varied occurrence of a salmon parasite · varied occurrence of a salmon parasite explaining the...
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Varied occurrence of a salmon parasite
Explaining the temporal and spatial
variations of Gyrodactylus salaris on the Swedish west coast
Johanna Lindberg
Degree project for Master of Science in Biology
Animal Ecology, 45 hec, 2016 Department of Biological and Environmental Sciences
University of Gothenburg
Supervisor: Johan Höjesjö Examiner: Lotta Kvarnmo
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The monogenean freshwater parasite Gyrodactylus salaris is known as the salmon killer after the disastrous effect it had on Norwegian salmon populations after 1975. On the Swedish west coast the parasite was first discovered in 1989, and today 15 salmon rivers are infected. The effect has not been the same as in Norway however, and today there are great variations in parasite numbers, both between rivers, and over time. This study aims to investigate the factors behind the variation, both temporal and spatial. There was significant difference in parasite intensity (mean number of parasites per infected fish) and prevalence (percentage of fish infected) between rivers, with Säveån and Hjärtaredsån having constantly lower values. Several factors are proven to effect G. salaris variation. The main factor that significantly determined both intensity and prevalence was conductivity. Conductivity positively correlated with both pH and alkalinity. The reason for the significant effect of conductivity is believed to be an indirect effect of aluminium (Al), that has been proven toxic to G. salaris. Al is associated with acidification, and low pH levels would thus lead to higher Al levels. High conductivity can be an indication of high pH and low Al levels, which would lead to higher levels of parasite infection. G. salaris varied significantly over years, and prevalence was significantly reduced after unusually warm summers, although the intensity remained unchanged. This is theorized to be an effect of the reduced survival of the parasite in warm waters, leading to reduced transmission success. Spring and autumn had significantly higher intensities than summer. This is in accordance with the temperature optimum of G. salaris of between 6 and 15 °C. G. salaris is predicted to spread from Rolfsån to neighbouring Kungsbackaån and Fälån, but, due to high values of Al in those two rivers, the infection intensities will probably be comparatively low, at least if the Al levels continues to be high. There are still much that remains to be understood about the G. salaris infections on the west coast, and suggestions for improvement of the current managing program, as well as suggestions for future studies are provided.
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Den sötvattenslevande haptormasken Gyrodactylus salaris är en parasit som även går under namnet laxdjävul, detta efter de katastrofala effekter den hade på norska laxpopulationer efter introduktionen i Norge år 1975. På den svenska västkusten upptäcktes parasiten först år 1989, och idag är 15 laxåar drabbade. Effekten har däremot inte varit den samma som i Norge och idag varierar antalet parasiter stort, både mellan åar och över tid. Den här studien syftar till att utreda faktorerna bakom denna variation, både över tid och rum. Det var en signifikant skillnad i parasitens intensitet (medelantal parasiter per infekterad fisk) och förekomst (antal procent av fisk som är infekterad) mellan de olika åarna, där Säveån och Hjärtaredsån hade konstant lägre nivåer. Flera faktorer har visat sig påverka förekomsten av G. salaris. Den faktor som hade en signifikant påverkan på både intensitet och förekomst var konduktivitet, som hade en positiv effekt på båda. Konduktivitet var även positivt korrelerat med pH och alkalinitet. Anledningen till att konduktivitet hade en signifikant påverkan tros vara en indirekt effekt av aluminium (Al), något som har visat sig vara giftigt för G. salaris. Al associeras med försurning, och lågt pH leder till högre nivåer av Al. Hög konduktivitet kan därför indikera högt pH och låga Al nivåer, vilket i sin tur kan leda till en större mängd parasiter. G. salaris varierade signifikant mellan olika år, och förekomsten minskade signifikant efter ovanligt varma somrar även om intensiteten var den samma. Detta kan eventuellt vara en effekt av att parasiterna överlever kortare tid i varmt vatten, vilket minskar antalet lyckade överföringar till nya värdar. Vårar och höstar hade signifikant högre intensitet än somrar. Detta stämmer överens med G. salaris optimala temperatur på mellan 6 och 15°C. G. salaris förutspås att spridas från Rolfsån till närliggande Kungsbackaån och Fälån, men på grund av de sannolikt höga nivåerna av Al i dessa två åar antas den framtida intensiteten av parasiten att bli relativt låg, i alla fall om Al nivåerna fortsätter att vara höga. Det finns fortfarande mycket kvar att undersöka när det gäller infektionen av G. salaris på den svenska västkusten, och förslag på förbättringar av det pågående övervakningsprogrammet, liksom förslag på framtida studier läggs fram.
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Introduction ....................................................................................................................... 5 Gyrodactylus salaris .................................................................................................................... 5 Effects in Norway ........................................................................................................................ 5 Difference in susceptibility between salmon stocks ..................................................................... 5 Different haplotypes of Gyrodacylus salaris ................................................................................ 6 Discovery and effects on the Swedish west coast ........................................................................ 6 Factors influencing variations in Gyrodacylus salaris intensity ..................................................... 6 Aim of the study .......................................................................................................................... 8
Materials and Methods ...................................................................................................... 9 Collection of data ........................................................................................................................ 9 Electrofishing .............................................................................................................................. 9 Counting of Gyrodactylus spp. ................................................................................................... 10 Statistical analysis and calculations ........................................................................................... 11
Results ............................................................................................................................. 16 Distribution of Gyrodactylus salaris ........................................................................................... 16 Differences between rivers and sampling sites .......................................................................... 17 Temporal variation .................................................................................................................... 18 ................................................................................................................................................. 22 Spatial variation ........................................................................................................................ 23
Discussion ........................................................................................................................ 26 Differences between rivers ........................................................................................................ 26 Temporal variation .................................................................................................................... 27 Spatial variation ........................................................................................................................ 29 Risk of future spreading of G. salaris within Rolfsån and to Kungsbackaån ................................ 35 Comments on the future management of G. salaris monitoring ................................................ 37 Conclusions ............................................................................................................................... 38
Acknowledgements .......................................................................................................... 39
References ....................................................................................................................... 40
Appendix .......................................................................................................................... 44
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Introduction Gyrodactylus salaris
The genus Gyrodactylus are widespread monogenean ectoparasites on freshwater fishes, and consists of app. 400 species. Gyrodactylus salaris infects the skin and fins of primarily Atlantic and Baltic salmon (Salmo salar) (Olstad 2013), where it feeds on the epidermis, causing grazing wounds. The parasite is viviparous, which means it gives birth to live young, but has a way of viviparity called hyperviviparity. This means that the offspring, at the time of the birth, is already fully grown and pregnant, and attach to the same host as the parent (Olstad et al. 2006; Denholm et al. 2013). This gives G. salaris a short generation time and it allows for rapid growth of parasites on infected individuals. When infected by hundreds or thousands of parasites the damage on the skin of the host can be extensive, causing secondary infections and osmoregulatory disturbance (Pettersen et al. 2012) sometimes referred to as gyrodactylosis.
G. salaris was first discovered by Malmberg in 1952, on salmons in a hatchery in River Indalsälven, Jämtland, and later described in 1957 (Malmberg & Malmberg 1993). The parasite was, at that time, not associated with disease on the Baltic salmon, and Gyrodactylus salaris was not considered pathogenic in the wild (Bakke et al. 2004). Effects in Norway
In 1975 G. salaris was discovered in Norway for the first time, likely introduced by transportation of farmed salmon from the Baltic sea in Sweden (Johnsen & Jensen 1991). In Norway the parasite spread rapidly, and has since its introduction been reported from 46 rivers (Johnsen et al. 2008). The effect on Norwegian salmon populations has been devastating, many salmon populations have crashed and the average density of salmon parr in infected rivers has been reduced by 86% (Johnsen & Jensen 1991; Bakke et al. 2004). Difference in susceptibility between salmon stocks
These large differences in G. salaris effect between the Swedish Baltic region and the Norwegian Atlantic region can likely be explained by difference in susceptibility between the two types of salmon. Many studies have shown the Baltic stocks of salmon to be less susceptible than the East Atlantic stocks (Bakke et al. 1990; Cable et al. 2000; Dalgaard et al. 2003). This is believed to be due to that Baltic salmon have coevolved with G. salaris since the last glacial period, leading to the population developing a heritable resistance to parasite infection (Dalgaard et al. 2003; Lumme et al. 2016). For example, Cable et al. (2000) showed that the time of first birth for G. salaris on Baltic stocks was extended and the survival lower compared to on Norwegian stocks of salmon.
There has been extensive research trying to determine what causes this apparent resistance. For example, an upregulation of cytokines in the skin, promoting mucus secretion has been observed in susceptible Atlantic, but not Baltic salmon. The mucus could serve as nutrition and as a chemoattractant, partially explaining the difference in susceptibility observed (Lindeström et al. 2006). There also seem to be multiple genes and genome regions involved in the differences in susceptibility, and the matter is not resolved (Gilbey et al. 2006; Kania et al. 2007).
There are some studies suggesting that the matter is not as simple as all East Atlantic salmons being more susceptible and all Baltic salmons being resistant (Bakke et al. 2004;
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Ramirez et al. 2015). Both complexes show great heterogeneity in resistance and adaption between local populations, both within and between rivers. (Ramirez et al. 2015; Lumme et al. 2016). Different haplotypes of Gyrodacylus salaris
To further complicate things, there are not only differences between salmon stocks in susceptibility, but also different clades and haplotypes of Gyrodactylus salaris with different virulence (Hansen et al. 2003; Hansen et al. 2007). Some of the Haplotypes, such as the Danish variant of Haplotype F, found in Danish rivers, seem to be non-‐pathogenic to salmon (von Gersdorff Jørgensen et al. 2008), whereas others, such as Haplotype A, which is found in many Norwegian rivers, is associated with severe gyrodactylosis (Hansen et al. 2007). Interestingly, haplotype F seems to be both pathogenic and non-‐pathogenic to Atlantic salmon in Norway (Ramirez et al. 2014). Several studies have used strains of this haplotype from Lierelva or Lærdalselva who grow exponentially on salmon (e.g. Bakke et al. 1990; Bakke et al. 1999; Cable et al 2000; Daalgard et al. 2003), while strains from the same haplotype from Pålsbufjorden mainly infects Arctic charr (Salvelinus alpinus), and is non-‐pathogenic to salmon (Ramirez et al. 2014).
The Haplotypes found on the Swedish west coast is Haplotype A; found in Viskan (Surtan) Ätran, Löftaån and Rolfsån, Haplotype C; found in Fylleån, Genevadsån, Nissan, Stensån, and Suseån, and finally Haplotype E; found in the river Säveån, in Göta älv (Hansen et al. 2003; Degerman et al. 2012; data from Degerman 2016). Discovery and effects on the Swedish west coast
The first discovery of Gyrodactylus salaris on the Swedish west coast occurred in the river Säveån 1989 (Degerman et al. 2012). Since then 15 salmon rivers have been infected and only 8 remains free of infection. The most recent river to be infected was Rolfsån, where the parasite was discovered in 2015.
The salmon on the Swedish west coast is of the East Atlantic type, closely related to the Norwegian salmon (Degerman et al. 2012). Due to the documented effects of G. salaris in Norway, many feared that the same catastrophic effects on salmon could occur on the Swedish west coast. This has however not been the case. All the salmon parr populations monitored on the west coast have declined over time since the end of 1980, but without any significant effect of G.salaris (Degerman et al. 2012). Why is not clear. One hypothesis is that Swedish west coast salmon have a higher resistance than the salmon from many Norwegian rivers, perhaps due to earlier presence of G. salaris during post glacial periods (Malmberg & Malmberg 1993). Some haplotypes, such as haplotype E from Göta älv, is even suggested to be a naturally occurring parasite population (Meinilä et al. 2004). In agreement, the south eastern Norwegian salmon stocks, the ones closest to the Swedish west coast, have lower G. salaris population growth rate than the northern or western stocks (Ramirez et al. 2015), suggesting that the salmon stocks in the Nordic south western area show a higher resistance. This, however, remains to be proven. The difference cold also be due to a difference in water properties between the Swedish and Norwegian rivers affecting G. salaris. Factors influencing variations in Gyrodacylus salaris intensity
The intensity of Gyrodactylus salaris varies greatly on the Swedish west coast, both between different rivers, and localities within the rivers, but also over time (Degerman et al.
