Statistic analysis

Female song songbirds

Purpose

• Run phylogenetic regression models on female song evolution

 

Report overview

• Run brms models
• Results
  • Predation models
  • Dale data models
  • Duet models
• Takeaways

trees <- read.nexus("./tree_hacket_allOTUs/output.nex")
trees <- trees[1:25]

Run brms models

Phylogenetic mixed models of occurrance and elab with natural history traits (predation)

### BEFORE RUNNING THIS CODE: run the 'Prepare data for
### phylogenetically controlled regression analyses' code above
### with 'categorical data as ordered' specified (turn off
### 'as.factor' and 'as.numeric' code using hashtags). Turn off
### code to set tip.labels as rownames.


############################## PREDATION RATE BRMS MODELS #
set.seed(123)
parallel <- 15
thin <- 100

### Present/absent running on a population of trees
df_pred <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_pred.RDS")

prsabs_pred_multibrm <- phylogenetic_uncertainty(formula = prs_abs ~
    DPR + (1 | gr(tip.labels, cov = A)), data = df_pred, phylos = prep_tree(trees,
    spp = df_pred$tip.labels), family = bernoulli(link = "logit"),
    chains = 1, cores = 1, iter = 20000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 15), parallel = parallel, init = 0, thin = thin,
    model.name = "prsabs_pred_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

### OCCURRENCE running on a population of trees
df_pred_occ <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_pred_occ.RDS")

occ_pred_multibrm <- phylogenetic_uncertainty(formula = occ ~ DPR +
    (1 | gr(tip.labels, cov = A)), data = df_pred_occ, phylos = prep_tree(trees,
    spp = df_pred_occ$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 15000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 15), parallel = parallel, init = 0, thin = thin,
    model.name = "occ_pred_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

### ELABORATION running on a population of trees
df_pred_elb <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_pred_elb.RDS")

elb_pred_multibrm <- phylogenetic_uncertainty(formula = elb ~ DPR +
    (1 | gr(tip.labels, cov = A)), data = df_pred_elb, phylos = prep_tree(trees,
    spp = df_pred_elb$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 14000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 15), parallel = parallel, init = 0, thin = thin,
    model.name = "elb_pred_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

### SONG LENGTH running on a population of trees
df_pred_len <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_pred_len.RDS")

len_pred_multibrm <- phylogenetic_uncertainty(formula = len ~ DPR +
    (1 | gr(tip.labels, cov = A)), data = df_pred_len, phylos = prep_tree(trees,
    spp = df_pred_len$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 15000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 15), parallel = parallel, init = 0, thin = thin,
    model.name = "len_pred_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

Phylogenetic mixed models - ORIGINAL FULL MODELS (Dale)

### BEFORE RUNNING THIS CODE: run the 'Prepare data for
### phylogenetically controlled regression analyses' code above
### with 'categorical data as ordered' specified (turn off
### 'as.factor' and 'as.numeric' code using hashtags). Turn off
### code to set tip.labels as rownames.

### See https://psyarxiv.com/x8swp/ for explanation of types of
### ordinal models implemented in brms.


# iter <- 10000
set.seed(123)
parallel <- 5
thin <- 100


### Present/absent
df_prsabs <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_prsabs.RDS")

df_prsabs$mating_system <- factor(as.character(df_prsabs$mating_system))
df_prsabs$paternal_care <- factor(as.character(df_prsabs$paternal_care))
df_prsabs$cooperation <- factor(as.character(df_prsabs$cooperation))
df_prsabs$Migratory <- factor(as.character(df_prsabs$Migratory))
df_prsabs$Territory <- factor(as.character(df_prsabs$Territory))

phylogenetic_uncertainty(formula = prs_abs ~ log_CRC_species_mass +
    SSD_wing + mating_system + paternal_care + cooperation + Migratory +
    Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A)),
    data = df_prsabs, phylos = prep_tree(trees, spp = df_prsabs$tip.labels),
    family = bernoulli(link = "logit"), chains = 1, cores = 1, iter = 40000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 15),
    parallel = parallel, init = 0, thin = thin, model.name = "prsabs_dale_factor_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

phylogenetic_uncertainty(formula = prs_abs ~ log_CRC_species_mass +
    SSD_wing + mating_system * paternal_care + cooperation + Migratory +
    Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A)),
    data = df_prsabs, phylos = prep_tree(trees, spp = df_prsabs$tip.labels),
    family = bernoulli(link = "logit"), chains = 1, cores = 1, iter = 40000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 15),
    parallel = parallel, init = 0, thin = thin, model.name = "prsabs_dale_intr_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

### OCCURRENCE
df_occ <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_occ.RDS")

df_occ$mating_system <- factor(as.character(df_occ$mating_system))
df_occ$paternal_care <- factor(as.character(df_occ$paternal_care))
df_occ$cooperation <- factor(as.character(df_occ$cooperation))
df_occ$Migratory <- factor(as.character(df_occ$Migratory))
df_occ$Territory <- factor(as.character(df_occ$Territory))


is.ordered(df_occ$Migratory)

phylogenetic_uncertainty(formula = occ ~ log_CRC_species_mass + SSD_wing +
    mating_system + paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_occ,
    phylos = prep_tree(trees, spp = df_occ$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 18000, prior = priors, parallel = parallel,
    init = 0, thin = thin, model.name = "occ_dale_factor_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

phylogenetic_uncertainty(formula = occ ~ log_CRC_species_mass + SSD_wing +
    mating_system * paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_occ,
    phylos = prep_tree(trees, spp = df_occ$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 18000, prior = priors, parallel = parallel,
    init = 0, thin = thin, model.name = "occ_dale_intr_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")


### ELABORATION
df_elb <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_elb.RDS")

df_elb$mating_system <- factor(as.character(df_elb$mating_system))
df_elb$paternal_care <- factor(as.character(df_elb$paternal_care))
df_elb$cooperation <- factor(as.character(df_elb$cooperation))
df_elb$Migratory <- factor(as.character(df_elb$Migratory))
df_elb$Territory <- factor(as.character(df_elb$Territory))



phylogenetic_uncertainty(formula = elb ~ log_CRC_species_mass + SSD_wing +
    mating_system + paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_elb,
    phylos = prep_tree(trees, spp = df_elb$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 20000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 12), parallel = parallel, init = 0, thin = thin,
    model.name = "elb_dale_factor_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")



phylogenetic_uncertainty(formula = elb ~ log_CRC_species_mass + SSD_wing +
    mating_system * paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_elb,
    phylos = prep_tree(trees, spp = df_elb$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 20000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 12), parallel = parallel, init = 0, thin = thin,
    model.name = "elb_dale_intr_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

### SONG LENGTH
df_len <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_len.RDS")

df_len$mating_system <- factor(as.character(df_len$mating_system))
df_len$paternal_care <- factor(as.character(df_len$paternal_care))
df_len$cooperation <- factor(as.character(df_len$cooperation))
df_len$Migratory <- factor(as.character(df_len$Migratory))
df_len$Territory <- factor(as.character(df_len$Territory))


phylogenetic_uncertainty(formula = len ~ log_CRC_species_mass + SSD_wing +
    mating_system + paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_len,
    phylos = prep_tree(trees, spp = df_len$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 35000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 12), parallel = parallel, init = 0, thin = thin,
    model.name = "len_dale_factor_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

phylogenetic_uncertainty(formula = len ~ log_CRC_species_mass + SSD_wing +
    mating_system * paternal_care + cooperation + Migratory + Territory +
    degrees_from_equator + (1 | gr(tip.labels, cov = A)), data = df_len,
    phylos = prep_tree(trees, spp = df_len$tip.labels), family = cumulative("probit"),
    chains = 1, cores = 1, iter = 35000, prior = priors, control = list(adapt_delta = 0.99,
        max_treedepth = 12), parallel = parallel, init = 0, thin = thin,
    model.name = "len_dale_intr_multibrm", save.model = TRUE, single.model = FALSE,
    path = "./models")

Phylogenetic mixed models of female song vs duets (duet)

### BEFORE RUNNING THIS CODE: run the 'Prepare data for
### phylogenetically controlled regression analyses' code above
### with 'categorical data as ordered' specified (turn off
### 'as.factor' and 'as.numeric' code using hashtags). Turn off
### code to set tip.labels as rownames.

set.seed(123)
parallel <- 5
thin <- 100



############################## TOBIAS DUET DATA BRMS MODELS #
df_duet <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_duet.RDS")

phylogenetic_uncertainty(formula = prs_abs ~ Duet + (1 | gr(tip.labels,
    cov = A)), data = df_duet, phylos = prep_tree(trees, spp = df_duet$tip.labels),
    family = bernoulli(link = "logit"), chains = 1, cores = 1, iter = 25000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 12),
    parallel = parallel, init = 0, thin = thin, model.name = "prsabs_duet_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

################################################ DUET vs FemSong
################################################ Natural Hist
################################################ MCMCglmm MODELS
################################################ #

df_duet$mating_system <- factor(as.character(df_duet$mating_system))
df_duet$paternal_care <- factor(as.character(df_duet$paternal_care))
df_duet$cooperation <- factor(as.character(df_duet$cooperation))
df_duet$Migratory <- factor(as.character(df_duet$Migratory))
df_duet$Territory <- factor(as.character(df_duet$Territory))

### ORDINAL FEMALE SONG / DUET SCORE
phylogenetic_uncertainty(formula = prs_abs_duet ~ log_CRC_species_mass +
    SSD_wing + mating_system + paternal_care + cooperation + Migratory +
    Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A)),
    data = df_duet, phylos = prep_tree(trees, spp = df_duet$tip.labels),
    family = cumulative("probit"), chains = 1, cores = 1, iter = 10000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 12),
    parallel = parallel, init = 0, thin = thin, model.name = "prs_abs_duet_factor_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

### OCCURRENCE
df_duet_occ <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_duet_occ.RDS")

phylogenetic_uncertainty(formula = occ ~ Duet + (1 | gr(tip.labels,
    cov = A)), data = df_duet_occ, phylos = prep_tree(trees, spp = df_duet_occ$tip.labels),
    family = cumulative("probit"), chains = 1, cores = 1, iter = 15000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 12),
    parallel = parallel, init = 0, thin = thin, model.name = "occ_duet_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")


### ELABORATION
df_duet_elb <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_duet_elb.RDS")

phylogenetic_uncertainty(formula = elb ~ Duet + (1 | gr(tip.labels,
    cov = A)), data = df_duet_elb, phylos = prep_tree(trees, spp = df_duet_elb$tip.labels),
    family = cumulative("probit"), chains = 1, cores = 1, iter = 15000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 12),
    parallel = parallel, init = 0, thin = thin, model.name = "elb_duet_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")


### SONG LENGTH
df_duet_len <- readRDS("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_duet_len.RDS")

phylogenetic_uncertainty(formula = len ~ Duet + (1 | gr(tip.labels,
    cov = A)), data = df_duet_len, phylos = prep_tree(trees, spp = df_duet_len$tip.labels),
    family = cumulative("probit"), chains = 1, cores = 1, iter = 15000,
    prior = priors, control = list(adapt_delta = 0.99, max_treedepth = 12),
    parallel = parallel, init = 0, thin = thin, model.name = "len_duet_multibrm",
    save.model = TRUE, single.model = FALSE, path = "./models")

dirs <- list.dirs(path = "/media/m/Expansion/Projects/temporary")
dirs <- dirs[nchar(dirs) > 45]

gsub.pat <- c("b_", "log_CRC_species_mass", "SSD_wing", "degrees_from_equator",
    "mating_system1", "mating_system2", "mating_system3", "paternal_care1",
    "cooperation1", "Migratory1", "Migratory2", "Territory2", "Territory3",
    "DPR")
gsub.rep <- c("", "Log mass", "Sexual size dimorphism", "Breeding latitude",
    "Mating system\n(<5% polygyny)", "Mating system\n(5-20% polygyny)",
    "Mating system\n(>20% polygyny)", "Paternal care\n(yes)", "Cooperative breeding\n(yes)",
    "Migration\n(partial)", "Migration\n(full)", "Territoriality\n(seasonal)",
    "Territoriality\n(year-round)", "Daily nest\npredation rate")

for (i in dirs) combine_model_rds(path = i, dest.path = "./100_tree_models",
    overwrite = FALSE, suffix = "-100_combined_model", check_data = FALSE,
    summary = TRUE, model.name = basename(gsub("-100_combined_model.RDS",
        "", i)), remove.intercepts = TRUE, gsub.pattern = gsub.pat,
    gsub.replacement = gsub.rep, highlight = TRUE)

Save model results

models <- list.files(path = "./100_tree_models", pattern = "100_combined_model.RDS$",
    full.names = TRUE)

gsub.pat <- c("b_", "log_CRC_species_mass", "SSD_wing", "degrees_from_equator",
    "mating_system1", "mating_system2", "mating_system3", "paternal_care1",
    "cooperation1", "Migratory1", "Migratory2", "Territory2", "Territory3",
    "DPR")

gsub.rep <- c("", "Log mass", "Sexual size dimorphism", "Breeding latitude",
    "Mating system\n(<5% polygyny)", "Mating system\n(5-20% polygyny)",
    "Mating system\n(>20% polygyny)", "Paternal care\n(yes)", "Cooperative breeding\n(yes)",
    "Migration\n(partial)", "Migration\n(full)", "Territoriality\n(seasonal)",
    "Territoriality\n(year-round)", "Daily nest\npredation rate")

for (i in models) {
    print(i)
    html_summary(read.file = i, model.name = basename(gsub("-100_combined_model.RDS",
        "", i)), remove.intercepts = TRUE, dest.path = "./100_tree_models",
        save = TRUE, gsub.pattern = gsub.pat, gsub.replacement = gsub.rep,
        highlight = TRUE)
}

