EFFECTS OF DOC AND
WATER TEMPERATURE
ON PREY USE AND
PERFORMANCE OF
NINE-SPINE
STICKLEBACK
Effects of doc and water temperature on prey use and performance of nine-spine stickleback
Ivan Berg Abstract
Climate change is causing water temperature to rise, and many lakes in the boreal zone will experience browning of waters (brownification) due to increased input of dissolved organic carbon (DOC). In fish, warming may cause resource limitation and decrease both fish size and population abundance. Many fish species display ontogenetic niche shifts during their lifetime, shifting to larger prey as they grow. Brownification may change the timing for, the benefits from, or prevent individuals from displaying, ontogenetic niche shifts by decreasing large prey abundance in the benthic zone or making fast-moving prey harder to see. This can cause resource limitations, suppressing growth and population growth. This study investigated the effects of increasing DOC and water temperature on ontogenetic diet shifts, size structure, and population abundance in nine-spine
stickleback (Pungitius pungitius) in an experimental pond system with a warming treatment and a gradient of DOC concentration. Warming had a negative effect on population number, biomass, maximum fish size, stomach fullness, and consumption of large prey. Contrary to expected outcomes, increasing DOC input resulted in larger population, biomass, and maximum sized fish. DOC did not negatively affect ontogenetic diet shifts. In the relatively shallow enclosures, the highest DOC concentration may not have reached the threshold where the shading effect of DOC overturns the benefits of extra nutrients associated with DOC. Hence, in shallow lake ecosystems, climate change induced DOC increase may support fish production, while warming may have strong negative effects on fish population abundance and size.
Table of content
1 Introduction ... 1 1.1 Purpose ... 2 2 Method ... 2 2.1 Study site ... 2 2.2 Data collection ... 3 2.3 Data processing ... 4 2.4 Statistical analysis ... 6 3 Result ... 63.1 Population and biomass ... 6
3.2 Fish length... 7 3.3 Fish condition ... 9 3.4 Diet ... 10 4. Discussion... 12 Acknowledgements... 15 Reference ... 15
1 Introduction
Anthropogenic climate change is causing air temperature to rise around the world. At the end of this century, temperature is predicted to have risen by an average of 1 – 3.7 °C globally (IPCC 2014). Climate change also increases water temperature (O’Reilly et al. 2015) and causes changes in precipitation (Nikulin et al. 2011). In Northern Europe, precipitation has increased and is expected to keep increasing (IPCC 2014; SMHI 2020). This means higher run-off and, in many cases an increased input of terrestrial dissolved organic carbon (DOC) into streams and lakes which causes browning of the water (brownification) (de Wit et al. 2016). Brownification can also increase via other
mechanisms, such as recovery from acidification and changes in land use (de Wit et al. 2016; Gavin et al. 2018; Kritzberg et al. 2019).
Brownification of lakes, common in the boreal zone, changes the basal production from dominantly benthic autotrophic to a more pelagic dominance with both autotrophic production but also heterotrophic bacterial production (Ask et al. 2009). Initially, increasing DOC can increase pelagic primary production at low to moderate DOC input due to increased nutrient input associated with DOC. However, diminishing light limits primary production severely in the benthic zone but also in the pelagic habitat at higher DOC concentrations (Ask et al. 2009; Seekell et al. 2015). Even though increasing DOC also stimulates bacterial production in the pelagic, this does not compensate for the declining benthic primary production, resulting in a total decrease in whole-lake basal production. This is because benthic production constitutes a substantial part of whole lake production (Ask et al. 2009; Vander Zanden, Vadeboncoeur and Chandra 2011). Among consumers, zooplankton may be able to compensate for the loss of pelagic
primary production by consuming more bacteria, which increases in the DOC-rich water. This can to some extent support the ecosystem (Solomon et al. 2011; Wenzel et al. 2012; Taipale et al. 2018). Still, zoobenthos abundance generally declines with increasing DOC as a consequence of reduced benthic production, resulting in ubiquitous changes in the community composition and abundance in benthic macroinvertebrates and fish (Karlsson et al. 2009; Jonsson et al. 2015; Arzel et al. 2020; van Dorst et al. 2020). Thus, to
compensate for changes in the prey community, fish feeding behavior may shift from feeding on more profitable benthic invertebrates to smaller pelagic zooplankton (Bartels et al. 2016). In addition, for visually feeding fish, feeding rate may decline since the brown water reduce the encounter rate with prey, which causes prey consumption rates to
decrease, leading to further negative effects on fish (Ranåker et al. 2012; Hedström et al. 2017).
