Do species feel overheated in the River Nedvědička? Veronika Konvičková & Bohuslav Uher Department of Botany and Zoology, Faculty of Science, Masaryk University, Kotlářská 2, 611 00 Brno, Czech Republic v.konvickova@mail.muni.cz In the river Nedvědička, a unique situation was found in the place where heated decontamination mining tributary (size 1m wide, from power plant) flows into the river. The quality of the water in the tributary was better than in the river. The only differences were found in increased temperature (20°C in winter), high conductivity (1500 µS.cmˉ¹) and higher sulphates (690 mg.l ˉ¹). The thermal regime of rivers plays an important role in the overall health of aquatic ecosystems. One of the most significant water quality parameters affected by urbanization is the temperature of water. The unnatural change in water temperature caused by human activities is thermal pollution. Numerous studies have demonstrated the danger of increased temperature on aquatic species. The importance of natural temperature for the ecosystem in running waters is undisputed; however there is still a lack of details in temperature research, especially a detailed study of herbivore assemblage influenced by thermal pollution is missing. The aim of this study is to investigate algal community and influence of herbivores in thermal polluted stream. The analysis of herbivores among localities influenced by thermal pollution The analysis of temperature data-sets We sampled quantitatively a macrozoobenthos and algae once a month over the course of the year. We also monitored temperatures (by thermometer) in four sampling places every 15 minutes from November 2007 until today. The main objectives of this study are to analyse the river at four representative places (the first - mining thermal tributary (MT), the second - up to the tributary of the river (UP), the third – directly under the tributary of river (UN), and the fourth – 2 km under the tributary (UT) with emphasis on thermal variability; also to observe the effects on the development of herbivore species and to relate variation in the phytobenthic assemblages to temperature. Coleoptera Diptera Gastropoda Ephemeroptera Chironomidae Trichoptera MT UP UN UT Plecoptera Boxplots (Figs 2-8) showed the difference of abundance of particular herbivores in localities. The abundance of Coleoptera was very high in the thermal tributary (next MT) and slightly increased the abundance in under to the tributary (next UN, UT). The abundance of Diptera in up to the tributary (next UP) was higher and there was reduction of abundance in UN, UT. The Gatropoda contained only too species. The high abundance was due to only one species Potamopyrgus antipodarum. This is very invasive species, which we didn´t find in UP. In MT have very good condition and higher temperature due to dispersion under the tributary. The opposite effect was appeared on the Ancylus fluviatilis, which its abundance was due to increasing the temperature under the tributary. The order Ephemeroptera, Trichoptera and suborder Chironomidae had lower abundance in UN and UT than UP, but caddisflies have higher abundance in MT. MT is locality full of algal mats and there live a few herbivorous caddisflies, which we didn‘t find in other localities (Tinodes unicolor, Hydroptila vectis). The opposite effect was appeared in Plecoptera, which their abundance was higher in UN, UT. We used Student's t-test for comparing the abundance (N.m²) of particular taxons in particular localities. The significant difference between up the tributary and under the tributary was in Coleoptera, Gatropoda, Ephemeroptera, Chironomidae and Trichoptera (Tab 2). p<0.05 UP-UN UN_UT UT-UP Coleoptera 0.003721 0.9232 0.02142 Diptera 0.4077 0.3965 0.05138 Gatropoda 0.008669 0.04785 0.1362 Ephemeroptera 0.04712 0.2545 0.6532 Chironomidae 0.04564 0.2328 0.7899 Plecoptera 0.2406 0.7005 0.3431 Trichoptera 0.02447 0.6121 0.02079 Tab. 2 Results of t.test in abundance (N.m²) between localities, p value < 0.05. Mean values °C MT UN UT UP mean summer 17.70 15.90 15.97 14.78 mean spring 16.23 9.88 9.93 8.27 mean autumn 17.96 11.89 12.24 10.12 mean winter 13.75 4.29 4.40 2.83 Extreme values max. temperature 24.15 20.63 20.75 19.03 max in this days 26/05/2009 16:45 31/05/2008 17:15 31/05/2008 16:15 23/06/2008 18:15 min. temperature 5.87 0.80 0.58 -0.06 min in this days 18/12/2007 20:30 19/12/2007 07:30 09/01/2008 08:30 09/01/2008 06:30 max. absolut temp in MT 24.15 26/05/2009 16:45 min. absolut temp in UP -0.06 09/01/2008 06:30 Daily amplitudes summer max 7.73 9.54 9.62 8.63 winter max 5.82 