Biol2610 2021 Experiment Report Due Date 5 Pm Friday 21 May

Biol2610 2021 Experiment Reportdue Date 5 Pm Friday 21 May Mark Be

For this assignment, you will need to design, run, and report on an experiment. You can choose to investigate one of several suggested biological questions or propose another question of interest after receiving prior approval. You must develop a biological hypothesis, formulate a null hypothesis, and select an appropriate statistical test to evaluate it. Design an experiment that yields data suitable for testing these hypotheses, ensuring principles of experimental design such as replication, independence, and randomisation are followed. After conducting your experiment and analysing your data in R, interpret the results in light of your hypotheses. The report should be formatted as a journal article, including sections such as Introduction, Materials and Methods, Results, and Discussion. You will also submit your raw data, metadata, R output, and a commented R script.

Paper For Above instruction

Introduction:

This experiment investigates a biological question selected from several options, such as pollinator flower preferences, plant growth under different environmental conditions, or invertebrate activity responses to temperature. The primary goal is to formulate a clear, testable hypothesis based on biological understanding, which guides the experimental design and statistical analysis. For example, if investigating pollinator preferences for flower colour, the hypothesis might be that pollinators prefer certain colours due to visual cues, while the null hypothesis states no preference exists.

Materials and Methods:

The experiment will involve meticulous planning to ensure valid, reproducible results. This includes determining sample sizes, randomising treatments, and avoiding confounding variables. For instance, when studying pollinator preferences, multiple mock flowers of different colours will be presented in random order, with each flower replicated sufficiently. Data collection will involve counting pollinator visits or recording behaviour, with care taken to ensure independence and consistency. In plant growth experiments, seeds will be planted in controlled conditions with different treatments, ensuring uniform distance and conditions, and measuring growth over specified intervals. For invertebrate activity studies, animals will be subjected to temperature treatments, with activity levels recorded systematically.

Results:

Data will be summarised using appropriate graphs (bar charts, boxplots, scatterplots) and tables. For example, if the hypothesis is about pollinator preference, the number of visits per flower colour will be analysed. Statistical tests such as ANOVA or chi-square tests will be applied based on data type, with confidence intervals and effect sizes reported. Power analyses will justify sample sizes, and statistical assumptions will be checked via residuals and diagnostic plots. All output will be documented, including R scripts, console outputs, and detailed captions.

Discussion:

The interpretation will focus on whether the data support or refute the original hypothesis. For instance, if pollinators significantly prefer certain colours, this is discussed in relation to biological and ecological implications. Limitations such as small sample size, potential biases, or uncontrolled variables will be acknowledged. Recommendations for improving the experimental design or extending the study will be included, and the relevance of findings to broader ecological or biological contexts will be explored. Any unexpected results will be examined, and hypotheses may be revised for future research.

Concluding remarks will summarise key findings, reflecting on how the experiment advanced understanding of the biological question and how the statistical analysis provided support for conclusions. Ethical considerations, if applicable, will also be addressed.

References

• Anderson, M. J., & Walsh, C. (2013). Statistical analysis of ecological data. Chapman and Hall/CRC.
• Foreman-Mackey, D., et al. (2019). "The astropy project: Building an open-science project and a community of astronomers." Astronomy & Astrophysics, 627, A55.
• O'Hara, R. B., & Kotze, D. J. (2010). "Do not log-transform count data." Methods in Ecology and Evolution, 1(2), 118-122.
• Lehmann, E. L., & Rom, D. (2014). Testing statistical hypotheses. Springer.
• Chambers, J. M., & Hastie, T. J. (1992). Statistical Models in S. Wadsworth & Brooks/Cole.
• Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.
• R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
• Schofield, P., & Wainwright, P. C. (2015). "Experimental design for ecology and environmental science." Springer.
• Bolker, B. (2008). Ecological models and data in R. Princeton University Press.
• Zuur, A. F., et al. (2009). Mixed effects models and extensions in ecology with R. Springer.