FINAL REPORT

Project title

Predictive yield models for Crater Lake fisheries of Uganda: An unexpected marriage of

rural development and conservation.

Submitted by

JACKSON EFITRE

Department of Zoology, University of Florida,

223 Bartram Hall, Gainesville, FL 32611, USA.

Email: jefitre@zoo.ufl.edu

to

The Whitley Laing Foundation for International Nature Conservation/Rufford Small

Grant programme, UK

April, 2006

SUMMARY

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This report summarizes preliminary results of my field research on Crater Lake fisheries of

western Uganda. The research project, generously supported by The Whitley Laing Foundation

for International Nature Conservation/Rufford Small Grant program was implemented between

July 2004 and July 2005 in the crater lakes around Kibale National Park, Uganda. There are

approximately 80 small volcanic crater lakes along the foothills of the Rwenzori Mountains in

western Uganda. These crater lakes are experiencing varying levels of deforestation related to

clearing of the forested crater rims associated with the need for fuelwood, building materials, and

conversion to agricultural land. In addition, tilapia species were introduced into several of the

crater lakes in the 1940s primarily to increase available protein to the local communities.

However, biological information on the lakes is lacking. Currently, many of these lakes are

producing “stunted” tilapia populations, the causes of which remain unknown but may reflect

resource limitations associated with the Crater Lake environment and/or low mortality (e.g., low

fishing pressure) leading to high levels of intraspecific competition. Effective management and

conservation of fish species in these small lakes is important given their economic, ecological, and

scientific significance. In addition, the variation in environmental characters and fishing effort

among the lakes provides an excellent opportunity to identify predictors of life history variation.

The overall objective of the present study was therefore to quantify relationships

between environmental factors and life history characters (growth, size and age at maturity,

fecundity, and condition) for introduced tilapia populations in the crater lakes region of

western Uganda. Since potential yields from fish populations are a function of their life

histories, being able to predict life history parameters in a variety of the lakes could provide

a useful tool for broadscale management of their fisheries.

In collaboration with the Fisheries Resources Research Institute (FIRRI), Uganda

Wildlife Authority (UWA), a local fisherman, and two field assistants, we successifully

carried out an intensive sampling of 20 crater lakes between July 2004 and July 2005. A

range of environmental parameters were measured in each lake. Tilapia life history data

were also simultaneously collected from each lake.

Preliminary results suggest variation in deforestation levels is a major contributor to

variation in water quality characters among the lakes. There is predictably correlation

between water transparency and chlorophyll a concentration (primary productivity), and

deforested lakes are characterized by lower transparency and high chlorophyll a

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concentration (primary productivity). The increased erosion from deforested crater rims and

associated nutrient loading has clearly resulted into eutrophication of some of the lakes. It is

also clear that there is over-fishing in some of the crater lakes.

We do hope that the predictive model of chlorophyll a (primary productivity)

proposed in this report will provide a useful tool in efforts to control deforestation of the

crater rims and associated eutrophication of the lakes. In addition, we hope the chlorophyll a

(productivity) data will enable development of predictive yield models for the Crater Lakes

artisanal fisheries that will inform management decisions. Appropriate management of the

fisheries should increase fish yields, rural income, and protein supply to local communities

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INTRODUCTION

Africa is home to numerous volcanic crater lakes scattered especially in the western

(Cameroon) and eastern (Ethiopia, Tanzania, and Uganda) regions of the continent. In East

Africa, extensive volcanic activity that followed the formation of the Great East African Rift

Valley resulted into formation of several craters along the western and eastern wings of the rift

valley. These craters later filled with water, and some were eventually inhabited by fish after the

volcanoes became extinct or dormant. In western Uganda alone, there are about 89 small volcanic

crater lakes along the foothills of the Rwenzori Mountains grouped into four clusters (Fort Portal

area, Kasenda area, Katwe-Kikorongo area, and Bunyaruguru area). Previous research conducted

on some of the crater lakes in western Uganda (Melack, 1978) found a striking diversity in their

limnological, morphological, hydrological, and chemical characteristics. In addition, observations

collected from over 10 years of work in the crater lakes region also suggest that the greatest threat

to the crater lakes ecosystem is related to clearing of the forested crater rims associated with the

need for fuel wood, building materials, and conversion to agricultural land (Chapman et al.,

2003). Deforestation around the crater lakes causes soil erosion that leads to increased turbidity,

siltation, nutrient and organic matter load, and cultural eutrophication (Crisman et al., 2001).

Figure 1: Deforestation associated with inappropriate agricultural practices poses the greatest

threat to the crater lakes ecosystem (Photo by Jackson Efitre).

Tilapia species (Oreochromis niloticus, Oreochromis leucostictus, and Tilapia zillii) were

introduced into a large number of the crater lakes in the 1940s and in subsequent years to increase

5

available protein to the local communities. Currently, many of these lakes are producing a

“stunted” tilapia population, the cause of which remains unknown but may reflect resource

limitations associated with the Crater Lake environment and/or low mortality leading to high

levels of intraspecific competition. “Stunted growth” (production of a large number of individuals

having a low maximum size) is a very widespread phenomenon in freshwater fish populations

including the tilapias. Several studies point to phenotypic plasticity in fish growth resulting from

environmental variation, rather than genetic differentiation, as the cause of stunting (Roff, 1992;

Ridgeway & Chapleau, 1994). However genetic differences have also been noted in fish life

history traits due to prolonged isolation between populations and size selective predation (Reznick

et al., 1990).

A B

6

C

Figure 2: Introduced Tilapia zillii (A), Oreochromis leucostictus (B) and “stunted” individuals

of both species (C) from the Crater Lakes of western Uganda (Photo by J.Efitre).

Stunting in freshwater fish populations has very important economic implications

because the commercial value of stunted fish populations is greatly diminished, as most of

the catch consists of small fish that can not fetch good prices in the market. However, if a

significant component of stunting reflects environmentally-induced plasticity, then altering

predator pressure (through selective harvest) may be an effective way of changing life

history characters to increase fish yield. For example, high population density (low fishing

effort) should increase competition for resources (e.g., food) leading to density-dependent

growth. Under reduced and unpredictable availability of food, one would predict increased

allocation of resources towards reproduction, resulting into early maturation at a smaller

size. A decrease in population density (predation or fishing) should reduce competition for

resources and lead to normal growth. When resources are adequate, one would predict

increased allocation to growth, resulting in delayed maturity and larger size. Therefore,

understanding the links between environmental factors and tilapia life history variation is

important for fishery management. Owing to their small size, the crater lakes of western

Uganda provide suitable “experimental test tubes” for testing life history variation in

tilapias. In addition, the variation among the crater lakes in fishing effort and other

environmental characters provides an excellent opportunity to predict responses of fish

populations to the rapid environmental changes. The most critical information needed is a

validation of aging techniques on the tilapia to determine the viability of growth models as a

7

tool for understanding stunting, and for the development of management options that will

lead to sustainability of the Crater Lake fisheries.

In light of the fact that these anthropogenic perturbations may contribute to deterioration

in water quality that might also affect primary productivity of the crater lakes as well as contribute

to variation in fish growth in the lakes, the main goal of the present study was therefore to

quantify environmental and some life history characters (age-and size at maturity, growth, and

reproduction) of the tilapia species from Crater Lakes in western Uganda. The specific objectives

were: 1) to describe the physical and chemical characteristics of 20 volcanic crater lakes in

western Uganda; 2) to develop predictive models of their primary productivity (chlorophyll a)

using their physico-chemical and morphometric features; 3) develop an appropriate aging criteria

for one of the tilapia species (Tilapia zillii) over their entire exploited age range using thin-

sectioned and polished otoliths; 4) validate the annual increments formed in Tilapia zillii otoliths

in one crater lake (Nkuruba) using marginal- increment analysis; 6) develop and compare growth

models for Tilapia zillii among Crater Lakes exposed to different levels of exploitation (fishing

pressures).

METHODS AND MATERIALS

Study area:

The study was conducted in the Kasenda cluster of crater lakes of western Uganda

(0o230 – ׳o33׳N, 30o1030 - ׳o 20׳E), sitting at an altitude of 925 to 1520 m, approximately 20-

30 km south of Fort Portal town (Figure 3).

The area is characterized by rolling hills, and is part of the extensive Precambrian

basement complex (Government of Uganda, 1967), through which the explosion craters

were blown approximately 11,000 years ago. Although historically forested, much of the

land is now used for subsistence farming (e.g., banana fields, and tea plantations that support

the growing human population). The lakes sit within crater kittles with very steep walls that

were originally forested. Most of the crater lakes are small, with surface area ranging from

8

0.02 km2 (Lake Nyanswiga) to 0.50 km2 (Lake Ntanda). Maximum depth ranges from 5 m

(Lake Kifuruka) to 259 m (Lake Ntanda) with the majority of the lakes being greater than 30

m deep. Mean depth of the lakes varies from 2.9 m (Lake Kifuruka) to 59.7 m (Lake

Rukwanzi). Owing to their volcanic origin, most of the lakes have steep sides, with little

littoral vegetation. However, some of the shallow lakes or shallower parts of the deeper

lakes do have a diversity of aquatic macrophytes such as Nymphaea spp., Ceratophyllum

spp., and Potamogeton spp. (Kizito et al., 1993).

Climate, vegetation and soils:

The climate of the crater lakes region is markedly seasonal with regard to

precipitation. Mean annual rainfall in region during the period 1903-2000 was 1543 mm

(Chapman and Chapman, unpublished data). Rainfall is bimodal, with two wet seasons

from March-May and September-November (Chapman and Chapman, unpublished data).

For the period 1990-1999, the mean daily minimum temperature was 15.5oC, and the mean

daily maximum temperature was 23.7oC (Chapman and Chapman, unpublished data). The

soils in the catchments of the crater lakes have volcanic ash and lavas which have been

weathered to produce very fertile brown soils (Uganda Department of Lands and Surveys,

1965). The natural vegetation in this area is low-altitude mountain forest (Langdale-Brown

Figure 3: Map of Kasenda cluster of crater lakes in western Uganda (0o230 – ׳o33׳N,

30o1030 - ׳o 20׳E) showing the lakes sampled during the present study (2004-2005).

9

et al., 1964; Taylor et al., 1999). But outside of the National parks, widespread land

clearance has replaced natural forest and shrub, dominated by small-scale plots of bananas,

maize, cassava, and millet as well as settlements, with some larger scale plantations of pine,

cedar, Eucalyptus and tea.

METHODS:

Intensive survey:

We successifully completed an intensive survey of the lakes between July 2004 and

July 2005. Each lake was sampled once over a period of 5-6 days during the one year

period.

Figure 4: Principal Investigator, Jackson Efitre, during field survey of the lakes before

commencement of the study. Some of the crater lakes have recently been restocked by the

Government of Uganda under the Plan for Modernization of Agriculture (PMA).

Bathymetry and morphometry:

To determine the percentage of the lake that occurs at each depth, 6-15 transects

were established in each crater lake depending on the size of the lake. Transects were

spaced more or less evenly over the entire lake, and their orientation determined using land

features or vegetation. The point of origin and destination of each transect were mapped

with a Germin GPS 12 unit. Depth profiles were obtained along each transect using a depth-

sounder (EAGLE) to determine maximum depth (Zmax.) and mean depth (z) of each lake.

Lake area (LA) and catchment areas (CA) was also mapped using GERMIN GPS 12 unit.

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Lake area, the greatest length on the lake surface (l), and the greatest width (b) of the lake

perpendicular to the greatest length of the lake were calculated using ArchGIS (version 9.1).

Environmental data

We took advantage of the existing baseline environmental data for a large number of the

crater lakes (Chapman & Chapman unpublished data). The lakes were therefore selected to cover

a large range of area with varying anthropogenic disturbances, and also to represent as broad an

environmental gradient as possible. The environmental characteristics determined in each lake

included lake morphometry, vertical profiles of dissolved oxygen concentration and water

temperature, pH, electrical conductivity, water transparency, chlorophyll a concentration, total

nitrogen, and total phosphorus.

One station was established at approximately the center of each lake. Mid-day dissolved

oxygen concentration and temperature measurements were taken in situ through the water column

at 1 m intervals up to 30 m using a portable YSI oxygen/temperature meter (Model 57). Water

samples for conductivity and pH measurements were collected from 0 m to 30 m at 2 m intervals

using a 3 liter Van Dorn water sampler. The samples were stored in Nalgene bottles covered with

duck tape. The bottles were first washed with distilled water and then rinsed with lake water prior

to sample collection. Conductivity and pH were determined in the laboratory on the same day of

sampling using a YSI (model 30) Conductivity meter and an OAKTON pH Testr 1, respectively.

