Primate Foraging Behavior and Their Mothers

Cleaning up data for final project

DATA SET 1: This dataset contains raw and corrected rates of primate behavioural innovation, tool use, extractive foraging, social learning and research effort

Citation: The evolution of primate general and cultural intelligence. Reader SM, Hager Y & Laland KN. Philosophical Transactions of the Royal Society B, 2011 366:1017-1027

Variables include: Species name, Taxon, Great Ape (y/n?), Times of recorded observations of: Innovation, Tool use, Extractive foraging, and Social learning.

DATA SET 2: Data from: Female reproductive aging in seven primate species: patterns and consequences

Citation: Campos, F., Altmann, J., Cords, M., Fedigan, L., Lawler, R., Lonsdorf, E., Stoinski, T., Strier, K., Bronikowski, A., Pusey, A., & Alberts, S. (2021). Data from: Female reproductive aging in seven primate species: patterns and consequences. Duke Research Data Repository.

The following data is looking at age-related chancles in female reproductive performance of seven primate populations. The variables include: Genus, ID of female born, Age of mother at birth, How long new infants are at risk, Mother ID, If the mother has had other births, and Population the individual was born into.

Data_ReaderHagerLalandPhilTrans2011 <- read_csv("Data_ReaderHagerLalandPhilTrans2011.csv")
View(Data_ReaderHagerLalandPhilTrans2011)
Primate_ToolUse <- Data_ReaderHagerLalandPhilTrans2011

#Removed columns that weren't useful to potential research questions
Primate_ToolUse <- Primate_ToolUse[-c(3,4,11:13,15:20)]
view(Primate_ToolUse)

Calculate and graph the mean of foraging behaviors (tool use, social learning, innovation, extractive foraging for the following genera; Cebus, Propithecus, Brachyteles, Cercopithecus, Papio, Pan, and Gorilla.

ToolUse_Means_Genus <- Primate_ToolUse %>%
  group_by(`Genus`) %>%
  filter(grepl('Cebus|Propithecus|Brachy|Cercop|Papio|Pan|Gorilla', Genus)) %>%
  summarise(Tool_Use = mean(`Tool use`))

ggplot(ToolUse_Means_Genus, aes(x= `Genus`, y= `Tool_Use`, fill= `Genus`)) +
  geom_bar(stat = "Identity") +
  labs(title = "Mean Observations of Tool Use", y = "Mean Observations", x = "Genus") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

SocialLearning_Means_Genus <- Primate_ToolUse %>%
  group_by(`Genus`) %>%
  filter(grepl('Cebus|Propithecus|Brachy|Cercop|Papio|Pan|Gorilla', Genus)) %>%
  summarise(Social_Learning = mean(`Social learning`))

ggplot(SocialLearning_Means_Genus, aes(x= `Genus`, y= `Social_Learning`, fill= `Genus`)) +
  geom_bar(stat = "Identity") +
  labs(title = "Mean Observations of Social Learning", y = "Mean Observations", x = "Genus") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

Innovation_Means_Genus <- Primate_ToolUse %>%
  group_by(`Genus`) %>%
  filter(grepl('Cebus|Propithecus|Brachy|Cercop|Papio|Pan|Gorilla', Genus)) %>%
  summarise(Innovation = mean(Innovation))

ggplot(Innovation_Means_Genus, aes(x= `Genus`, y= `Innovation`, fill= `Genus`)) +
  geom_bar(stat = "Identity") +
  labs(title = "Mean Observations of Innovation", y = "Mean Observations", x = "Genus") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

Foraging_Means_Genus <- Primate_ToolUse %>%
  group_by(`Genus`) %>%
  filter(grepl('Cebus|Propithecus|Brachy|Cercop|Papio|Pan|Gorilla', Genus)) %>%
  summarise(Foraging = mean(`Extractive foraging`))

ggplot(Foraging_Means_Genus, aes(x= `Genus`, y= `Foraging`, fill= `Genus`)) +
  geom_bar(stat = "Identity") +
  labs(title = "Mean Observations of Extractive Foraging", y = "Mean Observations", x = "Genus") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

Genus::Common Name - Cebus::Capuchin, Propithecus::Sifaka, Brachyteles::Muriqui, Cercopithecus::Blue Monkey, Papio::Baboon, Pan::Chimpanzee, Gorilla::Gorilla

Graphing Female Parity in 7 species: How many mothers have had more than one offspring (Multiparous) or was it the mother’s first offspring (Primiparous).

Graphing mean mother age for each of the species, as well all mothers’ ages per species

FemalePrimate_Reproduction <- read_csv("03_afr.csv")
view(FemalePrimate_Reproduction)

FemalePrimate_Reproduction %>%
  ggplot(aes(x=`mom_parity`, fill= mom_parity)) +
  geom_bar() +
  labs(title = "Female Parity", y = "Number of Mothers", x = "Mother Parity") +
  theme_classic(base_size = 10) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  facet_wrap(vars(species))

Mean_Mothers_Age <- FemalePrimate_Reproduction %>%
  group_by(`species`) %>%
  summarise(Mother_Age = mean(mom_age_years))

ggplot(Mean_Mothers_Age, aes(x= `species`, y= `Mother_Age`, fill= `species`)) +
  geom_bar(stat = "Identity") +
  labs(title = "Mean Mother's Age", y = "Mean Age", x = "Species") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

FemalePrimate_Reproduction %>%
  group_by(species) %>%
 ggplot(aes(x=`mom_age_years`)) +
  geom_histogram() +
  labs(title = "Age of Mother at Birth", x = "Mother's Age", y= "Frequency") +
  theme_bw(base_size = 10) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  facet_wrap(vars(species))

Potential Research Questions:

1. Is there a correlation between tool use and parity level of mothers?

2. Does a mother’s age affect recorded levels of social learing?

3. Does the age of mothers/general life history traits affect the frequency of tool use?

4. If journal searches indicate a bias in primate research, then are the number of observations of foraging behaviors (tool use, social learning, innovation, extractive foraging) skewed because more research is done on particular genera/species?