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2012). Some of this variation has been explained by the higher likeliness of encountering infected individuals later in the year, larger fish being more often infected than small, and grade of infection increasing with colder waters. Salmon density the year before the sampling was also positively correlated with G. salaris intensity. However, as much as 91% of the variation still remains unexplained, and it is believed that factors such as water chemistry may play a role in the variations observed between rivers (Degerman et al. 2012).
Indeed, there are many factors affecting G. salaris, both in positive and negative ways, where differences in water temperature, density off salmon parr, and water chemistry is the ones believed to have greatest effect. Temperature
G salaris is a cold water adapted parasite, and its mean life span decreases with higher temperature; from 33.7 days in 2.5°C to 4.4 days in 19°C. The number of offspring peaks between 6.5 and 13 °C, but the population growth increases with increased temperature up to 19°C (the highest temperature tested). This is due to the parasite giving birth at an earlier age and thus the generation time decreases sharply. The optimum temperature of G.salaris is considered to be between 6 and 15 °C (Jansen & Bakke 1991).
In accordance with this, a study of G. salaris in the Norwegian river Batnfjordselva showed that the mean number of parasites increased during the warm period of the year (summer and autumn) and decreased during the cold period of the year (winter and spring). After winters with water temperatures almost at 0°C for 2-‐3 months, some fish were even found to be uninfected (Mo 1992). Similar seasonal variation has been found for G. salaris on Arctic charr (Winger et al. 2008).
In short, it seems like warmer temperatures promote faster growth of G salaris populations, even though it decreases the life span of the individual parasites. The rate of transmission of G salaris from host to host is also positively correlated with temperature (Soleng et al. 1999). Salmon population density
Transmission of Gyrodactylus salaris from host to host has been suggested to occur via four different routes; (i) direct transfer via contact between live fish, (ii) transfer via contact between a live fish and a dead host, (iii) contact between a fish and detached parasites on the bottom substrate and (iv) contact between a fish and detached parasites drifting in the water column (Bakke et al. 1992). The relative importance of these transmission routes has not been studied in detail, but contact between live hosts is assumed to be important (Soleng et al. 1999; Olstad et al. 2006). Since physical contact between live fish occur more often when the population density of the salmon parr are higher, higher salmon parr density is hypothesised to lead to a higher transmission rate of G. salaris. Water chemistry
Aqueous aluminium (Al) and Zinc (Zn) has been shown to have negative effects on Gyrodactylus salaris, and there is a negative correlation between G salaris infections and these metal concentrations (Soleng et al. 1999; Poléo et al. 2004). In fact, today one of the Norwegian strategies of eliminating G. salaris in infected rivers consists of a treatment called AlS, where a combination of aqueous aluminium and sulfuric acid is released into the stream to eliminate the parasites (Eriksen & Pettersen 2016). Both Al and Zn, but especially Al, is associated with freshwater acidification, and elevated levels occur when pH drops (Poléo et
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al. 2004). pH in itself has also been shown to lower G. salaris survival and population growth, but only at very low levels. Survival is reduced when pH is 5.0 or lower, population growth is reduced when pH is 5.6 or lower, and populations are eliminated at pH 5.0 (Soleng et al. 1999). This suggests that acidified rivers are less susceptible to the parasite than others.
Another factor that has been shown to affect G. salaris is salinity, which has a negative effect on survival in salinities above 7.5‰, with a marked decrease in survival occurring in salinities at 10‰ or above. (Soleng & Bakke 1997) Other factor negatively affecting survival includes chlorine (Cl), where there is a negative correlation between population intensity and hypochlorite concentration (Hagen et al. 2014).
Low oxygen concentration and pollution has also been suggested to negatively affect gyrodactylids (Koskivaara et al. 1991), while eutrophication has showed both positive and neutral effects on gyrodactylid growth (Lafferty 1997; Vidal-‐Martínez et al. 2009). Nitrate has even been seen to reduce the number of gyrodactylids on guppies and make them less vulnerable to infection (Smallbone et al. 2016). Aim of the study
There are obviously many factors that can account for the large spatial and temporal variations in G. salaris intensity, and only few of them has been thoroughly examined in natural systems. This study aims to further investigate the temporal and spatial variations in infection rate of G. salaris on salmon parr observed in the rivers of the Swedish west coast, and which regulating factors that might exist. We will investigate if the temporal variations can be explained by changes in water temperature and if the spatial variations can be explained by factors such as temperature, water chemistry, salmon parr density, or distance to the sea.
We hypothesize that we will see a reoccurring seasonal variation in G. salaris intensity, with lower numbers of G. salaris in early spring, after the winter, and larger numbers in autumn, after long periods of warmth. There might also be large declines in both intensity and prevalence of G. salaris after long, cold winters or after unusually warm summers since G. salaris is adapted to cold waters.
Regarding the effect of salmon parr density, we hypothesize that higher salmon parr density will correlate positively with G. salaris intensity and prevalence, due to increased transmission rates. Regarding water chemistry, it is likely that we will see a negative effect on G. salaris with higher Al concentrations and possibly also with lower pH. Other factors such as conductivity, water coloration, and nutritional status is not hypothesised to show any effect on G. salaris, this since these factors has not been noted in earlier studies, except nutrients, and this only at very high levels unlikely to be seen in this study. Distance to the sea is not hypothesised to have any effect either since the salinities in the studied rivers are unlikely to reach high enough levels to negatively affect G. salaris. For example, the inner parts of the Kungsbacka fjord has a salinity below 10‰ (Sundqvist & Karlsson 2016), further up in the river the salinity is likely even lower, and will have little to no effect on the parasite population.
The aim is further to take eventual regulating factors into consideration and discuss how the infection might spread in newly infected Rolfsån and how likely the neighbouring Kungsbackaån is to be infected.
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Materials and Methods Collection of data
The Gyrodactylus salaris data was provided by SLU (Swedish University of Agricultural Science). This “gyro” database is part of a G. salaris monitoring program and contains information from 22 salmon rivers and 82 sites on the Swedish west coast and was obtained by electrofishing between 1989 to 2016. The database contains information like location, density and size (length and weight) of salmon, as well as number of Gyrodactylus spp. found on each fish. If the gyrodactylids has been determined to species and haplotype this is also noted.
The information on salmon parr density (0+ salmon, >0+ salmon, and total salmon density) was provided by SLU by standard electrofishing data available in the Swedish Electrofishing Register (aquarapport.slu.se), which covered most of the sites where the gyrodactylus electrofishing had occurred. The distance from the electrofishing local to the sea was also provided.
The water chemistry data was provided by the county administration boards in Halland and Västra Götaland. Some data were also collected from SLUs website Miljödata MVM, version 1.16 (miljodata.slu.se). This provided data on pH, alkalinity, total and inorganic aluminium (Al), total zinc (Zn), colour, absorbance, conductivity, nitrogen (N), nitrite (NO2
-‐) and nitrate (NO3
-‐), and phosphorus (P). Data on Swedish air
temperatures was collected from SMHI open data (opendata-‐catalog.smhi.se), and data on Norwegian air temperatures was collected from Yr, delivered by the Norwegian Meteorological institute and NRK. Electrofishing The electrofishing for the gyro-‐database is conducted at a yearly basis by the Swedish Anglers Association in Gothenburg for SLU. Some sites are fished during autumn and some during spring, in general, uninfected rivers are normally sampled in spring and infected in autumn. The sites are sampled when the water flow is sufficiently low, and when the water temperature is around 10°C. There are several predetermined sites for sampling
Table 1. This table displays the sites electrofished during 2016, the name of the stations, the river, and main river system they are situated in. The rivers are sorted from south to north, with Ätran being the river with its outlet located furthest south, and Örekilsälven furthest north.
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of Gyrodactylus salaris decided by SLU, and the electrofishing takes place at these locations. For this study, I assisted in most of the electrofishing during 2016.
In spring 2016, four rivers and 10 sites were sampled. The sites sampled were two sites in Himleån (15th of April), four in Rolfsån (11th of May), two in Kungsbackaån (26th of April), and two in Örekilsälven (28th of April). In autumn 2016 four rivers and 14 sites were sampled. There were four sites in Rolfsån (5th of October), the same ones as in spring, one site in Anråseån (6th of October), one site in Säveån (6th of October), and 8 sites in Ätran (31th of October). The name of the sites and the rivers they are located in can be seen in Table 1. A map of the more exact location of these rivers can be found in Figure 1.
The electrofishing at these sites was conducted until between 10 and 15 salmon parr were caught. The salmon were immediately after capture put in a bottle with 95% alcohol and were thus preserved until further analysis in the lab. After that, the water and air temperature were measured and noted. The site Bosgården in Rolfsån was sampled for the first time in 2016 and therefore a quick assessment of the stream habitat was made according to parts of the standard protocol for electrofishing. Counting of Gyrodactylus spp. The degree of infestation was assessed under a stereo microscope at 16x enlargement. The number of Gyrodactylus spp. was counted on the dorsal fin and both pectoral fins respectively.
Gyrodactylus salaris is not the only gyrodactylid to be found in the rivers of the Swedish west coast, there is also a species called Gyodactylus derjavini whose preferred host is trout (Salmo trutta). This species of Gyrodactylus is not associated with disease in salmon (Malmberg & Malmberg 1993; Bakke et al 1999). The two species, G. salaris and G. derjavini,
Figure 1. A map of the different rivers electrofished during 2016 and their location on the Swedish west coast. River number 1 is Ätran, number 2 is Himleån, number 3 is Rolfsån, number 4 is Kungsbackaån/Lindomeån, number 5 is Göta Älv, number 6 is Anråseån, and number 7 is Örekilsälven.
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are morphologically very similar, although G. derjavini is usually found in much smaller numbers than G. salaris. It is not possible to accurately determine the species by using only a stereo microscope. Therefore, any gyrodactylids found in previously uninfected or newly infected sites were sent to Veterinærinstituttet in Oslo, Norway, for further analysis of species and haplotype. This to determine if it was the infectious G. salaris, and if so which haplotype, or if it was the relatively harmless G derjavini.
Statistical analysis and calculations From the data in the gyro-‐database some basic information such as prevalence (percentage of salmons infected with G. salaris), infection intensity (mean number of G. salaris per infected fish) and total mean (mean number of G. salaris on all fish, including non-‐infected) was calculated. Not all rivers and years were represented, and if there were several electrofishings and/or water analyses in one year, the data from the date closest in time with the gyrodactylus sampling were chosen. The water chemistry measurements were sometimes taken at a station a few kilometres from the gyro stations, these data were still deemed okay to use, if the sites were in the same river and the stations were not separated by any lakes or major tributaries. Treatment of data The test for normality used in all cases was Shapiro-‐Wilk tests. If the data were not normally distributed, log transformed data (+10) were tested as well. All the statistical analyses were performed using IBM SPSS Statistics version 24, with the significance set to α =0,05. All graphs and tables were made in Microsoft Excel version 15.27. Differences between rivers and sampling sites The test of difference between sampling sites and rivers included sites that were infected with G. salaris and that had been electrofished in 2015 and/or 2016. The exception being
Table 2. A table of the different sampling sizes (N), or the total number of sites electrofished for G. salaris, in the test of temporal variations between years, months, and seasons. Years and months excluded from the test due to too few samples (<5) are marked with red. In a later test, without the data from Hjärtaredsån and Säveån, the year 2008 was excluded as well.
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Rolfsån where G. salaris was found too recently for a fair comparison to be made. Since the sampling at many of the stations did not start until the 21th century, while some of the stations has been sampled since the early 90’s, both a comparison of values only from the 21th century and a comparison with total values were made. It must be noted that the river Himleån was not infected until 2005, and the sampling at station Jonseredsdammen in Säveån did not start until 2008. Neither the prevalence, nor the intensity for rivers and stations were normally distributed, therefore, a Kruskal-‐Wallis ANOVA with a Dunn-‐Bonnferroni post hoc was used to determine differences. Temporal variation To test how the prevalence and intensity of G. salaris vary over years, months and seasons; December, January and February were classed as winter, March, April and May as spring, June, July and August as summer, and September, October and November as autumn. There were large differences in number of samplings done in the different years, months, and seasons, and 7 years and four seasons had to be excluded due to too few samples (Table 2).
