Survivorship of Anopheles darlingi (Diptera: Culicidae) in
Relation with Malaria Incidence in the Brazilian Amazon
Fábio Saito Monteiro de Barros1*, Nildimar Alves Honório2, Mércia Eliane Arruda3
1 Departamento de Zoologia, Universidade Federal de Pernambuco, Recife, Brazil, 2 Laboratório de Imunoepidemiologia, Centro de Pesquisas Aggeu Magalhães,
Fundação Oswaldo Cruz, Recife, Brazil, 3 Laboratório de Transmissores de Hematozoários, Departamento de Entomologia, Instituto Oswaldo Cruz-Fiocruz, Rio de Janeiro,
Brazil
Abstract
We performed a longitudinal study of adult survival of Anopheles darlingi, the most important vector in the Amazon, in a
malarigenous frontier zone of Brazil. Survival rates were determined from both parous rates and multiparous dissections.
Anopheles darlingi human biting rates, daily survival rates and expectation of life where higher in the dry season, as
compared to the rainy season, and were correlated with malaria incidence. The biting density of mosquitoes that had
survived long enough for completing at least one sporogonic cycle was related with the number of malaria cases by linear
regression. Survival rates were the limiting factor explaining longitudinal variations in Plasmodium vivax malaria incidence
and the association between adult mosquito survival and malaria was statistically significant by logistic regression (P,0.05).
Survival rates were better correlated with malaria incidence than adult mosquito biting density. Mathematical modeling
showed that P. falciparum and P. malariae were more vulnerable to changes in mosquito survival rates because of longer
sporogonic cycle duration, as compared to P. vivax, which could account for the low prevalence of the former parasites
observed in the study area. Population modeling also showed that the observed decreases in human biting rates in the wet
season could be entirely explained by decreases in survival rates, suggesting that decreased breeding did not occur in the
wet season, at the sites where adult mosquitoes were collected. For the first time in the literature, multivariate methods
detected a statistically significant inverse relation (P,0.05) between the number of rainy days per month and daily survival
rates, suggesting that rainfall may cause adult mortality.
Citation: Barros FSMd, Honório NA, Arruda ME (2011) Survivorship of Anopheles darlingi (Diptera: Culicidae) in Relation with Malaria Incidence in the Brazilian
Amazon. PLoS ONE 6(8): e22388. doi:10.1371/journal.pone.0022388
Editor: Nirbhay Kumar, Tulane University, United States of America
Received March 6, 2011; Accepted June 20, 2011; Published August 8, 2011
Copyright: ß 2011 Barros 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: The Interamerican Institute for Global Change Research, grant CRN no. 048. 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: fsaito1@yahoo.com.br
association of survival rates with malaria incidence was studied in
detail.
Introduction
Malaria remains one of the most important infectious diseases
in the world, and has reemerged in tropical regions that
experience rapid population growth [1]. The understanding of
age composition of anopheline mosquito populations has been
considered crucial for explaining transmission and evaluating the
success of control efforts [2]. The probability of survival of the
vector may be the most critical factor in the transmission cycle of
arthropod-borne diseases [3–4]. A considerable number of
individuals of a given vector species must survive long enough
to allow sporogonic development of Plasmodium spp. for it to be an
efficient vector. However, little is known of age composition and
survival rates of anopheline species in the Amazon region. We
have reported parous rates and methods for determining parity
status, as well as dispersal, of Amazonian anophelines [5–6].
Recently, Fouque et al. [7] have observed that peak malaria
incidence in the Maroni region of French Guiana was associated
with higher Anopheles darlingi survival rates, the most important
vector in the Amazon [8–10] but data were not statistically
significant. In this paper, we present longitudinal variations in the
age structure and survivorship of An. darlingi in a frontier zone of
the State of Roraima, in the northern Brazilian Amazon. The
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Materials and Methods
The protocol was approved by the ethics committee of the
Oswaldo Cruz Foundation.
Study site
The study was conducted within an agricultural frontier settlement
of Rorainópolis, 300 km south of Boa Vista, the capital of the
Province of Roraima, in the northern Brazilian Amazon. Climate
and ecoregional characteristics of Roraima have also been previously
reported [11]. Located deep inside the rainforest, most settlements in
the area are recent (,ten years, at the time of study) and composed of
multiple sideroads that run perpendicular to a main road, forming a
characteristic fish-bone pattern. Malaria in the area is unstable and
hypoendemic, predominantly due to Plasmodium vivax [12]. Sideroad
19 (00u519N, 60u219W) was chosen due to its higher malaria
endemicity, in comparison with neighboring sideroads (Rorainópolis,
Municipal Health Service, data not shown).
Sideroad 19 is a typical secondary road composed of 66
inhabited houses. Residences are spaced at 300 m intervals and
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Mosquito Survivorship and Malaria in the Amazon
lined up near the sideroad. The sideroad branches perpendicularly
from the main road for 18.8 km into the rainforest.
Mosquito collections
Adult collections were conducted during six bimestrial collecting
excursions from August, 2003 to July, 2004, including November,
2003; January, 2004; March, 2004; and May, 2004. Captures
were performed in six collecting stations, during four consecutive
days. Adult mosquitoes were collected on the act of landing and
identified using Consoli and Lourenço-de-Oliveira (1994) [14].
Residual spraying was performed irregularly and with large
intervals between applications. Collections were not performed
within three months of residual spraying. Bednets, which were
unimpregnated, were used irregularly by only three families in the
Sideroad. Collections were not performed in houses with bednets.
Further methodological details have been previously described
[15].
Climate and rainfall
In the study area, only two seasons can be distinguished, the dry
season and the rainy season. A well-defined six month-long dry
period lasts from November or December to April or May, with
55–60% of the precipitation occurring from May to July [13]. A
pluviometer was installed in the study site for daily monitoring of
rainfall throughout the collection period, from August, 2003, to
July, 2004. Annual rainfall was 1367 mm/m2. Total monthly
rainfall for the collection period is shown in Table 1. Temperature
and humidity were measured by digital devices during the
collection periods. Mean temperature was 28.0uC (SD = 1.11),
and relative humidity was around 58% (SD = 2.88), with little
yearlong variation.
Appearance of the ovarioles after oviposition and
duration of the gonotrophic cycle
Human malaria data
If the time for egg-development is known or accounted for, the
approximate duration of the gonotrophic cycle, i.e. the time
elapsed from one blood-feeding to the next, would vary as a
function of the time elapsed from oviposition to blood-feeding.
The time from oviposition to blood-feeding can be estimated by
examining the condition of the terminal portions of the ovarioles,
i.e. the ovariolar stalks, after passage of the eggs [2], in females in
Sella’s stage 1, 2 or 3 [16]. When the female oviposits, the eggs
pass through the terminal portions of the ovarioles. These
previously narrow conducts stretch to many times their original
size to allow passage of eggs. In females that have recently
oviposited (,24 h), the ovarioles still presents sac-like distensions
due to this stretching. Within one day, the dilated portions return
to their original size through progressive stages. It should be noted
that there is no way of recognizing if the females have taken more
than 24 h to find a host, since oviposition.
