Abundance and Dynamics of Anopheles (Diptera: Culicidae) Larvae in a Malaria Endemic Area of Bangladesh

Abundance and Dynamics of Anopheles (Diptera: Culicidae) Larvae in a Malaria Endemic Area of... Abstract Malaria is a major health burden in the border-belt areas of Bangladesh. There are recent data from adult mosquito collections that a number of vectors are involved in the transmission cycle. However, little information regarding the larval habitats of Anopheles mosquitoes are available in Bangladesh. To start filling this gap, a cross-sectional larval survey was conducted in Bandarban, Bangladesh from October 2011 to September 2012. Descriptive analysis, Poisson regression, spearman correlations and zero-inflated Poisson regression were used to calculate the degree of association between the abundance of larval Anopheles species and environmental factors. From the 300 larval habitats sampled, 5,568 Anopheles larvae were collected and of these, 2,263 (40.6%) were identified to species. Collections represented 16 Anopheles species with Anopheles vagus (26.4%, n = 598) as the dominant species. A total of 16 Anopheles larval habitat types were identified. Larval abundance was significantly different (P < 0.05) among habitats with pond (40%, n = 914) and rice field (34%, n = 779) implicated to be the most utilized. Larval abundance varied significantly (P < 0.05) with habitat characteristics. Most of the larvae were collected from sites with a range of pH from 7.0 to 8.0. Data obtained from this study revealed both natural and human-created larval habitats were favorable for anopheline larval survival and development. Such information elucidates plausible drivers of high anopheline diversity, high vector abundance, changes in relative species abundance from historic data, and sustained transmission of malaria in endemic areas of Bangladesh. Anopheles, larval production site, abundance, factor, Bangladesh At least 13 million people remain at risk for malaria in Bangladesh (NMCP 2015). Although once widespread throughout the country and accounting for 15% of annual deaths prior to the malaria eradication programme (MEP) in the 1960’s (Paul 1984), malaria is now restricted to 13 districts of Bangladesh bordering India and Myanmar. Of these districts, three in the Chittagong Hill Tracts (CHT); Rangamati, Bandarban, and Khagrachari, contribute 90% of the country’s total malaria cases of which 90% are due to Plasmodium falciparum Welch (Haemosporida: Plasmodidae) (NMCP 2015). During 2015, Bangladesh reported 39,719 malaria cases with nine deaths, where more than 90% (n = 35,968) of cases occurred in the CHT and nearly 50% (n = 18,262) of those cases were reported from Bandarban (MOHFW 2016). The CHT differs from all other regions of Bangladesh. Most of the CHT are difficult to reach both for surveillance and control which has resulted in refugia for malaria parasite transmission by the vectors indigenous to these areas. The topography of the CHT includes elongated ranges of hills, most of which are covered by forest, and intervening valleys with rivers and small lakes (NMCP 2015). During the last two decades, damming of hilly streams and rivers has allowed for high elevation irrigation from the resulting impoundments. These water bodies are suitable larval habitat for mosquitoes and might be one of the reasons for the persistence of malaria transmission in the CHT. Of the 36 Anopheles Meigen (Diptera: Culicidae) species reported in Bangladesh, only seven were recognized as important vectors of malaria (Irish et al. 2016). Historically, four of these species, Anopheles (Cellia) baimaii Sallum & Peyton, Anopheles (C.) philippinensis Ludlow, Anopheles (C.) sundaicus s.l. (Rodenwaldt) and Anopheles (C.) minimus Theobald were targeted for control by the MEP (Renshaw et al. 1996). Recent studies, however, have implicated previously unsuspected species such as Anopheles (C.) nivipes Theobald and Anopheles (C.) jeyporiensis James as playing key roles in malaria transmission (Alam et al. 2012b, Al-Amin et al. 2015). In addition to a decrease in the abundance of recognized malaria vectors, spatial and temporal changes in the distribution and densities of these vectors at both local and larger endemic scales has recently been observed (Al-Amin et al. 2015). Intact forest in the CHT had historically provided abundant larval habitat for principal vectors like An. baimaii (Rosenberg 1982). However, over the decades, land use and land cover have changed