Christopher J. Brown1,2 & Scott Spillias2,3
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania
Centre for Marine Socio-Ecology, University of Tasmania, Hobart, Australia
CSIRO Environment, Hobart, Australia
Below we provide an example of a readme.md file. This analysis asks how coral reef communities are impacted by pollution. The data was collected with diver surveys who identified different categories of benthic cover along standardized transects (Brown and Hamilton 2018).
To implement this analysis we would use an AI assitant like github
copilot. Then we’d simply place the readme.md in an
appropriate directory and prompt
Do this analysis @readme.mdThen we’d review the LLM’s decisions as it went and redirect when neccessary.
The readme file is below. Note that this file is completly self-contained and the workflow can be easily replicated, as the data are available via weblinks.
# Benthic Cover Analysis with Multidimensional Scaling (MDS)
## Introduction
This project analyzes benthic cover surveys to visualize patterns in ecological communities across different sites. Multidimensional Scaling (MDS) will be used to reduce the dimensionality of the data and reveal underlying patterns in site dispersion. We are particularly interested in how community structure relates to distance to logging.
Our hypothesis is that sedimentation caused by logging has impact coral cover.
## Aims
1. Visualize patterns in benthic cover composition across sites
2. Identify potential groupings of sites based on benthic cover composition
3. Relate ecological communities to logging
## Data methodology
The data was collected with the point intersect transect method. Divers swam along transects. There were several transects per site. Along each transect they dropped points and recorded the type of benthic organism (in categories) on that point. Percentage cover for one organism type can then be calculated as the number of points with that organism divided by the total number of points on that transect.
Transects should be averaged to give a single value for each site.
This data and study method is a simplified version of the study
[Brown, Hamilton. 2018. Estimating the footprint of pollution on coral reefs with models of species turnover. Conservation Biology. DOI: 10.1111/cobi.13079](http://onlinelibrary.wiley.com/doi/10.1111/cobi.13079/abstract), which should be cited.
## Analysis methodology
We'll use non-metric multidimensional scaling (nMDS) to represent patterns in community structure across sites. Site dispersion will be visualized with a 2D ordination. Be sure to show the 'stress' statistic on the figure, this is critical for interpretation. The nMDS will be created twice using two distance measures: Euclidean distance and Bray-Curtis distance.
We'll test for relationships among community structure and distance to logging using the `envfit()` algorithm from the `vegan` package.
## Tech context
- We will use the R program
- tidyverse packages for data manipulation
- vegan package for community analysis
- ggplot2 for data visualization
Keep your scripts short and modular to facilitate debugging. Don't complete all of the steps below in one script. Finish scripts where it makes sense and save intermediate datasets.
## Steps
As you go tick of the steps below.
[ ] Import the datasets
[ ] Wrangle data to make a combined dataset
[ ] Calculate dissimilarity matrices based on benthic cover composition. Here we will use Euclidean and Bray-Curtis distances
[ ] Perform nMDS analysis to visualize site dispersion in ordination space
[ ] Identify ecological patterns and potential groupings of sites
[ ] Assess the stress value to determine the reliability of the nMDS representation
[ ] Explore how ecological communities relate to distance to logging
[ ] Use your vision capabilities to interpret figures
[ ] Write an Rmd report summarizing the findings
As you go, document the methodology. Be sure to output both figures (as png files) and numbers that can be used in the final report.
## Meta data
The datasets are available at the URLs below
### benthic_cover.csv
[Benthic cover survey data in long format](https://raw.githubusercontent.com/cbrown5/example-ecological-data/refs/heads/main/data/benthic-reefs-and-fish/benthic_cover.csv)
Variables
- site: Unique site IDs
- trans: transect numbers, there are multiple transects per site
- code: benthic organism code
- cover: Number of points belonging to this habitat type
- n.pts: Total number of points sampled on the transect, used to normalize `Cover` to get per cent cover.
