Analysis on male color

Preliminaries

sample size

data_color_merged %>%
  select(pop, pred_trt, tutor_pop) %>%
  ftable() 

##              tutor_pop solo AH AL
## pop pred_trt                     
## AH  N                    20 19 20
##     Y                    20 20 16
## AL  N                    20 20 20
##     Y                    21 20 19

Data processing for plotting

#prep dataset for facet wrap
data_long <- data_color_merged %>%

  select("pop","tutor_pop","pred_trt",
         
         "body_black_num", "body_black_area", "body_black_prop",
         "body_fuzzyblack_num", "body_fuzzyblack_area","body_fuzzyblack_prop",
         "body_melanin_num", "body_melanin_area","body_melanin_prop",
         "body_yellow_num", "body_yellow_area","body_yellow_prop",
         "body_orange_num", "body_orange_area","body_orange_prop",
         "body_xanthophore_num", "body_xanthophore_area", "body_xanthophore_prop",
         
         "tail_black_num", "tail_black_area", "tail_black_prop",
         "tail_fuzzyblack_num", "tail_fuzzyblack_area","tail_fuzzyblack_prop",
         "tail_melanin_num", "tail_melanin_area","tail_melanin_prop",
         "tail_yellow_num", "tail_yellow_area","tail_yellow_prop",
         "tail_orange_num", "tail_orange_area","tail_orange_prop",
         "tail_xanthophore_num", "tail_xanthophore_area", "tail_xanthophore_prop",
         
         "body_area","tail_area",'gono_resid') %>%
  
  pivot_longer(-c("pop", "tutor_pop", "pred_trt"),
               names_to = "variable",
               values_to = "value") %>%
  
  group_by(variable) %>%

  #reorder for neater plotting
  mutate(variable = as.factor(variable)) %>%
  mutate(variable = fct_relevel(variable,
         "body_black_num", "body_black_area", "body_black_prop",
         "body_fuzzyblack_num", "body_fuzzyblack_area","body_fuzzyblack_prop",
         "body_melanin_num", "body_melanin_area","body_melanin_prop",
         "body_yellow_num", "body_yellow_area","body_yellow_prop",
         "body_orange_num", "body_orange_area","body_orange_prop",
         "body_xanthophore_num", "body_xanthophore_area", "body_xanthophore_prop",
         
         "tail_black_num", "tail_black_area", "tail_black_prop",
         "tail_fuzzyblack_num", "tail_fuzzyblack_area","tail_fuzzyblack_prop",
         "tail_melanin_num", "tail_melanin_area","tail_melanin_prop",
         "tail_yellow_num", "tail_yellow_area","tail_yellow_prop",
         "tail_orange_num", "tail_orange_area","tail_orange_prop",
         "tail_xanthophore_num", "tail_xanthophore_area", "tail_xanthophore_prop",
         
         "body_area","tail_area",'gono_resid'))

HP vs LP, pooling all treatments

ggplot(data_long, aes(x =pop,fill = pop, y = value)) + 
  geom_boxplot() + facet_wrap(variable ~., scales = 'free', ncol = 3) +
    theme_bw() + theme(panel.grid.major = element_blank(),
                       panel.grid.minor = element_blank()) +
    scale_fill_manual(values=c("steelblue", "lightsalmon1" ))

HP, plot by predator and social treatment

ggplot(data_long %>% filter(pop=="AH"), 
       aes(x =tutor_pop ,fill = pred_trt, y = value)) + 
  geom_boxplot() + facet_wrap(variable ~., scales = 'free', ncol = 3) +
    theme_bw() + theme(panel.grid.major = element_blank(),
                       panel.grid.minor = element_blank()) +
    scale_fill_manual(values=c("lightsalmon1", "steelblue" ))

LP, plot by predator and social treatment

ggplot(data_long %>% filter(pop=="AL"), 
       aes(x =tutor_pop ,fill = pred_trt, y = value)) + 
  geom_boxplot() + facet_wrap(variable ~., scales = 'free', ncol = 3) +
    theme_bw() + theme(panel.grid.major = element_blank(),
                       panel.grid.minor = element_blank()) +
    scale_fill_manual(values=c("lightsalmon1", "steelblue" ))

