Analysis of relative abundances with zeros on environmental gradients: a multinomial regression model

Negative and positive interspecific interactions involving jellyfish polyps in marine sessile communities

Sessile marine invertebrates on hard substrates are one of the two canonical examples of communities structured by competition, but some aspects of their dynamics remain poorly understood. Jellyfish polyps are an important but under-studied component of these communities. We determined how jellyfish polyps interact with their potential competitors in sessile marine hard-substrate communities, using a combination of experiments and modelling. We carried out an experimental study of the interaction between polyps of the moon jellyfish Aurelia aurita and potential competitors on settlement panels, in which we determined the effects of reduction in relative abundance of either A. aurita or potential competitors at two depths. We predicted that removal of potential competitors would result in a relative increase in A. aurita that would not depend on depth, and that removal of A. aurita would result in a relative increase in potential competitors that would be stronger at shallower depths, where oxygen is less likely to be limiting. Removal of potential competitors resulted in a relative increase in A. aurita at both depths, as predicted. Unexpectedly, removal of A. aurita resulted in a relative decrease in potential competitors at both depths. We investigated a range of models of competition for space, of which the most successful involved enhanced overgrowth of A. aurita by potential competitors, but none of these models was completely able to reproduce the observed pattern. Our results suggest that interspecific interactions in this canonical example of a competitive system are more complex than is generally believed.

Devil in the details: how can we avoid potential pitfalls of CATS regression when our data do not follow a Poisson distribution?

BACKGROUND

Community assembly by trait selection (CATS) allows for the detection of environmental filtering and estimation of the relative role of local and regional (meta-community-level) effects on community composition from trait and abundance data without using environmental data. It has been shown that Poisson regression of abundances against trait data results in the same parameter estimates. Abundance data do not necessarily follow a Poisson distribution, and in these cases, other generalized linear models should be fitted to obtain unbiased parameter estimates.

AIMS

This paper discusses how the original algorithm for calculating the relative role of local and regional effects has to be modified if Poisson model is not appropriate.

RESULTS

It can be shown that the use of the logarithm of regional relative abundances as an offset is appropriate only if a log-link function is applied. Otherwise, the link function should be applied to the product of local total abundance and regional relative abundances. Since this product may be outside the domain of the link function, the use of log-link is recommended, even if it is not the canonical link. An algorithm is also suggested for calculating the offset when data are zero-inflated. The relative role of local and regional effects is measured by Kullback-Leibler R2. The formula for this measure presented by Shipley (2014) is valid only if the abundances follow a Poisson distribution. Otherwise, slightly different formulas have to be applied. Beyond theoretical considerations, the proposed refinements are illustrated by numerical examples. CATS regression could be a useful tool for community ecologists, but it has to be slightly modified when abundance data do not follow a Poisson distribution. This paper gives detailed instructions on the necessary refinement.

Seasonal environmental variability drives microdiversity within a coastal Synechococcus population

Marine microbes often show a high degree of physiological or ecological diversity below the species level. This microdiversity raises questions about the processes that drive diversification and permit coexistence of diverse yet closely related marine microbes, especially given the theoretical efficiency of competitive exclusion. Here, we provide insight with an 8‐year time series of diversity within Synechococcus, a widespread and important marine picophytoplankter. The population of Synechococcus on the Northeast U.S. Shelf is comprised of six main types, each of which displays a distinct and consistent seasonal pattern. With compositional data analysis, we show that these patterns can be reproduced with a simple model that couples differential responses to temperature and light with the seasonal cycle of the physical environment. These observations support the hypothesis that temporal variability in environmental factors can maintain microdiversity in marine microbial populations. We also identify how seasonal diversity patterns directly determine overarching Synechococcus population abundance features.

Model-based joint visualization of multiple compositional omics datasets

The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.

Forecasting intensifying disturbance effects on coral reefs

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal.