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The prominence of competition as a central concept in plant ecology, and the unresolved debates that hinge on it, argue the need for a particularly deep understanding of how it works. But despite decades of research, that understanding remains elusive. One reason for this is that, although plants’ competitive interactions are dynamic, as evidenced by the vast literature on density-dependent self-thinning trajectories in communities (Harper, 1977), they are usually studied experimentally as if they are not. This perpetuates uncertainty and confusion about the process of competition and its ecological role (Grime, 1979, 2001; Tilman, 1982; Goldberg, 1990; Craine, 2005).
Competition is a multi-faceted concept, but this is not necessarily problematic provided that the facet of competition being examined is clearly defined for a particular study. Here, ‘competition’ is defined strictly as ‘the tendency of neighbouring plants to utilize the same quantum of light, ion of mineral nutrient, molecule of water, or volume of space’ (Grime, 1979, p. 8). This definition restricts itself to resource capture by two or more neighbours. It deliberately avoids saying anything about possible demographic consequences of competition. The influence of competition sensu Grime on population dynamics and community structure cannot yet be tested unambiguously. At that scale, ‘competition’ can mean anything from resource capture by neighbouring individuals, as above, through to the eventual extinction of one population by another. Here we do not use ‘competition’ to mean anything other than resource capture by neighbouring plants.
On that basis, biomass production by neighbouring plants is strictly an outcome of competition (and other processes), but one that obviously has a close functional relationship with resource capture. Put simply, bigger plants tend to capture more resources (i.e. are potentially stronger competitors) than smaller ones (Berntson & Wayne, 2000). We argue that, to fully understand plant competition as a process, it is necessary to perform experiments that measure both biomass production and resource capture by the competitors, and their functional interactions. Such experiments are conspicuous by their scarcity in the plant competition literature.
Other than those on self-thinning, plant competition experiments typically involve measuring biomass differences between neighbours at a single harvest (Connolly et al., 1990; Gibson et al., 1999; Damgaard et al., 2002). Explaining how and to what extent neighbours influence those differences is what most plant competition experiments aim to do. But such experiments are notoriously bad at doing this. This is because the biomass of a plant measured at one harvest is an outcome of the physiological and developmental processes that occurred throughout its life up to that point. Consequently, biomass differences between neighbours measured at one time cannot be explained fully without knowledge of the earlier, but unmeasured, processes that caused them. Explicitly dynamic perspectives on plant competition have been advocated by many modellers (e.g. Mutsaers, 1991; Damgaard et al., 2002; Dormann & Roxburgh, 2005), but investigated by relatively few experimentalists (Ross & Harper, 1970; Connolly et al., 1990; Robinson et al., 1999, 2010; Andersen et al., 2007; Aikio et al., 2009). Over a decade ago, the exclusive reliance on ‘final yield’ experiments to characterize plants’ competitive interactions was described by Gibson et al. (1999) as ‘…possibly the single most neglected and important issue in current practice’ and that criticism remains a valid one.
In the context of our definition of ‘competition’, it is clearly necessary to know how well an individual and its neighbours captured resources, how effectively those resources were converted into biomass, and how that biomass was then used to capture more resources, and so on over a defined period of the plant’s life. Such information would reflect the functional interdependence between growth and resource capture, the temporal dynamics of which are intrinsic to how these processes operate and which might contribute to eventual demographic success. In practice, characterizing the dynamics of plants’ competitive interactions demands measuring simultaneous changes in both resource capture and biomass production by competing individuals, preferably before and during a period when they would be likely to interact with one another, not just at one point in time. Surprisingly, this seems never to have been done in a way that allows those dynamics to be characterized reliably, which is why this was the first aim of this study.
