Type: Package
Title: Detecting Trait Clustering in Environmental Gradients
Version: 0.1.1
Author: Mateu Menendez-Serra, Vicente J. Ontiveros, Emilio O. Casamayor, David Alonso
Maintainer: Mateu Menendez-Serra <mateu.menendez@ceab.csic.es>
Description: The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. The method 1) determines whether exists or not a trait clustering on the sampled communities and 2) assess if the observed clustering signal is related or not to an increasing environmental constrain along an environmental gradient. Also, when the effect of the environmental gradient is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: matrixStats, vegan, Rcpp
Suggests: testthat, knitr, rmarkdown
LinkingTo: testthat, Rcpp
NeedsCompilation: yes
Packaged: 2020-06-12 17:17:37 UTC; macbookair
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2020-06-12 17:50:03 UTC

RTCC: Detecting trait clustering in environmental gradients with the Randomized Trait Community Clustering method

Description

A set of functions which allows to determine if the observed traits present clustering/overdispersion patterns on the observed samples, and if so, to stablish if the observed pattern is linked to the effect of an environmental gradient.

Details

The study of phenotypic similarities and differences within species along environmental gradients might be used as a powerful tool complementing taxon-based approaches when assesing the contribution of stochastic and deterministic processes in community assembly. For this, this package allows an easy implementation of a method for detecting clustering/overdispersion patterns along an environmental gradient (Triado-Margarit et al., 2019). A first function assesses if the observed traits exhibit a clustering/overdispersion pattern on the tested samples. If positive, two subsequent functions determine whether the observed pattern is linked to the effect of an environmental varible and its statistical significance.

Data entry

The data consists on presence-absence observations along a measured environmental gradient and trait quantitative information of the observed organisms.

References

Triado-Margarit, X., Capitan, J.A., Menendez-Serra, M. et al. (2019) A Randomized Trait Community Clustering approach to unveil consistent environmental thresholds in community assembly. ISME J 13, 2681–2689 . https://doi.org/10.1038/s41396-019-0454-4

Genomic data linked to saline lagoons.

Description

A dataset containing genomic data of 544 genomes that matched 16s rRNA data from saline lagoons of the Monegros desert area.

Usage

group_information

Format

A data frame with 544 rows and 14 variables:

genome

Genome IMG code

Genome_Size

Genome size

GC_perc

GC percentage

Coding_base_perc

Conding base percentage

CDS_perc

CDS percentage

RNA_perc

RNA percentage

rRNA_count

rRNA count

Transporter_perc

Transporter proteins percentage

Signal_peptide_perc

Signal peptide percentage

Transmembrane_perc

Transmembrane proteins percentage

Gene_Count

Gene count

min_env

Minimum environmental value where the organism has been observed

max_env

Minimum environmental value where the organism has been observed

rel_abundance

Relative abundance of the organism on the metacommunity

...

Source

Triadó-Margarit, X., Capitán, J.A., Menéndez-Serra, M. et al. A Randomized Trait Community Clustering approach to unveil consistent environmental thresholds in community assembly. ISME J 13, 2681–2689 (2019).

Salinity values of saline lagoons.

Description

A dataset containing salinity values of 136 lagoons on the Monegros desert area.

Usage

metadata

Format

A data frame with 136 rows and 2 variables:

sample_ID

Sample internal code

salinity

Sample salinity value

Source

Triadó-Margarit, X., Capitán, J.A., Menéndez-Serra, M. et al. A Randomized Trait Community Clustering approach to unveil consistent environmental thresholds in community assembly. ISME J 13, 2681–2689.2019.

Trait selection

Description

This function determines whether the selected traits exhibit or not a clustering/overdispersion signal on the tested samples. For each trait, compares the observed Mean Pairwise Distance (MPD) of each sample against a distribution of synthetic commmunities MPDs obtained by a randomization test. Each synthetic community is build maintaining the original sample richness and randomly selecting organisms form the global pool.

Usage

rtcc1(table1, table2, table3, traits_columns, repetitions)

Arguments

table1

A data frame containing organisms names on the first column and its trait values on the consecutive ones. It also has to contain two columns with the maximum and the minimum values of the tested environmental variable where the organisms have been observed.

table2

A presence-absence observations table with the organisms names on the first column and the sample names as consecutive colnames.

table3

A dataframe containing sample names on the first column and environmental parameters on the consecutive ones.

traits_columns

Table 1 column numbers where different trait values appear.

repetitions

Number of simulated synthetic communities distributions.

Value

The function returns a dataframe with trait names as colnames and the p-value distribution of the different traits.

Examples



data(group_information)
data(table_presence_absence)
data(metadata)
rtcc1(group_information, table_presence_absence, metadata, 2:11, 100)

Clustering signal along an environmental gradient

Description

For a given trait, this function determines whether the observed trait clustering/overdispersion on the metacommunity is linked to an environmental gradient. For this, it sequentially remove samples in decreasing order of the environmental variable and computes at each step the remaining metacommunity h-index. This index is based on the percentage of samples on a metacommunity presenting significant trait clustering/overdispersion.

