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BEES2041 Data Analysis for Life and Earth Scientists

Practical report 2
Independent data analysis

Background In recent years, there has been a very strong focus on the need for science
to be reproducible. This means that all the methods (in the laboratory and
field) are presented in enough detail for another researcher to be able to
repeat the work and check the conclusions. It also means that the data
collected, and all analytical methods used are also presented in a public
forum.
This movement has resulted in scientific journals frequently requiring authors
to publish their data and all code used to process, analyse and visualise their
data on publicly available platforms.
Support for open science comes from the desire to have publicly funded
research able to be scrutinised by anyone, for data and methods to be
shared freely to advance science more quickly, and from those involved in
synthetic research (i.e., meta-analysists who combine many studies to gain
an overall understanding of a problem) needing to assess the reliability of a
single study. The rare cases of scientific fraud can also be more readily
detected when data and methods are openly available.
Further reading on reproducibility in environmental sciences
• Powers et al. 2018 Ecological Applications link
• Liu et al. 2019. Eos link
Your task Open science requires the sharing of data sets and the code required to
process, visualise and analyse the data. When groups of researchers are
working on the same problem, they also need to share their work prior to the
study’s completion and publication.
To give you experience in producing a document that would allow open
sharing of all analytical methods, you are required to prepare notes and code
that would detail each of the steps required to address a single hypothesis

Step 1) Choose one of the two environmental science scenarios below (Frog
ID or Waterbird survey)

Step 2) Create a document (R Studio Notebooks are ideal for this), that will
include:
a) Notes to introduce the question and detail the hypotheses being
tested (1-2 paragraphs)
b) Notes and code to load any R packages that you will require
c) Notes and code to import the data set and extract relevant parts
d) Notes and code to create a graph that can visualise the how the data
addresses the question
e) Notes to describe the analytical methods (1-2 paragraphs)
f) Code to formally analyse the data to address the question
g) Notes to interpret the results (1-2 paragraphs)

The notes should include enough detail for a co-worker to be able to
understand what a given piece of code will be doing (imagine they are just
learning data analyses like you are).

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1. Use of citizen
science to monitor
frogs across
Australia



FrogID is a successful citizen science program led by Dr Jodi Rowley
(Australian Museum and School of Biological, Earth and Environmental
Sciences UNSW). Members of the public record frog calls on a smartphone
app which are then uploaded to contribute to a large, nation-wide data base
of frog species distributions.
Read more at www.frogid.net.au
The effectiveness of citizen science programs to document flora and fauna
depend on how well the public sampling represents the range of habitats and
areas available. To test this, Callaghan et al. 2020 recently contrasted the
number of frog species detected by FrogID (in each 30’ grid cells across
Australia) to the number of frog species known from a data set collected by
frog experts over many decades.

Your question:
• Does the data on species richness of frogs from FrogID accurately
predict the known species richness from previous expert data sets?

Data:
• The data sets from Callaghan et al. 2020 are provided on Moodle.
The number of frog species are provided in each 30’ grid cell.
• Hint: you will need to join data sets to get all data in the same
spreadsheet (see Combining data sets on Environmental
Computing)

Required reading:
Callaghan, CT; JD Roberts, AGB Poore, RA Alford, H Cogger & JJL.
Rowley. 2020. Citizen science data accurately predicts expert-
derived species richness at a continental scale when sampling
thresholds are met. Biodiversity and Conservation 29: 1323–
1337. link
Rowley, JJ and CT Callaghan. 2020. The FrogID dataset: expert-
validated occurrence records of Australia’s frogs collected by
citizen scientists. ZooKeys, 912, p.139. link


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2. Aerial Waterbird Surveys


The Centre for Ecosystem Science at UNSW (led by
Professor Richard Kingsford) has been monitoring
waterbirds across Australia’s wetlands since1983.
Read more at the Centre’s website.

Your question:
• 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.
• Wetlands with many observations include:
Lindsay-Walpolla-Chowilla, Lower Lakes &
Coorong, Lowbidgee, Kerang, Menindee,
Gwydir, Booligal, Narran Lake, Lake
Moondarra, Coolmunda Dam, Barmah-
Millewa, Hattah Lakes, Cumbung, Darling
River, Mullawoolka Basin, Macquarie
Marshes, Murray River, Paroo Overflow
Lakes, Chow_Lin_Wall, and Fivebough
Swamp

Data:
• The survey data are freely available at
https://aws.ecosystem.unsw.edu.au/ To
explore this yourself, you need to register
with a user name and password
• While you can export parts of this data set,
we have provided the entire dataset in a
spreadsheet for you on Moodle
(WaterbirdSearch_20200311-1247b.csv).
Please use this data file as we have added
a year variable that you will need for your
analyses.
• Hint: You will need to be familiar with the
functions in dplyr for selecting rows. Help on
Subsetting Data is on Environmental
Computing.

Required reading:
Kingsford RT & JL Porter 2009. Monitoring waterbird
populations with aerial surveys—what have
we learnt? Wildlife Research 36: 29–40. link
Kingsford RT, G Bino & JL Porter. 2017. Continental
impacts of water development on
waterbirds, contrasting two Australian river
basins: Global implications for sustainable
water use. Global Change Biology 23:
4958– 4969 link


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Assessment Submit the notes and code as a R Studio notebook (i.e. .rmd file) or R script for those few students that had troubles with notebook on older Mac laptops
Upload those files to the Practical Report 2 Assignment in Moodle (found in the
Assessment section)
Due date: Week 8, 11:59 pm, Monday 6th April
Marking:
a) Notes to introduce the question and detail the hypotheses being tested (10
marks)
b) Notes and code to load any R packages that you will require (5 marks)
c) Notes and code to import the data set and extract relevant parts (5 marks)
d) Notes and code to create a graph that can visualise the how the data
addresses the question (25 marks)
e) Notes to describe the analytical methods (15 marks)
f) Code to formally analyse the data to address the question (25 marks)
g) Notes to interpret the results (15 marks)