Ecological niche partitioning between Anopheles gambiae molecular forms in Cameroon: the ecological side of speciation

<p>Abstract</p> <p>Background</p> <p>Speciation among members of the <it>Anopheles gambiae </it>complex is thought to be promoted by disruptive selection and ecological divergence acting on sets of adaptation genes protected from recombination by polymorphic paracentric chromosomal inversions. However, shared chromosomal polymorphisms between the M and S molecular forms of <it>An. gambiae </it>and insufficient information about their relationship with ecological divergence challenge this view. We used Geographic Information Systems, Ecological Niche Factor Analysis, and Bayesian multilocus genetic clustering to explore the nature and extent of ecological and chromosomal differentiation of M and S across all the biogeographic domains of Cameroon in Central Africa, in order to understand the role of chromosomal arrangements in ecological specialisation within and among molecular forms.</p> <p>Results</p> <p>Species distribution modelling with presence-only data revealed differences in the ecological niche of both molecular forms and the sibling species, <it>An. arabiensis</it>. The fundamental environmental envelope of the two molecular forms, however, overlapped to a large extent in the rainforest, where they occurred in sympatry. The S form had the greatest niche breadth of all three taxa, whereas <it>An. arabiensis </it>and the M form had the smallest niche overlap. Correspondence analysis of M and S karyotypes confirmed that molecular forms shared similar combinations of chromosomal inversion arrangements in response to the eco-climatic gradient defining the main biogeographic domains occurring across Cameroon. Savanna karyotypes of M and S, however, segregated along the smaller-scale environmental gradient defined by the second ordination axis. Population structure analysis identified three chromosomal clusters, each containing a mixture of M and S specimens. In both M and S, alternative karyotypes were segregating in contrasted environments, in agreement with a strong ecological adaptive value of chromosomal inversions.</p> <p>Conclusion</p> <p>Our data suggest that inversions on the second chromosome of <it>An. gambiae </it>are not causal to the evolution of reproductive isolation between the M and S forms. Rather, they are involved in ecological specialization to a similar extent in both genetic backgrounds, and most probably predated lineage splitting between molecular forms. However, because chromosome-2 inversions promote ecological divergence, resulting in spatial and/or temporal isolation between ecotypes, they might favour mutations in other ecologically significant genes to accumulate in unlinked chromosomal regions. When such mutations occur in portions of the genome where recombination is suppressed, such as the pericentromeric regions known as speciation islands in <it>An. gambiae</it>, they would contribute further to the development of reproductive isolation.</p

Spatially Explicit Analyses of Anopheline Mosquitoes Indoor Resting Density: Implications for Malaria Control

Background: The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated. Methodology/Principal Findings: We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 565 km grid over a 2500 km(2) area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices. Conclusions/Significance: Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control

Apport de trois méthodes de détection des surfaces brûlées par imagerie Landsat ETM+ : application au contact forêt- savane du Cameroun

The majority of African savannas are traversed by fires each year for the benefit of hunting and agropastoral activities. The objective of this paper is to evaluate the contribution of Landsat imagery to the definition and the discretization of the zones affected by seasonal fires. The methodology used consists of three modes of treatment, namely the algorithm of detection of burned surfaces worked out by Eva and Lambin (1998), the unsupervised isodata classification realized on the thermal infra-red channel and the supervised classification by maximum likelihood carried out on optimized three-colour processes combining channels 6.1, 5 and 6.2. The treatments made it possible to highlight the areas affected by recent and old fires, the unburned forests and savannas. The method of thresholding of Lambin and Eva and the supervised classification give rather concordant results in quantitative terms as well as space covered. These two methods indicate that respective percentages of 28,25 and 27,9 of the total surface were affected by fires (old and recent). Automatic classification reduced the proportion of the territory affected by fires to 18%. This undervaluation of fires is related to the maladjustment of this last method to effectively discriminate the surfaces affected by relatively old or not very virulent fires and underlines the need for a joint use of channels 5 and 6 in the discretization of the burned surfaces. The distribution of the surface areas by type of vegetation indicates that 36% of shrub savannas, 23% of tree savannas, 7% of degraded forests and 0,4% of humid dense forests were burned at this period which corresponds just to the middle of the dry season in the study area. These experiments must however be taken again on other sites and at other periods of the year to better appreciate the contribution of the Landsat system in the study of vegetation fires in the Cameroonian territory

LISA results for <i>An. gambiae</i>.

<p>(A) and (C) are Moran scatter plots at the locality and at the house level, respectively. The name of the locality and the house number (in brackets) with large contributions to autocorrelation are displayed. (B) and (D) depict the locations of significant local Moran's <i>I</i> statistics and the type of spatial association between neighboring locations in sampling units with large contributions to the global autocorrelation. (A): locality level and (B): house level. ∧ significant (<i>p</i><0.01); bright red: <i>High-High</i>; light red: <i>High-Low</i>; deep blue: <i>Low-Low</i>; light blue: <i>Low-High</i>.</p

Distribution of indoor resting densities of <i>An. gambiae</i> (A) and other malaria vectors (B).

<p>Distribution of indoor resting densities of <i>An. gambiae</i> (A) and other malaria vectors (B).</p

Directional variograms.

<p><i>An. gambiae</i>: (A) locality level and (B) house level. <i>An. funestus</i>: (C) locality level and (D) house level. Envelopes of minimum and maximum values over 1000 randomizations are shown in grey scale from light (0°) to dark (135°).</p

Map showing the study area in Cameroon.

<p>The base map is a subset of a Landsat Enhanced Thematic Mapper (ETM+) satellite image with a color composite of red, near-infrared and green bands at 30 m resolution, on which a layer of main roads (in grey) and a 5×5 km grid (in white) are overlaid. In this pseudo-natural image, vegetation appears in shades of green and purple represents deforested areas, bare soils or pixels masked by clouds. The 100 surveyed localities are shown as red circles.</p

LISA results for <i>An. funestus</i>.

<p>(A) and (C) are Moran scatter plots at the locality and at the house level, respectively. The name of the locality and the house number (in brackets) with large contributions to autocorrelation are displayed. (B) and (D) depict the locations of significant local Moran's <i>I</i> statistics and the type of spatial association between neighboring locations in sampling units with large contributions to the global autocorrelation. (A): locality level and (B): house level. ∧ significant (<i>p</i><0.01); bright red: <i>High-High</i>; light red: <i>High-Low</i>; deep blue: <i>Low-Low</i>; light blue: <i>Low-High</i>.</p

Binary logistic regression models showing the estimated probability of occurrence of (A) adults of the M form (blue open dots, closed circles and continuous line); and (B) adults of the S form (red open dots, closed circles and continuous line) in relation to the Built Environment Index (BEI) calculated for 1×1 km quadrats including the 16 sites of the micro-geographic rural to urban transect of Fig. 1E.

<p>Tick marks on the floor and ceiling of each scattergram visualise occurrences (0 = absence, 1 = presence); larger thickness of the tick marks denotes higher frequency. Open dots represent the estimated probability of occurrence of the minimal adequate models including the BEI, <i>Anopheles gambiae</i> population density, and sampling effort as explanatory variables. The regression lines visualise the fitted probability of occurrence when only the BEI is included as explanatory variable. Closed circles show the mean observed response of occurrence (± standard errors) for five equally spaced classes of the BEI for visual assessment of ‘goodness-of-fit’.</p