Usage

rtcc2(
  table1,
  table2,
  table3,
  species_abundances,
  trait_col_number,
  min_env_col,
  max_env_col,
  env_var_col,
  h_iteration,
  repetitions,
  model
)

Arguments

table1

A data frame containing organisms names on the first column and its trait values on the consecutive ones. It also has to contain two columns with the maximum and the minimum values of the tested environmental variable where the organisms have been observed.

table2

A presence-absence observations table with the organisms names on the first column and the sample names as consecutive colnames.

table3

A dataframe containing sample names on the first column and environmental parameters on the consecutive ones.

species_abundances

A vector containing the relative abundance of the organisms on the whole data set on the same order as appear on Table 1.

trait_col_number

Table 1 column number of the tested trait.

min_env_col

Table 1 column number indicating the minimum value of the environmental variable were each organism has been observed.

max_env_col

Table 1 column number indicating the maximum value of the environmental variable were each organism has been observed.

env_var_col

Table 2 column number indicating the tested environmental variable.

h_iteration

Number of h-index calculations for computing a confidence interval.

repetitions

Number of simulated synthetic communities distributions.

model

Model selection. All models build synthetic communities based on the organisms richness of the observed communities.

- Model 1: organism are selected randomly from the global pool. - Model 2: organism are selected randomly with a probability based on its relative abundance on the global pool. - Model 3: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible. - Model 4: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible and the selection probability is based on its relative abundance on the global pool.

Value

The function returns a dataframe with the maximum of the environmental variable on the remaining metacommunity after the sequential removal, h-index calculation for each environmental value, and its confidence standard deviation.

Examples



data(group_information)
data(table_presence_absence)
data(metadata)
rtcc2(group_information, table_presence_absence, metadata, group_information$sums,
9, 12, 13, 2, 100, 100, model = 1)

Clustering signal significance.

Description

For a given trait and environmental variable, this function creates a null model of the clustering/overdispersion pattern in order to test if the observed pattern statistically differs from the expected by random. For this, it sequentially remove random samples from the metacommunity and computes at each step the remaining metacommunity h-index. This index is based on the percentage of samples on a metacoomunity presenting significant trait clustering/overdispersion. After h iterations, computes a 95 obtained h-index for each point of the environmental gradient.

Usage

rtcc3(
  table1,
  table2,
  table3,
  species_abundances,
  trait_col_number,
  min_env_col,
  max_env_col,
  env_var_col,
  h_iteration,
  repetitions,
  model
)

Arguments

table1

A data frame containing organisms names on the first column and its trait values on the consecutive ones. It also has to contain two columns with the maximum and the minimum values of the tested environmental variable where the organisms have been observed.

table2

A presence-absence observations table with the organisms names on the first column and the sample names as consecutive colnames.

table3

A dataframe containing sample names on the first column and environmental parameters on the consecutive ones.

species_abundances

A vector containing the relative abundance of the organisms on the whole data set on the same order as appear on Table 1.

trait_col_number

Table 1 column number of the tested trait.

min_env_col

Table 1 column number indicating the minimum value of the environmental variable were each organism has been observed.

max_env_col

Table 1 column number indicating the maximum value of the environmental variable were each organism has been observed.

env_var_col

Table 2 column number indicating the tested environmental variable.

h_iteration

Number of h-index calculations for computing a confidence interval.

repetitions

Number of simulated synthetic communities distributions.

model

Model selection. All models build synthetic communities based on the organisms richness of the observed communities.

- Model 1: organism are selected randomly from the global pool. - Model 2: organism are selected randomly with a probability based on its relative abundance on the global pool. - Model 3: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible. - Model 4: organism are selected randomly, but only those whose environmental range includes the value of the simulated community are elegible and the selection probability is based on its relative abundance on the global pool.

Value

The function returns a dataframe with the maximum value of environmental variable corresponding to the same number of samples on the ordered remova, h-index calculation for each environmental value, and the percentiles 0.025, 0.5 and 0.975 of the obtained distribution for each point (mean value and 95

Examples



data(group_information)
data(table_presence_absence)
data(metadata)
rtcc3(group_information, table_presence_absence, metadata, group_information$sums,
9, 12, 13, 2, 50, 20, model = 1)

Genome presence-absence data of 136 saline lagoons.

Description

A dataset containing presence-absence data of 544 genomes on 136 saline lagoons of the Monegros desert area.

