Energy Budget for the African Cichlid, Tropheus Duboisi

ENERGY BUDGET FOR THE AFRICAN CICHLID, TROPHEUS DUBOISI

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

CORTNEY KAY MORRIS B.S., Wright State University, 2011

2017 Wright State University

WRIGHT STATE UNIVERSITY

GRADUATE SCHOOL

November 27, 2017

I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION by Cortney Kay Morris ENTITLED Energy Budget for the African Cichlid, Tropheus duboisi BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science.

______Lynn Hartzler, Ph.D. Thesis Director

______David Goldstein, Ph.D., Chair, Department of Biological Sciences Committee on Final Examination

______Lynn Hartzler, Ph.D.

______Yvonne Vadeboncoeur, Ph.D.

______David Goldstein, Ph.D.

______Barry Milligan, Ph.D. Interim Dean of the Graduate School

ABSTRACT

Morris, Cortney Kay. M.S., Department of Biological Sciences, Wright State University, 2017. Energy Budget for the African Cichlid, Tropheus duboisi.

Herbivorous fish living in Lake Tanganyika rely on algae as their main source of food and are constantly feeding on low quality (low energy) food. The lake is warming leading to fewer nutrient upwelling’s that are likely limiting the amount of energy available to fish. I calculated an energy budget to compare energy intake against energy expenditure of the African cichlid, Tropheus duboisi, by measuring metabolic rates at rest, while feeding, digesting and being aggressive as well as the caloric content of food, feces, and urine. Fish fed a low quality diet of algae contaminated with sediments had lower growth rates, but they had no significant differences in daily behaviors. As Lake

Tanganyika warms I would expect fish living in the lake to have lower growth and reproduction rates. This could be detrimental to the fish populations, the ecosystems of which they are a crucial part, as well as for the many people depending on the lake for food.

iii

TABLE OF CONTENTS

Page

I. INTRODUCTION AND PURPOSE………………………………...………..1

II. METHODS…………………………………..………………………………..3

Fish Housing………………………………………………………………3

Fish Diet…………………………………………………………………...3

Caloric Content……………………………………………………………5

Metabolism...... …...5

Excretion………………………………………..…………………………8

Activity Budget……………………………………………………………9

Growth…………………………………………………………………….9

Statistical Analysis……………………………………………..………...10

III. RESULTS……………………………………………………………………14

Consumption……………………………………………………………..14

Metabolic Losses…...……………………………………………………14

Time Budgets……………………………………………………………15

Waste……………………………………………………………………..15

IV. DISCUSSION………………………………………………………………..24

APPENDIX A ……………………………………………………………….32

BIBLIOGRAPHY……………………………………………………………33

LIST OF FIGURES

Figure Page

1. Tank Setup………………………………………………………………….…………11

2. Flow Through Respirometry Setup………………………………..……………..……12

3. Fish metabolic rate stabilization …………………………………………………...…13

4. Caloric Content of Flake Food…………………………………………………….…..16

5a. Metabolic Rates of Tropheus duboisi……………………………..………………….17

5b. Metabolic Rates of Tropheus duboisi (Without Standard Metabolic Rate)…....…….17

6. 12 and 24 Hour Activity Budget…………………………………..…………………..18

7. Excretion Rates for Fish Body Size…………………………………………………...20

8. Excretion Rates for High and Fish Diets………………………………………..…….21

9. Ingestion and Egestion Rates for Fish Diets…………………………………....……..22

10. Growth Rates ………………..….……………………………………..….……..…..23

LIST OF TABLES Table Page

1. Time and Energy Comparison of Fish Diets……………………………………19

ACKNOWLEDGEMENTS

I first want to thank my advisor Lynn Hartzler for the continuous support, patience, and knowledge. I cannot imagine a better advisor and mentor and know that I would not be here today without her. I also want to thank Yvonne Vadeboncoeur for going above and beyond the role of a committee member. She challenged me to think for myself and gave me opportunities I never thought possible.

My sincere thanks also goes to Renalda Munubi for her encouragement and support. Not only has she become a great collaborator but a lifelong friend.

Last but not least, I want to thank my family. My husband, Tristan, has provided support and motivation for me. He was always willing to listen and read anything I put in front of him. Also, my parents, Marty and Tammy, who taught me that I can do anything

I put my mind to.

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I. INTRODUCTION

Lake Tanganyika is the second oldest and second largest freshwater lake in the world and is considered to be one of the most diverse freshwater ecosystems. It is bordered by Burundi, The Democratic Republic of Congo, Tanzania, and Zambia; these are four of the poorest countries in the world. Millions of people around the lake depend on fish for their energy and nutrient supply (Campbell et al., 2008). Lake Tanganyika has warmed 0.9°C over the past 90 years, resulting in fewer nutrient upwellings (Verburg et al., 2003). Without nutrient upwellings algal volumes decrease, compromising feeding important to supply energy for routine metabolism of maintenance, growth, and reproduction of fish species (Tierney et al., 2010, Liew, 2012). Lake Tanganyika contains upwards of 470 fish species, including about 300 cichlids and over 170 non- cichlid fish ((De Vos & Snoeks 1994; Snoeks 2000). These fish are highly sensitive to changes in temperature and sedimentation (Cohen et al., 1993). Settling sediments may blanket benthic algae, which affects algal communities directly and herbivorous fish indirectly by reducing foraging efficiency (Cohen et al., 1993). Herbivorous fish living in Lake Tanganyika such as Tropheus duboisi must deal with energy constraints resulting from decreased nutrient upwellings. Since Tropheus duboisi rely on algae (a low quality: low protein, low energy food) as their main food source they invest significant time in consuming sufficient algae to meet their energy needs.

