Not only warming: The consequences of thermal variability in the growth of Fucus serratus

Not only warming: The consequences of thermal variability in the growth of Fucus serratus Ángela Fernández García Mestrado em Ecologia, Ambiente e Território Departamento de Biologia 2013/2014 Orientador Francisco Arenas Parra, Investigador auxiliar, CIIMAR Co-orientador Brezo Martinez Díaz-Caneja, Professora, URJC Agradecimentos: Em primeiro lugar quero agradecer ao Francisco a oportunidade que me deu de trabalhar de novo no CIIMAR e nos seus projetos. Agradeço também a sua ajuda, paciência e orientação tanto na elaboração das próprias experiências como na deste trabalho escrito, por tudo o que me tem ensinado, o seu apoio, força e ilusão pela ciência e sobretudo por desempenhar em simultâneo as funções de chefe e amigo. Agradeço também a Brezo, minha co-orientadora, pelas dicas e ajuda demonstrada na escrita do trabalho. Ao Laboratório de Biodiversidade Costeira e toda a sua gente pela ajuda dada e por tornar o trabalho um pouco mais fácil, porque além de colegas muitos deles foram quase família durante a minha estadia cá. Obrigada pelas saídas de campo, viagens na nossa carrinha, aniversários, festas de Natal, almoços, cafezinhos ao sol...mas sobretudo por tudo o que me ensinaram tanto a nível pessoal como profissional. A minha familia por estar sempre ao meu lado e pelo seu apoio incondicional. Aos meus amigos de sempre e aos mais recentes, por estarem perto quando precisei deles. Mas em especial às minhas “formosas”, “preti” e meninos de Aldoar por partilharem comigo este ano e qualquer coisa na cidade do Porto, fazendo com que fosse único e inesquecível. E por último, agradecer à Alba pelos momentos de dança, à Patricia por mimar-me tanto e ser tão detalhista, ao meu “Gafotas” por partilhar comigo sua família e amigos e ser o melhor professor que tive, à Ángela pelos momentos de loucura e por devolver a inspiração à minha vida, ao meu cuñi e irmã por me acolherem sempre em sua casa e fazer com que nunca me sinta sozinha, mas sobretudo pela força dada durante estes últimos meses. Sem vôces não teria sido capaz. Obrigada por tudo. ABSTRACT Despite the current and future climate change scenarios forecast an increase in the intensity and variability in thermal and temporal stress events, most of the studies have focused on the impact of increases or decreases in average stress intensity. For this reason and in order to know the Fucus serratus resilience to temperature variability, we performed two manipulative experiments to evaluate the potential changes in the physiological and growth responses of this specie to extreme events of temperature variability. The first experiment analyzed variation in mean temperature seawater (optimal: 18ºC; sublethal: 22ºC and lethal: 26ºC) at three levels of thermal variability (control, low and high; δ = 0, 2 or 4 ºC respectively). In the second experiment, we examined simultaneously thermal variability (two levels, low and high; δ = 2 or 4 ºC) and temporal variance (two levels: low and high, i.e. stress events distribution homogeneously or not, respectively) in a sublethal temperature. Results showed that thermal variability of seawater have significant effects in addition to changes in mean temperature, suggesting that high thermal variability buffers negative effects of high temperature in the growth and some photosynthetic parameters. These results suggest that the mechanisms that govern this interaction could be involved in the current distributional patterns of F. serratus in the Iberian Peninsula. Results also suggest that it is important to consider the capacity to survive at short and repeated periods of extreme conditions. This study has significant implications for understanding macroalgae responses to climate change, but more studies may be done under climate change variability scenarios. Key words: Climate change, Fucus serratus, thermal variability, temporal variance, stress. RESUMO Apesar do que os cenários atuais e futuros de alterações climáticas prevêm um aumento na intensidade e variabilidade nos eventos de estresse térmico e temporal, a maioria dos estudos têm-se centrado no impacto dos aumentos ou diminuições da intensidade média deste stress. Por esta razão, e a fim de conhecer a resiliência de Fucus serratus a variabilidade da temperatura, foram realizadas duas experiências