Besides brownification, warming of waters affects organisms and ecosystems.
Phytoplankton can respond with higher growth rates, higher biomass, and changes in species composition, which can increase zooplankton abundance (Bergström et al. 2013, Hampton et. al. 2008). Fish are sensitive to warming because it increases metabolic rates which requires higher food intake to avoid starvation (Clarke and Johnston 1999). At high resource abundance, warming typically stimulates fish growth, but if resources are
limited, starvation in fish may occur due to unmet metabolic demands (Byström et al. 2006). In fish, smaller and younger individuals are often more efficient foragers on small prey and can therefore have positive growth at lower resource levels than larger
individuals (Persson et al. 1998; Byström and Andersson 2005). Furthermore, small individuals often have higher temperature optima regarding growth rates than larger individuals (Atkinson 1994; Christensen, Svendsen and Steffensen 2020). Warming can therefore lead to a shift towards smaller individuals and the need to mature at smaller body size, which in turn affect the size distribution of the population, potentially
negatively affecting the fish population biomass (Atkinson 1994; Sandström, Neuman and Thoresson 1995; van Dorst et al. 2019).
Many fish species, such as nine-spine stickleback (Pungitius pungitius), European perch (Perca fluviatilis), and the northern pike (Esox lucius), exhibit ontogenetic niche shifts during their life-stages, using larger prey as they grow (Amundsen et al. 2003; de Roos and Persson 2013; Merilä and Eloranta 2017; Linzmaier et al. 2018). Ontogenetic niche shifts are important for individual growth and population abundance. This may lead to higher population abundance as it, for instance, decreases competition among life stages since young and small individuals tend to feed on zooplankton, larger individuals on zoobenthos, while, in some species, the adults become piscivores (Mittelbach and Persson 1998; Persson and Crowder 1998). Thus, changes in stage-specific resources may change the size structure and density in fish species that undergo ontogenetic niche shifts.
Taken all the above into account, high levels of DOC and increased temperature may have the potential to shift size structure towards smaller fish individuals and decrease fish population abundance and biomass (van Dorst et al. 2019). Since warming and browning of lakes occurs simultaneously, it is important to understand how it can affect different organisms and ecosystem functions. A relatively simple experimental food-chain with zooplankton, zoobenthos, and nine-spine stickleback can be used as a model to study how brownification and warming may affect ontogenetic diet shifts in fish and its
consequences for size structure and population abundance. Nine-spine stickleback is a suitable model fish since it is common in the Nordic region, distributed in freshwater, brackish, and marine water (von Hippel et al. 2016). Its diet primarily consists of zooplankton and macroinvertebrates, but the nine-spine sticklebacks have also been found to be cannibalistic (Gallagher and Dick 2011; Merilä and Eloranta 2017).
1.1 Purpose
The purpose of this thesis was to study the effects of increased DOC input and water temperature in a semi-natural food web approach with a focus on ontogenetic diet shifts, size structure, and abundance of nine-spine stickleback.
I hypothesized that:
1) Nine-spine stickleback diets will change from zooplankton to benthic invertebrates with increasing stickleback size:
The effects of browning and warming will impact the sticklebacks as follows: 2) Diets will be more dominated by zooplankton with increasing DOC.
3) Individual performance, stomach fullness, condition, and growth (size) will be negatively affected by warming and increased DOC.
4) Smaller sticklebacks will be more dominant in populations subjected to both increasing DOC and warming.
5) Both warming and increased DOC will cause lower population abundance, and the effect will be even stronger with warming and increased DOC combined.
In order to test these five hypotheses, I analyzed nine-spine sticklebacks from an experiment pond system with a warming treatment and a DOC gradient. The analysis included stickleback population estimates and biomass, length, condition, and size-dependent diets of the stickleback as a response to the treatments.
2 Method
2.1 Study site
Samples for this study were collected at Umeå University’s Experimental Ecosystem Facility located in Röbäcksdalen in Umeå (63°48ʹN, 20°14ʹE). It is an open natural pond system with an ongoing long-term experiment. There are twenty separate enclosures with a size of 12.5 × 7.3 m and a depth of 1.5 m with soft-bottom sediment. The pond food web consists of zoobenthos, zooplankton, and primary producers that have naturally colonized the system. In spring 2018, 40 nine-spine sticklebacks were introduced into 16 of the
enclosures. The ongoing long-term experiment has an experimental design with eight enclosures with natural ambient water temperatures and eight enclosures with water heated to 3 °C above ambient temperatures. To both the ambient and warm treatment enclosures, DOC rich water from Hörneån (63°57′N, 19°25′E) was added at a gradient of DOC input levels. All enclosures received a total continuous input of 1.48 L water per minute. Two ponds received only clear groundwater (from Umeå municipality). The other ponds received different mixtures of groundwater and DOC rich water. Of the total 1.48 L/minute inflow, 0.07, 0.15, 0.30, 0.59, 0.89 L/minute was DOC rich water, and one pond received only DOC rich water (1.48 L/minute). This created a gradient of mean DOC concentration in the enclosures between 2.5 and 10.4 mg DOC/L during the 2020 growth season. The DOC levels vary during the season due to internal processes in the enclosures. A schematic view of the experiment design can be seen in figure 1.