Energy budgets are used to represent the flow of energy in an animal. They show the balance of energy intake against expenditure; therefore intake must equal outputs + growth/storage. Fish get their energy by oxidizing chemical bonds (CHO + O2  CO2 +

H2O) thereby releasing energy to lower energy states. By measuring metabolic rates of fish under various activity states and measuring caloric contents of food, feces, and urine,

I can determine an energy budget for the herbivorous fish Tropheus duboisi. The energy budget may be used to predict what the future holds for herbivorous fish populations as well as those depending on the fish for their energy needs.

Organisms at all life stages may have limited energy budgets (Glazier 1999,

Kozlowski & Teriokhin 1999, Stallings et al., 2010). Consumed energy (C) is first allocated to meet immediate metabolic requirements (M) that include specific dynamic action (increase in production of heat due to cost of processing food for use and storage) and activity (e.g. swimming, aggression). Egested waste (E) is lost through feces and urine, and remaining energy can then be allocated to somatic and gonadal growth (G). I have composed the energy budget model C= (M + E + G) for Tropheus duboisi experiencing a reduction in food quality and quantity to determine to what degree limited energy intake decreases metabolic activity of growth. The aim of this study is to answer the question, “Does low nutritional quality of algae alter energetic allocation of daily behaviors?”

II. MATERIALS & METHODS

FISH HOUSING

Two 57 liter aquaria were connected to a 190 liter aquarium that circulated water throughout the system (figure 1). Water was prepared by mixing 11 grams of Cichlid

Lake Salt (Seachem Laboratories Inc., Madison GA, USA) per 40 liters distilled water and 5g Tanganyika Buffer (Seachem Laboratories Inc., Madison GA, USA) per 40 liters of distilled water to mimic the water properties of Lake Tanganyika. Twenty percent of aquaria water was replaced every two weeks. 20 juvenile Tropheus duboisi were purchased from a local fish breeder (Blue Chip Aquatics, Cincinnati, OH, USA) ranging in size from 0.5-1.2g were randomly divided in two aquaria and acclimated to high and low quality diets for 4 weeks prior to testing. After acclimation, the experiments ran for

73 days. Fish in Tank A were fed a low quality diet and fish in Tank B were fed a high quality diet. Fish were fed 30% of their average initial dry body mass (assumed to be

10% of wet mass) once a day due to fish losing mass being fed only 25% during acclimation. The fish were weighed to the nearest centigram after patting them dry with a damp paper towel. Algae were allowed to grow on 15 tiles (12.7cm x 12.7cm x 1.3cm) placed in each tank as well. The fish could eat from these tiles ad libitum as it is their natural behavior to graze throughout the day. Unfortunately fishes’ algae consumption from these tiles was not able to be calculated in the energy budget, but is rather estimated based on growth as described later in the discussion section.

FISH DIET

Tank A fish were fed a low quality flake food prepared by freeze-drying algae collected from Lake Tanganyika and supplementing with krill and vitamins. The algae was

3 collected from Lake Tanganyika by scrubbing it off of rocks and collecting the algae mixed with sediments from the rock (Munubi 2015). Unfortunately, we could not remove the sedimentation. This diet is considered low quality since it contains more sediments and indigestible material than the high quality commercial diet. The food was mixed with 4% krill dry mass (San Francisco Bay Brand Inc., Newark, CA, USA), 1% animal vitamins (Dyets Inc., Bethlehem, PA, USA) and 95% Lake Tanganyika algae. Krill and vitamins were needed to supplement the diet due to fish losing body mass when fed the algae mixture alone. This mixture was poured into metal drying pans and placed in a drying oven for 6 hours or until dry. Flake food was stored at 0◦C. Tank B fish were fed a high quality diet consisting of Spirulina Flake Food (Ocean Star International, UT,

USA). They list their ingredients as:

“Spirulina flake ingredients: Fish Meal, Wheat Flour, Spirulina, Shrimp Meal,

Algae Meal, Fish Protein Concentrate, Torula Dried Yeast, Brine Shrimp, Fish

Oil, Beta Carotene, Lecithin, Vitamin A Palmitate, DL-Alpha-Tocopherol

Acetate, D-Activated Animal Sterol, Thiamine Mononitrate, Choline Chloride, L-

Ascorbyl-2-Polyphosphate, Calcium d-Pantothenate, Inositol, Niacinamide,

Riboflavin Supplement, Pyridoxine Hydrochloride, Folic Acid, Menadione

Sodium Bisulfite Complex, Biotin, Vitamin B12 Supplement, Ethoxyquin (as an

antioxidant). Spirulina flake nutrient content: Crude Protein 41.0% min; Crude

Fat 4.0% min; Crude Fiber 6.0% max; Moisture 8.0% max.”