para avaliar as possíveis variações nas respostas fisiológicas e de crescimento desta espécie aos eventos extremos de variação térmica. A primeira experiência analiçou a variação da temperatura média em água do mar (óptima: 18 °C; subletal: 22 °C e letal: 26 ºC) com três níveis de variabilidade térmica (controlo, baixa e alta; δ = 0, 2 ou 4 °C, respectivamente). Na segunda experiência, foram examinados simultaneamente a variabilidade térmica (dois níveis, baixo e alto; δ = 2 ou 4 º C) e variação temporal (dois níveis: baixa e alta, ou seja, se a distribuição de eventos de estresse era de forma homogênea ou não, respectivamente) em uma temperatura subletal. Os resultados mostraram que a variabilidade térmica da água do mar tem efeitos significativos, além das mudanças na temperatura média, sugerindo que altos níveis de variabilidade térmica amortiça os efeitos negativos da alta temperatura no crescimento e alguns parâmetros fotossintéticos. Estes resultados sugerem que os mecanismos que regem essa interação poderia ser envolvido nos atuais padrões de distribuição de F. serratus na Península Ibérica. Os resultados também sugerem que é importante ter em conta a capacidade de sobreviver em períodos curtos e repetidos de condições de estress. Este estudo tem implicações significativas para a compreensão das respostas das macroalgas nas alterações climáticas, mas mais estudos debem ser feitos em cenários variaveis de alterações climáticas. INDEX 1. LIST OF: _____________________________________________ 1 1.1. Figures _____________________________________________________ 1 1.2. Tables _____________________________________________________ 2 1.3. Abreviations _________________________________________________ 3 2. INTRODUCTION _______________________________________ 5 3. MATERIALS AND METHODS _____________________________ 7 3.1. Agae collection and acclimation _________________________________ 7 3.2. Exprimental design ___________________________________________ 8 Ambient variability and selection of stress _______________________________ 8 Thermal variability experiment _______________________________________ 9 Temporal variance experiment ______________________________________ 11 3.3. Functional responses _________________________________________ 12 Growth responses ________________________________________________ 12 Maximal quantum yield of photosynthesis (Fv/Fm) _______________________ 12 3.4. Statistical analysis ___________________________________________ 14 4. RESULTS ___________________________________________ 15 4.1. Thermal variability experiment __________________________________ 15 Growth ________________________________________________________ 15 Fv / Fm and RLC _________________________________________________ 17 4.2. Temporal variance experiment _________________________________ 20 Growth ________________________________________________________ 20 Fv / Fm and RLC _________________________________________________ 21 5. DISCUSSION ________________________________________ 25 6. REFERENCES _______________________________________ 29 FCUP 1 Not only warming: The consequences of thermal variability in the growth of Fucus serratus 1. LIST OF: 1.1. Figures Figure 1. a) Fucus serratus frond; b) View of Las Margaritas beach, A Coruña; c) Replicates individually labelled and hold from plastic frames. Figure 2. Piece-wise regression (segmented package for R from V. Muggeo, 2012) of the growth response of Fucus serratus after 14 days of laboratory culture at different temperatures, from 8 to 30ºC. Red line represents the positive slope of the curve and the green dotted line represents the negative slope. The red point is the breakpoint with SD. Figure 3. Daily temperature variation recorded on an intertidal rockpool at Praia Norte, Viana do Castelo during 2011. Figure 4. Temperature treatments diagram for a stress event (4 days). Figure 5. Representative units diagram with different mean temperature (18ºC-blue, 22ºC-green and 26ºC-orange). Squares represent the 20 l chambers where fronds were submerged during the experiment. Temperature range reach and variability levels are shown (left). Detailed view of experimental unit with chambers submerged in a water bath system (right). Figure 6. Representative unit diagram of high temporal variance at 22ºC of temperature. Squares represent the 20 l chambers where fronds were submerged during the experiment. Temperature range reach and variability levels are shown for both sequences (left). Detailed view of experimental unit with chambers submerged in a water bath system (right). Figure 7. Averaged growth (mean ± SE, n = 20) in thermal variability experiment. a) Represent values to 12 days growth and b) values to 20 days growth. Means with a common letter do not differ significantly based on Tukey HSD tests at p = 0.05 level. Figure 8. Photosynthetic parameters determined from ETR vs. Irradiance (PI), namely a) ETRm (μmol em-2 s-1), b) α [(μmol em-2 s-1) (μmol m-2 s-1)-1], c) Ek (μmol m-2 s-1) and d) β [(μmol em-2 s-1) (μmol m-2 s-1)-1] for the different treatments in thermal variability experiment. Mean ± SE (n= 20). FCUP 2 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Figure 9. Averaged growth (mean ± SE, n= 20) in temporal variance experiment. Open bars represent values to 12 days growth and filled bars correspond to 20 days growth. Figure 10. Photosynthetic parameters determined from ETR vs. Irradiance (PI), namely a) ETRm (μmol em-2 s-1), b) α [(μmol em-2 s-1) (μmol m-2 s-1)-1], c) Ek (μmol m-2 s-1) and d) β [(μmol em-2 s-1) (μmol m-2 s-1)-1] for the different treatments in temporal variance experiment. Mean ± SE (n= 20). 1.2. Tables Table 1. Distribution scheme of different stress events to low and high temporal variance treatments. Table 2. ANOVA summary for the significance of predictors of weighted mixed effect models for the growth at 12 days and 20 days in Fucus serratus. Predictors included mean temperature and thermal variability. Table 3. ANOVA of photosynthetic parameters determined from ETR vs. Irradiance (PI), namely ETRm, α, Ek and β for the different treatments in thermal variability experiment. Table 4. ANOVAs of effect of thermal and temporal variance with different sequences on growth at 12 days and 20 days in Fucus serratus. Table 5. ANOVA of photosynthetic parameters determined from ETR vs. Irradiance (PI), namely ETRm, α, Ek and β for the different treatments in temporal variance experiment. FCUP 3 Not only warming: The consequences of thermal variability in the growth of Fucus serratus 1.3. Abbreviations ºC: centigrade’s degrades δ: thermal variability N: north W: west µM: micromole N: nitrogen P: phosphorus NaNO3: sodium nitrate NaH3PO4: sodium phosphate FW: fresh water SD: standard deviation g: grams h: hour l: liter var: variance / variability seq: sequence W: weight t: time Fv/Fm: maximal quantum yield of photosynthesis RLC: rapid light curves ETR: electron transport rate FCUP 10 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Figure 4. Temperature treatments diagram for a stress event (4 days). Figure 5. Representative units diagram with different mean temperature (18ºC-blue, 22ºC-green and 26ºCorange). Squares represent the 20 l chambers where fronds were submerged during the experiment. Temperature range reach and variability levels are shown (left). Detailed view of experimental unit with chambers submerged in a water bath system (right). Seawater temperature was programmed and controlled using titanium heaters regulated by digital controllers and individual temperature probes (Aqua Medic ® AT Control System controllers, GmbH, Bissendorf, Germany). This system allowed a continuous control and record of seawater temperature with a programmed error of 0.1ºC. In order to ameliorate the effects caused by changes in air temperature and help heaters, chambers were submerged in a water bath system, set at the minimum temperature reached in each unit. Salinity was regularly monitored and chambers were refilled with freshwater to compensate for water evaporation every two days, mainly the 12 14 16 18 20 22 24 26 28 30 32 0 12 24 36 48 60 72 84 96 Temperature (ºC) Time (h) 18, δ= 0ºC 18, δ= 2ºC 18, δ= 4ºC 22, δ= 0ºC 22, δ= 2ºC 22, δ= 4ºC 26, δ= 0ºC 26, δ= 2ºC 26, δ= 4ºC FCUP 11 Not only warming: The consequences of thermal variability in the growth of Fucus serratus unit with higher temperature. Chambers were fitted within a constant aeration system to prevent stagnation and homogenize the experimental conditions. Temporal variance experiment The aim of the second experiment was to assess the effects of temporal variance of water temperature extreme events on F. serratus physiological and growth responses. Experimental design consisted of a set of 2 units at 22ºC. Each simulated conditions of low (unit described above for the previous experiment) or high temporal variance (Fig. 6). High temporal variance unit consisted of 8 white plastic chambers where two different sequences were simulated, each with two different levels of variability (δ = 2 or 4 ºC) changing over a 2, 4 or 6 days periods (Table 1). Treatments suffered always 10 days of high stress conditions. We used two replicated chambers for each combination of temperature treatments, i.e. 12 chambers (Fig 6). In each chamber ten fronds from F. serratus were placed during the experiment set on the CIIMAR terrace that lasted 20 days. Figure 6. Representative unit diagram of high temporal variance at 22ºC of temperature. Squares represent the 20 l chambers where fronds were submerged during the experiment. Temperature range reach and variability levels are shown for both sequences (left). Detailed view of experimental unit with chambers submerged in a water bath system (right). FCUP 12 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Table 1. Distribution scheme of different stress events to low and high temporal variance treatments. Days Temp. var. Thermal var. Seq 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Low ±2 1 20 20 24 24 20 20 24 24 20 20 24 24 20 20 24 24 20 20 24 24 Low ±4 1 18 18 26 26 18 18 26 26 18 18 26 26 18 18 26 26 18 18 26 26 High ±2 1 20 24 20 20 20 24 24 24 20 20 24 24 20 24 20 20 24 24 20 24 High ±4 1 18 26 18 18 18 26 26 26 18 18 26 26 18 26 18 18 26 26 18 26 High ±2 2 20 20 20 24 24 24 20 20 24 24 20 24 20 24 20 24 20 20 24 24 High ±4 2 18 18 18 26 26 26 18 18 26 26 18 26 18 26 18 26 18 18 26 26 3.3. Functional responses Growth responses Fronds were weighed at the beginning, in the middle (after 3 stress events, day 12 of experiment) and at the end (after the 10 days of stress events, day 20) of experiments. Fronds were weighed fully hydrated after dry it with absorbent paper to avoid possible errors associate with water excess, thus expressing growth as increase in fresh weight (FW) and determining the final size of the fronds (g). Growth was calculated as relative growth rate (Hoffmann & Pooter 2002): 12 12 )ln()ln( tt WW RGR where W2 is the fresh weight at time 2 (t2) and W1 is the fresh weight at time 1 (t1) Maximal quantum yield of photosynthesis (Fv/Fm) To characterize the physiological status of the algae in response to the different treatments, we measured maximum quantum yield of photosynthesis (Fv/Fm) in darkness using a MiniPAM (Heinz Walz GmbH, Effeltrich, Germany) as an indicator of physiological stress following Maxwell & Johnson (2000). FCUP 13 Not only warming: The consequences of thermal variability in the growth of Fucus serratus To know the starting fronds conditions, an initial Fv/Fm measurement previous experiments (predawn) in darkness was performed. We repeated this process on days 5 and 12 of experiments to ensure that algae were alive. At the end of experiment, after 20 days, we perform rapid light curves (RLC) to determine the electron transport rate (ETR). RLCs are plots of ETRs versus actinic irradiances (red light), making P-I curves (Hill 1996, Figueroa et al. 2003). Actinic light was increased every 15 seconds by exposing the sample to 9 increasing irradiances from 2 to 186 µmol photons m-2 s-1 (Withe & Critchley, 1999). ETR was calculated relating the effective quantum yield (Y II), which corresponds to the fluorescence of a frond not adapted to the darkness, and the eradiation intensity PAR according to the following modified formula from Schreiber et al. (1994): II FAEIIYETR )( where, E is the incident actinic irradiance, A the absorptance and FII is the fraction of chlorophyll associated to PSII being in brown algae 0.8 according to Grzymski et al. (1997). Absorptance (A), is the fraction of light that is actually retained by a sample. It was calculated using the following equation (Beer et al. 2000): 0 1E E At where E0 is the incident irradiance of PAR and Et is the transmitted irradiance with the algae being located on the light sensor. We use as absorptance value of 0.956 ± 0.051 (mean ± SE, n=90). There are several models that relate the light intensity and the rate of photosynthesis, but not all describe the degree of photoinhibition. For this reason light curves were fitted according to the model Platt & Gallegos (1980) to obtain efficiency values (ETR, equation 1), maximun ETR (ERT max, equation 2) and saturation irradiance (Ek, equation 3). )/()/( )1( PsEPsE SeePETR (eq 1) / ))/(())/((max S PETR (eq 2) max/ETREk (eq 3) FCUP 14 Not only warming: The consequences of thermal variability in the growth of Fucus serratus being α the maximal light utilization efficiency which coincides with the initial P-I curve slope and β the photoinhibition slope. 3.4. Statistical analysis Growth response of fronds to the treatments was highly heterogeneous, namely in those with higher stress, resulting in large variance heterogeneity among treatments. Data transformation was unable to lower heterocedasticity preventing the use of traditional ANOVA approach. Thus to examine growth effect we used weighted mixed effects models with mean temperature and thermal variability as fixed predictors and chamber as a random predictor. To account for larger residual spread in highly stress treatments we include a variance covariate term in the mixed effect model (Zuur et al. 2009). The selection of the most appropriate term was done comparing the different available structures in R and comparing the resulting models with AIC (Zuur et al. 2009). To examine a posteriori differences among treatments we used Tukey HSD poshoc test. All analyses using mixed effects models were done with R (R Development Core Team 2013) using lme and lsmeans packages. The effects of treatment on Fv/Fm (after 15 min and curve lights) were analysed using ANOVAs (Underwood 1997). The term mean temperature (optimun, 18ºC; sublethal, 22ºC and lethal, 26ºC) was fixed factor; thermal variability (control, δ=0; low, δ=2 and high, δ=4) was fixed and crossed with mean temperature and chamber (1 or 2) was included as a random factor nested in the interaction of both for first experiment. ANOVA were carried out with Statistica 10 (StatSoft Inc., Tulsa, OK, USA). In return, second experiment was an asymmetric design, where temporal variance (low or high) was fixed factor; sequence (1 or 2) was random factor and repeated only with the highest level of temporal variance; thermal variability (low, δ=2 and high, δ=4) was fixed and crossed with sequence and chamber (1 or 2) was included as a random factor nested within the interaction of the rest. To analyse the data from this study, we used a method described in Underwood (1993) that consists of combining the sum of squares values from separate analyses of variance. We made two different ANOVAS. The first did not to distinguish between the temporal variability factor, i.e. it considered the design like symmetrical and with three different sequences (two of high temporal variability and one of low temporal variability). With this analysis we obtain the SS interaction “sequence x thermal variability” will be split in several components because FCUP 15 Not only warming: The consequences of thermal variability in the growth of Fucus serratus this term is always additive. The second ANOVA compared just the two high temporal variance sequences, obtaining the SS interaction "sequence x thermal variability" within sequences of high variance. Using these last analyses, we completed the first ANOVA and split the interaction “sequence x thermal variability” in two different sources of variation additive (sequence x thermal variability within high variance + sequence x thermal variability high vs. low temporal variance) (for more detail, see Glasby 1997). Analyses were carried out with GMAV (1997) statistical package (University of Sidney, Australia). Homogeneity of variances for both experiments was tested using the Cochran´s test (Underwood 1997). We considered p < 0.05 as threshold value when testing the significant differences in the analysis of variance, i.e. the null hypothesis was rejected at the 95% confidence level. 4. RESULTS 4.1. Thermal variability experiment Growth Thermal variability experiment showed that frond growth was clearly affected by the interaction of main seawater temperature and thermal variability (F1, 4 = 6.0421; p = 0.0001 and F1, 4 = 7.5774; p < 0.0001; growth at 12 and 20 days respectively) (Table 2). Fronds growth followed the same pattern throughout the experiment, that is, data obtained after 12 and 20 days of experiment were similar. As expected, fronds of treatments at 18 ºC grow slightly more than those found at 22 °C reaching values of 0.030 ± 0.001g and 0.024 ± 0.001g (mean ± SE, n = 20) respectively at the end of the experiment. However, fronds in treatments at 26 ºC (lethal temperature) showed a decrease, -0.019 ± 0.007g (mean ± SE, n = 20), i.e. lost tissue, and were dead (approx. 