with deforestation for teak/rubber monoculture and agricultural expansion, urbanization, and expansion of human settlements (Uddin and Gurung 2010). These events have led to habitat change that benefits additional or alternative anopheline species (Al-Amin et al. 2015). Development projects such as dams and irrigation schemes can result in a net increase of suitable mosquito habitat (Sanchez-Ribas et al. 2012). In fact, small dams built in many parts of the world similar to those constructed in CHT for irrigation, have been correlated with increases in malaria transmission (Ghebreyesus et al. 1999, Norris 2004). Moreover, climate change could also favor the growth and survival of Anopheles immatures. Increased water temperatures associated with small shallow pools have been shown to enhance maturation of egg to larvae and subsequently adult, increasing the abundance of adult vectors and ultimately leading to increased pathogen transmission (Bayoh and Lindsay 2003). Anopheles larval ecology and identification of favorable aquatic habitat for malaria vectors is critical for understanding Plasmodium transmission and planning effective control measures (Amerasinghe et al. 1995). Unfortunately, there are few data available on the larval habitat of Anopheles mosquitoes from the CHT eco-zone. Notable reports focused either on An. baimaii (Rosenberg 1982) in the Sylhet region or An. philippinensis in the flood plains (Elias 1996). A single report by Renshaw et al. (1996) describes generalized Anopheles larval habitat in the flood plain with regards to implementation of the flood action plan (Renshaw et al. 1996). Despite these efforts in the 1990s, the larval biology of Anopheles mosquitoes has never been studied or well documented in the CHT. Therefore, the goal of this study was to identify and quantify potential oviposition habitats of Anopheles mosquitoes and the factors associated with larval presence and abundance in a malaria endemic area of the CHT. Materials and Methods An entomological survey of Anopheles larval habitats was conducted in Rajbila (22°20′42.48″N, 92°8′55.56″E) and Kuhalong (22°12′48.90″N, 92°12′11.46″E) unions of Bandarban District, from October 2011 to September 2012. Each of these two unions was further divided into 12 clusters for the purpose of the study. Description of the study areas with associated demography have been described elsewhere (Khan et al. 2011, Ahmed et al. 2013). Collection of Mosquito Larvae In each month larval surveys were undertaken in one cluster from each union. Once mosquitoes were collected from a cluster, no repeated visit was made for that cluster in the subsequent months. A team consisting of three to four persons walked along the longest possible transect of a cluster after obtaining information of cluster area from the Bandarban field office and inspected all artificial or natural habitats within line of sight where water could stand for few days. Collections were conducted if mosquito larvae or pupae were observed, irrespective to the size of the water bodies and habitat characteristics (type), and mosquito counts were recorded. Although many more mosquito-producing sites undoubtedly exist, collections were limited by available resources and no repeated visit or sampling at any one container was conducted. Mosquito larvae and/or pupae were sampled using standard dipping techniques (WHO 2003) where 5 to 7 dips in each larval habitat were made using a standard dipper (350 ml). In large water bodies, dipping was carried out over a 50 m walk. For small water reservoirs (e.g., animals hoof print, puddle, small artificial container and wheel tracts), a maximum amount of water was sampled using a serological pipette. All larvae and/or pupae were placed into screw cap plastic cups or Whirl-Paks (Nasco, Fort Atkinson, WI) with source water, labeled with collection date, and transported to the Bandarban field laboratory for further processing. Anopheles larvae were heat-killed by transferring them into hot water (50 to 70°C) after separating from other mosquito larvae, pupae, and insects, and morphologically identified using standard taxonomic keys (Stojanovich and Scott 1966, Rattanarithikul et al. 2006). Following larval identification, pupae were counted and reared to adults in cages labeled with collection site and date. After eclosion, adults were morphologically identified (Peyton et al. 1966, Nagpal and Sharma 1995) to maximize the accuracy of larval identifications. Data Collection of Habitat Characteristics and Climate Data Data on abiotic and biotic factors including pH, turbidity, presence of organic or inorganic waste materials, emergent grass, algae, floating vegetation, movement of water and exposure to sunlit was recorded on-site at the time of collection. pH was measured using a HANNA HI-98106 Champ Pocket pH Tester (Hanna Instruments Inc., Woonsocket, RI). Visual descriptions were made on the flow of water in the habitat as either stagnant or running; exposure to sunlight was recorded as shady, half shady and sunlit based on the presence or absence of trees, branches, or human constructions over the habitat; any floating vegetation were considered as aquatic floating and any emergent plants including aquatic and terrestrial immersed vegetation was considered as aquatic emergent plants. Muddy was qualified by taking sample water in a glass test tube and holding it against white background. If the water disrupts the visibility of the background by creating cloudiness and sediment precipitated to the bottom of the test tube, the sample was considered as muddy. Presence of algae was classified into three categories; algal turbidity (detected by visualizing water sample against a white background), algae encrusted and algae floating. Algae encrusted and floating were differentiated by having the habitat completely covered by algae or algae free floating. Visual observation was also conducted to describe if the water body contained any organic (i.e., animal feces) or inorganic (i.e., trash) waste materials. Data on rainfall, temperature, and humidity were collected from Soil Resource Development Institute, Bandarban. Data Analysis Temporal abundance of anopheline larvae across habitat types was explored by descriptive analysis. The relative abundance of the five most common mosquito species are presented in a stacked bar diagram (Fig. 1). The relative risk (RR) was calculated to identify associations between larval habitat types and mosquito abundance using Poisson regression. Since pond was observed to be the most common larval habitat type, it was compared with other habitat types in the regression analysis. Influence of climate factors including temperature, rainfall, and humidity on larval abundance were analyzed using Poisson regression. Only positive collections, i.e., any collection where at least one Anopheles species was present and identified, were included in the data analysis. Therefore, for each collection the total number of mosquito was calculated by aggregating the total number of species, which was never zero. The effect of habitat characteristics on overall mosquito abundance was explored by RR using Poisson regression. pH indicates the association of chemical and biological factors in the mosquito habitats upon which larval development depends (MacGregor 1929). Therefore, mosquito larval abundance with pH and habitat temperature was explored using spearman correlation. Most collections were dominated by a single species, with counts for the remaining species as zeros. Therefore, we applied zero-inflated Poisson regression instead of traditional regression to calculate RR of mosquito abundance on the basis of presence or absence of aforementioned habitat characteristics to explore the association of species presence and abundance with different habitat characteristics. All data were analyzed using Stata 13 (StataCorp LP, College Station, TX). Fig. 1. View largeDownload slide Abundance (%) of the five most common anopheline species in the most common larval habitats. Fig. 1. View largeDownload slide Abundance (%) of the five most common anopheline species in the most common larval habitats. Results A total of 300 mosquito-positive collections (158 from Rajbila and 142 from Kuhalong) were made over the study period. Mosquito larvae from these habitats represented five mosquito genera; Aedes Meigen (Diptera: Culicidae), Anopheles,Culex Linnaeus (Diptera: Culicidae), Mansonia Blanchard (Diptera: Culicidae), and Toxorhynchites Theobald (Diptera: Culicidae). Overall, 5,568 Anopheles larvae were collected from which a subset of 2,263 (40.6%) were morphologically identified to species (Table 1). The greatest number of larvae were collected in November (15.4%, n = 855) followed by July (14.3%, n = 799), January (13.1%, n = 728) and February (12.8%, n = 714) (Table 1). Most of the larvae (3,095, 55.6%) were collected during ‘dry’ months (November 2011 to April 2012) with less rainfall (Table 1). Table 