Evolutionary origin of human traits

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Question

Why and how did humans become so different from other apes?

Answer

Rationale

There are conflicting hypotheses to explain why the traits that so clearly distinguish humans from other primates originally evolved. One idea is that the ancestors of humans came to live in a different kind of environment than the ancestors of chimpanzees and gorillas, and hence experienced different selection pressures and obtained a suite of unique traits as adaptations to the new environment. What that new environment was and which selection pressures were most important has been debated, however, and a number of hypotheses based on ideas other than environmental adaptation have also been proposed. To date, general discussion on the topic seems mostly to have focused on finding merit or flaws in one hypothesis at a time. The purpose of this page is to provide a structured forum for the general evaluation and comparison of the different hypotheses on human origins.

Calculations

Data version 1: raw data in Excel from Webropol: free text columns removed [1]
Data version 2: cleaned with this code [2].

+ Show code - Hide code


library(OpasnetUtils)
library(ggplot2)

# Fetch the stored raw data (requires a password to open it).

objects.get("mg2Gf5eoR5FVZ8xw")

data <- objects.decode(etable, key)

# Rename columns.

titles <- c(
	"Gender",
	"Age",
	"Country",
	"Education",
	"Expertise.Anthropology",
	"Expertise.Animals",
	"Expertise.Ecology",
	"Expertise.Evolution",
	"Expertise.Genetics",
	"Expertise.Biology",
	"Expertise.Geology",
	"Expertise.Cardiovascular",
	"Expertise.Musculoskeletal",
	"Expertise.Nervous",
	"Expertise.Nurtition",
	"Expertise.Human",
	"Expertise.Paleoanthropology",
	"Expertise.Paleontology",
	"Expertise.Other",
	"Publications.scientific",
	"Publications.general",
	"EvolutionPublications.scientific",
	"EvolutionPublications.general",
	"Courses",
	"Familiarity",
	"Bipedalism.Running",
	"Bipedalism.Orangutang",
	"Bipedalism.Wading",
	"Bipedalism.Thermoregulation",
	"Bipedalism.Seeing",
	"Bipedalism.Foraging",
	"Bipedalism.CarryingFood",
	"Bipedalism.CarryingOffspring",
	"Bipedalism.Tools",
	"Bipedalism.Sexual",
	"Encephalization.Meat",
	"Encephalization.Fish",
	"Encephalization.Fire",
	"Encephalization.Social",
	"Encephalization.Hunting",
	"Encephalization.Speech",
	"Encephalization.Warfare",
	"Encephalization.Neoteny",
	"Encephalization.Bipedalism",
	"Encephalization.Nakedness",
	"Naked.OffspringContact",
	"Naked.SexContact",
	"Naked.Hygiene",
	"Naked.Extoparasite",
	"Naked.Swimming",
	"Naked.Cooling",
	"Naked.Size",
	"Naked.Clothes",
	"SubcutaneousFat.Storage",
	"SubcutaneousFat.Insulation",
	"SubcutaneousFat.Thermoregulation",
	"SubcutaneousFat.Sexual",
	"Larynx.Speech",
	"Larynx.SexualVoice",
	"Larynx.Diving",
	"Speech.Larynx",
	"Speech.WaterCommunication",
	"Speech.VoluntaryBreathing",
	"Speech.Reassuring",
	"Speech.Social",
	"Speech.Hunting",
	"Speech.Culture",
	"BabySwim",
	"Nose",
	"Smell",
	"Fingerweb",
	"EccrineGlands",
	"Sweating",
	"Swimming",
	"DivingReflex",
	"Bathing",
	"AAH.AgainstProcess",
	"AAH.EnvironDeterminism",
	"AAH.Redundant",
	"AAH.ComparativeAnatomy",
	"AAH.FurryAquatics",
	"AAH.Concidence",
	"AAH.ApesSwim",
	"AAH.SimplyFalse",
	"AAH.NoFossils",
	"AAH.FossilsAreTerrestrial",
	"AAH.WhenAndWhere",
	"AAH.NoTime",
	"AAH.Simplistic",
	"AAH.LessParsimonius",
	"AAH.LessConsistent",
	"AAH.Nonpredictive",
	"AAH.Feministic",
	"AAH.NotPeerReviewed",
	"AAH.NotProfessional",
	"AAH.Pseudoscience",
	"AAHFamiliarity",
	"AAHSource.Articles",
	"AAHSource.Morgan",
	"AAHSource.Books",
	"AAHSource.Media",
	"AAHSource.Courses",
	"AAHSource.Personal",
	"AAHSource.Blogs",
	"AAHSource.Wikipedia",
	"AAHAttitude.Rejected",
	"AAHAttitude.Described",
	"AAHAttitude.Plausible"
)

colnames(data) <- titles

data <- data[-(1:2) , ] # Remove old heading rows.

