1. Set up your data frame. This will involve creating a data frame with columns for each of the variables you have listed. The first column should be for the creek, followed by the treatment, followed by the fish, insects, shellfish, and benthos columns. Each row should represent a single creek, with the associated information for each of the other variables.

2. Calculate the Shannon Diversity Index for each of the variables. To do this, you can use the diversity() function in the vegan package in R.

3. Calculate the abundance and richness for each of the variables. To do this, you can use the abundance() and richness() functions in the vegan package in R.

4. Run a series of ANOVA tests to determine if there are significant differences in the Shannon Diversity Index, abundance, or richness between the different treatments for each of the variables.

5. Visualize the results. You can use the ggplot2 package in R to create a series of bar graphs or box plots to visualize the results of the ANOVA tests.

6. Interpret the results. Based on the results of the ANOVA tests and the visualizations, you can determine if there are significant differences in the Shannon Diversity Index, abundance, or richness between the different treatments for each of the variables.

Thank you Peter Donkor !

On this site https://www.dataanalytics.org.uk/comparing-diversity/

I read that I should use a Hutcheson t-test to compare the results. Is this also available in R?

Yes, the Hutcheson t-test is available in R. You can use the t.test() function in the native stats package to perform the test.

Hi Peter Donkor

I managed to load the vegan package and run the diversity() function. The value I get corresponds beautifully with the excel calculator on the dataanalytics page I mentioned before. However, the abundance() and richness() functions are not recognized in R. I get "could not find function richness" as error message... What could be going wrong?

It sounds like the vegan package is not properly installed. You can try reinstalling the package and see if that helps. If not, you may need to check for any missing dependencies that could be causing the issue. Additionally, you can check the vegan manual for troubleshooting tips.

It will take too long to learn R and by that time it's too late. You should use a simpler program like SPSS or Systat :)

Tamas:

With only 3, 9 or 18 samples per treatment (for each of the four assemblages you have data on), there is no chance at all that you can find either richness or the Shannon metric -- unless the system you are studying is very depauperate indeed.

Richness is not the count of species in your samples but the asymptote of a species-accumulation curve and you are most unlikely to get anywhere near that with so little data.

Software that purports to give you a Shannon value will (most probably) apply the equation for an infinitely-large dataset to your finite data. That is a very common mistake to make but a mistake nonetheless. Determining the correct value for Shannon is a complex, multi-step process and rarely attempted, while it probably needs about as many samples as are required to determine richness.

If you could find the true value of Shannon for each of your treatments (or if you decided to use the erroneous approximation), then you should certainly transform it to Exponential Shannon before proceeding to further analysis, as the relationship between community structure and the Shannon value is severely non-linear, leading to major errors of interpretation.

My recommendation, however, would be to forget all such measures of diversity, which tell you little about an ecosystem even if you can quantify them. You seem to have a replicated experiment (3 creeks per treatment). To me, that calls out for analysis by ANOVA, along with exploration through multivariate statistics (some sort of ordination to begin with, perhaps) and maybe building to MANOVA, if your data can support that.

Trevor John Kenchington

Thank you for this answer!

So far I have been playing around with my data and done the following:

1. Boxplots with count data (for instance Insect counts per treatment)

2. Boxplots with counted number of species per treatment (for instance nr Insect species per treatment)

3. Scatterplots to find correlations between the different types of populations, either by count or by nr species found (no correlations what so ever...)

4. ANOVA to find wether treatment influences either nr of species found or count data per type (ie insects, fish, shellfish or benthos).

Only Benthos "species richness' was significant with a p- value of 0.04.

A post hoc Tukey however did not show significance in any of the pairwise comparisons. (Intensive-Extensive, Intensive-Ungrazed, Extensive-Ungrazed).

5. Then I indeed used Shannon... Which I found very exciting, because there seemed to be some big differences. To calculate the Shannon index I used the VEGAN package in R and an excell sheet with Shannon and Hutcheson t-test calculator. Surprisingly both gave exactly the same values for the Shannon index.

Then I tried the Shannon and Hutcheson calculations with the ecolTest package in R. This gave me different values for the Shannon index... This was dissapointing and something I can not explain.

Basically you are saying I can not use these data in any meaningfull way... That is a bummer...

I will follow up your last comments and dive into what multivariate statistics and MANOVA entail.

I have added my result sheets as an attachment to this comment. If you are bored I would appreciate it if you would take a peek.

I also want to mention that this assignment is for a research project as part of my Masters in marine biology. The part I am working on here is just a small sample of a 4-5 year project and not intended for publication.

Tamas,

I certainly did not mean to say that you cannot use your data in any meaningful way! What I was trying to say was that diversity metrics are not very useful.

A bit of history: Back in the 1930s and '40s, ecologists were trying to raise their discipline from mere natural history into a quantitative science. They had very limited computational capabilities but they hoped for a dozen or so simple metrics that they might calculate by hand, then compare across ecosystems. Maybe they could then repeat, for ecology, what Copernicus, Kepler and Newton had achieved for physics, based on endless measurements of the movements of the planets.