1. Seasonal abundance of anophelines (October 2011 to September 2012) in Bandarban Season  Month  Number of anopheline larvae collected (%)  Number of morphologically identified specimens (%)  Wet  Oct.  602 (10.8)  305 (13.5)  Dry  Nov.  855 (15.4)  320 (14.1)  Dry  Dec.  339 (6.1)  115 (5.1)  Dry  Jan.  728 (13.1)  236 (10.4)  Dry  Feb.  714 (12.8)  124 (5.5)  Dry  Mar.  279 (5)  109 (4.8)  Dry  April  180 (3.2)  78 (3.4)  Wet  May  385 (6.9)  211 (9.3)  Wet  June  193 (3.5)  108 (4.8)  Wet  July  799 (14.3)  353 (15.6)  Wet  Aug.  82 (1.5)  74 (3.3)  Wet  Sept.  412 (7.4)  230 (10.2)  Total  5,568  2,263  Season  Month  Number of anopheline larvae collected (%)  Number of morphologically identified specimens (%)  Wet  Oct.  602 (10.8)  305 (13.5)  Dry  Nov.  855 (15.4)  320 (14.1)  Dry  Dec.  339 (6.1)  115 (5.1)  Dry  Jan.  728 (13.1)  236 (10.4)  Dry  Feb.  714 (12.8)  124 (5.5)  Dry  Mar.  279 (5)  109 (4.8)  Dry  April  180 (3.2)  78 (3.4)  Wet  May  385 (6.9)  211 (9.3)  Wet  June  193 (3.5)  108 (4.8)  Wet  July  799 (14.3)  353 (15.6)  Wet  Aug.  82 (1.5)  74 (3.3)  Wet  Sept.  412 (7.4)  230 (10.2)  Total  5,568  2,263  View Large A total of 16 Anopheles species were identified (Table 2). Overall, Anopheles (C.) vagus Dӧnitz (26.4%, n = 598) was the dominant Anopheles species followed by An. nivipes (20.8%, n = 471), Anopheles peditaeniatus Leicester (20.1%, n = 454), Anopheles (A.) barbirostris van der Wulp (10.3%, n = 234), and Anopheles (C.) varuna Iyengar (9.1%, n = 205) to round out the five most abundant species in the survey (Table 2). During the wet monsoon months (October 2011 and May to September 2012), An. vagus (27.8%, n = 357) was the dominant species, followed by An. nivipes (27.4%, n= 352), An. peditaeniatus (16.4%, n = 211), An. barbirostris (10.8%, n = 138), and An. varuna (5.2%, n = 67). In contrast, during the dry months, An. peditaeniatus (24.7%, n = 243) was dominant followed by An. vagus (24.5%, n = 241), An. varuna (14.1%, n = 138), An. nivipes (12.1%, n = 119), and An. barbirostris (9.8%, n = 96) (Tables 1 and 2). Table 2. Total numbers (%) of Anopheles larvae by month and species in Bandarban, October 2011 to September 2012   Number of Anopheles species (%)  Month  Anopheles (C.) aconitus Dӧnitz  An. barbirostris  Anopheles(C.) culicifacis Giles  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  Anopheles (A.) nigerrimus Giles  An. nivipes  An. peditaeniatus  Anopheles (C.) subpictus Grassi  Anopheles (A.) umbrosus (Theobald)  An. vagus  An. varuna  Anopheles (C.) willmori (James)  Oct.  0 (0)  6 (2.0)  2 (0.7)  0 (0)  12 (3.9)  4 (1.3)  2 (0.7)  0 (0)  14 (4.6)  105 (34.4)  89 (29.2)  0 (0)  0 (0)  71 (23.3)  0 (0)  0 (0)  Nov.  0 (0)  11 (3.4)  0 (0)  9 (2.8)  6 (1.9)  6 (1.9)  4 (1.3)  0 (0)  5 (1.6)  46 (14.4)  126 (39.4)  0 (0)  0 (0)  95 (29.7)  12 (3.8)  0 (0)  Dec.  0 (0)  16 (14.0)  0 (0)  6 (5.2)  2 (1.7)  5 (4.4)  3 (2.6)  2 (1.7)  2 (1.7)  12 (10.4)  36 (31.3)  0 (0)  0 (0)  15 (13.4)  16 (13.9)  0 (0)  Jan.  0 (0)  32 (13.6)  1 (0.4)  17 (7.2)  5 (2.1)  3 (1.3)  11 (4.7)  0 (0)  1 (0.4)  54 (22.9)  51 (21.6)  3 (1.3)  0 (0)  23 (9.8)  34 (14.4)  1 (0.4)  Feb.  0 (0)  3 (2.4)  0 (0)  1 (0.8)  0 (0)  1 (0.8)  8 (6.5)  0 (0)  0 (0)  3 (2.4)  10 (8.1)  0 (0)  0 (0)  54 (43.6)  44 (35.5)  0 (0)  Mar.  0 (0)  12 (11.0)  0 (0)  9 (8.3)  3 (2.8)  0 (0)  1 (0.9)  1 (0.9)  0 (0)  0 (0)  6 (5.5)  4 (3.7)  0 (0)  50 (47.9)  23 (21.1)  0 (0)  April  0 (0)  22 (28.2)  0 (0)  8 (10.3)  2 (2.6)  4 (5.1)  10 (12.8)  0 (0)  1 (1.3)  4 (5.1)  14 (18.0)  0 (0)  0 (0)  4 (5.1)  9 (11.5)  0 (0)  May  0 (0)  38 (18.0)  0 (0)  6 (2.8)  0 (0)  1 (0.5)  0 (0)  0 (0)  22 (10.4)  43 (20.4)  71 (33.7)  9 (4.3)  0 (0)  17 (8.1)  4 (1.9)  0 (0)  June  0 (0)  9 (8.3)  0 (0)  7 (6.5)  0 (0)  2 (1.9)  0 (0)  0 (0)  0 (0)  59 (54.6)  18 (16.7)  0 (0)  0 (0)  13 (12.0)  0 (0)  0 (0)  July  1 (0.3)  75 (21.3)  0 (0)  13 (3.5)  2 (0.6)  4 (1.1)  18 (5.1)  0 (0)  3 (0.9)  47 (13.3)  11 (3.1)  19 (5.4)  0 (0)  110 (31.2)  49 (13.9)  1 (0.3)  Aug.  