BIOL20020 Quantitative and Computational Methods Report 1R

Java Python Quantitative and Computational Methods, BIOL20020

Report 1: Testing Biological Hypotheses in Evolutionary Biology
Using the General Linear Model

General guidelines

For this assessment, you are asked to carry out three statistical analyses (outlined below), and write a short report consisting of three sections:

(i) a “Statistical Analysis” section detailing the statistical methods used,

(ii) a “Results” section consisting of a combination of text and figures reporting the results of your statistical analyses,

(iii) an “Appendix” containing the R code that you used to carry out the statistical analyses.

Your report should follow scientific writing and should emulate the Statistical Analysis [typically a sub-section of the Methods] and Results section of a peer-reviewed journal article.

The report word limit is a maximum of 1000 words. This does not include figures or figure legends, or the Appendix. You should include at least three figures as detailed below. You can include additional figures if you think it is appropriate, but note that redundancy between plots, for example two plots that show the same data in a different format, will be marked down.

You will need to submit the report as a PDF via Blackboard by the deadline: 4pm, 30^th October 2024.

The report will count for 50% of your total unit mark.

The aim of the report will be to test your ability to put into practice what you have learnt so far in the Quantitative and Computational Methods unit, particularly in Weeks 3 to 5, i.e., how to use the General Linear Model to analyse experimental data and test scientific hypotheses, and how to present the outcome of these analyses. You will be assessed on:

(i) the validity of the statistical methods used (R code in the Appendix),

(ii) the accuracy of the outputs of your statistical analyses (test statistics and p-values),

(iii) the clarity of presentation of the statistical methods used (Statistical Analysis section),

(iv) the clarity of presentation of the results and main take-home messages (Results section), and

(v) the quality of your figures.

Theme

The theme of the report is loosely linked to the Evolutionary Biology unit (BIOL20028), which many of you are currently taking. The aim of this link is to illustrate how the methods we teach in QCM are used to address real scientific questions from an active area of biological research. However, no knowledge from the Evolutionary Biology unit is necessary to complete the report, and you will not be disadvantaged in any way if you are not taking that unit.

The data

You will be provided with three biological datasets on Darwin’s finches. Each dataset will be used to test one specific hypothesis from evolutionary biology using the General Linear Model approach, as detailed below. Please note that these datasets contain real data extracted from online scientific publications, but they have each been simplified and cropped so the analytical methods, results and conclusions may differ from those published online.

Background: Darwin’s finches in the Galápagos archipelago are a very popular system to study natural selection and evolution in the wild. The Galápagos islands are subject to extreme weather fluctuations, resulting in extremely wet (El Niño) and extremely dry (La Niña) weather conditions which apply strong selection pressures on organisms. A landmark series of studies by Peter, Rosemary Grant and co-workers demonstrated a strong relationship between bird morphology (e.g. beak length, beak depth or body size) and fitness, as BIOL20020 Quantitative and Computational Methods Report 1R those traits determine a bird’s survival by being able to crack hard-shelled seeds. In this coursework, we ask you to carry out test three biological hypotheses that are related to three of the four main postulates of evolution by natural selection: that individuals within species are variable (postulate 1), that some of this variation is passed on to offspring (postulate 2), and that survival and reproduction are not random, but depend upon particular favourable or deleterious traits (postulate 4). The third postulate (that more offspring are produced that can survive) will not be considered in this work, although it is also clearly met in the population of Galápagos finches which show up to 84% mortality during periods of drought.

Statistical Analysis 1

Dataset for analysis: “cross_species_variability.csv”

In this dataset, each row corresponds to a unique ground finch collected on Santa Cruz Island, Galápagos. Column “species” indicates which species the finch belongs to: Geospiza fuliginosa (“fuliginosa”), G. magnirostris (“magnirostris”), G. scandens (“scandens”), and two morphotypes of G. fortis: large (“fortis_large”) and small ("fortis_small”). Column “beak_depth” contains the depth (in mm) of that individual’s beak.

Note: for the purpose of this analysis, you will treat the two morphotypes “fortis_large” and "fortis_small” as if they were two different species.

Using the General Linear Model approach, test whether beak depth differs between the five species of ground finches, and identify which species differ from one another.

Expectations:  

- for this analysis you should use a GLM with a single categorical predictor, followed by post-hoc tests if appropriate

- the Results section should contain one plot illustrating any differences in beak depth between species as well as intra-specific variability.

Statistical Analysis 2

Dataset for analysis: “beak_depth_heredity.csv”

In this dataset, each row corresponds to a unique family of ground finches recorded in the Galápagos islands. Column “species” indicates which species the family group belongs to: Geospiza scandens (“scandens”) or G. fortis: (“fortis”). Column “mid_parent” contains the average beak depth (in mm) of the two parents in this family. Column “mid_offspring” contains the average beak depth (in mm) of all offspring in this family.

Using the General Linear Model approach, test whether parental beak depth influences offspring beak depth in family groups, and whether the relationship between parental and offspring beak depth differs between species.

Expectations:  

- for this analysis you should use a GLM with one categorical predictor and one continuous predictor.

- the Results section should contain one plot illustrating the relationship between parent and offspring beak depth in both species.

Statistical Analysis 3

Dataset for analysis: “survival.csv”

In this dataset, each row corresponds to a unique ground finch collected on Santa Cruz Island, Galápagos. Column “species” indicates which species the finch belongs to: Geospiza fuliginosa (“fuliginosa”) or G. magnirostris (“magnirostris”). Column “tarsus” contains the length (in mm) of that individual’s tarsus (standard bird body length measurement, taken between the base of the tibia and the base of the toes), which is considered a good proxy for body size. Column “status” reports whether the individual had died or survived the following year.

Using the General Linear Model approach, test whether body size differs depending on whether individuals later died or survived, and whether the effect differs between the two species.

Expectations:  

- for this analysis you should use a GLM with two categorical predictors, followed by post-hoc tests if appropriate.

- the Results section should contain one plot illustrating the effect of survival on body size in the two species