Diversity is a concept central to ecology, and its measurement is essential for any study of ecosystem health. But summarizing this complex and multidimensional concept in a single measure is problematic. Dozens of mathematical indices have been proposed for this purpose, but these can provide contradictory results leading to misleading or incorrect conclusions about a community's diversity. In this review, we summarize the key conceptual issues underlying the measurement of ecological diversity, survey the indices most commonly used in ecology, and discuss their relative suitability. We advocate for indices that: (i) satisfy key mathematical axioms; (ii) can be expressed as so-called effective numbers; (iii) can be extended to account for disparity between types; (iv) can be parameterized to obtain diversity profiles; and (v) for which an estimator (preferably unbiased) can be found so that the index is useful for practical applications.

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The critical role that biodiversity plays in ecosystem functioning has motivated many studies of the mechanisms that sustain biodiversity, a notable example being cyclic competition. We extend existing models of communities with cyclic competition by incorporating variable community evenness and resource dependence in demographic processes, two features that have generally been neglected. In this way, we align previous approaches more closely with real-world microbial ecosystems. We demonstrate the existence of a trade-off between increasing biomass production and maintaining biodiversity. This supports experimental observations of a net negative biodiversity effect on biomass productivity, due to competition effects suffered by highly productive species in diverse communities. Our results also support the important role assigned by microbial ecologists to evenness in maintaining ecosystem stability, thus far largely overlooked in in silico approaches.

Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is timeconsuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.

Bioaugmentation often involves an invasion process requiring the establishment and activity of a foreign microbe in the resident community of the target environment. Interactions with resident micro-organisms, either antagonistic or cooperative, are believed to impact invasion. However, few studies have examined the variability of interactions between an invader and resident species of its target environment, and none of them considered a bioremediation context. Aminobacter sp. MSH1 mineralizing the groundwater micropollutant 2,6-dichlorobenzamide (BAM), is proposed for bioaugmentation of sand filters used in drinking water production to avert BAM contamination. We examined the nature of the interactions between MSH1 and 13 sand filter resident bacteria in dual and triple species assemblies in sand microcosms. The residents affected MSH1-mediated BAM mineralization without always impacting MSH1 cell densities, indicating effects on cell physiology rather than on cell number. Exploitative competition explained most of the effects (70%), but indications of interference competition were also found. Two residents improved BAM mineralization in dual species assemblies, apparently in a mutual cooperation, and overruled negative effects by others in triple species systems. The results suggest that sand filter communities contain species that increase MSH1 fitness. This opens doors for assisting bioaugmentation through co-inoculation with “helper” bacteria originating from and adapted to the target environment.

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