Figure 1 Schematic overview of the experiment design, Pond 1 – 8 are heated 3 °C above ambient temperature, pond 13 – 20 are ambient temperature, pond 9 – 12 serves as a buffer between the two temperature treatments. P# indicate pond number, number next to pond numbers indicates the treatment: L/minute DOC river water input, next number indicate the mean (±1 SD, n=9) DOC concentration in the enclosures during the 2020 growing season. The outer edges marked with a thick black line are a shoreline with natural vegetation.
During the experimental period, water chemistry of the river water was: dissolved organic carbon (DOC) 21.6 ± 4.5 mg/L, total phosphorus (TP) 555.0 ± 359.7 μg/L, total nitrogen
(TN) 1321.7 ± 864.9 μg/L, phosphate (PO4) 4.9 ± 2.7 μg/L, nitrate (NO3) 110.4 ± 135.3
μg/L, ammonium (NH4) 394.8 ± 528.6 μg/L and the ground water: dissolved organic
carbon (DOC) 0.4 ± 0.1 mg/L, total phosphorus (TP) 2.7 ± 3.8 μg/L, total nitrogen (TN)
66.7 ± 12.9 μg/L, phosphate (PO4) 0.3 ± 0.4 μg/L, nitrate (NO3) 37.9 ± 11.3 μg/L,
ammonium (NH4) 3.6 ± 1.7 μg/L (mean ± 1 SD, n=2). During the 2020 season, the DOC
concentration in the enclosures was measured nine times and the mean value of the DOC mg/L was used as an explanatory variable in the analysis (figure 1).
2.2 Data collection
For this thesis, sampling of nine-spine stickleback was conducted during summer 2020 at three occasions: 2020-06-29, 2020-08-13, and 2020-09-28. The sampling at 2020-06-29 was conducted by handheld dip-net. The net was dragged along half the side of the
shoreline edge of each pond. Captured sticklebacks were placed into a container with water. Ten fish per pond were randomly selected for further analysis, except for adult fish in spawning coloration, which were released back into the pond. In one enclosure, only
five sticklebacks were captured, so only five were sampled from that enclosure (#13, figure 1). The rest of the catch was released back into their respective ponds. The sampled fish were stored in a cooler box and within the same day put in a – 20° C freezer at Umeå University for later analysis. Samplings on 2020-08-13 and 2020-09-28 were conducted with a seine net, which was dragged across the whole pond, from the inner edge to the shoreline edge (figure 2). The catch was put into a container with water. The procedure was repeated until the new catch was less than half of the previous catch. This gave between 2 – 7 efforts per pond. Each effort was photographed from above together with a size reference, pond ID, and effort number. From the 2020-08-13 sampling event, twenty sticklebacks were semi-randomly selected: five randomly from the smallest size group in the sample, ten randomly from the middle-sized, and five randomly from the largest in each respective pond. This sampling was repeated in the 2020-09-28 sample occasion, with the difference being that only ten fish were sampled for the later analysis in this study, three of the smallest, three of the largest, and four of middle-sized fish. The rest of the catch was released back into the respective ponds. Sampled fish were stored in a cooler box and, within the same day, put in a – 20° C freezer at Umeå University for later analysis.
Ethical approval: The experimental design, sampling methods, collection of fish and method of sacrifices in this study comply with the present laws of Sweden and were approved by the local ethics committee of the Swedish National Board for Laboratory Animals in Umeå (license no. A13-2018 to Pär Byström).
Figure 2 Stickleback sampling with seine net at Umeå University’s Experimental Ecosystem Facility.
The stomach content of the stickleback was identified under a stereoscope (magnification 10-40 ×) to genus or family depending on taxa (Appendix table 1). The first ten diet items of each diet taxa (all if fewer were found) were measured in length in the stereoscope. Thereafter, each type was counted.
