Next Article in Journal
Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content
Previous Article in Journal
Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies
Previous Article in Special Issue
Riparian Ecological Infrastructures: Potential for Biodiversity-Related Ecosystem Services in Mediterranean Human-Dominated Landscapes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 13
Issue 24
10.3390/su132414049
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Do Cross-Latitude and Local Studies Give Similar Predictions of Phytoplankton Responses to Warming? An Analysis of Monitoring Data from 504 Danish Lakes

by
Erik Jeppesen
1,2,3,4,*,
Liselotte S. Johansson
1,
Sh Tserenpil
5,
Martin Søndergaard
1,2,
Torben L. Lauridsen
1,2 and
Per Andersen
1
1
Department of Ecoscience and WATEC, Aarhus University, 8600 Silkeborg, Denmark
2
Sino-Danish Centre for Education and Research, Beijing 101408, China
3
Limnology Laboratory, Department of Biological Sciences and Centre for Ecosystem Research and Implementation, Middle East Technical University, 06800 Ankara, Turkey
4
Institute of Marine Sciences, Middle East Technical University, 33731 Mersin, Turkey
5
Nuclear Research Center, National University of Mongolia, Ulaanbaatar 210646, Mongolia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(24), 14049; https://doi.org/10.3390/su132414049
Submission received: 20 November 2021 / Revised: 11 December 2021 / Accepted: 14 December 2021 / Published: 20 December 2021
(This article belongs to the Special Issue Freshwater Resources and Ecosystems in the Anthropocene: Challenges for a Sustainable Management)
Browse Figures
Versions Notes

Abstract

:
Cross-latitude studies on lakes have a potential to predict how global warming may cause major changes in phytoplankton biomass and composition, e.g., the development of favourable conditions for cyanobacteria dominance. However, results from these studies may be influenced by biogeographical factors, and the conclusions may, therefore, not hold when considering local response patterns. We used monthly monitoring data from 504 lakes in Denmark—a small and homogeneous geographical region—to establish empirical relationships between key phytoplankton groups and a set of explanatory variables including total phosphorus (TP), total nitrogen (TN), lake mean depth (DEP) and water temperature (TEMP). All variables had strong effects on phytoplankton biomass and composition, but their contributions varied over the seasons, with TEMP being particularly important in June–October. We found dominance of cyanobacteria in terms of biomass and also an increase in dinophytes biomass at higher TEMP, while diatoms and chlorophytes became less important. In May, however, the TEMP effect on total phytoplankton biomass was negative, likely reflecting intensified zooplankton grazing. Our results suggest that biogeographical effects are of minor importance for the response patterns of phytoplankton to temperature and that substantial concentration reductions of TN and TP are needed in eutrophic lakes to counteract the effect of the climate change-induced increase in TEMP.

1. Introduction

The global climate is changing [1,2], and this may affect the ecosystem structure and nutrient dynamics of lakes as well as their water quality. Several studies suggest that the biomass of phytoplankton and the concentration of chlorophyll a will increase, and also the relative contribution of cyanobacteria among the phytoplankton assemblage is expected to rise [3,4,5,6,7]. In correspondence with this, latitudinal gradient and cross-comparative studies have shown a greater importance of cyanobacteria in warm shallow lakes compared to colder temperate shallow lakes [6,8]. Moreover, warming intensifies stratification, and the mixing depth may become shallower, favouring cyanobacteria competition [9]; in addition, shallow lakes may become stratified [10]. This development creates optimal conditions for many cyanobacteria taxa due to their high optimum growth temperature and their ability to regulate buoyancy [11]. However, the sensitivity of cyanobacteria to temperature and nutrients varies in different lakes, which highlights the complex responses of lakes to multiple stressors [12].
A comparative study of 1656 lakes from subtropical Florida and temperate Denmark revealed an overall higher biomass of phytoplankton per concentration units of total phosphorus (TP) and total nitrogen (TN), as well as an overall higher contribution of cyanobacteria independent of the nutrient level [13]. Comparative studies are influenced by latitudinal changes in top–down control, which also affect the phytoplankton. For example, the community assemblage, size structure and dynamics of fish are expected to change markedly with global warming [14]. Cross-comparisons of fish populations in similar shallow lakes in subtropical South America and temperate Europe [15], as well as in lakes sampled along a latitudinal gradient within Europe, have collectively shown a greater number of fish at a given TP concentration in warm than in cold lakes [16]. Furthermore, when the percentage of omnivorous fish increases [17,18], the food webs become more truncated [19]. Such changes in fish assemblages are expected to result in a higher predation pressure on zooplankton and, consequently, a lower grazing pressure on phytoplankton, contributing to a higher algal biomass (chlorophyll a) per unit of TP [5]. This is further substantiated by the fact that the biomass of zooplankton is often lower in warm lakes than expected from the food resources available, i.e., there is a low zooplankton/phytoplankton biomass ratio [8,13,20,21]. While these changes may be a result of climate factors [22,23], biogeography-related factors cannot be excluded.
Models have also predicted an increase in phytoplankton biomass with warming, particularly an increase in the dominance and bloom frequency of cyanobacteria [24,25,26]. By contrast, warming experiments have revealed contrasting patterns in phytoplankton community structure—from no significant effect [25] to a reduction of mean biomass [27] and promotion of cyanobacteria [28], which may be partly reflected by differences in the food web structure present.
While biogeographical factors may interfere when using space-for-time approaches to elucidate the effects of climate [8], monitoring the data of lakes in specific regions is not affected by biogeography. Such data may thus be useful for studying the effects of temperature variations, at least in the short term, before biota adapted to the changes (genetically or by colonisation) is established. Here, we used monitoring data (monthly averages) from 504 Danish lakes sampled once or several times during the period from 1989 to 2017 to elucidate the role of temperature, while simultaneously considering variations in nutrients and lake depth. Based on the latitude gradient studies cited above, we expected to see an increasing biomass of phytoplankton and higher chlorophyll a concentrations at higher temperatures as well as a higher contribution of cyanobacteria and a longer duration of their blooming at the expense of, for instance, diatoms. We also expected cyanobacteria dominance with increasing mean depth (increasing degree of stratification).

