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Spatial variation of nutrients and primary productivity in the Rufiji Delta mangroves, Tanzania


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Spatial variation of nutrients and primary

productivity in the Rufiji Delta mangroves,


A Minu , J Routh , JF Machiwa & S Pamba

To cite this article:

A Minu , J Routh , JF Machiwa & S Pamba (2020) Spatial variation of nutrients

and primary productivity in the Rufiji Delta mangroves, Tanzania, African Journal of Marine

Science, 42:2, 221-232, DOI: 10.2989/1814232X.2020.1776391

To link to this article: https://doi.org/10.2989/1814232X.2020.1776391

© 2020 The Author(s). Co-published by NISC Pty (Ltd) and Informa UK Limited, trading as Taylor & Francis Group

Published online: 11 Aug 2020.

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African Journal of Marine Science is co-published by NISC (Pty) Ltd and Informa UK Limited (trading as Taylor & Francis Group)

Spatial variation of nutrients and primary productivity in the Rufiji Delta

mangroves, Tanzania

A Minu1 , J Routh2* , JF Machiwa1 and S Pamba1

1 Department of Aquatic Sciences and Fisheries, University of Dar es Salaam, Dar es Salaam, Tanzania 2 Department of Water and Environmental Studies, Linköping University, Linköping, Sweden

* Corresponding author, e-mail: joyanto.routh@liu.se

Determinations of spatial and temporal variations in organic matter and nutrient dynamics in water and sediments are crucial for understanding changes in aquatic bodies. In this study, we (i) determine the spatial dynamics of dissolved inorganic nutrients, during the transition from the dry to the rainy season, and (ii) provide future productivity predictions for the Rufiji Delta mangroves, Tanzania, based on the input of various nutrients. Water samples were collected from six locations, three times per year between April 2012 and January 2014, and analysed for dissolved nutrients, total organic and inorganic carbon, chlorophyll a, chlorophyll b and total carotenoids. The prediction of future net primary productivity in the Rufiji mangroves was undertaken using the software STELLA. The mean nutrient concentrations were of the order: nitrate > phosphate > ammonium > silica > dissolved organic carbon. The study revealed that high nutrient concentrations occurred in the northern part of the Rufiji Delta as a result of anthropogenic influence in the watershed. Modelling of nutrient inputs into the delta indicated enhanced primary productivity, which is expected to increase the vulnerability of water quality in the near future due to eutrophication.

Keywords: biogeochemical processes, chlorophyll, estuary, modelling, N:P ratios, nutrient input, plant pigments, water quality

Nutrients such as nitrogen, phosphorus, silica and sulphur are elements essential for plants and they are routinely monitored during water-quality assessments. When the input of nutrients is excessive it leads to an increase in aquatic productivity, leading in turn to eutrophication that can disrupt light penetration, temperature and oxygen levels in the water column (Rathore et al. 2016), and eventually to hypoxia (Zhu et al. 2017), forming a ‘system’ characterised by little ecological function. Nutrients in the marine system are cycled by biological pumping (Meyer et al. 2016), whereby plankton extract the dissolved nutrients in the water column and fix them in the organic matrix. When these organisms die, they sink and decompose, and the nutrients are once again released into the water column. For most nutrients, such as phosphorus, iron and silica, their external supply (i.e. source) is limited to atmospheric deposition and/or coastal and riverine inputs, whereas their main sink is the sedimentation of particulate matter. Nitrogen, however, has an additional biological source, namely fixation of N2 gas, together with the biological sink

involving denitrification and anaerobic ammonium oxidation (Risgaard-Petersen et al. 2006). The abundance of these nutrients and their bioavailability determines the productivity and trophic status of a water body.

The distribution of nutrients in estuarine waters is largely controlled by various biological, physical and chemical processes (Arndt et al. 2011) that are specific to the environment. However, anthropogenic inputs can generate a nutrient imbalance in the ecosystem, and such changes

can lead to different ecological consequences, such as a decrease in dissolved oxygen concentration as a result of increased biological oxygen demand (BOD). Since nutrient inputs in coastal areas have been increasing and this is most likely to persist in the future (van der Struijk and Kroeze 2010), it is particularly important to understand the relationship between nutrient loading and the extent to which these interactions may affect primary and secondary production in coastal ecosystems.

The Rufiji River in Tanzania forms the largest estuarine system along the western Indian Ocean, covering about 100 000 km2 (Kaaya 2019). However, this system has

received limited attention in past decades despite the fact that environmental investigations have indicated various problems associated with urban development in the upper catchment of the river. Some early studies from this region include that of Tafe (1990), who assessed the abundance of zooplankton in the Rufiji waters and its relation to salinity. Erftemeijer and Hamerlynck (2005) reported mortality in mangrove trees following severe and prolonged regional

El Niño events. Monga et al. (2018) reported the loss of

mangrove cover (~12.4%) in the Rufiji Delta during the past 24 years. While these studies consider flora and fauna in the Rufiji Delta, there is little information about ongoing biogeochemical processes in nearshore sediments or the water column, and their future outlook. Some of the studies are outdated and a fresh look at some of these issues is warranted. This work is aimed at (i) investigating the spatial dynamics of dissolved inorganic nutrients, during the Introduction

Open Access article distributed in terms of the Creative Commons Attribution License [CC BY 4.0] (http://creativecommons.org/licenses/by/4.0)


transition from the dry to the rainy season, and (ii) predicting future productivity in the Rufiji Estuary based on nutrient inputs and modelling. Hence, we focused on biogeochemical processes, climate variability and the influence of anthropogenic activities in the Rufiji Delta. Field surveys and subsequent modelling captured the spatial and temporal distribution of nutrients and future productivity trends in the Rufiji Delta. These investigations complement recent studies on the distribution of trace metals in the Rufiji sediments (Minu et al. 2018) and the accumulation of contaminants in a telescoping food-chain in estuarine waters (Shilla and Routh 2017; Shilla et al. 2019). The new information generated will help in the management of natural resources in terms of land-use practices, productivity changes in aquatic bodies, pollution and urban development in the catchment.

Materials and methods Study area

The Rufiji River is located between 39°07ʹ E, 7°41ʹ S and 39°45ʹ E, 8°15ʹ S in the tropics of East Africa and drains into the western Indian Ocean (Figure 1). The Rufiji River system consists of four components: a drainage basin, an alluvial valley, a deltaic plain, and a receiving basin. The deltaic plain, which is near the Indian Ocean margin, is approximately 23 km wide and 70 km long. The delta covers a total area of 532.55 km2 and hosts the largest continuous mangrove cover

along the East African coastline (Erftemeijer and Hamerlynck 2005). Currently, three main tributaries, namely the Ruaha, Kilombero and Luwengu rivers, feed the Rufiji River system (Maganga et al. 2004). The Rufiji system comprises seven main rivers, each interlinked by a series of minor channels. However, these rivers have changed their course several times in recent history. Thus, the Kikunya, Kiomboni and Bumba rivers are beneficiaries of freshwater inflow diverted from the Usimbe and Jaja rivers. The diversion of streams (Erftemeijer and Hamerlynck 2005) allowed people living in the northern part of the delta to expand their agricultural holdings. Unfortunately, this has resulted in rampant and illegal deforestation of the mangrove forests, as reported by the Kibiti Mangrove Office (S Malima, pers. comm.).

We selected six sampling stations in the mangrove forest based on the dominant mangrove species, tidal influence, and proximity to anthropogenic waste-disposal sites. The sampling sites in the northern part of the delta included station NR1 (dominated by Rhizophora mucronata), NR2 (Avicennia marina and A. alba), NR3 (A. marina) and NR4 (Heritiera littoralis). The central part of the forest was represented by one sampling station (CR1), where R. mucronata was the dominant species. The southern sampling station (SR1) was dominated by Sonneratia alba and A. marina; this site was influenced by tides and was inundated periodically.

Two surface-water samples (5 litres each) from each site were collected from the water column in pre-cleaned high-density polyethylene/polypropylene bottles (HDPE) and immediately placed in a cooler box for storage, before being transported to the laboratory for filtration and analysis. Sampling was carried out three times a year between April 2012 and January 2014. The samples were collected during high tides, and the sampling periods included the pre-southeast monsoon, post-southeast monsoon and

inter-monsoon seasons. The water samples were filtered using glassfibre (GF) filters for chlorophyll and carotenoid pigments. Nitrogen species and phosphorus were analysed within 48 h after sampling. The disks for chlorophyll and carotenoid analyses were stored in the dark (to prevent photodegradation), frozen at −4 °C, until further processing. Water samples (50 ml) for analysis of dissolved organic carbon (DOC) and silica were stored in a refrigerator.

Water-quality analyses

Temperature, dissolved oxygen, pH, salinity and conductivity in water samples were determined using a multi-parametric probe (YSI 556 MPS, USA). Measurement of these in situ parameters was done in triplicate. Standard methods were used to analyse ammonium (phenate), orthophosphate (ascorbic acid), silica (heteropoly blue) and nitrate (cadmium reduction) colorimetrically (APHA 2005). A correction was made for nitrite present in the samples by analysing without the reduction step. Standard curves were prepared with analytical-grade salts of ammonium chloride, anhydrous potassium dihydrogen phosphate, sodium metasilicate nonahydrate, and potassium nitrate; calibration blanks were measured during analyses. Dissolved carbon content in the water samples was measured using a total organic carbon (TOC) analyser (Shimadzu, TOC-5000); dissolved organic carbon (DOC) analysis was carried out in triplicate for each sample. The calibration curves for all nutrients showed R2 > 0.99.

Chlorophyll and total carotene analyses

Extraction of pigments from the GF filters was carried out using 90% acetone (Rysgaard et al. 1999). The extractions were conducted under dim light to reduce photodegradation. The GF filters were cut into small pieces and 12 ml of acetone was added, before the mixture was sonicated for 45 sec and centrifuged at 25 000 rpm for 25 min at 4 °C. The supernatant was separated and transferred into a 5-ml syringe using a Pasteur pipette (Sumanta et al. 2014). Absorbance was measured at 400–700 nm using a UV/ VIS spectrophotometer (Ultrospec 2100 Pro). Chl a showed maximum absorbance at 665 nm, Chl b at 650 nm, and total carotene at 475 nm; the measurements were repeated three times. The amounts of Chl a, Chl b and total carotene were calculated according to Eqn 1 (Sumanta et al. 2014). All analytical reagents used during the extraction process were of AR grade (Merck). A quartz cuvette (1 cm2) was used and

the corresponding solvent was used as a reference during spectrophotometric measurements. Thus:

Ca = 11.75 A1 – 2.350 A2

Cb = 18.61 A2 – 3.960 A1

Cx+c = 1 000 A3 – 2.270 Ca – 81.4 Cb/227 (1)

where Ca = concentration of Chl a; Cb = concentration of Chl b; Cx+c = concentration of total carotene; A1 = maximum

absorbance of Chl a; A2 = maximum absorbance of Chl b;

and A3 = maximum absorbance of total carotene.

Model description

The Biome-BGC (BioGeochemical Cycles) model (Luo et al. 2010) with minor modification was used to predict future


Mafia Island INDIAN OCEAN TANZANIA Tanzania AFRICA 39°15′ E See enlarged area Morogoro Region Ruaha Kilombero Luwengu Rufiji Rufiji T A N Z A N I A Mafia Island Kikunya River Simbauranga River Kiomboni River Bumba River NR 1 NR 2 NR 3 NR 4 CR 1 SR 1 Kiasi River Dima River Jaja River 10 km R U F I J I D E LTA Land Stations Mangrove 39°15′ E 39°30′ E 7°45′ S 8°15′ S 8° S RUFIJI—MAFIA CHANNEL

Rufiji River Basin I N D I A N O C E A N

Figure 1: Map of Rufiji Delta mangrove forests in Tanzania, showing the sampling stations used in the study of nutrients and primary


productivity in the Rufiji Estuary. While using the model, the following assumptions were made: (i) the meteorological input data may contain an unlimited number of years of data; (ii) gross photosynthesis and dark respiration in the shoot are proportional to the amount of nitrogen in the leaf and shoot; (iii) mangrove root respiration is the sum of respiration for maintenance of biomass, growth and ion uptake; (iv) mangrove respiration releases up to about half of the assimilated carbon by daily photosynthesis; (v) temperature responses of respiratory CO2 efflux rates

from mangrove plants and mangrove soil are modelled using exponential functions; and (vi) nitrogen uptake represents the main nutrient contributing to the growth of mangroves. Mathematical equations were used to describe the productivity-related biogeochemical processes in the conceptual model diagram. The conceptual diagram has three main compartments: atmosphere, plants and soil. The processes occurring in the sediment compartments are nutrient mineralisation, nitrification, denitrification, volatilisation, regeneration and litter decay. Photosynthesis, respiration, mangrove growth and harvesting occur in the plant biomass. Processes involving CO2 and O2 occur in

the atmospheric compartment; the turnover rate depends on soil temperature, moisture and texture, for the litter and sediment pools or plant biomes (Wang et al. 2010). Change in the pool size with time is governed by differential equations that are numerically integrated. The mass balance proposed by Mayo and Hanai (2014) was applied using Eqn 2 in the calculation. Thus:

Accumulation = Inflow – Outflow + Reactions =1 d ( ) + = + d m j i j i i o C V r Q C Q C V t ∑ (2)

where V = reactor volume (m3); m = number of reactions

that involve the substance; ri = volumetric reaction rate (g m–3 day–1); Q

i = influent flow rate (m3 day–1); Ci = influent concentration (g m–3); Q

o = effluent flow rate (m3 day–1);

C = effluent concentration (g m–3); and V (dC/dt) = volumetric

rate of change of substance in the reactor (g day–1).

Mass balance of ammonium-nitrogen

The mass balance of ammonium-nitrogen (NH4-N)

estimated the ammonium fraction in the mangrove system and the regeneration of ammonium from sedimentation and mineralisation of organic nitrogen. Outflow included the nitrification process, microbial uptake, volatilisation, and NH4-N that escaped from the system during flushing.

The mineralisation process of organic nitrogen was modelled using first-order kinetics, following Martin and Reddy (1997).

Mass balance of nitrate-nitrogen

The mass balance of nitrate-nitrogen (NO3-N) included

the inflow and outflow in the Rufiji Delta. During inflow, the processes considered were loading of NO3-N and

nitrification. Denitrification microbial uptake (mcruṕ) and plant uptake (plantuṕ) were included during outflow. Eqn 3, as reported in Mayo and Hanai (2014), describes the mass balance of NO3-N in the Rufiji Delta:

3 3

3 d(NO -N) = qi (NO -N i)

d d

(NO -Ne) n p

qe mcrup´ den pla t ´

– d u d m t t A t t A t t t ∂ ∂ +∂      ∂ ∂ +++∂     (3) where: 3 (NO -Ni) ∂

∂A = nitrate-nitrogen loading (g l–1 m–2) (where A

represents area);

3 (NO -Ne) ∂

∂A = nitrate-nitrogen in the effluent (g l

–1 m–2);

∂ ∂ n

t = nitrification rate (g m–2 day–1);



∂ = rate of water discharged into the delta (m3 day-1); qe


∂ = rate of water discharged into the ocean from the

delta (m3 day-1); and mcruṕ;

den ∂

∂t = denitrification rate (g m–2 day–1). Mass balance of nitrogen

The nitrogen pool in sediments depends on the rate of sedimentation of organic nitrogen and sediment regeneration. The nitrogen mass balance was estimated using Eqn 4, following Mayo and Hanai (2014):

d(N_sed)= reg d ∂ ∂ ∂ ∂ s t t t (4) where: ∂ ∂ s

t = sedimentation rate (g m–2 day–1), and reg

∂t = regeneration rate (g m–2 day–1).

The model accounted for the transformation and removal of nutrients in the mangrove ecosystem and is further explained below.


Mineralisation involves the biological transformation of organically combined nitrogen to ammonia as a result of microbial degradation. The process was modelled using first-order reaction kinetics (Eqn 5), following Martin and Reddy (1997):


t = [organic nitrogen] × k (5)

where k = the mineralisation rate of organic nitrogen.


The rate of nitrification was modelled by considering the bacterial biomass in suspended matter and biofilm. The biofilm was assumed to be composed of Nitrosomonas bacteria, which are attached to the surface of aggregates and plant roots (Bogino et al. 2013). The Monod model was used to model nitrification that was driven by bacterial processes in suspension. Nitrification in the biofilm was derived from Eqn 6, according to Polprasert and Agarwalla (1994):


4 org pH 4 1 2 NH -N DO = n +NH -N +DO N n T n r C C Y K K µ         ×   × × × ×         (6)

where rn = rate of nitrification; μn = specific growth rate of bacterial biomass (day–1); Y

n = yield coefficient (bacterial biomass of substrate) (g g–1); K

1 and K2 = the mass

transfer coefficient for ammonium and dissolved oxygen, respectively (day–1).

The nitrification process was influenced by temperature, pH, dissolved oxygen concentration and ammonia. Taylor et al. (2017) reported the influence of temperature and pH on nitrification, as indicated in Eqns 7 and 8:

CT = exp(0.098(T – T0) (7)

where CT = temperature-dependent factor; T = temperature (°C); and T0 = reference temperature (°C), and where:

CpH = 1 – 0.833(7.2 – pH) for pH < 7.2, and CpH = 1.0 for pH ≥ 7.2



The rate of denitrification was modelled by assuming the presence of denitrifying bacteria in the water column. Wang et al. (1995) described the denitrification process to follow Arrhenius kinetics within a temperature range between 3 and 28 °C. The first-order Arrhenius kinetic reaction (Eqn 9) was used to model the denitrification rate:




( 20)



20 1 3 = DR − NO -N − ∂  θ      T m t (9)

where DR_20 = denitrification rate constant at 20 °C (day–1)

(ranges from 0 to 1.0), and θ1 = Arrhenius constant (ranges

from 1.02 to 1.09).

Ammonia uptake by macroinvertebrates

Although ammonia is preferred, biomass uptake by autotrophic bacteria and algae involves NH3-N or nitrate.

In modelling the uptake of ammonia, it was assumed that ammonia would be consumed by microorganisms in the suspension and biofilm as long as it was available. The Monod kinetic equation for microorganisms in suspension and an equation for microorganisms in the biofilm were combined (Eqn 10), which was then used to model the uptake of ammonia by microorganisms (mcruṕ):

20 3 max_20 3 3 org 1 pH 2 N D mcru H -N ( )( N p´ ) NH - N d DO O −    µ θ  +   ∂ =  × ×   × ×       +    T K P t C K (10)

where μmax_20 = maximum growth rate of bacteria per day at

20 °C; K3 = ammonia uptake half-rate saturation constant;

and P1 = ammonia uptake preference factor.

Nitrate uptake by microorganisms

Autotrophic bacteria use dissolved nitrate as a terminal electron acceptor during bacterial respiration. However, it was assumed that nitrate would be consumed if all the NH3-N was depleted in the system. Eqn 11 was used to

model the uptake of nitrate by microorganisms (mcruṕ), following Mayo and Hanai (2014):

20 3 org max_20 pH 2 4 3 2 NO -N DO ( )( ) N d NO c -N DO m rup ´ T C P t K K −       ∂ = µ θ × × × ×       + +    (11) where K4 = nitrate uptake half-saturation constant (day–1),

and P2 = nitrate uptake preference factor.

Ammonia regeneration rate

The organic nitrogen pool that settled in the wetland due to sedimentation would eventually be regenerated into ammonia. Mayo and Bigambo (2005) described the regeneration process, which was assumed to follow the first-order kinetics, as indicated in Eqn 12:

reg (N_sed)

= × π

∂t (12)

where π = regeneration rate constant for ammonia per day.

Sedimentation rate

Organic nitrogen, algae and bacteria settle to form a layer in the aquatic system. Algae and bacteria contain organic nitrogen in their cells which, when deposited, contribute to the removal of nitrogen from the system. The net loss of organic nitrogen due to sedimentation depends on sedimentation and regeneration rates. Using Stoke’s law, the sedimentation rate in the mangrove was modelled using Eqn 13, described by Mayo and Hanai (2014):

( – ) 2 d 18s g d s t h ρ ρ ∂ = µ (13) where g = acceleration due to gravity (m s–2); ρ

s = density of

settling organic nitrogen particles (kg m–3); ρ = density of water

(kg m–3); d = diameter of the settling organic nitrogen particle

(m); μ = coefficient of dynamic viscosity of the fluid (Ns m–2);

and h = depth of water loaded into mangrove ecosystems (m). The dynamic viscosity of water is a function of temperature. For temperatures above 20 °C, viscosity varies with temperature, in accordance with Eqn 14:

2 20 1.3272(20 ) 0.001053( 20) log 105  µ =   µ÷   T T T (14) Volatilisation of ammonia

Factors influencing the volatilisation of ammonia are temperature and pH, which were included in the model. Ammonia in water exists as ammonium ions (NH4+) and dissolved ammonia

gas NH3 (g). At 20 °C and pH 9.4 in the water column, NH4-N

dominated the system. The volatilisation rate of ammonia was described using Eqn 15, after Montes et al. (2009):

4 0.056 exp(0.13( 20)) NH -N =       = × v T H r H (15) where H is the Henry’s law solubility constant.


Primary productivity

The net primary productivity (NPP) for the mangrove ecosystem comprises the difference between photosynthesis and respiration. The model represents physical and biological processes that control the fluxes of energy and mass. To calculate photosynthesis, a multiplicative expression of the function given in Eqn 16 was used as the main controlling factor:

P = f(LAI) × f(PAR) × Min{f(VPD),f(REW)} × f(T) × f(Tmin) ×

f(S) × f(CO2) (16)

where LAI is leaf area index; PAR is the photosynthetically active radiation; VPD is the vapour pressure deficit; REW is the soil relative extractable water; T is the temperature;

Tmin is the days of minimum temperature; and S is the

temperature history.

In principle, more leaves facilitate more photosynthesis in the vegetation cover; however, not all leaves are the same, because leaves shade each other and also resources are allocated differently to leaves in different places in the crown. It is indicated that LAI determines productivity in forest stands because of its role in intercepting radiation (i.e. red over infrared light) (Medhurst and Beadle 2001). At low light intensity, more light results in more photosynthesis. As light intensity increases and carbon fixation is at full capacity, the leaves cannot take full advantage of the extra light they receive. The relationship between LAI and photosynthesis is expressed in Eqn 17, after Zhang et al. (2019):

f(leaves) = 1/K × (1 – e–K × LAI) (17)

where K is the light extinction coefficient.

The relationship between PAR and photosynthesis is expressed in Eqn 18, following Zhang et al. (2019):

f(PAR) = Pmax × PAR/(PAR + B) (18)

where Pmax is the maximum photosynthesis, and B is the

plant biomass density (g m–2). Transpiration increases as

the air becomes dry.

If there is no control of water loss, the plant would become stressed and might desiccate. To avoid excess water loss, the stomata in the leaves adjust the transpiration rate by closing. Since stomata are the same pores through which CO2 enters the leaves, the closure causes a decrease in the

flow of CO2 and ultimately a reduction in photosynthesis, as

represented by Eqn 19 (Zhang et al. 2019):

f(VPD)= e–H×VPD (19)

where H is relative humidity at the site, and VPD is the vapour pressure deficit.

Statistical analysis

Comparison of nutrient and phytoplankton average concentrations between stations over the entire sampling session was done in SPSS 20 using one-way ANOVA and Pearson’s correlation coefficient after normalisation. Average values of nutrients, temperature, precipitation and dissolved oxygen were evaluated for significance.


Spatial variation of physicochemical parameters in water

The in situ-measured physicochemical parameters are presented in Table 1. Spatial and temporal concentrations of silica, nitrate, ammonium, orthophosphate, DOC, carotene and chlorophyll in the waters of the Rufiji River are presented in Figures 2 and 3. The calibration curves for all nutrients indicated R2 > 0.99.

Values for pH varied from 6.4 at station NR4 to 9.4 at station NR1. Water temperature ranged from 25.8 °C at NR4 in April 2013 to 34.9 °C at NR1 in January 2013. The lowest average water temperature was measured at SR1 followed by sampling stations CR1 and NR4, but the difference between stations was not significant. Dissolved oxygen concentration ranged from a minimum of 2.0 mg l–1

at CR1 and NR2 to a maximum of 7.4 mg l–1 at NR3.

Salinity varied from 0.2 at NR4 in April 2012 to 45.0at NR1 in January 2014 (Table 1).

Nutrient concentrations

Concentrations of NH4-N ranged from a minimum of

19.8 µg l–1 at SR1 to a maximum of 200 µg l–1 at NR1

(Figure 2), with a mean value of 71.0 µg l–1 (SE 9.5) in

the north, 70.8 µg l–1 (SE 20.0) in the central region, and

40.5 µg l–1 (SE 4.9) in the south. Nitrate concentrations

varied from 102 µg l–1 at NR3 to 0.44 µg l–1 at SR1

(Figure 2). The average concentration of NO3-N in the

northern, central and southern parts of the Rufiji Delta was 21.0 µg l–1 (SE 5.9), 11.3 µg l–1 (SE 5.7) and

5.2 µg l–1 (SE 3.7), respectively. The highest concentrations

were recorded at NR3 during April and September. There was considerable spatial variation of NO3-N, with large

differences between NR3 and the other stations.

Phosphate concentrations ranged from 65.2 µg l–1 at

CR1 to 622 µg l–1 at NR2 (Figure 2), with mean values

in the northern, central and southern parts of the Rufiji Delta mangroves of 263 µg l–1 (SE 30.4), 194 µg l–1 (SE

99.0) and 108 µg l–1 (SE 14.8), respectively. The highest

concentration of phosphate was measured in April 2012, followed by June 2013. The lowest silica concentration was measured at SR1 (2.72 µg l–1) and the highest at NR2

(61.8 µg l–1) (Figure 2). The average silica concentrations in

the northern, central and southern regions of the Rufiji Delta mangroves were 20.2 µg l–1 (SE 2.8), 11.8 µg l–1 (SE 4.0)

and 12.7 µg l–1 (SE 2.9), respectively. The highest value

of reactive silica was observed at NR4 during January. An ANOVA showed a significant difference between NR2, NR3 and NR4 (p < 0.05).

High concentrations of Chl a, Chl b and total carotene were recorded at NR3 (3.94, 6.46 and 605 µg l–1,

respectively) (Figure 3). The lowest concentration of Chl a was 0.11 µg l–1 at NR4 and CR1, for Chl b it was 0.03 µg l–1

at NR2, and for total carotenes it was 15.9 µg l–1 at NR2.

Seasonal variation of Chl a, Chl b and total carotenes was high during the dry season. A one-way ANOVA indicated a significant difference in chlorophyll and total carotene concentrations between stations (p < 0.05).

DOC concentrations ranged from a minimum of 2.83 µg l–1 at NR3 to 41.1 µg l–1 at NR1, with an overall


NH4-N NO3-N CONCENTRA TION (µg l −1) PO4-P SiO2-Si 50 100 150 200 250 20 40 60 80 100 100 225 350 475 600 12 24 36 48 60 CR1 NR1 NR2 NR3 NR4 SR1

Apr 2012 Sep 2012 Jan 2013 Apr 2013 Sep 2013 Jan 2014


Figure 2: Spatial and temporal variation of NH4-N, NO3-N, PO3-P and Si concentrations in water samples from the Rufiji Delta, Tanzania.

Error bars represent SE

Station Status pH Temperature(°C) Conductivity(mS cm–1) TDS(ppt) (mg lDO–1) Salinity(‰)

NR1 Maximum 9.4 34.9 30.7 19.2 6.4 40.5 Minimum 6.6 28.6 17.4 9.6 3.0 27.0 Average (SE) 7.2 (1.0) 31.2 (1.0) 24.2 (2.5) 14.2 (2.2) 5.3 (0.3) 34.0 (0.2) NR2 Maximum 9.2 31.6 30.0 17.2 7.0 12.0 Minimum 6.4 27.7 7.80 4.6 2.0 2.0 Average (SE) 6.6 (1.0) 29.3 (1.5) 18.6 (3.0) 10.5 (2.7) 3.8 (0.1) 6.0 (0.3) NR3 Maximum 8.8 31.4 9.90 29.2 7.4 21.0 Minimum 6.8 26.2 0.40 20.3 2.5 0.4 Average (SE) 7.0 (0.8) 28.9 (1.7) 9.60 (2.2) 25.3 (1.8) 3.3 (1.1) 7.0 (0.5) NR4 Maximum 8.8 30.7 8.60 28.2 6.2 4.0 Minimum 6.4 25.8 0.50 20.1 2.4 0.2 Average (SE) 6.5 (1.0) 28.7 (0.6) 2.70 (0.9) 26.3 (2.0) 3.6 (1.2) 2.0 (0.1) CR1 Maximum 9.3 30.2 59.6 32.7 5.8 40.0 Minimum 6.6 27.3 36.7 27.0 2.0 23.0 Average (SE) 7.0 (0.7) 28.7 (1.2) 51.1 (4.7) 29.3 (1.3) 3.6 (1.3) 32.0 (0.4) SR1 Maximum 8.9 29.8 44.8 33.5 4.1 40.0 Minimum 6.7 26.4 12.0 27.7 3.2 25.0 Average (SE) 6.9 (0.8) 28.3 (0.6) 35.4 (5.2) 30.8 (1.3) 3.6 (0.8) 32.0 (0.3)

Table 1: Physical and chemical characteristics of water in the Rufiji Delta, Tanzania, measured over the course of six sampling events.


average DOC in the northern, central and southern regions of the Rufiji Delta mangroves was 9.9 µg l–1 (SE 2.0),

14.9 µg l–1 (SE 6.7) and 12.8 µg l–1 (SE 5.3), respectively.

High concentrations of DOC occurred during April at NR1 and CR1, followed by September at SR1.

N:P ratios

The N:P ratio was calculated as the sum of NH4, NO3 and NO2

to the total soluble reactive phosphorus content. The mean N:P ratio ranged from 9 to 29 (Table 2). All sampling stations indicated excess nitrogen, particularly the northern (NR) sites.

Statistical relationships

To investigate the relationship between nutrients and climate-driven parameters (temperature and precipitation), the Pearson correlation coefficient was used, with a significance level of 0.05 (Sigma Plot 11.0). At NR1, Si was positively correlated with NO3-N and salinity, but negatively correlated

with NH4-N, PO4-P and DO. Si showed no significant

correlation with DOC, pH, temperature and precipitation.

Model calibration and validation

The simulated and observed values for ammonium in the waters of the Rufiji Delta are shown in Figure 4. In general,

model simulations followed the trend of the observed data. The removal of total nitrogen from the system was a close match between the simulated and observed data, indicating that the model closely predicted the transformation of nitrogen in the system. The model also indicated a strong correlation between the measured and simulated values as indicated by Pearson’s correlation factor of r = 0.67 (Figure 5).

The net primary productivity (NPP) was simulated in the Rufiji Delta by varying uptake rates, ranging from 0.1 to 3.0 µg l–1 day–1 in the delta, based on model optimisation.

The model was capable of predicting NPP for a period of 12 years (Figure 6). The NPP was observed to increase with time with a slight increase in uptake rates. However, NPP was expected to increase sharply after between 3 and 6 years.


Spatial and temporal variability of physical parameters

Temporal variability of physical conditions (Table 1) in the waters of the Rufiji Delta is mainly due to streamflow conditions (seasonality), soil content and vegetation cover, which pose the greatest effect on variability in water quality. The spatial variability in surface water temperature is driven

Total carotene Chl a Chl b 0.5 1.3 2.1 2.9 3.7 0.5 1.7 2.9 4.1 5.3 6.5 150 300 450 600 10 20 30 40 CONCENTRA TION (µg l −1) CR1 NR1 NR2 NR3 NR4 SR1

Apr 2012 Sep 2012 Jan 2013 Apr 2013 Sep 2013 Jan 2014


Figure 3: Spatial and temporal variation of Chl a, Chl b, total carotenes, and dissolved organic carbon (DOC) in water samples from the


primarily by the interactive effects of fluvial runoff, tidal exchange and processes near the oceanfront (Wooldridge and Deyzel 2012). The high pH value at station NR1 is probably because of the influence of seawater containing hydroxyl and carbonate ions, which have a high buffering capacity. This argument is supported by the average salinity value, which is high at this station. The pH values in the estuary (Table 1) indicate that the water is mostly of riverine origin.

The lowest DO concentrations observed at stations CR1 and SR1 are most likely attributable to the inflow of saline water from the ocean and the high temperatures recorded at these stations. Also, large freshwater inflows increase catchment-derived carbon and nutrient loads, leading to DO depletion at CR1 and SR1 and suppressed DO replenishment and mineralisation of organic matter, as reported by Zhu et al. (2017) in a stratified coastal lagoon. The high DO levels at stations NR3 and NR4 are due to the turbulent mixing of riverine waters resulting in increased dissolution of oxygen. The variation in salinity between the northern, central and southern regions of the Rufiji Delta is attributed to the different proximity of these stations to the ocean as well as to freshwater input from upstream. Salinity in the Rufiji Delta during all six sampling events did not exceed 40, which is below the critical value that affects mangrove productivity and growth (Noor et al. 2015).

Nutrient inputs

The spatial variation of NH4-N in the Rufiji Delta indicates

a high concentration in the northern part of the mangrove forests compared with in the central and southern parts. NH4-N inputs in the northern part of the delta are mainly

from farming and rearing of livestock in these rural settings, which are the main sources of livelihoods, as observed during sampling. Another source of NH4-N could be

the run-off from the catchment during the rainy season. Temporal variation of NH4-N indicated the highest value in

September at station NR1, which could be attributable to high temperatures which favour the formation of NH3-N in

equilibrium, rather than nitrification.

The spatial variation of NO3-N values in the estuary is

most likely caused by anthropogenic inputs from runoff, carrying with it decaying vegetation as noted during sampling. Small amounts of nitrogenous compounds originate from nearby agricultural fields (Bellos et al. 2004), particularly in the Morogoro region where the Rufiji River is recharged by three tributaries, the Ruaha, Kilombero and

50 100 150 200 250 300 Observed NHSimulated NH4-N 4-N PERIOD (y) NET PRIMAR Y PRODUCTIVITY (g C m −2 y −1) 0 2 4 6 8 10 12

Figure 4: Model calibration curve, showing simulated and observed

values for ammonium in waters of the Rufiji Delta, Tanzania

Station NR1 NR2 NR3 NR4 CR1 SR1

N (μmol kg−1) 17 485 14 693 54 544 22 260 12 086 6 717

P (μmol kg−1) 1 801 1 560 1 855 1 216 992 662

N:P 10 9 29 18 12 10

Table 2: Nitrogen-to-phosphorus ratio of water in the Rufiji Delta, Tanzania, sampled between

April 2012 and January 2014. Locations of sampling stations are provided in Figure 1

r2 = 0.67 50 100 150 200 250 300 30 60 90 120 150 180 OBSERVED NH4-N (µg l−1) SIMULA TED NH 4 -N (µg l −1)

Figure 5: Linear regression analysis between simulated and

observed ammonium-nitrogen in waters of the Rufiji Delta, Tanzania 10 20 30 40 50 Uptake 0.1 Uptake 0.8 Uptake 1.6 Uptake 3 PERIOD (y) NET PRIMAR Y PRODUCTIVITY (g C m −2 y −1) × 10 3 0 0 2 4 6 8 10 12

Figure 6: Predicted net primary productivity for a period of

12 years in the Rufiji Delta, Tanzania. Uptake rates varied between 0.1 and 3.0 µg l–1 y–1


Luwengu rivers, which support farming and agricultural practices in the watershed. Inputs derived from livestock (cattle) and fowl are also a source for NO3-N inputs within

the upper reaches of the estuary.

The spatial variation in phosphate (PO4-P) concentration

in the Rufiji Delta is most likely an outcome of both natural and anthropogenic activities. High temperature in surface water stimulates mineralisation processes that liberate the organic-bound P fraction (Niemistö et al. 2011). The spatial variation in PO4-P is also affected by the mixing of fresh

water with seawater as well as replenishment as a result of microbial decomposition of organic matter (Paytan and McLaughlin 2007). Desorption of particle-bound PO4-P

occurs as pH increases (Sundareshwar and Morris 1999), which is a function of temperature and precipitation. It is likely that the adsorbed PO4 pool may have been released

in the northern part of the Rufiji Delta as the pH is high. This release is probably also affected by inputs from domestic waste (Bellos et al. 2004), which could explain why stations NR1, NR2 and NR3 have high concentrations of PO4-P.

The temporal variation in PO4-P levels is affected by

precipitation, with higher concentrations measured in April than in January. The different sampling periods coincided with variability in rainfall, which affected the dissolved and particulate matter derived via erosion and fluvial transport (Paytan and McLaughlin 2007).

The variability in Si levels is most likely from the influx of fresh water containing Si eroded from the catchment (Saravanakumar et al. 2008). In addition, human activities such as damming, deforestation and the introduction of invasive species affect the natural terrestrial cycling of Si and its delivery to the ocean, as indicated in other studies (Humborg et al. 2000; Saravanakumar et al. 2008).

Temporal variation in DOC levels was characteristic between the sites. While high DOC levels occurred during April 2012 at NR1 and CR1, this trend changed in September due to the mineralisation process. In April 2013, DOC concentrations were high at NR2 and NR3. The highest DOC concentration at NR1 (Figure 3) is probably caused by the proximity of this station to discharge from household wastes in the vicinity. The statistical difference in DOC concentrations between sampling stations is probably due to the mineralisation of organic matter.

N:P ratio and primary productivity

The optimal N:P ratio for phytoplankton growth is 16:1, based on the Redfield ratio, which was recently revised to 14:1 (Allmon and Martin 2014). The mean value of N:P at NR1, NR2, NR4, CR1 and SR1 was less than the optimal N:P value (Table 2), indicating that nitrogen is the main limiting nutrient in the Rufiji Delta. This may affect phytoplankton diversity, biomass, species composition, and eventually food-web dynamics (Shilla and Routh 2017; Shilla et al. 2019). Station NR3 had an N:P value above the optimal ratio for phytoplankton growth (Table 2) and also had high concentrations of Chl a, Chl b and total carotene, which indicate the high primary productivity at this site. This was probably as a result of nutrient loading caused by the inflow of fresh water from upstream, making the habitat suitable for phytoplankton growth. Senthilkumar et al. (2008) reported a similar trend in an estuarine mangrove in southern India,

drawing a similar inference regarding the distribution of plant pigments and phytoplankton productivity. The lowest pigment concentrations were at NR4 and CR1, owing to tidal flushing that impacts these two sites. Chl a, Chl b and total carotenes (phytoplankton) had high concentrations during the rainy season (January 2013). This phenomenon can be explained in terms of the residence time of phytoplankton in the turbulent mixing layer, which is rich in soluble nutrients from runoff, and this in turn will favour phytoplankton growth (Anderson et al. 2002). Phytoplankton growth was low during September 2013. Hypothetically, this might have been due to cyanobacterial growth, which would hinder primary productivity (Hennemann and Petrucio 2010), but this needs to be confirmed based on monitoring data.

Modelling the surface-water chemistry and nutrient levels in the Rufiji Delta indicated that NPP increased with time (Figure 6). This might be because of utilisation of nutrients introduced into the Rufiji Delta mangroves from upstream sections of the river. In addition, an increase in mangrove litter and microorganisms is also expected to contribute to an increase in NPP. Plant litter derived from mangroves is the major source of nutrient input. When leaves fall, nutrients are released through their decomposition and accumulate in sediments (Srisunont et al. 2017). These nutrients can subsequently be used by plants in their metabolic processes. Consistent with this idea, the abrupt increase in NPP in the modelling simulation (Figure 6) suggests that, at some point, the delta acts as a source of nutrients, and productivity in the system increases as a result of internal nutrient cycling. The mass balance model also indicates that both dissolved nitrogen (especially ammonium) and phosphorus are important for mangrove productivity. This concurs with previous findings reported by Reef et al. (2010). There was no correlation between NPP and temperature, suggesting no direct relationship, but both photosynthesis and respiration might increase at progressively higher temperatures. While the geochemical analyses and modelling support these trends, more-detailed studies are needed to establish the nuanced changes that slowly build up over time and drive changes in this dynamic ecosystem. Moreover, additional ground-truthing is also needed to verify the predicted modelling trends and inferred changes in water chemistry and productivity, and hence to facilitate suitable management practices.


The distribution of nutrients in the waters of the Rufiji Delta, and their deposition into the sediments, are affected by both natural and anthropogenic processes prevalent in the upstream and downstream sections of the Rufiji River. The nutrient levels vary inter-annually and seasonally, depending on precipitation-driven fluvial runoff. Changing N:P ratios led to increased primary productivity at station NR3. Climatic parameters (temperature and precipitation) and independent variables (DO and salinity) might play a role in influencing the chemical processes and reactions that affect water quality. Accelerated nutrient loading, especially P and N from different anthropogenic sources, into the Rufiji Delta, affects primary productivity. In particular, the elevated concentrations of nutrients and phytoplankton (based on


pigment levels) indicate eutrophication, which influences the biogeochemical dynamics in the ecosystem. This is also affected by regeneration of N at this site, which suggests that primary production is most likely supported by rapid

in situ nutrient cycling. The results obtained from the model

indicate that the Rufiji Delta is a transitional and dynamic ecosystem governed by inputs derived from both terrestrial and aquatic processes. A combination of external factors such as pH, precipitation and light intensity strongly influence the biogeochemical functioning and NPP in the Rufiji Estuary. These trends follow the fundamental physical, biological and chemical regulators of C, P and N that are essential for predicting their interactive effects on nutrient dynamics and for developing effective management models for the Rufiji Delta mangrove ecosystem. Finally, the model indicates that the overall high level of nutrient export makes the Rufiji River system an important source of nutrients around coastal margins of the western Indian Ocean.

Acknowledgements — We thank H Mallya, R Masinde and

A Lugata for helping us with sample collection. L Lundman is thanked for providing valuable practical assistance in the laboratory. We thank both anonymous reviewers for their helpful suggestions. This study was funded by the Swedish Research Links Program – Africa (Grant 348-211-7408) to JR. AM thanks the Western Indian Ocean Marine Science Association (WIOMSA) for partly covering living expenses in Sweden through WIOMSA MARG-II.


Andrew Minu: https://orcid.org/0000-0002-5214-0692 Siajali Pamba: https://orcid.org/0000-0003-2299-2541 Joyanto Routh: https://orcid.org/0000-0001-7184-1593


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