Forest Birds
Cristina Soto
2026-03-24
knitr::opts_chunk$set(echo = TRUE)
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(lubridate)
setwd("~/Documents/School/Data 101/Data project/Birds")
birds <- read.csv("forest.birds.csv")R Markdown
Research Question: Do the number of years a forest has been left isolated really affect the bird abundance? (using the following variables, abundance, yrs.isolation)
Introduction: In this project I will demonstrate how environmental factors affect the Forest bird populations in Australia. I will also focus in on the number of years a forest has been isolated and the size of the bird’s abundance affected. These ongoing environmental issues are important because it severly affects the species of birds. This data is from OpenIntro Forest birds dataset, this dataset includes the following variables, abundance, patch.area, year.of.isolation, dis.nearest, dist.larger, grazing, intensity, altitude, yrs.isolation.
#Cleaning the Data, clean title, check for missing data.
colSums(is.na(birds))## abundance patch.area year.of.isolation dist.nearest
## 0 0 0 0
## dist.larger grazing.intensity altitude yrs.isolation
## 0 0 0 0names(birds) <- tolower(names(birds))
names(birds) <- gsub("\\.","_",names(birds))
head(birds)## abundance patch_area year_of_isolation dist_nearest dist_larger
## 1 5.3 0.1 1968 39 39
## 2 2.0 0.5 1920 234 234
## 3 1.5 0.5 1900 104 311
## 4 17.1 1.0 1966 66 66
## 5 13.8 1.0 1918 246 246
## 6 14.1 1.0 1965 234 285
## grazing_intensity altitude yrs_isolation
## 1 less than average 160 15
## 2 heavy 60 63
## 3 heavy 140 83
## 4 average 160 17
## 5 heavy 140 65
## 6 average 130 18summary(birds)## abundance patch_area year_of_isolation dist_nearest
## Min. : 1.50 Min. : 0.10 Min. :1890 Min. : 26.0
## 1st Qu.:12.40 1st Qu.: 2.00 1st Qu.:1928 1st Qu.: 93.0
## Median :21.05 Median : 7.50 Median :1962 Median : 234.0
## Mean :19.51 Mean : 69.27 Mean :1950 Mean : 240.4
## 3rd Qu.:28.30 3rd Qu.: 29.75 3rd Qu.:1966 3rd Qu.: 333.2
## Max. :39.60 Max. :1771.00 Max. :1976 Max. :1427.0
## dist_larger grazing_intensity altitude yrs_isolation
## Min. : 26.0 Length:56 Min. : 60.0 Min. : 7.00
## 1st Qu.: 158.2 Class :character 1st Qu.:120.0 1st Qu.:17.00
## Median : 338.5 Mode :character Median :140.0 Median :20.50
## Mean : 733.3 Mean :146.2 Mean :33.25
## 3rd Qu.: 913.8 3rd Qu.:182.5 3rd Qu.:55.50
## Max. :4426.0 Max. :260.0 Max. :93.00str(birds)## 'data.frame': 56 obs. of 8 variables:
## $ abundance : num 5.3 2 1.5 17.1 13.8 14.1 3.8 2.2 3.3 3 ...
## $ patch_area : num 0.1 0.5 0.5 1 1 1 1 1 1 1 ...
## $ year_of_isolation: int 1968 1920 1900 1966 1918 1965 1955 1920 1965 1900 ...
## $ dist_nearest : int 39 234 104 66 246 234 467 284 156 311 ...
## $ dist_larger : int 39 234 311 66 246 285 467 1829 156 571 ...
## $ grazing_intensity: chr "less than average" "heavy" "heavy" "average" ...
## $ altitude : int 160 60 140 160 140 130 90 60 130 130 ...
## $ yrs_isolation : int 15 63 83 17 65 18 28 63 18 83 ...Utilizing functions
birds_chosen <- birds |>
select(abundance, patch_area, yrs_isolation)Finding the mean and summary
mean(birds$abundance)## [1] 19.51429birds |>
group_by(yrs_isolation) |>
summarize(abundance = mean(abundance))## # A tibble: 25 × 2
## yrs_isolation abundance
## <int> <dbl>
## 1 7 34.4
## 2 9 29
## 3 10 22.8
## 4 11 27.1
## 5 13 29.5
## 6 15 11.0
## 7 16 27.4
## 8 17 24.5
## 9 18 15.1
## 10 19 28.9
## # ℹ 15 more rows#Creating a new variable using mutate
birds_df <- birds |>
mutate(size_available = ifelse(patch_area > median(patch_area), "Big", "Small"))#Scatterplot (Visualization)
library(ggplot2)
ggplot(birds, aes(x = yrs_isolation, y = abundance)) +
geom_point(aes(size = patch_area), alpha = 1/3) +
geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
This scatterplot shows the relationship between bird abundance and years
of isolation with patch area also included. The scatterplot shows how
bird abundance decreased over the years. It also shows how the larger
patch size have a higher bird abundance when compared to the smaller
patch sizes. This further shows how patch size and isolation have a role
in bird population outcome.
#Conclusion and future directions In conclusion this implies how the patch size and years of isolations affect the bird population (abundance) in the Australian forest. This demonstrates how the importance of having better habitats to preserve natural habitat. Also, how habitat isolation directly impacts animal population. More future research could have more variables available and maximizing the data to better understand how this affects bird population.
#References This data comes from OpenIntro Forest birds dataset.
Previous assignments and past chapters: Scatterplat formats: https://r4ds.had.co.nz/transform.html and Descriptive Statistics.Rmd Dataset cleaning under “cleaning data headings and variable names”: Loading Datasets.Rmd Future directions: help with https://www.sciencedirect.com/science/article/abs/pii/S0006320700000173