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Temperature effects on phytoplankton diversity — The zooplankton link
Journal of Sea Research 85 (2014) 359–364
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Temperature effects on phytoplankton diversity — The zooplankton link Aleksandra M. Lewandowska a,⁎, Helmut Hillebrand b, Kathrin Lengfellner c, Ulrich Sommer a a b c
Helmholtz Centre for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, 24105 Kiel, Germany Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of Oldenburg, Schleusenstrasse 1, 26382 Wilhelmshaven, Germany Department of Ecology and Environmental Sciences, Umeå University, 90187 Umeå, Sweden
a r t i c l e
i n f o
Article history: Received 21 September 2012 Received in revised form 5 July 2013 Accepted 10 July 2013 Available online 17 July 2013 Keywords: Climate warming Mesocosms Plankton Diversity
a b s t r a c t Recent climate warming is expected to affect phytoplankton biomass and diversity in marine ecosystems. Temperature can act directly on phytoplankton (e.g. rendering physiological processes) or indirectly due to changes in zooplankton grazing activity. We tested experimentally the impact of increased temperature on natural phytoplankton and zooplankton communities using indoor mesocosms and combined the results from different experimental years applying a meta-analytic approach. We divided our analysis into three bloom phases to define the strength of temperature and zooplankton impacts on phytoplankton in different stages of bloom development. Within the constraints of an experiment, our results suggest that increased temperature and zooplankton grazing have similar effects on phytoplankton diversity, which are most apparent in the post-bloom phase, when zooplankton abundances reach the highest values. Moreover, we observed changes in zooplankton composition in response to warming and initial conditions, which can additionally affect phytoplankton diversity, because changing feeding preferences of zooplankton can affect phytoplankton community structure. We conclude that phytoplankton diversity is indirectly affected by temperature in the post-bloom phase through changing zooplankton composition and grazing activities. Before and during the bloom, however, these effects seem to be overruled by temperature enhanced bottom-up processes such as phytoplankton nutrient uptake. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Current global warming pits us with the necessity to understand and predict the impact of rising temperatures on ecosystems. During the last two decades, ecological sciences have therefore put more focus on this important issue, for example to gain insight into how temperature affects properties and functioning of food webs. Recent marine studies revealed that temperature impacts marine organisms on different trophic levels and alters species interactions between and within trophic levels (Kordas et al., 2011; O'Connor, 2009; O'Connor et al., 2011). Phytoplankton generally forms the base of the pelagic food web and hence merits special attention. Although increased temperature speeds up metabolic processes of phytoplankton and might increase primary production in certain regions (Chavez et al., 2011; Doney, 2006), global phytoplankton decline with climate warming has been reported (Boyce et al., 2010; Moran et al., 2010). Two major processes were defined to be responsible for this decline: i) increasing resource limitations as a consequence of stronger water column stratification in the warmed ocean and ii) increasing top-down control of phytoplankton by zooplankton with rising temperature. Phytoplankton diversity is also expected to be altered by climate change but this link is less well understood. Recent studies draw different pictures: whereas controlled laboratory experiments reported ⁎ Corresponding author. Tel: +494316004411; fax: +49 4316004402. E-mail address: [email protected] (A.M. Lewandowska). 1385-1101/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.seares.2013.07.003
more rapid competitive exclusion resulting in a loss of species richness at higher temperature (Burgmer et al., 2011), field studies found an increasing number of species (richness) by immigrating warm-adapted species (Beaugrand et al., 2010). It seems, however, that irrespective of the net effect on richness, higher temperatures are strongly associated to higher species turnover (Hillebrand et al., 2012). Before species go extinct, rising temperatures will alter species dominance. Therefore, phytoplankton evenness (a measure of how equitable species are distributed within the community) is expected to be even more responsive to rising temperatures than richness (Hillebrand et al., 2008). Mesozooplankton can strongly reduce the biomass of microalgae and affect phytoplankton diversity (richness and evenness). Generally zooplankton can reduce the number of phytoplankton species by increasing phytoplankton mortality or can increase richness by feeding on dominant algae taxa and thus releasing rare species from interspecific competition. With respect to evenness, consumers predominantly have a positive effect as they reduce the proportion of the dominant species (Hillebrand et al., 2007). However, zooplankton can also decrease phytoplankton evenness if the dominant algal species are not included in the zooplankton food spectrum. Thus, the consumer effect on phytoplankton evenness depends on consumer quantity as well as on its identity and feeding preferences. Moreover, it depends on the quality of phytoplankton itself in terms of nutritional value and essential compounds (Hall et al., 2007). The impact of temperature on phytoplankton depends on its successional stage (Thackeray et al., 2008). Prior the peak in biomass, positive
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2. Material and methods
For straight comparison between experiments we used only data for ΔT = 0 °C and ΔT = 6 °C. Phytoplankton was sampled three times per week, preserved with Lugol's iodine and counted using the inverted microscope technique according to Utermöhl (1958) for species N5 μm. Flow cytometry technique (FACScalibur, Becton Dickinson) was used to count smaller species (Sommer and Lengfellner, 2008). Phytoplankton biomass was defined as carbon content calculated from cell volumes (Menden-Deuer et al., 2000) after an approximation of cell volumes to geometric standards (Hillebrand et al., 1999). Zooplankton was sampled once a week with a net (12 cm diameter, 64 μm mesh size), shock frozen with liquid nitrogen (experiments 2006 and 2007) or fixed with Lugol's iodine (experiments 2008 and 2009) and counted with a stereomicroscope. Copepods were specified to the genus level, Temora sp. and rare Eurytemora sp., as well as Pseudocalanus sp. and rare Paracalanus sp. were paired together, because their early copepodid stages are difficult to distinguish. Copepod biomass was estimated as a carbon content using species and stage specific conversion factors (Lengfellner, 2008). More details on the experimental setup and sampling procedure for each experiment can be found elsewhere (Lewandowska and Sommer, 2010; Sommer and Lengfellner, 2008; Sommer and Lewandowska, 2011).
2.1. Experimental setup and laboratory techniques
2.2. Diversity parameters and statistics
Eight (experiments 2006 and 2007) or twelve (experiments 2008 and 2009) mesocosms (1400 L volume, 1 m depth) were set up in temperature regulated climate rooms. Sea water containing the natural late winter plankton community (phytoplankton, bacteria and protozoa) from the Kiel Fjord, Baltic Sea, was pumped into a distribution tank and gravitationally transferred to the experimental units. The mesocosms were filled simultaneously to assure homogenous distribution of plankton. Mesozooplankton was added from net catches at appropriate concentrations for each experiment (Table 1) as it did not pass through the pumping system. The water column was gently mixed by a propeller. Temperature and light conditions simulated natural daily and seasonal patterns. There were two temperature scenarios tested in the experiments 2008 and 2009: a baseline corresponding to the decadal mean (1993–2002) of sea surface temperature in Kiel Fjord starting from 15th of February (ΔT = 0 °C) and a warming scenario where the temperature was elevated 6 °C above the baseline (ΔT = 6 °C) according to the most drastic warming scenario predicted by the Intergovernmental Panel on Climate Change (IPCC, 2007). In the experiment 2008 the factor temperature was combined with the factor light intensity with three levels of the initial surface irradiance (4.8, 5.7 and 6.5 mol quanta m−2 d−1) and in the experiment 2009 the factor temperature was combined with the factor initial copepod density with three start abundances (1.5, 4 and 10 ind. L−1), resulting in two replicates of each factor combination in each experiment (Lewandowska and Sommer, 2010; Sommer and Lewandowska, 2011). In the experiments 2006 and 2007 four temperature regimes: ΔT = 0 °C, ΔT = 2 °C, ΔT = 4 °C and ΔT = 6 °C were tested whereby each regime was replicated twice (Sommer and Lengfellner, 2008).