The low quality diet was derived from Lake Tanganyika algae scraped off the rocks of which it was growing. Therefore, it contained both algae and rock sediment (Munubi

2015).

CALORIC CONTENT

Gross energy content of food was determined by using a bomb calorimeter (Parr

Instrument Company, Moline, IL, USA). Algae samples were mixed with a mortar and pestle to ensure homogenization of samples, dried, and kept frozen until bombing in the calorimeter. When bombing, powdered food samples were placed into size 0 empty gelatin capsules (Parr Instrument Company, Moline, IL, USA). The calorimeter was calibrated by bombing 1g of Benzoic acid (standard). Next, a gelatin capsule along with the 1g Benzoic acid tablet was bombed to determine the energy content of the capsule.

METABOLISM

Closed system respirometry (Urbina et al., 2012) was used to measure metabolism in

. Tropheus duboisi. Metabolism is calculated as V O2 (mgO2/g/hr). It is the amount of oxygen used by the organism to convert energy from food energy to ATP energy molecules, the organism can use at the cellular level. Respirometers (61cm long and

700mL for experimental respirometer and 750mL for control (no fish, just water)) were constructed from PVC pipe with valves on both ends to facilitate flow-through water circulation during acclimation periods and then to allow the metabolic respirometer to be closed for recording oxygen depletion (Figure 2). The respirometers’ volumes are not exactly equal due to human error of the PVC being cut and glued.

Experiments were performed in a walk-in temperature controlled room set to 27◦C to mimic the current temperature of Lake Tanganyika’s near shore environment up to 10m,

5 considered the littoral zone (Sweke et al., 2013). For experiments, one oxygen probe

(YSI ProODO, Yellow Springs, OH, USA) was placed in each respirometer and one fish was placed in the experimental respirometer for 30 minutes to allow that fish to acclimate to the test respirometer. Upon entering the respirometer the fish would display escape behavior, but after 30 minutes the fish would calm and maintain a relatively stable position in the respirometer. During acclimation experimental respirometers were continuously flushed with water pumped from a tank equilibrated to room temperature and equilibrated with room air; after 30 minutes all four valves were closed to seal both respirometers. Oxygen content (mg/L) was recorded every 30 seconds for a total of 75 minutes or until dissolved oxygen reached 5.25 mg/L, whichever came first. In my experimental design I incorporated a control respirometer because, if algae or microbes were present in the water, oxygen content could increase due to photosynthesis or

. decrease due to microbial respiration. V O2 values of the control respirometer were subtracted from the experimental respirometer to account for any microbial metabolic activity (Herrmann & Enders, 2000). . (푂 푐표푛푠푢푚푒푑 )(푉표푙 − 푉표푙 ) V O = 2 푡표푡푎푙 푒푥푝푒푟𝑖푚푒푛푡푎푙 푟푒푠푝𝑖푟표푚푒푡푒푟 푓𝑖푠ℎ 2 푡푖푚푒

(푂 푐표푛푠푢푚푒푑 )(푉표푙 ) − 2 푡표푡푎푙 푐표푛푡푟표푙 푟푒푠푝𝑖푟표푚푒푡푒푟 푡푖푚푒

Standard Metabolic Rate: Fish were removed from their normal housing, placed in an aquarium with only water, and had food withheld for a period of 24 hours prior to measuring standard metabolic rates. During this time fish were in the temperature controlled tank with no algae tiles or other food source. Prior to measurements, fish were placed in the covered respirometer to mimic the dark cycle allowing the fish to rest.

While in the respirometer oxygen content values were measured continuously, metabolic

. rates (V O2) were calculated throughout the 75 minutes that the fish was in the respirometer. 75 minutes were used because for the first 30 minutes in the respirometer metabolic rates plateaued (Figure 3). The dissolved oxygen limit never reached

5.25mg/L during the 75 minutes for standard metabolic rate.

Metabolic Cost of Swimming: Two 2 in. x 2 in. ceramic tiles with algal growth were taken from the 15 gallon aquaria and placed in control and experimental respirometers during the experiment. The fish grazed on the algae on the tile throughout the experiment.

Metabolic Cost of Digestion: Fish were fed flake food ad libitum until they stopped eating and were immediately placed in the experimental respirometer. Metabolic rate markedly increases following feeding and reaches a peak level before gradually declining to the pre-feeding rate; this feeding-related increase in metabolism is termed specific dynamic action (Wang et. al., 2012). It is the total amount of energy expenditure above the standard metabolic rate attributed to the cost of processing food for use and storage.