2. Research Methods and Data

The study lakes are included in the nationwide Danish lake-monitoring programme on the aquatic environment, NOVANA, which has been running since 1989 and gathers data on key physical, chemical and biological variables [29]. Monitoring and samplings have been conducted by regional or national environmental authorities, and intercalibrations have been undertaken concurrently following standardised protocols [30]. A depth-integrated sample is taken at a mid-lake station in the photic zone and divided into a 2.5 L subsample for water chemistry analyses and a 100 mL subsample (fixed in Lugol’s solution) for phytoplankton determination. Phytoplankton was counted on settled (24 h) material using an inverted microscope, and specific algal biomasses were then calculated by fitting the different species and genera to geometric forms [31]. This study covers the period 1989–2017.
To assess the effects of changes in temperature, we analysed how the total phytoplankton biomass, chlorophyll a, the biomass of dominant phytoplankton groups (cyanobacteria, diatoms, chlorophytes and dinophytes) and their contribution (%) of the total phytoplankton biomass were related to temperature, nutrient level and lake depth. This was done by multiple regression analyses of monthly means based on the following equation:
Log (biomass or percentage + 1) = a log(TP) + b log(TN) + c log(DEP) + d log(TEMP)
where TP and TN are the concentrations of total phosphorus and total nitrogen in the lake surface water, respectively, DEP is the mean depth, and TEMP is the water temperature at 0.5 m of depth. Only data from lakes with a salinity <1 permille, a colour <30 pcu and an area >5 ha were included. Only data from March to October were used due to the scarcity of winter data. The dataset encompasses 504 lakes and 3206 lake-years for chlorophyll a, and 237 lakes and 1043 lake-years for phytoplankton biomass.

3. Results

The means and ranges of environmental variables used in the analysis are given in Table 1. The lakes were overall shallow, with high mean nutrient concentrations (0.16 mg TP L−1 and 1.83 mg TN L−1) and high mean chlorophyll a (45 µg L−1). Besides, the dataset covered a rather wide range of trophic states from oligotrophic to hypertrophic.
The coefficients (with SE) of the regression using Equation (1) are shown in Figure 1, Figure 2, Figure 3 and Figure 4, and all parameters, including tests of significance, are given in Table A1 in the Appendix A. While chlorophyll a was significantly positively related to TP during all months and to TN from April onwards, the DEP effect was negative or not significantly different from zero (Figure 1a). The TEMP effect varied from month to month but was strongly positive from June to October. However, the TEMP effect was negative in May. The total biomass of phytoplankton was also related to TP throughout the seasons and for TN, from April to October (Figure 1b). The TEMP effect followed the same pattern as that of chlorophyll a, being negative in May and otherwise positive and particularly pronounced during June–October. As for chlorophyll a, the DEP effect was negative or not significantly different from zero (Table A1).
Dividing the phytoplankton into the major groups revealed rather different patterns for the different groups. As for the biomass of cyanobacteria, the effect of TP was positive throughout all seasons, but for TN it was positive only from July to October (Figure 2a). The TEMP effect was positive during the whole period (significantly so from April onwards), being particularly pronounced in August and September. Apart from September and October, no significant DEP effect was observed (Table A1). The pattern of the cyanobacteria percentage of the total biomass partly followed the pattern observed for the biomass of cyanobacteria. The TEMP effect was positive throughout the season, being significant from April onwards, and was particularly pronounced during the period July–September, with a maximum in August (Figure 2b). The effect of DEP was also significantly positive from June to October. Thus, deep lakes generally hold a larger contribution of cyanobacteria than shallow lakes with the same TN, TP and TEMP levels during the summer stratification period.
As for the dinophyte biomass, the effects of TP and TN were overall low and mostly insignificant, although a significant positive effect of TP occurred in August–September. A significant TEMP effect (positive) was found in June–September, being particularly strong in July–September, and a significant positive DEP effect also appeared from June to September (Figure 3a). The pattern was clearer for the percentage of the dinophyte biomass (Figure 3b), for which, for most months, a negative, significant, relationship with both TP and TN emerged. The effect of TEMP was positive from August to October and negative in March–April, while the DEP effect was positive from May to October and negative in March–April (Table A1).
In the case of the diatom biomass, there was a significant positive effect of TP in all seasons and a positive effect of TN from March to June; after that, the effect was less clear (Figure 4a). The TEMP effect was generally negative except for March and June, while the DEP impact was negative from May to October and positive in March–April (Table A1).
As for chlorophytes (Figure 4b), a significant positive effect of TP could be traced during the entire seasons, whereas a positive effect of TN only occurred from June to October. The TEMP effect was weak and mostly insignificant, while a significant negative effect was found for DEP in all seasons (Table A1).
Below, we illustrate the relationships for two groups, cyanobacteria and dinophytes, as both include potentially toxic species. Figure 5 shows the calculated biomass of cyanobacteria as a function of TP and TEMP in August in a lake with an average depth of 3 m at different TN levels determined using Equation (1) (parameters in Table A1). The TP effects on the cyanobacteria biomass largely increased with the rising TEMP, and this was clearly amplified at high TN. The same 3D plot for the percentage of cyanobacteria shows a low contribution of cyanobacteria at low temperatures at all TP levels and high contributions at high temperatures, especially at high TN (Figure 6).
Figure 7 and Figure 8 demonstrate similar results for dinophytes in August. The dinophyte biomass increased with the increasing TP and further rose with the increasing TEMP, the highest biomass thus occurring at high TEMP and TP. In contrast to cyanobacteria, the dinophyte biomass decreased slightly with increasing TN. The dinophyte contribution of the total biomass increased as TP decreased and became even higher with TEMP at low TP, while the contribution decreased as TN increased.