Results

Predation

dirs <- list.dirs("./100_tree_models/")
dirs <- dirs[-1]

pred_dirs <- grep("pred", dirs, value = TRUE)
for (i in pred_dirs) read_summary(i, highlight = TRUE)

elb_pred_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	elb ~ DPR + (1 | gr(tip.labels, cov = A))	14000	100	1	7000	2093	0	9264.691	3277.452	365633745

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Daily nest predation rate	0.013	-0.26	0.278	1.008	9264.691	3277.452

len_pred_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	len ~ DPR + (1 | gr(tip.labels, cov = A))	15000	100	1	7500	1288	0	13223.94	3463.049	1691543030

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Daily nest predation rate	-0.18	-1.128	0.484	1.025	13223.94	3463.049

occ_pred_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	occ ~ DPR + (1 | gr(tip.labels, cov = A))	15000	100	1	7500	7	0	22273.38	14311.53	100496758

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Daily nest predation rate	0.057	-0.289	0.435	1.005	22273.38	14311.53

prsabs_pred_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs ~ DPR + (1 | gr(tip.labels, cov = A))	20000	100	1	10000	39	0	37959.98	21439.6	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Daily nest predation rate	-0.274	-1.57	0.761	1.006	37959.98	21439.6

Dale

dirs <- list.dirs("./100_tree_models/")
dirs <- dirs[-1]

dale_dirs <- grep("dale", dirs, value = TRUE)
for (i in dale_dirs) read_summary(i, highlight = TRUE)

elb_dale_factor_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	elb ~ log_CRC_species_mass + SSD_wing + mating_system + paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	20000	100	1	10000	0	0	1256.247	1664.828	593844420

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.446	0.188	0.775	1.045	1256.247	2040.413
Sexual size dimorphism	-0.128	-0.355	0.077	1.016	3392.530	5822.416
Mating system (<5% polygyny)	0.197	-0.385	0.801	1.008	7552.121	12573.944
Mating system (5-20% polygyny)	-0.261	-0.871	0.311	1.007	8623.840	11680.711
Mating system (>20% polygyny)	-0.971	-2.070	0.005	1.014	3898.025	5348.060
Paternal care (yes)	1.026	-0.025	2.165	1.02	2750.407	6323.422
Cooperative breeding (yes)	0.038	-0.354	0.423	1.007	9274.961	24234.421
Migration (partial)	0.452	-0.015	0.983	1.017	3215.722	4477.158
Migration (full)	0.079	-0.521	0.711	1.009	6584.733	11637.308
Territoriality (seasonal)	0.574	-0.036	1.259	1.014	3959.360	5232.417
Territoriality (year-round)	1.799	1.063	2.772	1.043	1317.968	1664.828
Breeding latitude	-0.030	-0.252	0.186	1.015	3818.361	8882.002

elb_dale_intr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	elb ~ log_CRC_species_mass + SSD_wing + mating_system * paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	20000	100	1	10000	0	0	1206.1	1477.375	593844420

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.445	0.182	0.785	1.046	1206.100	1899.959
Sexual size dimorphism	-0.129	-0.361	0.079	1.018	3102.527	5064.519
Mating system (<5% polygyny)	0.101	-2.685	2.896	1	837405.273	731476.401
Mating system (5-20% polygyny)	-1.146	-3.451	0.998	1.001	177772.025	689630.615
Mating system (>20% polygyny)	-0.743	-2.261	0.711	1.015	3718.770	7695.796
Paternal care (yes)	1.146	-0.136	2.537	1.026	2092.898	5757.868
Cooperative breeding (yes)	0.036	-0.363	0.426	1.007	8762.128	20900.166
Migration (partial)	0.474	0.000	1.018	1.019	2950.885	4036.491
Migration (full)	0.080	-0.529	0.721	1.009	6260.511	11888.874
Territoriality (seasonal)	0.598	-0.021	1.300	1.015	3706.165	4820.435
Territoriality (year-round)	1.839	1.087	2.848	1.047	1210.508	1477.375
Breeding latitude	-0.028	-0.253	0.192	1.015	3729.276	8713.824
Mating system (<5% polygyny):Paternal care (yes)	0.104	-2.690	2.892	1	837870.124	732216.743
Mating system (5-20% polygyny):Paternal care (yes)	0.917	-1.258	3.234	1.001	624833.738	688370.052
Mating system (>20% polygyny):Paternal care (yes)	-0.494	-2.329	1.266	1.009	6780.773	21217.014

elb_dale_intr_terr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	elb ~ log_CRC_species_mass + SSD_wing + mating_system * Territory + paternal_care + cooperation + Migratory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	20000	100	1	10000	0	2	701.718	1002.534	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.499	0.195	0.950	1.069	829.155	1229.213
Sexual size dimorphism	-0.141	-0.419	0.091	1.025	2152.221	2586.364
Mating system (<5% polygyny)	0.149	-1.271	1.535	1.003	39516.466	50002.081
Mating system (5-20% polygyny)	-1.286	-2.712	-0.035	1.019	3013.420	5327.243
Mating system (>20% polygyny)	-1.164	-2.720	0.226	1.016	3494.023	4511.087
Territoriality (seasonal)	0.549	-0.206	1.432	1.02	2845.508	3222.227
Territoriality (year-round)	2.032	1.088	3.505	1.083	701.718	1002.534
Paternal care (yes)	1.171	-0.047	2.566	1.032	1741.010	3505.150
Cooperative breeding (yes)	-0.010	-0.471	0.422	1.01	5449.695	6386.709
Migration (partial)	0.545	0.019	1.202	1.032	1725.503	2531.130
Migration (full)	0.020	-0.665	0.736	1.011	5099.146	7690.486
Breeding latitude	-0.032	-0.290	0.215	1.021	2681.445	4710.469
Mating system (<5% polygyny):Territoriality (seasonal)	0.367	-1.136	1.989	1.011	5725.949	8174.699
Mating system (5-20% polygyny):Territoriality (seasonal)	1.394	-0.011	2.997	1.021	2651.401	5188.871
Mating system (>20% polygyny):Territoriality (seasonal)	-0.068	-1.674	1.450	1.005	12610.110	9947.481
Mating system (<5% polygyny):Territoriality (year-round)	-1.936	-4.021	0.028	1.01	5858.694	9337.831
Mating system (5-20% polygyny):Territoriality (year-round)	0.543	-1.498	2.672	1.005	13747.434	18337.978
Mating system (>20% polygyny):Territoriality (year-round)	1.263	-2.224	4.749	1.005	14033.782	33931.651

len_dale_factor_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	len ~ log_CRC_species_mass + SSD_wing + mating_system + paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	35000	100	1	17500	0	0	1745.05	3049.362	891483484

