Experimental species removal alters ecological dynamics in a natural ecosystem

Strain dropouts reveal interactions that govern the metabolic output of the gut microbiome

The gut microbiome is complex, raising questions about the role of individual strains in the community. Here, we address this question by constructing variants of a complex defined community in which we eliminate strains that occupy the bile acid 7α-dehydroxylation niche. Omitting Clostridium scindens (Cs) and Clostridium hylemonae (Ch) eliminates secondary bile acid production and reshapes the community in a highly specific manner: eight strains change in relative abundance by >100-fold. In single-strain dropout communities, Cs and Ch reach the same relative abundance and dehydroxylate bile acids to a similar extent. However, Clostridium sporogenes increases >1,000-fold in the ΔCs but not ΔCh dropout, reshaping the pool of microbiome-derived phenylalanine metabolites. Thus, strains that are functionally redundant within a niche can have widely varying impacts outside the niche, and a strain swap can ripple through the community in an unpredictable manner, resulting in a large impact on an unrelated community-level phenotype.

Forecasting the Cumulative Effects of Multiple Stressors on Breeding Habitat for a Steeply Declining Aerial Insectivorous Songbird, the Olive-sided Flycatcher (Contopus cooperi)

To halt ongoing loss in biodiversity, there is a need for landscape-level management recommendations that address cumulative impacts of anthropogenic and natural disturbances on wildlife habitat. We examined the cumulative effects of logging, roads, land-use change, fire, and bark beetle outbreaks on future habitat for olive-sided flycatcher (Contopus cooperi), a steeply declining aerial insectivorous songbird, in Canada’s western boreal forest. To predict the occurrence of olive-sided flycatcher we developed a suite of habitat suitability models using point count surveys (1997–2011) spatially- and temporally-matched with forest inventory data. Flycatcher occurrence was positively associated with small (∼10 ha) 10- to 20-year-old clearcuts, and with 10–100% tree mortality due to mountain pine beetle (Dendroctonus ponderosae) outbreaks, but we found no association with roads or distance to water. We used the parameter estimates from the best-fit habitat suitability models to inform spatially explicit state-and-transition simulation models to project change in habitat availability from 2020 to 2050 under six alternative scenarios (three management × two fire alternatives). The simulation models projected that the cumulative effects of land use conversion, forest harvesting, and fire will reduce the area of olive-sided flycatcher habitat by 16–18% under Business As Usual management scenarios and by 11–13% under scenarios that include protection of 30% of the land base. Scenarios limiting the size of all clearcuts to ≤10 ha resulted in a median habitat loss of 4–6%, but projections were highly variable. Under all three management alternatives, a 50% increase in fire frequency (expected due to climate change) exacerbated habitat loss. The projected losses of habitat in western boreal forest, even with an increase in protected areas, imply that reversing the ongoing population declines of olive-sided flycatcher and other migratory birds will require attention to forest management beyond protected areas. Further work should examine the effects of multiple stressors on the demographic mechanisms driving change in aerial insectivore populations, including stressors on the wintering grounds in South America, and should aim to adapt the design of protected areas and forest management policies to projected climate-driven increases in the size and frequency of wildfires.

Decreased Ecological Resistance of the Gut Microbiota in Response to Clindamycin Challenge in Mice Colonized with the Fungus Candida albicans

