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Keywords:

  • Mosquito;
  • ecological niche models;
  • malaria;
  • phylogenetics;
  • SEEVA;
  • Albitarsis Group

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED

The Anopheles albitarsis group of mosquitoes comprises eight recognized species and one mitochondrial lineage. Our knowledge of malaria vectorial importance and the distribution and evolution of these taxa is incomplete. We constructed ecological niche models (ENMs) for these taxa and used hypothesized phylogenetic relationships and ENMs to investigate environmental and ecological divergence associated with speciation events. Two major clades were identified, one north (Clade 1) and one south (Clade 2) of the Amazon River that likely is or was a barrier to mosquito movement. Clade 1 species occur more often in higher average temperature locations than Clade 2 species, and taxon splits within Clade 1 corresponded with a greater divergence of variables related to precipitation than was the case within Clade 2. Comparison of the ecological profiles of sympatric species and sister species support the idea that phylogenetic proximity is related to ecological similarity. Anopheles albitarsis I, An. janconnae, and An. marajoara ENMs had the highest percentage of their predicted suitable habitat overlapping distribution models of Plasmodium falciparum and P. vivax, and warrant additional studies of the transmission potential of these species. Phylogenetic proximity may be related to malaria vectorial importance within the Albitarsis Group.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED

The Anopheles (Nyssorhynchus) albitarsis group comprises eight recognized species: An. albitarsis Lynch Arribalzaga, An. deaneorum Rosa-Freitas, An. janconnae Wilkerson and Sallum (previously An. albitarsis E), An. marajoara Galvão and Damasceno, An. oryzalimnetes Wilkerson and Motoki (previously An. albitarsis B), An. albitarsis F, An. albitarsis G, and An. albitarsis I; and the mitochondrial lineage, An. albitarsis H (Motoki et al. 2009, Ruiz-Lopez et al. 2012). The range of the group includes northern Colombia, Venezuela, Trinidad and Tobago, to Argentina (Ruiz-Lopez et al. 2012). These authors note that members of this group have also been reported from Bolivia, Costa Rica, French Guiana, Guatemala, Guyana, Panama, Peru, Suriname, and Uruguay, but specimens were not available to them for verification. McKeon et al. (2013) analyzed larval habitat characteristics (temperature, water chemistry, turbidity, water movement, shade, vegetation) of five species of Anopheles (Nyssorhynchus) in the Amazon, Brazil. They concluded that while An. janconnae and An. oryzalimnetes are habitat specialists, An. marajoara could not be so easily characterized. The Albitarsis Group is involved in malaria transmission in South America (Klein et al. 1991a, 1991b, Conn et al. 2002, Póvoa et al. 2006), but our knowledge of the vectorial importance, distribution, evolution, and ecological requirements of each individual species is incomplete.

According to Hanley et al. (2007), the concept of ecological niche assumes that a species can survive in a certain hypervolume of biotic and abiotic factors. Knowledge of evolutionary relationships may allow the identification of abiotic or ecological characters that exhibit phylogenetic constraints or changing importance within a lineage. Such characters could be considered for inclusion in ecological niche models (ENMs) that map the potential distribution of particular species. Some authors have concluded that niches can be conserved between sister species (e.g., Peterson et al. 1999, Kozak and Wiens 2006), and that even subtle changes in climatic gradients can promote geographic fragmentation into allopatric lineages. For example, past climate changes may have isolated montane species by making intervening lowland areas inhospitable. Others have found that closely related species can show considerable niche plasticity (Losos and Glor 2003, Graham et al. 2004), suggesting that divergence estimated by morphology or molecular genetics may underestimate the extent of niche divergence. Kozak and Wiens (2006) argue that conclusions about the role of natural selection in speciation based on comparisons of sister species' climatic niche models should be informed by knowledge of the specific role that climatic factors play in divergence.

We were interested in exploring the following evolutionary questions:

  • What changes occur in the environmental requirements of species and species clades as they have evolved?
  • Which environmental variable most strongly differentiates sister groups?
  • Is there greater ecological divergence between allopatric sister species than sympatric sister species?
  • Can phylogeny inform the vector potential of individual species?

We constructed ENMs to species of the Albitarsis Group based on the geographical origin of molecularly verified specimens, and environmental and bioclimatic data at these locations, using the program Maxent (Phillips et al. 2006). A similar approach was undertaken for the SE Asian An. dirus complex (Obsomer et al. 2012). We used the program FingerPrint 1.0 (Hanley et al. 2007) to visualize the species' ecological niche as defined by the Maxent distribution models for each species. The program SEEVA (Struwe et al. 2011) was used to explore environmental and ecological divergence associated with evolutionary splits, using a pre-defined hypothesis of phylogenetic relationships within the Albitarsis Group (Ruiz-Lopez et al. 2012) and the respective environmental and climatic data at collection localities. This approach has the potential to identify ecological adaptations that accompany or are instrumental in speciation. Actual and potential mosquito distribution was overlaid on models of malaria distribution (Guerra et al. 2008) to explore the possible vectorial importance of the member species.