Aggression: A mirror was placed on the outside of the respirometer allowing the fish to see itself. The fish continuously attacked the mirror in the same behavior that it would attack another fish; aggressive behavior can include chasing or biting, threatening, and engaging in border threats in another cichlid fish A. burtoni (Renn et. al., 2013). Since the fish would rest between attacking, aggression was calculated by averaging the three

. highest V O2 peaks from the 30 second intervals. Experiments lasted only 30 minutes since the dissolved oxygen decreased faster than it did due to the metabolic costs of swimming and digestion.

. Once the V O2 of each activity was determined, a metabolic budget was calculated by

. combining the time devoted to this activity with the energetic cost (V O2) for each activity.

Sample calculations are included in the Appendix.

EXCRETION

The analysis of ammonium concentration in the samples was determined following the methods of Holmes et al. (1999) and Taylor et al. (2007). Fish were placed in Ziplock® bags containing 250mL of water pre mixed with Tanganyika salt and buffer that has the same background chemical properties as the tank water. Fish were left in bags for ~30 minutes- after which 40 mL subsamples from bags were placed in brown Nalgene bottles and mixed with 10mL working reagent (precise details in Holmes et al. 1999). Samples were incubated for 4 hours and a fluorometer (Turner Designs, Sunnyvale, GA, USA) was used to measure the light emitted from the sample and placed in a standard

+ calibration curve (Holmes et al., 1999). From this curve the amount of NH4 (µmol/g/hr) was determined. A blank was taken from sample water that had not contained fish

(therefore, no ammonium) and subtracted from each sample’s fluorescence.

EGESTION

For fecal analysis, individual fish were placed in a separate tank with no algal growth.

Feces from fish were removed from the tank within 24 hours to keep feces from dissipating into the water. This ensured easy removal of feces without the need of filtering the entire tank. Samples were then dried and stored frozen until analyzed. Due to the small amount of feces collected I was unable to bomb the feces for caloric content.

There were originally 10 fish in each diet group, but due to fish being too stressed only 7 fish for the low quality and 8 fish for the high quality diet were included in the analysis.

The caloric content for egestion (kcal/g/d) was then calculated as an approximation.

Since the exact amount of food consumed for each fish is unknown I assumed the caloric content of ingested was the same as the caloric content excreted. This is an overestimation of the energy excreted.

ACTIVITY BUDGET

A video camera was used to record activities of fish in high food quality and low food quality tanks for 5 minutes every 30 minutes for 12 hours (to quantify daylight activities).

The video was uploaded to the computer and replayed to tally fish activity (swimming, feeding, resting (fasting), or aggressive behavior) on a sheet of paper every 5 seconds for each 5 minute fragment. If the activity of the fish was ambiguous, the video was played again. Fish were recorded as feeding if their mouths touched the tiles or sides of tank.

Fish were recorded as swimming if they were in the middle area of the tank. Fish were recorded as aggressive if they were chasing or fighting other fish. Fish were recorded as resting if they were stationary and performing none of the other three activities. Based on field recordings, fish are assumed to also be resting for 12 hours during the night so the light on the fish tanks is turned off to mimic night time. Total time of each tank’s activities were extrapolated to total a 24 hour day length (12 hours light and 12 hours dark) and multiplied by the metabolic rate for each activity to estimate the total metabolic cost of each activity in a 24 hour period.

GROWTH

After acclimation to the high and low quality diets and prior to start of experiments, fish were weighed to the nearest centigram after patting them dry with a damp paper towel.

Fish were weighed weekly during experiments, and the amount of food the fish were fed

9 was calculated and adjusted to their new masses. At the end of the experiment fish were weighed and the initial mass was subtracted from the final mass to determine average growth for both high and low quality diets. Juvenile fish are black with white spots, and they lose their spots as they mature. Adult fish have a single vertical blue band across their abdomen. I was unable to match the fish identity from the beginning to end of the experiment because of the change in markings and because the fish were group housed.

STATISTICAL ANALYSIS

A power analysis was performed before the start of experiments to determine the sample size of fish for each treatment (high and low quality diets). Ten fish for each treatment were determined to be used for experiments. T-tests were carried out to compare low vs. high quality diets for metabolic rates, caloric contents of food, and excretion rate experiments. P-values of ≤0.05 were considered significant. A 2 way repeated measures

ANOVA was run to compare the mean differences between activities for high and low quality diets and to compare each activity to the standard metabolic rate of that diet.

Figure 1: This cartoon diagrams the tank set-up. Arrows represent the direction of the flow of water. An EHEIM Jager heater and return pump were housed in the larger 50 gallon aquarium and a white fluorescent light on a 12 hour on/off timer was hung above the aquaria to ensure equal light distribution.

Figure 2: Set-up for flow-through respirometry. Red valves can be turned to the closed position for closed system respirometry. Water is continuously recirculated through an open, aerated aquarium before being pushed through the respirometer. Fish are allowed to acclimate to the respirometer prior to the system being closed. Po2 electrodes continuously measure oxygen levels (mg/L) in the water.