4. Discussion

Our results showed, except for May, an overall strong and positive effect of temperature on the total phytoplankton biomass and chlorophyll a, a positive effect on the biomasses of cyanobacteria and dinophytes and a negative effect on the diatom biomass, while the effect on the biomass of chlorophytes was modest (Figure 1, Figure 2, Figure 3 and Figure 4). The risk of cyanobacteria dominance increased with increasing temperatures, and the period with cyanobacteria importance is predicted to last long (TEMP effect became positive already from April), as suggested in other studies [32,33]. The results further show that the risk of high cyanobacteria contributions might increase even at relatively low TP concentrations (Figure 6). This concurs with findings from a comparative study of Florida and Danish lakes [13], where the median contribution of cyanobacteria of the total phytoplankton biomass was 80% in Florida lakes with TP ranging between 0.25 and 0.50 mg L−1 and only 10% in Danish lakes.
A shift in the regression coefficient of temperature to negative values in May was observed for both the total phytoplankton biomass and chlorophyll a. This might be attributed to intensified zooplankton grazing in early summer in warm years. Supporting this observation, zooplankton, especially the genus Daphnia, were strongly positively related to TEMP (when accounting also for TP, TN and DEP) in Danish lakes [15,34]. Later in the summer through autumn, there were fewer Daphnia in the Danish lakes in warm years, which was likely due to increased predation pressure from fish and particularly the newly recruited specimens [5,14]. The latter hypothesis is supported by changes in the size of zooplankton; thus, the size of all cladocerans as well as of Daphnia also decreased significantly in summer with increasing TEMP [15]. Accordingly, a multiple regression analysis, using only the August dataset on Danish lakes, demonstrated a decrease in the average size of cladocerans and copepods with increasing temperatures, which is usually an indication of enhanced predation by fish [14].
When focusing on seasonality, we found a positive effect of TP in all seasons and of TN from April to October for both chlorophyll a and total phytoplankton biomass, while TN was not significant in March, likely reflecting the fact that TN is typically high in Danish lakes during winter [35]. This indicates a dual effect of the two nutrients, although it can be difficult to fully elucidate the role of N using such data, as the level of inorganic N is often low in shallow lakes during summer, and most N is thus bound to phytoplankton [35], which affects the regression coefficient between TN and the phytoplankton biomass. The results further show a negative effect of DEP on both chlorophyll a and total phytoplankton biomass in the colder months (March and October) when the deeper lakes are typically fully mixed, leading to less light, on average, for the phytoplankton. The effect was also negative in May. This likely reflects an often more pronounced high zooplankton-grazing phase (before fish larvae become abundant) in deep temperate lakes, as fish predation at a given nutrient level is lower in deep lakes than in shallow lakes [36]. During summer, however, when the stratification is stronger in deep lakes, no DEP effect was found (no deep mixing in deep lakes).
The cyanobacteria biomass was positively related to TP in all seasons and, in late summer and autumn, also to TN; in fact, a higher regression coefficient for TN than for TP occurred during August–September. The coefficient was higher for TEMP. To illustrate the effect of TEMP, we used Equation (1) to calculate the cyanobacteria biomass and the percentage of the total biomass at two nutrient levels, one depth (3 m) and three different temperatures (see Table A1). At a TP of 0.1 mg L−1 and a TN of 3 mg L−1, the biomass was predicted to be 6.4 mm3 L−1 (27%) at 19.5 °C (a typical surface water temperature in August in Danish lakes today), but 7.8 (34%) and 8.6 (39%) mm3 L−1 at 22.5 and 24.5 °C, respectively. At 0.2 mg TP and 3 mg N l−1, it was 8.1, 9.6 and 10.7 mm3 L−1 or 30, 39 and 45%, respectively. These results show the substantial effects that warming will have on the risk of an increase in cyanobacteria biomass and dominance and also the clear synergistic effects of nutrients and temperature, emphasised also in other studies [12,15,18,37]. To avoid high concentration of cyanobacteria at the temperature level of today (19.5 °C) at a TN of 3 mg L−1, TP has to be reduced from 0.1 mg L−1 to 0.055 and 0.039 mg L−1 at 22.5 and 24.5 °C. Similarly, a TP of 0.2 mg L−1 should be reduced to 0.115 and 0.082 mg L−1 at 22.5 and 24.5 °C, respectively, suggesting that rather major changes in external loading are needed in a warmer future to meet the present-day levels and targets.
Contrary to the situation for chlorophyll a and total phytoplankton biomass, the regression coefficient was positive and relatively high also in May, indicating a strong effect on, particularly, cyanobacteria in May. It clearly demonstrates the prolonged dominance of cyanobacteria in warm years, as predicted from both modelling [21,22,38] and time series [39] and in comparative studies from contrasting climate zones [6,14].
Dinophytes were strongly and positively affected by both temperature and DEP during the stratification period June–September, the latter likely reflecting that these motile algae can migrate between a nutrient-rich layer (meta-/hypolimnion) and the surface to harvest light [40]. The stability of the stratification is higher under warmer conditions and often at a shallower depth [4,9]. Moreover, some fully mixed lakes may become temporarily or fully stratified in warm years [10], which may further stimulate both dinophyte and cyanobacteria growth. Regarding the relative biomass, the dinophytes were particularly stimulated by TEMP at lower nutrient concentrations, while the TEMP effect on cyanobacteria was high at higher nutrient levels. Therefore, although both genera are stimulated by temperature, our result indicates that cyanobacteria will be of superior importance in a future warmer world in nutrient-rich lakes, while dinophytes might become relatively more dominant in lakes with less nutrients.
In contrast to cyanobacteria and dinophytes, diatoms, in particular, seem to suffer in a warmer climate. Despite their earlier spring bloom stimulated by TEMP in March, the regression coefficients were negative for most of the remaining seasons, particularly from July to September. During this period, they were also negatively affected by DEP (Table A1). Chlorophytes were also negatively affected by TEMP from July to September and by DEP in all seasons (Table A1). Therefore, even though the temperature optimum for chlorophytes and cyanobacteria is rather similar [41], our results indicate that cyanobacteria will be dominating in a warmer climate at the expense of chlorophytes and diatoms, as demonstrated in several field studies [4,42]. The advantage of cyanobacteria can be attributed to their ability to migrate vertically and avoid sedimentation in warmer and stronger stratified waters as well as to their resistance to grazing, especially when warming reduces zooplankton body size [41].