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED

Specimens used

Specimens were collected over a period of 20 years by RCW, JFR, MMP, EB, and MAS and were individually identified by retrospective correlation of mtDNA cytochrome c oxidase I (COI) gene sequences (corresponding to the DNA barcoding region) with those 565 sequences published in Ruiz-Lopez et al. (2012). These include An. albitarsis s.s. (GenBank: JQ615201−JQ615309], An. deaneorum (JQ615310−JQ6153450, An. janconnae (JQ615346−JQ615441), An. marajoara (JQ615442−JQ615511), An. oryzalimnetes (JQ615512−JQ615562), An. albitarsis F (JQ614998−JQ615041), An. albitarsis G (JQ615042−JQ615146), An. albitarsis H (JQ615147−JQ615188), and An. albitarsis I (JQ615189−JQ615200). The full data set comprised 1,141 specimens, including those in Ruiz-Lopez et al. (2012). Data revealed 232 unique species-locality combinations of which 199 were unique at the resolution of the niche modeling (An. albitarsis, n = 38; An. deaneorum, n = 17; An. janconnae, n = 22; An. marajoara, n = 16; An. oryzalimnetes, n = 67; An. albitarsis F, n = 8; An. albitarsis G, n = 10; An. albitarsis I, n = 3; An. albitarsis H, n = 18). Novel sequences generated here are available under GenBank Accession numbers KJ011904-KJ012004 and KJ492398-KJ492558. Detailed individual specimen level collection data are available in the Mosquito Barcoding Initiative projects on the Barcode of Life (BOLD) website (www.boldsystems.org) under the Mosquitoes of the World container project, “MBIAA: Anopheles albitarsis complex”, and in VectorMap (www.vectormap.org). Voucher material, including DNA extracts, are housed at the Smithsonian Institution (National Museum of Natural History), Washington DC, the Natural History Museum, London, and University of São Paulo, Brazil.

Ecological niche modeling

ENMs were constructed using Maxent 3.3.3k (Phillips et al. 2006). Input layers were: WorldClim bioclimatic variables, hydrological variables (elevation, aspect, slope, flow accumulation, flow direction, compound topographic index (= Topo Index)), and soil layers (world soil suborders) (see Table 1). All geographical information systems (GIS) analyses were undertaken using ESRI's ArcMap 10. All variables were resampled to 0.04 degrees resolution and clipped by region via mask (Spatial Analyst Tools/Extraction/Extract by Mask). The following Maxent parameters were used: Bootstrapping, ten replicates, 0 random test percentage, 10,000 iterations, no clamping / extrapolation.

Table 1. Environmental and climate layer information used for the Maxent, SEEVA and Fingerprint analyses (x = included in analysis).
Analysis typeLayer nameLayer source
MaxentSEEVAFingerprint
 X1BIO1 = Annual Mean Temperaturehttp://www.worldclim.org/
XX2BIO2 = Mean Diurnal Range (Mean monthly (max temp – min temp))http://www.worldclim.org/
 X3BIO3 = Isothermality (BIO2/BIO7) (* 100)http://www.worldclim.org/
XX4BIO4 = Temperature Seasonality (SD*100)http://www.worldclim.org/
 X5BIO5 = Max Temperature of Warmest Monthhttp://www.worldclim.org/
XX6BIO6 = Min Temperature of Coldest Monthhttp://www.worldclim.org/
XX7BIO7 = Temperature Annual Range (BIO5-BIO6)http://www.worldclim.org/
XX8BIO8 = Mean Temperature of Wettest Quarterhttp://www.worldclim.org/
XX9BIO9 = Mean Temperature of Driest Quarterhttp://www.worldclim.org/
XX10BIO10 = Mean Temperature of Warmest Quarterhttp://www.worldclim.org/
XX11BIO11 = Mean Temperature of Coldest Quarterhttp://www.worldclim.org/
XX12BIO12 = Annual Precipitationhttp://www.worldclim.org/
 X13BIO13 = Precipitation of Wettest Monthhttp://www.worldclim.org/
 X14BIO14 = Precipitation of Driest Monthhttp://www.worldclim.org/
 X15BIO15 = Precipitation Seasonality (Coefficient of Variation)http://www.worldclim.org/
XX16BIO16 = Precipitation of Wettest Quarterhttp://www.worldclim.org/
XX17BIO17 = Precipitation of Driest Quarterhttp://www.worldclim.org/
XX18BIO18 = Precipitation of Warmest Quarterhttp://www.worldclim.org/
XX19BIO19 = Precipitation of Coldest Quarterhttp://www.worldclim.org/
XX20HYDRO-1K, Aspecthttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
XX21HYDRO-1K, Elevationhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
XX22HYDRO-1K, Flow accelerationhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
XX23HYDRO-1K, Flow directionhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
XX24HYDRO-1K, Slopehttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
XX25HYDRO-1K, Topo Indexhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/samerica
X 26Soils subordershttp://soils.usda.gov/use/worldsoils/mapindex/
  27Landcoverhttp://glcf.umiacs.umd.edu/data/landcover/
 X28LandScan Land Cover Classeshttp://www.usgs.gov/
 X29Treecoverhttp://glcf.umiacs.umd.edu/data/landcover/
 X Water holding capacityhttp://soils.usda.gov/use/worldsoils/mapindex/
 X Net primary productionhttp://daac.ornl.gov/NPP/npp_home.shtml
 X Major Habitat Code (Ecoregions 1998 – 1999)P. Hearn, Jr., T. Hare, P. Schruben, D. Sherrill, C. Lamar, P. Tsushima, 2003. Global GIS Database: Digital Atlas of the Earth, USGS Digital Data Series DDS-62-H, Flagstaff, AZ.
 X Biological Distinctiveness Index (Ecoregions 1998–99)P. Hearn, Jr. et al. 2003.
 X Geological ageP. Hearn, Jr. et al. 2003.
 X Soil Temperature Regimeshttp://soils.usda.gov/use/worldsoils/mapindex/
 X Soil organic carbonhttp://soils.usda.gov/use/worldsoils/mapindex/
 X Soil moisture regimeshttp://soils.usda.gov/use/worldsoils/mapindex/
 X Soil inorganic carbonhttp://soils.usda.gov/use/worldsoils/mapindex/
 X Phosphorus retention potentialhttp://soils.usda.gov/use/worldsoils/mapindex/
 X Inherent land qualityhttp://soils.usda.gov/use/worldsoils/mapindex/
 X Biomeshttp://soils.usda.gov/use/worldsoils/mapindex/
 X Anthropic landscapeshttp://soils.usda.gov/use/worldsoils/mapindex/
 X Plasmodium falciparum (stable)http://www.map.ox.ac.uk/
 X Plasmodium falciparum (unstable)http://www.map.ox.ac.uk/
 X Plasmodium vivaxhttp://www.map.ox.ac.uk/
 X LandScan 2011http://www.ornl.gov/sci/landscan/
 X Cattle densityhttp://www.fao.org/AG/AGAInfo/resources/en/glw/GLW_dens.html