HP vs LP, listing all treatments

ggplot(data_long,
       aes(x =tutor_pop:pred_trt ,fill = pop, y = value)) + 
  geom_boxplot() + facet_wrap(variable ~., scales = 'free', ncol = 3) +
    theme_bw() + theme(panel.grid.major = element_blank(),
                       panel.grid.minor = element_blank(),
                       legend.position = "top") +
    scale_fill_manual(values=c("steelblue","lightsalmon1" )) 

Correlation among colors and gonopodia

data_cor <- data_color_merged %>%
  gather(variable, value,
         body_melanin_prop, tail_melanin_prop, body_xanthophore_prop, tail_xanthophore_prop)

ggplot(data_cor, aes(x = value, y = gono_resid, color = pop, fill = pop)) + 
  geom_point(pch = 21, color = "black") + 
  geom_smooth(method = lm, alpha = 0.2) +
  stat_cor(method = "pearson") +
  theme_bw(base_size = 15) + 
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        aspect.ratio = 1,
        legend.position = "none") +
  scale_fill_manual(values = c("SteelBlue", "lightsalmon1")) +
  scale_color_manual(values = c("SteelBlue", "lightsalmon1")) +
  facet_wrap(~variable, nrow = 2)

## `geom_smooth()` using formula = 'y ~ x'

Analysis

Strategy: start with pop*(pred_trt + tutor_pop) and drop when non-significant

Body black

Both HP and LP males responded to social treatment. Body black solo > LP~HP

mod <- lmer(logit(body_black_prop) ~ pop+pred_trt + tutor_pop+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-1.365	0.076	-17.850
popAL	-0.043	0.088	-0.495
pred_trtY	-0.051	0.055	-0.932
tutor_popAH	-0.190	0.067	-2.836
tutor_popAL	-0.121	0.067	-1.812

Anova(mod)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: logit(body_black_prop)
##            Chisq Df Pr(>Chisq)  
## pop       0.2452  1     0.6205  
## pred_trt  0.8685  1     0.3514  
## tutor_pop 8.2954  2     0.0158 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emmeans(mod, ~ tutor_pop) %>% pairs() %>% kable(digits=3)

contrast	estimate	SE	df	t.ratio	p.value
solo - AH	0.190	0.067	224.381	2.823	0.014
solo - AL	0.121	0.067	216.399	1.807	0.170
AH - AL	-0.068	0.068	222.761	-1.002	0.577

Tail black

trend of pred+ having less tail black

mod <- lmer(car::logit(tail_black_prop) ~ pop+pred_trt + tutor_pop+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-1.847	0.239	-7.715
popAL	0.550	0.334	1.644
pred_trtY	-0.148	0.084	-1.772
tutor_popAH	-0.107	0.103	-1.034
tutor_popAL	-0.014	0.102	-0.134

Body melanin

LP overall have less body melanin compare to HP males They showed trend of responding differently to predator treatment; when reared in pred+, HP decreased melanin and LP increased.

mod <- lmer(logit(body_melanin_prop) ~ pop*pred_trt + tutor_pop+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-0.982	0.070	-14.012
popAL	-0.188	0.092	-2.055
pred_trtY	-0.095	0.062	-1.543
tutor_popAH	-0.006	0.053	-0.119
tutor_popAL	-0.036	0.053	-0.681
popAL:pred_trtY	0.160	0.087	1.840

emmeans(mod, ~ pred_trt|pop) %>% pairs() %>% kable(digits=3)

contrast	pop	estimate	SE	df	t.ratio	p.value
N - Y	AH	0.095	0.062	212.900	1.540	0.125
N - Y	AL	-0.065	0.062	217.401	-1.056	0.292

Tail melanin

Nothing

mod <- lmer(car::logit(tail_melanin_prop) ~ pop+pred_trt+tutor_pop+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-1.829	0.240	-7.622
popAL	0.563	0.335	1.681
pred_trtY	-0.127	0.083	-1.526
tutor_popAH	-0.080	0.103	-0.775
tutor_popAL	-0.020	0.101	-0.196

