Depth Study, Environmental Science Investigation
In 2019, CSIRO and the Bureau of Meteorology published Australia's State of the Climate report using 100 years of temperature records, showing Australia has warmed by 1.44°C since 1910, built entirely from secondary data.
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● Know
- The 6 stages of the Working Scientifically depth study structure
- The difference between primary and secondary data
- Reliable secondary data sources for environmental science (BOM, AIMS, CSIRO, NASA GISS)
- The key statistical concepts: mean, range, and correlation (conceptual)
● Understand
- How to write a testable, directional scientific question and hypothesis
- How to identify independent, dependent, and controlled variables
- How to evaluate limitations in a secondary-data investigation
● Can do
- Complete a 6-section depth study investigation scaffold
- Write a directional hypothesis in the form "If X increases, then Y will…"
- Identify limitations in a secondary-data method and suggest improvements
In 2022, a Year 10 student at a Sydney school downloaded 30 years of daily maximum temperature data from the Bureau of Meteorology website, plotted it against local bushfire frequency data, and found a statistically significant positive correlation, work good enough to present at a school STEM showcase. She never collected a single original measurement; everything came from existing government databases. A Stage 5 Working Scientifically depth study follows exactly this kind of structured approach, a 6-step process that mirrors how professional scientists conduct research, each step with specific requirements.
- Question: A testable, specific, scientific question about a measurable phenomenon. Good questions are narrow enough to investigate systematically. They often follow the form: "How does X relate to/affect Y in context Z?" They are not opinion questions and must be answerable with data.
- Hypothesis: A directional prediction of what you expect to find, written before you collect data. Format: "If [IV] increases/changes, then [DV] will [increase/decrease/change] because [scientific reasoning]." It must be falsifiable, you must be able to imagine data that would prove it wrong.
- Method: A step-by-step procedure for collecting data. For secondary data investigations: identify data sources, justify their reliability, specify what data will be extracted, and describe how it will be organised and graphed.
- Results: The collected data, organised in tables and/or graphs. Include trends, patterns, and descriptive statistics (mean, range). No interpretation yet, just the data.
- Discussion: Interpret the results. Do they support or contradict your hypothesis? What patterns appear? What do the statistics show? Identify limitations and discuss what they mean for confidence in the conclusions. Link findings to relevant scientific knowledge.
- Conclusion: A brief, direct answer to the original research question, supported by specific evidence from your results. State whether your hypothesis was supported or not. Suggest further investigations.
Environmental science often requires data spanning decades, far longer than any single researcher's project. Australia has outstanding long-term datasets: the Bureau of Meteorology has daily weather records since the 1900s; the Australian Institute of Marine Science (AIMS) has monitored the Great Barrier Reef since 1985; CSIRO has tracked ocean temperatures for decades. These datasets make environmental depth studies genuinely scientific, you are using real, peer-quality data to investigate real patterns.
One of the most common errors in student investigations is writing a research question or hypothesis that is too vague, not measurable, or not falsifiable. Here is how to distinguish good from poor examples.
Research question, poor examples and why:
- "Is climate change bad?", Not measurable; "bad" is a value judgement, not a scientific variable.
- "What is global warming?", This is a definition request, not an investigation question.
- "Why is the Great Barrier Reef dying?", Too broad; cannot be answered by a single dataset.
Research question, good example:
- "How has the mean annual sea surface temperature (SST) of the Great Barrier Reef changed between 1992 and 2022, and how does this correlate with the frequency of mass coral bleaching events?"
This question specifies: what is being measured (SST, bleaching frequency), the location (GBR), the time period (30 years), and the type of analysis (correlation). It is answerable with published data.
Hypothesis writing:
- Must state a direction: "increase," "decrease," "positive correlation," "negative correlation"
- Must include scientific reasoning: not just "I think X because that makes sense" but "because higher SST denatures coral proteins and expels zooxanthellae symbionts"
- Must be falsifiable: state what result would show the hypothesis is wrong
Good hypothesis example: "If mean annual sea surface temperature of the Great Barrier Reef has increased between 1992 and 2022, then the frequency of mass coral bleaching events will also have increased, because bleaching is triggered when SST exceeds the coral's thermal tolerance by 1°C for more than 4 weeks (AIMS, 2022)."
Environmental science depth studies rely on secondary data data collected and published by other organisations. The reliability of your conclusions depends on the reliability of these sources.
Recommended data sources for Australian environmental investigations:
- Bureau of Meteorology (BOM) weather and climate data: temperature, rainfall, sea levels, tropical cyclones; records from 1900s; publicly available at bom.gov.au
- CSIRO Australia's national science agency; publishes long-term ocean temperature, sea level, and climate change datasets
- Australian Institute of Marine Science (AIMS) Long-Term Monitoring Program data on GBR coral cover, bleaching events, and water quality since 1985
- NASA GISS (Goddard Institute for Space Studies) global surface temperature anomalies; 140+ years of instrumental records
- NOAA Mauna Loa Observatory continuous atmospheric CO₂ measurements since 1958
Key statistical tools for Stage 5 depth studies:
- Mean: average value; useful for comparing conditions across time periods. Calculate by summing all values and dividing by the number of values.