That dream died but it led to the idea that species-abundance data could be summarized through diversity indices. Those then turned out to be way more complicated to apply than anyone had expected and they were not very informative. Worse: They were very inefficient. To prepare a dataset of species abundances, you have to identify many, many specimens to their species. Yet diversity metrics entirely ignore the expensively-gathered species IDs. (You could re-name them "Species A", "Species B" etc. and which is A and which B can be different in each collection.) That is a terrible waste of expensively gathered information.

The last great authority on diversity metrics was Chris Pielou, who wrote a series of textbooks in the 1960s and 70s (which everyone should read before applying any diversity metric -- though almost nobody does any more). She then abandoned that approach and her textbook of the 1980s was on the use in quantitative ecology of packaged software for multivariate statistics -- far more powerful tools which had become available after 1975. Importantly, those tools (and their later developments) allow you to keep the information about which species is which, which massively increases the value of the data you have already collected.

Unfortunately, by 1980, a whole lot of students had been taught the basics of diversity measurements, without the detail. (I was one of them!) When the leading quantitative ecologists moved on to better things, a lot of us were left without much guidance and too many continued to naively apply the half-basked understanding that we had been given. Now, half a century later, your generation is attempting to use the techniques of the 1960s, when there have long been far, far more informative ways of analyzing the same data needed to estimate a Shannon value or a richness!

Prepare your data as matrices of species abundances (species X samples), one for each of your sampling techniques, then ordinate -- maybe with Multi-Dimensional Scaling, but there are many options to choose from. Look for patterns in the numbers and go from there. Go as far as the data will let you but remember that your are not engaged in abstract number-crunching and be guided by the structures of the ecosystems you are studying.

If there is anything to find, and there likely is something, then you will find it. And if there isn't you have your answer: Your three treatments would then be shown to have no detected effect on the species you have sampled.

Trevor

To compare the diversity of aquatic organisms in 9 creeks with 3 different grazing regimes, you can use a variety of techniques and methods. Here are some steps you can follow to compare the diversity of these creeks:

By following these steps, you can compare the diversity of aquatic organisms in the 9 creeks with 3 different grazing regimes, and identify any patterns or trends that may be influenced by the grazing regimes. It is important to consider the limitations andIt is important to consider the limitations and potential biases of the sampling and analysis methods when comparing the diversity of aquatic organisms in the 9 creeks. It is also important to consider other potential factors that may influence the diversity of the creeks, such as water quality, habitat characteristics, or the presence of other species. By carefully controlling for these factors and using appropriate statistical techniques, you can better understand the effects of the grazing regimes on the diversity of the creeks.

It is also important to consider the potential ecological and practical implications of the results of the study. Understanding the effects of grazing regimes on the diversity of aquatic organisms in creeks can provide valuable information for resource management and conservation efforts, and can help to inform decision-making about the management of these habitats.

Trevor John Kenchington

I am overwhelmed by your answers. Very much appreciated that you took the time to explain this to me.

@Ahmad Al Khraisat,

All of the sampling and determination (down to family level) has already been done. I am now in the phase of statistical analysis. On december 22nd I described what I have done so far, and the Shannon Index calculations are in there. The question now, is wether to use Shannon Indexes as one of the parameters to illustrate differences between treatments or not.

Here are the general steps you can follow to accomplish your analysis in R:

install.packages("vegan") library(vegan)

shannon_index = diversity(your_data, index = "shannon")

richness = richness(your_data)

abundance = apply(your_data, 1, sum)

I hope this helps! Let me know if you have any questions or need more clarification on any of the steps.

Ahmad Al Khraisat

Thank you very much for this! So far I managed to calculate the Shannon Index for each group and I used a t-test calculator in excel that I found on the internet. Both R and the calculator give me the same Shannon Index, so I have confidence in the t-test calculator.

I am using R-cloud (Posit-cloud as it is now formally called) for the rest of my statistical analysis.

Abundance and richness in Posit-cloud does for some reason not work with the Vegan package. It is a mystery to me and the helpdesk could not help either.

Unfortunately I can not install R on my laptop, because it is borrowed from my employer, so for now I have to skip these steps.

At the moment I am following part of Trevors advice in his earlier message to use EXP Shannon for the comparisons. This way I will be able to display the "effective number of species" and make a direct comparison between treatments, and also be able to show the magnitude of the differences.

Tamas Fulep It's good to hear that you've been able to calculate the Shannon Index and that you are using the t-test calculator in Excel to confirm your results. It's also good to hear that you are using Posit-cloud (formerly known as R-cloud) for your statistical analysis.

If the Vegan package is not working in Posit-cloud for calculating abundance and richness, you may want to consider using a different package or tool for these calculations. There are several other packages and tools that you can use for calculating these measures, such as the "vegan" and "ecodist" packages in R, or the "EstimateS" software.

As for using the EXP Shannon index for comparisons, this is a good approach. The EXP Shannon index can be used to estimate the "effective number of species" in a community, which can be useful for comparing the diversity of different communities. By comparing the EXP Shannon index for different treatments, you can get a sense of the magnitude of the differences in diversity between the treatments.

I hope this information is helpful! Let me know if you have any further questions or if you need more guidance with your analysis.

Ahmad Al Khraisat

Thank you again for your advise!

I will look into the other options for abundance and richness.