Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level

Temperature strongly affects phytoplankton growth rates, but its effect on communities and ecosystem processes is debated. Because phytoplankton are often limited by light, temperature should change community structure if it affects the traits that determine competition for light. Furthermore, the aggregate response of phytoplankton communities to temperature will depend on how changes in community structure scale up to bulk rates. Here, we synthesize experiments on 57 phytoplankton species to analyze how the growthirradiance relationship changes with temperature. We find that light-limited growth, light-saturated growth, and the optimal irradiance for growth are all highly sensitive to temperature. Within a species, these traits are co-adapted to similar temperature optima, but light-limitation reduces a species’ temperature optimum by 58C, which may be an adaptation to how light and temperature covary with depth or reflect underlying physiological correlations. Importantly, the maximum achievable growth rate increases with temperature under light saturation, but not under strong light limitation. This implies that light limitation diminishes the temperature sensitivity of bulk phytoplankton growth, even though community structure will be temperature-sensitive. Using a database of primary production incubations, we show that this prediction is consistent with estimates of bulk phytoplankton growth across gradients of temperature and irradiance in the ocean. These results indicate that interactions between temperature and resource limitation will be fundamental for explaining how phytoplankton communities and biogeochemical processes vary across temperature gradients and respond to global change. Global warming has underscored the need to understand how temperature affects organisms, populations, communities, and ecosystems. Predicting the ecological effects of temperature is difficult, in part because populations are typically limited by competition or predation, and so to predict growth or abundance we need to know how temperature modulates the multiple physiological processes that underlie species interactions (Vasseur and McCann 2005; Kordas et al. 2011; O’Connor et al. 2011). For example, simple scaling relationships for the temperature-dependence of ecosystem processes may only apply when resources are not limiting (Xu et al. 2004; L opez-Urrutia and Mor an 2007; De Castro and Gaedke 2008). Although resource limitation and other processes complicate the role of temperature, there still may be general rules for how temperature modulates physiology and species interactions, and quantifying such rules will enhance our ability to explain ecosystem responses to temperature gradients (Dell et al. 2014). Because light and nutrient limitation strongly affect primary producers, it is essential to characterize any general patterns for how temperature interacts with limitation by these resources. In this study, we synthesize monoculture experiments that characterize the interactive effects of light and temperature on phytoplankton growth. Phytoplankton contribute nearly half of global primary production, are the base of the food web in aquatic environments, and play a critical role in the feedbacks of the global carbon cycle to anthropogenic forcing (Falkowski et al. 1998; Field et al. 1998). Phytoplankton are very sensitive to environmental change (Doney et al. 2012), and both temperature and irradiance are among the key environmental drivers whose distribution is predicted to continue changing in the future (De Stasio et al. 1996; Boyd et al. 2015). The temperature and irradiance that *Correspondence: [email protected] Additional Supporting Information may be found in the online version of this article. 1232 LIMNOLOGY and OCEANOGRAPHY Limnol. Oceanogr. 61, 2016, 1232–1244 VC 2016 Association for the Sciences of Limnology and Oceanography doi: 10.1002/lno.10282 phytoplankton experience tend to be positively correlated, because solar radiation increases water temperature, and increased temperature drives stratification and shoals the mixed layer, thus increasing average irradiance experienced by phytoplankton. Nonetheless, phytoplankton occur over a wide range of temperature-irradiance combinations, including low irradiance at the deep chlorophyll maximum or below in warm waters (Fennel and Boss 2003; Cullen 2015), and saturating irradiance in shallow mixed layers in cold waters (e.g., due to meltwater in the summer in polar regions; Lancelot et al. 1993). Therefore, understanding the processes that control individual growth, community structure, and primary production requires us to understand how light and temperature interact, i.e., how temperature modulates the growth-irradiance relationship and how light modulates the growth-temperature relationship. Whether temperature has important direct effects on phytoplankton growth and community size structure in the ocean is currently debated (Mor an et al. 2010; Mara~ n on et al. 2012, 2014; Regaudie-de-Goux and Duarte 2012), and conflicting results may in part be driven by interactions between temperature and resource limitation. The independent effects of irradiance and temperature on phytoplankton growth have been intensively studied and are well-characterized. Growth increases nearly linearly at low irradiance, saturates at some optimal irradiance for growth, and then declines due to photoinhibition (Langdon 1988; Talmy et al. 2013; Edwards et al. 2015). Interspecific differences in this relationship are thought to be due to differences in pigment content, respiratory costs, cell size, and pathways for photoprotection and repair of photodamage (Langdon 1988; Six et al. 2007). Temperature responses are also unimodal, typically with a left-skew such that growth increases exponentially or linearly from low temperature, and declines more rapidly above the optimum (Eppley 1972; Montagnes et al. 2003; Thomas et al. 2012). Interspecific differences in this relationship are thought to be due to differences in protein structure (particularly the stability of enzymes, which is related to specificity and reaction rates), lipid composition of cell membranes, and chaperone protein production (Clarke 2003; Kingsolver 2009). For both irradiance and temperature responses, differences between genotypes or species measured in the lab have been correlated with differences in distributions across depths, seasons, or latitudes (Rodr ıguez et al. 2005; Johnson et al. 2006; Thomas et al. 2012, 2016; Edwards et al. 2013a,b). The interactive effects of temperature and irradiance on phytoplankton have been studied in many experiments (e.g., Dauta 1982; Verity 1982; Palmisano et al. 1987), but there is currently no clear consensus for how growth-irradiance relationships change with temperature, or how thermal optima change with irradiance. It is often expected that lightlimited photosynthesis and growth will be less sensitive to temperature than light-saturated rates, due to limitation of photosynthesis by photon absorption at low irradiance, but contradictory results have been observed (Raven and Geider 1988; Davison 1991; Nicklisch et al. 2008). Phytoplankton growth is often modeled as an exponential function of temperature under all resource conditions (Blackford et al. 2004; Taucher and Oschlies 2011), which assumes that resourcesaturated growth has the same monotonic temperature sensitivity as lightor nutrient-limited growth. In contrast, the initial slope of the chlorophyll-specific photosynthesis-irradiance curve is sometimes modeled as temperature-insensitive, while the maximum rate of photosynthesis is given an exponential temperature dependence (Geider et al. 1998; Moore et al. 2002). Importantly, the way in which temperature effects are modeled has large effects on projections of global primary production under climate change (Sarmiento et al. 2004; Taucher and Oschlies 2011). Many pressing ecological questions require us to “scale up” from community diversity and dynamics to aggregate ecosystem processes. For example, to understand the role of the biosphere in the global carbon cycle we need to know how complex communities respond to multiple environmental factors, and how community structure determines aggregate processes like primary production or carbon export to the deep ocean (Duffy and Stachowicz 2006; Boyd et al. 2015; Worden et al. 2015). Responses to temperature are an area where the difference between individual and aggregate outcomes are significant: even though individual species exhibit unimodal responses to temperature (Thomas et al. 2012; Dell et al. 2014), bulk ecosystem rates typically change monotonically with temperature, i.e., they do not decline above some optimum. This difference between species and community response was explained in an influential paper by Eppley (1972), which compiled measurements of phytoplankton growth rate as a function of temperature. Although individual species exhibited unimodal responses to temperature, the highest observed growth rates across species as a function of temperature increased exponentially. He characterized this with an exponential curve, l50.59 3 10, where l is specific growth rate (d) and T is temperature (8C). This curve is equivalent to a Q10 of 1.88, i.e., growth increases by a factor of 1.88 when temperature increases by 108C (a recent update using more data by Bissinger et al. (2008) found an essentially identical exponent, l50.81 3 10, or Q1051.88). This result implies that species will replace one another along a temperature gradient via competition, with the result that phytoplankton whole-community growth rate increases monotonically with temperature, if the maximum possible growth rate is higher for species adapted to higher temperatures (Eppley 1972; Norberg 2004; Bissinger et al. 2008). We will refer to the predicted wholecommunity curve, derived from the upper envelope of the single-species curves, as a trait envelope (Fig. 1A). Importantly, it is possible that the shape or slope of this envelope changes as a function of resource limitation (Fig. 1B). Edwards et al. Light–temperature interactions