Competition–colonization dynamics in experimental bacterial metacommunities

One of the simplest hypotheses used to explain species coexistence is the competition–colonization trade-off, that is, species can stably coexist in a landscape if they show a trade-off

between competitive and colonization abilities. Despite extensive theory, the dynamics predicted to result from competition–colonization trade-offs are largely untested. Landscape change,

such as habitat destruction, is thought to greatly influence coexistence under competition–colonization dynamics, although there is no formal test of this prediction. Here we present the

first illustration of competition–colonization dynamics that fully transposes theory into a controlled experimental metacommunity of two Pseudomonas bacterial strains. The

competition–colonization dynamics were achieved by directly manipulating trade-off strength and colonization rates to generate the full range of coexistence conditions and responses to

habitat destruction. Our study successfully generates competition–colonization dynamics matching theoretical predictions, and our results further reveal a negative relationship between

diversity and productivity when scaling up to entire metacommunities.

Competition–colonization (CC) trade-off models predict that species can coexist in landscapes with patch turnover by means of spatial niche partitioning1,2,3,4. Under CC dynamics, species

can occupy a ‘colonization niche’ by efficiently colonizing empty habitat patches (that is, ‘fugitive species’) or a ‘competition niche’ by outcompeting other species within sites5.

Depending on allocation trade-offs, such as those determined by life history, these alternative strategies (that is, ‘colonizers’ versus ‘competitors’) reduce the ratio of interspecific to

intraspecific competition and allow coexistence to occur without environmental heterogeneity among patches6. Although early CC models1,2 somewhat unrealistically assumed strict trade-offs,

including habitat homogeneity and the absence of both patch pre-emption and stochasticity, CC model predictions have been shown to be applicable to situations of greater complexity4,7.

CC models predict that changes to landscape structure impact coexistence even in the absence of changes in environmental conditions at the local scale8. A key prediction of such models is

that habitat destruction (or any other environmental change that reduces overall colonization rates) should preferentially suppress superior competitors or drive them extinct, whereas

increasing the regional abundance of inferior competitors8. If these landscape-level processes are resolved on long time scales, then extinction debts (that is, species committed to

extinction) are expected to develop9. It is also likely that changes in the prevalence of competitively dominant species could alter ecosystem functioning if such species are more productive

than inferior competitors5.

Despite a general acceptance of the theoretical framework, to our knowledge there is no direct experimental treatment of the general predictions of the CC theory; in fact, indirect tests

that attempted to document the existence of the CC trade-off have produced contradictory results in experimental10,11,12,13 and observational studies of natural species

assemblages14,15,16,17,18. Moreover, because the existence of a CC trade-off in an assemblage does not necessarily indicate that CC dynamics are determinant, indirect approaches have limited

power to evaluate the theory.

In this study, we present the first experimental illustration of a CC dynamic by manipulating the trade-off strength and colonization rate in an experimental system of Pseudomonas

fluorescens bacteria in 96-well plate metacommunities. Pseudomonas has emerged as a model for experimental ecology and evolution19,20,21. We first tailored the general CC model2,3 to our

experimental system, which allowed us to make quantitative predictions regarding the conditions under which different patterns of coexistence and dominance are predicted in the

metacommunity. We constructed and manipulated the two key mechanisms of CC dynamics in the model (trade-off strength and colonization rate) within a controlled setting to impose an

experimental CC dynamic. We subsequently tested the predicted consequences of this dynamic for coexistence, landscape change and ecosystem function by monitoring strain persistence, local

dominance, patch occupancy and metacommunity productivity. Our experiment is thus a targeted test of CC dynamics; however, it is an implementation rather than a test of underlying

mechanisms.

We used two Pseudomonas bacterial strains (analogous to ecological species22), a strong competitor (‘competitor’) and a poor competitor (‘colonizer’), to generate a strict asymmetry of

competition in local patches (that is, the colonizer is completely excluded from local patches). The rationale underlying the use of a two-strain system is consistent with other existing

experimental and empirical studies of the CC (for example, 13,15,18) and a variety of other studies that use microbes to investigate ecological phenomenon (for example, 23,24,25). We

manipulated the strength of the CC trade-off by controlling the colonization of the two strains independently; both strains could have the same colonization rate in the experiment (no

trade-off treatment) or the colonization rate of the colonizer could be strongly increased relative to the competitor (strong trade-off treatment). The manipulation of colonization rate was

achieved by diluting each strain when building the ‘colonizers pool’ (when inoculating a new microplate between two time steps of our experiment); greater dilution reduced the number of

cells that entered the colonizers pool and consequently reduced the probability that a strain would successfully colonize each inoculated well (see Methods and Supplementary Methods).