We defined three phases of the phytoplankton bloom and performed separate analyses for each of them. The period before the bloom was characterised by the mean biomass values from the beginning of the experiment to the phytoplankton total biomass maximum and represents the phase of exponential growth. Bloom period was characterised as a point of the phytoplankton total biomass maximum (a proxy for phytoplankton carrying capacity). The post-bloom phase was characterised by the mean values from the phytoplankton total biomass maximum to the end of the experiment and represents the phase, in which loss processes overrule phytoplankton growth. Phytoplankton species richness (S) was calculated as the total number of species, phytoplankton evenness ( J) was calculated according to the equation:
effects of temperature on the phytoplankton growth rates prevail (Reynolds, 2006). After the peak in biomass, loss rates exceed the growth rates and temperature acts on phytoplankton mainly indirectly modifying grazing activity of consumers. One can suspect that the phytoplankton diversity will change over entire time of the phytoplankton bloom and its response to increased temperature might also vary with the bloom development. In this study we hypothesised i) that phytoplankton biomass and diversity responses to increased temperature differ depending on the bloom phase and ii) that the temperature effects are mediated by increased zooplankton grazing activity under warmer conditions. Previously published results have shown a decline of phytoplankton biomass and size at the bloom maximum in response to increased temperature and copepod density (Sommer and Lewandowska, 2011). However, the periods before and after the bloom maximum have not been analysed yet. Thus, our present analysis extends previous analyses of the mesocosm experiments not only by its topical focus (on diversity) but also by paying attention to different phases of the bloom development.
J¼
H0 ln S
where H' is the Shannon diversity index (Shannon et al., 1949), which we based on biomass proportions, and S is the phytoplankton richness. We calculated the magnitude of the effect of temperature on phytoplankton richness, evenness and biomass for each experiment, using log response ratios (LRR): LRR ¼ ln
X 6 C X 0 C
where X6 °C is phytoplankton richness, evenness or biomass under high temperature (ΔT = 6 °C) and X0 °C is phytoplankton richness, evenness or biomass under low temperature (ΔT = 0 °C) accordingly.
Table 1 Experimental design of mesocosm experiments. Temperature elevation (ΔT), initial light intensities (I), initial copepod densities (ICD) and dominant copepod species. Experiment
ΔT (°C)
I (mol quanta m−2 d−1)
ICD (ind. L−1)
Bloom forming algae (% phytoplankton biomass)
References
2009
0, 6
5.7
0, 6
4.8, 5.7, 6.5
2007
0, 2, 4, 6
1.9
Diatoms (93 ± 6% SD) Diatoms (97 ± 6% SD) Silicoflagellate (42 ± 38% SD)
Sommer and Lewandowska, 2011
2008
1.5, 4, 10 (Acartia) 8 (Oithona) 4.5 (Pseudocalanus)
2006
0, 2, 4, 6
3.9
8.5 (Pseudocalanus)
diatoms (95 ± 2% SD)
Lewandowska and Sommer, 2010
Sommer and Lengfellner, 2008
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The magnitude of the overall effect across the studies was then calculated as: þ
i¼n
Ε ¼
Ε¼
Table 2 Effect sizes of increased temperature on phytoplankton diversity (richness and evenness) and biomass for three bloom phases.
Σi¼1 :Εi
Pre-bloom
i¼n Σi¼1 wi
Experiment
where wi is the inverse of variance and Ei is the effect size for study i. 95% confidence intervals (95% CI) were used to test, if the effects are significantly different from zero. The effect of copepods on phytoplankton richness, evenness and biomass for each experimental year were calculated as: 1 1þr ln 2 1−r
where r is the Fisher-z-transformed correlation coefficient between variables. We used the biomass of copepods (copepodites and adults only) as a proxy to copepods in calculating the effects. The overall effects across the experiments were calculated as previously stated (Eq. (3)). Because of a limited number of statistical units, the effects are saddled with large errors. Nevertheless, the general trend is still evident and important for further interpretation of our results. Taxonomic compositions of copepods were compared by conducting a nested analysis of similarities (ANOSIM), in which the factor temperature was nested within the factor year of the experiment to account for differences between the experimental years (Clarke, 1993). R statistic was applied to test for differences between groups (global R = 0 indicates completely random grouping). ANOSIMs based on the Bray–Curtis dissimilarity coefficients and multidimensional scaling plots (MDS) were used for graphical representation. All ANOSIM calculations were performed in Primer 5 (PRIMER-E Ltd). Redundancy analysis was performed to estimate the proportion of variance in phytoplankton biomass and diversity (richness and evenness) explained by experimental year, temperature, initial copepod abundance and light intensity. The analysis was performed using veganpackage in R (Development Core Team, version 2.15.2). 3. Results 3.1. Course of phytoplankton biomass In our experiments phytoplankton biomass was dominated by Skeletonema costatum. Phytoplankton biomass increased by 100–200% within the first 3 weeks and declined afterwards. An exception to this was the experiment 2007 where an initial stagnation of phytoplankton biomass occurred and the bloom peak was observed after 6 weeks from the start of the experiment. Phytoplankton community during the bloom in 2007 was dominated by the silicoflagellate Dictyocha speculum (Table 1). In all experiments phytoplankton biomass decreased in response to warming (for further details on changes in phytoplankton biomass at the bloom peak in particular years see Lewandowska and Sommer, 2010; Sommer and Lengfellner, 2008; Sommer and Lewandowska, 2011).
361
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
0.0585 −0.0263 −0.0203 −0.0073
0.0018 0.0011 0.0004 0.0001
−0.0771 0.1017 −0.1560 −0.0151
0.0029 0.0081 0.0008 0.0008
0.2908 −1.5219 −0.3181 0.4984
0.0132 0.3376 0.0975 0.2781
Effect size
Variance
Effect size
Variance
Effect size
Variance
0.0445 −0.2461 −0.0711 0.0290
0.0019 0.0035 0.0006 0.0002
−0.3596 0.5558 −0.0575 0.0530
0.0132 0.1664 0.0016 0.0060
−0.6774 −1.8984 −0.6136 −0.4289
0.0514 0.1678 0.0128 0.0637
Bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Post-bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
−0.0301 −0.3455 −0.2043 −0.0487
0.0001 0.0016 0.0005 0.0002
−0.0030 0.3859 0.3241 0.2823
0.0059 0.0762 0.0026 0.0081
−1.1534 −2.3453 −1.1281 0.3038
0.0085 0.4853 0.0357 0.1510
showed a negative response to increased temperature before the bloom and no response during the bloom. After the bloom, phytoplankton evenness increased with warming (Fig. 1). Again, the largest effects of temperature on phytoplankton diversity were observed in 2007. Redundancy analysis showed that temperature explained 27% of the variance in the data set in the pre-bloom phase (experimental year and initial copepod abundance explained 7% and 1%, respectively), 33% of the variance in the bloom phase (year explained 15% and copepods explained 2%) and 31% of the variance in the post-bloom phase (year and copepods explained 12% and 1%, respectively). 3.3. Copepod impacts on phytoplankton biomass and diversity The effects of copepods on phytoplankton biomass and diversity depended on experimental years (Table 3), which differed in initial copepod numbers and community composition. The overall effects of copepods on phytoplankton biomass and diversity were non-significant in most cases, except a strong positive effect on phytoplankton evenness
3.2. Temperature impacts on phytoplankton biomass and diversity The effects of temperature on phytoplankton biomass varied between the individual experiments, especially before the bloom, when algae were still growing (Table 2). The largest effects of temperature on phytoplankton biomass were observed in 2007. Total biomass of phytoplankton initially increased in response to warming, but during the bloom and especially after the bloom, a negative effect of increased temperature was observed. Phytoplankton species richness did not respond to increased temperature before and during the bloom (Fig. 1). However, richness declined with temperature after the bloom. Phytoplankton evenness
Fig. 1. Overall effect sizes (±95% confidence intervals, CI) of increased temperature on phytoplankton diversity (richness and evenness) and biomass in three bloom phases (pre-bloom, bloom, post-bloom).
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Table 3 Effect sizes of copepod abundance on phytoplankton diversity (richness and evenness) and biomass for three bloom phases. Pre-bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
−0.4053 −0.7083 −0.0781 0.6267
1.0000 1.0000 0.1111 0.1111
−0.0658 0.7175 −0.4457 0.7207
1.0000 1.0000 0.1111 0.1111
−0.1791 −0.8054 −0.3825 −0.5151
1.0000 1.0000 0.1111 0.1111
Effect size
Variance
Effect size
Variance
Effect size
Variance
−1.2783 −0.9099 −0.3987 0.7664
1.0000 1.0000 0.1111 0.1111
0.7884 0.9873 −0.5621 0.4939
1.0000 1.0000 0.1111 0.1111
1.6038 −0.8534 −0.4144 −0.8520
1.0000 1.0000 0.1111 0.1111
Effect size
Variance
Effect size
Variance
Effect size
Variance
4. Discussion
1.3099 −0.8667 −0.9374 −0.4625
1.0000 1.0000 0.1111 0.1111
−0.0212 2.9685 0.7060 0.8152
1.0000 1.0000 0.1111 0.1111
2.2846 −2.2388 −0.8226 0.3038
1.0000 1.0000 0.1111 0.1111
Consumer activity tends to increase in response to increased temperature (Lopez-Urrutia et al., 2006; O'Connor, 2009). For marine phytoplankton this means that with rising sea surface temperature topdown effects of their consumers will become stronger. Results of our mesocosm experiments confirm the field observations on declining phytoplankton biomass in response to climate warming and increasing grazing pressure (Figs. 1 and 2, see also Sommer and Lewandowska, 2011). In this article, we show that zooplankton not only reduces the biomass of phytoplankton, but also has a strong impact on phytoplankton diversity. However, the top-down control of algal consumers is often counteracted by bottom-up processes and a direct positive effect of warming on phytoplankton metabolism (Eppley, 1972). It should be emphasised that our experimental design had many limitations (Sommer and Lengfellner, 2008) and the differences in initial conditions between experimental years might be substantial. Although experimental year explained a large part of the variance in our data, temperature was the best predictor of changes in phytoplankton biomass and diversity. Moreover, most of the field studies, which address the impact of warming on phytoplankton, are confronted with potentially greater effects of confounding factors associated with seasonal and special variations. Thus, our conclusions are valid for the typical spring bloom conditions in temperate waters. While warming enhances grazing activity of zooplankton and thus leads to the reduction of the phytoplankton biomass, increased temperature has been shown to have a positive effect on algae's growth due to a faster nutrient uptake (O'Connor et al., 2009). Thus initially, when the loss of producer biomass due to grazing was balanced by increasing metabolic rates under enhanced temperature, we observed a slightly positive, however non-significant, effect of warming on the phytoplankton biomass (Fig. 1). Later on, when the top-down control prevailed and nutrients were depleted a strong reduction of the phytoplankton biomass could be observed in all experiments. This became most evident in 2007 where biomass was dominated by the silicoflagellate D. speculum in contrast to diatoms, which dominated in the other experimental years. An exception to this was the experiment 2009, during which we observed slightly positive effect of temperature on phytoplankton biomass after the bloom (Table 2). This unexpected response was probably caused by a trophic cascade effect, which is expected to become stronger under warmer conditions (O'Connor, 2009). After the decline of well edible (medium and moderately large) phytoplankton species copepods seem to have switched to feeding on ciliates,
Bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Post-bloom Experiment
2006 2007 2008 2009
observed. All mesocosms were initially dominated by Oithona (initial copepod density: 8 ind. L−1), but Temora and Centropages became more abundant in the warmer mesocosms (ΔT = 6 °C) after the bloom and started to dominate. The experiment 2009 was dominated by Acartia during the entire experiment and no differences in copepod composition could be noticed between colder (ΔT = 0 °C) and warmer (ΔT = 6 °C) treatments. Increased temperature altered the composition of copepods after the phytoplankton bloom (ANOSIM between temperature groups, global R = 0.426, p = 0.001, 999 permutations, Fig. 3), but not before and during the bloom (ANOSIM between temperature groups for the pre-bloom phase: global R = 0.186, p = 0.054, 999 permutations; ANOSIM between temperature groups for the bloom phase: global R = 0.096, p = 0.132, 999 permutations). The difference in copepod composition between colder (ΔT = 0 °C) and warmer (ΔT = 6 °C) mesocosms in the experiment 2006 could already be observed during the pre-bloom phase. In contrast, separation between mesocosms by temperature was not evident at the pre-bloom and bloom phases in the other experiments (2007–2009) (Fig. 3), but apparent during the post-bloom phase. The experiment 2009 was characterised by the strong variability within the year caused by the different copepod density treatments (1.5, 4 and 10 ind. L−1).
Richness
Evenness
Biomass
during the post-bloom phase (Fig. 2). The overall effects of copepods on phytoplankton diversity and biomass had the same directions as the effects of increased temperature (Fig. 1). 3.4. Variability of copepod composition between the years and bloom phases Initial composition of zooplankton varied between the years (ANOSIM between years for the pre-bloom phase: global R = 0.896, p = 0.01, 105 permutations, Fig. 3). The experiment 2006 was dominated by Pseudocalanus over the whole experimental period. The starting density of copepods was 8.5 ind. L−1. The copepods in the experiment 2007 were initially dominated by Pseudocalanus and Oithona, but the dominance slightly shifted towards Centropages at the end of the experiment in the warm treatments (ΔT = 6 °C). Overall, the initial density of copepods (4.5 ind. L−1) was lower than in the previous experiment. In the experiment 2008, a shift in copepod composition was
Fig. 2. Overall effect sizes (±95% confidence intervals, CI) of copepod abundance on phytoplankton diversity (richness and evenness) and biomass in three bloom phases (pre-bloom, bloom, post-bloom).
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Fig. 3. Multidimensional scaling plot of variation in copepod compositions among experimental years (2006–triangles, 2007–squares, 2008–circles, 2009–diamonds) and temperature treatments (ambient temperature – open symbols; warming scenario – filled symbols).
thereby releasing small phytoplankton from ciliate grazing pressure. In consequence, small algae would have been able to build up more biomass, resulting in the observed differences between warmer and colder mesocosms in our study. Another explanation of the increased phytoplankton biomass after the bloom in 2009 might be a stronger growth of benthic algae on the walls of the mesocosms under elevated temperature and faster remineralisation processes, which enhanced the available nutrient pool for phytoplankton growth. Although some “wall growth” in the mesocosms could be observed at the end of the experiments, there was no visible evidence of temperature driven changes between the mesocosms and enhanced nutrient concentrations in the post-bloom phase. During pre-bloom and bloom phases there were no significant changes in phytoplankton richness because phytoplankton mortality due to copepod grazing was well balanced by a positive phytoplankton growth and no new phytoplankton species were introduced to the system. After the bloom phytoplankton richness decreased because consumers reduced the number of edible phytoplankton species (Fig. 2) which in consequence fell below the detection limit and reduced apparent richness. Nevertheless, it should be noticed that other loss processes (e.g. sinking) should also be considered as possible reasons for the loss of species at the end of the experimental period. This is, however, less probable scenario as the phytoplankton communities after the bloom were dominated by motile species, which can counteract sinking. Interestingly, if copepods feed on the dominant phytoplankton species, as it happened in our experiments, they might have a positive effect on phytoplankton richness (Table 3, experiment 2006), because their impact on the dominant competitor is disproportionally greater and species below the detection limit might be released from competition and become detectable. If this is not the case, it would suggest that rare phytoplankton species avoided by grazers were not able to compensate the loss of dominant species under depleted nutrient conditions after the bloom. Overall, phytoplankton evenness negatively responded to increased temperature throughout the pre-bloom phase (Fig. 1). As the copepod
grazing seemed to have no significant effect on the phytoplankton evenness before and during the bloom (Fig. 2) we hypothesise that high initial nutrient contents promoted growth of a few dominant diatoms (mostly S. costatum) which “outgrew” the other phytoplankton groups and strongly dominated the blooms. Only in 2007, D. speculum dominated the phytoplankton community instead of diatoms, and this was also the only year when we observed a positive impact of temperature on phytoplankton evenness (Table 2). This suggests that the initial phytoplankton composition, especially the presence of good competitors and fast growing species, strongly affects the response of the whole phytoplankton community to environmental changes. In fact, the experiment 2007 was characterised by the slowest growth and the largest decrease of phytoplankton in response to warming. As soon as copepods started to play a major role in controlling the bloom, they increased phytoplankton evenness by feeding on the most dominant species, which is often observed in consumerproducer relationships (Hillebrand et al., 2007). Thus we observed a positive effect of copepods (Fig. 2) and temperature (the effect driven by the higher grazing activity under warmer conditions, Fig. 1) on phytoplankton evenness throughout the post-bloom phase. The year 2006 was an exception (Table 2) as the copepods abundance dropped down after the bloom (Lengfellner, 2008). The opposite effect of copepod grazing activity on phytoplankton evenness would be suspected if the bloom had been dominated by inedible phytoplankton (too small or too big species, toxic algae). In such a case we would suspect rather a decrease of phytoplankton evenness with increasing grazing pressure because copepods would feed mostly on the rare edible species thereby increasing phytoplankton dominance. Besides grazing pressure, which is related to enhanced zooplankton activity or abundance, zooplankton feeding preferences and thus community composition can also potentially influence phytoplankton diversity. Here, the role of microzooplankton, especially ciliates, as consumers of small phytoplankton species might be very important. In our experiments microzooplankton did not respond to temperature changes in terms of biomass (Aberle et al., 2012). Thus, we focused on
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copepods as the major phytoplankton consumers. Although copepods are expected to feed on the same phytoplankton size spectrum (N500 to 1000 μm3 cell volume, Sommer and Sommer, 2006), they have different feeding behaviours and some can switch between suspension feeding and raptorial feeding (Tiselius et al., 1990). Thus, copepod effectiveness in catching their prey might vary between species, thereby affecting phytoplankton diversity. It has been shown that rising sea surface temperatures provoke changes in zooplankton community composition towards warm water species (e.g. Centropages and Temora) similar to those observed in our mesocosms (Möllmann et al., 2000). Temperature induced responses in copepods such as taxonomic shifts (Fig. 3) and enhanced grazing activities, are possible explanations for the strong changes in phytoplankton diversity that we could observe in the post-bloom phase of our experiments. Similarly, changes in the initial zooplankton composition between the experimental years might explain part of the interannual variability in phytoplankton diversity responses to warming. Using our experimental setup, we were not able to directly disentangle the effects of different copepod species on phytoplankton diversity. The limited number of experiments included in our analysis, low statistical power due to replication of only two mesocosms per treatment combination or potential confounding effects due to differences in initial conditions should be also considered. Nevertheless our results suggest that the changes in zooplankton community composition might be crucial to understand the effects of warming on aquatic ecosystems and there is a need for further, well designed studies on consumer impact on phytoplankton diversity. This future work should be improved by sufficient treatment replication, longer experiment duration (to capture all development stages of zooplankton), repeated experiments with different initial communities (e.g. with the same experimental setup in different locations) and by parallel grazing experiments to better understand zooplankton feeding behaviour. In conclusion, the impact of temperature on phytoplankton diversity seems to be mostly mediated via changes in zooplankton activity and community structure, but the strength of the zooplankton impact on phytoplankton varies with the bloom development. There is still a lack of knowledge about how the naturally variable zooplankton community affects phytoplankton diversity and how this relationship changes in response to climate warming. Thus, there is a need of complex ecosystem studies, where community interactions could be fully represented. Acknowledgements This study was funded by DFG (German Research Foundation) within the priority programme 1162 “AQUASHIFT”. T. Hansen, H. Tomanetz and C. Meyer are acknowledged for their technical assistance. We thank N. Aberle for the microzooplankton data. References Aberle, N., Bauer, B., Lewandowska, A., Gaedke, U., Sommer, U., 2012. Warming induces shifts in microzooplankton phenology and reduces time-lags between phytoplankton and protozoan production. Marine Biology 159, 2441–2453. Beaugrand, G., Edwards, M., Legendre, L., 2010. Marine biodiversity, ecosystem functioning, and carbon cycles. Proceedings of the National Academy of Sciences of the United States of America 107 (22), 10120–10124. Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the past century. Nature 466, 591–596.
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Hillebrand, H., Gruner, D.S., Borer, E.T., Bracken, M.E.S., Cleland, E.E., Elser, J.J., Harpole, W.S., Ngai, J.T., Seabloom, E.W., Shurin, J.B., Smith, J.E., 2007. Consumer versus resource control of producer diversity depends on ecosystem type and producer community structure. Proceedings of the National Academy of Sciences of the United States of America 104 (26), 10904–10909. Hillebrand, H., Bennett, D.M., Cadotte, M.W., 2008. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89 (6), 1510–1520. Hillebrand, H., Burgmer, T., Biermann, E., 2012. Running to stand still: temperature effects on species richness, species turnover, and functional community dynamics. Marine Biology 159 (11), 2415–2422. IPCC, 2007. Climate Change 2007: The Physical Science Basis. IPCC, Geneva. Kordas, R.L., Harley, C.D.G., O'Connor, M.I., 2011. Community ecology in a warming world: the influence of temperature on interspecific interactions in marine systems. Journal of Experimental Marine Biology and Ecology 400 (1–2), 218–226. Lengfellner, K., 2008. The Impact of Climate Warming on Plankton Spring Succession: A Mesocosm Study. Christian Albrechts University of Kiel, Kiel 110 (PhD Dissertation). Lewandowska, A., Sommer, U., 2010. Climate change and the spring bloom: a mesocosm study on the influence of light and temperature on phytoplankton and mesozooplankton. Marine Ecology Progress Series 405, 101–111. Lopez-Urrutia, A., San, Martin E., Harris, R.P., Irigoien, X., 2006. Scaling the metabolic balance of the oceans. Proceedings of the National Academy of Sciences of the United States of America 103, 8739–8744. Menden-Deuer, S., Lessard, E.J., 2000. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45 (3), 569–579. Möllmann, C., Kornilovs, G., Sidrevics, L., 2000. Long-term dynamics of main mesozooplankton species in the central Baltic Sea. Journal of Plankton Research 22 (11), 2015–2038. Moran, X.A.G., Lopez-Urrutia, A., Calvo-Diaz, A., Li, W.K.W., 2010. Increasing importance of small phytoplankton in a warmer ocean. Global Change Biology 16 (3), 1137–1144. O'Connor, M.I., 2009. Warming strengthens an herbivore-plant interaction. Ecology 90 (2), 388–398. O'Connor, M.I., Piehler, M.F., Leech, D.M., Anton, A., Bruno, J.F., 2009. Warming and resource availability shift food web structure and metabolism. PLoS Biology 7 (8). O'Connor, M.I., Gilbert, B., Brown, C.J., 2011. Theoretical predictions for how temperature affects the dynamics of interacting herbivores and plants. American Naturalist 178 (5), 626–638. Reynolds, C.S., 2006. The Ecology of Phytoplankton. Cambridge University Press, Cambridge. Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. University of Illinois Press, Urbana, IL. Sommer, U., Lengfellner, K., 2008. Climate change and timing, magnitude, and composition of the phytoplankton spring bloom. Global Change Biology 14, 1–10. Sommer, U., Lewandowska, A., 2011. Climate change and the phytoplankton spring bloom: warming and overwintering zooplankton have similar effects on phytoplankton. Global Change Biology 17 (1), 154–162. Sommer, U., Sommer, F., 2006. Cladocerans versus copepods: the cause of contrasting topdown controls on freshwater and marine phytoplankton. Oecologia 147, 183–194. Thackeray, S.J., Jones, I.D., Frederiksen, M., Burthe, S., Bacon, P.J., Bell, J.R., Botham, M.S., Brereton, T.M., Bright, P.W., Carvalho, L., Clutton-Brock, T., Dawson, A., Edwards, M., Elliott, J.M., Harrington, R., Johns, D., Jones, I.D., Jones, J.T., Leech, D.I., Roy, D.B., Scott, W.A., Smith, M., Smithers, R.J., Winfield, I.J., Wanless, S., 2008. Long-term change in the phenology of spring phytoplankton: species-specific responses to nutrient enrichment and climatic change. Journal of Ecology 96 (3), 523–535. Tiselius, P., Jonsson, P.R., 1990. Foraging behaviour of six calanoid copepods: observations and hydrodynamic analysis. Marine Ecology Progress Series 66, 23–33. Utermöhl, H., 1958. Zur Vervollkommung der quantitativen Phytoplankton-Methodik. Mitteilung Internationale Vereinigung fuer Theoretische unde Amgewandte Limnologie 9, 263–272.
