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Habitat Hydrology and Geomorphology Control the
Distribution of Malaria Vector Larvae in Rural Africa
Andrew J. Hardy1,2, Javier G. P. Gamarra3, Dónall E. Cross2,3, Mark G. Macklin1, Mark W. Smith4, Japhet
Kihonda2, Gerry F. Killeen2,5, George N. Ling’ala2, Chris J. Thomas3*
1 Institute of Geography & Earth Sciences, Aberystwyth University, Aberystwyth, United Kingdom, 2 Biomedical and Environmental Sciences Thematic Group,
Ifakara Health Institute, Ifakara, Tanzania, 3 Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom,
4 School of Geography, University of Leeds, Leeds, United Kingdom, 5 Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United
Kingdom
Abstract
Background: Larval source management is a promising component of integrated malaria control and elimination.
This requires development of a framework to target productive locations through process-based understanding of
habitat hydrology and geomorphology.
Methods: We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles
habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of
Tanzania.
Results: Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of
vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after
stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological
setting and origin.
Conclusion: Hydrological and geomorphological processes governing the availability and productivity of Anopheles
breeding habitat need to be understood at the local scale for which larval source management is implemented in
order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established
practice providing a tractable way forward for developing important malaria management tools.
Citation: Hardy AJ, Gamarra JGP, Cross DE, Macklin MG, Smith MW, et al. (2013) Habitat Hydrology and Geomorphology Control the Distribution of
Malaria Vector Larvae in Rural Africa. PLoS ONE 8(12): e81931. doi:10.1371/journal.pone.0081931
Editor: Rick Edward Paul, Institut Pasteur, France
Received July 25, 2013; Accepted October 18, 2013; Published December 3, 2013
Copyright: © 2013 Hardy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Natural Environment Research Council (NERC), grant number NE/H022740/1 (http://www.nerc.ac.uk). The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: cjt@aber.ac.uk
Introduction
There is a growing need to target malaria vector mosquitoes
at their environmental resources through larval source
management [1–8]. To implement such strategies effectively
we need to be able to identify productive vector larval habitats
[2,7,9–13]. Vector aquatic habitats are controlled by temporal
and spatial hydrological dynamics [14] which need to be
understood if habitat targeted interventions are to be
successful [15,16].
Rainfall is a key determinant of malaria transmission [14], as
it governs the availability of aquatic habitats required for
breeding by vector mosquitoes. Despite this, observed
relationships between rainfall and malaria transmission are
variable [17] and poorly understood [18]. Recent advances in
understanding of thermal drivers of malaria transmission
[19–21] have not been matched by similar advances in our
understanding of response to precipitation, despite this being
the primary forcing climate variable in observed trends in
malaria transmission in Africa over the last century [22].
Studies have demonstrated a link between habitat type and
their ability to support vector larval populations
[2,9,11,13,23,24]. However, such studies do not classify
aquatic habitats according to the geomorphological and
hydrological processes that control their formation and
persistence [14]. This has led to inconsistencies when
identifying the relative vector productivity of water body types.
For example, Ndenga et al. [2] showed that the habitat type
‘puddles’ was the most productive, whereas Mutuku et al. [11]
demonstrated that puddles are the least productive, both
studies taking place in the western Kenyan highlands.
Hydrologically speaking, a puddle is an ambiguous term as
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they can form and persist due to a number of different
hydrological processes. For instance, pluvial puddles that are
rainfall fed will be vulnerable to evaporation and may not
provide productive habitats, whereas puddles that form due to
rising water tables may persist for a longer period of time and
may therefore be more productive. In this sense, the two
puddles are distinct in terms of their dynamics and their
responses to meteorological conditions, and should be
classified accordingly.
Do Manh et al. [25] also examined larvae in different water
body types for a rural area in Vietnam. However, these water
body types were classified by land use, with no consideration
of their geomorphological setting and hydrological controls. For
instance, ‘ground pools’ included buffalo wallows, borrow pits,
natural depressions, fish ponds, and manmade drains but
these habitats are controlled by different hydrological
processes. Borrow pits are likely to be fed by localised direct
runoff, whereas permanent fish ponds are likely to exist where
water table levels remain at the surface or where springs allow
the pool to exist independently of rainfall [14].