If egg-development is assumed to take two days [17–18], the
duration of the gonotrophic cycle (n), in days, can be estimated
from the ratio of mosquitoes with contracted ovariolar stalks (c)
c
and the total number collected (t) as follows: n~2z . This
methodology has been previously used [19–21] and it twill be
referred to, in this article, as ‘‘Charlwood’s minimum cycle
To explore seasonal incidences and spatial distribution, the
number of cases within the period from January, 2002, to
December, 2004, was analyzed. Malaria morbidity data was
collected retrospectively from the Rorainópolis Municipal Health
Service. Facilities were permanently available for diagnosis of cases
through microscopic examination of blood smears. Only the
number of patients with malaria per month was used in the study
and there was no need for storage of patient information in the
hospital database, which means written consent for research was
not needed. Thick films were stained with 10% Giemsa solution
and examined at 61,000 under oil immersion by an expert
microscopist with over 5 years of experience. Every 1–2 weeks,
health agents would periodically perform thick blood smears in
residents presenting non-specific acute febrile symptoms and their
household contacts. All new positive malaria cases, irrespective of
age, were enrolled for the study. Plasmodium vivax malaria cases
were promptly treated with chloroquine and primaquine and P.
falciparum with quinine or mefloquine. Cases that occurred less
than 50 days after the first day of drug treatment were excluded
from the study because it was unclear if they represented new
cases, treatment failure or disease relapse.
Table 1. Total monthly rainfall (mm/m2), number of wet days, from August 2003–July, 2004.
Month
Rainfall (mm)
No. wet days
Cumulative malaria cases (P. vivax)
Mean daily temperatures (6C)
Aug
106
19
7
27.4
Sep
103
20
10
27.8
Oct
99
18
16
28.4
Nov
85
17
20
28.8
Dec
5
8
23
29.0
Jan
23
1
25
28.9
Feb
39
4
16
28.8
Mar
2
3
21
29.0
Apr
150
8
18
28.4
May
364
11
13
26.6
Jun
278
18
10
26.4
Jul
113
20
7
26.0
Total
1367
147
186
The cumulative number of malaria cases per month diagnosed from January 2002 to December 2004 are also shown.
doi:10.1371/journal.pone.0022388.t001
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Mosquito Survivorship and Malaria in the Amazon
method’’ because only the minimum duration of the gonotrophic
cycle can be reliably determined and cycles longer than three days
cannot be detected.
Detinova (1945) [2] classified ovariolar stalk distension into five
categories, from A to E, where A is the most dilated and E the
most contracted. In A there is ‘‘no contraction’’; B represents
‘‘first signs of contraction’’; C ‘‘noticeable contraction’’; in D
‘‘large dilatations’’ have formed; and E corresponds to ‘‘well
defined dilatations’’. Types A to C were considered as
representing recently oviposited females. Females with A, B or
C type sacs were assumed to have returned to feed on the same
day they had laid eggs (a 2 day cycle) while those with type D or
E dilations were assumed to have delayed one day before
c
AzBzC
attempting to re-feed. Therefore, ~
, i.e.
t AzBzCzDzE
corresponds to the number of recently oviposited An. darlingi
females, divided by the total number of An. darlingi females
captured in that collection period.
Data for determining gonotrophic cycle duration was collected
twice, in January, 2004, and July, 2004. Gonotrophic cycle
duration during other dry season collections (January and May)
were considered similar to that determined in January. Cycle
duration during other wet season collections (August and
November) were considered similar to that determined in July.
Dissections were performed using a binocular dissecting
microscope (406) and a fiber-optics light source. Ovarian
observations were performed with a binocular conventional
clinical microscope (100–1,0006), in a field laboratory. Dissections
were performed immediately after identification and determination of Sella’s stage and only mosquitoes dissected less than six
hours after the moment of capture were considered for estimating
gonotrophic cycle durations.
constant densities year-round [25]. In frontier zones, dams are
built for creating readily available water collections, which are
used for bathing, washing, recreation and fishing. They are
created by blocking small streams with raised wooden and earth
barriers. Apparently, predation by introduced or native fish species
in these dams is not sufficient for controlling anopheline larval
densities. Mosquitoes collected within 1,000 m of the temporary
river were excluded from the study. This distance was chosen
based on the proposed flight range of An. darlingi mosquitoes [5],
only 4.5% of mosquitoes emerging in the river would be expected
to fly more than 1000 m.
Comparison between parous rates in different collection
periods
To determine if collection periods differed significantly in the
proportion of the binomial parameter ‘‘mosquito parity’’, a Chisquare statistic test with Yates correction for continuity was
performed using the 262 table describing the binomial variables
number of nulliparous and parous females in each month of
collection [27]. Statistical significance levels for multiple testing
were adjusted using Bonferroni correction for multiple compar0:05
and k = the number of comparisons within
isons, where a~
subgroups [28]. k
Density of mosquitoes of potentially dangerous age and
P. vivax sporogonic cycle duration
Using daily survival rates, the proportion of An. darlingi which
would survive long enough for the complete development of P.
vivax in the mosquito can be calculated. First the duration of the
sporogonic cycle, or extrinsic incubation period, must be known.
This represents the time required from the moment of infection in
the mosquito to complete maturation of sporozoites in the salivary
glands. The time for sporogonic development of Plasmodium spp. in
An. darlingi has not been specifically investigated. It is usually
presumed to vary according to temperature. For P. vivax it would
be around 10 days (range 8 to 13 days) at 27–28uC [29]. The
duration of the sporogonic cycle can be indirectly estimated by the
Moshkovsky method [30–31] which makes use of the Blunck
hyperbolic equation and the Bodenheimer formula for demonstrating the relationship between temperature and gonotrophic
cycle duration. In this method, first the mean daily temperatures
during the period of study must be known (Table 1). The duration
of the sporogonic cycle for P. vivax corresponds to the amount of
days required for the sum of degrees Celsius above the base
outdoor temperature, 14.5uC, on each day, to reach 105, the
degree-days. The Moshkovsky correction factor between the
mosquito resting place and the outdoor temperature was
considered to be 1.0. Once the duration of the sporogonic cycle
(n) is known, the percentage of mosquitoes surviving long enough
for complete maturation of P. vivax can be calculated by pn. Base
temperatures and degree-days for P. falciparum are 16uC and 111,
respectively. For P. malariae, these figures are 16uC and 144,
respectively.
Anopheles darlingi mosquitoes of potentially dangerous age are
here defined by those females surviving long enough for complete
development of P. vivax to an infective stage in their salivary glands
(i.e. the time needed for completion of at least one sporogonic
cycle). The density of dangerously aged mosquitoes (q) can be
estimated by q~HBR|pn , where HBR = human biting rate
(mosquitoes per man/night). Biting densities were directly
measured using the mean number of mosquitoes retrieved in
12 hour collections and are indicated in/man/night.
Data analysis: comparing ovariolar stalk dilations in
different collections
A Chi-square test was also performed on the 562 contingency table
for comparing types of ovariolar stalk dilations (A to E) in two collection
periods, but categories were grouped for obtaining cells .5.
Parous rates and survivorship
Parity was determined by checking multiple parameters, such
as tracheole structure, presence of yellow bodies, presence of
pedicular dilations or the appearance of the ovariolar stalks. Daily
survival
rates
ffi (p) were calculated by Davidson’s method as
pffiffiffiffiffiffiffiffiffiffiffiffi
p~ g parity, where parity represents the ratio between the
number of parous mosquitoes and the total number of females
collected, and g = the duration of the gonotrophic cycle in days
[22]. Ninety five percent confidence intervals (95% CI) for the
proportions of parous females where obtained, as proposed by
Bliss (1967) [23].
Accounting for recruitment fluctuations
Survival rates based on the proportion parous are only reliable
when there is constant recruitment [22]. For example, if one
increases the number of emerging adults, parous rates will tend to
decrease, if mortality is kept constant. Larval density may also
influence adult mosquito survival because high densities decrease
pupal and adult size and fitness [24]. Concomitant larval studies
verified that the quantity of larval habitats and larval densities
were high during the dry season in a temporary river. On the
contrary, larval densities were low during the wet season and the
species apparently disappeared for one month in the river,
presumably due to flushing away of larvae [25–26]. Meanwhile,
small dams provided stable yearlong larval habitats with almost
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per gonotrophic cycle. Dissections for studying follicular dilatations were performed either on the same night of capture or on the
following day, in order to decrease the number of recently
oviposited females. In females that have recently oviposited, i.e.,
types A or B ovariolar stalks where encountered, Polovodova’s
technique can only be applied to follicles that have degenerated in
an early stage of egg development [34]. Several of these nonfunctional follicles had to be visualized for a good estimate to be
made. Counting follicular dilatations in these recently oviposited
females is difficult and occasionally could not be performed. These
mosquitoes were tentatively classified as having one or two
dilatations and treated as ‘‘censored data’’ in the survival analysis
procedure. Multiparous dissections were performed in two study
occasions, in January and July 2004.
Expectation of life, expectation of infective life and
changes in longevity/density factors 1
and expectation
{loge p
of infective life, i.e. the average number of days of infective life per
pn
[32]. To determine
infected mosquito, was determined as
{loge p
the relative contributions of changes in density and longevity on
the impact of residual insecticidal spraying, Garret-Jones & Grab
(1964) [32] formulated the density factor (d) and the longevity
factor (l). The density factor reflects how many times the density of
females was affected and can be calculated as the ratio between pre and
log (ppost{spraying )
post-spraying expectations of life: d~ e
. The longevloge (ppre{spraying )
ity factor is the ratio of the mosquitoes’ expectation of infective lives,
pn pre{spraying
log (ppost{spraying )
| en
. The total
calculated as: l~
p post{spraying
loge (ppre{spraying )
impact of residual insecticide spraying is given by the product d6l. We
postulated that the similar principles could be used to study seasonal
variations of An. darlingi. Since HBR is a function of the proportion of
mosquitoes biting man and the total population, it will vary with
breeding rates, survival rates [33] and sampling error. Assuming
sampling error was small and the proportion biting man is a speciesspecific constant [15] we attempted to determine the relative
contribution of survival and breeding by verifying how much of the
density variation was due to survival rates. Survival and density are
interrelated factors and if both parameters vary concurrently, an
observed decrease in parous proportion will have an expected decrease
in HBR [32], because the equations imply that recruitment was kept
constant. A reduction in daily survival rates from 0.9 to 0.8
(exemplifying on a parasite with a 12 day sporogonic cycle), should
cause a 2.11 times reduction in adult mosquito density. Field
measurements can measure the amount of reduction in HBR that
actually occurred: if the observed equals the expected, it would be
assumed that only survival rates were affected. If the measured HBR
was less than expected, it would be assumed that breeding rates were
negatively affected, and if HBR is more than expected, then breeding
rates and recruitment would have increased. We compared the
expected decreases in mosquito density to actual decreases observed in
human biting rates. Changes in the longevity factor formula had to be
introduced to account for varying sporogonic cycle durations from the
pn1 dry
log pwet
dry to the wet season: l~
| ne
, where n1 and n2 are
loge pdry
p 2 wet
the sporogonic cycle durations in the dry and wet seasons, respectively.
The fit with the highest Komolgorov-Smirnoff d statistic and Pvalue was the unweighted exponential model (d = 0.076; P,0.05).
The exponential model was therefore adopted for further study
and survival curves were obtained by a least squares fit regression
of the exponential function y = bNexp(aNx), where b and a are
independent parameters, as described by Service (1993) [22]. A
weighted regression fit was used to account for the different sample
sizes in each age group.
Probabilities of surviving sporogony of different
Plasmodium spp
Survival and failure time analysis: Kaplan-Meier productlimit method and comparison between survival curves
The probability of a mosquito surviving the duration of the
sporogonic cycle varies with the Plasmodium spp. being considered.
We determined sporogonic cycle durations for P. falciparum and P.
malariae, for each collecting period with the Moshkovsky method.
For comparing transmission with that of P. vivax, we developed a
relative transmission ratio by dividing the probabilities of surviving
sporogony (PSS) of each species. The ratio of ‘‘probability of
surviving P. falciparum sporogony’’ divided by the ‘‘probability of
surviving P. vivax sporogony’’ estimates the degree to which a
mosquito population will survive to transmit falciparum malaria as
compared to vivax malaria.
For survival analysis, we used the ‘‘survival and failure time
analysis’’ module supplied in StatisticaTM version 6.0 (StatSoft,
Inc., Tulsa, OK). The survival function was estimated using the
product-limit estimator method, as described by Kaplan and
Meier (1958) [37]. A life table was created so that each time
interval contains exactly one case. Multiplying out the survival
probabilities across the ‘‘intervals’’ (i.e., for each single observation) the estimates survival function (S(t)) was calculated directly
from the continuous survival times as the non-parametric maximum
d(j)
i{j
, where P denotes
likelihood estimate using S(t)~ P
ti ~1 i{jz1
the multiplication (geometric sum) across all cases less than or equal
to t; i is the total number of cases, d(j) is a constant that is either 1 if
the j’th case is uncensored (complete), and 0 if it is censored.
For comparing survival curves obtained in January with July,
both Breslow-Gehan’s generalized Wilcoxon test and the Cox’s F
test were performed.
Expectation of life was determined by
Fitting of survival times with weighted least squares fit
and survival-failure analysis
Mosquitoes were grouped according to age (x) by counting the
number of dilatations in the ovarioles and the number of specimens
(y) in each category was determined. A regression procedure
attempted fitting of the life table into four theoretical distributions
(exponential, Weibull, Gompertz, and linear hazard) based on
algorithms proposed by Kennedy and Gehan (1971) [35]. The
logarithmic transforms of the hazard functions of all four theoretical
distributions were considered linear functions of the log-transformed survival times. Fitting was performed using unweighted least
squares goodness of fit, as well as two methods of weighted least
squares, as proposed by Gehan and Siddiqui (1973) [36]. Fitting was
evaluated by observing the Kolmogorov-Smirnoff goodness-of-fits
statistics and P-values, using a significance level of a = 0.01. The
presence of few categories and small sample sizes in older groups
(.4 parous mosquitoes providing cell counts under 5) prevented
distribution fitting using chi-square goodness-of-fit.