Model output is a continuous variable ranging from 0 to 100, indicating relative suitability. The median model output was given a threshold of the lowest value associated with an observed presence record, i.e., the minimum training presence (MTP) value (Pearson et al. 2007), averaged across the ten replicates. To facilitate model analysis, we created binary predictions of presence and absence by classifying as “present” any cell with suitability greater than or equal to the MTP.

SEEVA analysis

For the SEEVA analysis, we used the phylogeny suggested by Ruiz-Lopez et al. (2012), based on DNA barcodes (658 bp of the mtDNA cytochrome C oxidase gene – COI), and converted to NEXUS format, and extracted environmental data from collection localities, using ArcMap 10. Environmental variables used in the SEEVA analyses are listed in Table 1. SEEVA was limited to four states or categories per environmental layer, so each layer was re-categorized to fit this schema for the analyses. Quantitative variables were scored as ordered, continuous data divided into four quartile classes. Qualitative variables (soil, geology, and vegetation type) were treated as non-ordered, categorical data. When state frequencies were very low, we followed the approach of Struwe et al. (2011) by combining related types or (rarely) omitted the very few records with rare types present in fewer than four individuals.

Eight phylogenetic nodes or junctions occur in the phylogeny of Ruiz-Lopez et al. (2012) (Figure 1), leading to eight (independent) tests for each environmental feature. Node 1 combines the lineage pairs: An. marajoara, An. albitarsis G, An. albitarsis H, An. deaneorum, An. oryzalimnetes, An. albitarsis & An. albitarsis F, An. janconnae, An. albitarsis I. Node 2: An. marajoara, An. albitarsis G, An. albitarsis H, An. deaneorum & An. oryzalimnetes, An. albitarsis. Node 3: An. marajoara, An. albitarsis G, An. albitarsis H & An. deaneorum. Node 4: An. marajoara, An. albitarsis G & An. albitarsis H. Node 5: An. marajoara & An. albitarsis G. Node 6: An. oryzalimnetes & An. albitarsis. Node 7: An. albitarsis F & An. janconnae, An. albitarsis I. Node 8: An. janconnae & An. albitarsis I.

image

Figure 1. Location of collection points for species according to membership of the two major basal clades within the Albitarsis Group. Nodes in the phylogeny are numbered 1 to 8. Note the disjunction in the distribution north and south of the Amazon River (blue line).

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SEEVA calculates two indices to measure the strength of the phylogenetic–ecological association. The Impact Index is independent of sample size and degrees of freedom thereby providing a measure of the skewness for each node and variable; the Diversity Index is a proportional measure, bounded on the interval (0, 1). The Diversity Index conveys the magnitude of divergence better than the impact factor (Struwe et al. 2011), and we only report the former here. Nodes that have evolved a diversity of adaptations or ecological requirements are more likely to show a high index of diversity. For measuring statistical significance, we used a Bonferroni correction for an alpha of 0.05, which amounts to declaring a significant result for a particular node only if P = 0.0063, which is equivalent to an experiment-wise error rate of a = 1 − (1 − 0.0063)8 ≈ 8*(0.0063) = 0.05 for the set of eight independent nodal tests (Struwe et al. 2011).

Species fingerprinting

The program FingerPrint 1.0 (Hanley et al. 2007) was used to visualize the species' ecological niche, using the Maxent distribution models for each species, and the results projected into a visualization matrix. Species fingerprinting maps the probability of a species' existence through the entire range of each of the environmental factors, allowing ecological comparison across many species. Similarity indices comparing the level of niche overlap between species were calculated and the index over all environmental factors was estimated as a mean of all similarity indices. The program's default settings were chosen for the cut-off probability (70% – the lowest probability, below which the species' survival is unlikely) and the method (mean). The sequence and list of environmental and climate layers used in the Fingerprint analyses are given in Table 1.