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  1 (1.4)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  71 (96.0)  2 (2.7)  0 (0)  Sept.  0 (0)  10 (4.4)  3 (1.3)  0 (0)  1 (0.4)  1 (0.4)  0 (0)  0 (0)  0 (0)  98 (42.6)  22 (9.6)  6 (2.6)  2 (0.9)  75 (32.6)  12 (5.2)  0 (0)  Total  1 (0.0)  234 (10.3)  6 (0.3)  76 (3.4)  33 (1.5)  31 (1.4)  58 (2.6)  3 (0.1)  48 (2.1)  471 (20.8)  454 (20.1)  41 (1.8)  2 (0.1)  598 (26.4)  205 (9.1)  2 (0.1)    Number of Anopheles species (%)  Month  Anopheles (C.) aconitus Dӧnitz  An. barbirostris  Anopheles(C.) culicifacis Giles  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  Anopheles (A.) nigerrimus Giles  An. nivipes  An. peditaeniatus  Anopheles (C.) subpictus Grassi  Anopheles (A.) umbrosus (Theobald)  An. vagus  An. varuna  Anopheles (C.) willmori (James)  Oct.  0 (0)  6 (2.0)  2 (0.7)  0 (0)  12 (3.9)  4 (1.3)  2 (0.7)  0 (0)  14 (4.6)  105 (34.4)  89 (29.2)  0 (0)  0 (0)  71 (23.3)  0 (0)  0 (0)  Nov.  0 (0)  11 (3.4)  0 (0)  9 (2.8)  6 (1.9)  6 (1.9)  4 (1.3)  0 (0)  5 (1.6)  46 (14.4)  126 (39.4)  0 (0)  0 (0)  95 (29.7)  12 (3.8)  0 (0)  Dec.  0 (0)  16 (14.0)  0 (0)  6 (5.2)  2 (1.7)  5 (4.4)  3 (2.6)  2 (1.7)  2 (1.7)  12 (10.4)  36 (31.3)  0 (0)  0 (0)  15 (13.4)  16 (13.9)  0 (0)  Jan.  0 (0)  32 (13.6)  1 (0.4)  17 (7.2)  5 (2.1)  3 (1.3)  11 (4.7)  0 (0)  1 (0.4)  54 (22.9)  51 (21.6)  3 (1.3)  0 (0)  23 (9.8)  34 (14.4)  1 (0.4)  Feb.  0 (0)  3 (2.4)  0 (0)  1 (0.8)  0 (0)  1 (0.8)  8 (6.5)  0 (0)  0 (0)  3 (2.4)  10 (8.1)  0 (0)  0 (0)  54 (43.6)  44 (35.5)  0 (0)  Mar.  0 (0)  12 (11.0)  0 (0)  9 (8.3)  3 (2.8)  0 (0)  1 (0.9)  1 (0.9)  0 (0)  0 (0)  6 (5.5)  4 (3.7)  0 (0)  50 (47.9)  23 (21.1)  0 (0)  April  0 (0)  22 (28.2)  0 (0)  8 (10.3)  2 (2.6)  4 (5.1)  10 (12.8)  0 (0)  1 (1.3)  4 (5.1)  14 (18.0)  0 (0)  0 (0)  4 (5.1)  9 (11.5)  0 (0)  May  0 (0)  38 (18.0)  0 (0)  6 (2.8)  0 (0)  1 (0.5)  0 (0)  0 (0)  22 (10.4)  43 (20.4)  71 (33.7)  9 (4.3)  0 (0)  17 (8.1)  4 (1.9)  0 (0)  June  0 (0)  9 (8.3)  0 (0)  7 (6.5)  0 (0)  2 (1.9)  0 (0)  0 (0)  0 (0)  59 (54.6)  18 (16.7)  0 (0)  0 (0)  13 (12.0)  0 (0)  0 (0)  July  1 (0.3)  75 (21.3)  0 (0)  13 (3.5)  2 (0.6)  4 (1.1)  18 (5.1)  0 (0)  3 (0.9)  47 (13.3)  11 (3.1)  19 (5.4)  0 (0)  110 (31.2)  49 (13.9)  1 (0.3)  Aug.  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  1 (1.4)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  71 (96.0)  2 (2.7)  0 (0)  Sept.  0 (0)  10 (4.4)  3 (1.3)  0 (0)  1 (0.4)  1 (0.4)  0 (0)  0 (0)  0 (0)  98 (42.6)  22 (9.6)  6 (2.6)  2 (0.9)  75 (32.6)  12 (5.2)  0 (0)  Total  1 (0.0)  234 (10.3)  6 (0.3)  76 (3.4)  33 (1.5)  31 (1.4)  58 (2.6)  3 (0.1)  48 (2.1)  471 (20.8)  454 (20.1)  41 (1.8)  2 (0.1)  598 (26.4)  205 (9.1)  2 (0.1)  View Large Anopheline larvae were collected from 16 different habitat types. The most common mosquito-positive habitat type in the survey was ‘pond’, which was also the habitat type to account for most of the Anopheles larvae collected in this survey (40.4%, n = 914). The next most productive habitat types in the context of this survey were rice field (34%, n = 779), irrigation canal (4%, n = 90), puddle (3.7%, n = 83), animal hoof print (2.5%, n = 56) and well (2.2%, n = 50) (Table 3). Notably, 30 Anopheles larvae (1.3%) of An. barbirostris, Anopheles (C.) jamesii Theobald, Anopheles (C.) karwari James, An. peditaeniatus and An. vagus were identified from small artificial containers (plastic buckets) (Table 3). Anopheles larvae were also collected once from a bamboo hole but the specimen could not be identified, hence, it was not included in the analysis. Table 3. Total numbers (%) of Anopheles larvae identified by larval habitat type in Bandarban, October 2011 to September 2012 Habitat  Number of mosquito- positive observations  An. aconitus  An. barbirostris  An. culicifacis  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  An. nigerrimus  An. nivipes  An. peditaeniatus  An. subpictus  An. umbrosus  An. vagus  An. varuna  An. willmori  Total  Pond  111  0  153 (16.7)  3 (0.3)  32 (3.5)  5 (0.6)  8 (0.9)  27 (3.0)  3 (0.3)  13 (1.4)  140 (15.3)  111 (12.1)  19 (2.1)  0  216 (23.6)  184 (20.1)  0  914 (40.4)  Rice field  108  1 (0.1)  41 (5.3)  0  27 (3.5)  17 (2.2)  11 (1.4)  20 (2.6)  0  23 (3.0)  238 (30.6)  230 (29.5)  3 (0.4)  0  157 (20.2)  10 (1.3)  1 (0.1)  779 (34.4)  Permanent wetland  6  0  4 (11.1)  0  1 (2.8)  0  1 (2.8)  0  0  1 (2.8)  6 (16.7)  19 (52.8)  0  0  4 (11.1)  0  0  36 (1.6)  Temporary pond  4  0  0  0  1 (2.9)  3 (8.8)  2 (5.9)  4 (11.7)  0  0  4 (11.7)  2 (5.8)  8 (23.5)  0  10 (29.4)  0  0  34 (1.5)  Field flood pool  1  0  0  0  1 (2.8)  0  0  0  0  1 (2.8)  25 (69.4)  9 (25.0)  0  0  0  0  0  36 (1.6)  Puddle  10  0  0  3 (3.6)  0  2 (2.4)  0  1 (1.2)  0  0  12 (14.5)  7 (8.4)  1 (1.2)  0  57 (68.7)  0  0  83 (3.7)  Receding river  9  0  1 (2.3)  0  0  1 (2.3)  1 (2.3)  0  0  1 (2.3)  4 (9.3)  1 (2.3)  1 (2.3)  0  33 (76.7)  0  0  43 (1.9)  Irrigation canal  17  0  3 (3.3)  0  8 (8.9)  4 (4.4)  3 (3.3)  1 (1.1)  0  5 (5.6)  16 (17.8)  27 (30.0)  0  0  23 (25.6)  0  0  90 (4.0)  Irrigation pond  4  0  7 (18.9)  0  0  0  0  0  0  2 (5.4)  10 (27.0)  16 (43.2)  0  0  2 (5.4)  0  0  37 (1.6)  Stream  4  0  1 (11.1)  0  0  0  0  0  0  0  0  7 (77.8)  0  0  1 (11.1)  0  0  9 (0.4)  Well  7  0  5 (10. 