The prevalence and intensity of G. salaris were not normally distributed so a non-‐parametric Kruskal-‐Wallis ANOVA with a Dunn-‐Bonnferroni post hoc test was used. The prevalence had no outliers so it was used as it was, whereas the intensity was log transformed (+10), which removed the outliers.
A second, almost identical test of years, months, and seasons was made, in the same manner as above, with the same months excluded and only one additional year excluded (2008, N=4). This time without any values from Göta älv or Hjärtaredsån. This since I wanted to remove the possibility of the very low values in these stations affecting the whole test.
The air temperatures since 1990 were collected from SMHI, where mean temperatures from each month has been collected from stations at Halmstad, Varberg and Göteborg. To test the effect of cold winters, a comparison between cold winters (set as 3 months with mean air temperature under 0°C) and warm winters (no mean air temperatures under 0°C) was made. Only fish sampled in spring were investigated when looking at the effect of the winters. A total of 6 samplings could be found with data from spring in both a cold and a warm winter. The intensity was normally distributed after a log transformation (+10), the prevalence was however not, neither before nor after transformation. Thus, a paired t-‐test was used to test the difference in intensity and a Wilcoxon Signed Ranks test to test the difference in prevalence.
“Warm summers” were defined as summers with two or more months with mean air temperatures above 17.5°C alternatively one mean air temperature above 20°C, “unusually warm summers” was defined as summers with at least one mean monthly air temperature above 20°C and “cold summers” were defined as summer with no months with mean air temperatures above 17.5°C. Only samples in autumn was investigated to focus on the effect of the warm summer and was compared with years without any warm summers. The effect of unusually warm summers was tested by comparing the values obtained in the autumn after this summer with the values obtained in autumn the year before. Overall, 18 values of intensity and prevalence could be found. They were tested the same way as above, with a paired t-‐test to test the differences between intensities, and a Wilcoxon Signed Ranks test to test the difference in prevalence.
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Spatial variation To calculate the difference between haplotypes, all the samplings from rivers with known
haplotypes were used. The intensity and prevalence for each sample were grouped after haplotype, A, C or E. Since neither prevalence, nor intensity were normally distributed, a Kruskal-‐Wallis ANOVA with a Dunn-‐Bonnferroni post-‐hoc was used to determine any differences.
The test of differences in water quality and salmon density was performed with focus on pH, alkalinity, conductivity, colour, total nitrogen, nitrite and nitrate, total phosphorus, estimated salmon density of parr in the first year class (0+), estimated salmon density of older parr(>0+), and total estimated salmon density. Factors excluded from the study due to too few values were total and inorganic aluminium (Al), total zinc (Zn), and absorbance. pH and total nitrogen were the only factors normally distributed in the original form, after log transformation colour, nitrite and nitrate, total phosphorus, salmon density (0+), salmon density (>0+), and total salmon density also became normally distributed. Alkalinity and conductivity remained not normally distributed. An ANOVA with a Tukey’s post hoc was performed for each factor to determine the difference between rivers. All rivers where G. salaris are found with sufficient data were tested for differences. The rivers tested were Säveån, Himleån, Stensån, Ätran, Högvadsån, Fageredsån, Hjärtaredsån and Löftaån (Figure 2). There were not water chemistry data available for all sites in the rivers and even on sites where water chemistry data were available it rarely contained all variables mentioned above, leading to the water chemistry data being quite patchy.
Since Al is an important factor, but the Al-‐values in the database were too few to use statistically, a special test using not the gyrodatabase, but the raw data from the Halland county board was performed. This compared the Al-‐content in the rivers Hjärtaredsån, Högvadsån and Fageredsån. The reason for choosing these rivers was that Hjärtaredsån is suspected to be affected by high Al-‐levels (Degerman et al. 2012). The other rivers are, like Hjärtaredsån, tributaries to
Figure 2. A map of the rivers used to calculate the spatial variation of G. salaris. River number one is Stensån, river number two is Ätran, number three is Fageredsån, number four Högvadsån, number five Hjärtaredsån, number six Himleån, number seven Löftaån, and number eight Säveån. All rivers are infected with the parasite.
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Ätran but are not suspected to be as affected by Al-‐levels. They were also three of the very few rivers that had continuous Al-‐mesurements. To examine any difference between the rivers, all the raw data on total aluminium and on inorganic aluminium from the Halland count board were used. A few of the datapoints were given as lower than (<) rather than exact values, these values were excluded since they were not exact and could not be used statistically. One test was made with all the data, beginning in 1991 and ending in 2015. Two other test were made, one with data from 2001 to 2015 and one with only data from 2015 to see a more recent state of the rivers. From 2015 there were no total Al-‐ data, only data on inorganic Al. In the data from 1991-‐2015 neither the total Al, nor the inorganic Al, were normally distributed, they were thus tested using a Kruskal-‐Wallis ANOVA with a Dunn-‐Bonnferroni post hoc. In the data from 2001-‐2015 the total Al-‐values were normally distributed and the inorganic Al-‐values were normally distributed after log transformation. The data from 2015 were also normally distributed without need for transforming. For these tests a one-‐way ANOVA was used together with a Tukey post hoc.
In a test of difference in water quality and salmon density with focus on the uninfected Kungsbackaån and newly infected Rolfsån only rivers with the same haplotype as in Rolfsån, haplotype A, were chosen for comparison. The reason for this is that the infectivity of haplotype A is likely the same in all rivers, so the only difference observed in prevalence and intensity of G. salaris should be due to differences between rivers, in for instance water chemistry and salmon density. Also included in the test is Himleån; likely with G. salaris haplotype A, since Himleån is situated between Löftaån and Viskan, and Ätran, rivers that are all infected with haplotype A. The test was made with focus on the same factors as in the other water quality-‐test. Only pH was normally distributed in the raw data, but after log transformation all factors except alkalinity (p=0,034) and salmon density (>0+) (p=0,044) were normally distributed. Testing of differences was performed using a one-‐way ANOVA with a Tukey post hoc.
Since the data obtained from the county boards lacked Al-‐levels for most of these rivers the data was obtained from Miljödata MVM version 1.16. Exceptions were Löftaån, which was not in the MVM database, and Ätran, where there were no conductivity measurements in the MVM database. In these cases, the original data, obtained from the county boards, were used to allow a comparison between the rivers to be made. The data from MVM were obtained for only one station in each river since there were usually only one station measuring these levels relatively close to where the electrofishing had been conducted. There were only 6 data points in MVM for all stations, 3 from 2014 and 3 from 2015. The mean for these values was calculated, and for Al a median value was calculated as well and put in to the figure for comparison between rivers.
Testing the correlation between conductivity and pH, and conductivity and alkalinity were done with the same data used in the first test of difference in water quality. Since neither alkalinity, nor pH were normally distributed a Spearman’s rank correlation was used. Conductivity was also tested for correlation with total N, NO2
-‐ and NO3-‐, and total P with a
Spearman’s test. Testing which factors affects the intensity and prevalence of G. salaris was done using a
General Linear Model (GLM). The random factors tested in this model were river, sampling station, and year, while the covariates were water temperature, pH, alkalinity, colour, conductivity, toltal nitrogen (N), nitrite and nitrate (NO2
-‐ and NO3-‐), total phosphorus (P),
distance from the sampling station to the sea, density of salmon parr 0+, density of salmo parr older than 0+, and total salmon density. The dependent factors were intensity and
15
prevalence of G. salaris. Only rivers and stations with four or more samplings were included in the test. The dependent factors were not normally distributed, but after plotting the residuals there were no trends in the residuals found. Histograms show a slight skewness due to a lot of 0-‐values, but otherwise follow a fairly normal distribution. Normality increased after the dependent factors were log-‐transformed (+10) and since the residuals showed no trends, a parametric test, the GLM, was performed.
16
Results Distribution of Gyrodactylus salaris During the electrofishing conducted in spring 2016 gyrodactylids were found in 5 of the 10 sites sampled (Fig. 3). In three of these five sites, the two sites in Himleån and the site Gåsevadsholm in Rolfsån, Gyrodactylus salaris were already confirmed. In the site Island Pool in Rolfsån only one parasite of G. salaris had been encountered in 2015, and in Ålgårdsbacka in Kungsbackaån there had been no previous encounters of G. salaris. Analysis of the infected fins showed that the gyrodactylids found in Island Pool were indeed G. salaris, of haplotype A, while the gydodactylids found in Ålgårdsbacka, Kungsbackaån were Gyrodactylus derjavini. This means that the G. salaris infection has definitely spread to another site in Rolfsån, leading to a total of two sites out of four now being confirmed with a G. salaris infection. A few parasites of G. salaris had been found in Fälån in the sampling in 2015, but none were found this sampling. Meanwhile, Kungsbackaån together with Örekilsälven remains, as far as we know, free of infection.
Of the 14 sites electrofished in autumn 2016 G. salaris was already present at 11. Only the two sites Bosgården and Fälån in Rolfsån together with Anråseån were free of infection. After the sampling and counting were done, gyrodactylids were found in 12 of the 14 sites. The only sites without any gyrodactylids were Anråseån and Hjärtaredsån (where G. salaris is present). In both Bosgården and Fälån a few gyrodactylids were found, a total of two on two fishes in Fälån and a total of three on two fishes in Bosgården. These gyrodactylids are currently being analysed to find out whether they are G. salaris or G. derjavini.
0
50
100
150
200
250
300
350
400
450
G. slaris intensity
(mean nu
mbe
r of G
. salarispe
r infected
fish)
Name of station
Högvadsån FagerredsånHjärtaredsån
Himleån Rolfsån Fälån KungsbackaånLindomeån Munkedalsälven
Göta älv
Örekilsälven
Anråseån
Figure 3. The intensity of G. salaris (number of G. salaris per infected fish) at the different sites during 2016. The names of the stations are displayed on the x-‐axis, and below the brackets are the name of the rivers the stations are situated in. The name of the stations in Rolfsån appear twice, this is due to them being sampled in both spring and autumn. The first of the two columns are from the spring sampling and the second from the autumn sampling. The main river systems for the stations are indicated in Table 1. The red stars indicated were G. salaris was confirmed present before the sampling in 2016 started.
17
Differences between rivers and sampling sites
During 2016, there were large differences in number of G. salaris found at the different sites, from an intensity of 0 in Hjärtaredsån, Ätran to an intensity of 406 in Göingegården, Himleån (Fig. 3). Of the localities where G. salaris was present, the sampling stations in Hjärtaredsån and Säveån had much lower intensities than the rest. There were more variations in both prevalence and intensities between rivers than between stations. The coefficient of variation (CV) for intensity was 87 for stations and 92 for rivers, for prevalence it was 37 for stations and 52 for rivers.
The trend of high variation, and low intensities in Hjärtaredsån and Säveån was not just present in 2016, but appears to be quite constant over the years (Fig. 4). There was a significant difference in both prevalence (Kruskal-‐Wallis ANOVA, H(6)=64.10, p<0.001) and intensity (Kruskal-‐Wallis ANOVA, H(6)=55.47, p<0.001) of G. salaris between the rivers sampled in 2015 and/or 2016 regarding values since the year 2000. Post hocs showed the difference to be between between the following rivers; Säveån had significantly lower prevalence of G. salaris than all rivers except Hjärtaredsån and Ätran, and significantly lower intensities than all rivers except Hjärtaredsån. Hjärtaredsån in turn had significantly lower both prevalence and intensity than all rivers except Säveån and Ätran (P-‐ and N-‐values for the significant differences observed can be found in Table A1 and A2 in the appendix). The significant differences between the sampling stations can be observed in figure 4 (Exact p-‐
0
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300
350
Kungsbygget
Sten
sån
Kärram
ölla
Sten
sån
Källstorp ned
reSten
sån Fors
Ätran
Ätrafors
Ätran
Kogstorp
Högvadsån
Nydala kvarn
Högvadsån
Sumpafallet
Högvadsån
Århu
ltHö
gvadsån
Fridhe
msberg
Fageredsån
Ned
an iglasjö
bäcken
Hjärtaredsån
Göingegården
Himleån
Kvarnagården
Himleån
Ön Ro
lfstorp
Himleån
Jonsered
s fabrik
erSäveån
Jonsered
sdam
men
Säveån
G. sa
laris intensity
(mean nu
mbe
r of G
. salaris pe
r infected
fish) and
prevalence
Name of sampling station and river
Figure 4. The mean intensity (blue pillars) and mean prevalence (orange pillars, shown in percentage from 0-‐100) of G. salaris since the year 2000 in sites electrofished in 2015 or 2016. The sites from Himleån only displays mean value since 2005, since that is the year G. salaris was discovered in the system. The sites fished only in 2015 and not in 2016 are the sites from the river Stensån, the site Kvarnagården from Himleån and the site Jonseredsdammen from Säveån, Göta älv. Significant differences (p<0,05) are marked with brackets, orange for significant difference in prevalence, and blue for significant differences in intensity. Exact p-‐values can be found in tables 7 and 8 in the appendix.