5. Conclusions

Our data from a small and homogeneous geographical area (Denmark) show a similar response pattern to the variation in temperature as revealed by lake data from a large latitude gradient study with contrasting temperatures. This suggests that the biogeographical effects inherent in the dataset from different regions are of minor importance when explaining temperature effects on phytoplankton biomass and composition. Moreover, our results point out that warming increases the risk of dominance of cyanobacteria at high nutrient concentrations and of dinophytes at low nutrient concentrations. Moreover, the duration of cyanobacteria dominance will increase in a warmer climate. Our results confirm that a reduction of N and P concentrations (external loading) may, in part, counteract the effect of increasing temperature, due to global warming, on phytoplankton biomass and the risk of dominance of, for instance, cyanobacteria. However, substantial reductions in nutrient loadings are needed according to the current climate change projection of temperature rise.

Author Contributions

Data curation, E.J., L.S.J., M.S. and T.L.L.; investigation, E.J., P.A.; methodology, all authors; writing—original draft preparation, E.J., S.T.; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Danish Ministry of Environment, project: Miljøindikatorer—indikatorer til vurdering af effekter af klimaændringer. E.J. was also supported by the TÜBITAK BIDEB2232 program (project 118C250).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are from the national survey program and can be obtained from https://danmarksmiljoeportal.ze861ndesk.com/hc/da (accessed on 1 October 2020).

Acknowledgments

We thank all the individuals participating in the collection of the samples used in the construction of the large database used in this study and Anne Mette Poulsen for editorial help and Tinna Christensen for help with figures.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Regression coeffecient and intercept (INT) with Standard Error (SE) and significance level (p) obtained using equation 1 given in the main text. Abbreviations and units: Total phosphorus (TP, mg L−1), Total nitrogen (TN, mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m).
Table A1. Regression coeffecient and intercept (INT) with Standard Error (SE) and significance level (p) obtained using equation 1 given in the main text. Abbreviations and units: Total phosphorus (TP, mg L−1), Total nitrogen (TN, mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m).
March April May June July August September October
Int/
Regression Coefficient		SEpInt/
Regression Coefficient		SEpInt/
Regression Coefficient		SEpInt/
Regression Coefficient		SEpRegression CoefficientSEpRegression CoefficientSEpRegression CoefficientSEpRegression CoefficientSEp
Cyanobacteria
(mm3 L−1)
INT0.5290.09****0.4540.126***0.0480.24ns0.0370.416ns−0.2840.553ns−1.350.567**−1.2960.525*0.3540.31
TP0.1620.024****0.1930.021****0.2190.023****0.2880.032****0.3280.036****0.2820.034****0.2180.037****0.1950.035****
TN−0.030.021*−0.04870.02*−0.0250.026ns0.030.046ns0.2920.062****0.5070.058****0.5410.062****0.1480.053**
TEMP0.0610.037ns0.1420.05**0.3130.084**0.4530.139**0.6510.178***1.1360.185****1.040.184****0.3230.123**
DEP−0.0150.02ns−0.0050.019Ns0.010.024ns0.0030.036ns0.0080.043ns0.0210.04ns−0.1110.039**−0.0910.039*
Cyanobacteria (%)
INT1.220.24****0.7040.304*−0.1770.548ns0.4530.76ns−1.060.84ns−2.3940.783**−0.640.785ns1.0660.608ns
TP0.2190.063***0.2780.05****0.390.052****0.4420.059****0.2980.055****0.1910.047****0.1620.056**0.2110.069**
TN−0.2080.058***−0.1780.047***−0.1320.059*−0.1430.084ns0.310.093***0.6580.081****0.7110.093****3920.103***
TEMP0.0930.098ns0.4210.122***0.8770.192****0.7620.254**1.2650.271****1.6960.255****1.1340.274****0.3660.241ns
DEP0.0650.054ns−0.0520.047Ns0.0580.054ns0.2360.066***0.3010.066****0.3480.056****0.3030.058****0.4690.073****
Dinophytes (mm3 L−1)
INT0.360.08****0.230.06***−0.010.08ns−0.600.20**−0.780.36*−0.950.41*−1.510.34****−0.010.12ns
TP0.020.02ns0.010.01Ns−0.010.01ns−0.060.02****0.020.02ns0.090.02***0.090.02***0.010.01ns
TN−0.040.02**−0.030.01**−0.020.01*0.060.02**−0.010.04ns−0.050.04ns−0.120.04**−0.030.02ns
TEMP−0.050.03ns−0.050.03Ns0.010.03ns0.170.07**0.310.11**0.440.13***0.670.12****0.050.05ns
DEP−0.090.02****−0.020.01*0.020.01*0.180.02****0.380.03****0.420.03****0.280.03****0.020.01ns
Dinophytes
(%)
INT0.990.25****1.0350.267****−0.2390.446ns−0.7380.632ns0.6650.736ns−0.2950.732ns−20.681**−0.9720.377*
TP−0.1650.067*−0.1710.044****−0.3250.042****−0.4840.0491****−0.3210.048****−0.1950.043****−0.0880.0483ns−0.1430.042***
TN−0.1380.06*−0.1290.041**−0.1190.048*−0.0090.0699ns−0.3190.081****−0.2290.075**−0.4050.08****−0.1950.064**
TEMP−0.2140.102*−0.3620.107***−0.02580.156ns0.0620.211ns−0.1920.237ns0.2460.239ns0.9080.238****0.4790.149**
DEP−0.3670.056****−0.0690.041Ns0.2130.044****0.6630.054****0.8310.057****0.7910.052****0.6990.05****0.1170.045**
Diatoms
(mm3 L−1)
INT0.4230.197*1.3850.213****2.320.305****0.4490.341ns2.5890.428****2.9130.395****1.9220.375****1.3220.265****
TP0.090.05ns0.220.04****0.190.03****0.140.03****0.180.03****0.210.02****0.190.03****0.200.03****
TN0.250.05****0.320.03****0.200.03****0.150.04****0.100.05*−0.010.04ns0.030.04ns0.040.05ns
TEMP0.260.08***−0.060.09Ns−0.450.11****0.160.11ss−0.500.14***−0.560.13****−0.260.13*−0.010.11ns
DEP0.110.04*0.140.03****−0.140.03****−0.080.03**−0.140.03****−0.260.03****−0.220.03****−0.250.03****
Chlorophytes
(mm3 L−1)
INT0.110.13ns0.140.13Ns1.380.23****1.410.30****1.850.35****1.900.32****1.770.30****1.320.21****
TP0.390.03****0.370.02****0.420.02****0.380.02****0.280.02****0.200.02****0.170.02****0.200.02****
TN−0.110.03****−0.050.02*0.060.03*0.170.03****0.230.04****0.150.03****0.150.04****0.040.04ns
TEMP0.010.05ns0.060.05Ns0.150.08ns0.150.10ns−0.130.11ns−0.240.10*−0.240.10*−0.080.08ns
DEP−0.120.02****−0.110.02****−0.190.02****−0.300.03****−0.310.03****−0.310.02****−0.360.02****−0.330.02****
* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001

