Life Tables and Surviorship Lab - Gabriela Krochmal
Question 1
In Figure 1, I can make the conclusions that from 1970 to 2015 in terms of total births, there was a general increase from about the 1970s up until the 90s which can account for the baby boomers, then around the peak of the 90s there was a slight decrease in the total births that eventually picked back up and increased largely around 2005 and began its descent after 2005. This descent could be contributed to the Great Recession where the US was having an economic crisis and people probably couldn’t afford to have as many children. The crude birth rates follow a similar trend as total births but the increases are not as dramatic as total birth rates, and the lines are smoother which can show it was something that happened over time not just dramatic increases and decreases with changes in economy or health. Figure 2 demonstrates the crude birth rates of woman in different age groups. Over time there was an increase in crude birth rates for woman aged from 40-44 which can be attributed to the increasing medicines and technologies available that allow older woman to carry a child. There was a decrease in births from woman aged 15-19, I think this can be from education because it is hard to have a child so young mentally, and economically. The remaining age ranges from 25-29, 20-24, 30-34, ad 35-39 did not have as dramatic slopes over time, I think this can be because these ages are more likely to have children no matter the time in overall history.
Question 2
A total fertility rate of 2 would indicates stagnant population growth, because for example if a woman and her partner have two children, those children just replace them once they die so there isn’t actually any growth occurring. Looking at all of the fertility rates in 2010, most of them are below two which indicates that the population was barely growing because the states in the western side of the U.S that has fertility rates greater than 2.
Question 3
Shifting conservation efforts from nesting habitat protection to adult populations protection can benefit population growth because the adults are the ones that are capable to reproduce, so if there isn’t a strong population of adults then the population will not regenerate. Challenges when shifting to this new management strategy is that is it hard to implement. It is easy protecting nesting beaches because you know the location and the nest doesn’t move, but protecting adults is hard because you don’t know where they are all located, how fast they move, and what the main causes of their deaths are.
Question 4
Age-based life tables would be most effective when you are tracking an organism that is first easily traceable so that you are able to gather the data, then organisms that you can easily acknowledge what their age is. For example, you can track the age of a mountain sheep by potentially using the size of its horns. Size-based life tables would be most effective if you are unable to tell what the age of the organism is so you could just measure the size of the population. Using stage-based life tables can be beneficial when you also can’t tell the age of the organism but you do have a general idea of the different developmental stages throughout their life, like an egg, hatching, juvenile, etc.
Question 5
Organism that would follow a Type I survivorship curve are typically humans. Advances in technology and medicine have given us the chances of surviving through our youth and middle adult life, our highest mortality rates are at the older ages near the end of our lives. The biological implications of this curve in terms of reproductive strategy are that we wait to have children typically towards the middle of our lives, because we do live long we don’t rush as youth too. Organisms that follow a Type II survivorship curve are those whose mortality does not depend on its age. These organism will begin reproducing as soon as they are at the reproductive age because there isn’t knowledge of wen they will pass, examples are some adult birds and certain turtle species. Type III organisms produce thousands of individuals, most of which die right away. Examples of this are fish, seeds, and marine larvae. The reproductive strategy of these organisms is to produce a large number of offspring in hope that a few will survive.
Question 6
Using the survivorship curve plot, and plotting lx vs age x, I identified that Population A fits the Type II model of survivorship, Population B fits Type I surviorship, and Population C fits Type III survivorship. Population A must represent an organism whose mortality does not depend on its age, Population B must represent an organism that grows old and a lot of care is put into one individual, and Population C must represent a group of organisms that reproduce rapidly because of a high mortality rate at the start of life.