The mammalian gut microbiota is a complex community of microorganisms which typically exhibits remarkable stability. As the gut microbiota has been shown to affect many aspects of host health, the molecular keys to developing and maintaining a "healthy" gut microbiota are highly sought after. Yet, the qualities that define a microbiota as healthy remain elusive. We used the ability to resist change in response to antibiotic disruption, a quality we refer to as ecological resistance, as a metric for the health of the bacterial microbiota. Using a mouse model, we found that colonization with the commensal fungus Candida albicans decreased the ecological resistance of the bacterial microbiota in response to the antibiotic clindamycin such that increased microbiota disruption was observed in C. albicans-colonized mice compared to that in uncolonized mice. C. albicans colonization resulted in decreased alpha diversity and small changes in abundance of bacterial genera prior to clindamycin challenge. Strikingly, co-occurrence network analysis demonstrated that C. albicans colonization resulted in sweeping changes to the co-occurrence network structure, including decreased modularity and centrality and increased density. Thus, C. albicans colonization resulted in changes to the bacterial microbiota community and reduced its ecological resistance.IMPORTANCECandida albicans is the most common fungal member of the human gut microbiota, yet its ability to interact with and affect the bacterial gut microbiota is largely uncharacterized. Previous reports showed limited changes in microbiota composition as defined by bacterial species abundance as a consequence of C. albicans colonization. We also observed only a few bacterial genera that were significantly altered in abundance in C. albicans-colonized mice; however, C. albicans colonization significantly changed the structure of the bacterial microbiota co-occurrence network. Additionally, C. albicans colonization changed the response of the bacterial microbiota ecosystem to a clinically relevant perturbation, challenge with the antibiotic clindamycin.

Modelling microbiome recovery after antibiotics using a stability landscape framework

Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome's diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a 'stability landscape': the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.

Reconciling empirical interactions and species coexistence

Coexistence in ecological communities is governed largely by the nature and intensity of species interactions. Countless studies have proposed methods to infer these interactions from empirical data, yet models parameterised using such data often fail to recover observed coexistence patterns. Here, we propose a method to reconcile empirical parameterisations of community dynamics with species-abundance data, ensuring that the predicted equilibrium is consistent with the observed abundance distribution. To illustrate the approach, we explore two case studies: an experimental freshwater algal community and a long-term time series of displacement in an intertidal community. We demonstrate how our method helps recover observed coexistence patterns, capture the core dynamics of the system, and, in the latter case, predict the impacts of experimental extinctions. Collectively, these results demonstrate an intuitive approach for reconciling observed and empirical data, improving our ability to explore the links between species interactions and coexistence in natural systems.

Ecological Network Inference From Long-Term Presence-Absence Data

Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.

Viewing the effects of species loss in complex ecological networks

Species loss is becoming a major threat to ecosystems. An urgent task in ecology is to predict the consequence of species loss which requires an extending of our traditional study of the topology of network structure to the population dynamic analyses in complex food webs. Here, via numerical simulations of the model combining structural networks with nonlinear bioenergetic models of population dynamics, we analyzed the secondary effects of species removal on biomass distribution and population stability, as well as the factors influencing these effects. We found that the biomass of target species, the nutrient supply, and the trophic level of target species were the three most significant determiners for the effects of species loss. Species loss had large negative effect on the biomass of the species with small biomass or intermediate trophic levels, especially in infertile environment. The population stability of the species with large biomass or low trophic level is easily to be influenced especially in nutrient-rich environment. Our findings indicate the species which are easily to be affected by species loss in food webs, which may help ecologists to outline a better conservation policy.

Complex population dynamics in mussels arising from density-linked stochasticity

Population fluctuations are generally attributed to the deterministic consequences of strong non-linear interactions among organisms, or the effects of random stochastic environmental variation superimposed upon the deterministic skeleton describing population change. Analysis of the population dynamics of the mussel Mytilus californianus taken in 16 plots over 18-years found no evidence that these processes explained observed strong fluctuations. Instead, population fluctuations arose because environmental stochasticity varied with abundance, which we term density-linked stochasticity. This phenomenon arises from biologically relevant mechanisms: recruitment variation and transmission of disturbance among neighboring individuals. Density-linked stochasticity is probably present frequently in populations, as it arises naturally from several general ecological processes, including stage structure variation with density, ontogenetic niche shifts, and local transmission of stochastic perturbations. More thoroughly characterizing and interpreting deviations from the mean behavior of a system will lead to better ecological prediction and improved insight into the important processes affecting populations and ecosystems.