0)  0  3 (6.0)  1 (2.0)  1 (2.0)  3 (6.0)  0  0  6 (12.0)  4 (8.0)  2 (4.0)  2 (4.0)  21 (42.0)  2 (4.0)  0  50 (2.2)  Animal hoof print  4  0  0  0  0  0  0  0  0  0  5 (8.9)  0  4 (7.1)  0  47 (83.9)  0  0  56 (2.5)  Small artificial container  2  0  5 (16.7)  0  1 (3.3)  0  4 (13.3)  0  0  0  0  12 (40.0)  0  0  8 (26.7)  0  0  30 (1.3)  Channel  5  0  8 (32.0)  0  1 (4.0)  0  0  1 (4.0)  0  0  2 (8.0)  2 (8.0)  1 (4.0)  0  7 (28.0)  3 (12.0)  0  25 (1.1)  Drain  7  0  6 (20.0)  0  1 (3.3)  0  0  1 (3.3)  0  2 (6.7)  3 (10.0)  7 (23.3)  1 (3.3)  0  5 (16.7)  3 (10.0)  1 (3.3)  30 (1.3)  Wheel track  1  0  0  0  0  0  0  0  0  0  0  0  1 (9.1)  0  7 (63.6)  3 (27.3)  0  11 (0.5)  Habitat  Number of mosquito- positive observations  An. aconitus  An. barbirostris  An. culicifacis  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  An. nigerrimus  An. nivipes  An. peditaeniatus  An. subpictus  An. umbrosus  An. vagus  An. varuna  An. willmori  Total  Pond  111  0  153 (16.7)  3 (0.3)  32 (3.5)  5 (0.6)  8 (0.9)  27 (3.0)  3 (0.3)  13 (1.4)  140 (15.3)  111 (12.1)  19 (2.1)  0  216 (23.6)  184 (20.1)  0  914 (40.4)  Rice field  108  1 (0.1)  41 (5.3)  0  27 (3.5)  17 (2.2)  11 (1.4)  20 (2.6)  0  23 (3.0)  238 (30.6)  230 (29.5)  3 (0.4)  0  157 (20.2)  10 (1.3)  1 (0.1)  779 (34.4)  Permanent wetland  6  0  4 (11.1)  0  1 (2.8)  0  1 (2.8)  0  0  1 (2.8)  6 (16.7)  19 (52.8)  0  0  4 (11.1)  0  0  36 (1.6)  Temporary pond  4  0  0  0  1 (2.9)  3 (8.8)  2 (5.9)  4 (11.7)  0  0  4 (11.7)  2 (5.8)  8 (23.5)  0  10 (29.4)  0  0  34 (1.5)  Field flood pool  1  0  0  0  1 (2.8)  0  0  0  0  1 (2.8)  25 (69.4)  9 (25.0)  0  0  0  0  0  36 (1.6)  Puddle  10  0  0  3 (3.6)  0  2 (2.4)  0  1 (1.2)  0  0  12 (14.5)  7 (8.4)  1 (1.2)  0  57 (68.7)  0  0  83 (3.7)  Receding river  9  0  1 (2.3)  0  0  1 (2.3)  1 (2.3)  0  0  1 (2.3)  4 (9.3)  1 (2.3)  1 (2.3)  0  33 (76.7)  0  0  43 (1.9)  Irrigation canal  17  0  3 (3.3)  0  8 (8.9)  4 (4.4)  3 (3.3)  1 (1.1)  0  5 (5.6)  16 (17.8)  27 (30.0)  0  0  23 (25.6)  0  0  90 (4.0)  Irrigation pond  4  0  7 (18.9)  0  0  0  0  0  0  2 (5.4)  10 (27.0)  16 (43.2)  0  0  2 (5.4)  0  0  37 (1.6)  Stream  4  0  1 (11.1)  0  0  0  0  0  0  0  0  7 (77.8)  0  0  1 (11.1)  0  0  9 (0.4)  Well  7  0  5 (10. 0)  0  3 (6.0)  1 (2.0)  1 (2.0)  3 (6.0)  0  0  6 (12.0)  4 (8.0)  2 (4.0)  2 (4.0)  21 (42.0)  2 (4.0)  0  50 (2.2)  Animal hoof print  4  0  0  0  0  0  0  0  0  0  5 (8.9)  0  4 (7.1)  0  47 (83.9)  0  0  56 (2.5)  Small artificial container  2  0  5 (16.7)  0  1 (3.3)  0  4 (13.3)  0  0  0  0  12 (40.0)  0  0  8 (26.7)  0  0  30 (1.3)  Channel  5  0  8 (32.0)  0  1 (4.0)  0  0  1 (4.0)  0  0  2 (8.0)  2 (8.0)  1 (4.0)  0  7 (28.0)  3 (12.0)  0  25 (1.1)  Drain  7  0  6 (20.0)  0  1 (3.3)  0  0  1 (3.3)  0  2 (6.7)  3 (10.0)  7 (23.3)  1 (3.3)  0  5 (16.7)  3 (10.0)  1 (3.3)  30 (1.3)  Wheel track  1  0  0  0  0  0  0  0  0  0  0  0  1 (9.1)  0  7 (63.6)  3 (27.3)  0  11 (0.5)  View Large When data of the five most common mosquito species were analyzed, more than 90% of larvae collected from animal hoof prints were An. vagus. In fact, this species was common in most of the larval habitats identified. An. vagus was the most abundant species in puddles (75.0%), wells (55.3%), and ponds (26.9%). An. nivipes was the most common (35.2%) anopheline collected the rice fields. An. peditaeniatus was the dominant species observed in both small artificial containers (48.0%) and irrigation canals (39.1%). The remaining two most common species across all collections, An. barbirostris and An. varuna, did not dominate in any single habitat, but were very abundant in ponds (Fig. 1). Although, most anopheline larvae were collected from ponds, the RR for the abundance of mosquito larvae was 2.25 times higher for flood pools than ponds (RR 2.25; 95% CI 2.009, 2.523; P = 0.001) (Table 4). In contrast, significantly lower abundance of anopheline larvae was associated with permanent wetlands (RR 0.66; 95% CI 0.484, 0.913; P = 0.012), temporary ponds (RR 0.63; 95% CI 0.434, 0.942; P = 0.024), puddles (RR 0.58; 95% CI 0.424, 0.789; P = 0.001), and streams (RR 0.60; 95% CI 0.378, 0.958; P = 0.032) (Table 4). Table 4. Association of habitat types with anopheline larval abundance Factors  IRR  95% CI  P value  Permanent wetland  0.6652  0.4846–0.9133  0.012  Bamboo hole  1.0235  0.9133–1.1469  0.689  Rice field  1.0050  0.8610–1.1731  0.949  Temporary pond  0.6397  0.4342–0.9425  0.024  Field flood pool  2.2517  2.0094–2.5233  0.001  Puddle  0.5782  0.4240–0.7887  0.001  Receding River  0.8927  0.5397–1.4765  0.658  Irrigation canal  0.8429  0.6011–1.1819  0.322  Irrigation pond  0. 