- Range: maximum minus minimum; indicates spread of data and variability.
- Correlation (conceptual): Do two variables tend to increase together (positive correlation)? Or does one increase as the other decreases (negative correlation)? Use a scatter plot to visualise. Remember: correlation does not prove causation.
Evaluating data quality: Ask, Who collected it? How long has it been collected? Was the methodology consistent over time? Is it peer-reviewed or from a credible institution? Are there gaps or changes in methodology?
If sea surface temperature and coral bleaching frequency both increased between 1992 and 2022, that is a positive correlation. But correlation alone does not prove that rising SST caused bleaching. To establish causation you need: (1) correlation, (2) temporal precedence (cause before effect), and (3) elimination of alternative explanations. The biological mechanism (SST above thermal tolerance expels zooxanthellae) provides causal support, but your data analysis alone can only show correlation.
A student uses BOM sea surface temperature data to investigate whether the Great Barrier Reef has warmed over 30 years. They find a strong positive correlation between year and mean SST. They conclude: "Sea surface temperatures have caused the reef to warm." What limitation exists in this conclusion?
How close was your prediction?
✍ Copy Into Your Books
▾Investigation Structure
- Q → H → M → R → Discussion → Conclusion
- Hypothesis: directional + falsifiable + mechanism
Variables
- IV: what you change/compare (x-axis)
- DV: what you measure (y-axis)
- CV: what you keep the same (fair test)
Data Analysis
- Mean = sum ÷ count; Range = max − min
- Correlation = direction of relationship (not causation)
- Always evaluate source reliability
Sample question: "How has the mean annual sea surface temperature of the Great Barrier Reef changed between 1992 and 2022, and does this correlate with the frequency of mass coral bleaching events?"
Key data sources: AIMS Long-Term Monitoring Program (aims.gov.au/research-topics/monitoring); BOM sea surface temperature data; AIMS coral bleaching records.
Alternative investigation topics:
You may use any of these instead:
- CO₂ concentration over time: Mauna Loa Observatory data (1958–present), how has atmospheric CO₂ changed, and does it correlate with global temperature anomalies?
- Sea level rise: tide gauge vs satellite altimetry, how has sea level at Sydney Harbour changed between 1966 and 2022?
- Bushfire frequency in SE Australia vs annual maximum temperature (BOM data, 1970–2020)
Write a specific, measurable, testable research question. Include: what you're measuring, the location/context, and the time period if relevant. Do NOT write an opinion question.
Write a directional hypothesis using the "If…then…because…" format. State which direction you predict the relationship will go, and why (cite a scientific mechanism).
Identify your variables. Then describe your method: what data source(s) you will use, how you will extract and organise the data, and how you will analyse it.
| Variable type | Variable name and unit | How measured / source |
|---|---|---|
| Independent variable (IV) what you are comparing/changing | ||
| Dependent variable (DV) what you are measuring as the response | ||
| Controlled variables what you keep the same to ensure fair comparison |
Method summary, describe in 2–3 sentences how you will collect, organise, and analyse the data:
Record your data in the table below (use representative decades for the sample topic; fill in actual data if you have access to the sources). Then describe the trend you observe in 1–2 sentences. Do NOT interpret yet, just describe what the data shows.
| Decade | Mean annual SST °C (or your DV) | Bleaching events (or your second DV) | Notes |
|---|---|---|---|
| 1992–2001 | |||
| 2002–2011 | |||
| 2012–2022 | |||
| Mean / Range |
Describe the trend (what does the data show, no interpretation yet):
Interpret your results. Do they support your hypothesis? What patterns appear? What are the limitations? What do other published sources say?
Write a brief, direct answer to your research question (2–4 sentences). State whether your hypothesis was supported. Cite specific evidence from your results. Suggest one further investigation.
Can we trust this data?
- Source A: AIMS Long-Term Monitoring Program coral cover data (published annually since 1985 on aims.gov.au)
- Source B: An environmental blog post claiming "the GBR is totally fine, scientists are lying" (no author, no date, no references)
| Reliability criterion | Source A (AIMS) | Source B (blog) |
|---|---|---|
| Who collected it? (institutional credibility) | ||
| Time span and consistency of collection method | ||
| Peer review or independent verification |
The hook for this lesson raised something that isn't often discussed: real scientists often can't run their own experiments. Environmental scientists frequently rely on data collected by government agencies over decades, data they had no control over designing. That introduces real challenges around reliability, bias, and what conclusions you can legitimately draw.
Now that you've worked through a complete investigation framework, look back at your initial design for investigating coral bleaching. What assumptions did you make that you'd now want to question? How does understanding the limitations of secondary data change what conclusions you can honestly claim?
