Estimating diversity in networked ecological communities

Comparing ecological communities across environmental gradients can be challenging, especially when the number of different taxonomic groups in the communities is large. In this setting, community-level summaries called diversity indices are widely used to detect changes in the community ecology. However, estimation of diversity indices has received relatively little attention from the statistical community. The most common estimates of diversity are the maximum likelihood estimates of the parameters of a multinomial model, even though the multinomial model implies strict assumptions about the sampling mechanism. In particular, the multinomial model prohibits ecological networks, where taxa positively and negatively co-occur. In this article, we leverage models from the compositional data literature that explicitly account for co-occurrence networks and use them to estimate diversity. Instead of proposing new diversity indices, we estimate popular diversity indices under these models. While the methodology is general, we illustrate the approach for the estimation of the Shannon, Simpson, Bray-Curtis, and Euclidean diversity indices. We contrast our method to multinomial, low-rank, and nonparametric methods for estimating diversity indices. Under simulation, we find that the greatest gains of the method are in strongly networked communities with many taxa. Therefore, to illustrate the method, we analyze the microbiome of seafloor basalts based on a 16S amplicon sequencing dataset with 1425 taxa and 12 communities.

Automated summary

Comparing ecological communities across environmental gradients is challenging, especially when there are many different taxonomic groups. Traditional diversity estimation methods, such as maximum likelihood estimates of the parameters of a multinomial model, have strict assumptions and do not account for ecological networks. In this article, the authors leverage models from the compositional data literature to estimate diversity indices, such as Shannon, Simpson, Bray-Curtis, and Euclidean. They find that their method performs best in strongly networked communities with many taxa, as shown in a case study on the microbiome of seafloor basalts.