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Journal of Sea Research journal homepage: www.elsevier.com/locate/seares
Temperature effects on phytoplankton diversity — The zooplankton link Aleksandra M. Lewandowska a,⁎, Helmut Hillebrand b, Kathrin Lengfellner c, Ulrich Sommer a a b c
Helmholtz Centre for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, 24105 Kiel, Germany Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of Oldenburg, Schleusenstrasse 1, 26382 Wilhelmshaven, Germany Department of Ecology and Environmental Sciences, Umeå University, 90187 Umeå, Sweden
a r t i c l e
i n f o
Article history: Received 21 September 2012 Received in revised form 5 July 2013 Accepted 10 July 2013 Available online 17 July 2013 Keywords: Climate warming Mesocosms Plankton Diversity
a b s t r a c t Recent climate warming is expected to affect phytoplankton biomass and diversity in marine ecosystems. Temperature can act directly on phytoplankton (e.g. rendering physiological processes) or indirectly due to changes in zooplankton grazing activity. We tested experimentally the impact of increased temperature on natural phytoplankton and zooplankton communities using indoor mesocosms and combined the results from different experimental years applying a meta-analytic approach. We divided our analysis into three bloom phases to define the strength of temperature and zooplankton impacts on phytoplankton in different stages of bloom development. Within the constraints of an experiment, our results suggest that increased temperature and zooplankton grazing have similar effects on phytoplankton diversity, which are most apparent in the post-bloom phase, when zooplankton abundances reach the highest values. Moreover, we observed changes in zooplankton composition in response to warming and initial conditions, which can additionally affect phytoplankton diversity, because changing feeding preferences of zooplankton can affect phytoplankton community structure. We conclude that phytoplankton diversity is indirectly affected by temperature in the post-bloom phase through changing zooplankton composition and grazing activities. Before and during the bloom, however, these effects seem to be overruled by temperature enhanced bottom-up processes such as phytoplankton nutrient uptake. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Current global warming pits us with the necessity to understand and predict the impact of rising temperatures on ecosystems. During the last two decades, ecological sciences have therefore put more focus on this important issue, for example to gain insight into how temperature affects properties and functioning of food webs. Recent marine studies revealed that temperature impacts marine organisms on different trophic levels and alters species interactions between and within trophic levels (Kordas et al., 2011; O'Connor, 2009; O'Connor et al., 2011). Phytoplankton generally forms the base of the pelagic food web and hence merits special attention. Although increased temperature speeds up metabolic processes of phytoplankton and might increase primary production in certain regions (Chavez et al., 2011; Doney, 2006), global phytoplankton decline with climate warming has been reported (Boyce et al., 2010; Moran et al., 2010). Two major processes were defined to be responsible for this decline: i) increasing resource limitations as a consequence of stronger water column stratification in the warmed ocean and ii) increasing top-down control of phytoplankton by zooplankton with rising temperature. Phytoplankton diversity is also expected to be altered by climate change but this link is less well understood. Recent studies draw different pictures: whereas controlled laboratory experiments reported ⁎ Corresponding author. Tel: +494316004411; fax: +49 4316004402. E-mail address: [email protected] (A.M. Lewandowska). 1385-1101/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.seares.2013.07.003
more rapid competitive exclusion resulting in a loss of species richness at higher temperature (Burgmer et al., 2011), field studies found an increasing number of species (richness) by immigrating warm-adapted species (Beaugrand et al., 2010). It seems, however, that irrespective of the net effect on richness, higher temperatures are strongly associated to higher species turnover (Hillebrand et al., 2012). Before species go extinct, rising temperatures will alter species dominance. Therefore, phytoplankton evenness (a measure of how equitable species are distributed within the community) is expected to be even more responsive to rising temperatures than richness (Hillebrand et al., 2008). Mesozooplankton can strongly reduce the biomass of microalgae and affect phytoplankton diversity (richness and evenness). Generally zooplankton can reduce the number of phytoplankton species by increasing phytoplankton mortality or can increase richness by feeding on dominant algae taxa and thus releasing rare species from interspecific competition. With respect to evenness, consumers predominantly have a positive effect as they reduce the proportion of the dominant species (Hillebrand et al., 2007). However, zooplankton can also decrease phytoplankton evenness if the dominant algal species are not included in the zooplankton food spectrum. Thus, the consumer effect on phytoplankton evenness depends on consumer quantity as well as on its identity and feeding preferences. Moreover, it depends on the quality of phytoplankton itself in terms of nutritional value and essential compounds (Hall et al., 2007). The impact of temperature on phytoplankton depends on its successional stage (Thackeray et al., 2008). Prior the peak in biomass, positive
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2. Material and methods
For straight comparison between experiments we used only data for ΔT = 0 °C and ΔT = 6 °C. Phytoplankton was sampled three times per week, preserved with Lugol's iodine and counted using the inverted microscope technique according to Utermöhl (1958) for species N5 μm. Flow cytometry technique (FACScalibur, Becton Dickinson) was used to count smaller species (Sommer and Lengfellner, 2008). Phytoplankton biomass was defined as carbon content calculated from cell volumes (Menden-Deuer et al., 2000) after an approximation of cell volumes to geometric standards (Hillebrand et al., 1999). Zooplankton was sampled once a week with a net (12 cm diameter, 64 μm mesh size), shock frozen with liquid nitrogen (experiments 2006 and 2007) or fixed with Lugol's iodine (experiments 2008 and 2009) and counted with a stereomicroscope. Copepods were specified to the genus level, Temora sp. and rare Eurytemora sp., as well as Pseudocalanus sp. and rare Paracalanus sp. were paired together, because their early copepodid stages are difficult to distinguish. Copepod biomass was estimated as a carbon content using species and stage specific conversion factors (Lengfellner, 2008). More details on the experimental setup and sampling procedure for each experiment can be found elsewhere (Lewandowska and Sommer, 2010; Sommer and Lengfellner, 2008; Sommer and Lewandowska, 2011).