Survival curves obtained with the exponential model
Polovodova’s technique
To confirm the accuracy of survival rates determined by
Davidson’s method, survival was estimated using multiparous
dissections. We performed Polovodova’s technique [2] for
counting follicular stalk dilatations and determining survival rates
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Longitudinal variations of rainfall, daily survival rates, adult
mosquito human biting rates, and density of dangerously aged
mosquitoes are graphically shown in Figure 1. Rainfall appeared
to be inversely related with daily survival rates, density of
dangerously aged mosquitoes and malaria cases.
Data analysis: miscellaneous tests and associations
Two-tailed Mann-Whitney U tests were used for comparing the
means of variables, with the wet and dry seasons as the grouping
variable. Spearman’s rank correlation was used for studying the
association of various variables. Forward multiple regressions were
used for exploring the association of daily survival rates with
meteorological variables and of log(n+1) malaria cases with adult
mosquito variables. For describing pluviometry, three different
parameters were compared: monthly pluviometry, the number of
wet days, and degree of wetness. The number of wet days was
defined as the number of days per month with more than 1 mm/m2.
The degree of wetness was calculated by the product of monthly
rainfall and total number of rainy days. Log transformations were
performed when necessary for meeting assumptions on the normal
distribution of residuals.
A generalized simple linear regression model was used for
exploring the association of the density of dangerously aged
mosquitoes with the log(n+1) malaria cases.
For modeling percentages, the logit model was preferred over
linear regression, because it is not based on any assumptions
regarding the variance among collections periods. Additive logistic
regression modeling was used for exploring the association of the
dichotomic variable parity status (nulliparous or parous output)
with the number of wet days per month. Since daily survival rates
are derived from a transformation of parous percentages, a logit
model was also used for associating daily survival rates with the
log(n+1) malaria cases.
Appearance of the ovariolar stalks after oviposition and
duration of the gonotrophic cycle
The appearance of ovariolar stalks after oviposition is shown in
Table 2, as well as gonotrophic cycle duration. For obtaining cells
.5, grouping of categories A–C and D–E was necessary, resulting
in a 262 table for January and July 2004. A Yates corrected chisquare test of the table determined that the two groups differed
significantly (X2 = 10.07; P,0.005).
Parous rates and survivorship of dangerously aged
mosquitoes
Daily survival rates, expectations of life, and expectations of
infective life (for P. vivax) for An. darlingi in each collection period
are presented in Table 3.
The sporogonic cycle durations and PSS of An. darlingi for the
complete development of P. vivax varied from 0.52% in August to
50.41% in January, as shown in Table 3. The proportion of
mosquitoes surviving through 10 days is also given. The mean
biting density of An. darlingi is indicated, as well as the estimated
biting density of dangerously aged mosquitoes, which varied from
0.004 to 2.057 mosquitoes/man/night in August and January,
respectively.
Results
General results
Grouping collections into dry and wet season categories
A total of 2,193 An. darlingi were captured and 756 (34.47%)
were dissected for determining daily survival rates and/or
gonotrophic cycle duration. The number of malaria cases per
month in this area is shown in Table 1. In total, 186 malaria cases
of P. vivax malaria were reported. Only two P. falciparum cases were
encountered and none of P. malariae.
Yates corrected chi-square statistic test between parous rates of
An. darlingi in different collection periods are shown in Table 4.
The number of nulliparous and parous females were relatively
similar in July, August and November, as deduced from the nonsignificant chi-square statistics between July–August, July–November and August–November. However, these differed significantly
Figure 1. Davidson’s survival rates for Anopheles darlingi females dissected, number of malaria cases, log rainfall, density of adult
female mosquitoes and of epidemiologically dangerous mosquitoes. Data was obtained in Sideroad 19, from different collection periods,
from August, 2003, to July, 2004. Number of malaria cases were obtained from January 2002 to December 2004.
doi:10.1371/journal.pone.0022388.g001
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Table 2. Appearance of ovariolar stalks of An. darlingi after
oviposition determined in January and July 2004.
Table 4. Yates-corrected chi-square statistics between
number of nulliparous and parous Anopheles darlingi in
different collection periods.
Appearance of ovariolar stalks
January
No. (%)
July
No. (%)
A (no contraction)
58 (50.88)
54 (80.6)
August
B (first signs of contraction)
4 (3.51)
0 (0)
November
C (noticeable contraction)
2 (1.75)
0 (0)
January
D (large dilatations formed)
1 (0.88)
0 (0)
March
E (well defined dilatations)
49 (42.98)
13 (19.4)
May
Total
114 (100)
67 (100)
Gonotrophic cycle duration [2+(A+B+C)/
(A+B+C+D+E)]
2.43
2.19
November
January
0.48
March
May
July
57.46**
9.05*
17.99**
0.11
48.35**
4.90
12.22**
0.23
23.29**
13.03**
97.97**
1.38
13.43**
29.79**
Significant results are indicated:
*Bonferroni corrected P,0.05;
**Bonferroni corrected P,0.001.
doi:10.1371/journal.pone.0022388.t004
doi:10.1371/journal.pone.0022388.t002
longer the sporogonic cycle of the parasite, the more it will be
affected by a decrease in daily survival rates.
from the other three collections, i.e. January, March and May. Also
March and May had similar rates among each other, but that
differed from other months. January differed from all the other
months. The Chi-square statistic together with the parous
proportions and Bliss 95% CI permitted identification of two
groups: one comprising January, March and May; and a second
group August, November and July. The first group was considered
as representing the dry season collections and the second group, the
wet season. May was grouped as dry season presumably because
collections were performed during the first half of the month, while
heavy raining started mainly after the middle half of the month.
Estimated and observed density factors: determining
constant or variable recruitment
The longevity factor between January and July was determined
as 708.63. The density factor expected by the ratio of life
expectations was 6.30, which means that mosquito density was
expected to have decreased 6.3 times, i.e., from 4.08 mosquitoes/
man/night, in January, to 0.65 mosquitoes/man/night in July. In
July, the measured mosquito density was 0.60 mosquitoes/man/
night, which corresponds to a 6.80 times decrease, almost equal to
the predicted value by life expectation ratios. This suggests that the
observed changes in density were secondary to decreased survival
rates alone and not decreased breeding. The data is consistent with
no significant variation in recruitment between the two periods.
The data also mean that variations in survival rates were over two
orders of magnitude more important than variations in density, in
reducing the capacity to transmit malaria.
Probabilities of surviving sporogony of different
Plasmodium spp
The PSS in each collecting period, for P. falciparum and P.
malariae are shown in Table 5. Also shown are the PSS ratios
between these species and P. vivax. A PSS ratio of 1.0 would
predict similar malaria transmission rates and ,1.0 less efficient
transmission than that of P. vivax. This ratio was not constant but
varied along the year, reaching minimum values (less efficient
transmission) in August and July. This means that variations in
mosquito survival rates affect the transmission of Plasmodium spp.
differently, according to the duration of the sporogonic cycles. The
Survival curves
The number of mosquitoes in each multiparous dissection age
category is summarized in Table 6. Weighted linear regression
demonstrated significant fitting of the exponential curves shown in
Table 3. Number of nulliparous and parous Anopheles darlingi females dissected in different collection periods, from August, 2003,
to July, 2004.