References

  1. IPCC. Climate Change: The Physical Science Basis, Summary f1 1or Policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007. Available online: http://www.ipcc.ch (accessed on 1 November 2021).
  2. IPCC. The Physical Science Basis: Working Group 464 I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
  3. Huisman, J.; Matthijs, H.C.P.; Visser, P.M. Harmful Cyanobacteria; Springer: Dordrecht, The Netherlands, 2005. [Google Scholar]
  4. Jöhnk, K.D.; Huisman, J.; Sharples, J.; Sommeijer, B.; Visser, P.M.; Stroom, J.M. Summer heatwaves promote blooms of harmful cyanobacteria. Glob. Change Biol. 2008, 14, 495–512. [Google Scholar] [CrossRef] [Green Version]
  5. Jeppesen, E.; Kronvang, B.; Meerhoff, M.; Søndergaard, M.; Hansen, K.M.; Andersen, H.E.; Lauridsen, T.L.; Liboriussen, L.; Beklioglu, M.; Özen, A.; et al. Climate change effects on runoff, catchment phosphorus loading and lake ecological state; potential adaptations. J. Environ. Qual. 2009, 38, 1930–1941. [Google Scholar] [CrossRef]
  6. Kosten, S.; Huszar, V.L.M.; Bécares, E.; Costa, L.S.; van Donk, E.; Hansson, L.-A.; Jeppesen, E.; Kruk, C.; Lacerot, G.; Mazzeo, N.; et al. Warmer climate boosts cyanobacterial dominance in lakes. Glob. Change Biol. 2012, 18, 118–126. [Google Scholar] [CrossRef]
  7. Kakouei, K.; Kraemer, B.M.; Anneville, O.; Carvalho, L.; Feuchtmayr, H.; Graham, J.L.; Higgins, S.; Pomati, F.; Rudstam, L.G.; Stockwell, J.D.; et al. Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections. Glob. Change Biol. 2021, 27, 6409–6422. [Google Scholar] [CrossRef]
  8. Meerhoff, M.; Teixeira-de Mello, F.; Kruk, C.; Alonso, C.; González-Bergonzoni, I.; Pacheco, J.P.; Lacerot, G.; Arim, M.; Beklioğlu, M.; Brucet, S.; et al. Environmental warming in shallow lakes: A review of potential changes in community structure as evidenced from space-for-time-substitution approaches. Adv. Ecol. Res. 2012, 46, 259–349. [Google Scholar]
  9. Stockenreiter, M.; Navarro, J.I.; Buchberger, F.; Stibor, H. Community shifts from eukaryote to cyanobacteria dominated phytoplankton: The role of mixing depth and light quality. Freshwat. Biol. 2021, 66, 2145–2157. [Google Scholar] [CrossRef]
  10. Carey, C.C.; Ibelings, B.W.; Hoffmann, E.P.; Hamilton, D.P.; Brookes, J.D. Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate. Wat. Res. 2021, 46, 1394–1407. [Google Scholar] [CrossRef]
  11. Deng, J.; Paerl, H.W.; Qin, B.; Zhang, Y.; Jeppesen, E.; Cai, Y.; Xu, H. Climatically-modulated decline in wind speed may strongly affect eutrophication in shallow lakes. Sci. Tot. Environ. 2018, 645, 1361–1370. [Google Scholar] [CrossRef]
  12. Richardson, J.; Miller, C.; Maberly, S.C.; Taylor, P.; Globevnik, L.; Hunter, P.; Jeppesen, E.; Mischke, E.; Moe, J.; Pasztaleniec, A.; et al. Effects of multiple stressors on cyanobacteria biovolume varies with lake type. Glob. Change Biol. 2018, 24, 5044–5055. [Google Scholar] [CrossRef] [Green Version]
  13. Jeppesen, E.; Canfield, D.E., Jr.; Bachmann, R.W.; Søndergaard, M.; Havens, K.E.; Johansson, L.S.; Lauridsen, T.L.; Sh, T.; Rutter, R.P.; Warren, G.; et al. Towards predicting climate change effects on lakes: A comparative study of 1656 shallow lakes from subtropical Florida and temperate Denmark reveals substantial differences in nutrient dynamics, metabolism, trophic structure and top-down control. Inl. Wat. 2020, 10, 197–211. [Google Scholar] [CrossRef]
  14. Jeppesen, E.; Mehner, T.; Winfield, I.J.; Kangur, K.; Sarvala, J.; Gerdeaux, D.; Rask, M.; Malmquist, H.J.; Holmgren, K.; Volta, P.; et al. Impacts of climate warming on lake fish assemblages: Evidence from 24 European long term data series. Hydrobiologia 2012, 694, 1–39. [Google Scholar] [CrossRef] [Green Version]
  15. Jeppesen, E.; Søndergaard, M.; Lauridsen, T.L.; Liboriussen, L.; Bjerring, R.; Johansson, L.S.; Landkildehus, F.; Kronvang, B.; Andersen, H.E.; Trolle, D.; et al. Recent climate induced changes in freshwaters in Denmark. In Climatic Change and Global Warming of Inland Waters: Impacts and Mitigation for Ecosystems and Societies; Goldman, C.R., Kumagari, M., Robarts, R.D., Eds.; John Wiley & Son Ltd.: Hoboken, NJ, USA, 2012; pp. 156–171. [Google Scholar]
  16. Teixeira-de Mello, F.; Meerhoff, M.; Pekcan-Hekim, Z.; Jeppesen, E. Substantial differences in littoral fish community structure and dynamics in subtropical and temperate shallow lakes. Freshwat. Biol. 2009, 54, 1202–1215. [Google Scholar] [CrossRef]