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.390	0.163	0.661	1.031	1745.050	3049.362
Sexual size dimorphism	-0.114	-0.320	0.076	1.017	3144.504	8108.280
Mating system (<5% polygyny)	0.187	-0.416	0.789	1.003	25356.269	41892.195
Mating system (5-20% polygyny)	0.056	-0.518	0.625	1.004	18724.931	40850.503
Mating system (>20% polygyny)	-0.130	-1.050	0.770	1.004	16159.977	54496.072
Paternal care (yes)	1.587	0.523	2.737	1.009	6590.088	10864.726
Cooperative breeding (yes)	0.162	-0.198	0.536	1.008	7506.322	11112.903
Migration (partial)	0.478	0.030	0.971	1.013	4440.961	5880.459
Migration (full)	-0.027	-0.626	0.571	1.004	13879.169	34937.245
Territoriality (seasonal)	0.214	-0.338	0.804	1.008	7278.826	12426.114
Territoriality (year-round)	1.355	0.710	2.120	1.027	2058.754	3121.882
Breeding latitude	-0.009	-0.219	0.204	1.008	6936.857	21227.355

len_dale_intr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	len ~ log_CRC_species_mass + SSD_wing + mating_system * paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	35000	100	1	17500	0	0	1683.368	2911.637	891483484

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.389	0.160	0.663	1.032	1683.368	2911.637
Sexual size dimorphism	-0.114	-0.321	0.078	1.017	3121.097	7927.146
Mating system (<5% polygyny)	0.095	-2.695	2.886	1	1677908.556	1289065.552
Mating system (5-20% polygyny)	-0.688	-3.098	1.568	1.001	1363507.439	1236614.155
Mating system (>20% polygyny)	0.271	-1.371	1.956	1.002	45260.344	570397.018
Paternal care (yes)	1.884	0.337	3.543	1.006	9791.030	25796.450
Cooperative breeding (yes)	0.165	-0.199	0.543	1.008	7158.362	11335.667
Migration (partial)	0.500	0.047	1.002	1.013	4107.873	5497.981
Migration (full)	-0.023	-0.627	0.581	1.004	14117.335	37272.959
Territoriality (seasonal)	0.236	-0.325	0.836	1.008	7043.124	11684.555
Territoriality (year-round)	1.386	0.730	2.164	1.028	1954.286	3075.995
Breeding latitude	-0.008	-0.220	0.207	1.008	6806.334	20561.615
Mating system (<5% polygyny):Paternal care (yes)	0.095	-2.695	2.881	1	1672799.907	1297956.377
Mating system (5-20% polygyny):Paternal care (yes)	0.772	-1.498	3.188	1	1413905.155	1236918.235
Mating system (>20% polygyny):Paternal care (yes)	-0.582	-2.437	1.218	1.003	30073.098	886203.959

len_dale_intr_terr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	len ~ log_CRC_species_mass + SSD_wing + mating_system * Territory + paternal_care + cooperation + Migratory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	35000	100	1	17500	0	1	1045.615	1281.953	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.414	0.163	0.752	1.053	1045.615	1281.953
Sexual size dimorphism	-0.121	-0.354	0.083	1.026	2119.121	2969.789
Mating system (<5% polygyny)	0.223	-1.173	1.575	1.003	26327.581	25159.460
Mating system (5-20% polygyny)	-0.954	-2.268	0.280	1.005	12960.910	16827.650
Mating system (>20% polygyny)	-0.425	-1.802	0.931	1.002	58614.855	55153.077
Territoriality (seasonal)	0.069	-0.603	0.784	1.007	7915.217	7881.887
Territoriality (year-round)	1.315	0.553	2.305	1.047	1188.526	1311.207
Paternal care (yes)	1.607	0.394	2.963	1.013	4139.703	5350.382
Cooperative breeding (yes)	0.157	-0.228	0.567	1.012	4444.889	4223.621
Migration (partial)	0.527	0.042	1.103	1.028	1936.906	1890.192
Migration (full)	-0.091	-0.744	0.554	1.006	10864.874	11096.868
Breeding latitude	-0.014	-0.239	0.218	1.01	5788.914	9502.774
Mating system (<5% polygyny):Territoriality (seasonal)	0.085	-1.382	1.625	1.007	9143.247	11244.467
Mating system (5-20% polygyny):Territoriality (seasonal)	1.258	-0.120	2.732	1.009	6196.802	8719.448
Mating system (>20% polygyny):Territoriality (seasonal)	0.259	-1.132	1.622	1.003	43098.891	29750.346
Mating system (<5% polygyny):Territoriality (year-round)	-0.777	-2.765	1.166	1.003	19518.340	20900.173
Mating system (5-20% polygyny):Territoriality (year-round)	1.272	-0.810	3.440	1.003	24653.800	25357.779
Mating system (>20% polygyny):Territoriality (year-round)	0.769	-2.412	3.890	1.002	32905.182	43123.495

occ_dale_factor_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	occ ~ log_CRC_species_mass + SSD_wing + mating_system + paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	18000	100	1	9000	0	0	1796.143	4779.604	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.490	0.258	0.752	1.03	1796.143	4779.604
Sexual size dimorphism	-0.110	-0.316	0.086	1.018	3016.663	12434.280
Mating system (<5% polygyny)	0.061	-0.468	0.591	1.003	24601.561	271113.598
Mating system (5-20% polygyny)	-0.074	-0.593	0.440	1.006	9109.225	43387.827
Mating system (>20% polygyny)	-0.565	-1.434	0.280	1.006	10883.677	103422.453
Paternal care (yes)	1.268	0.265	2.311	1.012	4583.322	16101.685
Cooperative breeding (yes)	0.143	-0.199	0.488	1.008	7462.974	22858.880
Migration (partial)	0.487	0.049	0.950	1.008	6841.644	26265.494
Migration (full)	0.262	-0.289	0.831	1.007	8956.591	45834.126
Territoriality (seasonal)	0.736	0.125	1.406	1.012	4969.325	14166.884
Territoriality (year-round)	2.375	1.628	3.255	1.025	2196.714	5348.981
Breeding latitude	-0.090	-0.302	0.117	1.012	4614.521	16606.558

occ_dale_intr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	occ ~ log_CRC_species_mass + SSD_wing + mating_system * paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	18000	100	1	9000	0	0	1806.051	4838.144	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.494	0.262	0.757	1.03	1806.051	4838.144
Sexual size dimorphism	-0.115	-0.320	0.082	1.018	3074.441	14285.276
Mating system (<5% polygyny)	0.037	-2.750	2.815	1	1218694.106	679507.252
Mating system (5-20% polygyny)	-1.120	-3.381	0.971	1.001	995575.662	671272.054
Mating system (>20% polygyny)	-1.020	-2.466	0.425	1.007	8587.552	50020.953
Paternal care (yes)	0.934	-0.335	2.251	1.017	3194.597	13119.148
Cooperative breeding (yes)	0.140	-0.202	0.485	1.008	7667.304	24729.857
Migration (partial)	0.483	0.043	0.949	1.008	7294.533	26504.349
Migration (full)	0.255	-0.295	0.823	1.007	9190.044	42374.866
Territoriality (seasonal)	0.714	0.098	1.387	1.012	4809.471	14644.966
Territoriality (year-round)	2.356	1.605	3.237	1.026	2158.745	5636.080
Breeding latitude	-0.092	-0.304	0.115	1.012	4722.510	17655.688
Mating system (<5% polygyny):Paternal care (yes)	0.038	-2.741	2.823	1	1221239.370	683735.788
Mating system (5-20% polygyny):Paternal care (yes)	1.089	-1.024	3.365	1.001	1014376.152	673134.694
Mating system (>20% polygyny):Paternal care (yes)	0.600	-1.058	2.237	1.007	8094.528	51931.815

