Unit 2 - Environmental research and data collection

2.1 The scientific method

• Scientific method involves the interplay between observations and the formation, testing and evaluation of hypotheses

• Hypotheses are based on observations/experimental data

  • Consistently supported by investigation + observation → theory

• Investigations need controlled variables + to collect quantitative results

• IV = what is changing

• DV = what you’re measuring

• Some limitations to a study include: limited sample size, sampling/selection process, demographic, area of study → uncertainty in results

2.2 Environmental research in the context of climate change

• Reliability: obtain the same result each time a measurement method is carried out

  • Peer-reviewed & repeated experiments strengthen the results of an investigation

• Bias: systematic deviation between data interpretations and accurate description

  • Personal: scientist seeks to personally benefit from research/support their ideology

  • Institutional: organization interprets data to enhance its power

  • Misuse of data due to bias → unreliable data (in favor of one conclusion + inaccurate)

• Historical data developed by: development in scientific theory + technological advancements

  • Limited amount of historical data

• Unreliable data has led to false reporting of scientific conclusions

  • Limited amount of data

  • Lack of public + media knowledge

  • Uncertainty in climate models

2.3 Collection of environmental data

• Sampling strategies are used to collect representative data

• Random sampling: equal chance of selection for research

• Systematic sampling: follows an interval

• Random sampling + systematic sampling strategies → ensure samples are well distributed w/ low risk of bias

  • Suitability of the strategies is due to: size, ease of access, & knowledge of the environment

• Random vs. systematic sampling can be determined by:

  • Precision: random = equal chance of selection; systematic = possible sampling errors

  • Bias: random = reduces bias; systematic = possible data manipulation

  • Efficiency: random = time-consuming for large populations; systematic = faster & less expensive

2.4 Data collection techniques and data analysis

• ALWAYS IDENTIFY A LOCATION TO SAMPLE - WHETHER ITS RANDOM OR NOT

• Quadrats: state size of quadrat (e.g. 1m x 1m), sampling distances (e.g. every 5 m), count the organisms collected, repeat & average

  • Limitation: used for small animals & animals that don’t move much

• ACFOR: estimates abundance

  • Limitations: biased, qualitative, over/under estimations

• Pitfall traps: placed on the ground to collect crawling animals (assesses abundance + biodiversity)

  • Benefits: simple to set up & inexpensive

  • Limitations: don’t work with flying animals & time-consuming

• Sweep nets: collect samples of orthopods from grassy/bushy areas

  • Benefits: inexpensive, easy to use, doesn’t harm insects

  • Weaknesses: net easily damaged & time-consuming

• Beating trays: select a branch, shake the branch, collect falling insects on tray, count insects, count # of branches, multiply # of insects by # of branches

  • Benefits: can collect a sample in a quick manner; may release hidden insects

  • Limitations: only useful for certain species - might miss ground dwellers

• Kick sampling: use a net, open the net against the direction of flow, set period of time, count organisms, & repeat (e.g. in different location), average

  • Benefits: simple to use, inexpensive

  • Limitations: only useful in shallow bodies, vary with time of year + temp

• Light traps: capture nocturnal arthropods using a light source + capture sheet

  • Limitations: only effective in very dark areas

• Capture-mark-recapture: randomly capture animals & tag them (e.g. chips), recapture them, then use Lincoln Index to estimate population

  • Weakness: assumes no changes in population (e.g. migration, births, deaths)

• Water turbidity: measures the level of particles in a body of water

• Questionnaires: research method using written questions (contains open & closed questions)

  • Limitations: amount of data (too much/too little), possible bias, data might not be representative, can’t verify data

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2.5 The use of technology in data collection & analysis

• Methods of data collections that use technology:

  • Geospatial systems

  • Satellite sensors

  • Radio tracking

  • Computer modelling

  • Crowdsourcing

• Big data: a large amount of data that is collected rapidly using technology

  • Benefits: efficient (less time consuming), stores a lot of data, wide application of uses (e.g. healthcare)

  • Limitations: limited human input, data may be unreliable, people can rely to heavily on the data