Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics

The interplay of stochastic and ecological processes that govern the establishment and persistence of host-associated microbial communities is not well understood. Here we illustrate the conceptual and practical advantages of fitting stochastic population dynamics models to multi-species bacterial time series data. We show how the stability properties, fluctuation regimes and persistence probabilities of human vaginal microbial communities can be better understood by explicitly accommodating three sources of variability in ecological stochastic models of multi-species abundances: 1) stochastic biotic and abiotic forces, 2) ecological feedback and 3) sampling error. Rooting our modeling tool in stochastic population dynamics modeling theory was key to apply standardized measures of a community's reaction to environmental variation that ultimately depends on the nature and intensity of the intra-specific and inter-specific interaction strengths. Using estimates of model parameters, we developed a Risk Prediction Monitoring (RPM) tool that estimates temporal changes in persistence probabilities for any bacterial group of interest. This method mirrors approaches that are often used in conservation biology in which a measure of extinction risks is periodically updated with any change in a population or community. Additionally, we show how to use estimates of interaction strengths and persistence probabilities to formulate hypotheses regarding the molecular mechanisms and genetic composition that underpin different types of interactions. Instead of seeking a definition of "dysbiosis" we propose to translate concepts of theoretical ecology and conservation biology methods into practical approaches for the management of human-associated bacterial communities.

The structure and organisation of an Amazonian bird community remains little changed after nearly four decades in Manu National Park

Documenting patterns of spatiotemporal change in hyper-diverse communities remains a challenge for tropical ecology yet is increasingly urgent as some long-term studies have shown major declines in bird communities in undisturbed sites. In 1982, Terborgh et al. quantified the structure and organisation of the bird community in a 97-ha. plot in southeastern Peru. We revisited the same plot in 2018 using the same methodologies as the original study to evaluate community-wide changes. Contrary to longitudinal studies of other neotropical bird communities (Tiputini, Manaus, and Panama), we found little change in community structure and organisation, with increases in 5, decreases in 2 and no change in 7 foraging guilds. This apparent stability suggests that large forest reserves such as the Manu National Park, possibly due to regional topographical influences on precipitation, still provide the conditions for establishing refugia from at least some of the effects of global change on bird communities.

Assessing the Global and Local Uncertainty of Scientific Evidence in the Presence of Model Misspecification

Scientists need to compare the support for models based on observed phenomena. The main goal of the evidential paradigm is to quantify the strength of evidence in the data for a reference model relative to an alternative model. This is done via an evidence function, such as ΔSIC, an estimator of the sample size scaled difference of divergences between the generating mechanism and the competing models. To use evidence, either for decision making or as a guide to the accumulation of knowledge, an understanding of the uncertainty in the evidence is needed. This uncertainty is well characterized by the standard statistical theory of estimation. Unfortunately, the standard theory breaks down if the models are misspecified, as is commonly the case in scientific studies. We develop non-parametric bootstrap methodologies for estimating the sampling distribution of the evidence estimator under model misspecification. This sampling distribution allows us to determine how secure we are in our evidential statement. We characterize this uncertainty in the strength of evidence with two different types of confidence intervals, which we term “global” and “local.” We discuss how evidence uncertainty can be used to improve scientific inference and illustrate this with a reanalysis of the model identification problem in a prominent landscape ecology study using structural equations.

Ecological change points: The strength of density dependence and the loss of history

Change points in the dynamics of animal abundances have extensively been recorded in historical time series records. Little attention has been paid to the theoretical dynamic consequences of such change-points. Here we propose a change-point model of stochastic population dynamics. This investigation embodies a shift of attention from the problem of detecting when a change will occur, to another non-trivial puzzle: using ecological theory to understand and predict the post-breakpoint behavior of the population dynamics. The proposed model and the explicit expressions derived here predict and quantify how density dependence modulates the influence of the pre-breakpoint parameters into the post-breakpoint dynamics. Time series transitioning from one stationary distribution to another contain information about where the process was before the change-point, where is it heading and how long it will take to transition, and here this information is explicitly stated. Importantly, our results provide a direct connection of the strength of density dependence with theoretical properties of dynamic systems, such as the concept of resilience. Finally, we illustrate how to harness such information through maximum likelihood estimation for state-space models, and test the model robustness to widely different forms of compensatory dynamics. The model can be used to estimate important quantities in the theory and practice of population recovery.