Photos from sampling fishing at 2020-08-13 and 2020-09-28 were analyzed with an image-process software. In the program, each fish was counted, and all fish were length measured except for fish that were on top of each other and not possible to measure.
2.3 Data processing
Data on the population responses, which included the number of sticklebacks, biomass, mean fish length, and maximum fish length, were obtained from the photo analysis. The stickleback populations in each enclosure were estimated using the R package FSA, method “CarleStrub” (Ogle, Wheeler and Dinno 2021). The method was first described by Carle and Strub in 1978. It uses the count of removed individuals from each catch effort in
relation to the effort number. This creates a regression line from which the population can be estimated (Carle and Strub 1978).
The total biomass per pond could be estimated from the population estimation by conducting length-weight regression from the stickleback samples. Because of the relatively low number of samples from each enclosure, one length-weight regression was conducted from all 619 sampled stickleback and not separate length-weight regressions per ponds. From the measured length of the stickleback photos an estimation of the population biomass was obtained by applying the regression (Eq 1) where W = weight and
L = length (a= 0.000017, b=2.79, r2 = 0.96).
𝐸𝑞 1. 𝑊 = 𝑎 × 𝐿𝑏
Maximum length of sticklebacks in each enclosure was obtained from the photo analysis and defined as the mean values of the five largest stickleback per enclosure and sampling occasion in August and September.
Weight, length, and stomach content data were analyzed on 619 sticklebacks in total. The condition of the sticklebacks was calculated using the weight and length and by applying Fulton’s condition factor K (Eq 2), where W = weight in g, L = length in cm. 𝐸𝑞 2. 𝐾 = 100 × 𝑊/𝐿3
The dry weight of the stickleback diet items was calculated by length weight regressions, different regression for different taxa, obtained from literature (Appendix, table 1). To get a standardized estimate of the stickleback’s stomach fullness, the relation of its body weight to the weight of the stomach content was calculated by: Diet dry weight/Fish dry weight.
To obtain the mean length of the different diet items in stomachs, ten of each diet taxa were measured per stickleback. Since single heads without body, mostly Chironomidae, a few Ephemeroptera, and Trichoptera, were found in the stomach content (the body had been digested), the body length for these were estimated by using one body length-body weight regression (Eq 3) and one head width-body weight regression (Eq 4), where W = weight, L = length, H = head width, a, b, α and β are constants after Benke et al. (1999). These two equations were combined into equation 5 to estimate the body length for the heads. 𝐸𝑞. 3 𝑊 = 𝑎 × 𝐿𝑏 𝐸𝑞. 4 𝑊 = 𝛼 × 𝐻𝛽 𝐸𝑞. 5 𝐿 = √𝛼×𝐻𝛽 𝑎 𝑏
The mean length of the diet items per fish was then calculated by accounting for the total number of diet items per taxa.
The proportion of different taxa in the diet was calculated with taxa into three broader taxonomic units: “macroinvertebrates” (all zoobenthos except snails), “zooplankton”, (Rotifera, Copepoda, benthic and pelagic Cladocera) and “Gastropoda” (all snails). Biomass of macroinvertebrates was dominated by Chironomidae and to a smaller proportion, in declining order; Ephemeroptera, Coleoptera, Zygoptera, Trichoptera,
Copepoda and to smaller proportion, Daphnia and Bosmina. Rotifera constituted a very
small part of the total biomass and consisted of Keratella, Asplanchna and
Trichocercidae. Gastropoda consisted of Planorbidae, dominated by Gyraulus.
2.4 Statistical analysis
To test the relationship between the response variables and explanatory variables, I used linear mixed effect models. The explanatory variables included continuous and
categorical variables and included: fish length, temperature treatment ambient/warm, DOC concentration, and sampling date. The mixed-effect model allows for fitting multiple explanatory variables and accounts for random effects (Schielzeth et al. 2020). As each stickleback has individual differences and can respond to the treatment differently, fish ID was chosen as a random effect. Also, stickleback from the same enclosure is not independent, so each pond enclosure was also chosen as a random effect.
The statistical analysis was conducted in the program R (version 1.2.5001; RStudio Team 2019). The function ‘lme’ in the ‘nlme’ package (Pinheiro et al. 2021) was used to run the linear mixed effect models.
For the response variables, fish condition, stomach fullness, mean diet length, maximum diet length, and the proportion of macroinvertebrates, gastropods, and zooplankton in the diet, the full models were structured as follows:
Response variable ~ Fish length + DOC + Temperature + Date + Fish length:DOC + Fish length:Temperature + Fish length:Date + DOC:Temperature + DOC:Date + Temperature:Date + (~1|PondID/FishID). Where (~1|PondID/FishID) represents the random effects and variable:variable stands for an interaction between the two. Temperature stands for temperature treatment ambient/ warm. Date are the three sampling occasions 2020-06-29, 2020-08-13 and 2020-09-28.