8060  0.4301–1.5103  0.501  Stream  0.6013  0.3775–0.9578  0.032  Well  0.8188  0.6226–1.0768  0.153  Animal hoof print  0.7293  0.2808–1.8938  0.517  Small artificial container  0.9979  0.6196–1.6072  0.993  Othersa  0.8582  0.5428–1.3567  0.513  Factors  IRR  95% CI  P value  Permanent wetland  0.6652  0.4846–0.9133  0.012  Bamboo hole  1.0235  0.9133–1.1469  0.689  Rice field  1.0050  0.8610–1.1731  0.949  Temporary pond  0.6397  0.4342–0.9425  0.024  Field flood pool  2.2517  2.0094–2.5233  0.001  Puddle  0.5782  0.4240–0.7887  0.001  Receding River  0.8927  0.5397–1.4765  0.658  Irrigation canal  0.8429  0.6011–1.1819  0.322  Irrigation pond  0. 8060  0.4301–1.5103  0.501  Stream  0.6013  0.3775–0.9578  0.032  Well  0.8188  0.6226–1.0768  0.153  Animal hoof print  0.7293  0.2808–1.8938  0.517  Small artificial container  0.9979  0.6196–1.6072  0.993  Othersa  0.8582  0.5428–1.3567  0.513  Reference variable: Pond. RR determined by Poisson regression. aInclude channel—drain- and wheel track. View Large The abundance of Anopheles larvae were significantly associated with climate factors. Each millimeter increase of monthly rainfall was associated with an increase in larval abundance (0.437; 95% CI 0.366, 0.508; P = 0.001). However, increases in temperature (−0.019; 95% CI −0.037, −0.001; P = 0.037) and humidity (−0.006; 95% CI −0.011, −0.002, P = 0.003) were significantly associated with limits in anopheline larval abundance (Table 5, Fig. 2). Monthly mosquito larval counts peaked in November 2011 and July 2012, both observations followed high rainfall events by 1 to 2 mo suggesting a lag effect of rain on mosquito production in Bandarban (Fig. 2). Table 5. Association of abiotic climate factors on monthly abundance of anopheline larvae Factors  Regression coefficient  95% CI  P value  Rainfall  0.4375  0.366, 0.508  0.001  Temperature  −0.0192  −0.037, −0.001  0.037  Humidity  −0.0068  −0.011, −0.002  0.003  Factors  Regression coefficient  95% CI  P value  Rainfall  0.4375  0.366, 0.508  0.001  Temperature  −0.0192  −0.037, −0.001  0.037  Humidity  −0.0068  −0.011, −0.002  0.003  Value < 0.05. View Large Fig. 2. View largeDownload slide Abiotic climate factors and monthly abundance of anopheline larvae. Fig. 2. View largeDownload slide Abiotic climate factors and monthly abundance of anopheline larvae. Mosquito larvae were collected from habitats ranging in pH from <4.0 to >9.0, although anopheline larvae from these pH extremes were relatively uncommon. Water pH ranging from 7.0 to 8.0 was observed to be the most suitable for the five most abundant anopheline species although small numbers were collected outside this pH range (Fig. 3). An. vagus was recorded to be most abundant in the range of pH 7.6 to 8.0, with nearly 50% of An. vagus larvae collected at this pH and more than 70% of this species collected from a wider pH range, pH 7.0 to 8.0 (Fig. 3a). Likewise, nearly 70% of the An. nivipes larvae were collected from pH 7.0 to 8.0. However, unlike An. vagus, nearly 40% of the An. nivipes and An. peditaeniatus larvae were collected from the low end of this pH range, pH 7.0 to 7.5 (Fig. 3b and 3c). In contrast, more than 40% of An. varuna larvae were collected from larval habitats where the pH ranged from 7.6 to 8.0 (Fig. 3d) and nearly 60% of An. barbirostris larvae collected at pH 7.0 to 8.0 (Fig. 3e). Fig. 3. View largeDownload slide Association of pH and abundance of the five most common anopheline species. (a) An. vagus (b) An. nivipes (c) An. peditaeniatus (d) An. varuna (e) An. barbirostris. Fig. 3. View largeDownload slide Association of pH and abundance of the five most common anopheline species. (a) An. vagus (b) An. nivipes (c) An. peditaeniatus (d) An. varuna (e) An. barbirostris. Larval abundance did not significantly vary between habitats with and without organic waste, inorganic waste or both (Table 6). However, Anopheles larval abundance varied significantly (RR 1.14; CI 1.03, 1.26; P < 0.05) with the presence of floating debris