In contrast to most CC models that assume continuous colonization processes, we aimed to conduct our colonization treatments at discrete-time intervals for logistical feasibility. To achieve

this goal, we needed to derive a discrete version of the CC competition model that we could parameterize for our experimental system (see Methods and Supplementary Methods). The following

experiments and model conditions were implemented. An initial metacommunity was inoculated with each of the two strains in half of the wells of a 96-well microplate. Every 24 h, the regional

abundances (that is, microplate scale) of the two strains were estimated and used to build a ‘colonizer pool,’ which was subsequently used to inoculate a new microplate containing fresh

medium. The relative contribution of the two strains in the colonizer pool and the dilution of this pool were adjusted before each transfer (Fig. 1), to manipulate the absolute and relative

colonization rates (and thus the strength of the CC trade-off) of the two strains in the metacommunity. This procedure ensured that our experimental manipulations remained constant

throughout the experiment, but that potential deviations from the predicted trajectories in the preceding transfers would be taken into account in the computations of the colonization pool.

The overall dilution ensured that colonization rates were low enough to approximate limited or near-limited dispersal and generate an exponential growth phase. To prevent any possible

confounding of evolutionary changes in each strain from affecting the dynamics, at each new transfer, the bacterial colonization pool was reconstituted from initial frozen stock and not from

the bacteria of the previous transfer. Using this experimental setting and our model (Fig. 2, see Methods), we predicted the outcome of metacommunity dynamics under different trade-off and

colonization scenarios (Fig. 3). We subsequently assigned our primary experimental treatments along this parameter space to generate all predicted outcomes, which consisted of the exclusion

of the colonizer or competitor, coexistence and the extinction of both strains (Supplementary Table S1). In general, our experimental results closely corresponded to the predictions of our

model.

The experiment consisted of six basic steps: (1) set up an initial metacommunity with equal proportions of both strains in all wells (48 wells at random occupied by each strain); (2) build a

new ‘colonizer pool’ based on the regional abundances of the two strains after every 24 h; (3) modify the relative contribution of the two strains in the colonization pool and the dilution

of this pool according to each experimental treatment (see Supplementary Table 1) to manipulate the absolute and relative colonization rates (and thus the strength of the CC trade-off) of

the two strains in the metacommunity; and finally (4) inoculate new metacommunities (that is, new microplates containing fresh media); (5) measuring abundances in each microplate

metacommunity by estimating optical density; presence/absence results were confirmed via plating. Finally, (6) characterize colonizer pool using data from step 5 to estimate the relative

abundances of each strain in the colonizer pool. This experimental procedure was run for ten transfers.

Patch occupancies after one transfer (P1 for strain 1 and P2 for strain 2 at t +1) are functions of their current values through the probability of successful colonization following

dispersal (f1 and f2). The equilibrium is stable provided that f is concave (f′′1 for strain 1 and f2′(0)>1/(1−P1) for strain 2. One can determine the equilibrium occupancy of the competitor

(P1) and, subsequently, the equilibrium occupancy of the colonizer (P2), given P1.

These predictions take into account the colonization rates (that is, dilution rates) of strong competitor (competitor) and poor competitor (colonizer) strains. Numbers indicate the treatment

corresponding to each combination of strain colonization rates (obtained from the model presented in Fig. 2). Axes indicate the colonization rates of the competitor and colonizer strains as

the concentrations used before inoculation at each transfer; smaller values (that is, greater dilutions) indicate that fewer cells colonize each well (that is, are inoculated); greater

values indicate more cells being dispersed. Light gray, colonizer excluded by the competitor; white, coexistence; dark gray, competitor excluded by the colonizer; black, extinction of both

strains. Lines correspond to the four trade-off strengths: no trade-off (solid white line), weak, medium and strong trade-offs (all indicated by white dashed lines). Thicker arrows indicate

the predicted trajectories of isolation treatments (H7 and H11) subjected to reductions in colonization rate (70 or 90%). *Treatments whose observed dynamics are displayed in Fig 4. Dynamics

for the remaining treatments are shown in Supplementary Fig. S1.

The persistence of both strains was most prevalent and resulted in the most even relative patch occupancies at moderate trade-off strength (Figs 4 and Fig. 5, and also see Supplementary Fig.

S1). Treatments with no or weak trade-off strength resulted in the rapid exclusion of the colonizer, whereas strong trade-offs caused extinction of the competitor. Low absolute colonization

resulted in the extinction of both strains. There was a highly significant interaction between strain identity and trade-off strength in predicting equilibrium patch occupancy (Fig. 5;

generalized-linear model, P