For the response variables, population, biomass, mean fish length, and maximum fish length, the full models were structured as follow:
Response variable ~ DOC + Temperature + Date + DOC:Temperature + DOC:Date + Temperature:Date (~1|PondID). Where (~1|PondID) represents the random effect. Here, pond ID is used as a random effect because ponds with the same treatment can respond differently due to internal processes.
For the response variables net change from 2020-08-13 to 2020-09-28 in population, biomass, and mean fish length, the full models were structured as follow:
Response variable ~ DOC + Temperature + DOC:Temperature + (~1|PondID).
Model selection was conducted by using a backward stepwise regression, which entailed dropping fixed effects stepwise from the full model down to the most parsimonious model (Hocking 1976). The term with the highest p-value in an ANOVA output was dropped first. This was repeated down to the most parsimonious model. The Akaike’s Information Criterion (AIC) values from each model were compared, and the model with the lowest AIC value was chosen as the most parsimonious model.
3 Result
3.1 Population and biomass
Warming had a negative effect on the stickleback population number with higher
populations in the ambient enclosures compared to the warm, especially in the September sampling (F1,13=31.9, P<0.001). DOC (F1,13=7.4, P=0.017) had a positive effect with
higher populations number at higher DOC levels, but only in the ambient ponds (figure 3a, b). Change in population abundance (mortality) between August and September was
found to be higher with warming (F1,12=21.0, P<0.001) (figure 3c).
Figure 3 Estimated population number (95% CI) from the two sampling occasions a) 2020-08-13 and b) 2020-09-28 and c) the net change in population numbers between August and September sampling. X-axis represents a gradient of DOC ml/L.
Biomass displayed similar results as for population numbers with higher biomass in the ambient ponds than the warm ones and a trend towards higher biomass values as DOC increases in the ambient ponds (Appendix, table 2, figure 1a, b). There was an increase in biomass in all but one ambient enclosure and a decline in all but one of the warm
enclosures from August to September, and similarly as for population numbers, DOC increased the biomass in ambient enclosures (Appendix, table 2, figure 1c).
3.2 Fish length
Sampling date (reflecting individual growth) was found to affect stickleback mean length (F1, 15=47.3, P<0.001) with increasing values from August (figure 4a) to September (figure
August and September sampling occasions was higher with warming (F1, 14=8.4, P=0.012)
but no effect of DOC was found (figure 4c).
Figure 4 Mean stickleback length (±1 SE) from the two sampling occasions a) 2020-08-13 and b) 2020-09-28, c) is the net change in mean length between the first sampling occasion and the second. Horizontal line in a and b represents overall mean. Error bars in a and b are not visible due to low values.
Sticklebacks’ maximum length (mean of the five largest individuals) was overall larger in the ambient ponds compared to the warm ones both in August and in September.
Warming had thereby a negative effect on maximum length (F1, 13=75.8, P<0.001) (figure
5 a, b). Further, maximum length increased with increasing DOC (F1, 13=10.4, P=0.01)
Figure 5 Maximum stickleback length (± 1SE) from the two sampling occasions a) 08-13 and b) 2020-09-28. Horizontal lines represents overall mean.
3.3 Fish condition
Stickleback condition generally declined with increasing fish length (F1, 597=105.8,
P<0.001), but there was no significant effect of warming or DOC concentration (figure 6), although there was a non-significant trend towards declining fish condition over time in the three sampling occasions (figure 7a).
Figure 6 Fish condition (Fulton’s condition factor) in a) ambient and b) warm enclosures displayed over the three sampling occasions. Grey error ribbon represents ± 1 standard error.
Sickleback stomach fullness (Diet dry weight : Fish dry weight) declined over the summer
(Date F1, 602=10.8, P=0.001). In August alone, warming was found to have a negative
Figure 7 a) Mean fish condition (Fulton’s condition), b) Mean stickleback stomach fullness (Diet : Fish dry weight mg) displayed over the three sampling occasions (2020-06-29, 2020-08-13, 2020-09-28) and the two temperature treatments (ambient/ warm). Error bar represents ± 1 standard error.
3.4 Diet
Diet consisted of macroinvertebrates (dominated by Chironomidae larvae), Gastropoda,
Copepoda, pelagic Cladocera (Bosmina and Daphnia) and benthic Cladocera
(Chydoridae), and Rotifera (Appendix, figure 2a, b, c). Of the 619 analyzed stickleback samples, only one stickleback was cannibalistic, and 15 sticklebacks had empty stomachs.