occ_dale_intr_terr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	occ ~ log_CRC_species_mass + SSD_wing + mating_system * Territory + paternal_care + cooperation + Migratory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	18000	100	1	9000	0	0	1470.495	3600.12	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.526	0.274	0.819	1.037	1470.495	3600.120
Sexual size dimorphism	-0.119	-0.340	0.090	1.02	2734.828	10667.491
Mating system (<5% polygyny)	0.354	-1.031	1.724	1.002	77181.511	682991.517
Mating system (5-20% polygyny)	-1.473	-2.751	-0.275	1.007	8475.020	26880.249
Mating system (>20% polygyny)	-0.983	-2.390	0.374	1.005	13652.089	265728.697
Territoriality (seasonal)	0.582	-0.109	1.327	1.01	5991.647	19017.049
Territoriality (year-round)	2.402	1.562	3.397	1.031	1781.771	4370.974
Paternal care (yes)	1.223	0.085	2.424	1.017	3299.364	11760.586
Cooperative breeding (yes)	0.126	-0.236	0.490	1.009	5935.942	17725.774
Migration (partial)	0.554	0.088	1.057	1.012	4675.982	12034.667
Migration (full)	0.234	-0.350	0.839	1.008	7758.959	33926.356
Breeding latitude	-0.099	-0.326	0.120	1.014	3870.436	11898.902
Mating system (<5% polygyny):Territoriality (seasonal)	-0.169	-1.621	1.304	1.002	58240.437	647609.145
Mating system (5-20% polygyny):Territoriality (seasonal)	1.758	0.432	3.172	1.01	5507.005	16215.355
Mating system (>20% polygyny):Territoriality (seasonal)	0.449	-1.068	1.965	1.004	16563.185	510245.047
Mating system (<5% polygyny):Territoriality (year-round)	-1.151	-2.973	0.649	1.002	61317.896	662893.512
Mating system (5-20% polygyny):Territoriality (year-round)	1.040	-0.885	3.018	1.002	48296.695	636060.934
Mating system (>20% polygyny):Territoriality (year-round)	-0.261	-3.331	2.790	1.001	1047103.722	669978.809

prsabs_dale_factor_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs ~ log_CRC_species_mass + SSD_wing + mating_system + paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	40000	100	1	20000	0	1	1015.41	2704.702	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	1.127	0.440	2.142	1.054	1015.410	2704.702
Sexual size dimorphism	-0.450	-1.081	0.055	1.018	2945.051	6114.543
mating_system.L	-0.765	-2.240	0.577	1.015	3621.029	7684.639
mating_system.Q	-1.023	-2.412	0.180	1.017	3158.202	6366.314
mating_system.C	-0.047	-1.238	1.151	1.006	10451.309	20271.810
paternal_care.L	0.839	-0.704	2.477	1.03	1784.595	3921.522
cooperation.L	0.442	-0.218	1.262	1.016	3508.333	5472.965
Migratory.L	0.128	-0.893	1.190	1.007	7905.955	15123.195
Migratory.Q	-0.878	-1.793	-0.159	1.019	2776.702	5561.879
Territory.L	3.481	1.996	5.647	1.045	1227.152	3906.946
Territory.Q	0.935	0.127	1.991	1.027	2036.704	3875.090
Breeding latitude	0.260	-0.294	0.889	1.014	3996.505	7429.979

prsabs_dale_intr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs ~ log_CRC_species_mass + SSD_wing + mating_system * paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	40000	100	1	20000	0	1	955.128	2681.446	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	1.172	0.445	2.266	1.058	955.128	2681.446
Sexual size dimorphism	-0.470	-1.140	0.058	1.02	2746.819	5557.460
mating_system.L	-0.841	-2.635	0.839	1.017	3219.559	8124.136
mating_system.Q	-0.873	-2.930	1.092	1.009	6643.781	15190.260
mating_system.C	-0.046	-2.169	2.066	1.001	84635.957	1374806.758
paternal_care.L	0.973	-0.845	2.866	1.02	2646.881	6299.745
cooperation.L	0.468	-0.221	1.339	1.017	3163.583	5293.812
Migratory.L	0.134	-0.933	1.249	1.008	7255.483	13584.723
Migratory.Q	-0.919	-1.887	-0.171	1.021	2579.011	5457.898
Territory.L	3.625	2.051	5.920	1.049	1141.265	4043.088
Territory.Q	0.972	0.121	2.114	1.029	1834.331	3783.541
Breeding latitude	0.277	-0.302	0.946	1.015	3694.504	7019.640
mating_system.L:paternal_care.L	-0.074	-2.296	2.120	1.011	4966.291	14098.066
mating_system.Q:paternal_care.L	-0.419	-3.053	2.214	1.003	27874.412	1110234.002
mating_system.C:paternal_care.L	-0.073	-2.935	2.794	1.002	48686.424	1400814.095

prsabs_dale_intr_terr_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs ~ log_CRC_species_mass + SSD_wing + mating_system * Territory + paternal_care + cooperation + Migratory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	40000	100	1	20000	0	1	872.378	2444.134	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	1.316	0.485	2.613	1.065	872.378	2444.134
Sexual size dimorphism	-0.527	-1.330	0.069	1.026	2105.928	4245.709
mating_system.L	-1.090	-3.233	0.807	1.024	2276.254	7001.351
mating_system.Q	-0.745	-2.581	0.984	1.011	4879.199	11512.103
mating_system.C	-0.193	-2.048	1.631	1.009	6420.087	16091.479
Territory.L	3.795	1.689	6.302	1.023	2382.778	7078.875
Territory.Q	0.599	-0.831	2.092	1.007	8674.122	13350.076
paternal_care.L	0.902	-0.917	2.874	1.031	1730.761	3939.825
cooperation.L	0.519	-0.253	1.523	1.02	2729.137	4563.700
Migratory.L	0.125	-1.082	1.391	1.009	6475.507	11417.412
Migratory.Q	-1.037	-2.170	-0.199	1.027	1988.378	4623.829
Breeding latitude	0.303	-0.350	1.086	1.018	3055.662	5470.774
mating_system.L:Territory.L	0.266	-2.757	3.155	1.011	4800.948	14026.343
mating_system.Q:Territory.L	0.077	-2.750	2.978	1.009	5983.927	16370.683
mating_system.C:Territory.L	-1.228	-4.289	1.803	1.006	9932.943	57655.263
mating_system.L:Territory.Q	-0.182	-2.451	1.926	1.015	3737.794	10333.841
mating_system.Q:Territory.Q	1.203	-0.910	3.495	1.012	4457.755	10763.256
mating_system.C:Territory.Q	0.077	-2.245	2.362	1.008	6788.479	16316.807