Probability of
surviving
sporogony
(probability
of surviving 10
days) in %
Expectation
of life
(expectation
of infective
life), in days
Mean An. darlingi biting
density (estimated mean
biting density of mosquitoes
surviving .1 sporogonic
cycle) in mosquitoes/man/
night
0.72 (0.004)
No. of
nulliparous
females
dissected
No. of
parous
females
dissected
Total
dissected
Proportion
parous
(Bliss 95% CI)
Daily
survival
rates
P. vivax
sporogonic
cycle
duration, in
days
Aug
53
17
70
0.25 (0.15–0.36)
0.524
8.14
0.52 (0.16)
1.55 (0.01)
Nov
54
24
78
0.31 (0.20–0.41)
0.571
7.34
1.64 (0.37)
1.86 (0.04)
0.84 (0.014)
Jan
30
114
144
0.80 (0.72–0.86)
0.911
7.29
50.41 (39.43)
10.40 (5.16)
4.08 (2.057)
Mar
50
47
97
0.48 (0.38–0.59)
0.742
7.24
11.54 (5.07)
3.35 (0.39)
5.64 (0.650)
May
48
65
113
0.57 (0.48–0.67)
0.812
8.68
13.88 (10.27)
4.39 (0.61)
6.24 (0.866)
Jul
185
69
254
0.27 (0.24–0.31)
0.557
9.13
0.45 (0.29)
1.68 (0.01)
0.6 (0.003)
Total
420
336
756
Percent parous, Davidson’s survival rates, probabilities of surviving Plasmodium vivax sporogony, expectations of life and mean An. darlingi biting densities were
determined.
doi:10.1371/journal.pone.0022388.t003
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Table 5. Sporogonic cycle durations for Plasmodium falciparum and P. malariae, probability of surviving sporogony, density of
dangerously aged Anopheles darlingi and probability of surviving sporogony ratios between these species and P. vivax.
Plasmodium falciparum
Sporogonic
cycle duration
(days)
Probability of
surviving
sporogony (PSS)
Plasmodium malariae
falciparum/vivax
probability of
surviving
sporogony ratio
Dangerously aged
An. darlingi biting
density (mosquitoes/
man/night)
Probability of
Sporogonic
cycle duration surviving
sporogony
(days)
malariae/vivax
probability of
surviving
sporogony ratio
Aug
9.740
0.002
0.001
0.356
12.630
0.000
0.055
Nov
8.670
0.008
0.007
0.475
11.250
0.002
0.112
Jan
8.600
0.446
1.818
0.884
11.160
0.350
0.695
Mar
8.540
0.078
0.442
0.679
11.080
0.037
0.319
May
10.470
0.092
0.576
0.665
13.580
0.045
0.327
Jul
11.100
0.001
0.001
0.311
14.400
0.000
0.044
doi:10.1371/journal.pone.0022388.t005
Spearman’s rank correlation of daily survival rates with
meteorological variables
the Kaplan-Meier diagram (Figure 2). The observed number of
mosquitoes is shown as the cumulative proportion surviving and a
logarithmic % survival scale was used for obtaining linear regression
curves. For January, R2 = 0.91, F(1, 142) = 755.31 (P,,0.001). For
July, R2 = 0.97, F(1, 248) = 6985.55 (P,,0.001). The regression
equation, determined in January, was y = 4.087Ne(xN-0.426), where x
corresponds to the number of gonotrophic cycles that have already
been completed. The regression equation, determined in July, was
y = 5.177Ne(xN-1.148). Survival rates per gonotrophic cycle and per day
were determined as shown in Table 6.
Spearman’s rank correlation between daily survival rates and
the number of wet days was significant with non-corrected P-levels
(r = 20.88; P,0.05), but only marginally significant with Bonferroni adjusted significance (P = 0.07). Spearman’s rank correlation
was not significant between daily survival rates and temperature
(r = 0.02; Bonferroni adjusted P.1.0), humidity (r = 20.42;
Bonferroni adjusted P.1.0) or degree of wetness (r = 20.25;
Bonferroni adjusted P.1.0).
Comparison of survival curves in each season
Additive logistic model of parous rates and the number
of wet days
Cox’s F-Test revealed significant differences between survival
rates per gonotrophic cycle in January and July (F (500, 288) = 1.92;
T1 = 186.9; T2 = 207.1; P,,0.0001), with higher survival rates in
January. Similarly Gehan’s Wilcoxon Test was also significant (test
statistic = 27.52; P,,0.0001) for differences between these two
months.
Logit regression was performed using parity status as the dependant
variable, the number of wet days as the independent variable, and the
percent parous or nulliparous mosquitoes as the count variable. The
regression equation obtained was y = 0.92120.097Nx (X2 = 73.50
p,,0.001). This permits calculating the estimated %parous
according to the number of wet days (x). If x = 30, y = 21.99, for
example, parity can
obtained
by inverting the logit equation as
be
ey
follows: parity~
= 0.12, i.e. a parous rate of 12%. If the
1zey
number of wet days is 0, parous rates of 72% would be obtained, while
15 wet days correspond to a 37% parous rate.
Comparison of multiparous survival curves with daily
survival rates
With Davidson’s method we determined daily survival rates of
91% and 55% in January and July, respectively. The survival rate
for completing one sporogonic cycle (pn) corresponds to 79.1% in
January and 27.1% in July, while the survival rates determined by
multiparous dissections for each sporogonic cycle were 65% in
January and 30% in July (Table 6).
Number of malaria cases in dry and wet seasons
The number of malaria cases in the dry and wet seasons differed
significantly by Mann-Whitney U test (U = 4.50; P,0.05). The
number of malaria cases was higher during the dry season.
Forward multiple regression for associating daily survival
rates with meteorological variables
Forward stepwise multiple regression for associating
malaria cases with adult mosquito variables
To explore the association of daily survival rates with
meteorological parameters, survival rates in each month were
used as the dependant variable in a forward multiple stepwise
regression. Independent variables were the number of mosquitoes
dissected, the number of wet days in the month of collection, adult
densities, monthly rainfall, degree of wetness, temperature and
humidity. The only significant variable to enter the model was the
number of wet days (R2 = 0.80; F (1, 4) = 16.16; b = 20.89, SE
b = 0.22; B = 20.01; P,0.05).
A forward stepwise multiple regression was performed using the
log(n+1) number of malaria cases as the dependent variable.
Independent predictor variables were daily survival rate, parity ratio,
adult densities, rainfall, number of mosquitoes surviving more than 10
days, temperature, and humidity. The only variable to be admitted in
the model was daily survival rate (R2 = 0.92; F (1, 4) = 23.36; b = 0.92,
SE b = 0.19; B = 1.65; P,0.01).
Association between human biting rates, daily survival rates
and number of malaria cases by Spearman’s rank correlation
Parity in wet and dry seasons
A two-tailed Mann-Whitney U test of the percent parous in the
dry and rainy seasons revealed significant differences (P,0.05),
with higher parous rates in the dry season.
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Spearman’s rank correlation between adult densities and the
log(n+1) of malaria cases in Sideroad 19 was not significant
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Mosquito Survivorship and Malaria in the Amazon
per year (30 cases in three years) would be expected if parous rates
were around 71%, but only 1 case per year if parous rates were
around 16%.
Table 6. Number of dilatations in collections of Anopheles
darlingi collected in January and July, 2004.