2.1. Experimental setup and laboratory techniques
2.2. Diversity parameters and statistics
Eight (experiments 2006 and 2007) or twelve (experiments 2008 and 2009) mesocosms (1400 L volume, 1 m depth) were set up in temperature regulated climate rooms. Sea water containing the natural late winter plankton community (phytoplankton, bacteria and protozoa) from the Kiel Fjord, Baltic Sea, was pumped into a distribution tank and gravitationally transferred to the experimental units. The mesocosms were filled simultaneously to assure homogenous distribution of plankton. Mesozooplankton was added from net catches at appropriate concentrations for each experiment (Table 1) as it did not pass through the pumping system. The water column was gently mixed by a propeller. Temperature and light conditions simulated natural daily and seasonal patterns. There were two temperature scenarios tested in the experiments 2008 and 2009: a baseline corresponding to the decadal mean (1993–2002) of sea surface temperature in Kiel Fjord starting from 15th of February (ΔT = 0 °C) and a warming scenario where the temperature was elevated 6 °C above the baseline (ΔT = 6 °C) according to the most drastic warming scenario predicted by the Intergovernmental Panel on Climate Change (IPCC, 2007). In the experiment 2008 the factor temperature was combined with the factor light intensity with three levels of the initial surface irradiance (4.8, 5.7 and 6.5 mol quanta m−2 d−1) and in the experiment 2009 the factor temperature was combined with the factor initial copepod density with three start abundances (1.5, 4 and 10 ind. L−1), resulting in two replicates of each factor combination in each experiment (Lewandowska and Sommer, 2010; Sommer and Lewandowska, 2011). In the experiments 2006 and 2007 four temperature regimes: ΔT = 0 °C, ΔT = 2 °C, ΔT = 4 °C and ΔT = 6 °C were tested whereby each regime was replicated twice (Sommer and Lengfellner, 2008).
We defined three phases of the phytoplankton bloom and performed separate analyses for each of them. The period before the bloom was characterised by the mean biomass values from the beginning of the experiment to the phytoplankton total biomass maximum and represents the phase of exponential growth. Bloom period was characterised as a point of the phytoplankton total biomass maximum (a proxy for phytoplankton carrying capacity). The post-bloom phase was characterised by the mean values from the phytoplankton total biomass maximum to the end of the experiment and represents the phase, in which loss processes overrule phytoplankton growth. Phytoplankton species richness (S) was calculated as the total number of species, phytoplankton evenness ( J) was calculated according to the equation:
effects of temperature on the phytoplankton growth rates prevail (Reynolds, 2006). After the peak in biomass, loss rates exceed the growth rates and temperature acts on phytoplankton mainly indirectly modifying grazing activity of consumers. One can suspect that the phytoplankton diversity will change over entire time of the phytoplankton bloom and its response to increased temperature might also vary with the bloom development. In this study we hypothesised i) that phytoplankton biomass and diversity responses to increased temperature differ depending on the bloom phase and ii) that the temperature effects are mediated by increased zooplankton grazing activity under warmer conditions. Previously published results have shown a decline of phytoplankton biomass and size at the bloom maximum in response to increased temperature and copepod density (Sommer and Lewandowska, 2011). However, the periods before and after the bloom maximum have not been analysed yet. Thus, our present analysis extends previous analyses of the mesocosm experiments not only by its topical focus (on diversity) but also by paying attention to different phases of the bloom development.
J¼
H0 ln S
where H' is the Shannon diversity index (Shannon et al., 1949), which we based on biomass proportions, and S is the phytoplankton richness. We calculated the magnitude of the effect of temperature on phytoplankton richness, evenness and biomass for each experiment, using log response ratios (LRR): LRR ¼ ln
X 6 C X 0 C
where X6 °C is phytoplankton richness, evenness or biomass under high temperature (ΔT = 6 °C) and X0 °C is phytoplankton richness, evenness or biomass under low temperature (ΔT = 0 °C) accordingly.
Table 1 Experimental design of mesocosm experiments. Temperature elevation (ΔT), initial light intensities (I), initial copepod densities (ICD) and dominant copepod species. Experiment
ΔT (°C)
I (mol quanta m−2 d−1)
ICD (ind. L−1)
Bloom forming algae (% phytoplankton biomass)
References
2009
0, 6
5.7
0, 6
4.8, 5.7, 6.5
2007
0, 2, 4, 6
1.9
Diatoms (93 ± 6% SD) Diatoms (97 ± 6% SD) Silicoflagellate (42 ± 38% SD)
Sommer and Lewandowska, 2011
2008
1.5, 4, 10 (Acartia) 8 (Oithona) 4.5 (Pseudocalanus)
2006
0, 2, 4, 6
3.9
8.5 (Pseudocalanus)
diatoms (95 ± 2% SD)
Lewandowska and Sommer, 2010
Sommer and Lengfellner, 2008
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The magnitude of the overall effect across the studies was then calculated as: þ
i¼n
Ε ¼
Ε¼
Table 2 Effect sizes of increased temperature on phytoplankton diversity (richness and evenness) and biomass for three bloom phases.
Σi¼1 :Εi
Pre-bloom
i¼n Σi¼1 wi
Experiment
where wi is the inverse of variance and Ei is the effect size for study i. 95% confidence intervals (95% CI) were used to test, if the effects are significantly different from zero. The effect of copepods on phytoplankton richness, evenness and biomass for each experimental year were calculated as: 1 1þr ln 2 1−r
where r is the Fisher-z-transformed correlation coefficient between variables. We used the biomass of copepods (copepodites and adults only) as a proxy to copepods in calculating the effects. The overall effects across the experiments were calculated as previously stated (Eq. (3)). Because of a limited number of statistical units, the effects are saddled with large errors. Nevertheless, the general trend is still evident and important for further interpretation of our results. Taxonomic compositions of copepods were compared by conducting a nested analysis of similarities (ANOSIM), in which the factor temperature was nested within the factor year of the experiment to account for differences between the experimental years (Clarke, 1993). R statistic was applied to test for differences between groups (global R = 0 indicates completely random grouping). ANOSIMs based on the Bray–Curtis dissimilarity coefficients and multidimensional scaling plots (MDS) were used for graphical representation. All ANOSIM calculations were performed in Primer 5 (PRIMER-E Ltd). Redundancy analysis was performed to estimate the proportion of variance in phytoplankton biomass and diversity (richness and evenness) explained by experimental year, temperature, initial copepod abundance and light intensity. The analysis was performed using veganpackage in R (Development Core Team, version 2.15.2). 3. Results 3.1. Course of phytoplankton biomass In our experiments phytoplankton biomass was dominated by Skeletonema costatum. Phytoplankton biomass increased by 100–200% within the first 3 weeks and declined afterwards. An exception to this was the experiment 2007 where an initial stagnation of phytoplankton biomass occurred and the bloom peak was observed after 6 weeks from the start of the experiment. Phytoplankton community during the bloom in 2007 was dominated by the silicoflagellate Dictyocha speculum (Table 1). In all experiments phytoplankton biomass decreased in response to warming (for further details on changes in phytoplankton biomass at the bloom peak in particular years see Lewandowska and Sommer, 2010; Sommer and Lengfellner, 2008; Sommer and Lewandowska, 2011).
