Sexual Size Dimorphism in Hippopotomus

Abstract

This study examines sexual size dimorphism (SSD) in polygynous megaherbivores, focusing on the interplay between sexual selection and ecological constraints. It investigates the selection pressures acting on body size and weapon size in the context of male reproductive success. By analysing a dataset of 2994 individuals, the study demonstrates that the common hippopotamus exhibits SSD, with males possessing larger jaws and canines as powerful weapons. The research emphasizes the influence of species-specific ecology and physiology in determining SSD, where sexual selection favours weapon size over body bulk, as seen in hippos.The reader can Access the paper using this link.‘SSD in Hippopotamus’

Introduction

Sexual size dimorphism refers to the differences in size between males and females of a species. It is commonly observed in many animal species, including hippopotamuses (Hippopotamus amphibius) and various ungulates. Hippopotamuses are large semi-aquatic mammals found in sub-Saharan Africa. They exhibit significant sexual size dimorphism, with males being considerably larger and heavier than females. The history of sexual size dimorphism in hippos can be traced back to millions of years. Sexual size dimorphism (SSD) is mostly thought to have evolved from sexual selection through intra-sex competition or mate choice (Andersson 1994). The reasons for sexual dimorphism in ungulates are often associated with mating strategies, competition for mates, and sexual selection. In many species, males engage in battles or displays to establish dominance and gain access to females. Larger body size, along with antlers, horns, or other weapons, can give males an advantage in these competitive interactions. Emlen and Oring (1977) proposed that males should be sexually selected for gaining increased access to females and females should compete for access to food. Consequently, males undergo rapid growth for many years to attain the large size that enables them to obtain mating, whereas females favour high body condition and early sexual maturity at the expense of structural size (Andersson 1994). In polygynous ungulates, larger males should obtain greater reproductive success than smaller males by being able to allocate relatively more energy in intra-sexual competition such as fights, mate-guarding, territorial behaviour (e.g., defence of good food patches where females stand), or tending (Clutton-Brock 1989; Clutton-Brock et al. 1992; Pelletier et al. 2006), and by being more attractive to females (Byers and Waits 2006). In addition, most of the reproductive effort of polygynous males occurs during a relatively short run, during which feeding activities are usually sacrificed for mating activities (Clutton-Brock et al) Overall, sexual size dimorphism in hippopotamuses and ungulates has likely evolved as a result of sexual selection and the need for males to compete for mates and resources. The exact patterns and extent of dimorphism can vary among species based on their ecological and social characteristics.

This research investigated sexual size dimorphism (SSD) in common hippos (Hippopotamus amphibius) and its influencing factors. Previous belief linked SSD in polygynous species to larger male body mass and weapon size due to multiple female partners. However, not all polygynous species showed SSD, suggesting a more complex relationship. The study aimed to explore SSD extent in hippos and the factors shaping it. Using a dataset of 2994 hippos’ morphological measurements, the research confirmed hippos exhibit SSD, but the difference in male and female body mass was relatively small (5%), unlike other polygynous ungulates. However, males had significantly heavier jaws (44%) and canines (81%), indicating strong selection pressure for larger weapons. The study proposed several factors contributing to hippos’ SSD patterns. Their aquatic lifestyle and foregut fermenting morphology likely limit body size differences between sexes. Male-male competition in water reduces the importance of body size compared to land-based species. Ecological and physiological constraints on body size also play a role, as hippos’ digestion limits maximum attainable body size. Females may maximize body size to protect young and compete for resources during scarcity. The research reveals limited body size dimorphism in hippos compared to other polygynous ungulates, but significant weapon size divergence between sexes. This highlights the importance of selection pressure for larger weapons in establishing dominance and securing mating rights. The study emphasizes the complexity of SSD, influenced by species-specific ecology, physiology, and intra-sexual selection.