At the start you described how you might investigate coral bleaching using secondary data, and how you'd evaluate reliability.
Now that you have a complete investigation framework, what is the most important thing you understand about designing a depth study that you didn't know at the start?
Q1. Explain the difference between a primary data investigation and a secondary data investigation. Give one advantage and one limitation of each approach for an environmental science depth study. (3 marks)
Q2. A student investigating the relationship between atmospheric CO₂ and global average temperature finds a strong positive correlation using Mauna Loa and NASA GISS data. (a) Describe what this correlation means. (b) Explain why this correlation, while powerful evidence, does not alone prove that CO₂ increases caused the temperature rise. (c) What additional evidence would strengthen the causal claim? (4 marks)
Q3. Critically evaluate the use of long-term monitoring datasets (such as AIMS GBR data or BOM climate records) in environmental science investigations. In your answer, address: (i) why these datasets are scientifically valuable, (ii) what limitations they may have, and (iii) how a scientist would account for those limitations in their discussion. (5 marks)
Model answers (click to reveal)
MC 1, C
Option C specifies: what is measured (annual mean rainfall), location (Sydney), time period (1970–2020), and data source (BOM station data). It is narrow, testable, and answerable with real data. Options A–B–D are too vague, use value judgements, or lack specificity.
MC 2, B
The dependent variable is the response, what you measure. If you're investigating how SST (IV) affects bleaching frequency, then bleaching frequency is what changes in response and is the DV. SST is what you select or compare (IV). Location and year are controlled variables or data organisation parameters.
MC 3, D
AIMS LTMP is a credible, long-term, peer-reviewed monitoring program that has used standardised methods since 1985, ideal for detecting trends over time. Tourism websites, news articles, and Wikipedia are unsuitable as primary data sources in a scientific investigation.
MC 4, A
A conclusion must: (1) directly answer the research question with specific data as evidence; (2) state clearly whether the original hypothesis was supported or not; (3) optionally suggest further investigation. It should NOT repeat the full results, express opinions, or only list limitations without answering the question.
MC 5, C
Correlation shows association, not causation. Both SST and bleaching could be increasing due to a third factor (e.g. La Niña-driven warm water events), or the correlation could be partially coincidental. To claim causation you need a known mechanism (SST above bleaching threshold triggers zooxanthellae expulsion), temporal precedence (SST rise precedes bleaching), and elimination of alternatives, not data correlation alone.
Short Answer 1 (model)
Primary data is collected directly by the researcher through experiments, surveys, or field observations. Advantage: researcher controls the methodology, can design the study for their specific question. Limitation: limited time span and resources, a Year 10 student cannot collect 30 years of ocean temperature data. Secondary data is collected by other organisations and used by the researcher. Advantage: long-term datasets (decades) that no individual researcher could generate alone; enables investigation of trends at scales impossible for individuals. Limitation: the researcher cannot control the collection methodology, cannot verify raw data, and must trust that the original collectors were rigorous; changes in methodology over time can create artifacts in the data.
Short Answer 2 (model)
(a) A strong positive correlation means that as atmospheric CO₂ concentration increased over the period measured, global average temperature also increased, the two variables rose together in the same direction. (b) Correlation alone does not prove causation because: both variables could be responding to a third factor; the correlation might be partly coincidental over a limited time period; other greenhouse gases (methane, nitrous oxide) also increased over the same period and could be contributing. (c) Additional evidence strengthening causation includes: (1) the physical mechanism, CO₂'s absorption spectrum covers IR wavelengths emitted by Earth's surface, explaining how it traps heat; (2) isotopic fingerprinting, declining ¹³C/¹²C ratio in atmospheric CO₂ matches fossil fuel combustion; (3) stratospheric cooling concurrent with tropospheric warming, only explainable by greenhouse trapping, not solar forcing; (4) IPCC attribution studies using climate models that cannot reproduce observed warming without anthropogenic forcing.
Short Answer 3 (model)
(i) Scientific value: Long-term datasets like AIMS LTMP (GBR, 1985–) or BOM climate records are scientifically invaluable because they capture trends at timescales that eclipse any individual research project. They reveal decade-scale and century-scale changes invisible to short-term studies, the GBR dataset, for example, recorded five mass bleaching events between 2016 and 2022 that would be invisible in any study shorter than 10 years. They use standardised, peer-reviewed methods, ensuring data quality. (ii) Limitations: (1) Methodological changes over time, sensor upgrades, station relocations, or changes in sampling protocols can create apparent trends that are artifacts. (2) Spatial coverage, monitoring stations may not represent the full geographical extent of the system. (3) The researcher cannot interrogate raw data quality or repeat measurements. (iii) Accounting for limitations: A scientist would: acknowledge methodology changes in the discussion; reference peer-reviewed papers that account for these changes (e.g. homogenisation of BOM records); compare findings with independent datasets for cross-validation; state explicitly that conclusions are limited by the spatial and temporal coverage of the data; and recommend further investigation using complementary methods.