Body orange

Only LP fish is sensitive to social treatment; when reared alone they have more orange

mod <- lmer(car::logit(body_orange_prop) ~ pop*tutor_pop+ pred_trt+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-2.399	0.142	-16.913
popAL	0.322	0.197	1.638
tutor_popAH	0.024	0.129	0.187
tutor_popAL	0.002	0.127	0.016
pred_trtY	-0.026	0.071	-0.366
popAL:tutor_popAH	-0.390	0.175	-2.225
popAL:tutor_popAL	-0.183	0.173	-1.056

emmeans(mod, ~ tutor_pop|pop) %>% pairs() %>% kable(digits=3)

contrast	pop	estimate	SE	df	t.ratio	p.value
solo - AH	AH	-0.024	0.129	220.638	-0.186	0.981
solo - AL	AH	-0.002	0.127	210.121	-0.016	1.000
AH - AL	AH	0.022	0.134	220.651	0.165	0.985
solo - AH	AL	0.366	0.119	205.051	3.067	0.007
solo - AL	AL	0.181	0.118	203.047	1.532	0.278
AH - AL	AL	-0.185	0.119	201.300	-1.557	0.267

Tail orange

Both HP and LP responded to social treatment; solo < HP ~ LP

mod <- lmer(car::logit(tail_orange_prop) ~ pop+tutor_pop+ pred_trt+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-2.215	0.326	-6.788
popAL	0.131	0.443	0.296
tutor_popAH	0.264	0.175	1.504
tutor_popAL	0.446	0.173	2.580
pred_trtY	0.136	0.142	0.959

emmeans(mod, ~ tutor_pop) %>% pairs() %>% kable(digits=3)

contrast	estimate	SE	df	t.ratio	p.value
solo - AH	-0.264	0.176	210.379	-1.501	0.292
solo - AL	-0.446	0.173	205.567	-2.577	0.029
AH - AL	-0.182	0.178	208.678	-1.025	0.562

Body xantho

Population differ in response to tutor treatment; only AL is sensitive-ish, not significant.

mod <- lmer(car::logit(body_xanthophore_prop) ~ pop*tutor_pop+ pred_trt+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-1.974	0.119	-16.572
popAL	0.218	0.164	1.329
tutor_popAH	0.108	0.114	0.948
tutor_popAL	-0.041	0.113	-0.365
pred_trtY	0.002	0.063	0.034
popAL:tutor_popAH	-0.330	0.156	-2.115
popAL:tutor_popAL	-0.038	0.155	-0.245

Anova(mod)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: car::logit(body_xanthophore_prop)
##                Chisq Df Pr(>Chisq)  
## pop           0.5000  1    0.47951  
## tutor_pop     0.9359  2    0.62629  
## pred_trt      0.0011  1    0.97320  
## pop:tutor_pop 5.2359  2    0.07295 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emmeans(mod, ~tutor_pop|pop) %>% pairs %>% kable(digits=3)

contrast	pop	estimate	SE	df	t.ratio	p.value
solo - AH	AH	-0.108	0.115	222.671	-0.944	0.613
solo - AL	AH	0.041	0.113	211.962	0.364	0.930
AH - AL	AH	0.149	0.119	222.791	1.259	0.420
solo - AH	AL	0.222	0.106	206.156	2.082	0.096
solo - AL	AL	0.079	0.105	203.736	0.750	0.734
AH - AL	AL	-0.143	0.106	202.070	-1.345	0.372

Tail xantho

This ones pretty interesting. 1. Both HP and LP males are influenced by social treatment; when raised with AL tutor, they develop more xantho on tail (marginal) 2. Only HP is sensitive to pred treatment; they develop more xantho in predator water.

mod <- lmer(car::logit(tail_xanthophore_prop) ~ pop*pred_trt + tutor_pop+ (1|mom_ID),
            data = data_color_merged)

summary(mod)$coefficient %>% kable(digits = 3)

	Estimate	Std. Error	t value
(Intercept)	-1.907	0.301	-6.342
popAL	0.778	0.428	1.820
pred_trtY	0.295	0.142	2.082
tutor_popAH	-0.001	0.124	-0.006
tutor_popAL	0.274	0.122	2.241
popAL:pred_trtY	-0.427	0.201	-2.126