Species and malaria distribution overlap

The overlap of malaria and individual species predicted suitable habitat was quantified in ArcMap 10 (Raster Calculator and Zonal histogram tools), using the mean of ten mosquito models rendered to presence or absence via the MTP, and Plasmodium falciparum (stable: PfAPI ≥ 0.1 per thousand per annum, and unstable: PfAPI < 0.1 per thousand pa) for 2007 (Guerra et al. 2008) and P. vivax models for 2008 from the Malaria Atlas Project (http://www.map.ox.ac.uk/).

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED

The earliest phylogenetic split (node 1) separates Clade 1 (An. albitarsis F, An. albitarsis I, An. janconnae – north of the Amazon River from Clade 2 (all other species – south of the Amazon River) (Figure 1). The location of collection points shows that exceptions to this north-south distribution pattern are few, and comprise locations near the Amazon River and the species An. albitarsis G, An. janconnae, and An. marajoara. Interestingly, these species are all terminal taxa according to the phylogeny for this group. According to Ruiz-Lopez et al. (2012), An. janconnae (Clade 1) and An. marajoara (Clade 2) are sympatric in Pará state, Brazil.

Ecological niche modelling

The ENMs (median values) are shown in Figure 2. Except for a few areas, habitat suitability derived from the ENMs support the hypothesis that Clade 1 and Clade 2 species are north and south of the Amazon River, respectively.

image

Figure 2. Maxent ecological niche models for nine species within the Albitarsis Group. 1. Anopheles albitarsis, 2. An. deaneorum, 3. An. janconnae, 4. An. marajoara, 5. An. oryzalimnetes, 6. An. albitarsis F, 7. An. albitarsis G, 8. An. albitarsis I, and 9. An. albitarsis H. The higher the predicted habitat suitability, the darker the shade. Collection points used in the models for each species are shown as red crosses.

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Relative contributions of the environmental variables to the Maxent models are shown in Table 2. For percent contribution, soils had a high value across all species, but particularly for An. albitarsis F (58.8%). Precipitation contributions were high for many species, but hydrological variables were higher for An. deaneorum (25.8%), and temperature variables were higher for An. albitarsis, An. albitarsis F, An. albitarsis G, and An. albitarsis I (19.1−35.7%). Phylogenetically terminal (sister) taxa differed in the contribution of temperature vs precipitation. For example, for An. albitarsis I, a temperature variable (Bio11) contributed the most (35.5%) to the model compared to precipitation (Bio19, 31.3%) for the sister taxon An. janconnae. When a variables' contribution to the model was seen in isolation (see Table 2), soils were no longer as important, except for An. albitarsis F. Altitude was uniformly unimportant for Clade 1 species, yet was important for species in Clade 2. Temperature variables more commonly gave the highest values for basal taxa, such as An. albitarsis F, An. albitarsis, and An. oryzalimnetes.

Table 2. Relative contributions of environmental variables to Maxent model for species within the Albitarsis Group. Percent contribution (above) of each variable when all variables are considered together and permutation importance (below) when the contribution of each variable in calculated independent of other variables. The two highest values for each model are shaded, taxa are grouped according to clade and reciprocal monophyly (sister taxa in parentheses), and variables are grouped according to type (temperature, precipitation, hydrology, and soil).
Percent contribution
TypeVariableClade 1Clade 1Clade 1Clade 2Clade 2Clade 2Clade 2Clade 2Clade 2
  An. albitarsis F(An. albitarsis IAn. janconnae)(An. albitarsisAn. oryzalimnetes)An. deaneorumAn. albitarsis H(An. marajoaraAn. albitarsis G)
Tempbio_20.90.50.30.51.700.112
Tempbio_419.12.11.835.75.54.61.42.10.1
Tempbio_610.40.30.901.33.40.41.933.8
Tempbio_72.700.20.52.31.30.10.12.8
Tempbio_8016.803.80.80.12.800
Tempbio_92.50.10.50.34.100.10.51.4
Tempbio_100.200.20.57.700.81.90
Tempbio_111.235.52.50.51.201.50.50.3
Precipbio_1203.48.810.43.51.35.30.40.6
Precipbio_160.100.11.59.41.23.326.50.6
Precipbio_170.41.14.810.68.24.815.97.10.2
Precipbio_180.611.46.72.28.30.60.311.56.6
Precipbio_1903.731.30.35.44.910.31.91.2
Hydroh_aspect01.84.51.736.124.84
Hydroh_dem2.502.93.77.46.52.919.36.7
Hydroh_flowacc001.22.12.925.83.24.70
Hydroh_flowdir0.13.11.51.11.3311.63.60.6
Hydroh_slope0.4021.64.98.65.820.3
Hydroh_topoind0.10.80.10.40.20.10.41.71.1
Soilsoilso58.819.529.822.720.727.531.98.437.7
           
Permutation importance
Tempbio_22.51.32.10.11.60.10.52.31.9
Tempbio_420.56.50.143.77.63.11.712.60.1
Tempbio_61.30.90.600.30.30.20.10
Tempbio_710.708.31.41.600036.9
Tempbio_8043.22.83.83.90.3100.60.1
Tempbio_910.58.39.80.419.600.1013.6
Tempbio_100.101.21.74.50.11.20.20
Tempbio_110.633.514.610.8000.80
Precipbio_120528.46.52.80.51.22.53.9
Precipbio_160.801.11.9610.218.80.4
Precipbio_171.807.93.911.31.234.917.40.4
Precipbio_180.400.51.65.410.43.37
Precipbio_190.11.17.30.91.214.812.30.70.5
Hydroh_aspect000.40.61.40.313.30.8
Hydroh_dem7.7061620.263.520.921.531.5
Hydroh_flowacc000.110.35.31.12.90
Hydroh_flowdir0.20.210.20.315.91.80.4
Hydroh_slope0.705.97.73.81.74.46.60.4
Hydroh_topoind0001.60.70.20.62.30.2
Soilsoilso42.101.76.16.65.63.52.11.8