  17. Brucet, S.; Pédron, S.; Lauridsen, T.L.; Mehner, T.; Argillier, C.; Winfield, I.J.; Volta, P.; Emmrich, M.; Holmgreen, K.; Rask, M.; et al. Fish community structure in European lakes: The role of eutrophication, climate and hydromorphology alterations. Freshwat. Biol. 2013, 58, 1779–1793. [Google Scholar] [CrossRef] [Green Version]
  18. Moss, B. Climate change, nutrient pollution and the bargain of Dr Faustus. Freshwat. Biol. 2010, 55, 175–187. [Google Scholar] [CrossRef]
  19. Gonzalez-Bergonzoni, I.; Meerhoff, M.; Davidson, T.A.; Teixeira-de Mello, F.; Baattrup-Pedersen, A.; Jeppesen, E. Meta-analysis shows a consistent and strong latitudinal pattern in fish omnivory across ecosystems. Ecosystems 2012, 15, 492–503. [Google Scholar] [CrossRef]
  20. Iglesias, C.; Mazzeo, N.; Meerhoff, M.; Lacerot, G.; Clemente, J.; Scasso, F.; Kruk, C.; Goyenola, G.; Garcia, J.; Amsinck, S.L.; et al. High predation is the key factor for dominance of small-bodied zooplankton in warm lakes—Evidence from lakes, fish exclosures and surface sediment. Hydrobiologia 2011, 667, 133–147. [Google Scholar] [CrossRef]
  21. Gyllström, M.; Hansson, L.A.; Jeppesen, E.; García-Criado, F.; Gross, E.; Irvine, K.; Kairesalo, T.; Kornijow, R.; Miracle, M.R.; Nykänen, M.; et al. The role of climate in shaping zooplankton communities of shallow lakes. Limnol. Oceanogr. 2005, 50, 2008–2021. [Google Scholar] [CrossRef] [Green Version]
  22. Havens, K.E.; Beaver, J.R. Zooplankton to phytoplankton bbiomass ratios in shallow Florida lakes: An evaluation of seasonality and hypotheses about factors controlling variability. Hydrobiologia 2013, 703, 177–187. [Google Scholar] [CrossRef]
  23. Vidal, N.; Amsinck, S.L.; Gonçalves, V.; Azevedo, J.M.N.; Johansson, L.S.; Christoffersen, K.S.; Lauridsen, T.L.; Søndergaard, M.; Bjerring, R.; Landkildehus, F.; et al. Food web patterns in species-poor insular lakes resemble climate-related patterns in continental lakes. Water 2021, 13, 1380. [Google Scholar] [CrossRef]
  24. Trolle, D.; Hamilton, D.P.; Pilditch, C.A.; Duggan, I.C.; Jeppesen, E. Predicting the effects of climate change on trophic status of three morphologically varying lakes: Implications for lake restoration and management. Environ. Model. Softw. 2011, 26, 354–370. [Google Scholar] [CrossRef]
  25. Chen, W.; Nielsen, A.; Kuhlmann, T.; Hu, F.; Chou, Q.; Søndergaard, M.; Jeppesen, E.; Trolle, D. Modelling the ecological response of a temporarily summer-stratified lake to extreme heatwaves. Water 2020, 12, 94. [Google Scholar] [CrossRef] [Green Version]
  26. Feuchtmayr, H.; Moran, R.; Hatton, K.; Connor, L.; Heyes, T.; Moss, B.; Harvey, I.; Atkinson, D. Global warming and eutrophication: Effects on water chemistry and autotrophic communities in experimental hypertrophic shallow lake mesocosms. J. Appl. Ecol. 2009, 46, 713–723. [Google Scholar] [CrossRef]
  27. Kratina, P.; Greig, H.S.; Thompson, P.L.; Carvalho-Pereira, T.S.; Shurin, J.B. Warming modifies trophic cascades and eutrophication in experimental freshwater communities. Ecology 2012, 93, 1421–1430. [Google Scholar] [CrossRef] [PubMed]
  28. Hansson, L.-A.; Nicolle, A.; Granéli, W.; Hallgren, P.; Kritzberg, E.; Persson, A.; Björk, J.; Nilsson, P.A.; Brönmark, C. Food-chain length alters community responses to global change in aquatic systems. Nat. Clim. Change 2013, 3, 228–233. [Google Scholar] [CrossRef]
  29. Svendsen, L.M.; van der Bijl, L.; Boutrup, S.; Norup, B. (Eds.) NOVANA. National Monitoring and Assessment Programme for the Aquatic and Terrestrial Environments. Programme Description, Part 2; NERI Technical Report No. 537; National Environmental Research Institute: Aarhus, Denmark, 2005. [Google Scholar]
  30. Johansson, L.S.; Lauridsen, T.L. Feltmålinger, Profilmålinger Samt Udtagning af Prøver til Analyse af Vandkemiske Parametre i Søer—DCE National Cor Miljø og Energi. 2017. Available online: https://ecos.au.dk/forskningraadgivning/fagdatacentre/ferskvand (accessed on 1 November 2021).
  31. Olrik, K. Planteplankton Metoder—Prøvetagning, Bearbejdning og Rapportering ved Undersøgelser af Planteplankton i søer og Marine områder [Phytoplankton Methods—Sampling, Processing and Reporting at Phytoplankton Investigations in Lakes and Marine Environments]. Miljøprojekt nr. 187; Miljøministeriet/Miljøstyrelsen: Copenhagen, Denmark, 1991; ISBN 87-503-9411-8. 108p. (In Danish) [Google Scholar]
