Trade-offs between competition and defense specialists among unicellular planktonic organisms: the "killing the winner" hypothesis revisited

Abstract

A trade-off between strategies maximizing growth and minimizing losses appears to be a fundamental property of evolving biological entities existing in environments with limited resources. In the special case of unicellular planktonic organisms, the theoretical framework describing the trade-offs between competition and defense specialists is known as the "killing the winner" hypothesis (KtW). KtW describes how the availability of resources and the actions of predators (e.g., heterotrophic flagellates) and parasites (e.g., viruses) determine the composition and biogeochemical impact of such organisms. We extend KtW conceptually by introducing size- or shape-selective grazing of protozoans on prokaryotes into an idealized food web composed of prokaryotes, lytic viruses infecting prokaryotes, and protozoans. This results in a hierarchy analogous to a Russian doll, where KtW principles are at work on a lower level due to selective viral infection and on an upper level due to size- or shape-selective grazing by protozoans. Additionally, we critically discuss predictions and limitations of KtW in light of the recent literature, with particular focus on typically neglected aspects of KtW. Many aspects of KtW have been corroborated by in situ and experimental studies of isolates and natural communities. However, a thorough test of KtW is still hampered by current methodological limitations. In particular, the quantification of nutrient uptake rates of the competing prokaryotic populations and virus population-specific adsorption and decay rates appears to be the most daunting challenge for the years to come.

FIG. 1.

Schematic of the general structure of the “killing the winner” hypothesis.

FIG. 2.

Schematic of the mechanisms underlying the “killing the winner” hypothesis, also incorporating size- or shape-selective grazing by protozoa. There are potentially m different edible and q different inedible prokaryotic types that are infected by as many different virus types. The populations B_n and I_p do not grow fast enough to sustain stable virus populations V_n and W_p (indicated by excluding part of the rectangles referring to the population sizes of B_n and I_p). Thus, population B_n is only subject to mortality due to grazing, and I_p is controlled solely by resource limitation. Populations not able to establish stably in the system are indicated by dashed lines. The meanings of the symbols are given in Table 1.

FIG. 3.

“Killing the winner” mechanisms on three levels. Experimental data suggest that different strains of the same “species” may have profiles of different resistance to viruses. Thus, it appears that the influence of viruses on prokaryotes is on the strain level (B_ij; j = 1, 2, …, l) and not on the “species” level (B_i; i = 1, 2, …, n) commonly detected by PCR-based fingerprinting techniques targeting the 16S rRNA gene. This is illustrated here for the case of edible bacteria B_T; however, similar arguments also hold for the case of inedible bacteria I_T. The figure suggests that the abundance of the prokaryotic community B_T is controlled by protozoan grazing and that the abundance at the strain level B_ij is controlled by viral lysis. However, the controlling mechanism for population size at the “species” level B_i is not immediately obvious from this model. The meanings of the symbols are given in Table 1.

FIG. 4.

Possible scenarios for temporal changes in host abundance as a consequence of viral lysis. In scenario 1, the abundance of the host population changes periodically, resembling predator-prey oscillations. Thus, viral lysis regulates numerically dominant populations (similar to negative frequency-dependent selection). In scenario 2, the host population is resistant to viral infection and thus can maintain high host abundance. This scenario is unlikely due to the transient nature of resistance and the associated costs of resistance. Scenario 3 depicts the case of a population that coevolves with its virus. The oscillations are due to a change in the population's sensitivity to the virus. In scenario 4, the sensitive host population is kept at a low abundance due to strong viral control.