Just as the biomass measured at one harvest is the legacy of processes that preceded the harvest, so a plant’s resource capture is the accumulation of the resources captured up to the time of measurement. Net carbon and water capture could, in principle, be estimated nondestructively using continuous gas exchange systems, although partitioning of the total fluxes among competing individuals is technically difficult. Similarly, partitioning radiation interception among the canopies of competitors can be done, but with some difficulty (Beyschlag et al., 1990). However, for nutrients, net capture can be approximated most easily by determining the nutrient content of harvested plants, ignoring the relatively small losses (in young healthy plants, at least) via root efflux, losses of volatiles or soluble fractions from leaves, and herbivory. Given the relatively tight stoichiometric relations between elements in vegetation (Elser et al., 2010), it might be expected that temporal changes in resource accumulation would simply track those of biomass production, and that competition would have only a minor effect on those relationships, but that expectation has yet to be tested. Here we carry out such a test for the competitive capture of one essential resource, nitrogen (N), and that was the second aim of this study.
Rates of nutrient and water uptake and of photosynthesis change continually in response to environmental cues and internal signals (Robinson, 2005). The value of measuring instantaneous rates of resource capture among competitors, as opposed to cumulative resource capture, is that instantaneous rates reflect how plants are interacting with each other at the time when the measurement is made. Instantaneous rates reveal the simultaneous resource fluxes into each competitor. Such fluxes, if measured, would provide the closest approximations to and most unambiguous information about the actual competitive process. Instantaneous rates can also indicate periods when competitive interactions are at their most intense and potentially most decisive in influencing the competitors’ success. Cumulative biomass production and resource capture are the integrals of many instantaneous rates. Estimating those instantaneous rates repeatedly would reveal the temporal progression of those interactions as they unfold, providing a direct link between the process of competition and the physiological activities of the competing individuals. We argue that the most appropriate way to explore the dynamics of plants’ competitive interactions is in terms of measuring resource capture and growth by neighbouring individuals both cumulatively and instantaneously. Some information exists about how the growth rates of competing plants co-vary (Connolly et al., 1990). However, the temporal resolution of such data is poor because experiments are restricted at best to only a few harvests widely spaced in time (e.g. four harvests at intervals of 13–20 d up to 69 d after sowing in the experiment reported by Connolly et al. (1990); five harvests at intervals of 9–35 d up to 112 d after sowing in that by Andersen et al. (2007)) or many harvests over a very short period (e.g. eight harvests over only 21 d: Ross & Harper (1970)). Even that level of information appears to be lacking for competitive resource capture. By characterizing trajectories of biomass production and resource capture by competing plants we aimed, finally, to assess if interpretations of traditional, single-harvest competition experiments are potentially compromised if temporal dynamics are ignored.
We achieved these aims by statistically fitting an easily parameterized phenomenological model to measurements of cumulative biomass production and net N capture by competing and isolated plants (Robinson et al., 2010). From that model, we derived instantaneous rates of those processes at daily temporal resolution. This approach contrasts with the use of mechanistic models of plant competition (Baldwin, 1976; Huston & DeAngelis, 1994; Thornley et al., 1995; Schwinning & Parsons, 1996; Biondini, 2001; Raynaud & Leadley, 2004). Such models require the estimation of numerous parameters that cannot be measured routinely in any one experiment and are often assembled piecemeal from many experiments, some of which might involve no plant competition. This limits the extent to which mechanistic modelling is practical for the routine analysis of competitive interactions (Mutsaers, 1991), for which simple phenomenological models are an ideal alternative.
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This simple experiment has yielded new information about the dynamics of plant resource competition. This was achieved by: (1) measuring biomass and N contents of shoot tissues of competing and noncompeting plants at many frequent harvests; (2) fitting to the data a general phenomenological model to describe cumulative resource capture and biomass production as a function of time; (3) deriving from the model instantaneous per capita rates of net N capture and biomass production; (4) producing temporal trajectories of cumulative and instantaneous biomass production and N capture to determine how these processes co-varied temporally in competing individuals.