  32. Romo, S.; Villena, M.J.; Sahuquillo, M.; Soria, J.-M.; Giménez, M.; Alfonso, T.; Vicente, E.; Miracle, M.R. Response of a shallow Mediterranean lake to nutrient diversion: Does it follow similar patterns as in northern shallow lakes? Freshwat. Biol. 2005, 50, 1706–1717. [Google Scholar] [CrossRef]
  33. Blenckner, T.; Adrian, R.; Livingstone, D.M.; Jennings, E.; Weyhenmeyer, G.A.; George, D.G.; Jankowski, T.; Jarvinen, M.; Aonghusa, C.N.; Noges, T.; et al. Large-scale climatic signatures in lakes across Europe: A meta-analysis. Glob. Change Biol. 2007, 13, 1314–1326. [Google Scholar] [CrossRef] [Green Version]
  34. Jeppesen, E.; Moss, B.; Bennion, H.; Carvalho, L.; DeMeester, L.; Feuchtmayr, H.; Friberg, N.; Gessner, M.O.; Hefting, M.; Lauridsen, T.L.; et al. Interaction of climate and eutrophication. In Changing Climate and Changing Freshwaters: A European Perspective; Kernan, M., Battarbee, R., Moss, B., Eds.; Blackwell: Oxford, UK, 2010; pp. 119–151. [Google Scholar]
  35. Søndergaard, M.; Lauridsen, T.L.; Johansson, L.S.; Jeppesen, E. Nitrogen or phosphorus limitation in lakes and its impact on phytoplankton bbiomass and submerged macrophyte covers. Hydrobiologia 2017, 795, 35–48. [Google Scholar] [CrossRef]
  36. Jeppesen, E.; Jensen, J.P.; Søndergaard, M.; Lauridsen, T.L.; Pedersen, L.J.; Jensen, L. Top-down control in freshwater lakes: The role of nutrient state, submerged macrophytes and water depth. Hydrobiologia 1997, 342/343, 151–164. [Google Scholar] [CrossRef]
  37. Salk, K.R.; Venkiteswaran, J.J.; Couture, R.M.; Higgins, S.N.; Paterson, M.J.; Schiff, S.L. Warming combined with experimental eutrophication intensifies lake phytoplankton blooms. Limnol. Oceanogr. 2021. [Google Scholar] [CrossRef]
  38. Markensten, H.; Moore, K.; Persson, I. Simulated lake phytoplankton composition shifts toward cyanobacteria dominance in a future warmer climate. Ecol. Appl. 2010, 20, 752–767. [Google Scholar] [CrossRef] [PubMed]
  39. Bertani, I.; Steger, C.E.; Obenour, D.R.; Fahnenstiel, G.L.; Bridgeman, T.B.; Johengen, T.H.; Sayers, M.J.; Schuman, R.A.; Scavia, D. Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story? Sci. Tot. Environ. 2017, 575, 294–308. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Reynolds, C.S. The Ecology of Phytoplankton; Cambridge University Press: Cambridge, UK, 2006. [Google Scholar]
  41. Lürling, M.; Eshetu, F.; Faassen, E.J.; Kosten, S.; Huszar, V.L.M. Comparison of cyanobacterial and green algal growth rates at different temperatures. Freshwat. Biol. 2013, 58, 552–559. [Google Scholar] [CrossRef]
  42. Paerl, H.W.; Huisman, J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environ. Microbiol. Reps. 2009, 1, 27–37. [Google Scholar] [CrossRef] [PubMed]
Sustainability 13 14049 g001 550
Figure 1. (a) Regression coefficients (with Standard Error) for the relationship between log-transformed chlorophyll a (µg L−1) and TP (mg L−1), TN (mg L−1), water temperature in surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for the log-transformed total biomass of phytoplankton (mm3 L−1).
Figure 1. (a) Regression coefficients (with Standard Error) for the relationship between log-transformed chlorophyll a (µg L−1) and TP (mg L−1), TN (mg L−1), water temperature in surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for the log-transformed total biomass of phytoplankton (mm3 L−1).
Sustainability 13 14049 g001
Sustainability 13 14049 g002 550
Figure 2. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed total biomass of cyanobacteria (mm3 L−1) and TP (mg L−1), TN (mg L−1) and water temperature in the surface water (TEMP, °C), based on multiple regressions on a monthly basis. Mean depth (DEP, m) was overall not significant (Table A1) and not included in the final regression. (b) The same for the percentage contribution of cyanobacteria to the total biomass of phytoplankton. Mean depth was significant and included.
Figure 2. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed total biomass of cyanobacteria (mm3 L−1) and TP (mg L−1), TN (mg L−1) and water temperature in the surface water (TEMP, °C), based on multiple regressions on a monthly basis. Mean depth (DEP, m) was overall not significant (Table A1) and not included in the final regression. (b) The same for the percentage contribution of cyanobacteria to the total biomass of phytoplankton. Mean depth was significant and included.
