Afr. J. Ecol. 1994, Volume 32, pages 39-49

Factors influencing drift transport and concentration in a second-order high altitude tropical river in central Kenya

J U D E M. MATHOOKO* and KENNETH M. MAVUTI Department of’ Zoology, University of Nairobi, PO Box 301 97, Nairobi, Kenya

Summary The factors that influence drift transport and concentration in the Naro Moru River are recapitulated. The most important variables accounting for the highest proportions of drift transport variance are volume sampled by nets per 2 h (r=0.72, P<0.05; a f 95%CL= 1658.3 f 1388.1, b f 95%CL=2.2 f 1.0) and water velocity at net mouth ( ~ 0 . 7 1 , P<0.05; a f 950/cL= 1668.6 f 1469.7,

Starting with the factor with the greatest influence on drift transport, the following hierarchical order of factors is established: VOS, >VEL>DIS> VOS,>TRB>DOC>pH>CON>TMP.

b f 95%cL ~ 5 5 1 5 . 8 f 2439.8).

Key words: concentration, drift, factors, Kenya, river, transport

RCsumC On recapitule les facteurs qui influencent le transport derivant et sa concentra- tion dans la riviere Naro Moru. Les variables les plus importantes intervenant dans la variance des plus fortes proportions de transport derivant sont mesurkes par filets par deux heures (r=0,72, P<0,05; a f 95%, CL= 1658, 3 f 1388,1, b f 0,95% CL=2,2 f l,O), et la vitesse de I’eau a I’entree du filet (r=0,71, P<0,05; a f 95% CL= 1668, 6 f 1469,7, b f 95% CL=5515, 8 f 24393).

En allant du facteur ayant la plus grande influence sur le tranport derivant vers celui qui a la plus petite, on a etabli l’ordre hikrarchique suivant: VOS,>VEL>DIS>VOS,>TRB>DOC>pH>CON>TMP.

Introduction Drift is a passive and/or active movement which has velocity and direction components and is ethological in some aspects. It is essentially a ‘one-way ticket’ ecological movement unless where upstream migration is involved.

Drift transport (individuals passing a point per unit of time) and drift concentration (individuals per unit volume of water) (Statzner, Dejoux & Elouard, 1984) of zoobenthos are governed by a large number of factors. However, the interpretation of this widespread and ecologically important phenomenon has been beset with the failure to distinguish between the proximate cues and ultimate causes and many hypotheses have been advanced in putative explanation. The factors considered here permeate the drift literature and all of

*Present address: Department of Zoology, Egerton University, PO Box 536, Njoro, Kenya

40 J. M. Muthooko and K. M. Muvuti

them have been found to influence drift transport in diverse ways depending on the locale and the structure and function of the lotic ecosystem.

The Naro Moru River and the study site The Naro Moru River is a second-order (Strahler, 1957) river on the western side of Mount Kenya. It has a catchment area of about 83 km2 above the study site. The discharge of this river is of dual-peak nature and thus belongs to the 'I-Complex regime' according to the classification set up by Keller (1961). Physiographically, the river is composed of alternating pools and short, gentle riffles. The banks are lined with riparian vegetation which includes Oleu ufricunu, Podocarpus gracilior and Syzygium guineense. Coarse particulate organic matter (CPOM) from the riparian vegetation tends to accumulate on the bed sediment and forms debris dams at the pool exits (Pers. obs.). This allochthonous CPOM decays and releases humic acid and nutrients which are useful to the river biocoenoses as energy bases.

The study site was on a riffle which was about 0.08 km long (wetted area: 600 m2) (Fig. 1). The bed sediment was composed of gravel and pebbles.

Materials and methods Drift samples were collected from the Naro Moru River from 1986-87 using six nylon nets (105 pm mesh, 0= 16.0 cm., length= 105.0 cm., net mouth opening 0=6.7 cm.) placed along the channel cross-profile at 1200 h and samples collected every two hours for 24 h twice per month for twelve months at approximately two weeks intervals. The faunal contents of the nets were sorted, identified, enumerated and analysed.

Sunlight intensity was estimated just above the water surface using a photocell. The role of the moon was described in terms of phases (e.g. crescent (1/4), half-moon (1/2), gibbous (3/4), full-moon (1)) and these phase values were used in the simple linear regression analyses.

River water velocity and velocity at the net mouths were estimated using a float and a pitot tube, respectively. Discharge was estimated using the mid- section method and a flowmeter was used to determine the volume sampled by the nets. An improvised turbidimeter (Fig. 2), calibrated against an industrial turbidimeter Model 2 100A, was used to measure turbidity. A transparent plastic tube was sealed at the bottom with a piece of glass marked with a one millimetre thick cross (+) and a source of light was supplied below it. The whole structure was mounted on a one metre rule from where the height and Nephelometric turbidity units (NTU) of the water put into the tube could be read once the cross disappears.

Zoobenthos abundance was determined using 24 artificial substrate baskets (mean volume: 21 16.0 f 177.8 cm3; f SD) filled with smooth and rough black pebbles simulating the bed sediment and them implanted into the bed sediment. They were retrieved at random in sets of two at 2-day intervals for 24 days in a month. The organisms were sorted, identified and counted. For comparison with drift transport and concentration, each zoobenthic taxon found in the baskets at the time of maximum colonization was used.

D r f t transport in a Kenyun river 41

KENYA

Fig. 1. The location and map of the study site

The azide-modified Winkler method was used to determine dissolved oxygen concentration, with a correction factor of 1.28 for altitude. Water temperature was monitored using an ordinary mercury thermometer to the nearest 0.1"C and water temperatures by MadMin thermometers. Conductivity was determined

42 J. M. Mathooko and K. M. Mavuti

FRONT VIEW

NTL 5.5

64

6.9

7.9

9.2

11.0 13.8

184

27-6

55.2

-Marked piece of Light cm wood XK)

90

80 -Transparent 70 tube

60

50

40

f 30 Y % a t e r Clip outlet

SIDE VIEW

1 1

Support

5-1

P-i TOP VIEW

ng of wood round the light hole ansparent tube

Calibrated piece of wood

Pbwer cable I Cross [+I

Fig. 2. Plan of a simple turbidimeter. All measurements in centimetres

using a Hach conductivity meter and the pH using a digital pH meter (Model CG

The predator considered here is the resident rainbow trout, Oncorhynchs mykiss Walb. A total of 461 fish were analysed. Spearman’s rank correlation

a 1 aT).

Dr$t transport in a Kenyan river 43

coefficient (r,) was used to relate gut contents with the general drift transport. Simple linear regression analysis was used to relate drift transport of all taxa to predation and also to most of the factors considered in this study. A randomized block design analysis of variance (ANOVA) was used to test for variations in drift transport between the dry and wet seasons and as a result of net positions along the cross-profile. Stepwise multiple linear regressions were computed to determine the hierarchical order of the factors that influence drift transport.

Results The seasonal variations of the physical, chemical and biological variables are shown in Fig 3A-G. Drift concentration ranged from 0.19 to 0.42 ind.m - '. The most important variables accounting for the highest proportions of drift transport variance were volume sampled by nets per 2 h (rz0.72, P<0.05; a f 95%cL=1658.3 f 1388.1, b f 95%CL=2.2 f 1 .O) and water velocity at net mouth ( ~ 0 . 7 1 , PX0.05; a f 95%cL= 1668.6 f 1469.7, b f 95%cL= 5515.8 f 2439.8).

Whenever the zoobenthic abundance was high there was a corresponding increase in the drift transport (r,=0.76, W0.05) . The drift transport ( f 95%CL, n= 12) for daytime was 353.96 f 50.22, nighttime 435.17 f 74.60, dark nights 444.4 f 80.5 and for moonlit nights 425.9 f 71.4 ind. net - '.2 h ~ '. The null hypothesis (H,) that day and night drift catches came from normal populations with the same means and variances was tested by the F-test. It was revealed that the difference in the sample means was significant (F,,,,=7.89, W 0 . 0 5 ) and H , was thus rejected at the 5% level. The value of F for dark and moonlit nights was 0.08 showing that the means were not significantly different at the 5% level (DO-05) . Therefore H,, that there was no difference between drift transport means in dark and moonlit nights, is not disproved. Application of simple linear regression analysis on each variable against the drift transport of each taxa (Table 1) indicates that the variables had diverse effects. A randomized block design ANOVA indicated that there was a highly significant variation in drift transport between the seasons (F3,,=5.63, Pc0.05) and no spatial variation in the drift transport across the water column (F5,8=2.23, D0.05) . Most of the organisms drifted during the wet seasons rather than in the dry seasons.

No significant correlation existed between fish gut contents and the general drift transport (r,=O. 18, P>0.05). However, at the species and general levels, Diamesa ruwenzoriensis and Hydroptila indicated a significant relationship (see Table 1). Volume sampled by the nets was the main factor influencing drift transport (Table 2). The following hierarchical order of factors has been teased out (starting with the factor with the highest influence): VOS, >VEL> DIS>VOS,>TRB>DOC>pH >CON>TMP.

Discussion The influence of drift transport and, to a lesser extent, concentration by several factors, is evident. The fact that the night drift was higher than the day drift indicates that light intensity might be an important factor, corroborating studies carried out by Hughes (1970). However, some of the organisms were indifferent to light intensity. Generally, moonlight did not have a depressant effect on drift transport partially due to the intermittent occlusions of the moon. Similar results

44 J. M. Mathooko and K. M. Mavuti

- 5 -4 a= rnax.'Q' am b= rneap ',U T c = rnin. Q

. -

N 0 J F M A M J J A S Oi

1

In

200$

Max. temp.( 'C 1 range Min. temp. ('C 1

10

c

-1200 4 7,

-800 5 -600 g -400 2

-m U

t

In

t

-200 +

- 0 c L

Fig. 3. (A to D) (see legend opposite).

D r f t transport in a Kenyan river 45

I El +

1 1 I I

2000 Wet season Dry season Wet season

1000

0 N O J F M A M J J A S O

6B

N O J F M A M J J A S O 1906 1987

Fig. 3. (E, F, C)

Fig. 3. Graphs showing the limnological and biological changes in the Naro Moru River. (A) .--.=current velocity (m s - I ) ; F* S.D, n= 12; - - -=Mean ‘Q’. (B) 0--0 Turbidity (NTU). y* S.D, n= 12 Checked bars=mean monthly rainfall for Naro Moru Forest Guard Post. (C) Maximum, minimum temperatures and temperature range indicated. (D) III =Drift transport, 0 =Drift concen- tration, (E) 0--O=DOC; .-.=pH; .--.=Conductivity; A-A =Water temperature; V Start of the short rainy season; V Start of the long rainy season. (F) Seasons and zoobenthos abundances indicated. ( G ) @Indicates number of fish analysed, e.g. Nov. 54 fish.

46 J. M. Mathooko and K. M. Mavuti

Table 1. Regression values based on the linear model Y=a+bX for the various variables (independent) against taxon drift transport (dependent). Only the significant ones are indicated

Taxon Variable r a h n

Baeris spp.

Afronurus harrisoni (Brnrd.)

Dicercomyzon sp.

Caenis kungu (Kimm.)

Euthraulus elegans (Brnrd.)

Centroptilum sudajiricanum ( Lest.)

Simulium spp.

Limnophora sp. Diamesa ruwenzoriensis

Ceratopogonidae

Tanypodinae

Dicranota sp.

Chiniarrha sp

Hydroptila sp.

Hydropsyche sp.

LTI

DIS DOC

BEN TMP

VEL TRB LTI

DOC CON

PH vos,

vos ,

PH

PH

LTI CON

LTI CON

LTI CON

PH TRB TMP CON BEN PRE

TMP LTI

CON PH

VEL VOS,

DIS TRB BEN VEL DIS

BEN VEL DIS

TRB BEN

PRE LTI

DOC CON PH

TRB

PH

vos,

- 0,57* 0.63*** 0.48* 0.57* 0.60* 0,87*** 0.65* 0.57*

- 0 4 * - 0,54** - 0.84*** - 0.63*

0.64' - 0.70**

0.41*

0.63* - 0.68* - 0.65*

0.57* - 0.74**

0.62' - 0.57*

0.47* 0.58*

- 0.74** 0.79** 0.64, 0.73** 0.57*

0.75" 0.62*** 0.57** 0.59*** 0.50* 0.78** 0.45' 0.53** 0.80**

- 0.86***

- 0.84***

- 0,45* - 0.45' - 0.60***

0.57* - 0.40'

0.80** 0.73** 0.60*

- 0,67* 0.66'

- 0.55**

5258.1 191.8

1905.2 - 2561.2 - 6177.8

27.0 - 200.7 - 2680.9

82.8 73.2

792.4 1 1,650.9 14,501.7 27,280.6 - 52.1 497.9

1 1,759.3 21,707.8

10.7 - 284.5

744.3 - 8746.1 14,528.6

68.0 - 374.3 6191.0

140.2 118.1

- 127.2 18.0

1 126.2 - 1584.6

20.5 - 9.5 43.5 38.7 15.1 0.8 6.0

- 4.5 15.7 11.2 15.7 6.3

21.1 4.7

29.3 - 233.0

425.4 - 602.2

33.4

- 0.80 0.13

359.0 323.4 847.7

1 6 21.7

367.8 - 31.8 - 1.4 - 0.3

- 1268.6 350.2

- 35 15.4 0.02

- 0.3 280.5

- 2816.1 - 0.01

6.8 - 0.2 217.6

- 1830.5 4.4

50.7 - 135.4

0.9 10.3 10.6 0.01

- 25.5 212.1

53.5 0. I

16.7 1.5 0.6

16.1 6.2 1.3

- 11.3 - 3.8 - 0.5

1.0 - 0.01

1.3 0.0 1

30.6 - 9.1 83.8 - 0.9

12 24 24 12 12 12 12 12 24 24 12 12 12 12 24 12 12 12 12 12 12 12 12 24 12 12 12 12 12 12 12 12 24 24 24 24 12 24 24 12 24 24 24 12 24 12 12 12 12 12 24

Drqt transport in a Kenyan river 47

Table 1. Continued ~

Taxon Variable r a b n

H ydrophilidae Hydrachnellae

Neoperla sp.

Potamon jenneli

Dugesia sp.

Odonata

Hemiptera

Annelida: Hirudinea

Oligochaeta & Hirudinea (combined)

Isoptera (terrestrial) Culicidae Ancylidae Miscellaneous

VEL TMP CON

BEN BEN

DIS TRB LTI

CON BEN TRB

MON PRE LTI

DOC CON

PH BEN PRE LTI PH

BEN vos PRE PRE

PH

vos,

TMP LTI PH

- 0.51** 0.65*

0.76** 0,65* 0.73**

- 0.54** - 0.49* - 0.48* - 0.67*

0.57* - 0.60'

0.42* 0.41* 0.56*

- 0.92*** - 0.79**

0.65'

0.74** 0.78**

- 0.79** - 0.63'

0,61* - 0.42*

0.54* 0.90**

- 0.84***

- 0.96***

All insignificant All insignificant

0.81** 0,59* 0.75**

88.1 - 622.4 7416.3

10,408.9 105.1 - 2.5

14.8 7.1 8.1 8.9

- 196.2 2.8

- 0.01 0.1 0.1

1020.2 20,068.5 20,383.0 5 1,353.1

475.7 172.2

5.1 152.9

8.3 19.8 8.4

- 0.2

- 29.8 60.9

- 166.1 1404.9

1.5 1.9

- 0.01 - 2.1 - 0.2 - 0.004

4.7 - 0.04

0.03 1 .o 0.5

- 0.5 - 2220.1

489.6 - 6670.0

1.1 10.5 - 0.002 - 19.6

4.1 - 0.01

0.7 0.1

- 363.1 30.5

- 4097.6 549.8 55.5 0.03

24 12 12 12 12 12 24 24 24 12 12 12 24 24 12 12 12 12 12 12 12 12 12 12 24 12 12

12 12 12

n=the number of dta pairs. * P<0.05, ** P<O.OI, *** P<O.OOl Independent variables used here and hereinafter: VEL=water velocity at net mouth (ms I); DIS=river discharge (m3s- I ) ; CON=water conductivity (pS cm - I ) ; TRB= turbidity (NTU); VOS, =water volume sampled by net. 2 h - I (m'); VOS,=water volume sampled by net. 24 h I (m3); DOC=dissolved oxygen concentration (mg 1 I); TMP=temperature ('C); PRE=predation by fish; LTI=light intensity (pEs- I

m-'); pH=Hydrogen ion concentration; MON=moonlight (o=no moonlight, 1/4, 1/2, 3/4 & 1 =full moon); BEN=zoobenthic density (indset I )

have been obtained elsewhere (e.g. Fleituch, 1985; Statzner, Elouard & Dejoux, 1985).

Peak drift transport occurred during the rainy season, a time when the discharge was high with a consequently high current velocity. Whenever there- was a high discharge there was a corresponding increase in the general drift transport as surface crevices and instream organic matter structures, which act as refugia, were disturbed. The presence of a low drift concentration in the Naro Moru River was not an anomaly since they compare favourably with other estimates made in the tropics [0.1-1.9 in Ghana (Hynes, 1975) and 0.03-0.49 in

48 J. M. Mathooko and K. M. Mavuti

Table 2. Stepwise multiple linear regression. Dependent variable ( Y,,)=Drift transport (DRT 24 h I )

VAR (I ) r %VAR df ss MS F

1 TRB, pH, VEL, VOS,, DOC, VOS,, CON, DIS, (TMP) 0.783 61.3 (a ) 8 433,658.7 54,207.3 2.97*

(h ) 15 274,154.0 18,276.9 2 TRB, pH, VOS,, DOC, VOS,, DIS,

VEL. (CON, TMP) 0.783 61.2 (a) 7 433,443.8 61,920.5 3.61’ (h ) 16 274,368.9 17.148.1

3 TRB, VEL. VOS,, DOC. VOS,, DIS, (pH, CON, TMP) 0,778 60.6 (0) 6 429,089.4 71,514.9 4.36**

(h ) 17 278.723.3 16,395.5 4 TRB, VOS,, DIS. VOS,, VEL. (DOC,

pH, CON, TMP) 0,770 59.3 (0 ) 5 419,935.6 83.987.1 5.25** (h) 18 287,877.0 15.993.2

5 VEL, VOS,, DIS, VOS, (TRB, DOC, pH. CON, TMP) 0.751 56.4 (a) 4 399,211.0 99,802.7 6,14**

(h ) 19 308.601.7 16.242.2 6 VOS,, DIS, VEL, (VOS,, TRB, DOC,

pH. CON, TMP) 0.745 55.8 ( u ) 3 394,904.7 131.634.9 8.41*** (h ) 20 3 12,908.0 15,6454

7 VEL, VOS,, (DIS, VOS,, TRB, DOC, pH, CON, TMP) 0.720 51.9 (a) 2 367.3566 183.678.3 11.33***

( b ) 21 340,456.0 16,212.2 8 VOS,, (VEL, DIS, VOS,, TRB, DOC,

pH, CON, TMP) 0.708 50.2 (a) 1 355,016.8 355,016.8 22,14*** ( h ) 22 352,795.9 16,036.2

Significant of F: *P<0.05, **P<O.OI, ***P<O.OOI; rr=regression df, h=residual df; ‘%VAR. =percent variability accounted for by the variables in the equation; VAR (I)=independent variables; (CON, TMP etc.)=variable(s) not included in the equation; Abbreviations of variables as in Table I .

Florida (Cowell & Carew, 1976)l. One of the major effects of the dry seasons on stream biocoenoses is the expected overall decrease in the potentially colonizable habitats due to the reduction in river discharge. The presence of few drifters during the dry season might have been due to the presence of an interstitial water-film about 10cm. below the exposed sediment in which most of the zoobenthos passed the dry season.

Although the relationship between drift transport and benthos has been indicated, it is difficult to hypothesize that the significant relationships are as a result of the high zoobenthic density or as a result of the taxon’s greater propensity to drift. I t is also difficult to relate the zoobenthic density of a given sediment area with drift transport at a particular time since some of the drifters come from considerable distances upstream. The presence of significant relation- ships between drift transport of some taxa and benthic density in the present investigation is in line with other observations elsewhere (e.g. Lehmkuhl & Anderson, 1972). Some of the taxa did not have any significant relationship with the factors analysed (e.g. Culicidae and Ancylidae). Their drift might therefore be influenced by other factors within or outside them.

The lack of a significant relationship between predation, as determined by the gut contents, and drift transport is not surprising since similar results have been

Drqt transport in a Kenyan river 49

obtained by Chaston (1969). This could be explained partially by the fish foraging zoobenthos from the epilithic biofilm at the top of the bed sediment. These organisms subsequently become drift but do not necessarily indicate that the fish feed solely on the drift. Nevertheless, there were some taxa which showed significant relationships between predation and drift transport. Isoptera, though of terrestrial origin, showed a significant relationship.

A whole spectrum of factors can, and indeed have been shown to, influence drift transport. Some factors seem to be complementary, accessory and/or auxiliary. The heterogeneous nature of drift makes it difficult to explain it in terms of one or even a few factors. Differing results have been obtained by different workers (see Statzner, Dejoux & Elouard, 1984). This cannot be dismissed as “ecological noise” (Brittain & Eikeland, 1988) since such variations in results are obviously expected if different locales and streams are used. It is suspected that the failure to adduce clear-cut factors that influence drift transport is due to the heterogeneous nature of drift and the between-species and even within-species differences in responses towards the many factors that influence drift. This paper has attempted to bring forth in a hierarchical manner the factors that might influence drift transport and concentration but it is not an easy affair. Ingenuous research is required to tease apart “invisible factors” within the zoobenthos. Only then can the combination factors influencing drift transport and concentration be assessed accurately.

Acknowledgments We thank the National Council for Science and Technology for financial support (Grant No. NCST/5/003/D/38) and the management of the Naro Moru River Lodge for unrestricted access to the Naro Moru River. Thanks also to Jamhuri N. Mathooko for his field assistance. References BRITTAIN. J.E. & EIKELAND, T.J. (1988) Invertebrate drift-A review. Hydrohiologh 166, 77-93 CHASTON. I. (1969) Seasonal activity and feeding pattern of trout. Siilrrro trutta L. in a Dartmoor stream

COWELL, B.C. & CAREW, W.C. (1976) Seasonal and die1 periodicity in the drift of aquatic insects in a

FLEITUCH. T. JR. (1985) Macroinvertebrate drift in the middle course of the River Dunajec (Southern

HUGHES, D.A. (1970) Some factors affecting drift and upstream movements of Gummurus pules. Ecology

HYNES, J.D. (1975) Downstream drift of invertebrates in a river in southern Ghana. Fri~.rhic~at. Biol. 5,

KELLER, R. (I961 ) Gewusser und Wusserhciushiilt ties Fe.sr1unrie.s. Berlin. LEHMKUHL, D.M. & ANDERSON, N.H. (1972) Microdistribution and density as factors affecting down-

STRAHLER. A.N. (1957) Quantitative analysis of watershed geomorphology. Truns Am. geophys. Un, 38,

STATZNER, B., DEJOUX, C. & ELOUARD, J . M. (1984) Field experiments on the relationship between drift and benthic densities of aquatic insects in tropical streams (Ivory Coast). I. Introduction: review of drift literature, methods, and experimental conditions. Rev. Hydrubiol. Trop. 17, 319-334.

STATZNER, B., ELOUARD. J.M. & DEJOUX, C. (1985) Field experiments on the relationship between drift and benthic densities of aquatic insects in tropical streams (Ivory Coast). 11. Cheumutopsycliefiili~~~ru (Trichoptera: Hydropsychidae). J. Anbn. Ecol. 55, 93- 110.

in relation to availability of food. J. Fish. Res. Bd. Con. 26, 2165-2171.

subtropical Florida stream. Freshwt . Biol. 6, 587-.594.

Poland). Acta Hydrohiol. 27, 49-61.

51, 301-305.

515-532.

stream drift of mayflies. Ecology 53, 661-667.

9 13-920.

(Manuscript accepted 22 March 1993)