1.4

1.2 )

1 1

1 -

g 0.8 2

0.6

(mgO 2

VO 0.4

0.2

0 0 20 40 60 80 100 Time (min)

Figure 3: This representative graph shows the metabolic rate of a single fish in the respirometer. This typical response exemplifies that metabolic rates of fish plateau after 30 minutes in the respirometer. Therefore, for my metabolic determinations, I started my data collection after 30 minutes.

III. RESULTS

The energy budgets were calculated using the equation C = ∑ (M + E + G) where C = consumption, M = metabolism, E = excreted waste, and G = growth. M was calculated by the sum of the oxygen consumptions weighted for the time the fish spent in each activity: M = ∑ (Mstandard (time) + Mswimming (time) + Mdigestion (time) + Maggression (time)).

E was estimated from fecal egestion. G consisted of growth and storage which was calculated by subtracting the average beginning mass from the average end mass of fish for high and low quality diets. See Appendix A for detailed equations.

CONSUMPTION

The high quality fish food contained a higher amount of energy compared to the low quality food (Figure 4). The low quality fish food had an energy value of 1.97 kcal/g and the high quality fish food had an energy value of 4.81 kcal/g. Each fish was fed an average of 0.05 kcal/g/day for low quality and 0.13 kcal/g/day for high quality. The fish consumed everything they were fed within five minutes of feeding. Given that the low quality food’s energy content was less than 2 kcal/g, I estimate that at least half of the flake food was composed of sediments.

METABOLISM Standard metabolic rates of fish fed the high quality diet were significantly higher than fish fed the low quality diet (figure 5). There were no significant differences between metabolic rates of fish during digestion, aggression, and swimming between the high and

. . low quality diet. The dashed line represents the standard V O2; therefore all of the V O2 above the dashed line is attributed to the additional metabolic cost of that activity. The metabolic rate during swimming and feeding is significantly higher than the standard metabolic rate for both high and low quality diets. The metabolic rate for aggression is

14 significantly higher than the standard metabolic rate for fish fed the low quality diet

(Figure 5).

TIME BUDGETS Time budgets for both high and low quality diets showed that fish spend most of their time resting and feeding (Figure 6a & Figure 6b). Very little time was spent being

. aggressive. The V O2 for each activity was multiplied by the time the fish spent in each activity to determine how many kilocalories per day fish expend (standard, feeding, swimming, aggressive). Fish fed a high quality diet spent significantly more energy resting and feeding compared to fish fed a low quality diet (Table 1).

WASTE

Fish fed a higher quality diet had a significantly higher ammonium excretion rate compared to the low quality diet (Figure 8). Fish fed a high quality diet egested 0.057 kcal/g/d and fish fed a low quality diet egested 0.042 kcal/g/d (Figure 10). There was no significant difference in egestion rates between the two groups (unpaired t-test).

GROWTH

All 10 fish were measured at the beginning of the experiment and at the end of the experiment. Growth was determined by taking the average end mass and subtracting the average start mass. The growth (gfish) was then multiplied by 4cal/g/fish assuming fish is mostly protein and divided by the initial mass to determine fish growth (kcal/g/d). Fish fed a high quality diet had significantly more growth at the end of the experiment (Figure

10). Fish fed a low quality diet had no significant change in mass from the beginning to the end of the experiment.

0.25

0.2

)

∙

- g ∙ 0.15 Commercial Flake (High)

0.1 Tanganyika Flake (Low) Food EatenFood (kcal 0.05

0 Quality of Food

Figure 4: Fish fed commercial flake food (high quality diet) consumed 0.078 kcal∙g-1∙d-1 more energy compared to fish fed Tanganyika flake food (low quality diet) at the beginning of the experiment. Data are means  SD. n=10

0.7 

0.6 

 

) 1

- 0.5

1 -

g 0.4  2 *

0.3

(mgO 2

Vo 0.2

0.1

0 Standard Swimming Aggression Feeding/Digestion

low quality high quality

Figure 5a: Standard and feeding metabolic rates of Tropheus duboisi fed low quality diets were lower than those fed high quality diets (*= P<0.05). Oxygen consumption rates to above the dashed lines represents additional cost of activity in addition to standard metabolic rates. Data are means  SD. * indicates significant difference for high vs. low quality diet for each condition.  indicates significant cost of activity compared to standard.

Figure 5b: Metabolic rates of Tropheus duboisi with the standard metabolic rate subtracted for high and low quality diets.  indicates significant cost of activity compared to standard. The cost of swimming, aggression, feeding/digestion were all significant compared to the standard for fish fed a low quality diet and significant for swimming and feeding/digestion for fish fed a high quality diet (*= P<0.05).

Figure 6 shows the activity budget for fish fed high and low quality diets. 6A shows the activity budget for fish fed a low quality diet over 24 hours. Fish spent most of their time resting and feeding. 6B shows the activity budget for fish fed a low quality diet over 12 hours. Fish spent most of their time resting and feeding. 6C: Activity budget for fish fed a high quality diet over 24 hours. Fish spent most of their time resting and feeding. 6D: Activity budget for fish fed a high quality diet over 12 hours. Fish spent most of their time resting feeding.