Context-dependent landscape of fear: algal density elicits risky herbivory in a coral reef

Foraging theory posits that isolation from refuge habitat within a landscape increases perceived predation risk and, thus, suppresses the foraging behavior of prey species. However, these effects may depend fundamentally on resource availability, which could affect prey boldness and can change considerably through bottom-up processes. We conducted a field survey and experiment in a coral reef to test the effects of isolation from refuge habitat (i.e., reef structure) on herbivory by reef fishes and whether these effects depend on resource density. By fitting continuous-time, pure death Markov processes to our data, we found that at both the local and landscape scale distance from refuge habitat reduced herbivory in attractive resource patches of palatable benthic algae. However, our field experiment revealed that higher initial resource densities weakened negative effects of distance from refuge habitat on herbivory. Furthermore, we observed higher bite rates and greater total lengths of herbivorous fishes with greater distance from refuge habitat-responses consistent with higher perceived predation risk. Our results suggest that while the loss or fragmentation of refuge habitat reduces consumer control of resources, greater resource densities can partially counteract this effect by altering landscapes of fear of consumer species. Our findings emphasize the importance of considering the spatial context of species interactions that structure communities.

Evaluating the role of genome downsizing and size thresholds from genome size distributions in angiosperms

PREMISE OF THE STUDY

Whole-genome duplications (WGDs) can rapidly increase genome size in angiosperms. Yet their mean genome size is not correlated with ploidy. We compared three hypotheses to explain the constancy of genome size means across ploidies. The genome downsizing hypothesis suggests that genome size will decrease by a given percentage after a WGD. The genome size threshold hypothesis assumes that taxa with large genomes or large monoploid numbers will fail to undergo or survive WGDs. Finally, the genome downsizing and threshold hypothesis suggests that both genome downsizing and thresholds affect the relationship between genome size means and ploidy.

METHODS

We performed nonparametric bootstrap simulations to compare observed angiosperm genome size means among species or genera against simulated genome sizes under the three different hypotheses. We evaluated the hypotheses using a decision theory approach and estimated the expected percentage of genome downsizing.

KEY RESULTS

The threshold hypothesis improves the approximations between mean genome size and simulated genome size. At the species level, the genome downsizing with thresholds hypothesis best explains the genome size means with a 15% genome downsizing percentage. In the genus level simulations, the monoploid number threshold hypothesis best explains the data.

CONCLUSIONS

Thresholds of genome size and monoploid number added to genome downsizing at species level simulations explain the observed means of angiosperm genome sizes, and monoploid number is important for determining the genome size mean at the genus level.

An updated perspective on the role of environmental autocorrelation in animal populations

Ecological theory predicts that the presence of temporal autocorrelation in environments can considerably affect population extinction risk. However, empirical estimates of autocorrelation values in animal populations have not decoupled intrinsic growth and density feedback processes from environmental autocorrelation. In this study we first discuss how the autocorrelation present in environmental covariates can be reduced through nonlinear interactions or by interactions with multiple limiting resources. We then estimated the degree of environmental autocorrelation present in the Global Population Dynamics Database using a robust, model-based approach. Our empirical results indicate that time series of animal populations are affected by low levels of environmental autocorrelation, a result consistent with predictions from our theoretical models. Claims supporting the importance of autocorrelated environments have been largely based on indirect empirical measures and theoretical models seldom anchored in realistic assumptions. It is likely that a more nuanced understanding of the effects of autocorrelated environments is necessary to reconcile our conclusions with previous theory. We anticipate that our findings and other recent results will lead to improvements in understanding how to incorporate fluctuating environments into population risk assessments.