Anova(mod, type =3)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: car::logit(tail_xanthophore_prop)
##                Chisq Df Pr(>Chisq)    
## (Intercept)  40.2258  1  2.262e-10 ***
## pop           3.3138  1    0.06870 .  
## pred_trt      4.3360  1    0.03731 *  
## tutor_pop     6.5459  2    0.03790 *  
## pop:pred_trt  4.5184  1    0.03353 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emmeans(mod, ~tutor_pop) %>% pairs %>% kable(digits=3)

contrast	estimate	SE	df	t.ratio	p.value
solo - AH	0.001	0.125	205.446	0.006	1.000
solo - AL	-0.274	0.122	202.402	-2.240	0.067
AH - AL	-0.275	0.126	204.158	-2.183	0.076

emmeans(mod, ~pred_trt|pop) %>% pairs %>% kable(digits=3)

contrast	pop	estimate	SE	df	t.ratio	p.value
N - Y	AH	-0.295	0.142	202.771	-2.081	0.039
N - Y	AL	0.132	0.143	203.250	0.925	0.356

Gonopodium length among treatments

solo vs HP tutor vs LP tutor

Trend of solo fish having relatively shorter gonopodia, but not significant

mod <- lmer(gono_length ~ pop+pred_trt+tutor_pop+ length + (1|mom_ID) ,
            data = data_color_merged)

Anova(mod, type = 3)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: gono_length
##               Chisq Df Pr(>Chisq)    
## (Intercept) 12.9019  1  0.0003282 ***
## pop          0.7101  1  0.3994059    
## pred_trt     0.4304  1  0.5118131    
## tutor_pop    4.6454  2  0.0980079 .  
## length      91.4419  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emmeans(mod, ~tutor_pop) %>% pairs %>% kable(digits=3)

contrast	estimate	SE	df	t.ratio	p.value
solo - AH	-0.096	0.047	227.553	-2.044	0.104
solo - AL	-0.086	0.048	222.123	-1.787	0.176
AH - AL	0.009	0.041	224.401	0.228	0.972

solo vs tutor

Significant went grouping the two tutored groups together.

mod <- lmer(gono_length ~ pop+pred_trt+tutor+ length + (1|mom_ID) ,
            data = data_color_merged)

Anova(mod, type = 3)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: gono_length
##               Chisq Df Pr(>Chisq)    
## (Intercept) 13.1252  1  0.0002914 ***
## pop          0.7172  1  0.3970707    
## pred_trt     0.4316  1  0.5111979    
## tutor        4.6030  1  0.0319167 *  
## length      91.9168  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

emmeans(mod, ~tutor) %>% pairs %>% kable(digits=3)

contrast	estimate	SE	df	t.ratio	p.value
solo - tutor	-0.091	0.043	226.647	-2.134	0.034

Correlation among gonopodia, color, brain, and behavior

Exploratory scatterplots

Stats in this preliminary plot are based on Gaussian correlations with no random effects so could be different from the GLMM models but should show the same trends

scatterplot<- 
  ggpairs(data_color_merged, 
        columns = c("gono_resid", "BM_resid", "sigmoid_prop",
                    "body_melanin_prop", 'tail_melanin_prop', 'body_xanthophore_prop', 'tail_xanthophore_prop'), 
        aes(color = pop),
        title = "Scatterplots & Correlation matrix")

print(scatterplot)

gonopodium - behavior

Relative gonopodia length is negatively correlated with sigmoid proportion

# ID is added as OLRE to help with overdispersion
mod <-glmer(cbind(sigmoid, sneak)  ~ gono_resid+pop+
              (1|ID)+(1|mom_ID), 
            data = data_color_merged,
            family = binomial())
summary(mod)$coefficients %>% kable(digit = 3)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	-0.309	0.271	-1.140	0.254
gono_resid	-5.467	1.968	-2.778	0.005
popAL	-0.396	0.382	-1.037	0.300

mod.fit <- emmip(mod, pop~gono_resid, cov.reduce = range, type = "response", plotit = F)

gono_behav <- ggplot(data = data_color_merged)+
  geom_point(aes(x = gono_resid, y = sigmoid_prop, color = pop), alpha = 0.3) +

  stat_summary_bin(aes(x = gono_resid, y = sigmoid_prop, color = pop),
                   fun.data = mean_se, 
                   bins=8, size =0.5, position = position_dodge(0.02))+
  
  geom_line(size = 1 ,data = mod.fit, aes(x = xvar, y = yvar, color = pop))+
  labs( x = "Residual gonopodium length", y = "Sigmoid proportion") +
  
  scale_color_manual("Population origin",
                     labels = c("HP","LP"),
                     values=c("SteelBlue", "lightsalmon1")) +
  
  theme_bw(base_size = 15)+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "bottom",
        aspect.ratio=1) 

gono_behav

brain size - behavior

Marginal: HP males that have larger brain sigmoids proportionally more

mod <-glmer(cbind(sigmoid, sneak)  ~ pop*BM_resid+
              (1|ID) + (1|mom_ID), 
            data = data_color_merged,
            family = binomial(),
            control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))