#Population A
library(dplyr)## Warning: package 'dplyr' was built under R version 3.5.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionsurvivorImport <- read.csv("C:\\Users\\Gabriela Krochmal\\Downloads\\survivorship.csv")
survivorA <- subset(survivorImport, type=="A")
#graph section labeling- replace this title with each population
title <- "Population A"
#standardized survival
survivorA$lx <- survivorA$Sx / survivorA[1,"Sx"]
#creates column with Sx+1
survivorA$Sxplus1 <- lead(survivorA$Sx, n = 1L)
#age specific survivorship
survivorA$gx <- survivorA$Sxplus1 / survivorA$Sx
#survivorship curve plot
plot(lx ~ age_x, data=survivorA, log = "y", main= title) #plot log scale## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted
## from logarithmic plot
#Population B
survivorImport <- read.csv("C:\\Users\\Gabriela Krochmal\\Downloads\\survivorship.csv")
survivorB <- subset(survivorImport, type=="B")
#graph section labeling- replace this title with each population
title <- "Population B"
#standardized survival
survivorB$lx <- survivorB$Sx / survivorB[1,"Sx"]
#creates column with Sx+1
survivorB$Sxplus1 <- lead(survivorB$Sx, n = 1L)
#age specific survivorship
survivorB$gx <- survivorB$Sxplus1 / survivorB$Sx
#survivorship curve plot
plot(lx ~ age_x, data=survivorB, log = "y", main= title) #plot log scale## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted
## from logarithmic plot
#Population C
survivorImport <- read.csv("C:\\Users\\Gabriela Krochmal\\Downloads\\survivorship.csv")
survivorC <- subset(survivorImport, type=="C")
#graph section labeling- replace this title with each population
title <- "Population C"
#standardized survival
survivorC$lx <- survivorC$Sx / survivorC[1,"Sx"]
#creates column with Sx+1
survivorC$Sxplus1 <- lead(survivorC$Sx, n = 1L)
#age specific survivorship
survivorC$gx <- survivorC$Sxplus1 / survivorC$Sx
#survivorship curve plot
plot(lx ~ age_x, data=survivorC, log = "y", main= title) #plot log scale## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted
## from logarithmic plot
Question 7
Age-specific survivorship gives us the probability of an individual surviving from birth to the beginning of age x. Using the Age-specific survivorship plots we can interpret that Population A has an equal chance of morality for every age. Population B has a higher chance of mortality as age increases, and population C has a higher chance of mortality at birth and it decreases with age.
#Population A
title <- "Population A"
plot(gx ~ age_x, data=survivorA, main = title)
#Population B
title <- "Population B"
plot(gx ~ age_x, data=survivorB, main = title)
#Population C
title <- "Population C"
plot(gx ~ age_x, data=survivorC, main = title)
Question 8
Life expectancy is age-specific and it is the expected number of time-intervals remaining to members of a given age. The life expectancy for Population A decreases with age. The life expectancy for Population B increased with age but once it reaches around age 3 the life expectancy begins to decrease. Population C has a high life expectancy for the beginning of life but as it ages it slowly decreases.
#Population A
title <- "Population A"
plot(ex ~ age_x, data=survivorA, main = title)
#Population B
title <- "Population B"
plot(ex ~ age_x, data=survivorB, main = title)
#Population C
title <- "Population C"
plot(ex ~ age_x, data=survivorC, main = title)
survivorC$Ro <- survivorC$lx * survivorC$bx
sum(survivorC$Ro)## [1] 5.479Question 9
Population A: Ro= 0.94 Population is declining Population B: Ro= 0.88 Population is declining Population C: Ro= 5.47 Population is growing
Age-specific surviorship and fecundity play an interactive role in determining population growth because they provide information about the amount of individuals that survive from birth to perhaps, age of ability to reproduce, and the fecundity is the ability to produce offspring. So without surviving after birth and being able to produce offspring then we wouldn’t have an increasing population.
Question 10
Population A: Generation time 3.36, Intrinsic rate -0.04 Population B: Generation time 5.84, Intrinsic rate -0.02 Population C: Generation time 3.93, Intrinsic rate 0.43 The average age between parents and offspring when comparing the populations is about 4.37 (I took the mean of the generation times). Since the generation time is consistently greater than zero this means that the organisms are longer lived.
After 100 generations with exponential growth, the expected population size for Population A is 1.787807e+110, Population B is 4.382908e+71, Population C is 8.880603e+100
Question 11
From a conservation biology perspective, life tables can be used for population growth intervention because it provides data about the organisms life such as how quickly it reproduces, what age it reproduces at, how long do they normally live for, and all of this information can be used to help us understand at which point in the organisms life is it critical for us to monitor and help conserve the population. Without knowing the different survivorship curves of species we wouldn’t be able to make sceitnifcally-based adaptive management decisions.