July 2004
No. (%)
Simple regression of log malaria cases and biting density
of dangerously aged mosquitoes
53 (30.4)
185 (71.4)
40 (22.9)
38 (14.7)
2
30 (17.2)
18 (6.9)
3
17 (9.8)
8 (3.1)
4
3 (1.7)
1 (0.4)
5
1 (0.6)
0 (0)
Uncountable*
30 (17.2)
9 (3.5)
Total
174 (100)
259 (100)
Survival rate per cycle (per day**)
0.65 (0.83)
0.30 (0.58)
A simple regression between the log(n+1) of malaria cases as the
dependant variable and the density of dangerously aged
mosquitoes as the grouping variable showed a significant
association between the two parameters (R2 = 0.71, F(1, 4) = 9.97;
P,0.05). The regression equation obtained, converted from
log(n+1) cases to number of malaria cases, was the following:
:
no:malaria~100:916zx 0:294 {1, where x = density of dangerously
aged mosquitoes. This means that a density of one dangerously
aged An. darlingi per night would be associated with 5 cases of
malaria per year in Sideroad 19 (15 in three years). For every
increase in one dangerously aged mosquito per night the number
of malaria cases doubles: two is associated with 10 cases per year,
three with twenty etc.
No. of dilatations
January 2004
No. (%)
0
1
* = Presented sac-like dilatations and counting could not be performed because
abortive ovarioles were not encountered.
** = using a 2.43 and 2.19 day long cycle in January and July, respectively.
doi:10.1371/journal.pone.0022388.t006
Defining the importance of adult density and daily
survival rates: forward multiple regression
(r = 0.70; Bonferroni adjusted P.0.72). But rank correlation was
significant between daily survival rates and the log(n+1) of malaria
cases in Sideroad 19 (r = 0.88; Bonferroni adjusted P,0.05).
For differentiating adult densities from daily survival rates a
forward multiple stepwise regression was performed using the
log(n+1) malaria cases as the dependent variable and adult
densities (human An. darlingi biting rate) and daily survival rates as
predictor variables. A significant model (R2 = 0.81, F(1,4) = 16.98;
P,0.05) was achieved only if daily survival rates was the only
variable to be admitted. Also, higher b was found with daily
survival rates (b = .072) than with adult densities (b = 0.21) in the
two variable marginally significant model (R2 = 0.82, F(2,3) = 7.03,
P = 0.07). This suggests that malaria incidence is better explained
by daily survival rates than adult mosquito density.
Logistic regression of malaria cases and daily survival
rates
Logit regression was performed using parity status as the
dependant variable, the log(n+1) number of malaria cases per
month as the independent variable, and the percent parous or
nulliparous mosquitoes as the count variable. The regression
equation obtained was y = 23.344+2.851Nx (X2 = 73.75;
P,,0.001). This means that approximately 10 cases of malaria
Figure 2. Kaplan-Meier cumulative proportions plot with weighted least squares exponential regression fitting for Anopheles
darlingi caught in January (unbroken line and circles) and July 2004 (dotted line and squares).
doi:10.1371/journal.pone.0022388.g002
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Mosquito Survivorship and Malaria in the Amazon
relatively lower mean daily temperatures in the wet season (26uC, as
compared to 29uC).
Discussion
Gonotrophic cycle duration: literature and
methodological limitations
Methodological limitations: spatial distribution of larval
habitats; possible subtle temporal variations in
gonotrophic cycle duration; and sample size
considerations
For estimating adult survival rates, first the durations of the
gonotrophic cycles must be accurately known. Limited information is available on the duration of the gonotrophic cycle of An.
darlingi and methodological aspects have not been well defined. In
Aripuanã, Mato Grosso State, Central Brazil, Charlwood &
Wilkes (1979) [38], reported that only a few parous females
presented unstretched ovariolar terminals, and interpreted this as
indicating that the gonotrophic cycle duration required at least
three days. Charlwood’s minimum cycle method compared well
with capture-recapture experiments in Jaru, Rondonia, Western
Amazon, where Charlwood & Alecrim (1989) [20] determined
that An. darlingi had an approximate 2.3 day oviposition cycle. The
same authors also proposed a re-analysis of the data presented by
Roberts et al. (1983) [17] arriving at a 2.6 day long cycle, but the
data from the latter authors may have been slightly skewed
because dissections were performed the following morning after
collection. Our estimates of gonotrophic cycle durations for An.
darling are within the range observed in the literature.
We determined the gonotrophic cycle duration only twice
during the year, by performing dissections soon after capture on a
large number of mosquitoes. It is possible that short term temporal
variations in cycle duration may have been overlooked. Also,
minimal sample sizes for Charlwood’s minimum cycle method and
the scale of temporal variations are not yet understood. More
studies are necessary to better characterize the relative role of
meteorological and geographic variables and to demonstrate how
cycle durations vary in time. We suggest that capture-recapture
experiments or laboratory studies focusing on the time needed for
ovariolar stalk contraction may be particularly instructive.
It is possible that the presence or proximity to dams may have
biased the data. Dams may maintain constant recruitment because
they form stable larval habitats, as compared to rivers. Adult
density and survival rates near temporary rivers were not
evaluated in this study. The proximity to fish-farming dams may
also increase the proportion of parous to nulliparous mosquitoes,
because ovipositing females tend to actively seek and concentrate
at these areas [40]. The existence of spatial heterogeneity of
parous rates was not evaluated in the presence study.
Sideroad 19 differs from other sideroads in the area by the
quantity of dams, and this may cause variation in gonotrophic
cycle duration. Fish-farming dams are known important An. darlingi
larval habitats [41]. While 10 dams were present in Sideroad 19,
neighboring sideroads had a mean of only 4.25 dams (SD = 2.2).
The mean distance between fish-farms and adult collecting
stations used in the study was only 110 m (SD = 26.4 m). This
distance decreased by 5–10 m during the wet season. More studies
are necessary to demonstrate if gonotrophic cycle durations vary in
different geographical areas. We caution that parity studies that
make exclusive use of parous rates, instead of multiparous
dissections, should attempt to control for recruitment fluctuations.
To enable this, knowledge of larval binomics in the study area is
highly recommended.
Notes on the accuracy of Charlwood’s minimum cycle
method
Although Charlwood’s minimum cycle method appears to be of
limited applicability because the possibility of the real cycle being
longer than the estimate, the simplicity of the method is
particularly useful for repeated measures studies. The method
makes the following assumptions: 1- all females with well defined
dilations (type E) have returned to bite no more than 24 hours
after oviposition, i.e. have gonotrophic cycles of three days; 2there is no delay between blood-feeding and oviposition; 3- the
duration of egg development is constant. To account for the
possibility of longer cycles, we performed the same statistical
analyses assuming that these females had returned 2, 3, 4… up to
10 days after oviposition. Longer gonotrophic cycles increase the
daily survival rates during the wet season disproportionally more
than in the dry season, decreasing the overall magnitude of
variance between seasons. However, non-significant statistical
levels for the analyses performed (P.0.05) could only be obtained
if one assumed that mosquitoes with well defined dilations had
taken at least 4–5 days to find a host after oviposition, i.e. had
gonotrophic cycles of 6–7 days. This is a highly unlikely scenario,
since the variation in the literature has ranged from 2.3 to 4.4 days
for this species, with different sampling methods [7] and the
longest reported Nyssorhyncus cycle is 5 days [39].