Usage

table_presence_absence

Format

A data frame with 544 rows and 137 variables:

genome

Genome IMG code

MON_10

Sample presence-absence observations

MON_100

Sample presence-absence observations

MON_101

Sample presence-absence observations

MON_103

Sample presence-absence observations

MON_104

Sample presence-absence observations

MON_106

Sample presence-absence observations

MON_107

Sample presence-absence observations

MON_108

Sample presence-absence observations

MON_109

Sample presence-absence observations

MON_11

Sample presence-absence observations

MON_110

Sample presence-absence observations

MON_111

Sample presence-absence observations

MON_112

Sample presence-absence observations

MON_113

Sample presence-absence observations

MON_114

Sample presence-absence observations

MON_116

Sample presence-absence observations

MON_117

Sample presence-absence observations

MON_118

Sample presence-absence observations

MON_119

Sample presence-absence observations

MON_12

Sample presence-absence observations

MON_120

Sample presence-absence observations

MON_122

Sample presence-absence observations

MON_123

Sample presence-absence observations

MON_124

Sample presence-absence observations

MON_125

Sample presence-absence observations

MON_126

Sample presence-absence observations

MON_127

Sample presence-absence observations

MON_128

Sample presence-absence observations

MON_129

Sample presence-absence observations

MON_13

Sample presence-absence observations

MON_130

Sample presence-absence observations

MON_131

Sample presence-absence observations

MON_133

Sample presence-absence observations

MON_134

Sample presence-absence observations

MON_135

Sample presence-absence observations

MON_136

Sample presence-absence observations

MON_137

Sample presence-absence observations

MON_138

Sample presence-absence observations

MON_139

Sample presence-absence observations

MON_14

Sample presence-absence observations

MON_140

Sample presence-absence observations

MON_141

Sample presence-absence observations

MON_142

Sample presence-absence observations

MON_144

Sample presence-absence observations

MON_145

Sample presence-absence observations

MON_146

Sample presence-absence observations

MON_147

Sample presence-absence observations

MON_148

Sample presence-absence observations

MON_15

Sample presence-absence observations

MON_17

Sample presence-absence observations

MON_18

Sample presence-absence observations

MON_19

Sample presence-absence observations

MON_2

Sample presence-absence observations

MON_20

Sample presence-absence observations

MON_21

Sample presence-absence observations

MON_22

Sample presence-absence observations

MON_23

Sample presence-absence observations

MON_24

Sample presence-absence observations

MON_25

Sample presence-absence observations

MON_26

Sample presence-absence observations

MON_27

Sample presence-absence observations

MON_28

Sample presence-absence observations

MON_29

Sample presence-absence observations

MON_30

Sample presence-absence observations

MON_31

Sample presence-absence observations

MON_32

Sample presence-absence observations

MON_33

Sample presence-absence observations

MON_34

Sample presence-absence observations

MON_35

Sample presence-absence observations

MON_36

Sample presence-absence observations

MON_37

Sample presence-absence observations

MON_38

Sample presence-absence observations

MON_39

Sample presence-absence observations

MON_4

Sample presence-absence observations

MON_40

Sample presence-absence observations

MON_41

Sample presence-absence observations

MON_42

Sample presence-absence observations

MON_43

Sample presence-absence observations

MON_44

Sample presence-absence observations

MON_45

Sample presence-absence observations

MON_46

Sample presence-absence observations

MON_47

Sample presence-absence observations

MON_48

Sample presence-absence observations

MON_49

Sample presence-absence observations

MON_5

Sample presence-absence observations

MON_50

Sample presence-absence observations

MON_51

Sample presence-absence observations

MON_52

Sample presence-absence observations

MON_53

Sample presence-absence observations

MON_54

Sample presence-absence observations

MON_55

Sample presence-absence observations

MON_56

Sample presence-absence observations

MON_57

Sample presence-absence observations

MON_58

Sample presence-absence observations

MON_59

Sample presence-absence observations

MON_60

Sample presence-absence observations

MON_61

Sample presence-absence observations

MON_62

Sample presence-absence observations

MON_63

Sample presence-absence observations

MON_64

Sample presence-absence observations

MON_65

Sample presence-absence observations

MON_66

Sample presence-absence observations

MON_67

Sample presence-absence observations

MON_68

Sample presence-absence observations

MON_69

Sample presence-absence observations

MON_7

Sample presence-absence observations

MON_70

Sample presence-absence observations

MON_71

Sample presence-absence observations

MON_72

Sample presence-absence observations

MON_73

Sample presence-absence observations

MON_74

Sample presence-absence observations

MON_75

Sample presence-absence observations

MON_76

Sample presence-absence observations

MON_77

Sample presence-absence observations

MON_78

Sample presence-absence observations

MON_79

Sample presence-absence observations

MON_8

Sample presence-absence observations

MON_80

Sample presence-absence observations

MON_81

Sample presence-absence observations

MON_82

Sample presence-absence observations

MON_83

Sample presence-absence observations

MON_84

Sample presence-absence observations

MON_85

Sample presence-absence observations

MON_86

Sample presence-absence observations

MON_88

Sample presence-absence observations

MON_9

Sample presence-absence observations

MON_90

Sample presence-absence observations

MON_91

Sample presence-absence observations

MON_92

Sample presence-absence observations

MON_93

Sample presence-absence observations

MON_94

Sample presence-absence observations

MON_95

Sample presence-absence observations

MON_96

Sample presence-absence observations

MON_97

Sample presence-absence observations

MON_98

Sample presence-absence observations

MON_99

Sample presence-absence observations

...

Source

Triadó-Margarit, X., Capitán, J.A., Menéndez-Serra, M. et al. A Randomized Trait Community Clustering approach to unveil consistent environmental thresholds in community assembly. ISME J 13, 2681–2689 (2019).