Emergence of networks of shared restriction-modification systems in phage-bacteria ecosystems

Restriction-modification (RM) systems are the most ubiquitous bacterial defense system against bacteriophages and an important part of controlling phage predation. Using genomic sequence data, we show that RM systems are often shared among bacterial strains in a structured way. Examining the network of interconnections between bacterial strains within each genus, we find that in many genera strains share more RM systems than expected from a random network. We also find that many genera have a larger than expected number of bacterial strains with unique RM systems. We use population dynamics models of closed and open phage-bacteria ecosystems to qualitatively understand the selection pressures that could lead to these non-random network structures with enhanced overlap or uniqueness. In our models we find that the phages impose a pressure that favours bacteria with more RM systems, and more overlap of RM systems with other strains, but in bacteria dominated states this is opposed by the increased cost to the growth rate of these bacteria. Similar to what we observe in the genome data, we find that two distinct bacterial strategies emerge -- strains either have a larger overlap than expected, or they have more unique RM systems than one expects from a null model. The former strategy appears to dominate when the repertoire of available RM systems is smaller but the average number of RM systems per strain is larger.

Unveiling the recovery dynamics of Walleye after the invisible collapse

Walleye (Sander vitreus) populations in Alberta, Canada collapsed by the mid-1990s and were a case study in the paper Canada’s Recreational Fisheries: The Invisible Collapse? Here we fit age-structured population dynamics models to data from a landscape-scale monitoring program to assess Walleye population status and reconstruct recruitment dynamics following the invisible collapse. Assessments indicated that populations featured low F_msy values of approximately 0.2-0.3 under conservative assumptions for the stock-recruitment relationship but that many populations were lightly exploited during 2000-2018. Recruitment reconstructions showed that recovery from collapse in 33/55 lakes was driven in part by large positive recruitment anomalies that occurred during 1998-2002. Additionally, 15/55 lakes demonstrated cyclic recruitment dynamics. Both the recruitment anomalies and cyclic fluctuations could be due to environmental effect(s) and(or) cannibalism, and experimentation may be necessary to resolve this uncertainty. These findings contribute new information on the recovery dynamics of Walleye following the invisible collapse, and demonstrate the effectiveness of coupling traditional fisheries science models with broad-scale monitoring data to improve understanding of population dynamics and sustainability across landscapes.

Composition and Seasonality of Membrane Transporters in Marine Picoplankton

In this study, we examined transporter genes in metagenomic and metatranscriptomic data from a time-series survey in the temperate marine environment of the Baltic Sea. We analyzed the abundance and taxonomic distribution of transporters in the 3μm–0.2μm size fraction comprising prokaryotes and some picoeukaryotes. The presence of specific transporter traits was shown to be guiding the succession of these microorganisms. A limited number of taxa were associated with the dominant transporter proteins that were identified for the nine key substrate categories for microbial growth. Throughout the year, the microbial taxa at the level of order showed highly similar patterns in terms of transporter traits. The distribution of transporters stayed the same, irrespective of the abundance of each taxon. This would suggest that the distribution pattern of transporters depends on the bacterial groups being dominant at a given time of the year. Also, we find notable numbers of secretion proteins that may allow marine bacteria to infect and kill prey organisms thus releasing nutrients. Finally, we demonstrate that transporter proteins may provide clues to the relative importance of biogeochemical processes, and we suggest that virtual transporter functionalities may become important components in future population dynamics models.

Analysis and simulation of plant disease progress curves in R: introducing the epifitter package

The analysis of the disease progress curves (DPCs) is central to understanding plant disease epidemiology. The shape of DPCs can vary significantly and epidemics can be better understood and compared with an appropriate depiction and analysis. This paper introduces epifitter, an open-source tool developed in R for aiding in the simulation and analysis of DPC data. User-level functions were developed and their use is demonstrated to the reader using actual disease progress curve data for facilitating the conduction of several tasks, including (a) simulation of synthetic DPCs using four population dynamics models (exponential, monomolecular, logistic, and Gompertz); (b) calculation of the areas under disease progress curve and stairs; (c) fitting and ranking the four above-mentioned models to single or multiple DPCs; and (d) generation and customization of graphs. The package requires the installation of R in any desktop computer and the scripted analysis can be fully documented, reproduced, and shared. The epifitter R package provides a flexible suite for temporal analysis of epidemics that is useful for both research and teaching purposes.

Universal allometry from empirical parameters

Allometric settings of population dynamics models are appealing due to their parsimonious nature and broad utility when studying system level effects. Here, we parameterise the size-scaled Rosenzweig-Macarthur ODEs to eliminate prey-mass dependency. We define the functional response term to match experiments, and examine situations where metabolic theory derivations and observation diverge. We produce dynamics consistent with observation. Our parameterisation of the Rosenzweig-Macarthur system is an accurate minimal model across 15+ orders of mass magnitude.

