Foraging

Data entry

data_forage <- read.csv("../data_raw/ForagingAssayData_2021_10_08_10.03.csv")

data_forage <- data_forage %>%
  mutate(Tutor = factor(ifelse(Population == Tutor_pop, 'same', 'diff'))) %>%
  filter(Sex != 'dead') %>%
  filter(!(ID %in% c('ALF2_1O2O.2.6', 'AHF2_1O3Y.2.2', 'ALF2_1O3O.3.3', 'ALF2_1P2Y.2.4', 'ALF2_1P2Y.2.8', 'ALF2_3G4G.1.4', 'ALF2_1P3G.4.4', 'ALF2_1O3Y.3.7', 'ALF2_1P4G.3.4'))) %>%
  mutate(Sex = droplevels(Sex, c('', 'dead')))

kable(head(data_forage))

Population	ID	Sex	Mom_ID	Litter	Treatment	Pred_Tutor	Pred	Tutor_pop	Day	Date	Time	Water	Latency	Attempts	Notes	Tutor
AH	AHF2_1G4G.3.3	F	AHF2_1G4G	3	pred-/same	pred-/AH	pred-	AH	2	22-Mar		pred-	16	17		same
AH	AHF2_1G4G.3.3	F	AHF2_1G4G	3	pred-/same	pred-/AH	pred-	AH	1	21-Mar		pred+	73	99		same
AH	AHF2_1G4G.6.2	F	AHF2_1G4G	6	pred-/same	pred-/AH	pred-	AH	1	29-Sep	3:17 PM	pred-	35	166	first few in middle	same
AH	AHF2_1G4G.6.2	F	AHF2_1G4G	6	pred-/same	pred-/AH	pred-	AH	2	30-Sep	3:06 PM	pred+	58	7		same
AH	AHF2_1G4P.2.1	F	AHF2_1G4P	2	pred-/same	pred-/AH	pred-	AH	2	3-Mar		pred-	310	72		same
AH	AHF2_1G4P.2.1	F	AHF2_1G4P	2	pred-/same	pred-/AH	pred-	AH	1	2-Mar		pred+	39	55		same

Attempts/Pecks

First we analyze only females.

library(MASS)

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

model_pecks <- glm.nb(Attempts ~ Population + Tutor_pop + Pred * Water, subset(data_forage, Sex == 'F'))

kable(summary(model_pecks)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	3.6955872	0.1578089	23.418118	0.0000000
PopulationAL	-0.9051030	0.1298345	-6.971208	0.0000000
Tutor_popAL	-0.1830963	0.1294927	-1.413951	0.1573763
Predpred+	0.2280355	0.1814321	1.256864	0.2088029
Waterpred+	-0.3658130	0.1859709	-1.967045	0.0491781
Predpred+:Waterpred+	0.4479214	0.2585255	1.732600	0.0831668

(NOTE: We tested interactions up to 3-way, and removed non-significant ones)

There is a significant effect of population, whereby LP fish eat less than HP fish.
Testing water may have an effect whereby fish raised in pred- water forage less in pred+ water, but that is not the case for fish raised in pred+ water.
There is no clear effect of tutor

We can now try to plot the results.

plot_forag <- data_forage %>%
  filter(Sex == 'F') %>%
  group_by(Population) %>%
  ggplot(aes(y = Attempts, x = Pred, fill = Water)) +
  geom_boxplot() + facet_wrap(~ Population) +
  theme_bw()

plot_forag

## Warning: Removed 2 rows containing non-finite values (stat_boxplot).

Now we will repeat the same for males

model_pecks <- glm.nb(Attempts ~ Population + (Pred + Tutor_pop + Water)^2, subset(data_forage, Sex == 'M'))

kable(summary(model_pecks)$coefficients)

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	3.1238026	0.1932053	16.1683108	0.0000000
PopulationAL	-0.5044678	0.1396303	-3.6128825	0.0003028
Predpred+	0.0526737	0.2417398	0.2178942	0.8275116
Tutor_popAL	0.0136720	0.2315790	0.0590384	0.9529216
Waterpred+	-0.1157790	0.2326853	-0.4975777	0.6187817
Predpred+:Tutor_popAL	0.0168372	0.2806450	0.0599947	0.9521598
Predpred+:Waterpred+	-0.3800185	0.2806144	-1.3542373	0.1756607
Tutor_popAL:Waterpred+	0.4595863	0.2780435	1.6529297	0.0983452

The population result holds in males. And that’s about it

plot_forag <- data_forage %>%
  filter(Sex == 'M') %>%
  group_by(Population) %>%
  ggplot(aes(y = Attempts, x = Tutor_pop, fill = Water)) +
  geom_boxplot() + facet_wrap(~ Population) +
  theme_bw()

plot_forag

Latency

This is time-to-event data, and we will analyze it using Cox models.