The data that were used for the study were collected from the Queen Elizabeth national park, southwest of Uganda. It’s 1978 km2 in size and was a home to approximately 15000 hippos. Detailed morphological measurements were collected from 2994 hippos, which was culled as a management intervention to reduce overgrazing. All the hippos were sexed and subsequently aged using jaw characteristics, tooth replacement, and tooth wear before being assigned to one of the 15 rigorously defined age categories from 1-35 years. The mean age of each category was used in the analyses which provided an exceptional level of morphological data across the lifespan of this enigmatic species.The so collected data was arranged methodically by the aurthors distinguishing the two sexes on various parameters, the data was ordered and then used for analysis. Two sets of analyses were conducted. Firstly, the analyses of development of SSD with age using body mass(kg), body length(cm), and shoulder height(cm) as the metrics. Secondly, SSD in weapon size using two metrics: lower jaw mass(kg) and combined canine mass(kg), as a function of body size. We applied Cohen’s d effect size for each variable and the practical difference between male and female canines were assessed by examining the magnitude of the effect sizes. Cohen’s d is a standardized effect size for measuring the difference between two group means. Its commonly interpreted as small effects(d=0.2), medium effect(d=0.5) and large effect(d=0.8) referring to effect sizes.

Codes

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data1 <- read_excel('data.xlsx', sheet =1)
data2 <- read_excel('data.xlsx', sheet =2)

data3 <- data2[data2$Sex == 'Female',"Canine weight"]
data4 <- data2[data2$Sex == 'Male',"Canine weight"]

combined_data <- data.frame(
  Data = c(rep("female", length(data3$`Canine weight`)), rep("male", length(data4$`Canine weight`))),
  CanineWeight = c(data3$`Canine weight`, data4$`Canine weight`)
)


plot_combined <- ggplot(combined_data, aes(x = CanineWeight, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Canine weight of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

cohen.d(data3$`Canine weight`,data4$`Canine weight`)

## 
## Cohen's d
## 
## d estimate: -0.9665922 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.3042580 -0.6289264

### -0.9665922 (large)

mean1 <- mean(data3$`Canine weight`, na.rm = TRUE)
mean2 <-mean(data4$`Canine weight`, na.rm = TRUE)
mean1

## [1] 0.9282051

mean2

## [1] 1.588

percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] 71.08287

##71.08287

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data <- read_excel('data.xlsx', sheet =1)


data_m1 <- data[data$sex == 'M', "Girth"]
data_f1 <- data[data$sex == 'F', "Girth"]


combined_data <- data.frame(
  Data = c(rep("male", length(data_m1$Girth)), rep("female", length(data_f1$Girth))),
  Girth = c(data_m1$Girth, data_f1$Girth)
)


plot_combined <- ggplot(combined_data, aes(x = Girth, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Body weight of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

## Warning: Removed 2494 rows containing non-finite values (`stat_bin()`).

mean1 <- mean(data_m1$Girth, na.rm = TRUE)
mean2 <- mean(data_f1$Girth, na.rm = TRUE)

mean1

## [1] 303.6352

mean2

## [1] 316.6779

cohen.d(data_m1$Girth,data_f1$Girth, na.rm = TRUE)

## 
## Cohen's d
## 
## d estimate: -0.2742806 (small)
## 95 percent confidence interval:
##       lower       upper 
## -0.45124235 -0.09731888

## -0.2742806 (small)


percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] 4.29552

##4.29552

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data <- read_excel('data.xlsx', sheet =1)


data_m3 <- data[data$sex == 'M', "height_cm"]
data_f3 <- data[data$sex == 'F', "height_cm"]

combined_data <- data.frame(
  Data = c(rep("male", length(data_m3$height_cm)), rep("female", length(data_f3$height_cm))),
  Height_cm = c(data_m3$height_cm, data_f3$height_cm)
)


plot_combined <- ggplot(combined_data, aes(x = Height_cm, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Body height of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

## Warning: Removed 2488 rows containing non-finite values (`stat_bin()`).

mean1 <- mean(data_m3$height_cm, na.rm = TRUE)
mean2 <- mean(data_f3$height_cm, na.rm = TRUE)


cohen.d(data_m3$height_cm,data_f3$height_cm, na.rm = TRUE)

## 
## Cohen's d
## 
## d estimate: 0.2853732 (small)
## 95 percent confidence interval:
##     lower     upper 
## 0.1094562 0.4612902