Mean prey length increased with fish size (length) (F1, 585=122.9, P<0.001) and mean prey
length was lowest in September (Date F1, 585=54.0, P<0.001) (figure 8).
Figure 8 Mean prey length and maximum diet length in relation to fish length displayed over the three sampling occasions a) 2020-06-29, b) 2020-08-13, c) 2020-09-28. Data points are mean diet length. Error ribbon represents ±1 standard error.
Maximum prey length also increased with fish size (length) (F1, 586=100.9, P<0.001)
(figure 8). Additionally, DOC (F1, 13=9.2, P=0.010) and Warming (F1, 13=16.4, P=0.001)
was found to affect prey maximum length. Ambient enclosures showed generally larger maximum prey length than warm ones, especially at medium DOC levels (figure 9).
Figure 9 Maximum prey length in relation to fish length, temperature and DOC categories. DOC categories low = three lowest DOC levels, medium = three medium DOC levels, and high = two highest DOC levels in each temperature treatment a) ambient and b) warm, respectively. Horizontal black line represents mean value of maximum diet length. Error ribbon represents ±1 standard error.
Proportion macroinvertebrates in diet increased with fish size (length) (F1, 586=24.2,
P<0.001) and was higher in ambient enclosure (F1, 14=22.0, P<0.001) (figure 10).
Figure 10 Proportion of macroinvertebrates in stickleback diet in relation to fish length and temperature, a) ambient and b) warm. Error ribbon represents ±1 standard error.
The proportion of zooplankton showed the opposite pattern to proportion of
macroinvertebrates, i.e. it declined as fish size (length) increased (F1, 586=23.4, P<0.001)
Figure 11 Proportion zooplankton in diet (Rotifera, Copepoda, benthic and pelagic Cladocera) in relation to fish length and temperature, a) ambient and b) warm. Error ribbon represents ±1 standard error
Only time (F1, 586=23.4, P<0.001) was found to affect the proportion of gastropods in
stickleback diet. With highest values in the warm ponds in June. When June was tested
alone, warming (F1, 14=6.177, P=0.026) was confirmed to have an effect on this (figure 12).
Figure 12 Proportion gastropods (± 1 SE) in diet of sticklebacks displayed over the three sampling occasions and the temperature treatments, ambient and warm.
4. Discussion
This thesis focused on the effects of brownification and warming on ontogenetic prey shifts, size structure, and population abundance in nine-spine stickleback. Warming impacted stickleback populations negatively, with larger population declines over
summer, and an overall lower population and biomass, in warmed compared to ambient ponds. Contrary to what was expected, increasing DOC led to an increase in the
stickleback populations rather than a decline, as the highest population abundance was found in ambient ponds with high DOC levels.
Over the summer, the amount of prey biomass in stickleback stomachs (i.e., stomach fullness) generally declined, which indicated increasing resource limitation over time and
was likely caused by increased stickleback total resource consumption due to a new recruiting cohort of young-of-the-year sticklebacks. Stomach fullness was lower for sticklebacks in warm enclosures during August when water temperature was the highest and resource limitation likely the strongest. Increasing DOC concentration was not found to affect stomach fullness, suggesting that the light limitation was not severe enough to limit the stickleback’s ability to find prey. Brownification has been shown to affect some species’ foraging efficiency but not others (Ranåker et al. 2012; Hedström et al. 2017, van Dorst et al. 2020), although information on how brownification affect nine-spine
sticklebacks foraging behaviour appears to be lacking in the literature. Overall,
stickleback diet changed from smaller-sized zooplankton to larger macroinvertebrates with increasing fish size, as expected. Concentrations of DOC did not increase the proportion of zooplankton relatively to macroinvertebrates in the stickleback diets. On the other hand, warming caused a less clear diet shift with fish size from zooplankton to macroinvertebrates; in warm enclosures, larger sticklebacks relied more on zooplankton than in ambient enclosures. This may relate to changes in species composition anomg macroinvertebrates, Jonsson et al. (2015) found Chironomidae to decline with warming and other macroinvertebrates to increase in size, potentially becoming too large for sticklebacks to feed on. The proportion of gastropods in the diet did not show any correlation with fish length or DOC, but there was a positive effect (higher use of
gastropods in warm enclosures) of warming in June. The effects of higher consumption of gastropods in warm enclosure in June on stickleback performance is hard to know and even more so for the population development later in the summer. Still, this may reflect a shortage of zooplankton in June, which is an important resource for the newborn and juvenile stages of fish, including sticklebacks (Ghan and Sprules 1993; Byström and García-Berthou 1999; Lehtiniemi et al. 2007). This may have contributed to the general lower stickleback population abundance in August in the warm compared to the ambient enclosures. This speculation may render some further support from the fact that smaller sticklebacks displayed a lower body condition than larger individuals during June in warm ponds, though it was a non-significant trend.