18
and N-‐values can be found in table A3 and A4 in the appendix. Looking at values from all years, not just from the 21th century gives us a similar pattern in differences between rivers. There are significant differences in both parasite prevalence (Kruskal-‐Wallis ANOVA, H(6)=79.54, p<0.001) and intensity (Kruskal-‐Wallis ANOVA, H(6)=69.54, p<0.001). This time, according to the post doc, the differences were mostly between Säveån and Hjärtaredsån, and the other rivers. Säveån had significantly lower both prevalence and intensity of G. salaris than all the other rivers except Hjärtaredsån. Hjärtaredsån had significantly lower prevalence than the rivers Stensån, Högvadsån, Fageredsån and Himleån, and significantly lower intensity than Stensån, Högvadsån and Himleån. Interestingly there was also a significant difference in intensity of G. salaris between Ätran and Himleån with Ätran having lower intensities (The exact p-‐ and N-‐values can be found in table A5 and A6 in the appendix, significant differences between stations for all years can be found in Table A7 and A8).
Temporal variation
Differences between years
The prevalence and intensity of G. salaris varied both between months and years (Fig. 5). For example, the G. salaris intensity in Göingegården, Himleån decreased from a mean of 635 parasites per fish in 2006 to a mean of 11 in the following year 2007 (Fig. 5b). Large variations between years were also present in both Ätran and its tributaries, and in Säveån (Fig. 5a & 3c). In both Ätran and Himleån there were a clear decline in intensity in 2014 and a following increase in 2015. In Ätran there was also a decline in 2010, with levels in all rivers dropping. There were however no samplings in 2008 or 2009, so it is hard to compare with earlier years. Säveån has, as mentioned, generally lower intensities of G. salaris, and here the levels were constantly low, except for a peak increase in 2012. All rivers except Säveån experienced a sharp decline in G. salaris intensities in 2014 followed by an increase in 2015 which mostly continues in 2016 (Fig. 5a & 3b).
Testing the difference between years statistically for the stations sampled in 2015 and/or 2016 showed a significant difference between years in both prevalence (Kruskal-‐Wallis ANOVA, H(15)=34.87, p=0.003) and intensity (Kruskal-‐Wallis ANOVA, H(15)=35.26, p=0.002) of G. salaris. Post hocs revealed the difference in both prevalence and intensity to be between the years 2010 and 2005, with 2005 having higher levels of the parasite (p=0.023 for prevalence and p=0.038 for intensity).
When all stations with G. salaris were tested, there was a difference between years in both prevalence (Kruskal-‐Wallis ANOVA, H(18)=41.29, p=0.001) and intensity (Kruskal-‐Wallis ANOVA, H(18)=38.86, p=0.003), but the post hoc could not find any years that differed significantly from one another. But if the rivers Göta älv and Hjärtaredsån, which had constantly low levels of G. salaris, were removed there were some difference to the result. There was again a significant difference both in intensity (Kruskal-‐Wallis ANOVA, H(15)=35.3, p=0.002) and prevalence (Kruskal-‐Wallis ANOVA, H(15)=34.9, p=0.003). The years that, based on a post-‐hoc, were found to differ from one another this time were 2010, which had significantly lower prevalence than the years 1997 (p=0.022), 2003 (p=0.027), 2005 (p=0.008), 2015 (p=0.025), and 2016 (p=0.026) and significantly lower intensity than the years 2005 (p=0.007), and 2016 (p=0.017). Other years were, regarding prevalence, the year 1998 which was significantly lower than the years 1997 (p=0.026), 2003 (p=0.031), 2005 (p=0.006), 2015 (p=0.033), and 2016 (p=0.032). There were also significant differences between the years 2011 and 2005 (p=0.040), with the prevalence being lower in 2011. For
19
intensities, the levels were significantly lower in 1994 compared to the years 2005 (p=0.003), 2013 (p=0.040), 2015 (p=0.031), and 2016 (p=0.008) (Exact p-‐ and N-‐ values for difference between years can be found in table A9 and A10 in the appendix).
This gives us the years 1994, 1998, 2010 and 2011 with lower levels of G. salaris and the years 1997, 2003, 2005, 2013, 2015 and 2016 with higher levels.
20
0
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700
G. sa
laris intensity
(mean nu
mbe
r of G
. salaris
per infected fish)
Ätran with tributaries
Ätran Fors Ätran Ätrafors Högvadsån Kogstorp
Högvadsån Nydala kvarn Högvadsån Sumpafallet Högvadsån Århult
Fageredsån Fridhemsberg Hjärtaredsån Nedan iglasjöbäcken
A
Figure 5. The development of the G. salaris infections over time (years displayed on the X-‐axis) in the infected rivers sampled in 2016. The development in Rolfsån is not shown, since the infection there is so new, and there is therefore only G. salaris data for one or two years respectively. The different graphs (A, B, and C) represent different river systems, with the stations and tributaries within the system marked with different colours.
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600
700
G. sa
laris
intensity
(mean nu
mbe
r of G
. salaris
per infected fish)
Himleån
Göingegården Ön Rolfstorp
B
0100200300400500600700
G. sa
laris intensity
(mean nu
mbe
r of G. salarispe
r infected fish)
Säveån
Jonsereds fabriker
C
21
Differences between seasons Both Ätran and Högvadsån showed an increase in intensity during early summer and
winter, and a decrease during late summer (fig. 6b). In figure 6c, the station Ätran, Fors shows a slightly different pattern than during 1995
and stands apart from the other stations. There was an increase during early winter or late autumn (December and November) in both years, but the fluctuations were higher and this time there are low points during both early summers (June). Both Bussgaraget Ullared, Högvadsån, and Fridhemsberg, Fageredsån follow a similar pattern. Both show lower values during winter and higher values during summer.
There was a significant difference in prevalence between months (Kruskal-‐Wallis ANOVA, H(7)=24.3, p=0.001). A post hoc test showed lower prevalence of G. salaris in August than in both April (p=0.007), October (p=0.004), and November (p=0.019). The intensity of G. salaris also showed a significant difference between months (Kruskal-‐Wallis ANOVA, H(7)=39.6, p<0.001) where a post hoc revealed the intensity to be significantly lower in August compared to April (p<0.001), May (p=0.002), October (p<0.001), and November (p=0.001). September had also significantly lower intensity than April (p=0.019), and October (p=0.007). When it came to seasons, there were significant differences in both prevalence (Kruskal-‐Wallis ANOVA, H(3)=10.0, p=0.019) and intensity (Kruskal-‐Wallis ANOVA, H(3)=19.1, p<0.001). Post hocs revealed that summer had significantly lower prevalence and intensity than both spring (p=0.032 for prevalence and p<0.001 for intensity) and autumn (p=0.025 for prevalence and p=0.001 for intensity).
Removing the rivers Göta Älv and Hjärtaredsån, which had constantly low levels of G. salaris, reduces the variation within the months and seasons and makes sure that it is not just an effect of these stations we observe. This gives a slightly different result; for months, there were no significant difference in prevalence (Kruskal-‐Wallis ANOVA, H(7)=10.48, p=0.163), only in intensity (Kruskal-‐Wallis ANOVA, H(7)=25.19, p=0.001). Post hocs reveal that again, it is August that had lower intensity than April (p=0.008), May (p=0.004), October (p=0.001), and November (p=0.023). There were still no significant differences in prevalence for seasons (Kruskal-‐Wallis ANOVA, H(3)=6.00, p=0.112), but again a significant difference in intensity (Kruskal-‐Wallis ANOVA, H(3)=12.97, p=0.005) The seasons that, based on the post hoc, differed from one another were summer, which had a lower intensity than spring (p=0.018) and autumn (p=0.025).
Cold winters and warm summers
There were unfortunately few stations with both a warm and cold winter for comparison (N=6) and there was no significant difference, neither in prevalence (Wilcoxon Sign Rank test, Z=-‐0.365, p=0.715), nor in intensity (Paired samples t-‐test, t(5)=1.11, p=0.317). Testing the effect of unusually cold winters could not be performed due to lack of data from the following springs.
A comparison between warm and cold summers was also performed. There were more stations for comparison in this case (N=15), mostly due to more samplings being made in autumn than in spring. But there were no significant differences in prevalence (Wilcoxon Sign Rank test, Z=-‐1.40, p=0.161) nor in intensity between summers (Paired samples t-‐test, t(8)=-‐0.213, p=0.837) either. Testing only the effects of unusually warm summers compared to the year before show that there were no significant differences in intensity (Paired samples t-‐test, t(17)=-‐0.008, p=0.994). But there was however a significant difference in
22
prevalence (Wilcoxon Sign Rank test, Z=-‐2.69, p=0.007), with lower prevalence of G. salaris after warm summers compared to the year before.
01020304050607080
G. sa
laris intensity
(mean
numbe
r of G
. salarispe
r infected
Stensån -‐ Kärramölla 1998
050100150200250
G. sa
laris intensity
(mean
numbe
r of G
. salarispe
r infected
fish
1995
Ätran Fors Högvadsån Bussgaraget Ullared
B
0
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G. sa
laris intensity
(mean nu
mbe
r of
G. sa
laris
per infected fish
1998-‐1999
Ätran Fors Högvadsån Bussgaraget Ullared Fageredsån Fridhemsberg
C
Figure 6. The variation in G. salaris intensity over months (displayed on the x-‐axis). In A the variation in Stensån year 1998 is shown, in B the variation in Ätran and Högvadsån in 1995 is shown and in C the variation in Ätran, Högvadsån and Fagerredsån in the years 1998 and 1999 can be observed. Note the differences in scale on the Y-‐axes, this only illustrates differences in intensity between months, not between rivers and sites.
050100150200250
G. sa
laris intensity
(mean
numbe
r of G
. salarispe
r infected
fish
1995
Ätran Fors Högvadsån Bussgaraget Ullared
A
23
Spatial variation
Haplotypes Göta älv and the tributary Säveån generally displays lower values of G. salaris than
anywhere else on the Swedish west coast, with a possible exception of Hjärtaredsån. Salmon in Göta älv have another haplotype, haplotype E, of G. salaris than the other rivers, which have haplotype A or C. This might be a possible explanation for the low levels. In agreement, there is a significant difference between haplotypes in both prevalence (Kruskal-‐Wallis ANOVA, H(2)=63.67, p<0.001) and intensity (Kruskal-‐Wallis ANOVA, H(2)=52.58, p<0.001). Post hoc test show that it is haplotype E that differ from both haplotype A (p<0.001 for both prevalence and intensity) and C (p<0.001 for both prevalence and intensity). No significant differences in either prevalence nor intensity between Haplotype A and C were discovered.
Water quality and salmon density
There were significant differences between the rivers in both water chemistry and salmon density, as is displayed in Table 3. There is also a figure showing the mean G. salaris intensity and prevalence in these rivers since measurements or G. salaris infection started (fig. 7).