Duet

dirs <- list.dirs("./100_tree_models/")
dirs <- dirs[-1]

duet_dirs <- grep("duet", dirs, value = TRUE)
for (i in duet_dirs) read_summary(i, highlight = TRUE)

elb_duet_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	elb ~ Duet + (1 | gr(tip.labels, cov = A))	15000	100	1	7500	0	0	1683.961	4424.104	469733964

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Duet1	1.885	1.495	2.374	1.033	1683.961	4424.104

len_duet_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	len ~ Duet + (1 | gr(tip.labels, cov = A))	15000	100	1	7500	0	0	3044.222	9093.122	986515037

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Duet1	1.53	1.209	1.892	1.018	3044.222	9093.122

occ_duet_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	occ ~ Duet + (1 | gr(tip.labels, cov = A))	15000	100	1	7500	0	1	863.323	2905.451	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Duet1	2.685	2.178	3.355	1.067	863.323	2905.451

prs_abs_duet_factor_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs_duet ~ log_CRC_species_mass + SSD_wing + mating_system + paternal_care + cooperation + Migratory + Territory + degrees_from_equator + (1 | gr(tip.labels, cov = A))	10000	100	1	5000	0	0	1142.57	2924.285	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Log mass	0.433	0.170	0.735	1.033	1699.839	4505.877
Sexual size dimorphism	-0.107	-0.327	0.108	1.018	3155.403	10460.146
Mating system (<5% polygyny)	0.295	-0.298	0.910	1.011	5350.603	14230.835
Mating system (5-20% polygyny)	-0.039	-0.618	0.538	1.008	7892.878	38023.767
Mating system (>20% polygyny)	-0.877	-1.800	-0.022	1.017	3360.094	9767.606
Paternal care (yes)	0.566	-0.434	1.578	1.028	2006.585	8219.801
Cooperative breeding (yes)	0.022	-0.352	0.409	1.011	5107.215	17370.952
Migration (partial)	0.377	-0.091	0.873	1.011	5476.475	16653.117
Migration (full)	0.073	-0.535	0.691	1.009	6601.172	37883.823
Territoriality (seasonal)	0.530	-0.092	1.196	1.012	5110.226	16287.515
Territoriality (year-round)	2.925	2.092	3.972	1.05	1142.570	2924.285
Breeding latitude	-0.075	-0.310	0.156	1.015	3828.419	15404.517

prsabs_duet_multibrm

	priors	formula	iterations	chains	thinning	warmup	diverg_transitions	rhats > 1.05	min_bulk_ESS	min_tail_ESS	seed
1	Intercept-student_t(3, 0, 2.5) b-normal(0, 2) sd-student_t(3, 0, 2.5)	prs_abs ~ Duet + (1 | gr(tip.labels, cov = A))	25000	100	1	12500	0	0	11452.05	293022.6	1673048412

	Estimate	l-95% CI	u-95% CI	Rhat	Bulk_ESS	Tail_ESS
Duet1	7.731	5.942	9.83	1.005	11452.05	293022.6

Annotated phylogenetic trees

# consensus tree
trees <- read.nexus("./tree_hacket_allOTUs/output.nex") 
tree <- phangorn::maxCladeCred(trees)

dat <- read.csv("M-FsongOrdinal&AllNatHist_ForFinalAnalysis_v4_NoInfoLevelAdded.csv")

dat <- dat[!is.na(dat$TipLabel.for.tree), ]

# setdiff(dat$TipLabel.for.tree, tree$tip.label)
# setdiff(tree$tip.label, dat$TipLabel.for.tree)
tree <- drop.tip(tree, tip = setdiff(tree$tip.label, dat$TipLabel.for.tree))
dat <- dat[dat$TipLabel.for.tree %in% tree$tip.label, ]

dat <- dat[match(tree$tip.label, dat$TipLabel.for.tree),]
# dat$family <- sapply(dat$Latin.name, function(x) song_nat$Family[song_nat$Latin.name == x])
# dat$Family.name <- sapply(dat$Latin.name, function(x) song_nat$Family.name[song_nat$Latin.name == x])

dat$Family.name[dat$TipLabel.for.tree %in% c("Phaenicophilus_poliocephalus", "Calyptophilus_tertius", "Calyptophilus_frugivorus", "Rhodinocichla_rosea")] <- "Thraupidae"

famInfo <- split(dat$TipLabel.for.tree, dat$Family.name)

polyph_fams <- names(which(sapply(famInfo, function(x) !is.monophyletic(tree, tips = x))))

subtree <- drop.tip(tree, setdiff(tree$tip.label, unlist(famInfo)))

# sapply(polyph_fams, function(x)
#     length(famInfo[[x]]) /
#     Ntip(extract.clade(phy = subtree, node = getMRCA(subtree, tip = famInfo[[x]])))
# )

# remove most distant species
for (i in polyph_fams){

        spp <- famInfo[[i]] 
    
        dists <- cophenetic.phylo(drop.tip(tree, tip = setdiff(tree$tip.label, spp)))

        drop_sp <- names(which.max(apply(dists,2, mean)))

 if (length(spp) > 3)                
   famInfo[[i]] <- spp[spp != drop_sp]
}

subtree <- drop.tip(tree, setdiff(tree$tip.label, unlist(famInfo)))

# sapply(polyph_fams, function(x)
#     length(famInfo[[x]]) /
#     Ntip(extract.clade(phy = subtree, node = getMRCA(subtree, tip = famInfo[[x]])))
# )

polyph_fams2 <- names(which(sapply(famInfo, function(x) !is.monophyletic(drop.tip(tree, tip = setdiff(tree$tip.label, unlist(famInfo))), tips = x))))

# remove one species at the time
for(i in polyph_fams2){

        spp <- famInfo[[i]] 
    
        out <- sapply(spp, function(x){
            
            (length(spp) - 1) /
    Ntip(extract.clade(phy = subtree, node = getMRCA(subtree, tip = spp[spp != x])))
        })
        
        # print(i)
        # print(length(spp))
        # print(max(out))
    if (max(out) == 1) famInfo[[i]] <- spp[-which.max(out)]
}

subtree2 <- drop.tip(tree, setdiff(tree$tip.label, unlist(famInfo)))

polyph_fams3 <- names(which(sapply(famInfo, function(x) !is.monophyletic(drop.tip(tree, tip = setdiff(tree$tip.label, unlist(famInfo))), tips = x))))

#     thraup <- extract.clade(phy = subtree, node = getMRCA(subtree, tip = famInfo$Thraupidae))
#     thraup$tip.label
# setdiff(thraup$tip.label, famInfo$Thraupidae)
#     plot(thraup)
# 
# 
# sapply(polyph_fams3, function(x)
#     plot(extract.clade(phy = subtree, node = getMRCA(subtree, tip = famInfo[[x]])), main = x)
# )