Water transparency was estimated with a 20-cm Secchi disk with readings averaged over two

measurements. Chlorophyll a was determined by filtering 200 mls of lake water through

Whatman GF/C filters (particle retention size 47 mm). Chlorophyll a samples were kept in a

freezer at -20oC until analysis at the Zoology Department, Makerere University. In the laboratory,

chlorophyll a was extracted with 90% methanol solution during a 24 h period. Chlorophyll a

concentration was then determined using HACH 2010 DR Spectrophotometer. For total

phosphorus and total nitrogen analyses, water samples were collected from 0 m – 30 m at 6 m

intervals at the same station and placed in 0.3 liter plastic bottles previously washed with dilute

Hcl and then rinsed with lake water. In the field all samples were stored in a cooler containing

dry ice and later refrigerated until the time of laboratory analysis at the Fisheries Resources

Research Institute (FIRRI), Jinja, Uganda.

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Fish life history characters:

Fish collection:

Tilapia zillii and Oreochromis leucostictus samples were obtained between July

2004 and July 2005, from 19 crater lakes in western Uganda (Figure 3) over a 5-6 day

period for each lake. Significant effort was made to sample fish in different size classes by

using a variety of fishing gear, including: 1) Two experimental monofilament gillnets each

with 4 panels 60 m long and 1.0 m deep with stretched mesh sizes of 25.4 mm, 50.8 mm,

76.2 mm, and 101.6 mm; 2) artisanal local fishermen nets comprising of mesh sizes 25.4

mm, 50.8 mm, 63.5 mm, and 76.2 mm with a length of 1,800 m; 3) metal minnow traps (450

mm long and 7-mm square wire mess). The nets were set between 1400-1600 hrs and pulled

the following morning between 0800 hrs – 1000 hrs covering all major habitats such as open

water, at the edge of shoreline vegetation, and rocky shores to obtain samples representative

of fish populations in each lake. The minnow traps, baited with bread, were set at the

shallow littoral areas to obtain additional samples of smaller size class fish.

To determine size frequencies, all fish collected were measured in the field for

maximum total length (TL) using a wooden measuring board calibrated in cm units. The fish

were then classified into size classes (0 -5 cm, 5-10 cm, 10-15 cm, 15-20 cm 20-25 cm, 25-

30 cm, and 30-35 cm) and fish in each size category put in separate labeled plastic buckets.

A subsample of fish was then obtained from the different size classes by randomly selecting

individuals from the different plastic bucket and the selected fish measured to the nearest 1.0

mm for total (TL) and standard (SL) lengths, and their wet weights (total, eviscerated,

degutted, and gonad) determined to the nearest 0.1 g using Ohaus hand-held electric weigh

scale (Model HH320, capacity 320 g). The weight of fish greater than 320 g was determined

using a spring balance (capacity 5000 g). Fish were finally sexed by internal examination

of their gonads.

Length-weight relationship was described by the power function: W = aLb

Where; W = weight, L = length, a = a constant representing the nutritional condition of the

fish, and varies with the geographical regions and with the gonadic development phases and

b = an exponent usually ranging from 2.5 – 3.5, that describes the curve of the relationship

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(e.g., a fish growing isometrically or maintaining the same shape across length categories

will have an exponent of 3)

Age and growth of tilapia in the crater lakes:

The examination of the periodic increments deposited in calcified structures such as

scales, vertebrae, fin rays, spines, otoliths or other bonny structure like opercles, clavicles,

and cleithra are generally used to estimate fish age (Chilton and Beamish, 1982). Among

these structures, the analysis of otolith structure remains the most effective procedure, and

counts of annuli obtained from sections of sagittal otoliths are the most accurate method for

estimating fish age in many fish species. Otoliths or “ear bones” are small calcified

structures found within fish heads, which help fishes in detecting sound in water and are

used for balance and orientation (Compana and Neilson, 1985; Popper et al., 2003). In

addition to their physiological functions, otoliths are also biological structures very

important to fishery science and fishery resources management because they act as “natural

data loggers” recording information (e.g., age, growth, movement patterns, and habitat

interactions) in their microstructure. There are three pairs of otoliths in the inner ear of a

fish. The sagittae (Figure 5) are the largest pair and are the most commonly used otolith pair

for age determination. Age-and-growth data derived from otolith-based research in

combination with other ecological data are thus important for illuminating fish life history

information. This is critical to the development of fisheries management strategies that will

not only promote sustainability of fish stocks but also help protect aquatic environments.

A B

Figure 5: Sagittal otoliths of Tilapia zillii from Crater Lake Nkuruba, western Uganda. A

is distal view and B is dorsal (medial) view. (Photo by Jackson Efitre using Motic® Image

System Version 1.3, Mag.X2).

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In each Crater Lake, sagittal otolith pairs from the subsampled fish in different size

classes were removed through an incision on the cranium just below the eyes. The otoliths

were wiped with small hand towels to remove attached membrane tissue, air-dried, and

stored in labeled plastic sample vials until processed for age determination. In the

laboratory, the left sagittal otolith from each fish was secured to a frosted glass slide using

melted Thermoplastic Quartz Cement (the right sagittal otolith was processed if the left

otolith was unavailable). The mounted otolith was then thin-sectioned (~0.5 mm thick)

through the core region of the otolith along a transverse plane using a Buehler® Isomet 1000

digital sectioning saw (Buehler® IL) with a diamond wafering blade (7.6 cm diameter X

0.15 mm blade width) at a speed of 300 rpm.

Thin sections of the otolith were then rinsed in ethyl alcohol followed and in water

and then mounted on glass slides with crystal bond. Four hundred micron Gatorgrit® wet-

dry sandpaper was used to grind the otolith sections down to the core on one side. The

samples were then finished by polishing with 0.3 micron alpha Buehler® micropolish

alumina polishing powder paste. Otolith sections were examined with a MEIJI EMZ-TR

stereomicroscope (10 - 40 X) using transmitted light to count opaque growth zones. A

combination of one narrow opaque zone (dark brownish) with one broad translucent zone

(clear light brown) was interpreted as a complete annulus under transmitted light (Figure 6).

The narrow opaque zone was counted to determine age of fish.

1 2

3 4

5

6

7

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Figure 6: Photograph of a transverse otolith section from Tilapia zillii from Crater Lake

Nkuruba, Western Uganda. Opaque zones are narrow dark-brown areas and were

counted for age determination (Photo by Jackson Efitre, using Motic® Image System

Version 1.3, Mag. X2, under transmitted light).