There was a positive correlation between pH and conductivity (p<0.001, rs=0.699, N=163) as well as between alkalinity and conductivity (p<0.001, rs=0.866, N=163). Further, there was a positive correlation between conductivity and total N (p=0.007, rs=0.330, N=66), between conductivity and NO2
-‐ and NO3-‐ (p<0.001, rs=0.736, N=81), and between conductivity and
total P (p<0.001, rs=0.462, N=70). The differences in aluminium levels between Hjärtaredsån, Högvadsån and Fageredsån
measured from 1991 to 2015 showed a significant difference in total Al (Kruskal-‐Wallis ANOVA, H(2)=22.93, p<0.001). A post hoc revealed the difference to be between Fageredsån and the two other rivers (p=<0.001 for both) with Fageredsån having higher levels. There was also a significant difference in inorganic Al levels (Kruskal-‐Wallis ANOVA, H(2)=6.17, p=0.046), with the post hoc showing Hjärtaredsån as having the lowest levels (p=0.145 between Hjärtaredsån and Fageredsån and p=0.053 between Hjärtaredsån and Högvadsån). Testing the data between 2001 and 2015 showed that there was again a significant difference in total Al levels between the rivers (Oneway ANOVA, F(2,48)=4.54, p=0.016) post hoc test showed the difference to be between Hjärtaredsån and Fageredsån (p=0.019) with Fageredsån having higher levels. There were no significant differences in inorganic Al levels this time (Oneway ANOVA, F(2,148)=1.45, p=0.238). Neither was there a significant difference in those levels in the data from only 2015 (Oneway ANOVA, F(2,16)=1.70, p=0.215). But the mean values were again highest for Fageredsån and lowest for Hjärtaredsån. The levels of total Al measured from 2001 to 2015 ranged between 73-‐317 µg/l for Fageredsån, with a median of 189.5 µg/l, between 36-‐247 µg/l for Högvadsån, with a median of 134 µg/l, and between 51-‐255 for Hjärtaredsån, with a median of 124 µg/l.
To examine the possible spreading of G. salaris in newly infected Rolfsån and uninfected Kungsbackaån, the difference in water quality and salmon density between these rivers and already infected rivers was tested. The uninfected rivers were compared with rivers infected with G. salaris of haplotype A, since the infection in Rolfsån is of that haplotype. That means
Ätran, Högvadsån, Fageredsån, Hjärtaredsån and Löftaån. Included in the test was also Himleån, which is infected with an unknown haplotype, probably A. The results are displayed in Table 4.
24
The pH, alkalinity, conductivity and Al-‐levels were compared between the rivers of haplotype A together with Rolfsån and its tributary Fälån, and Kungsbackaån. There were only around 6 datapoints for each, and from only one station in each river. The samplings were also, with a few exceptions, all done in 2014 and 2015. This means that the results, displayed in Table 5, should be interpreted with much caution, but it gives an idea on the river conditions the last few years.
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300
350
400
450
Säveån Himleån Stensån Ätran Högvadsån Fageredsån Hjärtaredsån Löftaån
Intensity
(men
num
ber o
f G. salaris
per infected fish) and
prevalence of
G. sa
laris
Name of river
Table 3. Significant differences between the rivers (in green) in terms of water chemistry and salmon density (in orange). H stand for higher, L stand for lower and I stand for intermediate. When there were not enough data to perform a statistical test, there is an X. High, low and intermediate marks are only in comparison with each other, as separated by Tukey’s posh-‐hoc test. There was statistical significance between the three levels displayed, except for in Nitrite and nitrate (NO2+3) where there was no significant difference between Himleån and Löftaån, but they were still separated into different categories by the post hoc test.
Figure 7. Mean prevalence and intensity of Gyrodactylus salaris since measurements of G. salaris infection started in the rivers (stated at the x-‐axis). The mean prevalence (percentage of salmon infected) are the orange bars and the mean intensity (number of parasites per infected salmon) are the blue bars. There are some differences in sample size between rivers, the N for each river is Säveån=38, Himleån=34, Stensån=34, Ätran=33, Högvadsån=57, Fageredsån=23, Hjärtaredsån=13 and Löftaån=8.
25
GLM The intensity was positively affected by conductivity (GLM, F(1)=13.53, p=0.001) and water temperature (GLM, F(1)=7.41, p=0.011). Other factors that were not significant were river (GLM, F(1)=0.00, p=0.985), pH (GLM, F(1)=1.40, p=0.246), alkalinity (GLM, F(1)=3.02, p=0.093), colour (GLM, F(1)=1.45, p=0.238), and total P (GLM, F(1)=0.16, p=0.695). This model had a R2-‐value of 0.511. Similarly, the prevalence increased with conductivity (GLM, F(1)=11.54, p=0.002) and differed both between river (GLM, F(1)=7.76, p=0.009) and sampling station (GLM, F(1)=6.79, p=0.014), while alkalinity (GLM, F(1)=1.93, p=0.174), pH (GLM, F(1)=1.59, p=0.216), total N (GLM, F(1)=2.40, p=0.131), NO2
-‐ and NO3-‐ (GLM,
F(1)=1.81, p=0.188), and total P (GLM, F(1)=0.80, p=0.379) were not significant. This model had an overall R2-‐value of 0.517.
Table 4. The differences between the rivers (in green) in terms of water chemistry and salmon density (in orange). The rivers tested are newly infected Rolfsån, uninfected Kungsbackaån, and rivers infected with haplotype A, the same haplotype as in Rolfsån. Also Himleån, which haplotype is unknown but possibly A. H stand for higher, L stand for lower and I stand for intermediate. When there were not enough data to perform a statistical test there is an X. High, low and intermediate marks are only in comparison with each other, as separated by Tukey’s posh-‐hoc test.
Table 5. The mean levels of pH, alkalinity, conductivity, and aluminium (Al). It also shows the median value of Al. The rivers tested (in green) are rivers with confirmed or suspected Gyrodactylus salaris haplotype A and the rivers Rolfsån, with tributary Fälån, and Kungsbackaån who are at the risk of getting infected with G. salaris of haplotype A. The values are from 2014 and 2015 and from only one station in each river. There are, for all non-‐yellow marked values, only 6 data points. The X-‐marks in the figure shows where no values could be obtained. The yellow marked values are values that are obtained from the gyrodatabase for comparison, and are thus not from 2014 and 2015 only, and in the case of Ätran, from two stations and not just one.
26
Discussion
In contrast with our hypothesises, the factors shown to influence the prevalence of G. salaris in the rivers are: (1) river, (2) sampling site, and (3) conductivity. The factors influencing intensity are water temperature and conductivity. There were no effects of pH or salmon density. But there was a very strong correlation between conductivity and alkalinity, and a strong correlation between conductivity and pH. The effect of Aluminium (Al) could not be tested statistically, but high levels of aluminium (Al) were present in the Ätran tributaries Högvadsån, Fagerredsån and Hjärtaredsån, with Fagerredsån having the highest levels. In addition, both Fälån and especially Kungsbackaån has high levels of Al.
In contrast to the hypothesises regarding temporal variation there was no significant effect of cold winters, nor of warm summers. Unusually warm summers however reduced the prevalence of G. salaris in accordance with our hypothesis. The seasonal variation shows that the intensity of the parasite is higher in spring and autumn and significantly lower in summer, a pattern different from the one hypothesised. It is also clear that the parasite Gyrodactylus salaris is spreading in Rolfsån. It has established itself in a new site, and both prevalence and intensities are increasing. Differences between rivers There are large and significant differences between the rivers observed regarding intensity and prevalence of Gyrodactylus salaris infections. The variation between rivers are higher than between stations, and the variation constant for stations located within the same river are much lower. This indicates that the differences observed is due to some differences in the rivers, not just due to high variation between the sampling stations. Both Säveån and Hjärtaredsån have had significantly lower prevalence and intensity since the beginning of the 21th century than all the other rivers for Säveån, and all other rivers, except Ätran, for Hjärtaredsån. The difference between stations is a bit more varied, but generally consistent with the difference between rivers, with lower levels in the stations in Säveån and the station in Hjärtaredsån.
However, one thing one must note here though is that different rivers are sampled at different times of the year. The sampling, as it is done now, takes place in either autumn or spring, with roughly half of the stations sampled at each time. This has however not been constant; some earlier samplings have been done in the summer and some in winter as well. There is also a large skewness in the data. Of all the samplings at stations with G. salaris present, 148 were performed in the autumn whereas only 45 were performed in spring (Table 2), which is a large difference. Hence, we cannot know whether the difference observed between stations are due to a difference in haplotypes or in water quality, or just a reflection of when the sampling was done. In the case of Säveån and Hjärtaredsån it is likely that the difference observed is real and not just a matter of difference in sampling time. Säveån is usually sampled in spring, but has sometimes, like this year, been sampled in autumn instead. The low values of G. salaris occur in both seasons, while other rivers sampled in spring do not show the same low values. Hjärtaredsån, on the other hand, is almost always sampled on the very same day as the other rivers and stations in the Ätran system, and still shows significantly lower levels of G. salaris. So, difference in sampling time is not likely to be the cause behind the low levels observed in these two rivers.
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Temporal variation Difference between years The test of difference between years for all rivers, except Göta älv and Hjärtaredsån, show that 2010 was a year with significantly lower prevalence and intensity compared to five other years. When it came to intensity, salmon in 1994 also had significantly lower levels than in four other years. In terms of prevalence 1998 stood out with lower levels than five other years. 2005 was always in the top as having both high intensities and prevalence, as was 2015 and 2016. This indicates that both prevalence and intensities of G. salaris have been increasing during the last two years, which is a worrying trend. It remains to see if this is only a temporary peak, as has been observed earlier, or if this is a trend that will continue. Careful future monitoring is needed.
Testing cold winters against warm winters showed no significant differences of neither prevalence nor intensity of G. salaris. This might be surprising, since the reproduction and population growth of G. salaris has been shown to decrease in cold temperatures (Jansen & Bakke 1991). Cold winters have also been show to decrease the abundance of the parasite to almost zero in the Norwegian river Batnfjordselva (Mo 1992). One reason we do not see such an effect of our cold winters could be because the winters we tested might simply not be cold enough. Cold winters were, in this study, defined as 3 months with a mean air temperature below zero. In the study in Batnfjordselva, which is located far north from the Swedish west coast, the water temperature was almost 0 for two to three months. To get the water temperature that low for so long would require unusually cold winters on the west coast of Sweden. Unfortunately, there were too few samplings done in spring after the unusually cold winters to be able to test any effects.
One thing important to mention is that most samplings of G. salaris intensity and prevalence in 2010 and 1994, the years with the unusually cold winters, were done in autumn, not in spring. The lower intensities could of course still be an effect of a cold winter, and if the population decreased much after the cold winters the intensities would perhaps still be lower in the autumn, but this is not certain. More samples, especially from early spring are needed for a more reliable test.
The summers of 1994 and 2010 do not deviate from other summers much. They both have a quite high mean temperature in July (19.5°C and 19°C respectively), but years like 1997, 2002, and 2014 all had months with higher mean summer temperatures without having significantly lower intensities of G. salaris. Another thing worth mentioning is that the coldest winter air temperature measured was in December 2010, which would affect the year 2011. This year show significantly lower prevalence than the year 2005, but no significant difference in intensity from any other year.
The test of warm summers was not significant either, even though the GLM showed an effect of warmer temperature on intensity, the possible reasons for this will be discussed later in the “Spatial variation” part. But the test of unusually warm summers yielded some interesting results. The prevalence was significantly lower after the warmer summers, but the intensity was very far from being significant. This means that the number of fish infected decreased after a very warm summer (from app. 92% to app. 67%), but the mean number of parasites on the infected fishes stayed the same. This could indicate that the unusually warm temperatures do not affect parasite growth, but rather the transmission of parasites. Jansen & Bakke (1990) showed under experimental conditions that the growth of the parasite
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population was positively correlated with temperature up to 19°C, the highest temperature tested (Jansen & Bakke 1990). Interestingly, Soleng et al. (1999) showed that the transmission rate of G. salaris after direct host to host contact also increased with temperature, but only tested temperatures up to 12.2°C (Soleng et al. 1999). The life span of G. salaris does however decrease with temperature, both on live fish (Jansen & Bakke 1990), and away from a host (Olstad et al. 2006). So, if a large part of the transmission is via the substrate or via parasites surviving on dead host, as suggested by Soleng and Olstad (Soleng et al. 1999; Olstad et al. 2006), and not by direct contact, then warmer temperatures would likely decrease the number of successful transmissions to a new host. The increased transmission rate shown by Soleng et al. (1999) only tests temperatures well within the temperature optimum of G. salaris of between 6 to 15 °C. Temperatures above that, which significantly reduces the life span of the parasite, would perhaps yield a different result in transmission rate between live fish as well. Another theory is that the warm water might change the behaviour of the salmons in some way, and thus reduce the transmission rate. It is also possible that there is an increase in G. salaris resistance of the salmons during warmer temperatures. This has been suggested by Jansen & Bakke (Jansen & Bakke 1993b) and could perhaps explain the lower prevalence.