## boundary (singular) fit: see help('isSingular')

summary(mod)$coefficients %>% kable(digit = 3)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	-1.045	0.423	-2.470	0.014
popAL	0.768	0.527	1.458	0.145
BM_resid	3.199	1.768	1.809	0.070
popAL:BM_resid	-4.360	2.347	-1.858	0.063

emtrends(mod, ~pop, "BM_resid") %>% test

##  pop BM_resid.trend   SE  df z.ratio p.value
##  AH            3.20 1.77 Inf   1.809  0.0705
##  AL           -1.16 1.54 Inf  -0.754  0.4510

mod.fit <- emmip(mod, pop~BM_resid, cov.reduce = range, type = "response", plotit = F)

BM_behav <- ggplot(data = data_color_merged)+
  geom_point(aes(x = BM_resid, y = sigmoid_prop, color = pop), alpha = 0.3) +
  geom_line(size = 1 ,data = mod.fit, aes(x = xvar, y = yvar, color = pop))+
  
  stat_summary_bin(aes(x = BM_resid, y = sigmoid_prop, color = pop),
                   fun.data = mean_se, 
                   bins=8, size =0.5, position = position_dodge(0.025))+
  
  labs( x = "Residual brain mass", y = "Sigmoid proportion") +
  
  scale_color_manual("Population origin",
                     labels = c("HP","LP"),
                     values=c("SteelBlue", "lightsalmon1")) +
  scale_x_continuous(limits =c(-0.2506015, 0.4115183)) +
  
  theme_bw(base_size = 15)+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "bottom",
        aspect.ratio=1) 

BM_behav

brain size-gonopodium

Marginal: males with longer gonopodia have smaller brains

mod <-lmer(scale(BM)  ~ gono_resid+pop + weight +
              (1|mom_ID), 
            data = data_color_merged )

summary(mod)$coefficients %>% kable(digit = 4)

	Estimate	Std. Error	t value
(Intercept)	-2.4043	0.3498	-6.8739
gono_resid	-1.8360	0.9941	-1.8468
popAL	-0.3690	0.1681	-2.1948
weight	20.3387	2.5998	7.8231

Anova(mod)

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: scale(BM)
##              Chisq Df Pr(>Chisq)    
## gono_resid  3.4107  1    0.06478 .  
## pop         4.8172  1    0.02818 *  
## weight     61.2010  1  5.154e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod <-lmer(BM_resid  ~ gono_resid+pop +  (1|mom_ID), 
            data = data_color_merged)
mod.fit <- emmip(mod, pop~gono_resid, cov.reduce = range, type = "response", plotit = F)

BM_gono <- ggplot(data = data_color_merged)+
  geom_point(aes(x = gono_resid, y = BM_resid, color = pop), alpha = 0.3) +
  geom_line(size = 1 ,data = mod.fit, aes(x = xvar, y = yvar, color = pop))+
  
  stat_summary_bin(aes(x = gono_resid, y = BM_resid, color = pop),
                   fun.data = mean_se, 
                   bins=8, size =0.5, position = position_dodge(0.025))+
  
  labs( x = "Residual gonopodium length", y = "Residual brain mass") +
  
  scale_color_manual("Population origin",
                     labels = c("HP","LP"),
                     values=c("SteelBlue", "lightsalmon1")) +
  
  theme_bw(base_size = 15)+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = "bottom",
        aspect.ratio=1) 

BM_gono

Color

Didn’t run full sets yet - pending discussion on what we want to run.