SEEVA

Environmental features that showed phylogenetic ‘signal’ from the SEEVA analysis are shown in Figures 3 and 4. For the 47 environmental features considered, 92 of the 376 nodal comparisons were significant (P < 0.0063, indicated by *), and 57 had Index of Diversity values >0.5. Node 8 shows greatest ecological divergence, with the highest number of significant values, and nodes 2, 3, and 6 show the least ecological divergence with no significant values. The absence of any strong environmental effect on the An. oryzalimnetes and An. albitarsis clade is noteworthy, suggesting that ecological divergence (as measured here) did not play a role or accompany speciation in this clade. No nodes for any of the environmental layers that gave an Index of Diversity >0.5 were statistically significant, suggesting caution is needed in the interpretation of results. Node 1 had high (>0.5) Index of Diversity figures only for the temperature-related variables (BIO1, 2, 3, 6, 9, 10), with Clade 1 mainly restricted to warmer locations compared to a wider spectrum of temperature conditions recorded for species within Clade 2. Node 4 showed high Index of Diversity figures for a variety of climate (temperature and precipitation) variables, and for elevation. Node 5 showed high figures for soil-related variables and one precipitation variable (BIO13). Node 7 showed high figures for a variety of variables except temperature. Node 8 showed the greatest number and variety of variables with high Index of Diversity figures. However, numbers of An. albitarsis I were low (n = 3), suggesting caution should be used in the interpretation of these results for this taxon.

image

Figure 3. Example 1 output from the program SEEVA for variables mapped on a phylogram of the Albitarsis Group, based on mtDNA COI sequence data (Ruiz-Lopez et al. 2012). The bars indicate proportion of categories present at each sister group. Decimal numbers are the Index of Diversity and asterisks indicate statistically significant indices (P < 0.0063). Nodes in the phylogeny are numbered 1 to 8.

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image

Figure 4. Example 2 output from the program SEEVA for variables mapped on a phylogram of the Albitarsis Group, based on mtDNA COI sequence data (Ruiz-Lopez et al. 2012). The bars indicate proportion of categories present at each sister group. Decimal numbers are the Index of Diversity and asterisks indicate statistically significant indices (P < 0.0063). Nodes in the phylogeny are numbered 1 to 8.

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Comparison of the average nodal Index of Diversity revealed that Clade 1 was ecologically more divergent than Clade 2: 15 of 47 environmental features had values >0.5 (three of which were >0.75) for Clade 1, compared to none of 47 for Clade 2. For Clade 1, climate features with high diversity were all precipitation related rather than temperature related. For example, precipitation of the warmest quarter was higher and precipitation seasonality was lower in locations where An. albitarsis F and An. albitarsis I occurred compared to those for An. janconnae (data not shown).

Despite these average tendencies, individual nodes within Clade 1 and 2 could show high values for the Index of Diversity. For example: Clade 1 was notable in that no collections were made from the Woodland landscape category (Figure 3). Only An. albitarsis G from Clade 2 was absent from Woodland, although numbers were low. Clade 1 also was absent from locations with the lowest category of inorganic carbon (data not shown). For node 8, An. janconnae was located in areas with lower host (human) density than An. albitarsis I (data not shown). Members of node 6 (An. oryzalimnetes and An. albitarsis) tended to be associated with higher densities of humans. Within Clade 1, significant divergence occurred in temperature and precipitation variables, as well as altitude. Higher temperature range, especially with lower temperature conditions and lower precipitation in the driest and coldest time of the year, is a feature of locations supporting An. albitarsis H, probably due to the higher elevation of inland collection locations. Within Clade 2, significant divergence occurs, mainly in characteristics of the land rather than climate. These land conditions include: soil suborder, soil phosphorus retention potential, soil moisture regime, soil inorganic carbon, soil water holding capacity, inherent land quality, biome type, and Biological Distinctiveness Index.

Where comparisons of sister species were possible (nodes 5, 6, and 8) there was no consistency in the degree of divergences, which ranged from no appreciable divergence to large divergences, mainly in non-climate variables.

The youngest geological age category (Tertiary and Quaternary) made up the majority of sites from where the Albitarsis Group were collected (Figure 4).