Defining Integrated Weed Management: A Novel Conceptual Framework for Models

Weed population dynamics models are an important tool for predicting the outcome of alternative Integrated Weed Management (IWM) scenarios. The growing problem of herbicide resistance has increased the urgency for these tools in the design of sustainable IWM solutions. We developed a conceptual framework for defining IWM as a standardised input template to allow output from different models to be compared and to design IWM scenarios. The framework could also be used as a quantitative metric to determine whether more diverse systems are more sustainable and less vulnerable to herbicide resistance using empirical data. Using the logic of object-oriented programming, we defined four classes of weed management options based on the stage in the weed life cycle that they impact and processes that mediate their effects. Objects in the same class share a common set of properties that determine their behaviour in weed population dynamics models. Any weed control “event” in a system is associated with an object, meaning alternative management scenarios can be built by systematically adding events to a model either to compare existing systems or design novel approaches. Our framework is designed to be generic, allowing IWM systems from different cropping systems and countries to be compared.

Statistical Evaluation of Behavior and Population Dynamics Models Predicting Movement and Proportional Entrainment Loss of Adult Delta Smelt in the Sacramento–San Joaquin River Delta

There has been considerable debate about effects of entrainment of endangered Delta Smelt (Hypomesus transpacificus) at water export facilities located in the Sacramento–San Joaquin River Delta. In this paper we use a behavior-driven movement model (BMM) to simulate the movement of adult Delta Smelt, which, in conjunction with a population dynamics model, estimates the proportion of the population that is lost to entrainment, i.e., proportional entrainment loss (PEL). Parameters of the population model are estimated by maximum likelihood by comparing predictions to data from Fall Midwater Trawl (FMWT) and Spring Kodiak Trawl (SKT) surveys, as well as to daily salvage estimates. Our objectives are to evaluate different movement behavior hypotheses, to rank estimates of PEL based on how well predictions fit the data, and to sharpen our understanding of the data to inform future research and monitoring decisions. We applied the modeling framework to data from water year 2002—a year when salvage was high—and tested 30 combinations of six behavior and five population dynamics models. More complex process and observation assumptions in the population model led to much improved fits in most cases, but did not appreciably influence PEL predictions, which were largely determined by movement predictions from the BMMs. Estimates of PEL varied considerably among behaviors (2% to 40%). The model with the highest predictive capability explained 98% of the variation in FMWT data across regions, 70% of the variation in SKT data across regions and surveys, and 28% and 43% of the daily variation in salvage at federal and state fish screening facilities, respectively. The PEL estimate from this model was 35%, more than double the original estimate from Kimmerer (2008) of 15%. While PEL estimates provided in this study should be considered preliminary, our framework for testing combined behavior-driven movement models and population dynamics models is an improvement compared to earlier efforts.

Statistical Evaluation of Behavior and Population Dynamics Models Predicting Movement and Proportional Entrainment Loss of Adult Delta Smelt in the Sacramento–San Joaquin River Delta

There has been considerable debate about effects of entrainment of endangered Delta Smelt (Hypomesus transpacificus) at water export facilities located in the Sacramento–San Joaquin River Delta. In this paper we use a behavior-driven movement model (BMM) to simulate the movement of adult Delta Smelt, which, in conjunction with a population dynamics model, estimates the proportion of the population that is lost to entrainment, i.e., proportional entrainment loss (PEL). Parameters of the population model are estimated by maximum likelihood by comparing predictions to data from Fall Midwater Trawl (FMWT) and Spring Kodiak Trawl (SKT) surveys, as well as to daily salvage estimates. Our objectives are to evaluate different movement behavior hypotheses, to rank estimates of PEL based on how well predictions fit the data, and to sharpen our understanding of the data to inform future research and monitoring decisions. We applied the modeling framework to data from water year 2002—a year when salvage was high—and tested 30 combinations of six behavior and five population dynamics models. More complex process and observation assumptions in the population model led to much improved fits in most cases, but did not appreciably influence PEL predictions, which were largely determined by movement predictions from the BMMs. Estimates of PEL varied considerably among behaviors (2% to 40%). The model with the highest predictive capability explained 98% of the variation in FMWT data across regions, 70% of the variation in SKT data across regions and surveys, and 28% and 43% of the daily variation in salvage at federal and state fish screening facilities, respectively. The PEL estimate from this model was 35%, more than double the original estimate from Kimmerer (2008) of 15%. While PEL estimates provided in this study should be considered preliminary, our framework for testing combined behavior-driven movement models and population dynamics models is an improvement compared to earlier efforts.