All figures (old temp code for later use)

egg::ggarrange(gono_behav + theme(legend.position = "none"),
               BM_behav, 
               BM_gono + theme(legend.position = "none"), nrow = 1,labels = c("A","B","C"))

Variable key

1. Treatment variables & fish info:

• ID: unique ID of the fish
• flag: highlighted during analysis, y = uncertainty about ID handwriting, c = corrected when merging
• pop: fish origin; Aripo HP (AH) / Aripo LP(AL)
• pred_trt: Rearing water treatment; with or without predator chemical cues (Y/N)
  
• tutor_pop: Social treatment; reared with AH tutors, AL tutors, or by itself (AH/AL/solo)
• block: Experiment block; fish in the same block are processed on the same day
• mom_ID: the focal’s mom ID; fish with the same mom_ID are full siblings
• clutch_num: the number of the clutch the focal fish is from
• sex: Focal fish sex; male or female (M/F); all M in this dataset
  
• tutor_ID: ID of the tutors

 

2. Life history and morphology data:

• birth: date of birth
• mature_date: date of reaching sexual maturity (based on gonopodium hood; checked every week)
• mature_lat: latency (days) to sexual maturity
  
• assay_date: date of behavioral assay
• age: age (days) when assayed
• weight: weight (g) when assayed
• length: body length (mm) when assayed
• gono_length: gonopodium length (mm)
  
• eye_width: eye width(mm)
• body_width: body width (mm)
• tail_width: tail width (mm)
• BM: Brain mass (g)
• Cb: Cerebellum volume (mm^3)
• OT: Optic Tectum volume (mm^3)
• Tel: Telencephalon volume (mm^3)
• OB: Olfactory bulb volume (mm^3)
• Hyp: Hypothalamus volume (mm^3)
• BM_resid: Brain mass residual against body mass
• Cb_resid: Cerebellum volume against brain mass
• OT_resid: Optic Tectum volume against brain mass
• Tel_resid: Telencephalon volume against brain mass
• OB_resid: Olfactory bulb volume against brain mass
• Hyp_resid: Hypothalamus volume against brain mass

 

3. Color data:

• body_length: ImageJ measured body length (mm)
• body_area: body area (does not include tail) (mm^2)
• body_black_num: number of black spots
• body_black_area: total area of black spots
  
• body_black_prop: body_black_area / body_area
  
• body_fuzzyblack_num: number of fuzzy black spots
• body_fuzzyblack_area: total area of fuzzy black spots
  
• body_fuzzyblack_prop: body_fuzzyblack_area / body_area
  
• body_melanin_num: body_black_num + body_fuzzyblack_num
  
• body_melanin_area: body_black_area + body_fuzzyblack_area
  
• body_melanin_prop: body_melanin_area/ body_area
• body_yellow_num: number of yellow orange spots
  
• body_yellow_area: total area of yellow orange spots
  
• body_yellow_prop: body_yellow_area/body_area
• body_orange_num: number of orange spots
  
• body_orange_area: total area of orange spots
  
• body_orange_prop: body_orange_area/body_area
  
• body_xanthophore_num: body_yellow_num+body_orange_num
  
• body_xanthophore_area: body_yellow_area+body_orange_area
• body_xanthophore_prop: body_xanthophore_area/body_area
• tail_area: tail area (mm^2)
  
• tail_black_num: total number of black spots on tail
• tail_black_area: total area of black spots on tail
  
• tail_black_prop: tail_black_area/tail_area
  
• tail_fuzzyblack_num: total number of fuzzy black spots on tail
  
• tail_fuzzyblack_area: total area of fuzzy black spots on tail
  
• tail_fuzzyblack_prop: tail_fuzzyblack_area/tail_area
  
• tail_melanin_num: tail_black_num + tail_fuzzyblack_num
  
• tail_melanin_area: tail_black_area + tail_fuzzyblack_area
  
• tail_melanin_prop: tail_melanin_area/tail_area
• tail_yellow_num: total number of yellow orange spots on tail
  
• tail_yellow_area: total area of yellow orange spots on tail
  
• tail_yellow_prop: tail_yellow_area/tail_area
  
• tail_orange_num: total number of orange spots on tail
  
• tail_orange_area: total number of orange area on tail
  
• tail_orange_prop: tail_orange_area/tail_area
  
• tail_xanthophore_num: tail_yellow_num + tail_orange_num
• tail_xanthophore_area: tail_yellow_area + tail_orange_area
• tail_xanthophore_prop: tail_xanthophore_area/tail_area