In northern Angola a negative relationship was found
between malaria transmission and distance to rivers [26]. This
study was conducted in the dry season but the importance of
river channels for supporting productive vector habitats can
vary throughout the hydrological year. Specifically, large
perennial rivers with seasonally inundated floodplains can
support a number of productive vector habitats shortly after the
wet season, such as the Gambia [27] and the Nile in Sudan
[28]. Whereas the cessation of river flow in ephemeral
channels during the dry season can produce chains of shallow
pools [14] providing productive vector habitats [29] but will be
prone to flushing out during the wet season due to fast flowing
water [30,31].
To improve our understanding of vector larval habitats, it is
important to determine the geomorphological and hydrological
processes that govern the formation of vector aquatic habitats
[14]. Ignoring these can lead to misinterpretation of the
influence of rainfall patterns on malaria transmission [17] which
currently forms the basis, along with other environmental
components including temperature and humidity, for disease
mapping and modelling [14].
Earlier studies have shown the potential for linking
hydrological process based understanding to malaria [32] and
mosquito dynamics [33,34]. The aim of this study is to expand
this approach to the landscape scale by linking
geomorphological and hydrological processes with malaria
vector habitat productivity within a large sub-catchment
(200 km2). This was achieved by monitoring Anopheles larvae
over a 12 month period across a range of aquatic habitat types
classified according to their geomorphology and hydrology and
comparing them to changes in rainfall, river stage and water
table level.
Methodology
Ethics
Ethical approval was granted by the National Institute for
Medical Research, Tanzania, and Ifakara Health Institute's
Review Board. Before larval sampling, verbal consent was
requested from land owners and residents before entering
fields or crossing compounds.
Study Site
The Kilombero River has a drainage area of 31,700 km2
(Figure 1) and is one of the principal tributaries of the Rufiji
River, the largest river catchment in Tanzania. The Kilombero
Valley is located within an asymmetrical half-graben between
30-40 km wide and 200 km long. The floodplain lies between
210-250 m.a.s.l. and is flanked by the Udzungwa Mountains
(maximum elevation 2580 m) to the north and the Mahenge
Highlands (maximum elevation 1520 m) to the south [35].
These upland areas receive over 1400 mm rainfall annually
and the Kilombero Valley receives over 1000 mm [36] which is
usually divided into two rainy seasons. Short rains occur in
December and January with the main rainy season extending
from March through to May [37]. The Kilombero Valley, one of
the best characterised malaria transmission systems in Africa,
had some of the highest reported historical rates of malaria
transmission [38]. It is also one of the most advanced
examples of successful transmission control in an African
context, with near-elimination of Anopheles gambiae sensu
stricto [39], the most historically important malaria vector locally
[38] and across much of Africa [40], following the successful
scale up of long-lasting insecticidal nets [41].
The focus of this paper is a 200 km2 area surrounding the
village of Namwawala located 30 km to the west of Ifakara
(Figure 1). The landscape is generally flat with hilly terrain to
the north of the study area. The study area is drained by the
seasonal Idando River which is typically 10 m wide and 2-3 m
deep and is fed by two smaller tributaries at a confluence 5 km
downstream of Namwawala. 20 km south of Namwawala lies
the Kilombero River which flows throughout the year. During
particularly wet years the Kilombero inundates the lower 7 km
of the Idando sub-catchment but this did not occur during the
sampling period of the present study. A majority of the local
population are subsistence farmers [42] cultivating rice and
corn without the aid of irrigation. Extensive burning of arable
land takes place during the dry season to prepare the land for
the planting of crops during the short rains which are harvested
after the long rains in June [43].
Hydrological monitoring
Rainfall was measured using a network of tipping bucket rain
gauges. To account for the spatial variation in rainfall [44], eight
rain gauges were positioned throughout the study area,
ensuring a good geographical spread at a range of elevations
(Figure 1). River stage was recorded using three vented
pressure transducers positioned along the length of the Idando
River. The upper gauge was located in the village of
Namwawala; the middle gauge after the confluence of three
tributaries capturing a large proportion of the water that leaves
the Idando sub-catchment through the river channel system;
the lower gauge further downstream, 7 km from the main
Kilombero River which, during particularly wet seasons, can
flood, pushing water back up the Namwawala tributary. Water
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table depth was recorded in four shallow (< 3 m depth)
boreholes manually drilled into the soil.
Entomological sampling
Water body type. Potential malaria vector habitats within
the landscape were classified by their geomorphological and
hydrological setting according to the classification scheme
following Smith et al. [14]. Figure 2 provides a summary of the
different water body types identified in the Namwawala area.