Sustainability 13 14049 g002
Sustainability 13 14049 g003 550
Figure 3. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed biomass of dinophytes (mm3 L−1) and TP (mg L−1), TN (mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for the percentage contribution of dinophytes to the total biomass of phytoplankton.
Figure 3. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed biomass of dinophytes (mm3 L−1) and TP (mg L−1), TN (mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for the percentage contribution of dinophytes to the total biomass of phytoplankton.
Sustainability 13 14049 g003
Sustainability 13 14049 g004 550
Figure 4. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed biomass of diatoms (mm3 L−1) and TP (mg L−1), TN (mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for chlorophytes.
Figure 4. (a) Regression coefficients (with Standard Error) for the relationship between the log-transformed biomass of diatoms (mm3 L−1) and TP (mg L−1), TN (mg L−1), water temperature in the surface water (TEMP, °C) and mean depth (DEP, m), based on multiple regressions on a monthly basis. (b) The same for chlorophytes.
Sustainability 13 14049 g004
Sustainability 13 14049 g005 550
Figure 5. Biomass of cyanobacteria in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Figure 5. Biomass of cyanobacteria in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Sustainability 13 14049 g005
Sustainability 13 14049 g006 550
Figure 6. Percentage contribution of cyanobacteria in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Figure 6. Percentage contribution of cyanobacteria in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Sustainability 13 14049 g006
Sustainability 13 14049 g007 550
Figure 7. Biomass of dinophytes in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Figure 7. Biomass of dinophytes in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Sustainability 13 14049 g007
Sustainability 13 14049 g008 550
Figure 8. Percentage contribution of dinophytes in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Figure 8. Percentage contribution of dinophytes in a lake with an average depth of 3 m in August plotted against water temperature (°C) and TP (mg L−1) at a TN concentration of 2 mg L−1 (left) and at two lower TN concentrations (right). Equation (1) was used, and the parameters are given in Table A1.
Sustainability 13 14049 g008
Table
Table 1. Mean and ranges of environmental variables for the 504 lakes used in the analysis.
Table 1. Mean and ranges of environmental variables for the 504 lakes used in the analysis.
MeanMinimumMaximum
Total phosphorus (mg L−1)0.160.0043.84
Total nitrogen (mg L−1)1.830.268.10
Chlorophyll a (µg L−1) 451266
Mean depth (m)2.40.216.4
Lake area (km2)2.00.0539.5
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jeppesen, E.; Johansson, L.S.; Tserenpil, S.; Søndergaard, M.; Lauridsen, T.L.; Andersen, P. Do Cross-Latitude and Local Studies Give Similar Predictions of Phytoplankton Responses to Warming? An Analysis of Monitoring Data from 504 Danish Lakes. Sustainability 2021, 13, 14049. https://doi.org/10.3390/su132414049

AMA Style

Jeppesen E, Johansson LS, Tserenpil S, Søndergaard M, Lauridsen TL, Andersen P. Do Cross-Latitude and Local Studies Give Similar Predictions of Phytoplankton Responses to Warming? An Analysis of Monitoring Data from 504 Danish Lakes. Sustainability. 2021; 13(24):14049. https://doi.org/10.3390/su132414049

Chicago/Turabian Style

Jeppesen, Erik, Liselotte S. Johansson, Sh Tserenpil, Martin Søndergaard, Torben L. Lauridsen, and Per Andersen. 2021. "Do Cross-Latitude and Local Studies Give Similar Predictions of Phytoplankton Responses to Warming? An Analysis of Monitoring Data from 504 Danish Lakes" Sustainability 13, no. 24: 14049. https://doi.org/10.3390/su132414049

APA Style

Jeppesen, E., Johansson, L. S., Tserenpil, S., Søndergaard, M., Lauridsen, T. L., & Andersen, P. (2021). Do Cross-Latitude and Local Studies Give Similar Predictions of Phytoplankton Responses to Warming? An Analysis of Monitoring Data from 504 Danish Lakes. Sustainability, 13(24), 14049. https://doi.org/10.3390/su132414049

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop