and ostracods), large crustacean specialist (feeding on large decapods, mysids, or krill), fish specialist (feeding mainly on actinopterygian larvae, juveniles, or adults), gelatinous specialist (feeding mainly on other siphonophores, medusae, ctenophores, salps, and/or doliolids), and generalist (feeding on a combination of the aforementioned taxa, without favoring any one prey group). These were selected to minimize the number of categories while keeping the most different types of prey separate. The gut content observations on *Forskalia* sp. were synonymized to an arbitrary *Forskalia* species on the tree (*F. tholoides*) for comparative analyses.
Data wrangling for comparative analyses: For comparative analyses, we removed species present in the tree but not represented in the morphology data, and *vice versa*. Although we measured specimens labeled as *Nanomia bijuga* and *Nanomia cara*, we are not confident in some of the species-level identifications, and some specimens were missing diagnostic zooids. Thus, we decided to collapse these into a single taxonomic concept (*Nanomia* sp.). All *Nanomia* sp. observations were matched to the phylogenetic position of *Nanomia bijuga* in the tree. We carried out all phylogenetic comparative statistical analyses in the programming environment R [@team2017r], using the Bayesian ultrametric species tree (S8), and incorporating intraspecific variation estimated from the specimen data as standard error whenever the analysis tool allowed it. R scripts and summarized species-collapsed data available in the Dryad repository. For each character (or character pair) analyzed, we removed species with missing data and reported the number of taxa included. We tested each character for normality using the Shapiro-Wilk test [@shapiro1965analysis], and log-transformed those that were non-normal.
Data wrangling for the variance-covariance analyses: When fitting all variance-covariance terms simultaneously (S16-18), we selected the largest set of characters that would allow the analysis to run without computational singularities. This excluded many of the morphometric characters which are linearly dependent on other characters. Since the functions do not tolerate missing data, we ran the analyses in two ways: One including all taxa but transforming absent states to zeroes, and another removing the taxa with absent states. These analyses could only be carried out on the subset of taxa for which diet data is available, and only among character pairs that are not computationally singular for that taxonomic subset. Gelatinous specialist correlations could only be estimated for a small subset of characters present in *Apolemia* (S21F, S22E, S23D) and should be interpreted with care.
Comparative tools used to test character associations: To test for correlated evolution among binary characters, we used Pagel’s test [@pagel1994detecting]. To characterize and evaluate the relationship between continuous characters, we used phylogenetic generalized least squares regressions (PGLS) [@grafen1989phylogenetic]. To compare the evolution of continuous characters with categorical aspects of the diet, we carried out a phylogenetic logistic regression (R nlme::gls using the 'corBrownian' function for the argument ‘correlation’).
DAPC optimization: Some taxa have inapplicable states for certain absent characters (such as the length of a nematocyst subtype that is not present in a species), which are problematic for DAPC analyses. We tackled this by transforming the absent states to zeroes. This approach allows us to incorporate all the data, but creates an attraction bias between small character states (*e.g.* small tentilla) and absent states (*e.g.* no tentilla). Absent characters are likely to be very biologically relevant to prey capture and we believe they should be accounted for in a predictive approach. We limited the number of linear discriminant functions retained to the number of groupings in each case. We selected the number of principal components retained using the a-score optimization function (R adegenet::optim.a.score) [@jombart2010discriminant] with 100 iterations, which yielded more stable results than the cross validation function (R adegenet::xval). This optimization aims to find the compromise value with highest discrimination power with the least overfitting. The discriminant analysis for feeding guild (7 principal components, 4 discriminants) produced 100% discrimination, and the highest loading contributions were found for the characters (ordered from highest to lowest): Involucrum length, heteroneme volume, heteroneme number, total heteroneme volume, tentacle width, heteroneme length, total nematocyst volume, and heteroneme width (S10).
## Supplementary Materials {-}
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## References {-}