361
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
0.0585 −0.0263 −0.0203 −0.0073
0.0018 0.0011 0.0004 0.0001
−0.0771 0.1017 −0.1560 −0.0151
0.0029 0.0081 0.0008 0.0008
0.2908 −1.5219 −0.3181 0.4984
0.0132 0.3376 0.0975 0.2781
Effect size
Variance
Effect size
Variance
Effect size
Variance
0.0445 −0.2461 −0.0711 0.0290
0.0019 0.0035 0.0006 0.0002
−0.3596 0.5558 −0.0575 0.0530
0.0132 0.1664 0.0016 0.0060
−0.6774 −1.8984 −0.6136 −0.4289
0.0514 0.1678 0.0128 0.0637
Bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Post-bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
−0.0301 −0.3455 −0.2043 −0.0487
0.0001 0.0016 0.0005 0.0002
−0.0030 0.3859 0.3241 0.2823
0.0059 0.0762 0.0026 0.0081
−1.1534 −2.3453 −1.1281 0.3038
0.0085 0.4853 0.0357 0.1510
showed a negative response to increased temperature before the bloom and no response during the bloom. After the bloom, phytoplankton evenness increased with warming (Fig. 1). Again, the largest effects of temperature on phytoplankton diversity were observed in 2007. Redundancy analysis showed that temperature explained 27% of the variance in the data set in the pre-bloom phase (experimental year and initial copepod abundance explained 7% and 1%, respectively), 33% of the variance in the bloom phase (year explained 15% and copepods explained 2%) and 31% of the variance in the post-bloom phase (year and copepods explained 12% and 1%, respectively). 3.3. Copepod impacts on phytoplankton biomass and diversity The effects of copepods on phytoplankton biomass and diversity depended on experimental years (Table 3), which differed in initial copepod numbers and community composition. The overall effects of copepods on phytoplankton biomass and diversity were non-significant in most cases, except a strong positive effect on phytoplankton evenness
3.2. Temperature impacts on phytoplankton biomass and diversity The effects of temperature on phytoplankton biomass varied between the individual experiments, especially before the bloom, when algae were still growing (Table 2). The largest effects of temperature on phytoplankton biomass were observed in 2007. Total biomass of phytoplankton initially increased in response to warming, but during the bloom and especially after the bloom, a negative effect of increased temperature was observed. Phytoplankton species richness did not respond to increased temperature before and during the bloom (Fig. 1). However, richness declined with temperature after the bloom. Phytoplankton evenness
Fig. 1. Overall effect sizes (±95% confidence intervals, CI) of increased temperature on phytoplankton diversity (richness and evenness) and biomass in three bloom phases (pre-bloom, bloom, post-bloom).
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Table 3 Effect sizes of copepod abundance on phytoplankton diversity (richness and evenness) and biomass for three bloom phases. Pre-bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Effect size
Variance
Effect size
Variance
Effect size
Variance
−0.4053 −0.7083 −0.0781 0.6267
1.0000 1.0000 0.1111 0.1111
−0.0658 0.7175 −0.4457 0.7207
1.0000 1.0000 0.1111 0.1111
−0.1791 −0.8054 −0.3825 −0.5151
1.0000 1.0000 0.1111 0.1111
Effect size
Variance
Effect size
Variance
Effect size
Variance
−1.2783 −0.9099 −0.3987 0.7664
1.0000 1.0000 0.1111 0.1111
0.7884 0.9873 −0.5621 0.4939
1.0000 1.0000 0.1111 0.1111
1.6038 −0.8534 −0.4144 −0.8520
1.0000 1.0000 0.1111 0.1111
Effect size
Variance
Effect size
Variance
Effect size
Variance
4. Discussion
1.3099 −0.8667 −0.9374 −0.4625
1.0000 1.0000 0.1111 0.1111
−0.0212 2.9685 0.7060 0.8152
1.0000 1.0000 0.1111 0.1111
2.2846 −2.2388 −0.8226 0.3038
1.0000 1.0000 0.1111 0.1111
Consumer activity tends to increase in response to increased temperature (Lopez-Urrutia et al., 2006; O'Connor, 2009). For marine phytoplankton this means that with rising sea surface temperature topdown effects of their consumers will become stronger. Results of our mesocosm experiments confirm the field observations on declining phytoplankton biomass in response to climate warming and increasing grazing pressure (Figs. 1 and 2, see also Sommer and Lewandowska, 2011). In this article, we show that zooplankton not only reduces the biomass of phytoplankton, but also has a strong impact on phytoplankton diversity. However, the top-down control of algal consumers is often counteracted by bottom-up processes and a direct positive effect of warming on phytoplankton metabolism (Eppley, 1972). It should be emphasised that our experimental design had many limitations (Sommer and Lengfellner, 2008) and the differences in initial conditions between experimental years might be substantial. Although experimental year explained a large part of the variance in our data, temperature was the best predictor of changes in phytoplankton biomass and diversity. Moreover, most of the field studies, which address the impact of warming on phytoplankton, are confronted with potentially greater effects of confounding factors associated with seasonal and special variations. Thus, our conclusions are valid for the typical spring bloom conditions in temperate waters. While warming enhances grazing activity of zooplankton and thus leads to the reduction of the phytoplankton biomass, increased temperature has been shown to have a positive effect on algae's growth due to a faster nutrient uptake (O'Connor et al., 2009). Thus initially, when the loss of producer biomass due to grazing was balanced by increasing metabolic rates under enhanced temperature, we observed a slightly positive, however non-significant, effect of warming on the phytoplankton biomass (Fig. 1). Later on, when the top-down control prevailed and nutrients were depleted a strong reduction of the phytoplankton biomass could be observed in all experiments. This became most evident in 2007 where biomass was dominated by the silicoflagellate D. speculum in contrast to diatoms, which dominated in the other experimental years. An exception to this was the experiment 2009, during which we observed slightly positive effect of temperature on phytoplankton biomass after the bloom (Table 2). This unexpected response was probably caused by a trophic cascade effect, which is expected to become stronger under warmer conditions (O'Connor, 2009). After the decline of well edible (medium and moderately large) phytoplankton species copepods seem to have switched to feeding on ciliates,
Bloom Experiment
2006 2007 2008 2009
Richness
Evenness
Biomass
Post-bloom Experiment
2006 2007 2008 2009
observed. All mesocosms were initially dominated by Oithona (initial copepod density: 8 ind. L−1), but Temora and Centropages became more abundant in the warmer mesocosms (ΔT = 6 °C) after the bloom and started to dominate. The experiment 2009 was dominated by Acartia during the entire experiment and no differences in copepod composition could be noticed between colder (ΔT = 0 °C) and warmer (ΔT = 6 °C) treatments. Increased temperature altered the composition of copepods after the phytoplankton bloom (ANOSIM between temperature groups, global R = 0.426, p = 0.001, 999 permutations, Fig. 3), but not before and during the bloom (ANOSIM between temperature groups for the pre-bloom phase: global R = 0.186, p = 0.054, 999 permutations; ANOSIM between temperature groups for the bloom phase: global R = 0.096, p = 0.132, 999 permutations). The difference in copepod composition between colder (ΔT = 0 °C) and warmer (ΔT = 6 °C) mesocosms in the experiment 2006 could already be observed during the pre-bloom phase. In contrast, separation between mesocosms by temperature was not evident at the pre-bloom and bloom phases in the other experiments (2007–2009) (Fig. 3), but apparent during the post-bloom phase. The experiment 2009 was characterised by the strong variability within the year caused by the different copepod density treatments (1.5, 4 and 10 ind. L−1).