Evolutionary Paths That Expand Plasmid Host-Range: Implications for Spread of Antibiotic Resistance

The World Health Organization has declared the emergence of antibiotic resistance to be a global threat to human health. Broad-host-range plasmids have a key role in causing this health crisis because they transfer multiple resistance genes to a wide range of bacteria. To limit the spread of antibiotic resistance, we need to gain insight into the mechanisms by which the host range of plasmids evolves. Although initially unstable plasmids have been shown to improve their persistence through evolution of the plasmid, the host, or both, the means by which this occurs are poorly understood. Here, we sought to identify the underlying genetic basis of expanded plasmid host-range and increased persistence of an antibiotic resistance plasmid using a combined experimental-modeling approach that included whole-genome resequencing, molecular genetics and a plasmid population dynamics model. In nine of the ten previously evolved clones, changes in host and plasmid each slightly improved plasmid persistence, but their combination resulted in a much larger improvement, which indicated positive epistasis. The only genetic change in the plasmid was the acquisition of a transposable element from a plasmid native to the Pseudomonas host used in these studies. The analysis of genetic deletions showed that the critical genes on this transposon encode a putative toxin-antitoxin (TA) and a cointegrate resolution system. As evolved plasmids were able to persist longer in multiple naïve hosts, acquisition of this transposon also expanded the plasmid's host range, which has important implications for the spread of antibiotic resistance.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series.

First principles modeling of nonlinear incidence rates in seasonal epidemics

In this paper we used a general stochastic processes framework to derive from first principles the incidence rate function that characterizes epidemic models. We investigate a particular case, the Liu-Hethcote-van den Driessche's (LHD) incidence rate function, which results from modeling the number of successful transmission encounters as a pure birth process. This derivation also takes into account heterogeneity in the population with regard to the per individual transmission probability. We adjusted a deterministic SIRS model with both the classical and the LHD incidence rate functions to time series of the number of children infected with syncytial respiratory virus in Banjul, Gambia and Turku, Finland. We also adjusted a deterministic SEIR model with both incidence rate functions to the famous measles data sets from the UK cities of London and Birmingham. Two lines of evidence supported our conclusion that the model with the LHD incidence rate may very well be a better description of the seasonal epidemic processes studied here. First, our model was repeatedly selected as best according to two different information criteria and two different likelihood formulations. The second line of evidence is qualitative in nature: contrary to what the SIRS model with classical incidence rate predicts, the solution of the deterministic SIRS model with LHD incidence rate will reach either the disease free equilibrium or the endemic equilibrium depending on the initial conditions. These findings along with computer intensive simulations of the models' Poincaré map with environmental stochasticity contributed to attain a clear separation of the roles of the environmental forcing and the mechanics of the disease transmission in shaping seasonal epidemics dynamics.

Stability of a promiscuous plasmid in different hosts: no guarantee for a long-term relationship