That the intensities of G. salaris are not higher in unusually warm summers than in normal ones could be a result of the parasites possibly reaching their maximum growth rate at around 19°C. The growth rate cannot increase indefinitely with temperature, there would have to be a threshold where maximum growth rate would occur.
All years were unfortunately not comparable, nine years had to be excluded from this study due to too few samplings of G. salaris performed. In addition, there is still a large difference in number of stations sampled each year (Table 2) which makes it hard to do an accurate study of the difference between years. For example, 1996 had an unusually cold winter but was excluded due to a lack of data, if this year had shown the same drop in G. salaris intensity as 1994 and 2010 it would have been a clear indicator on the effects of cold winters.
Difference between months and seasons
The difference between months and seasons observed in this study showed that salmon in summer had lower intensities of G. salaris than in spring and autumn. In August, there were significantly lower intensities of G. salaris on salmon than in the spring months April and May, and autumn months October and November. It is obviously in late spring and in late autumn that we recorded the highest levels of G. salaris, with a low point in summer. Winter shows equally low mean intensities as summer, but there are probably too few and varied samples from this season to get a significant result.
The pattern of low levels of G. salaris in the summer, and higher in the autumn and spring are not in accordance with other studies. Degerman et al. (2012) showed that the grade of infection was higher in colder waters and the likeliness of encountering an infected individual increased later in the year. This would mean higher intensities and prevalence in autumn than in spring. A common pattern is also the one showed by Mo (1992), where the abundance of G. salaris increased in summer and early autumn and decreased in winter and early spring in Batnfjordselva. Winger et al. (2008) studied G. salaris on Arctic charr in Skibotnelva and Signaldalselva, which also exhibited higher levels of infection in autumn and lower in spring.
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The present study, in contrast, shows high levels in both spring and autumn and low levels in summer and winter. Again, one must remember though, that the studies in Norway are made in rivers far north of the Swedish west coast rivers, with a colder climate. If we take the study in Skibontelva and Signaldalselva for example, the mean temperature for the summer months July and August is almost never above 15°C, but more usually between 11 and 13 °C. This is the mean temperatures we expect to see in the months of May and September on the Swedish west coast, and well within the temperature optimum of G. salaris. The area around the river Batnfjordselva shows higher mean temperatures than the Skibontelva area, but still generally lower than on the Swedish west coast. A study in southern Norway, with temperatures closer to what we observe in our study, showed an increase in G. salaris abundance during spring up until June, after that the abundance of the 0+ salmons continued to increase, while the abundance of older salmon parr showed a significant decrease (Jansen & Bakke 1993a). The same result was evident under experimental conditions, where several of the parasite populations declined during summer after June (Jansen & Bakke 1993b). This is hypothesised by Jansen & Bakke to be because of an increase in salmon resistance to G. salaris as summer proceeds (Jansen & Bakke 1993b). This could be the reason why we in our study also see a decline in parasite intensity during summer. In agreement, August showed especially low values of G. salaris, and if the resistance increases as summer proceeds the salmons would have the highest resistance then. The results could thus be said to be in accordance with the theory by Jansen and Bakke (1993b). It is also in accordance with the G. salaris temperature optimum at between 6 and 15 °C.
One would perhaps expect a larger difference between winter and summer months, as observed in many studies (e.g. Mo 1992; Winger et al. 2008). But winter is the season with the lowest number of samplings performed, and most winter samplings are done in early December (Table 2). In early December we are unlikely to see any effect of cold waters, since the temperatures on the Swedish west coast are usually more like the temperatures in autumn at that point. There are very few samplings done in January (a total of five) and February (a total of two), where an effect of the cold winter temperatures would be more likely. More samplings during the winter months, and an equal number of samples during all months, are likely to give a better picture of the seasonal variation on the Swedish west coast. Spatial variation Haplotypes The difference in haplotypes between the rivers might be one explanation for the differences observed between Göta älv and the other rivers. Both prevalence and intensity differed between Göta älv, infected with haplotype E, and rivers infected with haplotype A and C. This indicates that haplotype E might not be as infective as the other two haplotypes. That haplotype A is highly pathogenic has been shown in several studies (e.g. Bakke et al. 2004; Hansen et al. 2007; Paladini et al. 2014), but neither the pathogenicity of haplotype E, nor C has been experimentally studied.
The fact that there was no significant difference in parasite intensity and prevalence between haplotype A and C suggests that they have the same pathogenicity in natural systems on the Swedish west coast. This somewhat contradicts theories that haplotype C, being closely related to haplotype B from the southern Baltic, might be mostly naturally
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occurring on the west coast of Sweden, while haplotype A is introduced via stocking of Baltic salmon (Meinilä et al. 2004). If one haplotype would be naturally occurring and one recently introduced, we would expect to see a difference in the salmon resistance to the parasite, but there seems to be none. This indicates that the haplotypes A and C might have a similar origin, i. e. introduced or naturally occurring.
Since Norway is the country hardest affected by this parasite, with salmon stocks crashing and millions of dollars lost (Bakke et al. 2004), it is only natural that most articles focus on the parasite haplotypes found in Norwegian rivers. The disastrous effects in Norway is likely what sparked the multitude of papers studying G. salaris that we see today. In Norwegian rivers parasites associated with disease of the haplotypes A, B and F have been found (Hansen et al. 2003), and thus the infectivity of these strains is quite well known for different types of salmon. Haplotype C and E have only been observed on the Swedish west coast (Hansen et al. 2003), and are therefore relatively unstudied. It would be interesting to conduct an experimental study comparing the infectivity of the three strains found at the Swedish west coast; Have for example the haplotype A found in Sweden the same infectivity as the haplotype A found in Norway? Since we do not see the same effects in Sweden as in Norway, there are likely some differences, either in G. salaris infectivity, salmon susceptibility, or water quality of the rivers. By testing G. salaris of haplotype A from Sweden on salmon from both Norway and Sweden, and compare that to data on haplotype A from Norway one could get an answer to whether there are any differences in the infectivity of the parasites or in the susceptibility of the salmon. Also, if haplotype E is naturally occurring on the Swedish west coast, as hypothesised, it would be interesting to see the effect of that haplotype on Baltic salmon. If haplotype E is not found in the Baltic, perhaps we could see an inverted pattern to the one we are used to see, with Atlantic salmon being resistant and Baltic salmon being susceptible. It could also show if the infectivity of haplotype E is lower for all Atlantic salmon, or if it is a case of local adaption by the salmon from Göta älv. Local adaptations have been observed earlier, for instance in Torne älv (Lumme et al. 2016) and it is possible that this is yet another case. Or maybe the low infection in Göta älv is just due to some hidden poor river conditions, and haplotype E is just as pathogenic as the other haplotypes in standardised water conditions. But Säveån has no problems with acidification in the lower parts, and the water quality does not differ much from any river with higher parasite intensities (Table 3). There would have to be some other pollutant responsible for the low values in that case. To test haplotype C infectivity on both Atlantic salmon and Baltic salmon could also be very interesting, it could be another step towards determine if the haplotype is naturally occurring on the west coast, as hypothesised by Meinilä et al. 2004, or introduced from the Baltic, by seeing if any of the salmon stocks shows some inherited resistance.
Aluminium levels As mentioned above, the observed lower infection of G. salaris could possibly be due to poor river conditions. This might be the case in Hjärtaredsån, which share G. salaris haplotype A with the other tributaries in the Ätran system, but with constantly lower prevalence and intensity (see figures 1, 2 & 5). This has been suggested to be due to high levels of Al and humic substances in Hjärtaredsån (Degerman et al. 2012), but has not been investigated. This study shows that the total Al levels in Hjärtredsån and Högvadsån are actually significantly lower than in the other Ätran tributary Fageredsån, according to the data from 1991 and onwards. There was also a significant difference in inorganic Al, and a
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clear trend that Hjärtaredsån had lower values than Högvadsån. The mean levels were also lower than in Fageredsån, but not significantly so. Looking at more recent data might perhaps change that, since the Ätran tributaries have had problems with acidification and have been regularly limed since 1978 (Enheten för Naturvård & Miljöövervakning 2010). One might thus expect the Al value to change over time as the liming proceeds, since Al is associated with acidified waters (Poléo et al. 2004). But the Al levels from 2001-‐2015 still show that the levels of total Al are significantly lower in Hjärtaredsån than in Fageredsån. In the values from 2015, showing only inorganic Al, there are no significant differences, but Hjärtaredsån remains the river with the lowest mean Al of the three tributaries.
Fageredsån thus show higher levels of Al but also higher levels of G. salaris. This is indeed a somewhat surprising finding. Especially with regards to the levels of Al found in the rivers. In the study by Soleng et a.l 1999, total Al levels of 126 µg/l eliminated G. salaris infections after 9 days and Al levels of 250 µg/l eliminated the infections after just 4 days (Soleng et al. 1999). The total Al levels measured in 2001-‐2015 goes up to 255 µg/l in Hjärtaredsån, to 247 µg/l in Högvadsån, and to 317 µg/l in Fageredsån. The median levels are 125 µg/l, 134 µg/l and 189.5 µg/l respectively. The higher range of Al levels found in these rivers is thus enough to eliminate G. salaris infections in a few days under experimental conditions. Over half of the measurements in all rivers had Al-‐levels over, or close to, the threshold that eliminated G. salaris after 9 days in the study by Soleng et al. (1999). That the infections are not eliminated in these rivers might be explained by the fact that a natural system is much more complicated than an experimental one, water chemistry can vary within small areas and over short periods of time. Also, in a later study by Poléo et al (2004), Al-‐levels around 180 µg/l led to a decline of the G. salaris population to 12% of its initial size, and only levels of around 200 µg/l and 300 µg/l almost completely eliminated the parasite populations (Poléo et al. 2004). Perhaps these are more ecologically relevant levels. If that is the case, the Al-‐levels measured in the three Ätran tributaries are still high enough to have a negative impact on the G. salaris populations in the rivers, even if not high enough to completely eliminate them. If the Al levels would drop and stay constantly at lower levels like 100 µg/l it is likely that we will see a subsequent increase in G. salaris.
There are however factors that might further complicate the situation. All forms of Al are not equally toxic, the most toxic form is the inorganic, or labile Al. The binding of Al to organic compounds greatly reduces the toxicity. Thus, using the total Al-‐values might lead to an overestimation of the toxic effects (Driscoll et al. 1980). A significant difference in total Al might thus not represent a significant difference in toxicity, but one should rather look at the levels of inorganic Al. In the three rivers investigated there was no significant difference in the levels of inorganic Al, but there was still a clear trend towards lower levels in Hjärtaredsån and higher in Högvadsån. Also, non-‐labile aluminium has been shown to correlate strongly with organic carbon concentration in water, such as humic substances (Driscoll 1984). This means that rivers might have higher organic, less toxic, Al levels due to higher humic content. Fagerredsån have significantly higher colouration than Högvadsån and Hjärtaredsån (Table 3), which indicates higher levels of humic substances. Thus, it might not be surprising that we see significantly higher total Al levels there. It is also likely be the reason why we do not see reduced levels of G. salaris in Fagerredsån compared to the other two rivers, since the binding of Al by humic substances reduces the toxicity. This might also explain the discrepancy between the levels observed to eliminate G. salaris in experimental conditions, and the Al levels observed here without G. salaris being eliminated. In experimental conditions, there are likely very low levels of organic carbon in the water for Al
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to bind to. Thus, the total Al levels are likely mostly made up of unbound, labile Al with higher toxicity. In natural conditions a large part of the total Al might be bound to organic carbon, and thus not as toxic. But, there were no significant differences in inorganic Al levels between Hjärtaredsån and Fargeredsån, and we still observe a significant difference in G. salaris intensity between those two rivers. This means that the theory that the low prevalence and intensity of G. salaris in Hjärtaredsån is caused by high levels of Al alone can probably be discarded, even though Al can likely play a part in explaining the low infectivity.