Using this approach, we were able to visualize how the competitive interaction between neighbouring plants unfolded (Fig. 3d). The trajectories of instantaneous N capture rate by competing plants were largely distinct from those of the isolated plants, indicating a strong neighbour effect on instantaneous N capture at all but the earliest harvests. This effect was predictable given the time needed for leaves of one plant to over-top those of a neighbour and for concentration depletion zones around the roots of adjacent plants to overlap as root densities in soil increase (Newman & Andrews, 1973; Baldwin, 1975; Newman, 1983). The contest for N between P. lanceolata and D. glomerata shifted gradually in favour of the latter species. Robinson et al. (2010) attributed the ultimately superior growth of D. glomerata in this experiment to a relentless increase in its root : shoot ratio while that of the P. lanceolata with which it grew remained relatively fixed. The strong allocation response by D. glomerata to P. lanceolata would be circumstantial evidence for a superior competitive capture of N and other nutrients by D. glomerata (Freckleton & Watkinson, 2001). That suspicion was confirmed by the direct evidence of greater N capture by D. glomerata (Fig. 3). Progressively larger investment in roots by D. glomerata would have allowed it to gradually attain faster rates of N capture than P. lanceolata, and that is indeed what happened. Greater N capture would have resulted in more biomass production by D. glomerata, and this again is supported by the data.
It is notable that the point at which D. glomerata overtook P. lanceolata in terms of cumulative or instantaneous N capture preceded by 3–4 d the point at which it gained the advantage in terms of biomass production (Fig. 3). That is consistent with faster N capture by D. glomerata leading to greater biomass production when growing with P. lanceolata. That simple cause-and-effect is unlikely to be the full story, however. The competitive capture of resources that were not measured (light, water and nutrients other than N) would also have influenced the capacity of D. glomerata to eventually grow better than its neighbour, but those influences cannot be evaluated using data from this experiment. Although we focused on only one of the resources for which plants can compete, the competitive dynamics of N capture nevertheless illustrate how that process is linked to those of biomass production, and ultimately how one individual could gain a competitive advantage over a neighbour.
Instantaneous N capture by isolated D. glomerata and P. lanceolata followed a similar, but wider and more rapid, trajectory to that of the competitors, D. glomerata eventually overtaking P. lanceolata. This indicates that the N capture trajectory of the competing plants was determined not only by the nature of their interaction, but also by external conditions such as soil N availability, which would have affected the time-courses of growth and N capture by isolated plants as much as those of the competitors, and by phenological differences between D. glomerata and P. lanceolata. In the small pots in which the plants were grown, eventual exhaustion of available N was possible, even with regular fertilizer additions during the experiment. That would have limited per capita rates of N capture. This is supported by the near-zero instantaneous N capture rates of the isolated plants of both species at the later harvests compared with their rapid rates at c. 40–50 d, a pattern typical of plants that are depleting their available N supply (Van Vuuren et al., 1996). Therefore, the characteristic ‘footprint’ of competitive dynamics is not solely the trajectory of instantaneous resource capture by neighbouring plants, but the comparison of that trajectory with that of isolated plants, as in Fig. 3(d).
It would be instructive to test the extent to which resource capture trajectories such as those in Fig. 3(d) can be modified by intermittent additions of N and other resources during competition. Campbell & Grime (1989) demonstrated the potential importance of the frequency, duration and predictability of nutrient pulses to the growth rates of two perennial grasses, Festuca ovina and Arrhenatherum elatius, with contrasting ecological distributions. In the British Isles, F. ovina is a slow-growing dominant of low-productivity, nutrient-poor grasslands, and A. elatius a large, fast-growing species characteristic of productive, nutrient-rich habitats (Grime et al., 1987). F. ovina grew faster than A. elatius when nutrient pulses were short, infrequent and unpredictable, matching, presumably, the nutrient-supply characteristics of undisturbed, infertile soils. The position was reversed when pulses were long and predictable, as would be expected of fertile soils. The implications of this for understanding how neighbours respond to resource supplies in different habitats are obvious, but direct tests of how temporal or spatial heterogeneity in resource supply influences competitive dynamics among co-existing plants have yet to be performed.