Broad-host-range (BHR) IncP-1 plasmids have the ability to transfer between and replicate in nearly all species of the Alpha-, Beta- and Gammaproteobacteria, but surprisingly few data are available on the stability of these plasmids in strains within their host range. Moreover, even though molecular interactions between the bacterial host and its plasmid(s) exist, no systematic study to date has compared the stability of the same plasmid among different hosts. The goal of this study was to examine whether the stability characteristics of an IncP-1 plasmid can be variable between strains within the host range of the plasmid. Therefore, 19 strains within the Alpha-, Beta- or Gammaproteobacteria carrying the IncP-1beta plasmid pB10 were serially propagated in non-selective medium and the fraction of segregants was monitored through replica-picking. Remarkably, a large variation in the stability of pB10 in different strains was found, even between strains within the same genus or species. Ten strains showed no detectable plasmid loss over about 200 generations, and in two strains plasmid-free clones were only sporadically observed. In contrast, three strains, Pseudomonas koreensis R28, Pseudomonas putida H2 and Stenotrophomonas maltophilia P21, exhibited rapid plasmid loss within 80 generations. Parameter estimation after mathematical modelling of these stability data suggested high frequencies of segregation (about 0.04 per generation) or high plasmid cost (i.e. a relative fitness decrease in plasmid-bearing cells of about 15 and 40 %), which was confirmed experimentally. The models also suggested that plasmid reuptake by conjugation only played a significant role in plasmid stability in one of the three strains. Four of the 19 strains lost the plasmid very slowly over about 600 generations. The erratic decrease of the plasmid-containing fraction and simulation of the data with a new mathematical model suggested that plasmid cost was variable over time due to compensatory mutations. The findings of this study demonstrate that the ability of a so-called 'BHR' plasmid to persist in a bacterial population is influenced by strain-specific traits, and therefore observations made for one strain should not be generalized for the entire species or genus.

The population biology of bacterial plasmids: a hidden Markov model approach

Horizontal plasmid transfer plays a key role in bacterial adaptation. In harsh environments, bacterial populations adapt by sampling genetic material from a horizontal gene pool through self-transmissible plasmids, and that allows persistence of these mobile genetic elements. In the absence of selection for plasmid-encoded traits it is not well understood if and how plasmids persist in bacterial communities. Here we present three models of the dynamics of plasmid persistence in the absence of selection. The models consider plasmid loss (segregation), plasmid cost, conjugative plasmid transfer, and observation error. Also, we present a stochastic model in which the relative fitness of the plasmid-free cells was modeled as a random variable affected by an environmental process using a hidden Markov model (HMM). Extensive simulations showed that the estimates from the proposed model are nearly unbiased. Likelihood-ratio tests showed that the dynamics of plasmid persistence are strongly dependent on the host type. Accounting for stochasticity was necessary to explain four of seven time-series data sets, thus confirming that plasmid persistence needs to be understood as a stochastic process. This work can be viewed as a conceptual starting point under which new plasmid persistence hypotheses can be tested.

Modeling the impact of periodic bottlenecks, unidirectional mutation, and observational error in experimental evolution

Antibiotic resistant bacteria are a constant threat in the battle against infectious diseases. One strategy for reducing their effect is to temporarily discontinue the use of certain antibiotics in the hope that in the absence of the antibiotic the resistant strains will be replaced by the sensitive strains. An experiment where this strategy is employed in vitro produces data which showed a slow accumulation of sensitive mutants. Here we propose a mathematical model and statistical analysis to explain this data. The stochastic model elucidates the trend and error structure of the data. It provides a guide for developing future sampling strategies, and provides a framework for long term predictions of the effects of discontinuing specific antibiotics on the dynamics of resistant bacterial populations.

Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution

Temporarily discontinuing the use of antibiotics has been proposed as a means to eliminate resistant bacteria by allowing sensitive clones to sweep through the population. In this study, we monitored a tetracycline-sensitive subpopulation that emerged during experimental evolution of E. coli K12 MG1655 carrying the multiresistance plasmid pB10 in the absence of antibiotics. The fraction of tetracycline-sensitive mutants increased slowly over 500 generations from 0.1 to 7%, and loss of resistance could be attributed to a recombination event that caused deletion of the tet operon. To help understand the population dynamics of these mutants, three mathematical models were developed that took into consideration recurrent mutations, increased host fitness (selection), or a combination of both mechanisms (full model). The data were best explained by the full model, which estimated a high mutation frequency (lambda = 3.11 x 10(-5)) and a significant but small selection coefficient (sigma = 0.007). This study emphasized the combined use of experimental data, mathematical models, and statistical methods to better understand and predict the dynamics of evolving bacterial populations, more specifically the possible consequences of discontinuing the use of antibiotics.