Plasticity in Male Coloration

Data Preparation

There are two datasets containing the relevant data:

Plasticity Color Data.csv contains the color measurements. We will call it data_col.
F2_Data_122821.csv contains the metadata of the experiment. We will call it data_exp.

Because color should not respond to the immediate environment (at least not like we’re measuring it), we will only use the data from the \(pred^-\) treatment.

data_col<-read.csv("../data_raw/Plasticity Color Data.csv")
data_exp<-read.csv("../data_raw/F2_Data_122821.csv") %>%
  filter(assay_water == "pred-")

data <- right_join (data_exp, data_col, by = "ID") %>%
  mutate(tutor_pop = ifelse(is.na(tutor_pop), "0", tutor_pop))

Orange Body

model_orange <- glmmTMB(Body.Orange.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_orange)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-3.0279803	0.1449583	-20.8886305	0.0000000
popAL	0.3817716	0.1881825	2.0287307	0.0424857
pred_trtY	0.0193086	0.0681104	0.2834899	0.7768013
tutor_popAH	-0.2490617	0.0806309	-3.0889126	0.0020089
tutor_popAL	-0.2130395	0.0803220	-2.6523165	0.0079942

plot_ob <- plot_results(model_orange, trait = "Body Orange")
plot_ob

Xanthophore Body

model_xantho <- glmmTMB(Body.Xanthophore.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_xantho)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-2.8779178	0.1336049	-21.5405059	0.0000000
popAL	0.3074338	0.1708181	1.7997727	0.0718965
pred_trtY	-0.0140733	0.0670113	-0.2100134	0.8336572
tutor_popAH	-0.2926029	0.0800063	-3.6572489	0.0002549
tutor_popAL	-0.2242972	0.0799065	-2.8069954	0.0050006

plot_xb <- plot_results(model_xantho, trait = "Body Xanthophore")
plot_xb

Black Body

model_black <- glmmTMB(Body.Black.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_black)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-1.4478348	0.0740513	-19.5517857	0.0000000
popAL	-0.0396747	0.0851796	-0.4657771	0.6413751
pred_trtY	-0.0564906	0.0546020	-1.0345874	0.3008617
tutor_popAH	-0.1115785	0.0669139	-1.6674925	0.0954165
tutor_popAL	-0.1466930	0.0663947	-2.2094089	0.0271462

plot_bb <- plot_results(model_black, trait = "Relative Black")
plot_bb

Melanistic Body

model_mela <- glmmTMB(Body.Melanistic.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_mela)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-1.2427688	0.0611392	-20.3268599	0.0000000
popAL	-0.0709926	0.0645424	-1.0999370	0.2713596
pred_trtY	-0.0442109	0.0491121	-0.9002035	0.3680120
tutor_popAH	-0.0425365	0.0598988	-0.7101397	0.4776175
tutor_popAL	-0.1248912	0.0597034	-2.0918610	0.0364510

plot_mb <- plot_results(model_mela, trait = "Melanistic Body")
plot_mb

Orange Tail

model_otail <- glmmTMB(Tail.Orange.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_otail)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-1.4264611	0.3397561	-4.1984853	0.0000269
popAL	0.2802265	0.4391477	0.6381146	0.5233991
pred_trtY	0.0480832	0.1098119	0.4378690	0.6614812
tutor_popAH	-0.0720344	0.1397059	-0.5156147	0.6061235
tutor_popAL	0.0594725	0.1308539	0.4544949	0.6494726

plot_ot <- plot_results(model_otail, trait = "Orange Tail")
plot_ot

Black Tail

model_btail <- glmmTMB(Tail.Black.Relative.Area ~ 
                          pop + (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'ordbeta', data)

kable(summary(model_btail)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-1.8438320	0.2777521	-6.6384093	0.0000000
popAL	0.6176345	0.3746099	1.6487403	0.0992009
pred_trtY	-0.0735815	0.0914806	-0.8043394	0.4212010
tutor_popAH	-0.2038920	0.1138814	-1.7903896	0.0733913
tutor_popAL	-0.0285915	0.1094231	-0.2612935	0.7938662

plot_bt <- plot_results(model_btail, trait = "Black Tail")
plot_bt

Orange Number

model_onr <- glmmTMB(Body.Orange.Number ~ 
                          pop * (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'genpois', data)

## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation

## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation

## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation

## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation

kable(summary(model_onr)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	0.2197138	0.2124562	1.0341607	0.3010610
popAL	0.1455943	0.2966396	0.4908122	0.6235593
pred_trtY	0.0696245	0.0688513	1.0112303	0.3119062
tutor_popAH	-0.0895483	0.0846228	-1.0582059	0.2899616
tutor_popAL	-0.2651231	0.0894610	-2.9635604	0.0030410
popAL:pred_trtY	-0.0521478	0.0940562	-0.5544322	0.5792831
popAL:tutor_popAH	0.0289782	0.1178495	0.2458915	0.8057662
popAL:tutor_popAL	0.2516081	0.1178501	2.1349839	0.0327623

plot_on <- plot_results(model_onr, trait = "Orange Spots")
plot_on

Black Number

model_bnr <- glmmTMB(Body.Black.Number ~ 
                          pop * (pred_trt + tutor_pop) +
                          (1|mom_ID), 
                        family = 'genpois', data)

kable(summary(model_bnr)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	1.4574978	0.0715445	20.3718946	0.0000000
popAL	0.0218482	0.1026157	0.2129132	0.8313946
pred_trtY	-0.0453144	0.0656282	-0.6904717	0.4898976
tutor_popAH	0.0665402	0.0796043	0.8358867	0.4032186
tutor_popAL	0.0604472	0.0814005	0.7425907	0.4577295
popAL:pred_trtY	-0.0239052	0.0953106	-0.2508134	0.8019584
popAL:tutor_popAH	-0.2540757	0.1188058	-2.1385806	0.0324697
popAL:tutor_popAL	0.0194073	0.1116936	0.1737548	0.8620581

plot_bn <- plot_results(model_bnr, trait = "Black Spots")
plot_bn

sjPlot::tab_model(model_orange, model_xantho,
                  model_black, model_mela,
                  model_otail, model_btail,
                  model_onr, model_bnr,
                  show.se = TRUE, 
                  show.ci = FALSE, 
                  collapse.se = TRUE,
                  show.icc = FALSE,
                  show.r2 = FALSE)

	Body Orange Relative Area		Body Xanthophore Relative Area		Body Black Relative Area		Body Melanistic Relative Area		Tail Orange Relative Area		Tail Black Relative Area		Body Orange Number		Body Black Number
Predictors	Estimates	p	Estimates	p	Estimates	p	Estimates	p	Estimates	p	Estimates	p	Incidence Rate Ratios	p	Incidence Rate Ratios	p
(Intercept)	0.05 (0.01)	<0.001	0.06 (0.01)	<0.001	0.24 (0.02)	<0.001	0.29 (0.02)	<0.001	0.24 (0.08)	<0.001	0.16 (0.04)	<0.001	1.25 (0.26)	0.301	4.30 (0.31)	<0.001
pop [AL]	1.46 (0.28)	0.042	1.36 (0.23)	0.072	0.96 (0.08)	0.641	0.93 (0.06)	0.271	1.32 (0.58)	0.523	1.85 (0.69)	0.099	1.16 (0.34)	0.624	1.02 (0.10)	0.831
pred trt [Y]	1.02 (0.07)	0.777	0.99 (0.07)	0.834	0.95 (0.05)	0.301	0.96 (0.05)	0.368	1.05 (0.12)	0.661	0.93 (0.08)	0.421	1.07 (0.07)	0.312	0.96 (0.06)	0.490
tutor pop [AH]	0.78 (0.06)	0.002	0.75 (0.06)	<0.001	0.89 (0.06)	0.095	0.96 (0.06)	0.478	0.93 (0.13)	0.606	0.82 (0.09)	0.073	0.91 (0.08)	0.290	1.07 (0.09)	0.403
tutor pop [AL]	0.81 (0.06)	0.008	0.80 (0.06)	0.005	0.86 (0.06)	0.027	0.88 (0.05)	0.036	1.06 (0.14)	0.649	0.97 (0.11)	0.794	0.77 (0.07)	0.003	1.06 (0.09)	0.458
pop [AL] × pred trt [Y]													0.95 (0.09)	0.579	0.98 (0.09)	0.802
pop [AL] × tutor pop [AH]													1.03 (0.12)	0.806	0.78 (0.09)	0.032
pop [AL] × tutor pop [AL]													1.29 (0.15)	0.033	1.02 (0.11)	0.862
Random Effects
σ²	3.02		2.94		1.76		1.58		1.52		1.82		0.14		0.10
τ₀₀	0.20 _{mom_ID}		0.16 _{mom_ID}		0.03 _{mom_ID}		0.01 _{mom_ID}		1.18 _{mom_ID}		0.90 _{mom_ID}		0.53 _{mom_ID}		0.01 _{mom_ID}
N	30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}		30 _{mom_ID}
Observations	194		194		194		194		194		194		194		194

plot_ob + plot_xb + 
plot_bb + plot_mb +
plot_ot + plot_bt +
plot_on + plot_bn +
  plot_layout(ncol = 1,
              axis_titles = "collect",
              guides = "collect") +
  plot_annotation(title = "                 High Predation                   Low Predation") &
  theme(legend.position = 'bottom')