50% survival rate, compared with 100% in other temperature treatments). These results are in agreement with those obtained in a previous experiment, where growth fronds were tested at different seawater temperatures recorded lower growth at 18 and 22 °C (0.022 ± 0.001g and 0.021 ± 0.001g, mean ± SE, n = 20, respectively) and higher at 26 ºC (0.0002 ± 0.001g) of temperature than in our experiment (data not shown). It was observed that increased thermal variability made that the effects FCUP 16 Not only warming: The consequences of thermal variability in the growth of Fucus serratus caused by a higher or lethal temperature being ameliorated (Fig. 7). Chamber was consistently significant, suggesting some environmental heterogeneity of the experimental prototypes. Table 2. Growth model summary for the significance of predictors of weighted mixed effect models for the growth at 12 days and 20 days in Fucus serratus. Predictors included mean temperature and thermal variability. Growth 12 days 20 days Variables df F p F P Intercept 1 1508.63 <0.0001 1795.81 <0.0001 Mean Temp -T 2 247.1389 <0.0001 215.3919 <0.0001 Thermal Var. - Th v 2 1.6194 0.3334 1.0775 0.4439 Chamber - C 1 1508.631 <0.0001 1785.813 <0.0001 T x Th v 4 6.0421 0.0001 7.5774 <0.0001 Residual 168 Significant differences at α < 0.05 are shown in bold. Figure 7. Averaged growth (mean ± SE, n = 20) in thermal variability experiment. a) Represent values to 12 days growth and b) values to 20 days growth. Means with a common letter do not differ significantly based on Tukey HSD tests at p = 0.05 level. -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC Thermal var. (δ) Mean temp. a a a a c a a,b a a,b c b b d c d a a a Growth (g/day) a. Growth 12 days b. Growth 20 days FCUP 17 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Fv/Fm and RLC. Values of Fv/Fm at predawn (before the experiment) and after 5 days of experiment were high, about 0.777 ± 0.004, (mean ± SE, n= 64) and 0.713 ± 0.009 (mean ± SE, n= 72), respectively, suggesting optimal photosynthetic performance before start and at the beginning of the experiment and without trends noticeable. Fv / Fm values after 12 days were also high in general, around 0.716 ± 0.009 (mean ± SE, n= 72), however appeared statistically significance differences between high temperature treatment (0.635 ± 0.027, mean ± SE, n= 24) and low and medium temperature treatment (0.759 ± 0.005 and 0.755 ± 0.003, mean ± SE, n= 24, respectively). The effects of mean temperature and thermal variability in terms of the photosynthesis electron transport were determined using the curves ETR vs. Irradiance (PI) as we discussed previously. Figure 8 shows a detailed summary of the photosynthetic parameters obtained from these curves. Visually nor treatment effects or trends were found in these physiological parameters. Also, no statistically significant differences were found in the analysis of variance (Table 3) for photosynthetic parameters except for photoinhibition rate (β) which showed a significant mean temperature x thermal variability interaction (F4, 54= 8.465, p =0.004). This interaction shows that in the control treatment (δ=0) photoinhibition rate shows a slight increase as we increase the mean temperature of the treatments. However, treatments with low thermal variability (δ=2) shows that the rate of photoinhibition decreases drastically as we increase the mean temperature, reaching the maximum values at 18 °C and the minimum at 26 ºC. Finally, the high thermal variability treatments (δ=4) show very similar values of photoinhibiton rate at 18 °C and 26 °C, significantly lower than values obtained for 22C. Noteworthy that there are practically no differences in values obtained at 22 °C for all thermal variability (Figure 8.d). No significant differences in means based on SNK test at p = 0.05 level were observed for this interaction. FCUP 18 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Figure 8. Photosynthetic parameters determined from ETR vs. Irradiance (PI), namely a) ETRm (μmol em-2 s-1), b) α [(μmol em-2 s-1) (μmol m-2 s-1)-1], c) Ek (μmol m-2 s-1) and d) β [(μmol em-2 s-1) (μmol m-2 s-1)-1] for the different treatments in thermal variability experiment. Mean ± SE (n= 20). 