First we need to convert the Latency variable into a Surv object. We will call it time2peck

library(coxme)

## Warning: package 'survival' was built under R version 3.6.2

data_forage <- data_forage %>%
  mutate(event = ifelse (Latency ==600, 0, 1),
         time2peck = Surv(Latency, event = event))

Now we can run the cox model

model_latency <- coxph(time2peck ~ Population + Pred + Tutor + Water, subset(data_forage, Sex == 'F'))

kable(summary(model_latency)$coefficients)

	coef	exp(coef)	se(coef)	z	Pr(>\|z\|)
PopulationAL	-0.5806389	0.5595408	0.1173234	-4.9490468	0.0000007
Predpred+	0.0692910	1.0717481	0.1157988	0.5983744	0.5495901
Tutorsame	0.2660450	1.3047937	0.1174636	2.2649145	0.0235179
Waterpred+	-0.0821559	0.9211283	0.1154480	-0.7116267	0.4766960

Let’s plot the population differences crossed with water.

library(ggfortify)

## Warning: package 'ggfortify' was built under R version 3.6.2

data_forage <- data_forage %>%
  mutate(popwater = factor(paste(Population, Water)))
autoplot(survfit(time2peck ~ popwater, subset(data_forage, Sex == 'F')))

And the males:

model_latency <- coxph(time2peck ~ Population + Pred + Tutor_pop + Water + Tutor_pop:Water + Population:Pred, subset(data_forage, Sex == 'M'))

kable(summary(model_latency)$coefficients)

	coef	exp(coef)	se(coef)	z	Pr(>\|z\|)
PopulationAL	-0.1627945	0.8497658	0.1680416	-0.9687751	0.3326574
Predpred+	0.4191978	1.5207411	0.1855000	2.2598262	0.0238320
Tutor_popAL	-0.1815125	0.8340079	0.1771369	-1.0247013	0.3055041
Waterpred+	-0.3697676	0.6908949	0.1780368	-2.0769162	0.0378093
Tutor_popAL:Waterpred+	0.3267695	1.3864818	0.2523559	1.2948754	0.1953632
PopulationAL:Predpred+	-0.3543520	0.7016280	0.2557136	-1.3857373	0.1658271

FIGURES

Fig1. Comparison of foraging behaviors (attacks and latency) among the two populations of origin.

fig1a <- ggplot(data_forage, aes(Population, Attempts)) +
  stat_summary(fun = mean, geom = "point") +
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.07) +
  theme_bw()
  
fig1a

## Warning: Removed 2 rows containing non-finite values (stat_summary).

## Warning: Removed 2 rows containing non-finite values (stat_summary).

missing Fig2b: latency

Fig2. Effect of testing water on the feeding attempts (attacks) of fis from different origins, sexes, and treatments.

fig2 <- data_forage %>%
  drop_na(Sex) %>%
  mutate(Tutor_pop = recode(Tutor_pop, "AH" = "HP", "AL" = "LP"),
         Population = recode(Population, "AH" = "HP", "AL" = "LP"),
         Sex = recode(Sex, "M" = "Male", "F" = "Female")) %>%
  group_by(Pred, Tutor) %>%
  ggplot(aes(y = Latency, x = Water, group = Pred_Tutor, col = Pred, linetype = Tutor_pop)) +
    stat_summary(fun = mean, geom = "point") +
    stat_summary(fun = mean, geom = "line") +
    stat_summary(fun.data = "mean_se", geom = "errorbar", width = 0.1) +
    facet_grid(Sex ~ Population, drop = T) +
    theme_bw() + scale_colour_grey(start = 0, end = 0.7) +
    labs(col = "Raising Environment", linetype ="Tutor Origin") +
    theme(strip.background = element_blank(),
          strip.text.x = element_text(size = 15),             strip.text.y = element_text(size = 15),
          axis.title.x = element_text(size = 15),
          axis.title.y = element_text(size = 15),
          axis.text.x = element_text(size = 12),
          axis.text.y = element_text(size = 12))

fig2

## Warning: Removed 2 rows containing non-finite values (stat_summary).

## Warning: Removed 2 rows containing non-finite values (stat_summary).

## Warning: Removed 2 rows containing non-finite values (stat_summary).

Fig3 Environment (Pred) vs. Learning (Tutor) effect.

Foraging

Swanne Gordon

10/11/2021

Data entry

Attempts/Pecks

Latency

FIGURES