Below is a description of the hydrological mechanisms that
control surface water availability within the water body types
and their potential for providing a vector habitat. A photograph
of each water body type is provided in Figure 3.
a. Topographic convergence water bodies represent
areas of subsurface moisture accumulation [14]. Typically,
such areas include valley and gully bottoms in small (< 1
km2) zero order catchments that do not have well
developed channel networks. These are located in the hilly
terrain in the north of the study area where rising water
tables may intercept the surface resulting in surface
ponding. This mechanism has previously been shown to
be an important driver of vector habitat development in
areas such as the western Kenyan highlands [16,45–49].
b. Floodplain basins are shallow depressions lying close
to river channels, particularly those with prominent, natural
levees. These are inundated when river levels exceeds the
height of the river banks and overtop levees. Some studies
have found this to be a key process for the generation of
vector breeding habitats. Notably, Bøgh et al. [27] found
that most breeding habitats of An. gambiae sensu lato in
the Gambia were generated by this mechanism. Similarly,
Ageep et al. [28] showed that habitats supporting An.
arabiensis in an area of northern Sudan were mainly
driven by overbank flooding from the River Nile.
c. Palaeochannels are sinuous linear depressions
marking abandoned river channels that are no longer
connected to active river channels. If the water table is
sufficiently high, these depressions become saturated in a
process similar to water bodies in areas of topographic
convergence. During particularly wet years
palaeochannels may reactivate with flowing water [14].
d. River channel water bodies are located within river
networks. During the dry season river levels can decrease
sufficiently for the river to stop flowing. This forms a series
of disconnected pools along the river channel, whose
location are controlled by the topography of the channel
bed [14]. These pools have been identified as a source of
malaria vector habitats in a number of studies [11,50,51].
For instance, van der Hoek et al. [51] found the majority of
vector habitats in Sri Lanka to be associated with pools
formed in streams and river beds. During the wet season,
river levels increase and pools reconnect causing water to
start flowing within the channel. Larvae of most Anopheles
species can only tolerate still or slowly moving water and
are therefore vulnerable to high river flows, highlighted by
the modification of channels to augment water flow as a
larval control method [4].
e. Spring-fed pools are water bodies fed by groundwater
recharge and can persist throughout the year,
independently of rainfall. This makes them important for
sustaining vector populations through the dry season when
many other water body types are likely to dry up [52–54].
Specifically, this provides a potential habitat for species
Figure 1. Kilombero Valley study area. The location of hydrological monitoring instruments is shown that recorded rainfall, river
stage and water table depth over a 12 month period. Background elevation data is provided by the Shuttle Radar Topography
Mission (SRTM) with a 90 m grid resolution.
doi: 10.1371/journal.pone.0081931.g001
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that prefer permanent water bodies such as An. funestus
[53].
Landsat satellite imagery acquired on 10th July 2001 (30 m
grid resolution) was used to identify the main river channels
and their floodplains (Figure 4). This imagery was also used to
identify palaeochannels which appear as distinctive sinuous
linear features either infilled with fine grained sediment (silts
and clays) and organic matter that retain moisture making them
appear darker than the surrounding landscape or comprising
sandy deposits that form levees making them appear bright.
A Digital Elevation Model (DEM) was extracted from 50 cm
stereo Worldview satellite imagery acquired on 12th February
2012 using standard photogrammetric techniques [55]. This
was carried out using the DEM extraction tools within the
image processing software ENVI [56] producing a DEM with a
grid resolution of 2 m and a vertical accuracy of approximately
2 m [55]. This was used to identify areas of topographic
convergence which have potential for the accumulation of
moisture [16]. The DEM was also used to identify low-lying
areas adjacent to river channels where flooding might occur.
The features identified using the imagery and DEM were
checked using field observations. There was also a single
Figure 3. Examples from each water body type. The water
body types were classified according to their geomorphological
and hydrological characteristics. (A) Topographic convergence:
saturated areas driven by topographic convergence of
subsurface moisture; (B) Floodplain basins: depressions within
floodplains of active river channels with well-developed levees;
(C) Palaeochannels: associated with relict palaeochannel
systems; (D) River channels: pools located in perennial or
seasonally active river channels; and (E) Spring-fed pools.
doi: 10.1371/journal.pone.0081931.g003
Figure 2. Diagram showing the different water body types. Included is a description of the key hydrological processes taking
place in the dry and wet seasons within each water type found in the Namwawala area, Kilombero Valley, southern Tanzania.
doi: 10.1371/journal.pone.0081931.g002
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groundwater spring in the study area. This was not evident in
the remotely sensed data and was mapped in the field.
Water body sampling. The landscape was divided into
distinct geomorphological zones through manual interpretation
of Landsat imagery and DEM data and a mapped groundwater
spring. Random stratified sampling was used to distribute 65
sample locations within these zones (Figure 4) in proportion to
the observed frequency of each water body type in the study
area. All locations were situated within 3 km of an occupied
house, within the typical flight range of female An. gambiae
[57]. Eight points were located in floodplain basins, eleven
points were located in areas of topographic convergence, and
only one located in the groundwater spring. Numerous habitats,
however, were located in seasonally active river channels (22)
and depressions within palaeochannel systems (23).
Sample locations were determined by field visits during the
dry season in September-October 2011. At the first field visit to
each location, the closest standing water body within a 150 m
radius was selected as the sampling location, and its position
recorded using a handheld Garmin Etrex GPS receiver with a
horizontal accuracy of approximately 5 m. If a water body was
not found within the search area the sample location was
centred in an area where a water body was most likely to
occur. This was determined by looking for depressions in the
Figure 4. Larval sample locations categorised by water body type. The background image was captured on 10th July 2001 by
Landsat Thematic Mapper. The image is displayed as a false colour composite (red = band 7, green = band 5 and blue = band 4)
with bright green indicating developing vegetation, dark green indicating mature or sparse vegetation, purple indicating bare soil and
black representing water.
doi: 10.1371/journal.pone.0081931.g004
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local terrain and identifying features such as caked mud, the
presence of hydrophilic vegetation and dried hoof prints
representing a potential watering hole for cattle.
Each location was visited 13 times from November 2011 to
October 2012 at a frequency of approximately once every four
weeks. At each visit, all water bodies within a 25 m radius of
the location were identified and up to five water bodies were
selected at random for surveying and their location recorded
using the handheld GPS. A description of the site was taken,
including the width and length of the water body. This was used
to estimate habitat size by calculating the area as an ellipse
and taking the outer 50 cm to represent the shallow edges of
the water body where larvae tend to occur [23].
A purposive dipping strategy was employed [7,24] using a
350 ml dipper, whereby dips were made in places most likely to
harbour larvae, such as around clumps of vegetation or
protruding substrate, amidst floating debris, and along the
periphery of the water body. The number of dips was decided a
priori based on the size of the water body to be surveyed. A
minimum of 10 dips were taken at each water body with the
number increasing up to 40 for large water bodies (> 40 m in
length). Other studies have adopted the use of sweep nets to
determine the abundance of larvae [2] but the dimensions of
these nets exceed the size of small scale aquatic habitats,
such as hoof prints, at the fringes of larger pools of water.
Each dip was examined in a white plastic tray. Anopheline
and culicine larvae were differentiated macroscopically based
on body position and morphology [54]. Counts were made of
early (1st-2nd instars) and late-stage (3rd-4th instars) anopheline
larvae [58]. Where the total number of larvae caught in all dips
at a water body exceeded 10, a random sample of 10 larvae
was taken and specimens were stored in separate 1.5 ml
eppendorfs in 98% ethanol for subsequent molecular species
identification. Where the total number of larvae per water body
was 10 or fewer, all larvae were taken for species identification.
Pupae were not counted because anopheline pupae cannot
readily be morphologically distinguished from culicine pupae in
the field [2,54].
Genomic DNA was extracted from individual larvae and the
amplification of ribosomal DNA was made using a multiplex
polymerase chain reaction (PCR) for identification of the four
sibling species of the Anopheles gambiae complex (An.
arabiensis, An. gambiae s.s., An. merus and An.
quadriannulatus ) [59]. Unamplified DNA was tested by a
further PCR assay with the capacity to identify five species of
the Anopheles funestus group including An. funestus s.s., An.
leesoni, An. parensis, An. rivulorum and An. vaneedeni [60].
Data analysis
Hydrometric data. Daily total rainfall was calculated for
each rain gauge. Pairwise relationships between the gauges
were analysed using Spearman rank correlations. The gauges
were used to calculate areal average rainfall for the study area.
Hourly water table depths were calculated by subtracting
recorded water depth from the depth of the pressure
transducer below the surface.
Entomological data. In order to focus on indicators of
habitat quality for malaria vectors, the number of late instar An.
arabiensis larvae per dip was estimated [61–63]. Analysis was
restricted to An. arabiensis as this was the only primary malaria
vector species found in sufficient numbers (Table 1). Estimated
numbers per dip and confidence intervals were derived using
Generalized Estimating Equations (GEE) over the total number
per dip of late instar stage anophelines and the proportion of
An. arabiensis found in the PCR samples via bootstrapping of a
mixture distribution. Analyses were performed with the geepack
package [64], and the boot package [65] for R [66]. Contrasts
in the number of late instar An. arabiensis larvae per dip
between water body types were calculated using the Method of
Variance Estimates Recovery (MOVER) [67]. A detailed
description of the statistical analyses of entomological data can
be found in the Methods S1.
It is important to note that the above methods make a
number of inferences that must be acknowledged. The quality
of the data was not optimal owing to the presence of zeros in
larval numbers, inaccessibility of some locations during the wet
season, over-dispersion, unbalanced surveys, and lack of
knowledge regarding the covariance structure of the residuals.
As a result, our analysis is free from a time dependent structure
accounting for time correlations in the number of larvae.
However, as a precautionary measure, our sampling dates
were set at periods long enough to minimise causality due to
autoregressive processes (i.e. periods longer than a
generation).
The bootstrap estimated number of late instar An. arabiensis
larvae per dip, including upper and lower 95% confidence
intervals, were multiplied by the total area of available habitat
per water body type, based on field observation of water body
dimension per sample round, to derive an area-weighted
abundance estimate of late-stage An. arabiensis larvae. This
was compared with the hydrometric data, aggregated to
monthly time steps to match the entomological sampling
frequency, using Cross Correlation Functions in R [66] taking
into consideration lagged relationships. Due to the highly
variable nature of the larval data autoregressive time series
Table 1. Total Anopheles species count gathered
throughout the sampling period and relative proportions.
Count % of total
An. gambiae s.l. complex
An. Arabiensis 503 25.2
An. gambiae s.s. 0 0
An. Merus 0 0
An. Quadriannulatus 0 0
An. funestus group
An. funestus s.s. 37 1.9
An. Leesoni 1 0.1
An. Parensis 0 0
An. Rivulorum 12 0.6
An. Vaneedeni 0 0
Non-amplified specimens
All 1445 72.3
Total 1998
doi: 10.1371/journal.pone.0081931.t001
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analysis was not possible; this would require a much larger
number of sample locations.
Results
Hydrology
The study area received a total of 1175 mm rainfall over the
12 month study period compared to a historical (1969-2010)
annual average of 1186 mm recorded at a gauge near Ifakara.
December was wetter than average and February was
considerably drier receiving over 100 mm less rainfall
compared to the historical average (Figure 5). Total monthly
rainfall masks the intensity of individual rainfall events. Most
notably, over 40% of the rainfall in both December and March
occurred over a 24 hour period on 19th December and 16th
March, respectively (Figure 6A).
Precipitation was not distributed evenly over the study area.
For instance, on 19th December 131 mm was recorded at one
rain gauge and just 18 mm was recorded at another less than
15 km away. Pairwise Spearman rank correlations showed that
daily rainfall totals recorded at one pair of gauges were not
significantly correlated (p = 0.52). The gauges were located
only 20 km apart with a difference in elevation of less than 20
m. Rainfall recorded at all the other gauges were significantly
correlated (p < 0.01).
River stage rose rapidly in response to rainfall (Figure 6B),
particularly following intense rainfall events in December 2011
and March 2012. For instance, following the 16th March rainfall
event the stage at the upper river gauge rose from 0 cm to 115
cm and fell to 4 cm over a period of four hours. Further
downstream at the middle and lower gauges persistent rainfall
kept stage heights above zero from April through to mid-July.
During April and May 2012 the stage height exceeded the
height of the river banks at the middle and lower gauges
leading to overbank flooding. During this period, the water table
remained high (Figure 6C) with one gauge positioned close to
the Kilombero River recording negative depth values indicating
that water was pooling at the surface.
Entomology
Of the 1998 larvae taken for species identification, a majority
were unamplified in the PCR process (Table 1) and were likely
to be other species of Anopheles which are not malaria vectors
(PCR tested for all significant vectors in the region). No An.
gambiae s.s. larvae were found and less than 2% were
identified as An. funestus. An. arabiensis made up over 25% of
the total count. Most of the specimens identified as An.
funestus (33) were found in water bodies located within
ephemeral river channels. These habitats persisted throughout
the hydrological year as shallow pools in the dry season which
connect during the wet season as flowing water.
The variation in estimated number of late-stage An.
arabiensis larvae per dip (Figure 7A) and area-weighted
abundance estimate of late-stage An. arabiensis larvae (Figure
7B) over the sampling period were similar suggesting that
habitat size did not control the density of larvae found in each
water body type. The abundance of An. arabiensis larvae
increased in areas of topographic convergence from May to
July 2012 following the peak of the long rainy season. Habitats
within floodplain basins also showed an increase during this
period following a peak in river stage which exceeded the bank
level leading to flooding. River channel and palaeochannel
habitats had background levels of vector larvae for most of the
sampling period; however, both showed a reduction at the
height of the long rains in April and May 2012. River channel
habitats supported relatively high abundance of vector larvae
over the dry season and short rains, from December 2011 to
March 2012, when the river was not flowing, leaving a series of
disconnected pools in the river bed. The spring fed pond was
also shown to support high larval abundance during dry
periods, most notably in August 2012. Despite this, very few
numbers were found in the spring fed pond during the short
and long rains.
The estimated number of late-stage An. arabiensis larvae
per dip in each water body type were shown to be variable over
the sampling period (Figure 8). This was particularly true for the
sample round immediately following the wet season (19th June
2012), during which estimated vector larvae per dip were
shown to be significantly different between every water body
type. Throughout the sampling period the spring fed pond
habitat type tended to be distinct from other water body types,
with the highest larval densities in the study area recorded in
four out of the thirteen sample rounds.
Cross Correlation Function analysis showed that the area-
weighted abundance estimate of late-stage An. arabiensis
larvae across all the sites was not significantly related to the
hydrometric data (Table 2). However, relationships existed
when the abundance estimate was aggregated by water body
type. Abundance estimates from areas of topographic
convergence were positively related to river stage and were
negatively related to water table depth reflecting a dependence
Figure 5. Total monthly rainfall. Rainfall was recorded using
a network of eight tipping bucket rain gauges positioned
throughout the study area. Historical mean (1969-2010) is
calculated using rain gauge measurements recorded near the
town of Ifakara located 30 km east of the study area.
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on the wet season to raise water tables resulting in surface
ponding. Floodplain basin abundance estimates were also
related to river stage but with a one month lag. Here, flooding is
widespread during the peak of the wet season but these water
bodies only support vector larvae once the flood water has
receded, due to infiltration and evaporation, to form smaller,
shallower pools of water. Abundance estimates in
palaeochannels had a positive relationship with river stage with
a three month lag indicating that these habitats cannot support
vector larvae during or shortly after wet periods.
Discussion
Nearly three quarters of the larvae identified to species level
were not An. gambiae s.s., An. arabiensis or An. funestus, the
major contributors to malaria transmission in Africa [39,41]. No
Figure 6. Graphs showing (A) areal averaged rainfall
from eight gauges distributed throughout the study area
summarised as daily totals; (B) mean hourly river stage
height with bank level reference line for the middle and
lower gauges; and (C) mean hourly water table depth
below the surface.
doi: 10.1371/journal.pone.0081931.g006
An. gambiae s.s. were found and only small numbers of An.
funestus were identified, whereas one quarter of the larvae
tested were identified as An. arabiensis. The low densities of
An. funestus are consistent with previous surveys of adult
mosquitoes in Namwawala village [35,37,42,68]. The apparent
absence of An. gambiae s.s. parallels observations in the adult
population, reflecting the success of and long-lasting
insecticidal net (LLIN) distribution programmes [39,41],
suppressing anthropophagic species, such as An. gambiae s.s.
and An. funestus, that are highly dependent on obtaining
human blood indoors [69]. By contrast, An. arabiensis is not
only more zoophilic, exophagic and exophilic, it also appears
capable of safely entering and exiting houses containing LLINs
even where and when it remained fully susceptible to their
pyrethoid active ingredients [70,71], so this species can be
described as being resilient to this vector control intervention
[8,72].
A previous study has demonstrated an increase in vector
population and subsequent malaria transmission at the height
of the wet season [29]. However, this study found that overall
area-weighted abundance estimate of late-stage An. arabiensis
larvae fell across all habitat types during periods of prolonged
rainfall associated with the height of the wet season in April
and May 2012. This included a spring fed pond which is a
permanent water body and is therefore assumed by the global
index of malaria stability to be independent of seasonal
fluctuations in rainfall [73]. Numbers of An. funestus were
restricted almost exclusively to ephemeral river channels where
water bodies persisted throughout the dry season as shallow
disconnected pools, which reconnect in the wet season as a
flowing river. This is consistent with observations of An.
funestus behaviour showing them to have a preference for
more persistent water bodies [53,74,75]. Although the precise
location of such habitats may be difficult to predict, the
ephemeral channels in which they are located are often
mapped or can be readily identified in high spatial resolution (2
m) satellite imagery. Despite the availability of surface water
throughout the hydrological year, no An. funestus were found in
the spring-fed pond. Factors leading to this absence are
uncertain and can perhaps be attributed to the relatively short
study period leading to anomalous observations. However,
environmental factors may also account for the absence of An.
funestus, for instance the spring-fed pond is open and sunlit,
whereas river channel habitats are characterised by
overhanging tree canopies providing shade, a factor which has
previously been shown to be significantly related to the
abundance of An. funestus larvae [76].
Large-scale studies into climatic drivers of malaria
transmission are often based on monthly aggregates of
environmental data, including precipitation [14]. However, total
monthly rainfall recorded in the Namwawala area over the
sampling period masked the intensity of individual rainfall
events, which can be an important indicator of a reduction in
larval numbers due to the flushing out of habitats and
displacement or death due to rain pounding [31,74]. This study
found that daily measurements of rainfall are sufficient to
capture these events.
Anopheles Hydrology
PLOS ONE | www.plosone.org 8 December 2013 | Volume 8 | Issue 12 | e81931
Figure 7. Plots of An. arabiensis estimates per water body type. (A) Bootstrap prediction estimates of late-stage An. arabiensis
larvae per dip and (B) area-weighted abundance estimate of late-stage An. arabiensis larvae for each water body type. Area-
weighted abundances and their 95% confidence intervals were calculated by multiplying estimated habitat size by the number of
late-stage An. arabiensis larvae per dip estimated by bootstrapping a mixture distribution generated from GEE estimates of number
of late-stage anophelines and the probability of finding An. arabiensis in the PCR samples. The hydrometric data is added for
reference including hourly areal average rainfall, river stage recorded in the middle of the study site catchment and water table
depth recorded towards the south of the study area.
doi: 10.1371/journal.pone.0081931.g007
Anopheles Hydrology
PLOS ONE | www.plosone.org 9 December 2013 | Volume 8 | Issue 12 | e81931
Area-weighted abundance estimates of late-stage An.
arabiensis larvae were not significantly related to monthly
aggregates of rainfall, river stage and water table. However,
relationships became clear after the larval data was
aggregated by water body type defined on the basis of its
geomorphological setting and origin. For instance, a significant
relationship existed between estimated vector larval
abundance in areas of topographic convergence with water
table depth, reflecting the topographic organisation of water in
the landscape and the formation of pools following the wet
season [16,45,46]. Vector larval abundance within floodplain
basins was also related to river stage, but with a one month
lag, representing the development of An. arabiensis vector
larvae during the drying out phase of flood waters [28,77,78].
By contrast, vector abundances within palaeochannel habitats
were related to river stage with a three month lag. This likely
reflects the lack of dependence of An. arabiensis on this water
Figure 8. Contrasts in bootstrap estimated number of late-
stage An. arabiensis larvae per dip using Method of
Variance Estimates Recovery [67]. Black = significant
difference (95% confidence), grey = no significant difference,
blank = not available (due to absence of larvae in one or both
habitat types). T = topographic convergence, F = floodplain
basin, P = palaeochannel, R = river channel and S = spring-fed
pond.
doi: 10.1371/journal.pone.0081931.g008
body type during the height of the wet season. Again, the
drying out phase of aquatic habitats appears to play a crucial
role [28,77,79] with smaller, shallower and more turbid water
bodies [74] providing dry season refuges in palaeochannels as
the availability of water in other habitat types disappears,
specifically floodplain basins and areas of topographic
convergence.
