The authors have constructed a linear array of coupled, microscale patches of …

• Abstract
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• Acknowledgements
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Biology Articles » Biophysics » Bacterial metapopulations in nanofabricated landscapes » Discussion

Discussion

- Bacterial metapopulations in nanofabricated landscapes

The ecological corridors coupling MHPs are critical to our work: by weakly linking the habitat patches we are able to build heterogeneous landscapes as designed combinations [K_i, J_i,i+1, _i] of locally interacting MHPs while preserving the parallel and distributed nature of the (local) demographic process in each MHP, a spatially structured, adapting metapopulation (4). Under strong coupling our device behave like a giant 0D one, by loosing its patchy structure. In this case the population does not structure itself into localized demographic units.

Cell–cell communication through a signaling field C_t(x, y) determines chemotaxis-based bacterial movement (20) within each MHP. Keller–Segel flux (21) accounting for diffusion J₀ (with strength D) and chemotaxis ₁ (with strength X),

is usually used to describe the spatial dynamics _tt = G + of bacterial densities _t as a function of local growth G and chemotactic spatial coupling . At the local scale, the balance between dispersive forces (₀) and chemotaxis-based aggregation (₁) is critically dependent on density (22). The oscillatory behavior of local density, which we learned from the 0D analysis (Fig. 3), leads us to conjecture that MHP demographics is to be shifting between two different regimes: (i) a ₀-dominated regime (dispersive) when the average MHP density is below the critical value _c, and (ii) a ₁-dominated regime (attractive) when it is above _c. These regimes are expected to be important in generating stochastic propagule dispersal events, implementing migration between nearby MHPs (see Movie 2, which is published as supporting information on the PNAS web site). As we have shown here within our patchy landscapes, density aggregates at more than one scale. So we would expect patterns of molecular response in the cellular assemblage to also match characteristic scales embedded within the interaction between the topology of the habitat and a strain's life-history strategy. The question is, what are the scales (1) at which spatial coupling and "local" growth G operate for a given strategy [, _r, _m] in a given landscape [K_i, J_i,i+1, _i]?

The "shifting balance" (11) between evolutionary forces (phenotypic plasticity, mutation, genetic drift, and selection) embedded in the adaptive gradients across the landscape in conjunction with demographic process determine the evolution of competitive advantage and the struggle for existence (17) in our devices. Notice that the local adaptation observed could be the result of physiology or mutation; however, we do not yet know to which degree each of these processes are involved. Although conservative estimates of mutation rates are typically in the order of one mutation per 10⁹ bp per generation, it is clear now that this number is highly variable depending on the various stresses imposed to the organism by our devices (23–25).

Growth advantage in stationary phase mutants are expected to take over (25) ecotopes with high density of (stress) _min MHPs. Notice that stationary phase here can be induced not only by resource starvation (bottom-up) but also by space limitation (top-down). In this manner, the discrete nature of the environment allows for parallel stationary-phase states to be developed in a metapopulation that is otherwise expanding across the landscape. This slows down total growth but it increases the complexity of the assemblage by favoring stationary phase advantage phenotypes in different, but connected, local populations. Notice that space-limited vs. resource-limited patches can be used in a (niche) complementary fashion by two cooperating strategies exploiting different ecotopes across the landscape. Thus, in our devices the spatial implication of growth advantage in stationary phase phenomena in spatially explicit and heterogeneous settings as well as its responses to different adaptative topologies should be studied further. From the natural history comparison between rugged and B&W landscapes, preliminary results suggest that the number and complexity of ecotopes enhances the adaptive capacities of the metapopulation to high stress (_min) territories.

Our arrays have an intrinsic dynamic variability due to the nonlinear nature of the demographic coupling. Weakly coupled logistic oscillators, are prone to spatiotemporal chaos (26) depending on the balance between local growth (G) and dispersal coupling J_i,i+1. A clear limitation of our study is its temporal extent t. Longer periods of culturing (as well as larger arrays) are needed to understand the evolution of ecological advantages under controlled adaptive topologies.