Since the haplotype present in the rivers is haplotype A, one of the haplotypes causing extensive damage to the Norwegian salmons, it is possible that the reason we do not observe the same deleterious effects of G. salaris on the Swedish west coast is due to the high levels of Al in e.g. the Ätran system. The only other river displayed in figure 7 with known haplotype A is Löftaån, which has much higher pH (Table 3), and also much higher prevalence and intensities of G. salaris (fig. 7). Even though we should bear in mind that those are only measurements from three years, 2002-‐2004. It is possible that the reason why we see such a high difference between two river systems with the same haplotype is a difference in Al-‐levels. Unfortunately, Al-‐levels are not measured in Löftaån, but since high Al-‐levels, especially labile Al, are associated with low pH (Poléo et al. 2004) we can assume that the levels are much lower in Löftaån than in the Ätran system. If the Al levels in the Ätran tributaries would sink due to higher pH after liming, we will perhaps see a G. salaris infection more like the one in Löftaån in the future.
This raises some interesting questions. There is no doubt that low pH and high Al-‐levels are harmful to the rivers and the fish and invertebrates inhabiting them. But if higher pH and lower Al-‐levels leads to an increase in G. salaris, this could have a negative impact on the salmon population. So, from the perspective of the salmon, the Al levels we observe now would perhaps be preferable compared to lower levels.
Water quality
The Al-‐levels alone cannot explain the lower values of Hjärtaredsån, since equally or higher levels are found in neighbouring tributaries. Humic content is probably neither the reason for this, since the colour is higher in Fageredsån (Table 3). Hjärtaredsån does not stand out from the neighbouring tributaries Högvadsån and Fageredsån in any of the factors displayed in this figure, except the salmon density for parr older than 0+, which is significantly lower in Hjärtaredsån (Table 3). It shares the lower levels of salmon older than 0+ densities with Löftaån and Säveån, however only Säveån correspons to the same haplotype. Löftaån has, contrastingly to Hjärtaredsån, the highest prevalence and intensity of G. salaris of all the rivers measured (figure 7). The main difference between Högvadsån and Löftaån, except for the parasite intensities, is that Löftaån has significantly higher pH, and thus likely much lower levels of Al, as mentioned before. It is possible that a combination of low densities of salmon older than 0+, and high levels of Al explain the low levels in Hjärtaredsån. There are no differences in total salmon densities between Hjärtaredsån and neighbouring Högvadsån tough.
There are other possible explanations as well, since not all the water chemistry factors have been measured, and thus cannot be included in this study. G. salaris has been shown to be sensitive not just to Al, but also to Zn (Poléo et al. 2004), and it is possible that other heavy metals could have similar negative effects. Chlorine (Cl) has also been proven to have a negative effect even in low concentrations (Hagen et al. 2014). There is also a possibility for other pollutants to be present. Many freshwater parasites, including monogeneans, have
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been shown to be negatively affected by pollution (Valtonen et al. 1997; Gilbert et al. 2016). Thus, it is possible that the lower levels of G. salaris could be due to higher levels of pollution, Cl-‐concentration, or heavy metals that are currently not measured in the rivers.
The general linear models show that the factors influencing the prevalence of G. salaris are (1) river, (2) sampling station and (3) conductivity. For intensity, the important factors are conductivity and water temperature. Both water temperature and conductivity have a positive effect on G. salaris intensity, and in the case of conductivity, also on prevalence.
The significant effect of station and river on prevalence is not very surprising, we have already observed large variations between stations and rivers in e.g. figure 4.
The significant effect of conductivity is a rather surprising result. An earlier study has shown a negative correlation between conductivity and monogenean infection (Gilbert & Avenant-‐Oldewage 2016), where high conductivity levels were associated with high levels of trace elements and pollution. This study shows the exact opposite effect, higher conductivity was associated with higher parasite levels. Conductivity is a measurement of ion concentration and salts in the water and can be influenced by both heavy metal ions, ions associated with alkalinity, and nutrient salts. In this study, high levels of conductivity are found in rivers with high levels of pH and alkalinity (Table 3 and 4).
The detected correlation between conductivity and alkalinity, and to some extent pH, found here has also been found in other studies (e.g. Siver 1993). The correlation coefficient of 0,866 indicates a very strong correlation between conductivity and alkalinity in the studied rivers. If higher levels of Al in this case are associated with low levels of pH and alkalinity, and therefore with low levels of conductivity, then there would indeed be more G. salaris in rivers with higher conductivity. That would explain the positive effect of conductivity on G. salaris found in this study.
The lack off effect of pH might at first seem a bit surprising, but in studies with pH and G. salaris, negative effects were not observed until pH was at 5.0 (Soleng et al. 1999). In this study, the lowest pH level recorded were 6.2, which are far above the levels where we would expect negative effects. That lower pH does not indicate higher levels of Al as well as conductivity is also somewhat surprising, but it might be that pH and alkalinity vary more in this study, and that it is thus harder to find significant differences than for conductivity.
The effect of water temperature on intensity is likely a result of the different sampling time of the rivers. The two rivers with highest G. salaris intensities, Löftaån and Himleån are both sampled in spring, sometimes as late in the year as in June, when the water is warmer. Of the 30 warmest sampling temperatures collected, 23 are from either Löftaån or Himleån. The water temperature during sampling is not likely to give any real effect, since the size of the parasite population on the salmons are likely influenced by the river temperature the latest months, and does not vary on a day to day basis. One can argue though, that the reason for seeing such high levels in both Löftaån and Himleån is an effect of the late spring sampling, and that the population had all spring to grow in optimal temperatures. But at least Himleån has on several occasions been sampled in autumn as well, in which case the G. salaris intensities were still very high. So, in Himleån the high levels seem to be relatively constant, and not only an effect of the sampling being performed in late spring.
None of the nutrients seems to have a significant impact on neither intensity nor prevalence of G. salaris. An earlier study did show that nitrate could reduce the infection intensity of G. turnbulli on guppies, but only at as high nitrate levels as between 50 and 250 mg/l (Smallbone at al. 2016). In this study, the highest level of nitrite and nitrate measured were 1. 8 mg/l. So, it is not surprising that we did not observe any effects. Interestingly
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though, the two rivers with the highest G. salaris intensities, Löftaån and Himleån, are both suffering from eutrophication with high levels of nutrients (VISS Löftaån n.d; VISS Himleån n.d). They are not the only rivers with that problem though, Stensån has eutrophication problems as well (VISS Stensån n.d). There was also a strong correlation between nitrite, nitrate and conductivity, albeit not as strong as the one between conductivity and alkalinity. Hence, this could indicate an effect of nutrients on G. salaris, perhaps salmons are stressed by the eutrophic conditions and thus more sensitive to infections? However, there was no significant effect of nutrients in the GLM. It is also possible that the two rivers with G. salaris of haplotype A and without acidification problems also just happens to be affected by eutrophication. In agreement, Stensån is facing acidification problems (VISS Stensån n.d) and had lower intensities of G. salaris despite also being eutrophic. Note that Stensån has haplotype C, not A, though, even if the haplotypes have not been shown to differ in infectivity.
Unfortunately, the water chemistry data are very patchy for the sampled sites in the gyrodactylus monitoring programme. Not all sites have all the chemistry and density data we investigated in this study, some lack data altogether and for yet some the data are only on a few factors, like pH and alkalinity, or nutrients. The chemistry of Löftaån is for example based on data from only one station during five years. This makes it hard to see any differences between stations regarding water chemistry, there are simply not enough data most of the time. There is also a big difference in the amount of data between the different rivers, from five samplings in Löftaån to 48 in Hjärtaredsån regarding pH and alkalinity. This makes the comparison a bit unsecure.
Salmon density
Salmon density was hypothesised to have a large effect on G. salaris prevalence and intensity. This was however not the case in this study. Earlier studies on guppies show that Gyrodactylid populations increase with host population density (Johnson et al. 2011). However, guppies are social fish, while salmon parr are territorial, with little direct contact unless competing for territory. Even when competing for territories, it is mostly threat displays rather than physical contact such as nipping fins (Bakke et al. 1992). This has led to suggestions that alternative routes, such as transmission from dead hosts or from the bottom substrate, might be more important for transmission of G. salaris than direct contact between live salmon parr (Soleng et al 1999; Olstad et al 2006). Salmon parr are bottom-‐dwelling, so transmission via bottom substrate might be a common way of transmission (Soleng et al. 1999). This would not be as dependent on salmon density as would direct contact, and could explain why we do not see any effects. Salmon densities might also be high enough in all rivers to facilitate competition and physical contact such as nipping fins. Thus, higher density must then not necessarily lead to a higher rate of contact.
Water chemistry in Rolfsån and Kungsbackaån
There are unfortunately few Al measurements available for many of the rivers in Table 4. To investigate the risk of G. salaris spreading in Rolfsån and to Kungsbackaån these measurements might be important. A rough comparison between the mean levels of pH, alkalinity, conductivity and Al is presented in Table 5. These are values from only one point in each river and only from 2014 and 2015 and must be interpreted with caution. But the figure still might give a hint of the values present in each of the rivers.
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According to VISS, the rivers in Table 4 with acidification problems are Högvadsån, Fageredsån, Hjärtaredsån, Fälån, and Kungsbackaån. Ätran has had troubles with acidification in the past but is not acidified nowadays. The upper parts of the catchment area of Himleån has also had acidification problems in the past, but not anymore (Enheten för Naturvård & Miljöövervakning 2010).
In Table 5 Himleån is seen to have relatively low levels of pH and alkalinity, almost close to the levels of acidified rivers. This is quite different from Table 3 and 4, where the pH and alkalinity levels are high. In this case, the tables 3 and 4 probably give a more correct picture, since they contain more data. Another factor to consider is that the values from Table 5 come only from the station Rolfstorp, which is situated some 10 kilometres up in the catchment area, compared to the other stations sampled. The other stations are close to the river outlet. This might also explain the surprisingly high levels of Al present. It is possible that the station Rolfstorp still receives water with lower pH and higher Al levels from lakes higher up in the system. These levels change on the way down in the river system, and the pH would be higher, and the Al levels likely much lower in the stations further down. The relatively high Al levels might actually be why we see lower G. salaris intensities at Rolfstorp compared to the other stations in Himleån (figures 1 & 2).
Another surprise is the relatively high levels of pH in Kungsbackaån, in Table 4 Kungsbackaån is one of the rivers with high both pH and alkalinity. But Kungsbackaån is classed as having problems with acidification, and the very high Al levels support this. Yet the levels of pH and alkalinity are still relatively high. This might be due to the extensive liming in the river, and again that the acidification problem is bigger higher up in the catchment area (Vattenmyndigheterna n.d.). There still seems to be a problem with high levels of Al even further down the river tough, considering the very high levels in Table 5.
Risk of future spreading of G. salaris within Rolfsån and to Kungsbackaån The parasite G. salaris is established in two out of four stations in Rolfsån, in Gåsevadsholm and in Island Pool, where the first parasites were found in August 2015. Since then, both prevalence and intensities of the parasite have been rising with each sampling (fig. 3). A few parasites of G. salaris have been found in Fälån in 2015, but no parasites were found in spring 2016. In autumn however, two parasites, which are currently being analysed to determine species and haplotype, were found. It is clear that G. salaris has established itself in Gåsevadsholm and Island pool, with intensities rising since the first discovery. In Fälån the parasite has been present, but it does not seem established like in the other two sites.
But how will the infection proceed in Rolfsån, and will it spread into neighbouring Kungsbackaån? From this study, it would seem like the important factors that determine the levels and spreading of G. salaris in a river are (i) haplotype of G. salaris, (ii) Al-‐levels, and (iii) conductivity.
The haplotype in the Rolfsån system is haplotype A, the same as in Ätran, Löftaån and maybe Himleån. This haplotype is known to be very infective from studies in Norway (e.g. Bakke et al. 2004; Hansen et al. 2007; Paladini et al. 2014) and it has been shown to reach high levels of intensities in Himleån and Löftaån on the Swedish west coast (fig. 7).
The river Rolfsån is not classed as having any problems with acidification in the main stem, this generally means higher levels of pH and alkalinity, and lower levels of Al. Both Fälån and Kungsbackaån however have acidification problems. Rolfsån has relatively low levels of Al, while Fälån and Kungsbackaån have much higher values (Table 5). Al levels as low as in Rolfsån have not been shown to have any significant effect on the G. salaris
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population (Poléo et al. 2004) whereas Al levels as the ones in Fälån and Kungsbackaån could lead to a sharp decrease of the parasite population. There, mean levels of Al as high as 240 µg/l would be enough to almost completely eliminate the parasite population under experimental conditions (Soleng et al. 1999; Poléo et al. 2004). Both Kungsbackaån and Rolfsån have lower colour (Table 4), lower than any other rivers examined, which means that there are fewer humic substances to bind the Al and make it less toxic. The proportion of total Al made up by labile Al would perhaps not be as high as during experimental conditions, but still likely higher than in any other river.
