Introduction and review
- Top of page
- Introduction and review
- Discussion of possibilities beyond phytoplankton
- Further discussion
- Supporting Information
Temperature is a key abiotic driver of ecological systems and a fundamental dimension of the metabolic theory of ecology (Gillooly et al. 2001; Brown et al. 2004). Yet, the effects of temperature are complex: organism performance, the distribution and abundance of species and the structure of ecological communities do not depend just on the direct impacts of temperature on physiology, but also on how these direct effects play out in the context of other processes. Ever since Darwin, it has been recognized that the distribution of species along environmental temperature gradients reflects interactions among species, not just direct effects. For instance, climate can impose species range limits directly, through increased mortality or decreased reproduction (e.g. Gaston 2003), but also indirectly, through geographically varying competitor abundance and modified interactions with competitors (Gross & Price 2000; Price & Kirkpatrick 2009). Southern range margins of northern-hemisphere species are believed often to be set by competition, with northern limits set by physiological tolerances (MacArthur 1972; Gaston 2003).
Many examples have been documented of temperature affecting competitive outcomes. For instance, classic experiments showed that temperature can influence competition in Tribolium beetles (Park 1954). When grown on their own, both Tribolium confusum and T. castaneum persisted across the entire range of temperatures tested. When placed together, competitive exclusion was observed. At and high humidity, T. castaneum always won. At , T. confusum prevailed about 70% of the time.
Dunson & Travis (1991) provided a broad overview of abiotic influences on community organization, and in their Table 1 list several other examples (including plants, flatworms, barnacles, fruit flies and fish) of experimental demonstrations that competition can be strongly influenced by temperature. A recent review of 688 studies found ubiquitous evidence that climate change affects several types of species interaction, including competition (Tylianakis et al. 2008). Woodward et al. (2010), Gilman et al. (2010) and Kordas et al. (2011) provided useful recent overviews of the potential impact of climate change on interspecific interactions. Amarasekare (2007, 2008) provide examples exploring the effects of a temperature-based temporal refuge on intraguild predation.
Table 1. Summary of model parameters. Parameters are defined in eqns (eqn 8) and 9. K and . Here has units eV, has units , and and are dimensionless
| ||−0·28||0·018||Edwards et al. (2012)|
| ||0·65||0·059||Edwards et al. (2012)|
| ||0·474||0·036||Eppley (1972); Bissinger et al. (2008)|
| ||0·29||0·091||Edwards et al. (2012), this paper|
The above examples are informative, but they and other studies of competition lack at least one of two important features: either the physiological mechanisms are not understood by which environmental conditions (e.g. temperature) influence survival, fecundity and other vital rates, or the population-dynamic mechanisms are not understood by which changes in vital rates lead to differences in competitive outcomes, or both. For instance, Park (1954) realized that temperature and humidity affect developmental and physiological processes in Tribolium, but he had no precise description of these effects, nor did he know how they influence population dynamics. Excellent population-dynamic models of T. castaneum and T. confusum now exist (Benoit et al. 1998; Dennis et al. 2001; Reuman et al. 2008), but to our knowledge, parameters of the models have not been related to environmental conditions. Tilman et al. (1981) considered a population-dynamic model and parameterized it experimentally for each of his two species at each temperature he considered. However, the physiological link between environmental conditions and species parameters was not explicitly described, making it hard to generalize findings. Buckley et al. (2014) have described the link between environmental temperatures and aspects of physiology in grasshopper populations on an altitude gradient, but have not yet linked physiology to population dynamics and competition.
The aim should be to describe thermal dependence quantitatively, without having to measure parameters species by species. We assume that parameters are not entirely idiosyncratic among species and can be inferred from species traits. Understanding and predicting the ecological effects of climate change has become a major goal of ecology. But this goal is hampered by the lack of generality that characterizes most studies of competition and that arises from the two shortcomings in mechanistic understanding described above.
If one has an explicit model of competition, a protocol for examining how temperature (or another factor) might influence competition is to make the parameters of the model functions of temperature. This basic approach was suggested by Gilman et al. (2010). Lafferty & Holt (2003) provide a comparable host–pathogen example, Vasseur & McCann (2005) developed a predator–prey example and Ohlberger et al. (2011) explore an example that examines intraspecific competition. A general model sheds light on the proposed approach. Consider the Lotka–Volterra model of competition, where we have abstractly expressed the model parameters as functions of temperature, T:
- (eqn 1)
where i ≠ j and i,j = 1,2 (Baskett 2012). Here, is the density of competitor i, measures the strength of intraspecific competition, and measures the strength of interspecific competition. If a species is alone in an environment with a constant temperature, it reaches an equilibrium population density , and it should persist, provided its intrinsic growth rate is positive (and ignoring demographic stochasticity). If we assume temperature is constant, then we can in the usual way write down conditions for coexistence (so that each species can increase when it is rare and its competitor is at its equilibrial density): . If intraspecific and interspecific density dependence are equivalent, so the two outer ratios are unity, then coexistence is impossible; the species with the higher carrying capacity, which is also the species with the higher intrinsic growth rate, will win. Growth rate is a measure of species competitive performance for this scenario. Both temperature and body size have a strong effect on growth rate (Savage et al. 2004; Amarasekare & Savage 2012), so will influence competitive dominance.