Detinova’s classification
There is still incomplete knowledge of the time needed for
moving between each stage of sac-like dilations after oviposition
occurs. Studies have traditionally considered sacular dilation to be
present or absent. In this study we arbitrarily considered types A to
C as representing recently oviposited females. Confusion could arise
when classifying C and D dilations as recently oviposited or not, but
these represented together less than 3% of dissected females. A, B
and E types accounted for the majority of mosquitoes dissected. To
our knowledge, this is the first attempt to relate Detinova’s sacular
classification to gonotrophic cycle duration. We propose that the use
of this classification may be advantageous, compared to the two
category method, since it permits regression modeling and more
accurate weighted means can be obtained. An accurate gonotrophic
cycle determination is particularly important due to the sensibility of
survival data to this estimate.
On assuming constant gonotrophic cycles in longitudinal
studies
To our knowledge, this is the first study to document seasonal
variation in gonotrophic cycle duration, for a Neotropical
anopheline. We caution that using a constant gonotrophic cycle
duration for fitting parous rate data must be performed with care. It
may obscure changes in breeding site availability that may occur in
accordance to rainfall. However, in our study, the gonotrophic cycle
variations estimated in the dry and wet seasons was not important
and controlling for cycle duration with a constant parameter would
not change any of the observations, since very similar results would
have been obtained if any constant gonotrophic cycle from 2.5 to
4.5 days was to be used. Shorter gonotrophic cycle durations in each
season could reflect the increased abundance of larval habitats in the
wet season, which appears to have been more determinant than the
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Variation in daily survival rates appears to be related to
rainfall
In our study, daily survival rates were lower during the wet
season, as compared to the dry season. The best meteorological
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Mosquito Survivorship and Malaria in the Amazon
significant with daily survival rates but not with density. This data
suggests that malaria incidence in Sideroad 19 was better
explained by daily survival rates than adult densities. To our
knowledge, this paper is the first statistical study of variation of
survival rates of Neotropical malaria vectors in relation to malaria
incidence.
Variations in malaria transmission have been related to
mosquito survival rates per extrinsic incubation period, but not
survivorship per feeding cycle [44]. In the Maroni River, French
Guiana, we performed a reanalysis of the entomological data
collected by Fouque et al. (2010) [7] with permission of the author,
comprising almost 2,700 dissections during two years. The total
number of mosquitoes, the number of parous mosquitoes and the
number of malaria cases were significantly higher during the dry
season, as compared to the wet season, by Mann Whitney U tests
(P,0.001). Spearman rank correlation was significant (P,,0.001)
between the number of malaria cases in Maripasoula and the
number of parous mosquitoes per month. The simple linear
regression equation between the number of malaria cases and the
number of parous mosquitoes was also significant (P,,0.001),
although neither survival rates alone nor mosquito density alone
produced significant regressions with malaria incidence. We
verified that recruitment fluctuations, secondary to decreased
breeding rates, were likely to have occurred because decreases in
adult density were higher than that explained by decreases in
survival alone. These results suggest that, in the riverine Maroni
region, where there are no dams, both survival rates and density
variations caused by changes in larval breeding rates may be
correlated to malaria incidence. It is possible that in areas near
year-round stable water collections only survival rates will be
important for malaria incidence, while near small temporary
rivers, where breeding may vary due to wet season flushing of
larvae, both variations in survival rates and breeding will influence
transmission.
Fouque et al. (2010) [7] have proposed, as an alternative to the
vectorial capacity for longitudinal studies, the use of a more
simplified parameter describing the number of infected mosquitoes
able to transmit malaria (IMT), calculated by IMT~HBR:pn :b,
where b = proportion of infected mosquitoes. However, longitudinal studies frequently fail to encounter infected mosquitoes on
many occasions and entolomological inoculation rates are
determined with pooled data for the entire period of observation.
We believe that b should be omitted from the equation, as was
done in this study, to arrive at the simpler dangerously aged
mosquitoes equation: q~HBR:pn .
predictor of daily survival rate was the number of wet days per
month. We suggest that heavy rainstorms may cause increased
adult mortality by direct impact of droplets on resting or flying
mosquitoes. The frequency of raining may be more important
than the amount of rain, but more studies are necessary to better
evaluate this. Rainfall in Southern Roraima is of the convective
type, with large drop size, high rain-rates and large amounts of
water per downpour, typically reaching 20 to 40 mm/m2 in 2–
3 hours. Wind speed was not systematically measured in this
study, but values from 12 to 18 km/h were common, as measured
visually by the Beaufort scale, and could influence mosquito flight,
dispersal [42] or even survival.
Capture-recapture experiments performed by Charlwood &
Alecrim (1989) [20], at the beginning of the rainy season in that
region of the Amazon, yielded daily survival rates of 83%. For the
same period, we reported 81%, in May. A reanalysis of
Charlwood’s (1980) [21] data from Mato Grosso, during the end
of the rainy season in that region, was performed. Exponential
regression models using the table provided by these authors,
calculated daily survival rates of 63% with Davidson’s method,
using the 2.3 day long cycle proposed by Charlwood & Alecrim
(1989) [20]. We report 57% for the same season in Roraima.
Fouque et al. (2010) [7] have also reported higher survival rates in
the dry season than the wet season, in the Maroni area of French
Guiana.
Studies suggest that An. darlingi survival rates may vary as a
function of seasonal influences. Variations may occur synchronously throughout areas of the Amazon with similar rainfall
patterns, but more studies are necessary to verify this.
Methodological limitations: possible subtle temporal
variations in parous rates
Parous rates were only determined once every two months. It is
possible that transient variations in Davidson’s parous rates, in the
order of weeks, could have been missed in the study. Fouque et al.
2010 [7], reported variations around 10–30% (SD = 12–24%)
from one month to the next, but the possibility of recruitment
fluctuation hindered better evaluation. More studies, with smaller
sampling intervals and larger samples, are needed to verify more
precisely how these rates vary in time.
Mathematical basis for comparisons of survival and
density
The greater influence of parous rates in determining transmission can be explained by analyzing classic malaria modeling
formulae [43]. Malaria incidence varies in a 1:1 proportion to the
density of vectors and
in a 1:1 proportion to the expectation of
pn
). In this sense, a 16 fold increase in
infective life (
{loge p
entomological inoculation rate could be brought about by an
increase in 16 times in the man-biting rate or by an increase in
20% in survival rate [4].
