Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE).

A key question in ecology and evolution is the relative role of natural selection and neutral evolution in producing biogeographic patterns. Here we quantify the role of neutral processes by simulating division, mutation and death of 100k individual marine bacteria cells with full 1 Mbp genomes in a global surface ocean circulation model. The model is run for up to 100k years and output is analyzed using BLAST alignment and metagenomics fragment recruitment. Simulations show the production and maintenance of biogeographic patterns, characterized by distinct provinces subject to mixing and periodic takeovers by neighbors (coalescence), after which neutral evolution re-establishes the province and the patterns reorganize. The emergent patterns are substantial (e.g., down to 99.5% DNA identity between North and Central Pacific provinces) and suggest that microbes evolve faster than ocean currents can disperse them. This approach can also be used to explore environmental selection. Main Text:An important ongoing endeavor in ecology and evolution is to understand the mechanisms underlying the geographic distribution patterns of organisms. Natural selection by contemporary environmental factors acting on adaptive mutations or a persistent seed bank of species is one mechanism that can create such patterns. Neutral evolution (selectively neutral mutations and genetic drift) coupled with dispersal limitation or isolation is another mechanism (1-6). These processes are not mutually exclusive and, for microbes in the global surface ocean, molecular observations (e.g., shotgun sequencing, (7)) provide support for the role of both mechanisms (8-11).Here we ask: How does neutral evolution influence the biogeographic distribution of surface ocean microbes? To what extent does dispersion allow for different operational taxonomic units (OTUs) to develop and persist? Are there emerging spatial patterns (e.g., provinces, (12)) and how do these change in time?Several approaches are available to quantify the contribution of the various processes in generating and maintaining biogeographic patterns among ocean microbes (2). A common empirical approach involves correlating observations (e.g., microbial composition) with environmental variables, subtracting out this environment effect and then correlating with geographic distance. In the ocean, hydrodynamic models coupled with tracers, either Eulerian concentration or Lagrangian particles, can be used as a measure of "connectivity" to supplement empirical relations (10,13). A problem with this approach is that one can never be sure that the distance effect is not actually caused by an unmeasured environmental variable. Mechanistic models constitute an alternative approach. Eulerian models with coupled phytoplankton ecology and biogeochemistry can simulate biogeographic patterns produced by environmental selection (14). However, the Eulerian approach generally assumes all species are present everywhere and does not consider dispersal limitation.An alternative approach is to u...

Karst systems have a high degree of heterogeneity and anisotropy, which makes them behave very differently from other aquifers. Slow seepage through the rock matrix and fast flow through conduits and fractures result in a high variation in spring response to precipitation events. Contaminant storage occurs in the rock matrix and epikarst, but contaminant transport occurs mostly along preferential pathways that are typically inaccessible locations, which makes modeling of karst systems challenging. Computer models for understanding and predicting hydraulics and contaminant transport in aquifers make assumptions about the distribution and hydraulic properties of geologic features that may not always apply to karst aquifers. This paper reviews the basic concepts, mathematical descriptions, and modeling approaches for karst systems. The North Coast Limestone aquifer system of Puerto Rico (USA) is introduced as a case study to illustrate and discuss the application of groundwater models in karst aquifer systems to evaluate aquifer contamination.