Species fingerprinting

The results of the FingerPrint analysis are shown in Figure 5. Hydrological layers (e.g., low values of flow acceleration and higher values of Topo Index) appear important for the ecological niche of An. oryzalimnetes and soil types for An. albitarsis F. Lower mean annual temperature range is more important for An. albitarsis F than for An. albitarsis; An. albitarsis F and An. albitarsis I have higher precipitation requirements than the other species. Temperature and hydrological features appear to be unimportant for the ecological niche of An. albitarsis I, and temperature and precipitation appear to be unimportant for An. janconnae. A phylogram constructed in MEGA 5.02 (Tamura et al. 2011) using dissimilarity values output from FingerPrint (Figure 6) differed from the DNA-derived phylogeny, with the relatively unrelated An. albitarsis F (Clade 1) and An. oryzalimnetes (Clade 2) being ecologically distinct from each other and the other species. Anopheles albitarsis F differed mainly in temperature and precipitation variables and slope, and An. oryzalimnetes differed mainly in hydrological variables (aspect, Topo Index, and flow acceleration and direction), and both species differed simultaneously in soils, land cover, and flow direction. The output matrix of the mean Full Dissimilarity Index from FingerPrint (Figure 6) showed low values for sister species within Clade 1 (n = 1.00), high values for non-sister species within Clade 1 (n = 8.00), and intermediate values for Clade 2 sister and non-sister comparisons (n = 4.00 and 3.85, respectively). The average mean Full Dissimilarity Index for Clade 1 was 5.67 and for Clade 2 was 3.87. The greatest dissimilarity values were between An. albitarsis I and An. oryzalimnetes, which showed non-overlap in precipitation variables (Figure 5). Dissimilarity is perhaps not surprising given the non-overlapping and restricted predicted habitat suitability of An. albitarsis I, and extensive distribution of An. oryzalimnetes (see Figure 2). However, low dissimilarity was seen between other non-overlapping species (e.g., An. albitarsis I and An. deaneorum).

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Figure 5. Visual representation of the ecological fingerprint for 29 environmental variables (columns in the matrix for each species) according to the nine species within the Albitarsis Group (the more red the color, the higher the probability of occurrence and the more important for the ecological niche of that species). The numbers across the top of each species matrix refer to the different environmental layers used in the modeling and are divided by a solid black vertical line into general types. From left to right these are: temperature (1 – 11), precipitation (12 – 19), hydrological (20 – 25), soil (26), and landscape variables (27 – 29) (see methods). The values on the left refer to 20 different value levels for each environmental layer, e.g. for temperature, low = 1 and high = 20. The bottom row of three species comprise Clade 1 taxa.

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image

Figure 6. Matrix of mean P based Full Dissimilarity Index output for species within the Albitarsis Group from the program FingerPrint. Those values with a blue background refer to Clade 1 comparisons, white refers to Clade 2, and grey refers to cross clade comparisons. Values with a red font refer to sister taxa comparisons, black font refers to non-sister taxa comparisons. Also shown is a Neighbor Joining phylogram, constructed in MEGA 5.02, using the matrix values, to further illustrate the relative ecological similarity of species within the Albitarsis Group.

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The comparison of sister species (nodes 5, 6, and 8) gave an average dissimilarity score of 3.00, compared to an overall average of 4.31 (Figure 6), supporting the idea that phylogenetic proximity is related to ecological similarity, i.e., that niches are conserved. As noted by Ruiz-Lopez et al. (2012) for a subset of the current data, some species and lineages were found to be sympatric: An. marajoara with An. oryzalimnetes and An. janconnae in Pará, Brazil; An. albitarsis H, An. deaneorum, An. marajaora, and An. oryzalimnetes in Mato Grosso state, Brazil; An. oryzalimnetes and An. albitarsis in São Paulo state, Brazil, among others. The average dissimilarity score for these sympatric species was 4.78 (Figure 6), suggesting that geographic proximity was not correlated with ecological similarity.

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED

Zeisset and Beebee (2008) discussed five factors that have been used to explain biogeographic patterns in tropical South America. Of these, the barrier effect of major Amazonian rivers (the River hypothesis), the Refuge hypothesis, and the Climatic/River refuge hypothesis are the most relevant to mosquitoes. Major rivers, but not usually minor tributaries or headwaters, can act as partial barriers to gene flow in some animals (Zeisset and Beebee 2008). Molecular evidence suggests that the Amazon River has been a significant barrier for several taxa, including monkeys (Lavergne et al. 2010), tapirs (de Thoisy et al. 2010), carnivores (Eizirik et al. 2001), and birds (Fernandes et al. 2012). Geological data suggest that the Amazon River attained its current flow during the late Pleistocene (Rossetti et al. 2005), and Solomon et al. (2008) noted that diversification at time subsequent to the formation of the Amazon River (5−12 mya) would be consistent with the riverine barrier hypothesis. Pedro et al. (2010) analyzed a fragment of the COI mitochondrial gene of An. triannulatus (Neiva and Pinto) and An. darlingi Root and concluded that river and mountain barriers block gene flow for more than 1,000 km for both species and likely played a part in historical gene flow. They found that populations northeast of the Amazon and in southeastern Brazil are generally reciprocally monophyletic to the remaining groups. They speculated that human-aided transport and unassisted dispersal across the islands of the Amazon delta may have played a part in explaining cross Amazon River haplotypes. Although dating of the phylogenetic nodes of the Albitarsis Group has yet to be done, the role of the Amazon River in the speciation of this group appears important, initially in the separation of Clade 1 and 2 and more recently in the co-location near the Amazon River of the sister taxa An. marajoara and An. albitarsis G. A geographically more detailed study of vector species distribution in relation to the Amazon River appears warranted.

