Introduction

Aerial survey of waterbirds provides one the few quantitative, large scale biodiversity datasets that can monitor changes in the distribution and abundance of 50 waterbird species, including threatened species, and the health of rivers and wetlands. Changes in waterbird numbers provides a tangible way of indicating and measuring changes in the ecological health of river and wetland systems. This survey is run by the Centre for Ecosystem Science at the University of NSW in partnership with the NSW Office of Environment & Heritage, with additional funding provided by the South Australian Department of Environment, Water, and Natural Resources, the Queensland Department of Environment and Heritage Protection, the Victorian Department of Environment, Land, Water & Planning, the Victorian Game Management Authority and the Department of Environment of the Australian Government.

Loading data and libraries

Data

The Eastern Australian Waterbird Survey is one of the largest wildlife surveys in Australia, surveying major wetland sites in the Murray-Darling Basin, providing invaluable information on the ecosystem health of wetlands and rivers

# Original csv dataset 
birds_orig <- read.csv("WaterbirdSearch_20200311-1247b.csv")
birds_orig

The data is composed by 154130 observations (rows) and 13 variables (columns: Wetland, Lattitude, Longitude, Survey Program, Date, Year, Band, Replicate.Count, Common.Name, Scientific.Name, Bird.Count , Nest.Count, Brood.Count ).

Libraries

library(dplyr)
library(ggplot2)
  • dplyr to subset the data.
  • ggplot to visualize the data.

Question to be solved

Is there a decline in numbers of waterbirds observed across the years surveyed? (Address this question for a bird species of your choice in one of the wetlands that has many observations)

Data analysis

  • To do this, first we need to choose a Waterland with high count of species.
# Selection of important variables for the analysis
birds <- birds_orig %>% 
  select(Wetland, Year, Common.Name, Scientific.Name, Bird.Count) %>% 
  filter(Bird.Count != 0)
birds

Only 5 of the 13 variables are selected to proceed with the analysis. This variables are important because they contain the most basic and sufficient information to solved the question asked. The variables are:

  • Wetland name of the Wetland.
  • Year year of the observation taken.
  • Common.Name of the species obeserved.
  • Scientific.Name of the species observed.
  • Bird.Count count of single species per obervation.

The next step consis in picking a Wetland with high count of birds.

birds %>% 
  group_by(Wetland) %>% 
  summarise(count = sum(Bird.Count)) %>% 
  arrange(desc(count))

So this table shows the Wetlands of the dataset with birds count in descending order. Among the top ones, Narran Lake, with 1 615 764 birds is picked to continue the analysis.

# Subset of only Narran Lake waterbirds data
Narran_lake <- birds %>% 
  filter(Wetland == "Narran Lake") 
Narran_lake

The next step is to pick one of the many species in this Wetland.

Narran_lake %>% 
  group_by(Common.Name, Scientific.Name) %>% 
  summarise(Count = sum(Bird.Count)) %>% 
  arrange(desc(Count)) # Threskiornis spinicollis is picked (among random top ones)

From the subset of the survey data containing only the ones in Narran Lake, the species Straw-necked Ibis (Threskiornis spinicollis) is picked among the ones with high total count.

# Straw-necked Ibis subset of the Narran Lake
Ibis <- Narran_lake %>% 
  filter(Scientific.Name == "Threskiornis spinicollis") %>% 
  group_by(Year) %>% 
  summarise(Count = sum(Bird.Count))
Ibis
ggplot(data = Ibis, aes(x = Year, y = Count)) +
  geom_point(size = 3) +
  geom_smooth() +
  labs(title = expression(paste("Straw-necked Ibis (", italic("Threskiornis spinicollis"), 
                                ") count in Narran Lake along the years")), 
       y = "Species Count") +
  theme_classic()

Conclusion

With the last plot we can prove that there has been a significant decrease in this species count over the years of the study.

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