Prediction of Mosquito Abundance in Temperate Regions, Using Ecological, Hydrological and ...

172 Pages·2014·4.65 MB·English

Prediction of Mosquito Abundance in Temperate Regions, Using Ecological, Hydrological and
Remote Sensing Models
by
Yun Jian
Department of Environment
Duke University

Date:_______________________
Approved:

___________________________
Marco Marani, Supervisor

___________________________
William Pan, Co- supervisor

___________________________
Sonia Silvestri

___________________________
Song S. Qian

___________________________
Gabriel Katul

Dissertation submitted in partial fulfillment of
the requirements for the degree of Doctor
of Philosophy in the Department of
Environment in the Graduate School
of Duke University

2014
ABSTRACT
Prediction of Mosquito Abundance in Temperate Regions, Using Ecological,
Hydrological and Remote Sensing Models
by
Yun Jian
Department of Environment
Duke University

Date:_______________________
Approved:

___________________________
Marco Marani, Supervisor

___________________________
William Pan, Co- supervisor

___________________________
Sonia Silvestri

___________________________
Song S. Qian

___________________________
Gabriel Katul

An abstract of a dissertation submitted in partial
fulfillment of the requirements for the degree
of Doctor of Philosophyin the Department of
Environment in the Graduate School of
Duke University

2014
Copyright by
Yun Jian
2014
Abstract
New and old mosquito-borne diseases have emerged and re-emerged in temperate
regions over the recent past, but an understanding of mosquito population dynamics, a
fundamental step toward disease control, remains elusive. In particular, we are still
lacking reliable predictive models of mosquito abundance in temperate areas due to the
subtle links between the fluctuation of mosquito population and highly heterogeneous
environmental drivers. Hence, this doctoral dissertation presents an interdisciplinary
approach towards an improved understanding and prediction of the fluctuations in
mosquito abundance in temperate regions. In the first part of this dissertation a
hierarchical Gompertz-based model is used to assess the relative importance of
endogenous (density dependence) and exogenous (environmental forcings) controls and
their interactions in regulating the dynamics of a West Nile Virus vector (Culex pipiens)
in the Po River delta in Italy. The results clearly detect the effects of density-dependence
in the observed population dynamics for the mosquito species analyzed and highlight
the controls exerted by environmental forcings and habitat conditions. Subsequently, the
characteristic scales of temporal variability in mosquito populations, and the
representativeness of observations at different sampling resolutions, are investigated
using a 10 year daily mosquito sample from Brunswick County, North Carolina. The
species present in the sample (among which Aedes vexans and Culiseta melanura are
addressed in greater detail, as vectors of East Equine Encephalitis and West Nile Virus)
iv
are investigated using a combination of time series analysis, individual based
simulations, and density-dependent modeling approaches. Significant population
fluctuations with characteristic periodicity between 2 days and several years are found
in response to different regulation mechanisms. In particular, the observed fast
fluctuations are importantly determined by a varying mosquito activity, rather than by
reproduction/mortality processes, driven by rapid changes in meteorological conditions.
Finally, in the third part of this study, a state space reconstruction (SSR) approach is
used to understand how the predictability of mosquito abundance varies with
aggregation time scale and with the prediction horizon, and how much can the
prediction of mosquito abundance be improved by using daily observations compared
to the commonly used once-per-week samples. The results show that the predictability
of mosquito abundance decreases as the time scale of the models increases from one
week to one month, while the predictability of per capita growth rate increases together
with the modeling scale. It is also shown that the prediction of mosquito per capita
growth rate can be improved using daily abundance observations. Furthermore, many
mosquito models compare the observed and predicted abundance as a measure of
model performance. However, my results suggest that short term forecasts of mosquito
abundance may appear to have a significant capability due to the positive
autocorrelation between abundance in subsequent time steps, even when the model’s
v
ability to predict the abundance change is low. Model capability should thus be
evaluated comparing observed and modelled per capita rates of change.

vi
Contents

Abstract .................................................................................................................................... iv

Contents ................................................................................................................................. vii

List of Tables............................................................................................................................. x

List of Figures ......................................................................................................................... xii

Acknowledgements .............................................................................................................. xvi

1. Introduction .......................................................................................................................... 1

1.1 Mosquito – borne diseases in temperate regions: an emerging challenge .............. 1

1.2 An overview of our current understanding of mosquito population dynamics .... 3

1.3 Density dependence in mosquito population dynamics .......................................... 8

1.4 Hydrologic controls in mosquito population dynamics ......................................... 11

1.5 The representativeness of adult mosquito observations ......................................... 13

1.6 Dissertation Objectives and Organization ............................................................... 16
2. Environmental Forcing and Density-dependent controls of Culex pipiens Abundance in

a Temperate Climate (Northeastern Italy) ........................................................................... 21

2.1 Introduction ................................................................................................................ 21

2.2 Materials and methods .............................................................................................. 24

2.1.1 Study area and data collection ............................................................................. 24

2.1.2 Modeling Approach .............................................................................................. 27

2.1.2.1 Soil moisture dynamics ................................................................................. 28

2.1.2.2 Mosquito population modeling .................................................................... 29

2.2 Results ......................................................................................................................... 37