If it were possible to combine Campbell & Grime’s (1989) approach with that used here, it is easy to envisage that the resulting trajectories of instantaneous N capture could be very different from those in Fig. 3(d). We can predict that if a pulse of N were to become available when the plants’ capacities to take up N were constrained by the initial supply being close to exhaustion, the N capture trajectories would again increase, instead of heading towards zero as happened towards the end of this experiment. The resulting effects of such a response of instantaneous N capture on N accumulation and biomass production would depend on the relative and absolute sizes of the competitors at the time when the N pulse occurred, and on their capacities to use newly available N to produce new biomass. The competitive trajectories presented in Fig. 3(d) can reflect only a small range of the rich and varied dynamics that neighbouring plants in real communities might express. Because plant size changes with time, and per capita resource capture is size-dependent (Berntson & Wayne, 2000), the competitive dynamics of plants are necessarily size- as well as time-dependent. Size, not time, is probably the more important determinant of plants’ short-term competitive dynamics, but one that is itself dependent on those dynamics, a co-dependence illustrated by the trajectories in Fig. 4.
The implications of Fig. 3 for traditional plant competition experiments are serious. Comparing biomass produced or N accumulated by competitors and isolated plants at only a single point in time is clearly inadequate to fully explore the scale and nature of neighbours’ interactions. Whether comparing cumulative biomass production or N capture, or their instantaneous equivalents, different quantitative and qualitative outcomes would be obtained depending on when the comparison was made; this is also clear from the arable crop data of Andersen et al. (2007) and even from the 21-d pot experiment on D. glomerata reported by Ross & Harper (1970). Any arbitrarily chosen harvest along the trajectories shown in Fig. 3 would generate estimates of ‘competitive effect’ (Weigelt & Jolliffe, 2003) or ‘competitive strength’ (Andersen et al., 2007) very different from those produced at earlier or later harvests; even determining which competitor ‘wins’ is strongly time-dependent, as Fig. 3 demonstrates. It is impossible to estimate how many traditional, single-harvest, plant competition experiments have been influenced by such an artefact of sampling, but given their ubiquity and the temporally dynamic nature of resource capture and biomass production, the most likely answer is ‘all of them’. Single-harvest comparisons provide not merely snapshots of plants’ dynamic interactions, but potentially misleading ones. This has been acknowledged for a long time (Newman, 1983; Connolly et al., 1990, 2001; Gibson et al., 1999), but the scale of the problem has remained unknown. It is perhaps unsurprising that different single-harvest experimental studies of plant competition can give contradictory or inexplicable results when plants are sampled at arbitrary points along unknown dynamic trajectories. Small differences in initial conditions, such as in seedling size, from one competition experiment to another can also generate unexpected outcomes at subsequent harvests (Andersen et al., 2007).
Fig. 3(d) also reveals the striking possibility of an experiment falsely providing evidence for facilitation (Brooker et al., 2008), rather than competition. Had instantaneous N capture been measured only at 59 d or later, rates of N capture by competitors would have exceeded those by isolated plants. Such an effect, if genuine, would be difficult to explain mechanistically, but we can see that it was an artefact of the different temporal trajectories of competitors and isolated plants. Because the trajectories of isolated plants proceeded faster than those of the competitors, so that the former probably exhausted available N before the latter, more N would have been available to the competitors in the later stages of the experiment, allowing those plants to sustain faster N capture rates at those times. But that explanation can be suggested only because the trajectories were known. This problem of ‘pseudo-facilitation’ would not have arisen in this experiment had the comparison been based on biomass, but there is no certainty that that would always be the case.
The most surprising information to emerge from this experiment was the extent to which the dynamics of N capture and biomass production were modified by neighbours, compared with the corresponding dynamics of isolated plants. The presence of a neighbour induced a wider separation of the times of maximum instantaneous biomass production and N capture rates compared with those in isolated plants (Fig. 2). This could be interpreted as an example of temporal niche separation (Bretagnolle & Thompson, 1996), by which the activities of co-existing individuals in exploiting available resources are adjusted to occur partly at different times, with the potential effect of minimizing competition between neighbours and, hence, the potentially negative effects of one individual on another. But it is more probable that this effect was the secondary consequence of the closer temporal coupling between maximum biomass production and N capture rates, albeit at different times in each species: 48–52 d in P. lanceolata; 65–68 d in D. glomerata.