Sister species couplets are: An. marajoaraAn. albitarsis G (node 5, overlapping distribution, ecological divergence in soil features), An. albitarsisAn. oryzalimnetes (node 6, overlapping southern distribution, no strong ecological divergence in characters considered here), and An. janconnaeAn. albitarsis I (node 8, disjunct distribution, greatest ecological divergence). Anopheles deaneorum and An. albitarsis H are sympatric, with overlapping middle to western Amazonian distribution. Sister species either had overlapping or sympatric potential distribution with little ecological divergence, or allopatric potential distribution with great ecological divergence. Although numbers are low, phylogenetic proximity more often corresponds with geographic and ecological proximity.

Distribution patterns

Based on present day distribution, some speculation about past distributions at the time of speciation is possible. For example, the sister taxa An. janconnae and An. albitarsis I may have arisen in allopatry at the eastern and western edges, respectively, of a more widespread ancestral taxon. The geographically intermediate distribution of the phylogenetically basal An. albitarsis F possibly reflects the ancestral ecological niche of Clade 1. For example, a hotter, drier past climate may have isolated this ancestral species to higher, cooler areas, enabling allopatric speciation of intervening lowland areas to occur. Anopheles albitarsis appears to have specialized to more southern environments, and An. oryzalimnetes retained the more widespread ancestral (node 1 and 2) niche, judging from the similarity of the latter species to the hypothesized ancestral ecological spectra (e.g., temperature annual range, Figure 4). These sister taxa are unique in their occurrence in dry environments – the deserts and xeric shrub lands biome – an adaptation that may have played a role in the evolution of this clade. Although roughly co-located in western Brazil, An. albitarsis H appears to have become specialized for hillier, higher elevation and less-forested locations that are hotter and drier than the more ancestral An. deaneorum. The clade comprising An. marajoara and An. albitarsis G occurs in the proximity of the Amazon River and shows evidence of ecological divergence in features of the soil rather than climate, e.g., An. albitarsis G occurs in locations with wetter soils characterized by lower soil phosphorus retention potential. Soil, organic detritus, and aquatic chemistry have powerful inter-related effects on mosquito distribution and abundance (Gardner et al. 2013). Access to a robust molecularly-dated phylogenetic tree and projecting what we know about the ecological niche and geological history onto scenarios of past climate may show spatial habitat suitability patterns that could shed light on the factors involved in speciation in the Albitarsis Group.

We found a widespread occurrence of An. oryzalimnetes and more ancestral-like environmental spectra of this species (Figures 3 and 4). McKeon et al. (2013) considered this species an evolutionary specialist and found that the number of larval habitats that were shared among this and other species was one of the lowest within the study sites sampled. The fingerprint analysis reported here confirmed that this species is very different in its associated climatic and environmental characteristics compared to the other species, which suggests the exploitation of a unique niche (but not a geographically limited niche). McKeon et al. (2013) based their findings on the observation of a more restricted distribution of this species than we detected and on its abundance being associated with the water chemistry variables that they measured. The present study measured occurrence of adults over a wider geographic area and may better reflect more evolutionarily constrained physiological tolerance. Also, McKeon et al. (2013) measured larval abundance, which is partially related to more plastic behavioral oviposition preferences and less easily interpreted optimal conditions for growth. A combination of a detailed understanding of the phylogeography of the species within the Albitarsis Group across their distribution range and studies such as McKeon et al. (2013) that investigate the nature and degree of tolerance in different geographical populations of the different species with varying exposures to water chemistry may assist an understanding of the population structure and history of adaptations within this group.

Limitations of this study include: the phylogenetic hypothesis (branching order) is based on a single gene and may not be correct; collection sites may be subject to sampling bias and were sometimes few in number (e.g., very low numbers for An. albitarsis I, n = 3); additional cryptic species may be present that were not accounted for; categorization of layers to four states may not represent biological reality and could distort the analysis; correlations of ecology and phylogeny do not prove causality thereby limiting our conclusions; limits on distribution may be historical rather than relating to ecological niche; and the limited resolution of the remote sensing data may make species with different ecologies but the same general location appear closer in their niche requirements than is the case. The biological reality of ecological niche predictions could be improved with additional collection locations, especially those remote from sites already sampled. According to the SEEVA manual, the generic null hypothesis for all tests is that the cross-classified factors are completely independent, and the hypothesis driving the work is that species or clade distributions are influenced by specific environmental variables, as shown by non-random associations of particular species or clades and their environmental situations. However, it should not be assumed that environmental variables are independent, and an assumption of independency is not necessary for the SEEVA analysis. In general, correlations between divergence and environmental variables can be inferred as trends and tendencies within phylogenetic lineages and not as the definite cause for the divergence until verified by further research. The role of ecology in speciation more widely is unclear, but the observation of apparent niche conservatism in this study argues against it in the Albitarsis Group. However, as stated by Svennson (2012), “the ecology of species differences is not the same as ecological speciation, just like the genetics of species differences does not equate to the genetics of speciation.”

Species and malaria distribution overlap

Collection locations and ENMs can be overlaid with human and malaria models to incriminate vector species (by eliminating from contention those species that do not coincide with malaria) and to predict the likely species encountered in malaria field studies. Given the caveats that correlation does not imply causation, that this study does not consider the role of non-Albitarsis Group vectors, and that ‘anophelism without malaria’ (Fantini 1994) is possible, collection locations and ENMs available in this study can be used to explore the potential importance of individual species in transmission. The expectation for a species that is a ‘good’ vector throughout its range is that it will have a high co-occurrence with malaria. If it does not, then the mosquito or Plasmodium models are inaccurate or malaria distribution depends on other (non-vector) factors. The expectation for a species that is a ‘poor’ vector throughout its range is that it will have a low co-occurrence with malaria. If it does not, then the non-vector species is usually sympatric with vector species, the result occurs through chance, or both mosquito and malaria distribution are responding to an unknown third factor.