###cohen.d value= 0.2853732 (small)

percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] -3.670918

###-3.670918

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data <- read_excel('data.xlsx', sheet =1)


data_m2 <- data[data$sex == 'M', "total_body_length_cm"]
data_f2 <- data[data$sex == 'F', "total_body_length_cm"]

combined_data <- data.frame(
  Data = c(rep("male", length(data_m2$total_body_length_cm)), rep("female", length(data_f2$total_body_length_cm))),
  Bodylength_cm = c(data_m2$total_body_length_cm, data_f2$total_body_length_cm)
)


plot_combined <- ggplot(combined_data, aes(x = Bodylength_cm, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Body length of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

## Warning: Removed 203 rows containing non-finite values (`stat_bin()`).

mean1 <- mean(data_m2$total_body_length_cm, na.rm = TRUE)
mean2 <- mean(data_f2$total_body_length_cm, na.rm = TRUE)


cohen.d(data_m2$total_body_length_cm,data_f2$total_body_length_cm, na.rm = TRUE)

## 
## Cohen's d
## 
## d estimate: -0.0003688658 (negligible)
## 95 percent confidence interval:
##       lower       upper 
## -0.07581574  0.07507801

###-0.0003688658 (negligible)



percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] 0.0040257

##0.0040257

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data <- read_excel('data.xlsx', sheet =1)


data_m <- data[data$sex == 'M', "body_mass_kg"]
data_f <- data[data$sex == 'F', "body_mass_kg"]


combined_data <- data.frame(
  Data = c(rep("male", length(data_m$body_mass_kg)), rep("female", length(data_f$body_mass_kg))),
  Bodymass_kg= c(data_m$body_mass_kg, data_f$body_mass_kg)
)


plot_combined <- ggplot(combined_data, aes(x = Bodymass_kg, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Body Mass of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

## Warning: Removed 1743 rows containing non-finite values (`stat_bin()`).

mean1 <- mean(data_m$body_mass_kg, na.rm = TRUE)
mean2 <- mean(data_f$body_mass_kg, na.rm = TRUE)


cohen.d(data_m$body_mass_kg,data_f$body_mass_kg, na.rm = TRUE)

## 
## Cohen's d
## 
## d estimate: 0.04450629 (negligible)
## 95 percent confidence interval:
##       lower       upper 
## -0.06818268  0.15719526

##0.04450629 (negligible)

percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] -1.088953

#-1.088953%

getwd()

## [1] "C:/Users/91779/Downloads"

setwd('C:/Users/91779/Downloads')
library(effsize)
library(ggplot2)
library(readxl)

data <- read_excel('data.xlsx', sheet =1)

dataF <- data[data$sex == 'F',"jaw_weight_kg"]
dataM <- data[data$sex == 'M',"jaw_weight_kg"]

combined_data <- data.frame(
  Data = c(rep("male", length(dataM$jaw_weight_kg)), rep("Female", length(dataF$jaw_weight_kg))),
  JawWeight_Kg = c(dataM$jaw_weight_kg, dataF$jaw_weight_kg)
)


plot_combined <- ggplot(combined_data, aes(x = JawWeight_Kg, fill = Data)) +
  geom_histogram(bins = 30, alpha = 0.6) +
  labs(title = "Jaw weight of male and female") +
  scale_fill_manual(values = c("blue", "red"))

print(plot_combined)

## Warning: Removed 2749 rows containing non-finite values (`stat_bin()`).

mean(data$jaw_weight_kg,na.rm = TRUE)

## [1] 9.449102

mean1 <- mean(dataF$jaw_weight_kg,na.rm = TRUE)
mean2 <- mean(dataM$jaw_weight_kg,na.rm = TRUE)

cohen.d(dataF$jaw_weight_kg,dataM$jaw_weight_kg, na.rm = TRUE)

## 
## Cohen's d
## 
## d estimate: -0.8771942 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.1407271 -0.6136613

###-0.8771942 (large)

percentage_diff <- function(mean1, mean2) {
  diff <- mean2 - mean1
  percent_diff <- (diff / mean1) * 100
  return(percent_diff)
}


result <- percentage_diff(mean1, mean2)
print(result)