The mechanism by which that effect arose is unknown, but presumably it would have involved neighbour-detection processes (Karban, 2008; Cahill & McNickle, 2011). These include red:far-red radiation reflectance from, and transmission through, the leaves of a neighbour (Smith, 2000) and rhizosphere signalling to detect the presence of roots of another individual (Bais et al., 2006). Irrespective of the detailed signal reception and transduction pathways involved, the gross effect in this experiment was to more closely align the dynamics of instantaneous rates of biomass production and N capture (Fig. 4b). The corresponding trajectories of cumulative N capture and biomass production (Fig. 4a) were not those expected of a progressive dilution of captured N by the subsequent production of new biomass, as was seen in the isolated plants. Therefore, it seems likely that, when D. glomerata and P. lanceolata competed in this experiment, the plants used captured N differently from when they grew in isolation. One possibility is that less N was diverted to internal storage pools in competitors than in isolated plants and was instead used immediately by the competitors to produce more biomass (Lemaire & Millard, 1999), as if there was a greater imperative for immediate N use when that N was captured competitively rather than when it was acquired free of that constraint. In the case of D. glomerata, new biomass produced from the competitive capture of N and other resources was increasingly allocated to roots (Robinson et al., 2010). This would have facilitated the capture of more N and other nutrients. This suggests a positive feedback between resource capture and growth by D. glomerata that was decisive in determining its eventual dominance over P. lanceolata in the experiment. That such a response was confined to competing plants indicates the physiological and developmental flexibility with which plants can respond to the presence of a neighbour.
A second unexpected result was the different statistical effect of neighbours on cumulative biomass production and N capture (Table 1). Neighbours influenced only the ultimate amount of biomass produced (Ymax), not its rate constant (r), a result confirming that found by Damgaard & Weiner (2008) for the growth of Chenopodium album individuals in dense monospecific stands. By contrast, neighbours influenced both Ymax and r for cumulative N capture by both D. glomerata and P. lanceolata. Testing the generality of this result depends on further experiments on more species that compete under a wider range of conditions. Meanwhile, we note that measurements of biomass production and N capture can provide similar, but not identical, information about plants’ competitive interactions, as indicated by the analyses in Tables 1 and 2. As argued in the Introduction, experimental investigations of plant competition should measure resource capture by neighbours, biomass production being an outcome of competitive resource capture and of many other processes. But it is obviously vital to have information about both resource capture and biomass production as these processes interact so intricately, and iteratively, when individuals compete. Combining such information with measurements of resource availabilities (in the case of N, variations in soluble N production rates in the rooting volume) would allow direct tests to be made of Goldberg’s (1990) ideas about how plants change the availability of resources for which they compete and the resulting responses of plants to those changes (respectively, ‘competitive effects’ and ‘competitive responses’ in Goldberg’s terminology). Doing so would further improve our understanding of the competitive process.
There is a need to extend the approach described here to explore competitive dynamics over a larger fraction of plants’ lifespans, especially across successive growing seasons to account for phenological responses (Bretagnolle & Thompson, 1996) and regenerative processes (Grubb, 1977) under field conditions. There is evidence that the long-term population dynamics of some plant communities can be dominated by intraspecific, not interspecific, interactions between individuals (Rees et al., 1996). Therefore, a necessary development of the approach described here is to characterize the dynamics of intra- vs interspecific competition; this has recently been done for D. glomerata (M. C. Woo et al., unpublished data). It would also be valuable to know if, for example, seed production by competitors correlates with their resource capture during vegetative growth, and how competitive resource capture trajectories respond to local stochastic events (defoliation, disturbance, spatio-temporal fluctuations in resource availability, and climate), to temporal variations in population density, and to large disparities in neighbour size. Such information would allow plant community dynamics to be modelled in terms of local competitive processes (Travis et al., 2006; Berger et al., 2008). If competition does influence the structure and long-term dynamics of plant communities, then that influence will originate from the kinds of short-term competitive dynamics between individuals that are reported here.