At least three species are vectors of human malaria parasites in Brazil: An. deaneorum (Klein et al. 1991a, 1991b), An. marajoara (Conn et al. 2002), and An. janconnae (as An. albitarsis E, Póvoa et al. 2006). Collection locations of these species (plus An. albitarsis G and H) largely coincide with areas predicted suitable for P. falciparum (Figure 4). Interestingly, An. janconnae and An. albitarsis G were unique in that they were always in areas predicted suitable for P. falciparum (stable) and P. vivax but never in areas that were predicted to be suitable for P. falciparum (unstable).

The percentage overlap of ENMs with malaria distribution models is shown in Figure 7. On average, higher percentage of overlap is observed for P. vivax compared with P. falciparum, and for P. falciparum (stable) compared with P. falciparum (unstable). The widespread An. oryzalimnetes was predicted to most often coincide with malaria (data not given), but when considering the percentage of this species' suitable habitat that coincides with malaria the result is very different. Anopheles albitarsis I, An. janconnae, and An. marajoara had the highest levels of overlap with P. falciparum and P. vivax, as a percentage of the total area predicted suitable for each mosquito species. Moderate to high percentages of overlap were noted for An. albitarsis G, An. albitarsis H, and An. deaneorum. The sister taxa An. oryzalimnetes and An. albitarsis showed the lowest percentage of overlap and, on this basis, do not appear to be important malaria vectors. This observation also suggests that phylogenetic proximity may be related to malaria vectorial importance within the Albitarsis Group. Gutiérrez et al. (2010) used the phylogenetic proximity to An. janconnae of a new lineage (= An. albitarsis I, Ruiz-Lopez et al. 2012) from Colombia to argue for its possible role in malaria transmission. Mapping malaria to a vector species phylogeny is not meant to imply that speciation is related to malaria, but this type of analysis could reveal phylogenetic constraints and pre-adaptations important to malaria transmission or vector control.

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Figure 7. Percentage of the area predicted suitable, based on niche models, for each species within the Albitarsis Group that is also suitable for malaria type according to models of malaria distribution for 2007/2008 from the Malaria Atlas Project.

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The current results agree with the list of known vector species above and also supports the suggestion of a role in malaria transmission for An. albitarsis I (Gutiérrez et al. 2010). Low collection numbers of this species limit conclusions, but the present results are suggestive and warrant additional studies to be undertaken on the transmission potential of this species.

Other information can be considered in relation to vectorial importance. For example, An. marajoara, followed by An. deaneorum and An. albitarsis H, tended to be associated with low to medium human densities (data not shown), as expected in rural areas where malaria is more likely and with land of higher vulnerability to anthropogenic activities (i.e., lower Inherent Land Quality, see Figure 3). By contrast, An. albitarsis G and An. janconnae collection locations tended to be in areas with higher land quality. Low resilience and performance soils in agricultural areas are likely to be related to lower crop yields and increased levels of poverty, poorer nutrition, and lower access to health services; poverty is a risk factor for malaria (Tusting et al. 2013).

Knowledge of species distributions may assist vector control workers to identify the likely members of the Albitarsis Group, regardless of their vectorial importance, in given areas. ENMs may also suggest new areas for mosquito surveys and geographically isolated populations that are genetically distinct. For example, the vicinity of São Luís, Maranhão, was not sampled but appears suitable for An. marajoara and An. albitarsis G. Analyses such as from SEEVA may shed light on the evolutionary history and likely responses of individual species to aspects of climate/environmental change. For example, long term changes in precipitation of the wettest quarter may have very different effects on the abundance and distribution of An. marajoara and An. albitarsis G compared to An. albitarsis (see Figure 3). Knowledge of the abiotic conditions, biotic factors, geographic connectivity, and evolutionary capacity of a species to adapt to new conditions is necessary to fully appreciate the relationship between a species' fundamental ecological niche and the geographic areas where it is found (Soberón and Peterson 2005). This knowledge could also suggest how a species might react (e.g., González et al. 2010) given future anthropogenic changes in aspects of the climate, habitat, and the types of species interactions, including with hosts and pathogens, for an area of interest.

Acknowledgments

This investigation received financial support from the Global Emerging Infections Surveillance and Response System (a Division of the Armed Forces Health Surveillance Center – AFHSC/Div of GEIS Ops) (grant P0149_13_WR to DHF); from the National Institute of Health, USA (grant R01 AI50139–02 to JEC); and from the Foundation for Research Support of the State of São Paulo, FAPESP (Processo no. 2011/20397–7 to MAMS). This manuscript was prepared while YML held a National Research Council Research Associateship Award at the Walter Reed Army Institute of Research. We thank Simon Hay, Carlos Guerra, Catherine Moyes, and Peter Gething of the Malaria Atlas Project for access to malaria models. This research was performed under a Memorandum of Understanding between the Walter Reed Army Institute of Research and the Smithsonian Institution, with institutional support provided by both organizations. The published material reflects the views of the authors and should not be construed to represent those of the Department of the Army or the Department of Defense.

REFERENCES CITED

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. REFERENCES CITED
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