Photoperiod influences the shape and scaling of freshwater phytoplankton responses to light and temperature

Abstract

Light fluctuations are ubiquitous, exist across multiple spatial and temporal scales, and directly affect the physiology and ecology of photoautotrophs. However, the indirect effects of light fluctuations on the sensitivity of organisms to other key environmental factors are unclear. Here, we evaluate how photoperiod regime (period of time each day where organisms receive light), a dynamic element of aquatic ecosystems, can influence the interactive effects of temperature and irradiance (intensity of light) on the growth rate of phytoplankton populations. We first completed a literature review and meta-analysis that suggests photoperiod alters the individual effects of temperature -but not irradiance -on algal growth rates and that highlights how few studies experimentally manipulate photoperiod, temperature and irradiance. To address this empirical gap, we conducted a set of laboratory experiments on three freshwater phytoplankton species (Chlamydomonas reinhardtii, Chlorella vulgaris and Cryptomonas ovata). We measured performance surfaces relating growth rate to irradiance and temperature gradients for each species in constant (24:0 h of light:dark) environments. We then evaluated whether analogous surfaces measured under different photoperiods (6:18, 12:12 and 16:8 h of light:dark) and scaled by the duration of light availability could be inferred from results under constant light. For a majority of the combinations of species and photoperiods examined, photoperiod meaningfully altered the intercept and shape of performance surfaces. These differences were most pronounced under the shortest photoperiod (6:18 h light:dark), where populations underperformed expectations. Alterations to performance surfaces were non-linear and mostly structured by temperature with higher temperatures yielding higher than anticipated growth rates. Collectively, these experiments and synthesis reveal the potential for photoperiod regime to influence the effects of temperature, irradiance and their interaction on phytoplankton growth. Beyond the environmental variables and organisms presently considered, this research highlights the capacity for dynamic, abiotic variables to exert direct effects while also influencing relationships among other environmental factors.

Environmental variability in aquatic ecosystems: Avenues for future multifactorial experiments

“…It is important to note that beyond the illustrative examples described here, other environmental variables also interact in complex ways altering performance of a variety of biological systems and therefore highlighting the relevance of considering such complex multifactorial scenarios in variability research. Temperature has been shown to interact with irradiance affecting phytoplankton growth rate, and the effect of this interaction is shaped by the photoperiod regime (light fluctuations, Theus et al 2022). Furthermore, other relevant interactive effects of environmental factors on performance have been highlighted in the literature, like the combined effects of temperature and oxygen concentration on marine invertebrates and fish metabolism and physiology (Pörtner 2010; Vaquer‐Sunyer and Duarte 2011; Roman et al 2019), or temperature and salinity effects on mussel's vital rates (Guzmán‐Agüero et al 2013), but less is known about these interactions under fluctuating conditions.…”

“…For environmental variables other than temperature the information is sparse, and approaches differ among variables, but some comparisons between constant and fluctuating environments are available. Variable light affects phytoplankton performance differently than constant light (Shatwell et al 2012; Theus et al 2022) and the effect of fluctuations depends on the average irradiance and the period of fluctuations (i.e., the part of the response curve that is analyzed and the frequency of fluctuations) (Litchman 1998, 2000; Shatwell et al 2012). Experimental studies using salinity gradients showed that marine mussels' vital rates (filtration, respiration, among others) might have nonlinear response curves (Guzmán‐Agüero et al 2013; Peteiro et al 2018), but the effects of salinity fluctuations have not been addressed using the performance framework.…”

“…In addition to experimental approaches, theoretical studies have increasingly addressed the effects of variability on performance, but model predictions often lack experimental tests required to extrapolate their expectations to ecologically relevant conditions (Morash et al 2018). How accurately reaction norms established for constant or ramp treatments anticipate the performance of ecological systems experiencing environmental fluctuations has been tested in aquatic ecosystems, and many studies showed deviations between observed and predicted patterns for a variety of organisms (anuran larvae, phytoplankton, macroinvertebrates: Niehaus et al 2012; Wang et al 2019; Marshall et al 2021 for temperature, and Litchman 2000; Theus et al 2022 for light). These mismatches can be instructive to understand the resulting biological process that contributed to their occurrence ( see Section How to approach biological consequences of environmental variability).…”

Photoperiod effects in a freshwater community: Amphibian larvae develop faster and zooplankton abundance increases under an early‐season photoperiod

“…We predicted that phytoplankton abundance would increase under the longer (late-season) photoperiod due to longer light exposure (Seyfabadi et al, 2011;Shatwell et al, 2012;Tang & Vincent, 2000;Theus et al, 2022), but we had no a priori predictions for periphyton and zooplankton.…”

Resource limitation determines realized thermal performance of consumers in trophodynamic models

“…Although numerous studies have sought to improve the link between TPCs and predictions of fitness in natural environments (Fey et al, 2019 ; Khelifa et al, 2019 ; Kremer et al, 2018 ; Sinclair et al, 2016 ), these have mainly focused on the effects of life‐history, behaviour and physiology. Recent work has shown that thermal performance curves can also respond to changes in the availability of resources such as food or light (Theus et al, 2022 ; Thomas et al, 2017 ) further challenging the practice of making a prediction based on ‘idealized’ TPCs. Building on the work of Brett et al ( 1969 ) and Brett ( 1971 ), who showed that the individual growth of salmon was optimized at lower temperatures as the availability of food declined, Thomas et al ( 2017 ) found that the optimal temperature for phytoplankton population growth occurred at lower temperatures under reduced nutrient availability and used a model to derive a mechanistic understanding of this shift.…”