0 2 4 6 8 10 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC 0 5 10 15 20 25 30 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC 0 40 80 120 160 200 0 2 4 0 2 4 0 2 4 18ºC 22ºC 26ºC Thermal var. (δ) Mean temp. Thermal var. (δ) Mean temp. c. Light saturation coefficient (Ek) a. Maximun electron transport rate (ETRm) d. Photoinhibition rate (β) b. Maximal light utilization efficiency (α) FCUP 19 Not only warming: The consequences of thermal variability in the growth of Fucus serratus Table 3. ANOVA of photosynthetic parameters determined from ETR vs. Irradiance (PI), namely ETRm, α, Ek and β for the different treatments in thermal variability experiment. Fv/Fm ETRm α Ek β Variables df MS F p MS F p MS F p MS F p Mean Temp -T 2 0,119 0,016 0,984 0,006 0,197 0,825 13,526 0,487 0,630 2130,149 4,525 0,044 Thermal Var. - Th v 2 3,692 0,508 0,618 0,008 0,263 0,774 15,091 0,543 0,599 2820,243 5,990 0,022 Chamber - C 9 7,263 1,087 0,388 0,031 3,478 0,002 27,770 0,568 0,817 470,795 0,219 0,990 T x Th v 4 8,749 1,205 0,373 0,005 0,165 0,951 96,937 3,491 0,055 3985,500 8,465 0,004 Residual 54 Significant differences at α < 0.05 are shown in bold. FCUP 26 Not only warming: The consequences of thermal variability in the growth of Fucus serratus of extreme conditions is another aspect to consider (Bertocci et al. 2005, Overgaard et al. 2014). Our results suggest that a high temporal variability reduces the growth rate and fitness and therefore its ability to recovery or acclimation after several consecutive short-term stress events, as in sequence 2. It is also interesting to highlight that growth rates in both experiments were similar to those suggested from the functional response to temperature in previous experiment that we use to define our treatment temperatures. That is, if we consider the average temperatures of our treatments (18, 22 or 26 ° C) and compare these growth rates with those obtained at the same temperatures in the previous experiment (Arrontes 1993, Chapman 1995, 2002, Martínez et al. 2012), we observed that the values are similar, although in small increments or decreases due to different thermal or temporal variability treatments. This suggested some predictability of species from the average temperature conditions in variables environmental systems. Despite the growing amount of research exploring the consequences of global warming on marine ecological systems, few studies have focused on stressors variability or extreme effects. In fact, most of the work have examined the impact of increases or decreases in average intensity of stressors. This is the case of seaweeds, where most of ecophysiological studies focused on responses to changes in the average intensity of stressors (Pearson et al 2009) and their potential interactions (e.g., Martinez et al. 2012, Ferreira et al. 2014). In fact, the only research focused on the role of variability of stressors on macroalgal assemblages aimed to understand the impact of temporal variance of physical disturbance at community level responses like changes in assemblages structure or stability (Bertocci et al. 2005, 2007, Benedetii-Cechi et al. 2006, Vaselli et al. 2008). Only a recent study tried to examine the interaction between temporal variability of stressors and its intensity (Trilla 2013). To our knowledge, no manipulative experiments have been carried out concerning a thermal variability on macroalgae, despite being a very important and novel subject in other research fields, like on insect’s research (Lalouette et al. 2007, Engelbrecht et al. 2010, Folgueira et al. 2011, Williams et al. 2012, Bozinovic et al. 2011). In these studies with insects, it was found that an increase in environmental variability may have both, positive or negative effects on fitness and growth species populations. Recent models in insects indicate that thermal variance could have as much (or more) of an impact on fitness as does the mean temperature, since an increase in thermal variability when mean temperature is close to the optimal, impair performance while if FCUP 27 Not only warming: The consequences of thermal variability in the growth of Fucus serratus temperature is far from the optimal can improve growth and fitness population (Bozinovic et al. 2011). In particular, Terblanche et al. (2010), using a