Table 1: Combined time and energy budget for high and low quality diets were determined by multiplying the metabolic rates times the time the fish spent in each activity times the conversion factor for oxygen consumption to energy (3.24 kcal/gO2) (Brett and Groves 1979). Fish spent significantly more time and energy feeding and swimming in a high quality diet compared to a low quality diet. Diet had no effect on standard and aggression behavior (*= P<0.05). Low Quality (kcal/d) High Quality (kcal/d)

Standard 7.54 12.21

Feeding* 9.45 12.67

Swimming* 4.95 5.79

Aggression 0.34 0.15

1.2

)

- hr

∙ 1

∙ 4 0.8 y = 0.2926x + 0.4116 R² = 0.5795 0.6 Low Quality 0.4 High Quality y = 0.5885x + 0.0966 R² = 0.9015

0.2 ExcretionRate (µmol NH 0 0 0.5 1 1.5 2 2.5 Mass (g)

Figure 7: On average, fish fed a high quality diet had higher ammonium secretions.

1 * 0.9

0.8

)

- hr

∙ 0.7

- g

∙ 0.6 mol µ 0.5 Low Quality High Quality 0.4

0.3

ExcretionRate ( 0.2

0.1

0 Food Quality

Figure 8: Fish had a higher excretion rate of ammonium when fed a high quality diet compared to a low quality diet. Data are averages  SD (n=10) (*= P<0.05).

0.06

0.05

0.04

)

- ∙d 0.03 Ingested

Egested Food (g Food 0.02

0.01

0 High Quality Low Quality

Figure 9: Ingestion and egestion rates for fish fed a high and low quality diet. There was no statistical significance between diets. Data are averages  SD (n=8).

1.8 * 1.6 1.4 1.2 1 Low Quality 0.8

High Quality Weight Weight (g) 0.6 0.4 0.2 0 Initial Final

Figure 10: Fish fed a high quality diet had a significantly higher growth at the end of the experiment (*= P<0.05). Fish fed a low quality diet had no significant change in mass from the beginning to the end of the experiment. Data are averages  SD (n=10).

IV. DISCUSSION

I hypothesized that low nutritional quality of algae alters energetic allocation of daily behaviors. Overall, I found fish fed a low quality diet did not change the time dedicated to each behavior when compared to fish fed a high quality diet (Figure 6a &

6b). The differences in energy budgets for fish fed high and low quality diets reflect differences in consumption (C), metabolic rates (M), excretion (E), and growth (G). The energy budget is the balance of energy in (Consumption (C)) with energy out (metabolic rates (M) and excretion (E) ± growth/storage (G)).

Low Quality (kcal/g/d): C = ∑ (M + E + G) 0.05 ± 0.002 = (0.034 ± 0.008 + 0.042 ± 0.012 + 0.004) 0.05 ± 0.002 = 0.08 (range = 0.059 - 0.099)

High Quality (kcal/g/d): C = ∑ (M + E + G) 0.128 ± 0.002 = (0.038 ± 0.005 + 0.057 ± 0.085 + 0.026) 0.128 ± 0.002 = 0.121 (range = 0.064 - 0.213)

If consumption is lower than the sum of metabolic requirements plus excretion and growth then fish should be in a negative energy balance. Fish consuming the low quality diet had a lower input (consumed less digestible food) than output. Fish consuming the high energy diet had a higher input than output. Since fish eating the low quality diet gained mass over the experiment (Figure 10) their diets must have been compensated with algae growing on the tiles. Assuming that algae contains 4.0 kcal/g dry weight, I estimate that the fish in the low quality diet group must have consumed between

0.023-0.123g algae each day off of the tiles to account for their activity and growth. The high quality diet data fit within the measurement error for each component of my budget.

Both the low and high quality fish were observed eating algae off of the tiles.

In the budget presented, I do not have standard deviation for growth because I could not identify the fish from the beginning to the end of the experiment. Therefore, I was unable to calculate a complete energy budget for Tropheus duboisi because I cannot match growth with the energetic costs of metabolism and excretion for each fish. The budget would have been more successful if I had calculated each fish individually

(growth, consumption, excretion, metabolism) in the beginning of the experiment and continued through to the end. My budget is an estimation since the fish mass was averaged for each group and used to determine the amount of flake food to feed each tank for consumption. If I were repeat this experiment, I could tag each fish, separate fish using containers, or use photographic images to differentiate fish (update frequently to account for changing color patterns). Consumption was estimated based on the average fish mass in each tank. Both high and low quality diets had tiles with algae the fish could eat ad libitum. The algae on the tiles was not calculated in each budget resulting in an underestimation of consumption. Ammonium excretion was not incorporated in the energy budget. While there is some metabolic cost to filtering and excreting ammonium,

Brett and Groves 1979 energy budget for herbivores shows ammonium excretion requires negligible energy. Since ammonium excretion requires so little energy and is less than the errors of my other measurements I left the cost of ammonium excretion out of my energy budget.