4.02 4.75 4.68 summer mean 1.09 2.53 2.43 2.74 winter mean 2.62 2.14 2.51 2.47 spring mean 3.09 3.20 2.76 2.90 autumn mean 2.35 2.17 2.51 2.43 Cumulative degree-days 23.11.2007-22.6.2009 (578 days) 565 288 288 246 The temperature data-sets were tested for a normal distribution using Shapiro-Wils W Test. For all temperature parameters, the W statistic was significant (p < 0.05), indicating data-sets were not normally distributed. We used this test for all testing in all data, which was used in analyses. We used Wilcoxon test to test data-sets to confirm differences between localities. The difference between UP and UN is highly significant (p-value < 2.2e-16) and between UP and UT is significant too. (Fig. 1) The main temperature characteristic shown in Tab 1. The temperature under the tributary was on average 1,6°C higher than in UP and the temperature in MT was on average 7,1°C higher than in UN. Tab. 1 Temperature parameters Fig. 1 Boxplots of temperature in localities. Functional feeding group Figs 2-8. Boxplots showed the difference among localitites in particular herbivores. Invertebrate taxa were assigned to functional groups by reference to Moog (1995). We divided herbivores to 3 functional group: Grazer-collectors (GC), Shredders (S) and Filtrators (F) and we wanted to find the difference of groups among localities. We tested the difference by Student's t-test again, but we found no statistically significant difference, but we can see some differences. (Figs 10-13) The almost balanced assemblages were up to the tributary (UP). We can see the difference among other localities under the tributary. The grazers collectors predominated in all localities, but the mostly predominated in thermal tributary. The higher amount of GC than S and F was find under the tributary. The higher abundance of algal assemblage in MT and under the tributary could due to more GC under the tributary. MT UP UN UT MT UP UN UT MT UP UN UT MT UP UN UT MT UP UN UT MT UP UN UT MT UP UN UT Benthic algal community Fig. 9. The circle graphs of algal communities We found 76 species of algal communities. The graph 3 shows the proportional representation of benthic groups in particular localities. The different is between up to the tributary and under the tributary. We can see great amount of green algae in MT, UN and UT, but not in UP. On the other side, there was a decrease of red algae assemblage under the tributary. We used Student's t-test for comparing the abundance (N.cm²) and number of species of algal community in localities (Tab. 3). We can see the significant difference in number of species and in abundance between up to the tributary and under the tributary. (Fig. 15,16) The number of species and the abundance of algal community were lower in UP than UN. The thermal tributary probably caused the higher abundance and the number of algal species under the tributary. p<0.05 UP-UN UN_UT UT-UP Number of species 0.00539 0.1959 0.1233 Abundance 0.00736 0.00048 0.1233 Tab. 3 Results of t.test between localities, p value < 0.05 Results Fig. 14. The result of redundant analysis, used restricted permutation with split plot design, factor season like covariate, used Monte Carlo permutation test, first axis = 17,5% , second axis = 31,2,%. Figs 10-13. Boxplots show the difference of abundance in feeding groups among localities. Code Significant factors p value t actual actual temperature measured during sampling 0 SO42 phosphates 0 t max maximal temperature during month 0,001 conduct conductivity 0,002 t mean mean temperature during month 0,002 TC total carbon 0,021 pH pH 0,03 We used a redundancy analysis RDA of species abundance data (invertebrate and algal assemblages) and environmental parameters, where we can find the main direction of variability. The significant parameters shown in Tab 4. From graphical output it is clear that localities are divided according to gradient of changed parameters, which was influenced by thermal tributary. On the surface it seems that the thermal tributary had very good quality of water (it‘s very clear), but these parameters like warmer water, high conductivity and SO4ˉ² significantly changed the quality of water under the tributary and changed the plant and animal communities. Tab 4. Significant parametres UP UN UT 3 2 4 5 6 7 8 1 9 10 11 12 13 14 15 16 Figs 15-16. Boxplots showed the difference among particular localities. Our photos from sampling. Acknowledgement: Financial support for this study was provided by the Funding of Ministry of Education of Czech Republic; project No. MSM0021622416. Thermal tributary 27.8.2008 [USEMAP]