Studies in areas such as the western Kenyan highlands have
established relationships between hydrology and malaria
vector numbers using terrain analysis because the distribution
of water in the landscape is controlled by topography
[16,45,46,49]. However, the present study site requires
consideration of more than just topographical controls on
hydrology including the influence of flowing water in rivers and
palaeochannels, overbank flooding and habitats fed by spring
water. We have shown that significant differences in vector
larval abundance occur in habitats when they were classified
by their hydrology and geomorphological setting. Furthermore,
significant correlations existed between larval abundance and
simple hydrometric data. This process based understanding
can be used to model and forecast the spatial and temporal
dynamics of malarial aquatic habitats. These findings should be
incorporated into models of malaria transmission, particularly
those that are limited to the influence of climate and weather on
parasite and vector development [80–82].
The main finding of this study is that the spatial and temporal
variation in malaria vector larvae can be explained according to
the hydrological processes that govern the formation and
persistence of different habitat types. Vector larvae productivity
shifts to different water body types throughout the hydrological
year in response to rainfall and subsequent changes in water
table and river stage. Specifically, floodplain basins and areas
of topographic convergence became dominant in the wet
season with vector larvae retreating to palaeochannels,
ephemeral river channels and a spring fed pond during the dry
season. These dynamics are driven by hydrological and
geomorphological processes, many of which can be mapped
using remotely sensed data with the exception of spring-fed
ponds which are reliant on ground mapping. This approach can
Table 2. Correlation coefficients between the hydrometric data and area-weighted abundance estimate of late-stage An.
arabiensis larvae per water body type.
0 lag 1 month lag 2 month lag 3 month lag
Rain Stage WT Rain Stage WT Rain Stage WT Rain Stage WT
T0.62 0.97** -0.7* 0.58 0.48 -0.39 0.03 -0.08 0.16 -0.06 -0.57 0.39
F-0.99** -0.25 0.1 0.24 0.78* -0.42 0.53 0.29 -0.3 -0.25 0.03 0.06
P-0.46 -0.47 0.59 -0.47 -0.28 0.25 -0.33 0.05 -0.14 -0.02 0.64* -0.56
R-0.28 -0.05 0.11 0.08 -0.07 0.15 -0.09 -0.19 0.26 -0.3 -0.15 0.27
S-0.17 -0.2 0.33 -0.05 -0.27 0.11 -0.29 -0.11 -0.02 -0.05 0.61 -0.47
All -0.35 -0.29 0.41 -0.09 -0.23 0.14 -0.26 -0.07 -0.01 -0.12 0.52 -0.41
Analysis carried out using Cross Correlation Functions. T = topographic convergence, F = floodplain basin, P = palaeochannel, R = river channel, S = spring-fed pond, WT =
water table.Analysis carried out using Cross Correlation Functions.
Significant to Bonferroni adjusted confidence intervals at 99%** and 95%*.
Rain = rainfall, Stage = river level, WT = water table depth
T = topographic convergence, F = floodplain basin, P = palaeochannel, R = river channel, S = spring-fed pond, WT = water table.
doi: 10.1371/journal.pone.0081931.t002
Anopheles Hydrology
PLOS ONE | www.plosone.org 10 December 2013 | Volume 8 | Issue 12 | e81931
provide valuable information for larval source campaigns for
targeting productive habitats, particularly during the dry
season.
Supporting Information
Methods S1. Description of the statistical analysis of
entomological data: Generalized Estimating Equations and
Method of Variance Estimate Recovery.
(DOCX)
Acknowledgements
We would like to thank the Ifakara Health Institute for their
support, including Stefan Dongus, Caroline Harris, Jason
Moore, Issa Lyimo, the late Innocent Njoka and Deogratius
Roman Kavishe. In addition, we would like to thank the people
of Namwawala for their warm welcome and guidance. The
authors are grateful to Jeffrey Shaman and an anonymous
reviewer for helpful feedback on the manuscript.
Author Contributions
Conceived and designed the experiments: AJH JGPG DEC
MGM MWS JK GFK CJT. Analyzed the data: AJH JGPG DEC
CJT. Wrote the manuscript: AJH JGPG DEC MGM MWS GFK
CJT. Field work and data collection: DEC AJH JK GNL.
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PLOS ONE | www.plosone.org 13 December 2013 | Volume 8 | Issue 12 | e81931