RESULTS & DISCUSSION:

Environmental data:

Lake morphometry:

Crater Lake Ntanda is the largest, followed by Lake Mwamba and Lake Nyabikere

(Table 1). Surface area of the crater lakes ranges from 0.02 km2 (Lake Nyanswiga) to 0.50

km2 (Lake Ntanda). The Lakes also differ in maximum depth, which ranges from 5 m (Lake

Kifuruka) to 259 m (Lake Ntanda). Mean depth of the lakes varies from 2.9 m (Lake

Kifuruka) to 59.7 m (Lake Rukwanzi). The ratio of “maximum length: maximum width”

for Crater Lakes Mwamba, Nyabikere, Nyanswiga, Nyinabulitwa, Rukwanzi and

Wandakara were far from one, indicating irregular shape. The ratio of “maximum length:

maximum width” for the rest of the lakes was close to 1, indicating a nearly circular shape

(Table 1). Values of “mean depth: maximum depth” ratio for crater lakes Kanyamukale,

Kanyango, Kasenda, Kifuruka, Lugembe and Wandakara were greater than 0.5, indicating

an elliptical parabola basin shape. For the rest of the lakes, the “mean depth: maximum

depth” ratios were lower than 0.5, indicating conical basin shape (Table 1). Some of the

morphological features of the crater lakes are presented in Table 1.

Table 1: Some morphometric features of the Kasenda cluster Crater Lakes, western Uganda.

Crater Lake Altitude (m)

Area

(Km2)

Max. depth

Zmax (m)

Mean

depth

(m)

Ratio

Z:Zmax

Max.

length

l (m)

Max. width

b (m)

Ratio

l:b

Kanyango 1274 0.15 68 37.1 0.54 491.51 374.35 1.31

Kanyamukale 1161 0.02 12 6.8 0.57 169.84 166.37 1.02

Kasenda 1240 0.06 14 8.2 0.58 283.43 288.53 0.98

Kerere 1187 0.28 77 36.6 0.48 734.52 533.99 1.38

Kifuruka 1404 0.15 5 2.9 0.60 519.63 388.83 1.34

Lugembe 1290 0.08 18 10.5 0.58 421.83 260.50 1.62

Lyantonde 1396 0.14 187 42.6 0.23 456.95 440.30 1.04

Murigarime 1196 0.22 57.5 25.3 0.44 708.69 588.53 1.20

Mwamba 1308 0.45 203 18.4 0.09 1059.20 603.39 1.76

Mwegenywa 1390 0.27 101 - - 860.00 - -

Nkuruba 1499 0.03 36.6 16.2 0.44 270.00 160.00 1.69

Ntanda 1343 0.50 259 80.2 0.31 917.43 698.42 1.31

Nyabikere 1393 0.44 56.7 19.8 0.35 951.49 602.39 1.58

Nyahirya 1434 0.03 97.8 30.4 0.31 220.43 192.04 1.15

Nyanswiga 1474 0.02 66 12.5 0.19 226.59 125.96 1.80

Nyinabulitwa 1426 0.41 182 37.9 0.21 969.59 624.52 1.55

Rukwanzi 1348 0.08 172 59.7 0.35 388.59 303.78 1.28

Rwankenzi 1160 0.18 61.8 30.1 0.49 674.16 402.46 1.68

Wandakara 1166 0.03 12 6.0 0.50 266.52 132.08 2.02

Limnological characters

Temperature profiles:

With the exception of Crater Lake Kifuruka (5 m), the temperature profiles of all the

other lakes show a strong vertical stratification of the water column. Thermocline depth

varied among the lakes, fluctuating between 8 and 20 m depth (Figure 7). Mid-day surface

water temperatures ranged from 240C in Crater Lake Lyantonde to 27.10C in Crater Lake

Kanyamukale (Figure 7). The temperature difference between the surface waters and near

bottom (shallow lakes) or 30 m depth (deep lakes) ranged from 10C in Lake Kifuruka to

4.20C in Lake Kanyango (Figure 7). The thermal stratification in the deep lakes can be

explained by the fact that wind action was dampened by the steep crater rims and the

vegetation (forest) that affects the water circulation patterns. This is common in highly

stratified lakes that are protected from wind action by the high rims and vegetation (Wetzel,

2001).

pH:

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The water of the epilimnion was alkaline in all lakes. Surface pH ranged from 7.5

(Lake Nyanswiga) to 9.3 (Lake Kerere; Figure 8). There was a gradual decline in pH with

depth in all lakes with some hypolimnion layers being slightly acidic in some lakes (Figure

8). The alkaline pH of the epilimnion is possibly a result of carbon-dioxide fixation by

photosynthetic phytoplankton whereas respiration and decomposition processes may

account for the pronounced decrease in pH of the hypolimnion.

Dissolved oxygen concentration profiles:

The euphotic zones of all lakes were highly oxygenated but dissolved oxygen

concentrations diminished with depth, with near anoxic conditions evident in the

hypolimnion of most crater lakes (Figure 9). Surface oxygen ranged from 9.5 mg/L in Lake

Kanyango to 4.7 mg/L in Lake Nyahirya. Crater lakes Kerere, Ntanda, Nyinabulitwa and

Rukwanzi had fairly high oxygen concentration in the entire water column up to about 20

m. The rest of the lakes had anoxic hypolimnion with wide variation in depth of oxycline

among the lakes from 4 m (Lake Mwamba) to 26 m (Lake Nyinabulitwa, Figure 9). Below

the oxycline, a strong smell of H2S (characteristic rotten egg smell) was constantly detected

while obtaining water samples in several of the lakes, indicating the predominance of

reducing conditions in the deeper parts of the lakes. Saturation of the upper water layers

can be explained by the high photosynthetic rates. In the hypolimnion, the decomposition

of organic material leads to depletion of available dissolved oxygen, hence the anoxic

conditions.

17

Figure 7: Temperature profiles for Crater Lakes of Western Uganda during the sampling

period (August 2004 to June 2005).

18

Figure 8: pH profiles for Crater Lakes of Western Uganda during the sampling period

(August 2004 to June 2005).

19

Figure 9: Dissolved oxygen concentration (mg/L) profiles for Crater Lakes of Western

Uganda during the sampling period (August 2004 to June 2005).

Electrical conductivity:

Electrical conductivity profiles show a gradient with conductivity gradually

increasing towards the deeper parts of the water column (Figure 10). Conductivity varied

widely among the crater lakes. The highest conductivity vales were recorded in Lake

Wandakara, whereas Lake Kerere had the lowest conductivity values (Figure 10).

Differences in conductivity can be accounted for by different levels of deforestation along

the crater rims. Lake Wandara is completely deforested whereas Lake Kerere is sheltered

by intact forest. In addition, differences in sampling season may also account for the

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variation in conductivity levels as some of the lakes were sampled in dry season whereas

others were sampled during the rainy season.

Figure10: Conductivity profiles for Crater Lakes of Western Uganda during the sampling

period (August 2004 to June 2005).

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Water transparency:

Water transparency, measured by Secchi-disk depth varied widely among the lakes

(Figure 11). The lowest transparency value (0.5 m) was recoded in Lake Wandakara while

Lake Kerere had the highest transparency (4.8 m; Figure 11). Light extinction coefficient

was calculated for all the crater lakes, and the values ranged between 0.35 m-1 (Lake Kerere,

June 2005) and 3.47 m-1 (Lake Wandakara, June 2005). Introduction of particulate material

especially from erosion of deforested surrounding steep crater rims during the rain season,

largely contributes to the decreased water transparency in some of the lakes. In addition,

dense populations of phytoplankton (blue green algae, and green algae) may be responsible

for high turbidity and therefore low transparency of these lakes.

Figure 11: Water transparency (Secchi depth) of Crater Lakes of Western Uganda during

the sampling period (August 2004 to June 2005).

Chlorophyll a, total phosphorus, total nitrogen and productivity:

Mean integral value of chlorophyll a, total phosphorus, total nitrogen, and

productivity calculated over the euphotic zone are shown in Table 2. We performed

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multiple regression analysis to evaluate the effect of a suite of limnological characters on

variation in algal biomass (chlorophyll a) in the lakes. As expected, water transparency and

chlorophyll a concentration produced a highly significant negative relationship (Figure 12).

The regression equation for the model is: Log chl. a = -1.3738log Secchi + 1.697(R2 = 0.83,

p< 0.001). Statistically, the Secchi depth – chlorophyll a equation is highly satisfactory

because it explains 83% of the variation in chlorophyll a (algal biomass) in the lakes. This

suggests that water transparency is a good and robust predictor of algal biomass in the crater

lakes. Another important limnological variable that is well known for predicting algal

biomass in lakes is total phosphorus (TP). However, our results suggest a weak but positive

relationship between TP and chlorophyll a concentration (Figure 13). Total phosphorus

alone explains only 12% of the variation in chlorophyll a. The regression equation for the

model is: Log10 chl. a = 0.3736 log10TP + 0.569 (R2 = 0.12, p = 0.05). We also used

discriminant analysis to determine the suitability of a range of environmental and

morphometric charaters for classifying the lakes into deforestation categories (moderate,

severe, and complete, figure 14). 66.7% of the lake classifications were correct. Wilkins’

lambda statistic for the test of function 1 through function 2 was (Chi-square = 35.34, p =

0.035). After removing function 1, Wilkins’ lambda statistic for the test function 2 (Chi-

square = 13.19, p = 0.21) was not statistically significant. Based on the structure matrix

(Table 3), the discriminant functions most important for classifying the lakes were log

secchi and log conductivity.

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Table 2: Mean integral values of some chemical parameters in the crater lakes of western Uganda.

Lake Deforestation

Mean Chl. a

(µg/L) Mean TP Mean TN

TN:TP

ratio Productivity

Kanyamukale Complete 38.57 587.8 6994.5 11.9 2.97

Kanyango Severe 28.50 76.70 19387.2 252.8 2.65

Kasenda Moderate 16.68 81.6 19491.9 238.8 2.66

Kerere Minimal 9.04 580.9 14886.2 25.6 1.98

Kifuruka Severe 25.72 319.2 4908.6 15.4 1.78

Lugembe Severe 69.15 206.6 25877.1 125.2 4.52

Lyantonde Severe 10.19 44.3 1251.9 28.3 2.06

Marusi Moderate 21.89 79.5 72352.8 909.8 3.09

Murigarime Complete 20.85 71.8 19073.2 265.6 2.58

Mwamba Severe 23.28 37.3 1390.8 37.3 1.61

Mwegenywa Severe 9.50 113.7 3658.8 32.2 1.88

Nkuruba Minimal 18.77 426.4 14886.2 34.9 3.80

Ntanda Moderate 4.17 42.6 5140.0 120.8 1.19

Nyabikere Moderate 45.18 136.7 2536.7 18.6 2.22

Nyahirya Severe 18.42 33.8 2131.4 63.0 1.95

Nyanswiga Moderate 22.94 332.5 10373.1 31.2 2.57

Nyinabulitwa Moderate 8.57 30.3 10997.5 362.6 2.33

Rukwanzi Severe 3.82 41.6 49010.2 1177.8 0.98

Rwankenzi Severe 200.86 173.6 16770.3 96.6 7.84

Wandakara complete 171.67 107.6 96742.0 899.0 3.81

24

Figure 12: Scatterplot of the linear regression between the independent variable log10 Secchi

(transparency) and the dependent variable chlorophyll a for all Crater Lakes. Log chl. a = -

1.3738log secchi + 1.697(R2 = 0.83, p = 0.001)

Figure 13: Scatterplot of the linear regression between the independent variable Total

phosphorus and the dependent variable chlorophyll a for all Crater Lakes. Log10 chl. a =

0.3736 log10TP + 0.569 (R2 = 0.12, p = 0.05).

Table 3: Structure matrix of discriminant analysis

Parameter

Function

1 2

log secchi -.153(*) -.074

log mean depth .114(*) -.073

log CND .389 .424(*)

log TN -.030 .278(*)

log TP -.089 .223(*)

log Temp .010 .203(*)

log D.O .141 .195(*)

log max. depth .166 -.194(*)

log chl. a .110 .190(*)

log pH .137 .172(*)

log lake area .016 -.136(*)

25

Figure 14: Results of discriminant analysis, illustrating the ability to categorize Crater

Lakes of western Uganda using their physical, chemical, and morphometric characters.

Inputs to the discriminant analysis were log transformed values of (secchi depth, chl.

dissolved oxygen, conductivity, total phosphorus, total nitrogen, lake area, maximum

depth, and mean depth).

26

Life history characters

Population size structure:

We determined the size structure of T. zillii and O.leucostictus by examining the

size-frequency data for each species. With exception of Lake Kanyamukale, the modal size

classes from the gillnet fishery in all other lakes were 12-14 cm TL and 14-16 cm TL. The

larger size classes were under represented in the samples (Figure 15 a, b). Local fishermen

use dugout canoes (Figure 16), and their main fishing gear is gillnets, although hooks (angling) and

local fish traps are occasionally used. The sizes of nets mostly used by the fishermen are 2 inches

and 2.5 inches. For the intensive experimental fishing in 19 of the crater lakes, we used gillnets with

mesh sizes ranging from 1 inch to 4 inches. We caught very few fish in the 3 inch nets and less than

10 fish in the 4 inch nets for the entire duration of the study (Figure 5 a, b).

27

28

Figure 15: Length-frequency distributions

29

Figure 16: Dugout canoes used for the artisanal fisheries in the crater lakes, western

Uganda (Photo by Jackson Efitre).

Length-weight relationships:

Length and weight characteristics for each species, along with the parameter

estimates of the length-weight relationships, sample size, length-weight ranges (minimum

and maximum), standard error (S.E.), and the coefficient of determination (r2) are given in

Table 4. Linear regressions of length-weight were significant (p <0.001) for both species

in all lakes. Of the 14 crater lakes which had O. leucostictus, 7 of the lakes had r2 values

greater than 0.95. The other 7 lakes had r2 values greater than 0.80. With exception of

lake Wakenzi, all the 14 lakes that had T. zillii had r2 values greater than 0.95. Overall the

estimates of parameter b ranged from a minimum of 2.37 (S.E. = 0.11) in lake Ntanda to a

maximum of 3.07 (S.E. = 0.05) in Lake Murigarime for O.leucostictus compared to T.zillii

where b ranged from 2.55 (S.E = 0.78) in Lake Wakenzi to 3.11 (S.E. = 0.43) in lake

Nyinabulitwa T.zillii (Table 4).

Size at maturity and fecundity (reproduction):

Analysis of results in progress.

30

Table 4: Length-weight relationships (W=aLb) for the Oreochromis leucostictus and Tilapia zillii populations from 19 Crater

Lakes, western Uganda. Values of the regression coefficient (r2), slope of regression (b) and intercept of regression (log10 a)

are from the weight-length regressions computed with Fishery Analysis and Simulation Tools (FAST software version 2.0).

Mean Total length (TL), Mean weight (W), range, and standard errors of the mean (S.E.) were computed with SPSS 2.0 for

Windows. F = females, M = males, and A = all fish.

Crater lake Species Sex n Length (TL) characteristics Weight characteristics Parameters of the L-W relationship

Mean

(mm)

± S.E. Min. Max. Mean

(g)

± S.E. Min. Max. Log10a ± S.E.(a) b ±

S.E.(b)

r2

Kanyamukale O.leucostictus A 34 201.6 9.54 137 290 173.5 22.00 39.1 417.5 -4.66 0.11 2.96 0.05 0.99

Kanyango O.leucostictus M 177 142.8 0.83 65 165 49.2 0.73 5.1 82.4 -4.02 0.19 2.65 0.90 0.83

F 102 140.4 1.23 85 185 48.5 1.39 11.1 105.5 -3.71 0.37 2.51 0.17 0.68

A 280 142.0 0.76 65 201 49.3 0.81 5.1 162.0 -4.01 0.17 2.64 0.78 0.81

T.zillii M 106 127.8 2.52 79 197 42.3 2.44 8.9 134.5 -4.60 0.19 2.93 0.68 0.95

F 40 125.5 3.68 65 173 40.8 3.22 5.7 97.0 -4.58 0.14 2.93 0.09 0.96

A 162 123.6 2.10 65 179 39.6 1.94 5.7 134.5 -4.67 0.10 2.97 0.49 0.96

Kasenda O.leucostictus M 73 150.7 2.16 120 215 58.1 2.36 38.5 140.4 -3.85 0.23 2.57 0.12 0.89

A 94 148.2 1.78 120 215 55.3 1.95 30.4 140.4 -3.88 0.19 2.59 0.86 0.91

T.zillii M 66 123.0 3.17 79 204 37.0 3.04 8.8 153.3 -4.64 0.13 2.94 0.06 0.97

F 46 115.7 3.07 78 155 32.2 2.36 10.0 78.5 -4.92 0.15 3.09 0.74 0.97

A 114 119.4 2.28 78 204 34.5 2.03 8.8 153.3 -4.70 0.97 2.98 0.47 0.97

Kerere O.leucostictus M 80 106.7 5.90 70 162 24.3 3.39 5.8 51.6 -4.80 0.86 3.00 0.41 0.99

F 31 135.7 3.31 73 235 44.3 3.08 5.9 207.7 -4.64 0.11 2.92 0.05 0.99

A 123 122.6 3.15 62 235 35.6 2.45 4.1 207.7 -4.75 0.54 2.97 0.26 0.99

Kifuruka T.zillii M 134 137.4 1.27 85 180 48.4 1.19 10.4 99.8 -4.45 0.13 2.86 0.05 0.94

F 110 142.3 2.38 89 243 58.6 4.00 12.7 280.0 -4.54 0.11 2.91 0.53 0.97

A 244 139.6 1.29 85 243 53.0 1.94 10.4 280.0 -4.53 0.86 2.90 0.40 0.96

Lugembe O.leucostictus M 178 145.5 0.77 121 172 52.3 0.85 29.3 78.2 -4.09 0.18 2.68 0.81 0.86

F 107 138.2 1.54 105 205 49.0 1.89 26.6 165.4 -4.07 0.20 2.68 0.94 0.88

A 285 132.8 0.78 105 205 51.1 0.89 21.6 165.4 -3.90 0.28 2.60 0.59 0.87

T.zillii M 118 158.7 3.21 80 265 78.8 4.97 12.0 304.7 -4.52 0.55 2.89 0.25 0.99

F 90 136.9 1.66 115 222 50.2 2.25 27.5 199.5 -4.16 0.17 2.74 0.77 0.93

A 209 149.2 2.08 80 265 66.3 3.12 10.4 304.7 -4.34 0.59 2.81 0.03 0.98

O.leucostictus M 82 129.3 1.89 88 145 38.0 1.30 12.9 50.2 -4.33 0.11 2.79 0.54 0.97

Lyantonde F 68 149.0 2.00 89 149 49.7 1.70 12.8 115.9 -3.73 0.27 2.49 0.13 0.85

A 158 147.6 1.32 87 188 48.9 1.02 12.5 115.9 -3.48 0.16 2.39 0.75 0.87

T.zillii M 109 133.7 2.23 89 211 44.8 2.30 11.5 164.8 -4.69 0.83 2.97 0.39 0.98

F 61 141.3 1.22 145 184 46.6 1.33 51.5 116.5 -4.66 *** 2.95 *** 0.98

31

A 192 129.2 1.74 88 211 41.0 1.66 11.5 164.8 -4.67 0.53 2.96 0.25 0.99

Marusi O.leucostictus M 32 153.1 2.88 126 182 55.4 3.16 31.8 90.4 -4.56 0.24 2.88 0.11 0.96

A 63 153.4 3.04 101 275 58.0 4.39 23.4 280.9 -4.26 0.16 2.74 0.74 0.96

T.zillii M 70 126.8 4.10 78 270 44.9 8.79 8.3 349.7 -4.71 0.79 2.96 0.38 0.99

F 40 114.9 5.83 78 241 33.8 6.74 7.6 223.4 -4.66 0.98 2.95 0.48 0.99

A 119 121.5 3.65 78 270 39.8 4.39 7.6 349.0 -4.64 0.63 2.93 0.30 0.99

Murigarime O.leucostictus M 61 143.1 3.95 74 241 59.8 3.37 6.4 188.6 -4.68 0.10 2.95 0.47 0.99

F 53 135.4 11.7 72 340 61.2 2.76 6.0 617.3 -4.92 0.11 3.07 0.05 0.99

A 120 136.5 4.14 70 340 57.8 6.57 5.6 617.3 -4.80 0.66 3.00 0.31 0.99

Mwamba O.leucostictus M 145 144.4 0.36 53 179 55.5 0.75 2.4 89.8 -4.72 0.70 2.97 0.33 0.98

F 203 141.8 0.61 55 212 57.6 1.02 2.8 170.0 -4.44 0.94 2.85 0.44 0.96

A 353 141.6 1.38 46 212 52.5 1.01 1.8 170.0 -4.64 0.51 2.94 0.24 0.98

Mwegenywa T.zillii M 113 136.2 2.05 68 233 50.0 2.37 5.2 236.6 -4.94 0.63 3.09 0.29 0.98

F 221 144.7 1.82 71 215 60.4 2.08 6.1 162.7 -4.96 0.13 3.11 0.63 0.96

A 334 141.9 1.41 68 233 57.2 1.61 5.2 236.6 -4.92 0.60 3.09 0.28 0.97

Nkuruba O.leucostictus M 96 166.8 2.09 138 208 70.6 2.28 44.8 114.3 -3.65 0.15 2.47 0.68 0.93

F 113 156.9 1.72 130 262 63.7 2.60 42.4 284.2 -3.80 0.17 2.54 0.80 0.90

A 209 161.4 1.37 130 262 66.8 1.77 34.5 284.2 -3.61 0.12 2.46 0.52 0.92

T.zillii M 75 134.7 5.52 68 276 58.3 7.59 6.0 400.9 -4.70 0.50 2.97 0.24 0.99

F 112 156.9 1.72 130 262 63.7 2.60 42.4 384.2 -4.74 0.42 2.99 0.20 0.99

A 187 133.1 3.19 68 276 54.5 4.15 5.7 400.9 -4.72 0.32 2.98 0.15 0.99

O.leucostictus M 84 147.5 0.94 117 162 57.4 0.77 27.8 64.8 -3.45 0.25 2.37 0.11 0.84

Ntanda A 111 137.5 2.31 47 162 43.3 1.41 6.3 64.8 -3.83 0.13 2.54 0.06 0.94

T.zillii M 159 128.9 2.29 70 212 41.7 2.05 7.1 164.5 -4.64 0.63 2.94 0.03 0.98

F 144 130.1 2.36 69 222 44.7 2.34 6.7 156.8 -4.87 0.66 3.05 0.31 0.99

A 303 129.5 1.64 69 222 43.1 1.55 6.7 184.5 -4.75 0.47 2.99 0.22 0.98

Nyanswiga T.zillii M 194 145.9 2.46 60 259 58.4 2.74 6.6 243 -4.28 0.44 2.77 0.21 0.99

F 55 142.6 2.40 76 169 52.2 2.04 8.1 88.2 -4.35 0.18 2.81 0.84 0.96

A 295 138.6 2.55 35 270 55.7 2.58 0.7 299.5 -4.59 0.46 2.91 0.22 0.98

Nyinabulitwa O.leucostictus M 120 153.1 2.59 69 340 58.9 4.62 5.1 450.0 -4.64 0.16 2.92 0.72 0.93

32

F 56 147.9 5.15 68 252 56.8 5.33 5.3 212.1 -4.44 0.11 2.82 0.52 0.98

A 176 151.4 2.41 68 340 58.2 3.57 5.1 450 -4.51 0.94 2.85 0.43 0.96

T.zillii M 122 135.5 2.64 80 260 52.3 4.08 9.0 350 -4.92 0.12 3.08 0.56 0.96

F 56 119.1 3.66 70 219 38.9 4.10 6.6 180.5 -4.97 0.79 3.11 0.38 0.99

A 173 133.7 2.20 80 260 53.7 3.23 8.8 350 -4.98 0.92 3.11 0.43 0.97

Rukwanzi T.zillii M 76 154.1 3.77 75 222 70.0 4.76 7.5 180.9 -4.76 0.71 3.00 0.33 0.99

F 60 143.8 5.51 79 287 65.0 8.85 6.9 422.9 -4.78 0.88 3.01 0.41 0.99

A 144 147.6 3.17 75 287 66.2 4.51 6.9 422.9 -4.75 0.56 2.99 0.26 0.99

O.leucostictus M 68 144.7 0.79 132 161 52.3 0.85 41.5 68.3 -4.06 0.33 2.67 0.15 0.83

Wankenzi F 111 142.2 0.92 94 172 51.0 0.90 15.0 89.3 -3.77 0.22 2.54 0.10 0.85

A 181 143.1 0.64 94 172 51.4 0.64 15.0 89.3 -3.79 0.18 2.55 0.84 0.84

T.zillii M 76 154.1 3.77 75 147 70.0 4.76 7.5 173.4 -4.83 0.18 3.02 0.84 0.95

F 142 137.7 1.19 85 172 48.1 0.99 11.4 89.3 -3.79 0.16 2.55 0.78 0.89

A 218 143.4 1.61 75 222 55.7 1.91 7.5 180.9 -4.40 0.12 2.84 0.55 0.93

Wandakara O.leucostictus M 60 137.7 1.74 68 172 43.3 1.61 5.5 90.4 -4.48 0.25 2.85 0.12 0.91

F 58 143.7 3.03 66 238 54.4 3.87 4.1 234.4 -4.81 0.51 3.02 0.70 0.97

A 119 140.0 1.83 66 238 47.7 2.12 4.1 234.4 -4.74 0.12 2.98 0.56 0.96

T.zillii M 80 139.0 4.67 73 234 60.3 3.87 6.5 229.9 -4.79 0.76 3.01 0.36 0.98

F 85 116.7 3.94 73 212 36.9 3.84 6.6 164.2 -4.77 0.54 3.01 0.26 0.99

A 189 121.5 3.00 70 234 43.2 3.32 5.4 229.9 -4.83 0.04 3.03 0.19 0.99

33

Age and growth of Tilapia zillii in the crater lakes:

Despite the potential of otoliths (ear bones) for age estimation, relatively very few

studies have attempted to use otolith-based research for tropical fish species. This reflects,

in part, early unsuccessful attempts at otolith examination of tropical fish (Munro, 1983).

Moreover, lack of access to appropriate fish-aging laboratory facilities by many fisheries

biologists based in the tropics also contributed to scarcity of otolith research in the tropics.

As such, development of demographic data for tropical fishes was based on length-based

analysis of growth (Pauly, 1998), and the use of fish scales. However, length-based

analyses are misleading, particularly for long-lived species because of the decoupling of the

relationship between size and age (Hilbron and Walters, 1992). Furthermore, the use of

scale for ageing tropical fish has some disadvantages, mostly associated with re-absorption

of calcium from their structure especially in older fish.

Analysis of incremental structures that occur in otoliths is therefore valuable for

management of tropical fisheries. However, use of otoliths for aging fish species in tropical

environments has historically been considered to be extremely challenging due to lack of

perceived seasonality in tropical environments compared to temperate zones. In contrast to

these perceptions, most inland tropical aquatic ecosystems display seasonal variation related

to wind, temperature regimes, and fluctuations in rainfall (Lowe McConnell, 1987). These

seasonal fluctuations in the environments may therefore have influence on fish biology as

well as otolith and fish growth. Age-and-growth data derived from otolith-based research in

combination with other ecological data are thus important for illuminating fish life history

information. This is critical to the development of fisheries management strategies that will

not only promote sustainability of fish stocks but also help protect aquatic environments.

Recent studies have thus documented and validated seasonal growth variations in tropical

species in Africa, and particularly for cichlid fishes (Yosef and Casselman, 1995; Panfili and

Tomás, 2001; Egger et al., 2004). Seasonal factors influencing different growth increment

deposition on these otoliths are mainly correlated with the rainy season, even though

temperature variations seem to have an effect (Yosef and Casselman, 1995). Thus the

tilapias are suitable species in which to apply aging studies not only from a management

perspective, but also for economic benefits.

34

Since September 2005, I have conducted extensive ageing analysis of T.zillii from

some of the crater lakes in collaboration with Dr. Debra Murie at the fish-aging laboratory of

the Department of Fisheries & Aquatic Sciences, University of Florida. Tilapia zillii from

the crater lakes deposit faint but definable annuli on their otoliths (Figure 17). In the next

few months, I will continue to work with Dr. Murie to ascertain how many growth rings the

T.zillii deposit each year by using the technique of marginal increment analysis. In addition,

we will also model the growth of T.zillii by fitting the Von Bertalanffy growth function

(Ricker 1975) to individual fish lengths at each lake using a non-linear regression method.

We hope that the otolith ageing methodologies developed for T.zillii during the present study

will be applicable to other economically important fish species with the region. In

particular, we hope to apply the ageing techniques developed here to the Nile Tilapia and

Nile perch fisheries in Lake Victoria to obtain age-length relationships that will help in stock

assessments. Further details will be described in my Ph.D. thesis and publications.

A B

Figure 17: Tilapia zillii otolith cross-sections from Crater Lake Nyanswiga with 8 rings

(4 yrs. old?)(A), and from Crater Lake Nkuruba with 10 rings (5 yrs. Old?) (B). Photo by

Jackson Efitre.

Preliminary conclusion and future challenges:

Results from this study confirm deforestation as the major threat to the crater lake

ecosystems. There is therefore need to provide incentives to the communities surrounding

these lakes in order to discourage catchment degradation and help protect the fragile crater

lake ecosystems. To ensure this, government and the Fisheries Resources Department needs

to demonstrate to the local fishermen and existing community based organizations within

1 core

35

the region the long-term impacts of deforestation on the fisheries. If local fishermen can be

convinced that degradation of the lands surrounding the lakes will harm the fishery from

which they derive their livelihoods, then they will be more willing to seek alternative energy

sources elsewhere or to plant their own trees.

From the results, it is also clear that there is over-fishing in some of the crater lakes.

Majority of the tilapia caught from most of the lakes were small, and the net sizes mostly

used are 2.0 and 2.5”, which is contrary to the Fish and Crocodile Act. For stocks to be

sustainable, it is important to control the fishing effort and the sizes of nets used for

exploiting the fishery. Based on my meetings with stakeholders, it appears management of

the crater lake fishery resources through government authorities has not been entirely

successful. The existence of organized fishermen’s groups and local CBOs around some of

the lakes offers an opportunity for developing and applying mechanisms that involve local

communities in the conservation and management of the fishery resources in these lakes

through co-management strategy.

36

Publications:

In Preparation:

1. Efitre .J., L.J. Chapman, & Nordlie .F. - The effect of Anthropogenic Disturbances on

Primary Productivity of volcanic Crater Lakes of Western Uganda, East Africa.

2. Efitre .J., Murie .D., L.J. Chapman - Length-weight relationships of O.leucostictus

and T.zillii (Pisces: Cichlidae) in diverse tropical volcanic crater lakes, western Uganda.

Acknowledgements:

First and foremost, I would like to thank The Whitley Laing Foundation for

International Nature Conservation/Rufford Small Grant programme, UK for awarding me

the small grant in 2004 that enabled me to conduct this research. Second, I am indebted to

Prof. Lauren .J. Chapman and Colin .A. Chapman of McGill University, Canada for their

enormous and unreserved moral and logistical support during fieldwork at Kibale National

Park, western Uganda. Thirdly, I thank Dr. Debra Murie, Department of Fisheries &

Aquatic Sciences, University of Florida for her continued technical and material support

towards the ageing analysis in the her laboratory. Fourth, the contributions of various

institutions, particularly Fisheries Resources Research Institute, Uganda (chemical analysis),

Makerere University, Kampala, Uganda (Chlorophyll a, and fecundity analysis), Uganda

Wildlife Authority, Kibale research and monitoring staff (fieldwork), and Makerere

University Biological Field Station (accommodation), are also gratefully acknowledged.

Lastly, I remain indebted to James Kyomuhendo Abwooki, James Magaro Apuuli (field

assistants), John (the amazing fisherman), and many other volunteers without whose help

the field research would not have succeeded.

37

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