This dataset consists of 2 zip files and 2 README.txt files:
(1) Species-by-site abundance matrices - this zip file contains the coral and fish species-by-sites matrices of counts analyzed in the linked publication in Ecology Letters.
(2) R code to fit the unified model - this ZIP file contains 5 files needed to fit the unified model to the data in the CSV files (or similar data, formatted in the same way), to conduct parametric bootstrap goodness-of-fit testing, and to generate out-of-sample predictions of unshared species.
README.txt files as follows:
The ZIP file Abunds.zip contains the Indo-Pacific coral and fish count data analyzed in the associated publication.
Each of the 30 CSV files contains the data for one metacommunity. The file name indicates the taxon (corals or fishes), the habitat (crest, flat, or slope), and the region (note FP=French Polynesia, and PNG = Papua New Guinea).
Within each CSV file, rows are species, and columns are sites. The files have a column heading. Values in the data matrix are counts. For corals, they are the number of distinct intercepts observed at the site for each species. For fishes, they are the number of individual fishes observed. Note that the counts of large fishes were made on wider transects than the counts of small fishes, so the fish counted on the wide transects have been subsampled to represent equivalent sampling effort. The field methods, and the subsampling algorithm, are described briefly in the linked publication, and in more detail in Connolly et al. (2005).
Connolly, S. R., T. P. Hughes, D. R. Bellwood, and R. H. Karlson. 2005. Community structure of corals and reef fishes at multiple scales. Science 309: 1363-1365.
The ZIP file Code.zip contains the example code for fitting the unified model to a species x sites abundance matrix (formatted in the same way as the CSV files in Abunds.zip), conducting parametric bootstrap goodness of fit testing, estimating metacommunity species pool size, and conducting the out-of-sample prediction test.
There are 5 files:
FitUnifiedModel.R: Reads in a CSV file of count data, and fits the unified model, as well as the alternative model where there is a gamma metacommunity species-abundance distribution instead of a lognormal SAD. This script includes estimation of metacommunity species pool richness.
GOFtest_UnifiedModel.R: Conducts parametric bootstrapping of the unified model for the data analyzed and the fitted model produced by FitUnifiedModel.R
OutOfSamplePrediction_UnifiedModel.R: Calculates the predicted number of species for the out-of-sample prediction test shown in Fig. 4. Specifically, for each site in the metacommunity CSV file, it fits the unified model to all of the other sites, and predicts the number of species that are observed in at least one of the fitted sites that would NOT be observed at the site that has been excluded from the fitting.
NBLNPL_LLfxn.R: Functions needed to calculate the log-likelihood for the unified model.
NBGPL_LLfxn.R: Functions needed to calculate the log-likelihood for the alternative model with a gamma metacommunity species-abundance distribution.
Abstract [Related Publication]: Abundance patterns in ecological communities have important implications for biodiversity maintenance and ecosystem functioning. However, ecological theory has been largely unsuccessful at capturing multiple macroecological abundance patterns simultaneously. Here, we propose a parsimonious model that unifies widespread ecological relationships involving local aggregation, species-abundance distributions, and species associations, and we test this model against the metacommunity structure of reef-building corals and coral reef fishes across the western and central Pacific. For both corals and fishes, the unified model simultaneously captures extremely well local species-abundance distributions, interspecific variation in the strength of spatial aggregation, patterns of community similarity, species accumulation, and regional species richness, performing far better than alternative models also examined here and in previous work on coral reefs. Our approach contributes to the development of synthetic theory for large-scale patterns of community structure in nature, and to addressing ongoing challenges in biodiversity conservation at macroecological scales.