## [1] 36.44784

##36.44784

Discussion

In this study, we investigated the differences in various physical characteristics between male and female hippopotamus. The analysis focused on canine weight, girth, height, body length, body size, and jaw weight. We computed Cohen’s d effect size for each variable and examined the magnitude of the effect sizes to assess the practical significance of the differences between male and female Hippopotamuses. For canine weight, the effect size was calculated to be -0.9665922, indicating a large effect. This suggests that there is a substantial difference in weight between male and female canines. Further analysis revealed a percentage difference of 71.08287, with male canines being approximately 71.08% heavier than female canines on average. In terms of girth, the effect size was found to be -0.2742806, which is classified as a small effect. This indicates a minor difference in girth between male and female hippopotamus. The percentage difference was computed as 4.29552, indicating that female hippopotamus have approximately 4.30% greater girth compared to males. When examining height, the effect size was determined to be 0.2853732, indicating a small effect. This suggests that there is a slight difference in height between male and female hippopotamus. The percentage difference was found to be 3.670918, implying that male hippopotamus, on average, have approximately 3.67% greater height than female hippopotamus. For body length, the effect size was calculated to be -0.0003688658, which is considered negligible. This indicates that there is almost no difference in body length between male and female hippopotamus. The percentage difference was computed as 0.0040257, suggesting a minute variation between the two groups. In terms of body size, the effect size was found to be 0.04450629, also classified as negligible. This indicates that there is a negligible difference in body size between male and female hippopotamus. The percentage difference was computed as 1.088953%, indicating a minimal variation in body size between the two groups,showing that females have an upperhand in this aspect. Finally, for jaw weight, the effect size was determined to be -0.8771942, indicating a large effect. This suggests a substantial difference in jaw weight between male and female hippopotamus. Further analysis revealed a percentage difference of 36.44784, with males having approximately 36.45% greater jaw weight compared to females. Overall, our findings indicate significant differences between male and female hippopotamus in terms of weight, girth, height, and jaw weight. These differences were classified as large effects for weight and jaw weight, small effects for girth and height, and negligible effects for body length and body size. Understanding these differences can provide valuable insights into the physical characteristics of male and female hippopotamus and contribute to our knowledge of sexual dimorphism in this species.The observed differences have significant biological implications. These differences contribute to sexual dimorphism, providing insights into the evolutionary history and reproductive strategies of hippopotamus. They may reflect physiological adaptations, such as energy requirements and reproductive physiology, and can be associated with behavioral variations and ecological interactions. Understanding these differences is crucial for effective conservation and management efforts, guiding population monitoring, habitat management, and species protection. Overall, studying these variations enhances our understanding of hippopotamus as a species and informs decision-making in wildlife conservation.

Result

From the given data we can infer that males tend to have higher canine length and jaw with respect to females which can be a positive factor in terms of reproduction, fertility and give offspring a good Antenatal care. The observed differences in physical characteristics between male and female hippopotamus have significant implications for sexual size, dimorphism, polygony, reproduction, and survival. Sexual size dimorphism is evident with larger and heavier males, indicating a specialized role in intra-sexual competition and dominance. These differences are associated with polygynous mating systems, where males with larger body size, weight, and jaw strength have advantages in securing access to multiple females. In terms of reproduction, female preference for larger males may influence mate selection, while male physical traits can impact reproductive behavior and success. Moreover, the variation in physical characteristics between the sexes can influence survival strategies. Larger male body size and jaw strength contribute to hunting prowess and territorial defense, while smaller female size may provide advantages in foraging and predator evasion. Understanding the role of these differences provides insights into the evolutionary adaptations, behaviors, and ecological dynamics of hippopotamus, contributing to our understanding of sexual selection and the interplay between morphology, behavior, and reproductive success in the animal kingdom.

References

• Canadian Journal of Zoology 4 October 2011 https://doi.org/10.1139/z11-062
• Tamás Székely, Robert P. Freckleton, and John D. Reynolds, Rensch’s rule of size dimorphism , August 10, 2004
• R. Terry Bowyer, Dale R. McCullough, Janet L. Rachlow, Simone Ciuti, Jericho C. Whiting, Evolution of ungulate mating systems: Integrating social and environmental factors, 15 April 2020
• Causes of sex-biased adult survival in ungulates: sexual size dimorphism, mating tactic or environment harshness? Carole Toïgo, Jean-Michel Gaillard, https://doi.org/10.1034/j.1600-0706.2003.12073.x
• Potential causes and life-history consequences of sexual size dimorphism in mammals JOANNE L. ISAAC,