## OCC TREE
occInfo <- split(dat$TipLabel.for.tree, dat$DimorphSongOcc_NoInfo)

fam_df <- data.frame(family = unique(dat$Family.name))
fam_df$mrca.node <- sapply(fam_df$family, function(x){ 
    if (length(famInfo[[x]]) > 1)
        getMRCA(tree, tip = famInfo[[x]]) else
            NA
})

fam_df <- fam_df[complete.cases(fam_df), ]
fam_df <- fam_df[order(fam_df$mrca.node), ]
fam_df$bar.color <- rep(c("red","gray"), 65)[1:nrow(fam_df)]

tree <- groupOTU(tree, occInfo)
attr(tree, "group") <- factor(attr(tree, "group"), levels = c("No occurrence score", "Absent", "Rare", "Occasional", "Regular", "Same as male", "More than male"))

# fix overalpping families
fam_df$family[fam_df$family == "Menuridae"] <- "\nMenuridae"
# fam_df$family[fam_df$family == "Orthonychidae"] <- "\nOrthonychidae"
fam_df$family[fam_df$family == "Scotocercidae"] <- ""
fam_df$family[fam_df$family == "Mohouidae"] <- " "
fam_df$family[fam_df$family == "Cnemophilidae"] <- "  "
fam_df$family[fam_df$family == "Callaeidae"] <- "   "

occ_tree <- ggtree(tree, layout='circular', aes(color=group)) + #guides(color = "none") + 
    guides(color = guide_legend(override.aes = list(line.width = 1))) +
    # scale_color_viridis_d(option = "plasma", direction = -1) + 
    scale_color_manual(values = c("gray", plasma(6, direction = -1))) + 
    labs(color = "Female song ocurrence") +
                       theme(legend.position = c(0, 0.83), legend.text=element_text(size=15), legend.title = element_text(size=15), legend.background = element_rect(fill = "transparent")) +
    
  new_scale_color() +
     geom_cladelab(data = fam_df, align = TRUE, 
                             angle = "auto", show.legend = FALSE,
                        mapping=aes(
                             node=mrca.node, 
                             label=family#,
                             # color = bar.color
                             )  
  ) +
    scale_color_manual(values = c("black", "gray30"))

occ_tree

# ggsave(occ_tree, filename =  "./output/occurrence_annotated_tree_dpi70.png", width = 20, height = 12, dpi = 70)
# 
# ggsave(occ_tree, filename =  "./output/occurrence_annotated_tree_dpi300.png", width = 20, height = 12, dpi = 300)


# no labels
occ_tree_ntl <- ggtree(tree, layout='circular', aes(color=group)) + #guides(color = "none") + 
    guides(color = guide_legend(override.aes = list(line.width = 1))) +
    # scale_color_viridis_d(option = "plasma", direction = -1) + 
    scale_color_manual(values = c("gray", plasma(6, direction = -1))) + 
    labs(color = "Female song ocurrence") +
                       theme(legend.position = c(0, 0.83), legend.text=element_text(size=15), legend.title = element_text(size=15), legend.background = element_rect(fill = "transparent")) +
    
  new_scale_color() +
     geom_cladelab(data = fam_df, align = TRUE, 
                             angle = "auto", show.legend = FALSE, textcolour = "white",
                        mapping=aes(
                             node=mrca.node, 
                             label=family
                             
                             )  
  ) +
    scale_color_manual(values = c("black", "gray30"))
occ_tree_ntl

# ggsave(occ_tree_ntl, filename =  "./output/occurrence_annotated_tree_no_labels_dpi70.png", width = 20, height = 12, dpi = 70)
# 
# ggsave(occ_tree_ntl, filename =  "./output/occurrence_annotated_tree_no_labels_dpi300.png", width = 20, height = 12, dpi = 300)

## ELB TREE
elbInfo <- split(dat$TipLabel.for.tree, dat$DimorphSongElb_NoInfo)

fam_df <- data.frame(family = unique(dat$Family.name))
fam_df$mrca.node <- sapply(fam_df$family, function(x){ 
    if (length(famInfo[[x]]) > 1)
        getMRCA(tree, tip = famInfo[[x]]) else
            NA
})

fam_df <- fam_df[complete.cases(fam_df), ]
fam_df <- fam_df[order(fam_df$mrca.node), ]
fam_df$bar.color <- rep(c("red","gray"), 65)[1:nrow(fam_df)]

tree <- groupOTU(tree, elbInfo)
attr(tree, "group") <- factor(attr(tree, "group"), levels = c("No elaboration score", "Absent", "Much less", "Somewhat less", "Same as male", "More than male"))

# fix overalpping families
fam_df$family[fam_df$family == "Menuridae"] <- "\nMenuridae"
# fam_df$family[fam_df$family == "Orthonychidae"] <- "\nOrthonychidae"
fam_df$family[fam_df$family == "Scotocercidae"] <- ""
fam_df$family[fam_df$family == "Mohouidae"] <- " "
fam_df$family[fam_df$family == "Cnemophilidae"] <- "  "
fam_df$family[fam_df$family == "Callaeidae"] <- "   "


elb_tree <- ggtree(tree, layout='circular', aes(color=group)) + #guides(color = "none") + 
    guides(color = guide_legend(override.aes = list(line.width = 1))) +
    # scale_color_viridis_d(option = "plasma", direction = -1) + 
    scale_color_manual(values = c("gray", plasma(6, direction = -1))) + 
    labs(color = "Female song elaboration") +
                       theme(legend.position = c(0, 0.83), legend.text=element_text(size=15), legend.title = element_text(size=15), legend.background = element_rect(fill = "transparent")) +
    
  new_scale_color() +
     geom_cladelab(data = fam_df, align = TRUE,
                             angle = "auto", show.legend = FALSE,
                        mapping=aes(
                             node=mrca.node, 
                             label=family#,
                             # color = bar.color
                             )  
  ) +
    scale_color_manual(values = c("black", "gray30"))
elb_tree

# ggsave(elb_tree, filename =  "./output/elaboration_annotated_tree_dpi70.png", width = 20, height = 12, dpi = 70)
# ggsave(elb_tree, filename =  "./output/elaboration_annotated_tree_dpi300.png", width = 20, height = 12, dpi = 300)

## LEN TREE
lenInfo <- split(dat$TipLabel.for.tree, dat$DimorphSongLen_NoInfo)

fam_df <- data.frame(family = unique(dat$Family.name))
fam_df$mrca.node <- sapply(fam_df$family, function(x){ 
    if (length(famInfo[[x]]) > 1)
        getMRCA(tree, tip = famInfo[[x]]) else
            NA
})

fam_df <- fam_df[complete.cases(fam_df), ]
fam_df <- fam_df[order(fam_df$mrca.node), ]
fam_df$bar.color <- rep(c("red","gray"), 65)[1:nrow(fam_df)]

tree <- groupOTU(tree, lenInfo)
attr(tree, "group") <- factor(attr(tree, "group"), levels = c("No length score", "Absent", "Much shorter", "Somewhat shorter", "Same as male", "Longer than male"))

