Landscape movements of <em>Anopheles gambiae</em> malaria vector mosquitoes in rural Gambia.

BACKGROUND: For malaria control in Africa it is crucial to characterise the dispersal of its most efficient vector, Anopheles gambiae, in order to target interventions and assess their impact spatially. Our study is, we believe, the first to present a statistical model of dispersal probability against distance from breeding habitat to human settlements for this important disease vector. METHODS/PRINCIPAL FINDINGS: We undertook post-hoc analyses of mosquito catches made in The Gambia to derive statistical dispersal functions for An. gambiae sensu lato collected in 48 villages at varying distances to alluvial larval habitat along the River Gambia. The proportion dispersing declined exponentially with distance, and we estimated that 90% of movements were within 1.7 km. Although a ‘heavy-tailed’ distribution is considered biologically more plausible due to active dispersal by mosquitoes seeking blood meals, there was no statistical basis for choosing it over a negative exponential distribution. Using a simple random walk model with daily survival and movements previously recorded in Burkina Faso, we were able to reproduce the dispersal probabilities observed in The Gambia. CONCLUSIONS/SIGNIFICANCE: Our results provide an important quantification of the probability of An. gambiae s.l. dispersal in a rural African setting typical of many parts of the continent. However, dispersal will be landscape specific and in order to generalise to other spatial configurations of habitat and hosts it will be necessary to produce tractable models of mosquito movements for operational use. We show that simple random walk models have potential. Consequently, there is a pressing need for new empirical studies of An. gambiae survival and movements in different settings to drive this development

Habitat Hydrology and Geomorphology Control the Distribution of Malaria Vector Larvae in Rural Africa

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. 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. 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. 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

Probability of dispersal.

<p>Cumulative probability of female <i>Anopheles gambiae</i> s.l. dispersal into villages in The Gambia versus distance from village to nearest alluvial sediments (solid line) estimated from non-linear regression. Grey shaded areas indicate 95% CI of the curve and red marker lines indicate distances over which 95% of the population has dispersed; A) two parameter negative exponential model; B) two-parameter half-Cauchy model.</p

Random walk simulation.

<p>Two parameter negative exponential (blue line) and half-Cauchy (black line) regression models of female <i>Anopheles gambiae</i> s.l. dispersal into villages and simulated dispersal distances (red, 1000 simulations) from a random walk (daily survival = 0.8; daily movement = 0.35 km) of 10,000 mosquitoes emerging from a point source and dispersing until population extinction or day 30, whichever came first.</p

Mosquito dispersal in The Gambia.

<p>A) Geometric mean (GM) female <i>Anopheles gambiae</i> s.l./night in villages in the rainy season (Jul–Dec) 1996 at different distances from the landward edge of alluvial sediments; B) Two-parameter negative exponential non-linear regression model (solid line) with 95% CIs of curve (dashed lines) with points from A in grey for reference; C) Two-parameter half-Cauchy non-linear regression model (solid line) with 95% CIs of the curve (dashed lines) with points from A) in grey for reference.</p

Predicted dispersal of adult female <i>Anopheles gambiae</i> s.l. mosquitoes away from breeding sites in The Gambia, using a 2-parameter negative exponential regression model and a 2-parameter half-Cauchy regression model.

<p>Predicted dispersal of adult female <i>Anopheles gambiae</i> s.l. mosquitoes away from breeding sites in The Gambia, using a 2-parameter negative exponential regression model and a 2-parameter half-Cauchy regression model.</p

Map of the study area.

<p>Central Gambia, showing study villages (red squares), other villages (green circles), main channel of The River Gambia and tributaries (blue lines), alluvial sediment (solid yellow). The Gambia nation is shaded grey, surrounded by Senegal in white. Inset: location of the study area in The Gambia.</p

Contrasts in bootstrap estimated number of late-stage <i>An</i>. <i>arabiensis</i> larvae per dip using Method of Variance Estimates Recovery [67].

<p>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.</p

Examples from each water body type.

<p>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.</p

Plots of <i>An</i>. <i>arabiensis</i> estimates per water body type.

<p>(A) Bootstrap prediction estimates of late-stage <i>An</i>. <i>arabiensis</i> larvae per dip and (B) area-weighted abundance estimate of late-stage <i>An</i>. <i>arabiensis</i> 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 <i>An</i>. <i>arabiensis</i> larvae per dip estimated by bootstrapping a mixture distribution generated from GEE estimates of number of late-stage anophelines and the probability of finding <i>An</i>. <i>arabiensis</i> 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.</p