Meeting the dietary needs of an herbivore can be challenging since the food they consume is low in quality and hard to digest, requiring a large amount of food to satisfy these demands (Wagner et al., 2009). Brett and Groves (1979) created the following generalized energy budget for young fish:

Carnivores: 100C= 29P + 44R + 7U + 20F

Herbivores: 100C= 20P + 37R + 2U + 41F

In this relationship C (consumption) is the energy content of food, P (production) is the energy available for growth and reproduction, R (respiration) is the energy lost as heat, U

(urinal loss) is nitrogenous excretory products, and F (fecal loss) is the energy lost in feces. This relationship shows that carnivores and herbivores use the available gross energy consumed differently. It suggests that herbivores should have lower growth rates, respiratory rates, urinal losses and a higher feces excretion rate. The quality of food consumed between herbivores and carnivores are not the same. Herbivore diets are lower in nutritional value and have more indigestible material. My low quality food data support Brett and Groves energy budget for herbivores with my proportions being:

100C = 5G + 42.5M + 52.5E

My growth (G) is the equivalent of the production (P) in Brett and Groves budget, metabolism (M) is equivalent to respiration (R), and excretion (E) is equivalent to fecal losses (F) and urinal losses (U). My growth component was much lower than Brett and

Groves budget. This is due to my excretion and metabolism having higher values which I previously described as an over estimation.

Not only do herbivorous fish living in Lake Tanganyika face challenging dietary needs, but they also face increased climate challenges. As Lake Tanganyika warms and less mixing occurs, the availability of nutrients from the deep water nutrient reservoir essential for primary production will decrease (O’Reilly et. al., 2003). Fish fed a low quality (low calorie diet) consumed 0.78 kcal/g/d less energy than those fed the high quality (high calorie) diet (Figure 3). The high quality diet was used as an over

26 estimation of the quality of food typically available to an herbivore to see if there is a difference in daily behaviors. For example, do fish fed a high quality diet spend less time grazing? Figure 6 shows that they do not. Do fish fed a high quality diet have greater growth? Figure 10 shows that they grew half again as much. Will fish fed a high quality diet be more aggressive? Figure 6 shows that they are not.

Another change that results from a change in diet is ammonium excretion.

Ammonium can become extremely toxic to fish if allowed to accumulate in their body.

Fish fed a high quality diet excreted more ammonium compared to the low quality diet

(figure 7). I expected this diet to have higher excretion rates because the higher quality diet contains larger amounts of protein. As the proteins are catabolized by the fish ammonium is the end product. Kraemer et al. (unpublished data) performed ammonium excretion experiments with Tropheus sp. in Lake Tanganyika and my results fall within their excretion range (personal communication with Benjamin Kraemer). Ammonium excretion rates in fishes are variable, depending on the state of the animal, the environmental conditions, and the species (Randall and Wright, 1987). If the food quality available to fish in Lake Tanganyika is predicted to decline then fish ammonium excretion rates should decline as well. This would exacerbate the nutrient problem to the ecosystem because this ecosystem relies on the release of nitrogen from fish. The ratio of

Carbon:Nitrogen:Phosphorous (C:N:P) may be affected by the decrease in this in N and P availability increasing the amount of C and overall ratio (Kim 2014). A high C:N:P ratio is associated with poor quality food (Lichtenbelt, 1992; Schindler & Eby, 1997).

Activities of fish were recorded to see how they spend their time and metabolic costs were determined. Activities were recorded only during the daylight hours since

27 herbivorous fish feed mainly during the daylight hours (Choat & Clements, 1993). There is no record in the literature of cichlids being active at night. When looking at the results of my experiment I found fish spent most of their time resting (standard) and feeding

(Figure 6). I expected fish consuming a lower quality diet to spend more time resting and feeding since that requires the least amount of energy (Figure 4). Fish fed a high quality diet were expected to use their additional energy available to swim more and be more aggressive (Figure 6).

Metabolic rates in animals increase as activity levels increase. Fish have significantly higher metabolic rates when aggressive compared to their standard metabolic rate (Brett and Groves, 1979; Grantner and Taborsky, 1998). Tropheus duboisi fed a high quality diet had significantly higher metabolic rates for standard and feeding

(figure 4). Cost of each activity was compared to the standard metabolic rate. When calculating M in the energy budgets, the standard metabolic rate was added to the cost of each activity since certain levels of specific dynamic action is always maintained in a natural situation where the fish feed more or less constantly during the day (Gashagaza

1988). For the low quality diet, digestion and aggression were significantly higher compared to standard metabolic rate. I kept fish in both treatments in the same size range. Metabolic rate increases as body size increases. Kleiber’s law states that an animal’s metabolic rate scales to the ¾ power of an animal’s mass (Kleiber 1975). This trend should be used for warm-blooded animals and does not show as well in ectothermic organisms such as fish (Glazier, 2006). Fish have shown to have a higher ratio than

Kleiber’s law suggests. I directly measured metabolic rate in Tropheus duboisi and did not use Kleiber’s law as an estimation.