# fix overalpping families
fam_df$family[fam_df$family == "Menuridae"] <- "\nMenuridae"
# fam_df$family[fam_df$family == "Orthonychidae"] <- "\nOrthonychidae"
fam_df$family[fam_df$family == "Scotocercidae"] <- ""
fam_df$family[fam_df$family == "Mohouidae"] <- " "
fam_df$family[fam_df$family == "Cnemophilidae"] <- "  "
fam_df$family[fam_df$family == "Callaeidae"] <- "   "


len_tree <- ggtree(tree, layout='circular', aes(color=group)) + #guides(color = "none") + 
    guides(color = guide_legend(override.aes = list(line.width = 1))) +
    # scale_color_viridis_d(option = "plasma", direction = -1) + 
    scale_color_manual(values = c("gray", plasma(6, direction = -1))) + 
    labs(color = "Female song length") +
                       theme(legend.position = c(0, 0.83), legend.text=element_text(size=15), legend.title = element_text(size=15), legend.background = element_rect(fill = "transparent")) +
    
  new_scale_color() +
     geom_cladelab(data = fam_df, align = TRUE,
                             angle = "auto", show.legend = FALSE,
                        mapping=aes(
                             node=mrca.node, 
                             label=family#,
                             # color = bar.color
                             )  
  ) +
    scale_color_manual(values = c("black", "gray30"))
len_tree

# ggsave(len_tree, filename =  "./output/length_annotated_tree_dpi70.png", width = 20, height = 12, dpi = 70)
# 
# ggsave(len_tree, filename =  "./output/length_annotated_tree_dpi300.png", width = 20, height = 12, dpi = 300)

 

Takeaways

 

---

 

Session information

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=pt_BR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_CR.UTF-8        LC_COLLATE=pt_BR.UTF-8    
##  [5] LC_MONETARY=es_CR.UTF-8    LC_MESSAGES=pt_BR.UTF-8   
##  [7] LC_PAPER=es_CR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.7    ggtree_3.2.1        ggdist_3.2.0       
##  [4] posterior_1.3.1     brmsish_1.0.0       ape_5.6-2          
##  [7] ggplot2_3.4.0       brms_2.18.0         Rcpp_1.0.9         
## [10] viridis_0.6.2       viridisLite_0.4.1   xaringanExtra_0.7.0
## [13] rprojroot_2.0.3     formatR_1.11        knitr_1.42         
## [16] kableExtra_1.3.4   
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0           backports_1.4.1      fastmatch_1.1-0     
##   [4] systemfonts_1.0.4    plyr_1.8.7           igraph_1.3.5        
##   [7] lazyeval_0.2.2       splines_4.1.0        crosstalk_1.2.0     
##  [10] TH.data_1.1-0        rstantools_2.2.0     inline_0.3.19       
##  [13] digest_0.6.31        yulab.utils_0.0.5    htmltools_0.5.4     
##  [16] fansi_1.0.3          magrittr_2.0.3       checkmate_2.1.0     
##  [19] remotes_2.4.2        RcppParallel_5.1.5   matrixStats_0.62.0  
##  [22] xts_0.12.2           sandwich_3.0-1       svglite_2.1.0       
##  [25] prettyunits_1.1.1    colorspace_2.0-3     rvest_1.0.3         
##  [28] xfun_0.36            dplyr_1.0.10         callr_3.7.3         
##  [31] crayon_1.5.2         jsonlite_1.8.4       lme4_1.1-27.1       
##  [34] phangorn_2.7.1       survival_3.2-11      zoo_1.8-11          
##  [37] glue_1.6.2           gtable_0.3.1         emmeans_1.8.1-1     
##  [40] webshot_0.5.4        distributional_0.3.1 pkgbuild_1.4.0      
##  [43] rstan_2.21.7         abind_1.4-5          scales_1.2.1        
##  [46] mvtnorm_1.1-3        DBI_1.1.1            miniUI_0.1.1.1      
##  [49] xtable_1.8-4         tidytree_0.4.0       gridGraphics_0.5-1  
##  [52] stats4_4.1.0         StanHeaders_2.21.0-7 DT_0.26             
##  [55] htmlwidgets_1.5.4    httr_1.4.4           threejs_0.3.3       
##  [58] ellipsis_0.3.2       pkgconfig_2.0.3      loo_2.4.1.9000      
##  [61] farver_2.1.1         sass_0.4.5           utf8_1.2.2          
##  [64] labeling_0.4.2       ggplotify_0.1.0      tidyselect_1.2.0    
##  [67] rlang_1.0.6          reshape2_1.4.4       later_1.3.0         
##  [70] munsell_0.5.0        tools_4.1.0          cachem_1.0.6        
##  [73] cli_3.6.0            generics_0.1.3       ggridges_0.5.4      
##  [76] evaluate_0.20        stringr_1.5.0        fastmap_1.1.0       
##  [79] yaml_2.3.7           processx_3.8.0       purrr_1.0.0         
##  [82] packrat_0.9.0        pbapply_1.6-0        nlme_3.1-152        
##  [85] mime_0.12            projpred_2.0.2       aplot_0.1.7         
##  [88] xml2_1.3.3           compiler_4.1.0       bayesplot_1.9.0     
##  [91] shinythemes_1.2.0    rstudioapi_0.14      gamm4_0.2-6         
##  [94] sketchy_1.0.2        treeio_1.18.1        tibble_3.1.8        
##  [97] bslib_0.4.2          stringi_1.7.12       highr_0.10          
## [100] ps_1.7.2             Brobdingnag_1.2-9    lattice_0.20-44     
## [103] Matrix_1.5-1         nloptr_1.2.2.2       markdown_1.3        
## [106] shinyjs_2.1.0        tensorA_0.36.2       vctrs_0.5.2         
## [109] pillar_1.8.1         lifecycle_1.0.3      jquerylib_0.1.4     
## [112] bridgesampling_1.1-2 estimability_1.4.1   cowplot_1.1.1       
## [115] patchwork_1.1.2      httpuv_1.6.6         R6_2.5.1            
## [118] promises_1.2.0.1     gridExtra_2.3        codetools_0.2-18    
## [121] boot_1.3-28          colourpicker_1.2.0   MASS_7.3-54         
## [124] gtools_3.9.3         assertthat_0.2.1     withr_2.5.0         
## [127] shinystan_2.6.0      multcomp_1.4-17      mgcv_1.8-36         
## [130] parallel_4.1.0       quadprog_1.5-8       ggfun_0.0.7         
## [133] grid_4.1.0           tidyr_1.1.3          coda_0.19-4         
## [136] minqa_1.2.4          rmarkdown_2.20       shiny_1.7.3         
## [139] base64enc_0.1-3      dygraphs_1.1.1.6