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Text S7: Temporal variability of population density at the individual-house level
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For uncertainty in the temporal CV (calculated with a time interval of 15 days
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instead of the daily interval due to the large data storage requirement for densities of each
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life stage over 730 days for 5000 simulations) of population density at the individual-
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house level, our results show that CVs are relatively high where there is low population
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density (Figure S7.1 a and S7.2 a). The nominal survival rates of female adults and larvae
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and the coefficient of metabolic weight loss, which are important parameters contributing
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to population density, are also important parameters contributing to temporal variability
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(Figure S7.1 b, c , d and Figure S7.2 b, c , d). In contrast to the temporal variability in
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population density at the community level, our results show that spatial dispersal can be
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an important parameter contributing to the temporal variability at the individual-house
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level (Figure S7.2 f), with higher spatial dispersal probability leading to lower temporal
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variability in the population dynamics (see Figure S7.3). Spatial dispersal also contributes
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to the temporal variability in pupal population density, but to a much lesser extent (see
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Figure S7.1 f). The coefficient of food dependence is very important for the temporal
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variability of pupal population density in houses with relatively small food input and
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relatively high egg density (see Figure S7.2 g), where there are stronger density-
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dependence effects on larval growth. The low temperature limit (10-20oC) for nominal
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survival of pupae/larvae, which is important for temporal variability at the community
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level, is not detected as an important parameter at the individual-house level. This is due
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to the fact we use a longer time interval (15 days) to calculate the CVs, making
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occasional temporal variability (e.g., due to temperature falling below the low
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temperature limit for nominal survival of pupae and larvae) less detectable. Since the
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high temperature limit (25-35oC) for predator-dependent egg survival is more frequently
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exceeded for the weather in Iquitos, it is an important parameter contributing to temporal
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variability in population density at the individual-house level (Figure S7.2 e).
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(a) Mean temporal CV based on
5000 FAST samples
(b) Proportion of uncertainty in temporal CV
contributed by female adult survival rate
Legend
Legend
Mean.img
SensEff9.img
Value
Value
High : 2.16
High : 0.27
Low : 0.32
Low : 0
(d) Proportion of uncertainty in temporal CV
contributed by coefficient of metabolic activity
(c) Proportion of uncertainty in temporal CV
contributed by larval survival rate
Legend
Legend
SensEff51.img
SensEff7.img
Value
Value
High : 0.03
High : 0.06
Low : 0
Low : 0
(f) Proportion of uncertainty in temporal CV
contributed by female adult short-range dispersal
probability
(e) Proportion of uncertainty in temporal CV
contributed by high temperature limit for
predator-dependent egg survival
Legend
Legend
SensEff62.img
SensEff40.img
Value
Value
High : 0.02
High : 0.04
Low : 0
Low : 0
(g) Proportion of uncertainty in temporal CV
contributed by coefficient of food dependence
Legend
SensEff50.img
Value
High : 0.20
Low : 0
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Figure S7.1 Mean temporal coefficient of variation (CV) for pupal population density at
the individual-house level (a) and the proportion of uncertainty in CV contributed by
different model parameters (b-g). To simplify this figure, only parameters with maximum
uncertainty contributions larger than 0.03 are plotted except for panel (f) for the
comparison of the importance of mosquito dispersal for different life stages. We did not
plot the proportion of uncertainty in CV contributed by the nominal survival rate for male
adults since the uncertainty contribution is mainly due to its correlation with the nominal
survival rate for female adults.
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(a) Mean temporal CV based on
5000 FAST samples
(b) Proportion of uncertainty in temporal CV
contributed by female adult survival rate
Legend
Legend
Mean.img
SensEff9.img
Value
Value
High : 1.10
High : 0.40
Low : 0.36
(c) Proportion of uncertainty in temporal CV
contributed by larval survival rate
(d) Proportion of uncertainty in temporal CV
contributed by coefficient of metabolic activity
Legend
Legend
SensEff7.img
SensEff51.img
Value
Value
High : 0.07
High : 0.04
Low : 0.01
Low : 0
(e) Proportion of uncertainty in temporal CV
contributed by high temperature limit for
predator-dependent egg survival predation
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Low : 0.11
(f) Proportion of uncertainty in temporal CV contributed
by female adult short-range dispersal probability
Legend
Legend
SensEff40.img
SensEff62.img
Value
Value
High : 0.03
High : 0.04
Low : 0
Low : 0
Figure S7.2 Mean temporal coefficient of variation (CV) for female adult population
density at the individual-house level (a) and the proportion of uncertainty in CV
contributed by different model parameters (b-f). To simplify this figure, only parameters
with maximum uncertainty contributions larger than 0.03 are plotted. We did not plot the
proportion of uncertainty in CV contributed by the nominal survival rate for male adults
since the uncertainty contribution is mainly due to its correlation with the nominal
survival rate for female adults.
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0.65
0.60
0.55
Coefficient of variation in female adult population density
0.1
0.2
0.3
0.4
0.5
Short-range dispersal probability for female adults
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Figure S7.3 Dependence of temporal variability in female adult population density at an
specific individual house (located at 4th row from top and 5th column from left) on shortrange spatial dispersal. The short-range dispersal accounts for 3.8% of uncertainty in the
temporal variability as measured by the temporal coefficient of variation during the
second simulation year. The curves are fitted to the scatter plot of parameter values
sampled by FAST and the corresponding predicted coefficient of variation in pupal
density during the second simulation year using a cubic smoothing splines with the
SemiPar R package [1]. The shaded areas are the 95% confidence intervals of the fitted
lines.
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References
1. Wand MP, Coull BA, French JL, Ganguli B, Kammann EE, et al. (2005) SemiPar 1.0.
R package. http://cran.r-project.org.
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