Taken together, the overall development of body condition over time indicates a general increase in resource limitation over time in the enclosures. Larger fish also displayed lower body condition, likely because smaller fish perform better under low resource availability compared to large fish (Persson et al. 1998; Byström and Andersson 2005). That resources likely declined over time was further supported by the stomach analysis since stomach fullness declined over time.
Warming typically increases the growth rate of fish if resources are abundant, but can also amplify resource limitation, decrease growth, and cause starvation if resource supply cannot meet increased metabolic demands (Clarke and Johnston 1999; Byström et al. 2006; Crozier et al. 2010). Correspondingly, maximum fish sizes were larger in ambient enclosures, which suggests stronger resource limitation in warm enclosures. The higher use of larger macroinvertebrates by large sticklebacks in ambient enclosures may also have decreased intraspecific competition from smaller individuals. Hence, this broader ontogenetic variation in resource use in ambient enclosures likely enhanced a sustained population with bigger individuals in ambient compared to the warm enclosures.
Moreover, warming may also have caused the largest individuals to starve to death due to too low resource levels to meet the biggest individuals’ metabolic demand (Byström et al. 2006). The size effects seen in this study are also in line with other studies that have found that warming changes the size distribution of fish populations towards smaller individuals (Sandström, Neuman and Thoresson 1995; van Dorst et al. 2019).
DOC, on the other hand, had a small but positive effect on stickleback size with slightly larger fish with greater DOC. This may relate to the in some cases positive effect of DOC at intermediate concentrations on resources and fish production (Finstad et al. 2014; Bergström and Karlsson 2019). The highest mean DOC concentration measured in this
study was 10.4 mg/L. In a similar experiment by Feuchtmayr et al. (2019), though under more eutrophic baseline conditions, the highest DOC treatment used was 8.5 mg/L, and like the results in this study, they did not find a negative effect of brownification, but a decreased population of three-spined stickleback with warming. Bergström and Karlsson (2019) investigated lakes in northern Sweden and found a threshold of around 11 mg/L DOC concentration in water, where, below the threshold, phytoplankton production was positive, and above it, the pelagic production was negatively affected. This can mean that in this study all enclosures in the experiment had DOC concentration under the threshold from where the diminishing light overrides the positive effects of the increasing nutrient input for pelagic production. Furthermore, fish production has also been found to have a unimodal response to the browning of waters (Finstad et al. 2014). Conflicting to
Bergström and Karlsson (2019), Seekell et al. (2015) found the threshold for when DOC starts to suppress primary production to be at 4.8 mg/L DOC.
A substantial part of the whole ecosystem production in clear, nutrient-poor lakes takes place in the benthic habitats (Ask et al. 2009; Karlsson et al. 2009; Seekell et al. 2015). Rodríguez et al. (2016), which used the same experimental facility as this thesis, found the benthic production to constitute around half of the total net ecosystem production in a clear-water baseline setting. Vander Zanden, Vadeboncoeur and Chandra (2011) examined energy pathways from primary producers to fish and found the benthic and littoral zone to contribute to 57% of fish biomass on average, and the benthic primary production to represent 36% of the whole-lake primary production in average. If the benthic zone is shaded by DOC, the benthic primary production (and secondary
zoobenthos production) will decrease, and at higher levels of DOC, the possible increase in pelagic primary production will not compensate for the loss in the benthic (Ask et al. 2009; Karlsson et al. 2009; Seekell et al. 2015). Thus, the unimodal response in fish production to increasing DOC is primarily driven by light-dependent benthic primary production since zoobenthos constitutes a substantial part of the energy pathway for fish (Vander Zanden and Vadeboncoeur 2002; Solomon et al. 2011). However, in this context, it is also important to consider the depth of the study system. Shallow lakes are less sensitive to light limitation compared to deep lakes, as light penetration also decreases with depth. Thus, the DOC concentration threshold should also be dependent on the depth of the lake (Finstad et al. 2014). Seekell, Byström and Karlsson (2018) found the strongest negative effect on fish yield of decreasing light at a mean depth between 2.1 – 3.5 m, but no negative effect for lakes shallower than 2.1 m. Since the depth of the
experimental enclosures in this thesis was 1.5 m, it is possible that the highest DOC levels did not reach the threshold where the shading effect of DOC overturns the benefit of extra nutrient input associated with DOC.