2.3 Discussion and conclusions ....................................................................................... 45
vii
3. The Temporal Spectrum of Adult Mosquito Population Fluctuations: Conceptual and

Modeling Implications ........................................................................................................... 52

3.1 Introduction ................................................................................................................ 52

3.2 Materials and methods .............................................................................................. 54

3.2.1 Study area and data .............................................................................................. 54

3.2.2 Data analysis .......................................................................................................... 57

3.2.3 Fourier analysis ..................................................................................................... 58

3.2.4 Autocorrelation, partial correlation, and data splitting ..................................... 59

3.2.5 Models .................................................................................................................... 60

3.2.5.1 Individual-Based life cycle simulation model ............................................. 60

3.2.5.2 Density dependent population models ........................................................ 65

3.3 Results ......................................................................................................................... 67

3.4 Discussion and conclusions ....................................................................................... 77

4. Predicting Mosquito Abundance across Temporal Scales .............................................. 82

4.1 Introduction ................................................................................................................ 82

4.2 Methods ...................................................................................................................... 84

4.2.1 Study area and data .............................................................................................. 84

4.2.2 State space reconstruction (SSR) and Simplex Projection (SP) .......................... 84

4.3 Results ......................................................................................................................... 94

4.4 Discussion and conclusions ..................................................................................... 106

5. Dissertation conclusions .................................................................................................. 111

5.1 Overview of the results ........................................................................................... 111

5.2 Major contributions.................................................................................................. 115
viii
Appendix A .......................................................................................................................... 118
A1. Conditional plots for per capita growth rate, log of abundance and temperature

......................................................................................................................................... 118

A2. Relationship between rainfall and soil moisture .................................................. 118

A3. WinBUGS model script .......................................................................................... 119

A4. Model evaluation .................................................................................................... 120

Appendix B ........................................................................................................................... 121

B1. IBS model formulation, developmental stages, and processes. ........................... 121

B2. Power spectrum of observed weather data ........................................................... 122

B3. Detailed functions and parameter values used in the IBS models ...................... 123

B4. Empirical relations between activity factor and rainfall ....................................... 126
B5 ACF and PACF of Gompertz models for Ae.vexans with different density

dependence (results for the Ricker model, not shown, are analogous) ..................... 127

B6. ACF for observations and for models with different density dependence. ........ 128

B7. Estimated coefficients for density dependent models .......................................... 129

Appendix C .......................................................................................................................... 132
C1. Change of correlation between the predicted and observed values for prediction

horizon ............................................................................................................................ 132

C2. Correlation between the predicted and observed values for co-prediction ....... 134

C3. SP results of 7-day-ahead prediction using the once-per-week samples ............ 137

C4. SP results of 7-day-ahead prediction using the daily samples ............................ 138

References ............................................................................................................................. 139

Biography ............................................................................................................................. 153
ix
List of Tables

Table 1: Model performance when soil moisture or rainfall was used as predictors. ...... 41
Table 2: Mean and standard deviation of multiplicative coefficients of exogenous factors
in eq. (4). Soil water content, radiation, hours and wind speed were calculated as the
average values one week before mosquito samplings. NDVI was calculated for a 75m x
75m window around each sampling site. (levels of significance are: * p <0.1, ** p<0.05,

and *** p<0.01) ........................................................................................................................ 43
Table 3: Density-dependent models (showing lag 0 as examples), where t is index of
sample date; N is adult mosquito abundance; r is the maximum per capital growth rate;
m

K is the carrying capacity ...................................................................................................... 67
Table 4: The correlations and RMSE for the best models at three time scales for

Cs.melanura and Ae.vexans ..................................................................................................... 94
Table 5: The variables selected for the best models at three time scales for Cs.melanura

and Ae.vexans ........................................................................................................................ 100

Table 6: The correlations and RMSE for the random walk models at three time scales . 106
Table 7: Estimated coefficients for density dependent models for mosquito abundance in

the Brunswick County NC .................................................................................................. 129
Table 8: Correlation between the predicted and observed per capital growth rate

changes for prediction horizon ........................................................................................... 132
Table 9: Correlation between the predicted and observed abundance changes for

prediction horizon ............................................................................................................... 133
Table 10: Correlation for co-prediction at weekly scale. Index for species: 1, Ae.vexans; 2,
Cs.melanura; 3, Cx.salinarius; 4, Cx.restuans; 5, Ps.columbiae; 6, Ps.ferox; 7, An.crucians;
8, Oc.atlanticus; 9, Oc.fulvus.pallens; 10, Cx.nigripalpus; 11, Cx.erraticus; 12, Cx.pipiens;

13, Ps.ciliata; 14, An.quadrimaculatus; 15, Ps.howardii; 16, Oc.canadensis .................... 134
Table 11: Correlation for co-prediction at biweekly scale. Index for species: 1, Ae.vexans;
2, Cs.melanura; 3, Cx.salinarius; 4, Cx.restuans; 5, Ps.columbiae; 6, Ps.ferox; 7,
An.crucians; 8, Oc.atlanticus; 9, Oc.fulvus.pallens; 10, Cx.nigripalpus; 11, Cx.erraticus;
12, Cx.pipiens; 13, Ps.ciliata; 14, An.quadrimaculatus; 15, Ps.howardii; 16, Oc.canadensis

............................................................................................................................................... 135
x