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RAMAS - Community Software
A free web app for characterizing temporal change in ecological communities. This web application implements the methods for measuring temporal change in communities described in Spencer (2015)(1). The paper introduces a measure of change in relative abundances (the “shape” of a community) that complements measures of mean proportional change in abundance (the “size” of a community) such as the Living Planet Index (2).
The application also implements a wide range of other measures of change in relative abundance, reviewed in Spencer (2015)(1), and offers interactive visualization of results. Uncertainty in abundance data can be handled through interval arithmetic, approximate confidence intervals based on simple sampling models, or through user-supplied Monte Carlo estimates.
An R package implementation is also available on GitHub
Spencer (2015) takes an axiomatic approach to choosing a measure of change in relative abundances. The most important property is that proportional growth rates contain all the relevant information about habitat quality from an organism`s point of view. This leads to the recommendation that the among-species standard deviation of proportional population growth rates be used to measure change in relative abundances. The “shape” change measure developed in Spencer (2015)1 follows this recommendation. It complements measures based on the among-species mean of proportional population growth rates, such as the Living Planet Index (Loh et al. 2005, link).
One obvious challenge with measures of proportional change is that colonization and extinction events, which undoubtedly occur, represent larger changes than any change in abundance that does not involve colonization or extinction. The application uses Surreal Arithmetic to describe colonization and extinction events in a way that satisfies this requirement.
Other measures of change in relative abundance may also be useful, such as those developed by Foster and Tilman (2000)3, Jassby and Goldman (1974)4, Lewis (1978)6, and measures based on Bray-Curtis (Field et al., 1982)5 or chi-square (Legendre and Gallagher, 2001)7 distances. The application implements all of these.
Users can upload data as plain text or .Rdata files. The data for each species can be scalar-valued (point estimates), interval-valued (lower and upper bounds on abundance), or a set of Monte Carlo samples (e.g. from a Bayesian population model).
Uploading vs. Committing
The first tab users will see is theDatatab on the right. This is where they can upload their own datasets for personal use, or select datasets uploaded by other users. If they wish, users can commit their datasets to the public database. This `Commit` option will upload the dataset permanently, allowing other users to use the dataset on the application.
Uploading
Under the upload tab, users can select a file to upload, this file can be in the form of a text/csv file, R data frame, or R matrix/array. datasets can be scalar (single point values over time), interval (left and right endpoints), or Monte Carlo simulation results.
Interval datasets
Interval datasets should take the form of a 3D array, consisting of two tables such as the one shown above. The first table should contain the left endpoints of the dataset, and the second should contain the right endpoints of the dataset.
Monte Carlo Simulations
As with interval datasets, Monte Carlo simulation results should take the form of a 3D array, consisting ofntables, where each table is an individual replicate.
Once the data has been uploaded, users can select which variable denotes time, and which (if any) denote comparison variables. Users can then choose to `Commit` the dataset, in which case they must fill in the required fields (such as title, description, and location). They can also choose to use the dataset privately, in which case they should only press the `Upload` button. Upon uploading, users should see a preview of the dataset in a table at the bottom of the page.
Committing
If users wish, they can upload data to the public database, allowing other users access to their data. By default, datasets are shared under the Creative Commons CC BY license with attribution. This means that anyone using your datset must give you credit as the source. More details on the license can be found here. If you wish, you can select a different license to share the data under. This should be specified under the License field when committing the data.
If you accidentally commit your dataset, you can delete it from the database using the `Undo Commit` botton by specifying the title of the dataset in the `Title` field.