# Replace answer numbers with actual responses, and turn them into factors.

leve <- list()
leve[[1]] <- c("Male", "Female")
leve[[2]] <- c("29 or less", "30-39", "40-49", "50-59", "60 or more")
leve[[3]] <- c("None", "Bachelor's degree", "Master's degree", "Doctor's degree")
leve[[4]] <- c("Anthropology or archaeology", "Biology (animal physiology, anatomy or morphology)", "Biology (ecology)", 
	"Biology (evolution)", "Biology (genetics or molecular biology)", "Biology (other, please specify)", "Geology", 
	"Human cardiovascular or respiratory system", "Human musculoskeletal system", "Human nervous system", "Human nutrition", 
	"Other aspects of human biology (please specify)", "Paleoanthropology", "Paleontology", "Other, please specify")
leve[[5]] <- c("none", "1-10", "11-40", "41 or more")
leve[[6]] <- c("Yes", "No")
leve[[7]] <- c("Not at all", "I have some idea", "I know the hypotheses well")
leve[[8]] <- c("Very likely", "Moderately likely", "No opinion", "Moderately unlikely", "Very unlikely")
leve[[9]] <- c("Fully agree", "Mostly agree", "No opinion", "Mostly disagree", "Strongly disagree")
leve[[10]] <- c("No", "Yes")
leve[[11]] <- c("Considerably", "A little", "Not at all")
leve[[12]] <- c("Common", "Rare", "Not seen")

# Which answer list is used for which question (NA: do not use an answer list. This applies to country).

chooselevel <- c(1, 2, NA, 3, rep(4, 15), rep(5, 4), 6, 7, rep(8, 51), rep(9, 20), 10, rep(11, 8), rep(12, 3))

for(i in 1:length(chooselevel)) {
	if(!is.na(chooselevel[i])) data[[i]] <- as.factor(leve[[chooselevel[i]]][as.numeric(as.character(data[[i]]))])
}

## New function for making uppercase Initials. See http://127.0.0.1:21251/library/base/html/chartr.html

capwords <- function(s, strict = FALSE) {
    cap <- function(s) paste(toupper(substring(s,1,1)),
                  {s <- substring(s,2); if(strict) tolower(s) else s},
                             sep = "", collapse = " " )
    sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s)))
}

# Rename written country names into standard names and turn into a factor.

data[[3]] <- ifelse(is.na(data[[3]]), "", as.character(data[[3]]))

data[[3]] <- capwords(data[[3]], strict = TRUE)

data[[3]] <- ifelse(data[[3]] == "Brasil", "Brazil", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Canada And France", "Canada", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Ch", "Switzerland", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Czech Republich", "Czech Republic", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Iyaly", "Italy", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Korea, South", "Korea", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Mexi", "Mexico", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Northern Ireland. U.k.", "UK", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Scotland Uk", "UK", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Sweden (currently, But Of U.s. Origin)", "Sweden", data[[3]])
data[[3]] <- ifelse(data[[3]] == "The Uk", "UK", data[[3]])
data[[3]] <- ifelse(data[[3]] == "United Kingdom", "UK", data[[3]])
data[[3]] <- ifelse(data[[3]] == "U,.s.", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "U. S. A.", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "U.s.a.", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "U.k.", "UK", data[[3]])
data[[3]] <- ifelse(data[[3]] == "United States", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "United States Of America", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "U.s.", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Us", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Usaf", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Wales, Uk", "UK", data[[3]])

data[[3]] <- ifelse(data[[3]] == "Usa", "USA", data[[3]])
data[[3]] <- ifelse(data[[3]] == "Uk", "UK", data[[3]])

data[[3]] <- ifelse(data[[3]] == "", NA, data[[3]])
data[[3]] <- as.factor(data[[3]])

#MPF-731
#sininen farmariaudi
#16.40 10.3.2013
#Orivesi-Jämsä itään n. 150 km/h

# Print and plot examples of the cleaned data.

# oprint(head(data))

X <- colnames(data)[X]
Y <- colnames(data)[Y]
fill <- colnames(data)[fill]

ggplot(data, aes_string(x = X, weight = 1, fill = fill)) + 
	geom_bar(position = "fill", na.rm = TRUE) + 
	theme_grey(base_size = 24)

# Save the cleaned data table in a secure format.

etable <- objects.encode(data, key)
objects.put(etable)

cat("Cleaned table 'etable' successfully saved.\n")

Keywords

References

Related files