In summary, warming caused stickleback population and biomass to decline, caused a lower stomach fullness and a smaller maximum size. This can be attributed to the combination of higher mortality and increased resource demand, caused by the higher water temperature, in an already resource-limited system. In contrast to what was expected, DOC had a positive effect on the population, biomass and population growth, and individual size. DOC did not affect ontogenetic diet shifts and large sticklebacks still used larger macroinvertebrate prey under high DOC concentrations. The highest DOC concentration used in this study may not have reached the threshold where the shading effect of DOC reduces the benthic primary production and benthic macroinvertebrate availability. Thereby, the food web, including the sticklebacks, was supported by the extra nutrient input associated with DOC rather than being limited by light. Hence, in shallow systems increasing DOC may support fish production while warming, on the other hand, may have strong negative effects on fish population abundance and size.
Acknowledgements
I would like to thank Pär Byström and Shuntaro Koizumi who have supervised and supported me during my work with this thesis.
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Appendix
Table 1 Identified diet taxa and the reference used to conduct length-dry weight regression. (1) Diptera regression equales small proportions of different species within Diptera. (2) Nematoda regression as
Ceratopogonidae, which have long slender body shape. (3) Tardigrada regression as Collotheca, which is a
relatively big Rotifera up to 1,2 mm with elongated shape.
Diet taxa dry weight regression reference
“Rotifera”
Asplanchna Bottrell et al. 1976 Keratella cochlearis Bottrell et al. 1976 Keratella quadrata Bottrell et al. 1976 Trichocercidae Bottrell et al. 1976
“Zooplankton”
Bosmina Dumont et al. 1976 Chydorus Dumont et al. 1976 Cladocera benthic Dumont et al. 1976 Copepod nauplii Dumont et al. 1976 Copepoda Dumont et al. 1976
Daphnia Dumont et al. 1976
Eurycercus Dumont et al. 1976 Ostracoda source unknown resting egg source unknown
“Macroinvertebrates”
Chironomidae head Benke et al. 1999
Chironomidae larvae Benke et al. 1999
Chironomidae subadult Benke et al. 1999 Coleoptera adult Benke et al. 1999 Coleoptera larvae Benke et al. 1999 Corixidae adult Benke et al. 1999
Diptera larvae1 Benke et al. 1999
Ephemeroptera head Benke et al. 1999 Ephemeroptera larvae Benke et al. 1999 Ephemeroptera subadult Benke et al. 1999 Odonata larvae Benke et al. 1999 Trichoptera head Benke et al. 1999 Trichoptera larvae Benke et al. 1999 Zygoptera larvae Benke et al. 1999
“Gastropoda”
Planorbidae Baumgärtner and Rothhaupt 2003
“other”
Asellus source unknown Bivalvia Benke et al. 1999
Hydrachnidiae Baumgartner and Rothaupt 2003
Nematoda2 Benke et al. 1999
Tardigrada3 Bottrell et al. 1976
Nine-spine stickleback this thesis Terrestrial Sage 1982 Unidentified Sage 1982
Table 2 ANOVA table of the mixed effect model with biomass (wet weight) Stickleback as response variable and mixed effect model with net change in biomass as response variable.
numDF denDF F-value p-value Biomass (Intercept) 1 16 111.3 <0.001
DOC 1 13 14.0 0.002
Temperature 1 13 28.5 <0.001
Net change
biomass (Intercept) DOC 1 1 12 12 5.4 4.9 0.038 0.047
Temperature 1 12 11.5 0.005
DOC:Temperature 1 12 6.5 0.025
Figure 1 Estimated biomass, wet weight g, of Stickleback. From the two sampling occasion a) 2020-08-13 and b) 2020-09-28, and c) is the net change in wet weight between the first sampling occasion to the second. X-axis represent the gradient of DOC ml/L, blue represents ambient ponds and orange represents warm ponds.
Figure 2 Proportion of simplified taxa in the stickleback diet. Displayed over the three sampling occasions a) 2020-06-29, b) 2020-08-13 and c) 2020-09-28. Fish size small represents stickleback <30 mm and large represents >30 mm. DOC level low = three lowest DOC levels, medium = tree medium DOC levels and high = two highest DOC levels in each temperature treatment ambient and warm, respectively. In a) fish size “small” is only represented in treatment warm, medium and high DOC, this is because there were no sticklebacks larger than 30 mm in those samples.