Species performance as a function of temperature typically is unimodal, albeit often with sharper declines at high temperatures than low temperatures (Angilletta 2009; Dell et al. 2011; Amarasekare & Savage 2012). Along an environmental temperature gradient, one might observe patterns of species replacement, with local dominance by whichever species has the higher local growth rate. This can happen in two different ways. First, two species may have similar performance curves, but with different optima (Fig. 1a). Second, one species may be more tolerant of a broader range of conditions, without there being any difference in optima (Fig. 1b). In case one, both species are equally generalized, but to different temperatures. In the latter case, one species is more specialized. In both cases, species 1 dominates below the points x on Fig. 1 and is supplanted at temperatures above x. So different biological interpretations can underlie a given pattern of species replacement along a temperature gradient. More complex scenarios can also occur, where the strengths of density dependence are not equal and vary as a function of temperature. A species may be an inferior competitor not because it has low carrying capacity, but because it is particularly sensitive in a given thermal regime to the per capita effects of the competing species. This simple model (eqn 1), while informative about possibilities, is clearly not mechanistic enough to fully explore the consequences of temperature change; models with truly mechanistic formulations of physiology–temperature relationships and more realistic depictions of dynamics are needed. In the simple case considered, if no crossing point, x, exists, then one species is strictly inferior and can only exist if the superior competitor has not dispersed to its range.
Figure 1. Competition at different temperatures according to the simple general model of the Introduction, (eqn 1). (a) Two species with similar performance curves as a function of temperature, T, but with different optima. (b) Two species with the same optimum temperature, but one is more cold tolerant. More details are in the Introduction. Kordas et al. (2011) produced a similar figure.
Download figure to PowerPoint
Reduced body size of ectotherms has been called a universal ecological response to global warming (Daufresne et al. 2009), and warming-related size reductions have been documented in many species (Millien et al. 2006; Daufresne et al. 2009; Sheridan & Bickford 2011). Geographical variation in body size along a temperature gradient, with smaller sizes in warmer areas, is long studied (Bergmann 1847). Phytoplankton, specifically, are smaller in warmer regions of the ocean or in response to experimental temperature manipulation (Figs 2 and 3 and Winder et al. 2009; Daufresne et al. 2009; Morán et al. 2010; Yvon-Durocher et al. 2011 for examples). Shrinking phytoplankton may be practically important because fisheries are based mainly on phytoplankton production, and marine consumer–resource relationships are size-structured (Barnes et al. 2010b). Also, carbon export to the deep ocean proceeds by sinking plankton (Smetacek 1985, 19991999), and differently sized plankton sink at different rates (Miklasz & Denny 2010).
Figure 2. An empirical example of smaller phytoplankton at warmer temperatures, from the marine environment. The figure presents similar information to Barnes et al. (2010a) in adapted format, using their data; it shows size distributions of phytoplankton in 361 water samples collected in the North Atlantic, South Atlantic, Benguela upwelling, Bergen fjord, Irminger Sea, Long Island Sound, North Sea, Norweigan Sea, and Oregon upwelling. , and refer to the cell sizes below which 10%, 50% and 90% of the biomass of the sample was represented, depicted with blue downward triangles, black open circles and red upward triangles, respectively, on the plot. Lines show ordinary least-squares regressions through the three point types.
Download figure to PowerPoint
Various explanations have been advanced for smaller size in ectotherms at warmer temperatures (Sheridan & Bickford 2011). Perhaps the best known explanation relates to metabolism: if the total metabolic rate of a trophic level increases with increasing population abundance, temperature and body size, and if nutrient inputs to the trophic level remain fixed, then increased metabolic demand due to warmer temperatures can be compensated for through reduced abundance or reduced body size, or a combination (Sheridan & Bickford 2011). Competition is not mentioned explicitly in this explanation, nor in any of the other common explanations (Sheridan & Bickford 2011), though it seems likely to play a role. The important question of how competition among differently sized ectotherms causes observed size reductions will serve as a focal point for our proposed approach.
This study has three main goals. We will (i) examine whether changes in the competitive abilities of differently sized plankton may be responsible for observed reductions in size under warming, using a model of phytoplankton nutrient uptake, growth and competition for nutrients. Phytoplankton are a good place to start, because of the extensive information that is available on their resource requirements, cell size dependencies and basic population dynamics. We write model parameters in terms of cell size and temperature. We explore how and invasion fitness are expected to vary with cell size at different temperatures. In so doing, we will (ii) illustrate our proposed theoretical framework and advocate its broader application. We will also (iii) examine a general model of resource exploitation using the same approach to assess possibilities for inferences about ectotherms generally.