Research
Currently, there are four broad themes of work in the lab:
Ecology and evolution of dispersal
Dispersal is a complex, multi-stage process which is polygenic and can have complex interactions with environment. Therefore, disentangling the various causes that can affect dispersal evolution, and their consequences, requires a long-term research program consisting of a series of careful experiments performed under highly controlled environments. Since 2014, we have established such a program in the lab.
Using experimental evolution as well as controlled ecological experiments on replicate laboratory populations of Drosophila melanogaster, we have shown that increased dispersal can evolve very fast (Tung et al, 2018a). Interestingly, once evolved, the selected flies tend to disperse more even in the absence of proximate drivers for dispersal (Tung et al, 2018a). Since, dispersal is an energy-intensive process, one naively expects that organisms selected for dispersal would pay steep fitness costs. However, we found that the dispersal selected populations had similar body size, fecundity and longevity as the controls, yet evolved significantly greater locomotor activity, exploratory tendency, and aggression (Tung et al, 2018b). This implied that organisms can evolve greater dispersal without necessarily paying any cost in terms of their life-history traits. Dispersal evolution was also shown to lead to greater amounts of glucose, AMP and NAD (suggesting elevated cellular respiration) and higher levels of octopamine and other neurotransmitters (Tung et al, 2018b). Dispersal evolution can also reduce the pattern of density dependence in the selected populations (Mishra et al, 2020). Interestingly, traits that are seen to be associated with greater dispersal in the wild-type flies, need not necessarily be the ones that evolve in response to selection for dispersal (Mishra et al, 2018). This counter-intuitive observation puts a question mark on the common usage of extant trait-associations to predict evolutionary outcomes.
Effects of fluctuating environments and population size on microbial evolution
There is considerable understanding about how laboratory populations respond to predictable (constant or deteriorating environment) selection for single environmental variables such as temperature or pH. However, such insights may not apply when selection environments comprise multiple variables that fluctuate unpredictably, as is common in nature. Through a number of experimental evolution studies on laboratory populations of E. coli, we have shown that exposure to complex, fluctuating environments can lead to evolution of efflux activities, which in turn can increase fitness under multiple antibiotics (Karve et al, 2015). Such fluctuation-selected populations can have greater mean growth rate and lower variation for growth rate over all the selection environments, but face no trade-offs across environments (Karve et al, 2016). Interestingly, predictability of environmental fluctuations does not play a role in determining the extent of adaptation, although the extent of ancestral adaptation to the chosen selection environments is of key importance (Karve et al, 2018).
We are also interested in the effects of population size on microbial evolution. Again, using the tools of experimental evolution, we have shown that even in genetically identical populations that adapt to the same selection environment, depending on their population sizes, a given fitness determining character can have very different evolutionary fates (Chavhan et al, 2019). Interestingly, we showed that this effect was entirely modulated by selection and not drift. We have also shown that adapting in larger numbers can increase the vulnerability of bacterial populations to future environmental changes (Chavhan et al, 2019). Such larger microbial populations tend to evolve heavier fitness trade-offs and undergo greater ecological specialization (Chavhan et al, 2020).
Controlling unstable dynamics
Although a large number of methods have been proposed to control the non-linear dynamics of unstable populations, very few have been verified using biological populations. We have proposed a new method of control called Adaptive Limiter Control, and have numerically as well as empirically shown that it can stabilize spatially-structured as well as spatially-unstructured populations (Sah et al, 2013; Sah et al 2014). We have investigated the effects of four methods of controlling the dynamics of biological populations using laboratory populations of Drosophila melanogaster. We have also performed biologically realistic simulations to ascertain the generalizability of our results. We have shown that control methods that incorporate either culling or restocking cannot simultaneously enhance multiple stability properties (Tung et al, 2016a). On the other hand, control methods that incorporate both culling and restocking can simultaneously enhance multiple stability properties, but at potentially very high economic costs (Tung et al, 2016b).
We have also created a very detailed Individual-Based Model (IBM) for dynamics of laboratory populations of Drosophila (Tung et al, 2019). We have then calibrated the model using data from a 49-generation long time-series of Drosophila laboratory populations under four different nutritional regimes. Our model provides excellent fit to multiple aspects of these time series. We have then used this model to explore the effects of nutritional availability on Sterile Insect Release Technique (SIT).
Extended Evolutionary Synthesis
In association with Foundations of Genetics and Evolution Group (FOGEG), we are also interested in various aspects of the ongoing Extended Evolutionary Synthesis. Please visit the FOGEG page for more details of our activities.