The conductivity levels in Rolfsån and Kungsbackaån seems to be comparable to the ones in Himleån and Ätran (Table 5). But we know that the conductivity levels in the stations closer to the outlet in Himleån are much higher.
Considering these facts, the likely future scenario in Rolfsån is a continued spreading of G. salaris. It will probably not take long until it is discovered in the station Bosgården as well. Studies in natural conditions have shown that G. salaris can spread rapidly in a stream and may reach a prevalence of 100% after a short summer season, even at lower temperatures than on the Swedish west coast (Hendrichsen et al. 2015). The observed increase in intensity and prevalence of the parasite in Island pool from spring to autumn is in accordance with this study. The spreading will likely continue until the whole river is infected, and the intensities of the parasite will probably be close to the intensities observed in Ätran or Himleån. The haplotype is the same, the conductivity levels are comparable and the low levels of Al observed will likely not have any negative effects on the parasite populations. Unless there are high levels of pollution different from Himleån and Ätran that could affect G. salaris, which I deem unlikely, there is no reason why the parasite intensities would be lower in Rolfsån.
Fälån is a different story however. The pH, alkalinity and conductivity is very similar to the levels in Högvadsån, another river affected by acidification (Table 5). The Al levels are higher in Fälån though, comparable to the ones observed in Fagerredsån (Table 5). These Al levels are high enough to have a negative impact on the G. salaris population (Soleng et al. 1999; Poléo et al. 2004), and will likely lead to Fälån having lower levels of G. salaris than Rolfsån. The levels might end up similar to the ones in Högvadsån or Fagerredsån, or possibly to the ones in Hjärtaredsån. Since we do not know exactly what makes the G. salaris levels so much lower in Hjärtaredsån than in Fagerredsån and Högvadsån we cannot say if the same effect will occur in Fälån. But the fact that the parasite has not established itself in Fälån after its initial discovery in 2015 is likely because of the acidification problems and the high levels of Al present. The development in Rolfsån has been quite different with higher G. salaris prevalence and intensities in every sampling after discovery.
For the parasite to spread to Kungsbackaån from Rolfsån it need to pass either through a very small stream connecting the two rivers, or trough Kungsbackafjorden, where both rivers have their outlets. G. salaris is a freshwater adapted parasite, and survival time is negatively correlated with salinity (Soleng & Bakke 1997). But interriver dispersal of the parasite has been suggested to occur in Norwegian fjord systems. Freshwater inflow and distance between the river outlets determine how far the parasite can spread to start a new population, and distances as far as 40 km have been suggested, even though most dispersals probably take place within distances of 10 km (Jansen et al. 2007). Numbers as high as 1105 G. salaris have been found on migrating smolt in brackish fjord water, 25 km from the river mouth. The parasite can be transmitted in salinities up to 20‰, but lower salinity leads to higher transmission rates. This means that migrating salmon smolt can bring G. salaris to
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estuaries where they can infect other hosts and thus a new river (Soleng et al. 1998). G. salaris can infect not only a wide range of salmonids, but also other fish such as brook lamprey (Lampetra planeri), common minnow (Phoxinus phoxinus), three-‐spined stickleback (Gastrosteus aculeatus), European eel (Anguilla anguilla), and European flounder (Platichtys flesus) (Bakke et al. 2002). These fish could then act as vectors and transport the parasite to new rivers. The distance between the outlets of Rolfsån and Kungsbackaån is less than one kilometre, and the outlets are located at the upper end of the Kungsbacka fjord. This distance in brackish water is, as suggested by earlier studies, not a problem for G. salaris and a transmission is likely to occur. The study by Jansen et al. (2007) suggests that if there is sufficient inflow of freshwater and a short distance between the rivers, an inter-‐river dispersal might be expected to happen within a year or two (Jansen et al. 2007). This is for Norwegian fjords where the rivers sustain salmon populations with very high G. salaris intensities, but if the parasite intensity in Rolfsån increases and the parasite spreads further in the water shed, it will likely not take long until G. salaris reaches Kungsbackaån as well.
When the parasite is introduced to Kungsbackaån what can we expect then? The river has had acidification problems, but Table 4 and 5 suggests that both pH and alkalinity levels are relatively high, at levels comparable to Himleån, or at least to the station Rolfsån in Himleån. But there are very high levels of Al present. A mean level of 240 µg/l Al should be enough to have a strong negative impact on the parasite (Soleng et al. 1999; Poléo et al 2004). Thus, it is likely that G. salaris will reach Kungsbackaån, but if such high levels of Al are present in the entire river it will probably be hard for the parasite to establish itself and the prevalence, as well as the intensities will be low. But Kungsbackaån is currently being limed, and both pH and alkalinity is rising, if this leads to lower Al levels in the future, then we can probably expect higher G. salaris intensities in turn. Comments on the future management of G. salaris monitoring The gyrodactylus project has been monitoring the spread and development of G. salaris infections on the Swedish west coast since the early 90’s. This has been of great use, and led to further understanding of the G. salaris situation in Sweden.
In this study, I have tried to study both development of the infection over time, as well as differences between stations and rivers. The problems encountered studying difference over time is that, for an ideal comparison between years, the samplings should be made during roughly the same time period. This is mostly done well today; samplings are performed either in spring or in autumn. But there are some shifts, for instance, Säveån, which has been sampled in spring for several years consecutively, is suddenly sampled in autumn. Such shifts have been made earlier as well, in several rivers. This makes comparisons over years much harder, and should be avoided if possible. It is also important to make sure that the rivers of interest are sampled every year. As it is now there are many years missing in a lot of the stations, and important changes might thus be missed or misinterpreted.
If one would like to study the difference between stations however, the ideal would be if all the stations were sampled during roughly the same time period. As it is now, there might always be a difference between seasons or months explaining some of the variance observed between rivers.
The best approach would be if, in the future, all the stations were sampled in both spring and autumn. Then the differences over time could still be compared with earlier data, and the different stations could be better compared with each other. It would also give us a better clue of the variations between seasons within each station. For a better statistical
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basis, we need to have long, unfragmented, series of data from many rivers, and it is therefore important to keep monitoring the river, long after G. salrais is discovered. Ideally, all rivers with G. salaris on the west coast should be sampled in spring and autumn. To better understand the seasonal variation more data is also needed from summers, and especially from winters. More water chemistry data are also needed, preferably from the same stations, or stations very close to the gyrodactylus sampling stations. Here, values of Al, both organic and inorganic, pH, alkalinity, conductivity, colour, and maybe Zn and other heavy metals are deemed to be important factors to monitor. To coordinate the samplings of G. salaris with the water chemistry samplings made by the county boards would be ideal for future studies on the spatial variation. Also, to test the hypothesised effect of Al, experiments including liming could be of interest. To lime parts of a water shed and not others to see if liming affects the abundance of G. salaris by affecting the pH-‐ and Al levels could be very helpful in determining future management approaches. Finally, a series of experimental studies to determine the infectivity of the different Swedish haplotypes are important. Then we would know whether it is the water quality or the haplotype that are responsible for the low infectivity in Säveån, and whether it is only the water quality in our rivers that differ from Norway, and not the pathogenicity of our parasites. These future studies would be a good start in helping future understanding of G. salaris on the west coast, and with it currently spreading to new rivers at a quick rate, this is deemed to be of importance.
This would lead to a lot more sampling needing to be done, and is both more time consuming and, as a consequence, more expensive. It is thus important to either give this project increased funding to better understand the variations occurring on the Swedish west coast, or if that is not possible, to decide what the data will be used for and which studies are more important, and design the samplings accordingly. Conclusions
I conclude that there are large and significant differences in both the prevalence and intensity of the parasite Gyrodactylus salaris between different rivers and stations, but also between different years and seasons.
The GLM shows that the prevalence of G. salaris is affected by the river and sampling station, and that intensity is affected by temperature, although tis can arguably be an effect of the rivers with the highest intensities usually being sampled late in spring. Further, conductivity is also shown to have a positive effect on both intensity and prevalence of G. salaris. Conductivity in turn positively correlates with pH, alkalinity and nutrients. The effect of conductivity is thus believed to be an effect of lower pH leading to higher levels of Al, which has been proven harmful for G. salaris. As such, higher levels of conductivity would lead to lower Al levels and in turn increased parasite infections.
Hjärtaredsån and Säveån show constantly lower G. salaris infections than the other rivers sampled. This is believed to be, in the case of Säveån, because of a lower infectivity of the G. salaris haplotype E, found in the river. But since this is the only river with this certain haplotype, other factors might also be responsible. However, Säveån did not stand out from other rivers in any of the water chemistry factors or salmon densities investigated. In the case of Hjärtaredsån the reason for these low levels are believed to be in part due to the high Al levels observed, but since two other Ätran tributaries show equal or higher levels of both labile and organic Al this cannot be the only explanation and further studies are needed.
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Summer had lower intensity and prevalence of G. salaris than spring and autumn which corresponds well to the G. salaris temperature optimum of 6 to 15 °C, the likely spring and autumn water temperature. The prevalence of G. salaris are significantly reduced in the autumn after an unusually warm summer compared to the autumn the year before, which is hypothesised to be due to the reduced survival time of G. salaris in warmer waters, making transfer of parasites to a new host less successful. The intensity is not affected, which is in accordance with earlier studies where the increased fecundity of G. salaris makes up for the shorter life span in warmer waters.
This study has furthered our knowledge of G. salaris on the Swedish west coast, and gives some explanations to the variations in prevalence and intensity observed. But there are still many factors that are not fully explained or fully understood, and further studies on the subject are needed.
Acknowledgements There are many people that I owe my thanks to for helping me with this study. First, and
foremost, my supervisor Johan Höjesjö that has helped me during this entire time, encouraged me and answered my questions. Also Per-‐Erik Jacobsen at Sportfiskarna who let me join him in in the electrofishing for G. salaris in 2016 and who taught me how to identify and count the parasites. And to Erik Degerman at SLU who suggested this study and who gave me a lot of good ideas and input. Johannes Olsson gave me some good advice concerning statistics and have my thanks. Last, but not least, I want to thank Arvid Enemar, for cheering me up, helping me and encouraging me all the way. Thank you all!
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Appendix
Table A1. This figure displays p-‐ and N-‐values for significant differences in prevalence between rivers sampled since the year 2000. River low is the rivers with significantly lower prevalence, and river high the rivers with significantly higher prevalence.
Table A2. This figure displays p-‐ and N-‐values for significant differences in intensity between rivers sampled since the year 2000. River low is the rivers with significantly lower intensity, and river high the rivers with significantly higher intensity.
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Table A3. This figure displays p-‐ and N-‐values for significant differences in prevalence between stations sampled since the year 2000. Station low is the stations with significantly lower prevalence, and station high the stations with significantly higher prevalence.
Table A4. This figure displays p-‐ and N-‐values for significant differences in intensity between stations sampled since the year 2000. Station low is the stations with significantly lower intensity, and station high the stations with significantly higher intensity.
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Table A5. This figure displays p-‐ and N-‐values for significant differences in prevalence between rivers for all years sampled. River low is the rivers with significantly lower prevalence, and river high the rivers with significantly higher prevalence.
Table A6. This figure displays p-‐ and N-‐values for significant differences in intensity between rivers for all years sampled. River low is the rivers with significantly lower intensity, and river high the rivers with significantly higher intensity.
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Table A7. This figure displays p-‐ and N-‐values for significant differences in prevalence between stations for all years sampled. Station low is the stations with significantly lower prevalence, and station high the stations with significantly higher prevalence.
Table A8. This figure displays p-‐ and N-‐values for significant differences in intensity between stations for all years sampled. Station low is the stations with significantly lower intensity, and station high the stations with significantly higher intensity.
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Table A9. This figure displays p-‐ and N-‐values for significant differences in prevalence between years for all rivers except Hjärtaredsån and Säveån. Year low is the years with significantly lower prevalence, and year high the years with significantly higher prevalence.
Table A10. This figure displays p-‐ and N-‐values for significant differences in intensity between years for all rivers except Hjärtaredsån and Säveån. Year low is the years with significantly lower intensity, and year high the years with significantly higher intensity.