similar approach to our experiment, and Bozinovic et al. (2011) found that a large variability could limit thermal plasticity responses, thus reducing the fitness of flies, their target specie. They proposed that at low mean temperatures, the critical thermal minimum of the species, i.e. the minimum sublethal temperature, was slightly higher with a greater thermal variability. Similarly, at high mean temperatures, greater thermal variability increased the critical thermal maximum. Those results suggest that at low mean temperatures increased thermal variability had a negative effect, whereas at higher temperatures mean increasing the variability had a positive effect on the fitness species. They found that for the low thermal variability scenarios responses of acclimatization for most of their parameters measured were typical, however for high variability scenarios poorer resistance to climatic stress in some but not all parameters, was detected. Authors suggest that this response may be related to heat shock protein production (Kalosaka et al. 2009), enhanced by the heat shock response found in all living organisms, which offers an effective defense against exposure to adverse environments (Lindquist 1986). These results have some similarities to our thermal variability results. We found the expected effects of mean temperature and more interestingly under the most extreme experimental temperatures, greater thermal variability had a positive effect on growth, i.e. Fucus serratus grew better with high thermal variability (δ = 4) than with low (δ = 2) or no thermal variability (δ = 0) at high temperatures. All this could be related to increase thermal variability in high temperatures treatments might cause the increase of critical thermal maximum, slowing down its decrease or tissue lost as occurred in 26 °C treatments. The mechanisms behind this paradoxical results required more research and could be involved in the current distributional patterns of Fucus serratus in the Iberian Peninsula. Higher temperature variability under extreme conditions (i.e 26 ºC on average) meant that fronds endured temporary the highest temperature events (i.e 30 ºC) but also the lowest temperature conditions (i.e. 22 ºC) of the whole set of treatments with mean temperature of 26 ºC. The current area of distribution of Fucus serratus in Portugal (around Viana do Castelo) has in terms of mean seawater temperatures values similar to other areas in the Cantabrian Sea where there are no longer F. serratus populations. However coastal summer seawater temperatures are among the lowest of the whole Atlantic Iberian shores due to the persistent upwelling events from March to November FCUP 28 Not only warming: The consequences of thermal variability in the growth of Fucus serratus (Cacabelos 2013). These cooling events may well enable seaweeds to recover from very extreme emersion stress and reduce the impacts of increasing air temperatures. The current and future climate change scenarios forecast an increase in the intensity and variability in thermal and temporal stress events (Easterling et al. 2000, McGregor et al. 2005, Meehl et al. 2007). In particular, up to 4 °C increase in water temperature at the end of the 21st century (Müller et al. 2009) and a higher frequency, between 5 and 10 times more of heat waves over the next 40 years (Schär et al. 2004, Barriopedro et al. 2011) is anticipated along North-Atlantic shores. Species respond to these changes with phenological changes and in their distributional ranges harboring local extictions such as Fucus serratus in the north of the peninsula (Viejo et al. 2011). In order to better understand the possible species responses to this variability impact and their demographic consequences it is important to investigate and understand their phenotypic plasticity and adaptive evolvability, that in Fucus serratus is quite small (Bijlsma & Loeschcke 2012) because their flow and genetic diversity is low (Coyer et al. 2003, Hampe & Petit 2005, Pearson et al. 2009), since it is a perifical and isolated population after suffering the last glaciation (Hoarau et al 2007). In conclusion, our results revealed interactive effects of mean intensity and both thermal and temporal variability of seawater stress events on Fucus serratus growth and physiological response. 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