Not only is the decrease in food quality a problem for fish living in Lake

Tanganyika but increasing water temperatures should affect them as well. The increase in metabolic rate as temperature increases is well-studied among vertebrates (Barbieri,

2005; Litzgus & Hopkins, 2003). An increase on global water temperature can be expected to have major effects on the distribution of costal fishes. Growth and temperature studies on juvenile fish species suggest that there may be substantial changes in the growth performance, and therefore possibility of survival of common estuarine fishes (Booth et al., 2013). As the lake temperature increases metabolic rates will increase, requiring the fish living in Lake Tanganyika to either eat more of the low quality algae or decrease growth rates (Kim 2014). Kim (2014) showed that African cichlids kept at 32ºC grew about half the rate of those at 26ºC and 29ºC. Despite increasing temperatures, which have caused an increase in the photosynthesis rates of algae, primary production within Lake Tanganyika has decreased in correlation with warming events (O’Reilly et al., 2003).

Climate warming is diminishing productivity in Lake Tanganyika. It is estimated that primary productivity may have decreased about 20% which would roughly decrease fish yields by about 30% (O’Reilly et al., 2003). The fish living in Lake Tanganyika could be impacted by the increase in water temperatures leading to a higher metabolic rate which could be detrimental to the fish if it cannot eat enough food to support basic functions for survival. As the waters warm, the impact on fish populations in the lake will be detrimental to the fish and also the millions of people who depend on the lake.

From the results it appears that a decrease in food quality had the largest impact on growth. Fish consuming a lower quality diet had a lower growth rate compared to fish

29 on a high quality diet (Figure 10). Consumed energy is first allocated to metabolic requirements (waste losses through feces, urine and specific dynamic action), and energy remaining is allocated to somatic and gonadal growth (Horodysky et al., 2011). I expected the higher quality diet to have a higher growth rate because they would have more energy left over after metabolic requirements to devote to growth and reproduction.

Further studies could be dedicated to reproductive strategies for herbivorous fish when faced with a decrease in food quality and/or quantity. Reproduction is energetically expensive and digestive constraints may limit the timing of reproduction of some rodents

(Gross et al., 1985). My study shows that a decrease in food quality negatively affects growth (figure 10) and metabolic requirements (figure 4) of Tropheus duboisi but does not give any insight if reproduction of the species will be affected. Tropheus duboisi are mouth brooders and invest a lot of energy in reproduction. Food deprivation during mouth brooding is very common. Other African cichlid mouth brooders lose a significant amount of body mass when carrying eggs (Schurch and Taborsky, 2005; Grone et al.,

2012). One study on the African cichlid Astatotilapia burtoni concluded that food- deprived or mouthbrooding animals are likely to require more time to replenish energy stores and mate again, potentially decreasing future reproductive fitness (Grone et al.,

2012). Observations in Lake Malawi cichlids suggest shortening of the gut in mouthbrooding females that are unable to feed regularly (Reinthal 1989).

From this study I have concluded that fish fed a low quality diet have lower growth rates, excretion rates and metabolic rates. I would expect fish living in Lake

Tanganyika to be a smaller size and reproduce less as the lake warms and fewer nutrients are available. This could be detrimental to the fish populations, the ecosystems of which

30 they are a crucial part, as well as the many people depending on the lake for food and resources.

APPENDIX A

Consumption - Fish were fed 30% of their body weight per day. - Dry body mass = 10% of wet body mass.

0.85g (wet) * 10% = 0.085g (dry) 0.026g food 0.085g (dry) * 30% g/d = gfish∙day 0.026g 1.1965kcal * = 0.050 kcal/g∙d d g Low quality: 0.050 kcal/g∙d High quality: 0.128 kcal/g∙d

Egestion - I assumed same caloric content out as in because I could not experimentally determine a value due to the small amount of feces collected.

4.81kcal 0.13g feces * = 0.057 kcal/g∙d g feces 1.34 g fish∙d Low Quality: 0.042 kcal/g∙d High Quality: 0.057 kcal/g∙d

Growth - I used 4kcal/g assuming fish is mostly protein.

g (final−initial) 4 kcal * ÷ g fish (final) = kcal/g∙d 70 days g Low Quality: 0.004 kcal/g∙d High Quality: 0.026 kcal/g∙d

Metabolism - Metabolism was measured for digestion/feeding, aggression, swimming, and standard. - 0.69978 is the conversion from mg/L to mL/L (Weber et al., 2008).

mgO mgO 2 ÷ g fish = 2 hr g∙hr

mgO2 standard mgO2 - * g fish * hrs activity = mgO2 푔∙ℎ푟 g∙hr

0.6998 mgO2 * = LO2 1000

4.83 kcal LO2 * ÷ g fish = kcal/g∙d LO2 Low Quality: 0.034 kcal/g∙d High Quality: 0.038 kcal/g∙d

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