Richness
Evenness
Biomass
during the post-bloom phase (Fig. 2). The overall effects of copepods on phytoplankton diversity and biomass had the same directions as the effects of increased temperature (Fig. 1). 3.4. Variability of copepod composition between the years and bloom phases Initial composition of zooplankton varied between the years (ANOSIM between years for the pre-bloom phase: global R = 0.896, p = 0.01, 105 permutations, Fig. 3). The experiment 2006 was dominated by Pseudocalanus over the whole experimental period. The starting density of copepods was 8.5 ind. L−1. The copepods in the experiment 2007 were initially dominated by Pseudocalanus and Oithona, but the dominance slightly shifted towards Centropages at the end of the experiment in the warm treatments (ΔT = 6 °C). Overall, the initial density of copepods (4.5 ind. L−1) was lower than in the previous experiment. In the experiment 2008, a shift in copepod composition was
Fig. 2. Overall effect sizes (±95% confidence intervals, CI) of copepod abundance on phytoplankton diversity (richness and evenness) and biomass in three bloom phases (pre-bloom, bloom, post-bloom).
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Fig. 3. Multidimensional scaling plot of variation in copepod compositions among experimental years (2006–triangles, 2007–squares, 2008–circles, 2009–diamonds) and temperature treatments (ambient temperature – open symbols; warming scenario – filled symbols).
thereby releasing small phytoplankton from ciliate grazing pressure. In consequence, small algae would have been able to build up more biomass, resulting in the observed differences between warmer and colder mesocosms in our study. Another explanation of the increased phytoplankton biomass after the bloom in 2009 might be a stronger growth of benthic algae on the walls of the mesocosms under elevated temperature and faster remineralisation processes, which enhanced the available nutrient pool for phytoplankton growth. Although some “wall growth” in the mesocosms could be observed at the end of the experiments, there was no visible evidence of temperature driven changes between the mesocosms and enhanced nutrient concentrations in the post-bloom phase. During pre-bloom and bloom phases there were no significant changes in phytoplankton richness because phytoplankton mortality due to copepod grazing was well balanced by a positive phytoplankton growth and no new phytoplankton species were introduced to the system. After the bloom phytoplankton richness decreased because consumers reduced the number of edible phytoplankton species (Fig. 2) which in consequence fell below the detection limit and reduced apparent richness. Nevertheless, it should be noticed that other loss processes (e.g. sinking) should also be considered as possible reasons for the loss of species at the end of the experimental period. This is, however, less probable scenario as the phytoplankton communities after the bloom were dominated by motile species, which can counteract sinking. Interestingly, if copepods feed on the dominant phytoplankton species, as it happened in our experiments, they might have a positive effect on phytoplankton richness (Table 3, experiment 2006), because their impact on the dominant competitor is disproportionally greater and species below the detection limit might be released from competition and become detectable. If this is not the case, it would suggest that rare phytoplankton species avoided by grazers were not able to compensate the loss of dominant species under depleted nutrient conditions after the bloom. Overall, phytoplankton evenness negatively responded to increased temperature throughout the pre-bloom phase (Fig. 1). As the copepod
grazing seemed to have no significant effect on the phytoplankton evenness before and during the bloom (Fig. 2) we hypothesise that high initial nutrient contents promoted growth of a few dominant diatoms (mostly S. costatum) which “outgrew” the other phytoplankton groups and strongly dominated the blooms. Only in 2007, D. speculum dominated the phytoplankton community instead of diatoms, and this was also the only year when we observed a positive impact of temperature on phytoplankton evenness (Table 2). This suggests that the initial phytoplankton composition, especially the presence of good competitors and fast growing species, strongly affects the response of the whole phytoplankton community to environmental changes. In fact, the experiment 2007 was characterised by the slowest growth and the largest decrease of phytoplankton in response to warming. As soon as copepods started to play a major role in controlling the bloom, they increased phytoplankton evenness by feeding on the most dominant species, which is often observed in consumerproducer relationships (Hillebrand et al., 2007). Thus we observed a positive effect of copepods (Fig. 2) and temperature (the effect driven by the higher grazing activity under warmer conditions, Fig. 1) on phytoplankton evenness throughout the post-bloom phase. The year 2006 was an exception (Table 2) as the copepods abundance dropped down after the bloom (Lengfellner, 2008). The opposite effect of copepod grazing activity on phytoplankton evenness would be suspected if the bloom had been dominated by inedible phytoplankton (too small or too big species, toxic algae). In such a case we would suspect rather a decrease of phytoplankton evenness with increasing grazing pressure because copepods would feed mostly on the rare edible species thereby increasing phytoplankton dominance. Besides grazing pressure, which is related to enhanced zooplankton activity or abundance, zooplankton feeding preferences and thus community composition can also potentially influence phytoplankton diversity. Here, the role of microzooplankton, especially ciliates, as consumers of small phytoplankton species might be very important. In our experiments microzooplankton did not respond to temperature changes in terms of biomass (Aberle et al., 2012). Thus, we focused on
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copepods as the major phytoplankton consumers. Although copepods are expected to feed on the same phytoplankton size spectrum (N500 to 1000 μm3 cell volume, Sommer and Sommer, 2006), they have different feeding behaviours and some can switch between suspension feeding and raptorial feeding (Tiselius et al., 1990). Thus, copepod effectiveness in catching their prey might vary between species, thereby affecting phytoplankton diversity. It has been shown that rising sea surface temperatures provoke changes in zooplankton community composition towards warm water species (e.g. Centropages and Temora) similar to those observed in our mesocosms (Möllmann et al., 2000). Temperature induced responses in copepods such as taxonomic shifts (Fig. 3) and enhanced grazing activities, are possible explanations for the strong changes in phytoplankton diversity that we could observe in the post-bloom phase of our experiments. Similarly, changes in the initial zooplankton composition between the experimental years might explain part of the interannual variability in phytoplankton diversity responses to warming. Using our experimental setup, we were not able to directly disentangle the effects of different copepod species on phytoplankton diversity. The limited number of experiments included in our analysis, low statistical power due to replication of only two mesocosms per treatment combination or potential confounding effects due to differences in initial conditions should be also considered. Nevertheless our results suggest that the changes in zooplankton community composition might be crucial to understand the effects of warming on aquatic ecosystems and there is a need for further, well designed studies on consumer impact on phytoplankton diversity. This future work should be improved by sufficient treatment replication, longer experiment duration (to capture all development stages of zooplankton), repeated experiments with different initial communities (e.g. with the same experimental setup in different locations) and by parallel grazing experiments to better understand zooplankton feeding behaviour. In conclusion, the impact of temperature on phytoplankton diversity seems to be mostly mediated via changes in zooplankton activity and community structure, but the strength of the zooplankton impact on phytoplankton varies with the bloom development. There is still a lack of knowledge about how the naturally variable zooplankton community affects phytoplankton diversity and how this relationship changes in response to climate warming. Thus, there is a need of complex ecosystem studies, where community interactions could be fully represented. Acknowledgements This study was funded by DFG (German Research Foundation) within the priority programme 1162 “AQUASHIFT”. T. Hansen, H. Tomanetz and C. Meyer are acknowledged for their technical assistance. We thank N. Aberle for the microzooplankton data. References Aberle, N., Bauer, B., Lewandowska, A., Gaedke, U., Sommer, U., 2012. Warming induces shifts in microzooplankton phenology and reduces time-lags between phytoplankton and protozoan production. Marine Biology 159, 2441–2453. Beaugrand, G., Edwards, M., Legendre, L., 2010. Marine biodiversity, ecosystem functioning, and carbon cycles. Proceedings of the National Academy of Sciences of the United States of America 107 (22), 10120–10124. Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the past century. Nature 466, 591–596.
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