Wet season-predominant alluvial malaria appears to
differ epidemiologically from frontier zone or highland
dry season malaria
Parity studies may be particularly useful for explaining malaria
incidence in areas where there is little seasonal change in
anopheline biting rates. In this sense, malaria transmission in
frontier zones appears to be epidemiologically distinct from
alluvial malaria [45–46]. In frontier zones, the absence of large
rivers and seasonal flooding, mean that larval habitats are
relatively scarce and mosquito densities are lower. Vector breeding
is dependent on the natural or artificially created water collections,
being less dependent on the level of the water table. Malaria
transmission occurs during the dry season [8,46–47] or the dry-wet
and wet-dry transitions [9,21,48]. Meanwhile, near large rivers,
the role of female mosquito density could be more important than
survival because flooding occurs during the wet season, increasing
the larval population and high adult densities are obtained [49–
51]. Longitudinal survival studies should keep this geographical
Malaria incidence was better related to daily survival
rates than adult densities
Parous rates and adult densities were well correlated (0.82
correlation). This occurred because the changes observed in
density were basically those expected by the decrease in survival
rates, suggesting that survival, and not breeding, was the limiting
factor determining density. Also, the longevity factor was much
higher than the density factor and daily survival rates explained
better malaria incidence than adult densities with multivariate
analysis. Spearman’s rank correlation of malaria cases was also
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decreases in daily survival rates than P. vivax. This is compatible
with the prevalence of Plasmodium spp. in Sideroad 19, where only
two cases of P. falciparum malaria were detected, both in the dry
season, and no cases of P. malariae, based on thick smears only.
These results were not confirmed with Polymerase Chain Reaction
(PCR) and asymptomatic subjects were not regularly sampled.
Hypoendemic frontier malaria in the Amazon, the second
colonization phase of frontier malaria [45], is composed mainly of
P. vivax, followed by P. falciparum. Cases of P. malariae are
considered to be relatively uncommon, when only thick smears
are analyzed. We postulate that the more efficient transmission of
P. vivax could be secondary to a vector survival-dependent limiting
factor for disease transmission, compatible with sporogonic cycle
durations of Plasmodium spp. It is possible that performing effective
insecticide spraying increases the P. vivax to P. falciparum malaria
ratios. The relative prevalence of Plasmodium spp. in the human
population may also aid in identifying localities where mosquito
survival is the limiting factor in disease transmission, but more
studies are necessary to verify this.
heterogeneity in mind. Parous rate studies using Davidson’s
method may yield incorrect results near large rivers, where
recruitment fluctuations are important.
Multiparous dissections can be used to validate
Davidson’s daily survival rates and gonotrophic cycle
durations
By comparing multiple methods of age determination in
Anopheles farauti, Charlwood (1986) [52] suggested that rates based
on multiparous dissections were more reliable than rates
determined by parous dissections. This method does not require
calculating gonotrophic cycle durations. For this reason, we
dissected mosquitoes for obtaining survival curves. In our study,
daily survival rates obtained by Davidson’s method compared well
to the survival curves by multiparous dissections. The good
correlation between the two data sets suggests that the gonotrophic
cycle durations and daily survival rates that were determined with
Davidson’s method were relatively accurate.
Difficulties with Polovodova’s technique
Survival curves and constant versus age-dependant
survival
Many investigators have encountered problems while performing the Polovoda technique for age-grading [53] and Hugo et al.
[54] have found that it enabled correct classification of only 57.5%
nulliparous and 1, 2 or 3-parous Ae. vigilax females. Hoc & Wilkes
(1995) [55] have proposed that ovariole sacs contract to form basal
distentions which are larger than the typical dilatations. These
distensions would obscure signs of the typical dilatations, i.e.
previous gonotrophic activity. The typical dilatations described by
Polovodova would form only when follicles degenerate at an early
stage during egg development. This means that in parous
mosquitoes, multiple ovarioles must be checked to find those with
one or more typical dilatations, as was performed in this study.
The availability of the new molecular methods may help solve the
problem of an adequate age-grading technique [54].
Fitting of the exponential model of survival curves initially
suggest constant mortality rates in the different age groups of the
mosquito population, rather than age dependant survival rates.
However, studies with larger samples are needed to verify this and
the existence of age-dependant survival rates is possible. Agedependant survival would be suggested if the data fitted better a
Gompertz [56] or a Weibull distribution rather than an
exponential distribution. If mortality is, in fact, age-dependant,
models will be more complex, p cannot be regarded as a constant,
mathematical equations must be adapted and values recalculated.
Age-independent models tend to overestimate the transmission
potential of older mosquitoes, overestimating vectorial capacity
[57].
Duration of the sporogonic cycle of Plasmodium spp
Final comments
The duration of the sporogonic cycle of P. vivax and other
Plasmodium spp. in An. darlingi is poorly characterized. Although
long experience with the Moshkovsky method in the former USSR
has shown that it is useful for epidemiological analysis in temperate
climates, we are unaware of any systematized validation with
tropical malaria strains. For this reason, when we report the
estimated percent of mosquitoes surviving enough to transmit
malaria, both the percentage that would survive one or more
cycle, as estimated by the Moshkovsky method, as well as the
percentage that would survive 10 days is given, to show the results
if temperature-dependant changes in sporogonic cycle duration
were ignored. Relatively similar data were obtained with either
method, with more dangerously aged mosquitoes in the dry
season. However, the density of dangerously aged mosquitoes was
better associated with malaria incidence than the 10 day old
densities. For the latter parameter, the simple linear regression
with log(n+1) malaria was not significant (P.0.1).
Our results indicate that survival rates of An. darlingi may vary in
accordance to rainfall and the wet season could be associated with
lower adult survival rates, resulting in decreased malaria
transmission. More studies are necessary to better evaluate the
influence of meteorological parameters with survival rates.
In small temporary rivers, An. darlingi breeding may be limited to
the dry season [25–26]. Rainfall may cause larval mortality in
small rivers by the following mechanisms: flushing out larvae with
strong currents, direct impact of raindrops on the water,
depending on the size of raindrops; ejection of immature stages
onto muddy surroundings; and exhaustion of larvae by constantly
moving away from the surface to avoid being struck by raindrops
[58]. Malaria transmission in this setting would be limited by both
mosquito survival and breeding rates. But the construction of small
dams, blocking small waterways, may enable breeding throughout
the year [25–26]. Dams permit constant recruitment of relatively
small densities of An. darlingi and Barros et al (2011) [26] have
proposed that the species prefers areas with obstructions to river
flow, decreased luminance (shade) and proximity to human
dwellings. Malaria transmission in this setting would be basically
limited by mosquito survival alone. It appears that anthropic
modification of the environment may remove the natural balance
of limiting factors such as river currents caused by heavy raining
[58]. Determining key transmission factors may help direct control
efforts. If survival is the limiting factor, residual insecticide
spraying would be particularly useful. If larval and/or adult
Probability of surviving sporogony ratios and duration of
the sporogonic cycles
The duration of the extrinsic incubation periods of Plasmodium
spp. are, in decreasing order: P. malariae.P. falciparum.P. vivax.
Longer cycles decrease the probability that a mosquito will survive
sporogony. For every day increase in extrinsic incubation cycles
the probability of surviving sporogony decreases by pn+1.
Plasmodium spp. with longer extrinsic incubation periods, such as
P. falciparum and P. malariae, were more affected by seasonal
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density is determinant, larvicidal methods or increasing the
distance between larval habitats and humans should be favored.
their field data for analysis and for the discussions that helped shape this
paper.
Acknowledgments
Author Contributions
To José Francisco Luitgards Moura, for the kind support and for helping
our group with logistical problems. To Wanderli P. Tadei for the insightful
comments that inspired this work. To Dr. Florence Fouque for providing
Conceived and designed the experiments: FSB MA NH. Performed the
experiments: FSB NH. Analyzed the data: FSB. Contributed reagents/
materials/analysis tools: FSB NH MA. Wrote the paper: FSB NH MA.
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