## benthic_variables.csv
[Database linking benthic codes to full names](https://raw.githubusercontent.com/cbrown5/example-ecological-data/refs/heads/main/data/benthic-reefs-and-fish/benthic_variables.csv)
Variables
- code: benthic organism code, matches `Code` in benthic_cover
- category: Long format name of benthic organism
## fish-coral-cover-sites.csv
Variables
- site: Unique site IDs, use to join to benthic_cover.csv
- reef.ID: Unique reef ID
- pres.topa: number of Topa counted (local name for Bolbometopon)
- pres.habili: number of Habili counted (local name for Cheilinus)
- secchi: Horizontal secchi depth (m), higher values mean the water is less turbid
- flow: Factor indicating if tidal flow was "Strong" or "Mild" at the site
- logged: Factor indicating if the site was in a region with logging "Logged" or without logging "Not logged"
- coordx: X coordinate in UTM zone 57S
- coordy: Y coordinate in UTM zone 57S
- cb_cover: Number of PIT points for branching coral cover
- soft_cover: Number of PIT points for soft coral cover
- n_pts: Number of PIT points at this site (for normalizing cover to get per cent cover)
- dist_to_logging_km: Linear distance to nearest log pond (site where logging occurs) in kilometres. Below we provide a second example of a readme.md file, this time for experimentally collected data from (King et al. 2022).
# Analysing of effects of interacting stressors on algal growth
## Introduction
We'll analyse an experimental dataset to understand interactions among multiple stressors and how they affect algal density over time.
## Aims
1. Quantify the interactive effects of light and diuron on algal growth.
## Data methodology
This datasets is from an experiment where algal grown in replicate containers were exposed to multiple stressors of varying light and diuron (herbicide exposure). The number of algal cells and a measure of photosynthetic capacity were measured at several time intervals. The dataset thus contains replicate measurements over time (longitudinal data)
as well as the potential to look at effects of interacting pressures on two response variables.
There are 4 blocks, one with missing data for algal cell density. In each block there is a crossed design between multiple light (3 levels) and diuron levels (5 levels). Measurements of cell density are repeated at intervals over 72 hours.
This data were published in this study:
[King OC, van de Merwe JP, Campbell MD, Smith RA, Warne MSJ, Brown CJ. 2022 Interactions among multiple stressors vary with exposure duration and biological response. Proc. R. Soc. B 289: 20220348. https://doi.org/10.1098/rspb.2022.0348](https://doi.org/10.1098/rspb.2022.0348)
## Analysis methodology
Build a GAM of log(cell density) over time (hours). Use this GAM formula:
`ln_cell_density ~ s(block, bs = "re") + s(hours, by = diuron) + s(hours, by = light) + s(diuron, light)`
Use a Gaussian family and include an offset for initial log cell density.
We will do a thorough verification of all GAM assumptions.
## Tech context
- We will use the R program
- tidyverse packages for data manipulation
- mgcv package for statistical analysis
- ggplot2 for data visualization
Keep your scripts short and modular to facilitate debugging. Don't complete all of the steps below in one script. Finish scripts where it makes sense and save intermediate datasets.
## Steps
As you go tick of the steps below.
[ ] Import the datasets
[ ] Wrangle data to make sure all categorical variables are factors and we have all neccessary variables
[ ] Fit the GAM
[ ] Do GAM verification of residuals and concurvity
[ ] Plot effects with CIs using visreg
[ ] Write an Rmd report summarizing the findings
As you go, document the methodology. Be sure to output both figures (as png files) and numbers that can be used in the final report.
## Meta data
### diuron_data.csv
[Contains all experimental raw data (i.e. four replicates) for the responses of diuron and light exposure on algal growth and photosynthetic activity. Block 1 is missing cell density data.](https://raw.githubusercontent.com/cbrown5/example-ecological-data/refs/heads/main/data/algal-stressors/diuron_data.csv)
Variables:
- hours: Measurement time in hours (0.33, 2, 24, 48, 72)
- Light_num: Light level in micromoles of photons per square meter per second (5, 20, 80)
- Diuron_num: Amount of Diuron (herbicide) in ug per liter (0, 0.11, 0.33, 1, 3)
- block: Experimental block (1-4), representing independent algae cultures
- sample_id_celld: Unique identifier for cell density samples, combining treatment and block
- t0_celld: Initial cell density at time zero
- celld: Cell density at the specified measurement time
- sample_id_Yield: Unique identifier for photosynthetic yield samples
- t0_Yield: Initial photosynthetic yield (Y(II)) at time zero
- Yield: Photosynthetic yield, calculated as (Fm - F)/Fm, at the specified measurement time