and trash materials. Interestingly, presence of algae including sites encrusted with algae (RR 0.85; CI 0.76, 0.95; P < 0.05) and floating algae (RR 0.81; CI 0.72, 0.89; P < 0.05) and movement of water in the habitat had significant positive association with Anopheles larval abundance (P < 0.05) whereas emergent grass, floating vegetation or amount of sunlight did not (Table 6). Table 6. Association of habitat characteristics on anopheline larval abundance Habitat characteristics  RR  95% CI  P value  Polluted organic waste  1.0084  0.9376, 1.0845  0.821  Polluted mineral waste  0.8103  0.48821, 1.3451  0.416  Polluted mineral and organic waste  1.1732  0.9187, 1.4983  0.200  Floating debris and trash  1.1462  1.0377, 1.2661  0.007  Algae turbid  1.1756  1.0469, 1.3202  0.006  Algae crusted  0.8544  0.7635, 0.9562  0.006  Algae floating  0.8058  0.7265, 0.8937  0.000  Aquatic emergent grass  1.0720  0.9910, 1.1596  0.083  Aquatic floating vegetation  1.0498  0.9718, 1.1340  0.217  Water stagnant  0.5357  0.4604, 0.6232  0.000  Water running  1.8975  1.6214, 2.2206  0.000  Sunlit  1.0335  0.9785, 1.0917  0.237  Half shady  0.9647  0.9113, 1.0213  0.217  Shady  1.0049  0.8966, 1.1263  0.932  Habitat characteristics  RR  95% CI  P value  Polluted organic waste  1.0084  0.9376, 1.0845  0.821  Polluted mineral waste  0.8103  0.48821, 1.3451  0.416  Polluted mineral and organic waste  1.1732  0.9187, 1.4983  0.200  Floating debris and trash  1.1462  1.0377, 1.2661  0.007  Algae turbid  1.1756  1.0469, 1.3202  0.006  Algae crusted  0.8544  0.7635, 0.9562  0.006  Algae floating  0.8058  0.7265, 0.8937  0.000  Aquatic emergent grass  1.0720  0.9910, 1.1596  0.083  Aquatic floating vegetation  1.0498  0.9718, 1.1340  0.217  Water stagnant  0.5357  0.4604, 0.6232  0.000  Water running  1.8975  1.6214, 2.2206  0.000  Sunlit  1.0335  0.9785, 1.0917  0.237  Half shady  0.9647  0.9113, 1.0213  0.217  Shady  1.0049  0.8966, 1.1263  0.932  View Large The abundance of An. peditaeniatus decreased significantly with increased pH (r = −0.15, P value < 0.05) as revealed by a Spearman correlation analysis, but in contrast, the abundance of An. vagus increased with a rise in pH (r = 0.15, P value < 0.05) (Table 7). An. nivipes abundance was observed to increase (r = 0.23, P value < 0.05) significantly with increased habitat temperature whereas, An. varuna larval abundance increased (r = −0.18, P value < 0.05) with lower habitat temperature (Table 7). Table 7. Association of habitat characteristics with the five most abundant anopheline species Habitat characteristics  An. barbirostris  An. peditaeniatus  An. nivipes  An. vagus  An. varuna  Correlation  pH  −0.10  −0.15*  −0.06  0.15*  0.04  Habitat temperature  0.05  0.06  0.23*  0.13  −0.18*  Habitat characteristics RR (95% CI)  Muddy  1.19 (0.89–1.58)  0.76 (0.60–0.95)*  0.54 (0.43–0.69)*  0.93 (0.79–1.10)  1.71 (1.27–2.30)*  Fresh  0.70 (0.51–0.97)*  1.48 (1.21–1.80)*  1.39 (1.14–1.69)*  0.80 (0.66–0.99)*  0.94 (0.60–1.47)  Polluted with organic waste  0.92 (0.66–1.28)  0.89 (0.67–1.19)  1.18 (0.92–1.50)  0.68 (0.49–0.93)*  0.80 (0.50–1.29)  Polluted with mineral waste  0a  0a  0.64 (0.20–1.98)  0.40 (0.10–1.62)  0a  Polluted with both organic and mineral waste  0a  0a  0a  1.12 (0.62–2.04)  0a  Floating debris/trash  0.98 (0.63–1.53)  0.35 (0.12–1.01)*  0.33 (0.15–0.74)*  0.66 (0.47–0.94)*  1.02 (0.63–1.68)  Algae turbid  0.46 (0.20–1.04)  0.92 (0.57–1.48)  0.75 (0.40–1.40)  1.30 (0.97–1.74)  0.30 (0.08–1.10)  Algae crusted  0.29 (0.12–0.73)*  0.57 (0.30–1.07)  0.40 (0.22–0.73)*  0.67 (0.43–1.02)  0.02 (0.002–0.12)*  Algae floating  0.57 (0.32–0.99)*  0.60 (0.34–1.06)  0.63 (0.38–1.02)  0.41 (0.24–0.71)*  0.01(0.002–0.11)*  Aquatic emergent grass  1.25 (0.90–1.74)  0.64 (0.46–0.89)*  1.50 (1.18–1.90)*  0.61 (0.41–0.89)*  0.81 (0.57–1.14)  Aquatic floating vegetation  1.50 (1.12–2.01)*  0.64 (0.45–0.89)*  1.04 (0.79–1.37)  0.69 (0.49–0.99)*  0.89 (0.63–1.27)  Live grass—choked  0.56 (0.09–3.36)  0a  0a  0.79 (0.29–2.22)  0a  Dead grass—choked  0.55 (0.09–3.36)  0a  0.33 (0.09–1.19)  0.05 (0.007–0.36)*  0a  Tree leaf  0a  0a  0a  0.32 (0.08–1.15)  0.27 (0.08–0.84)*  Stagnant water  16.78 (5.36–52.50)*  2.83 (1.41–5.67)*  1.20 (0.67–2.19)  2.25 (1.24–4.10)*  2.29 (0.38–13.96)  Running water  0.06 (0.02–0.19)*  0.39 (0.20–0.78)*  0.82 (0.45–1.50)  0.47 (0.25–0.88)*  0a  Sunlit  1.01 (0.74–1.35)  0.65 (0.53–0.79)*  1.13 (0.94–1.37)  2.25 (1.74–2.91)*  0.95 (0.70–1.28)  Half shady  0.95 (0.68–1.32)  1.56 (1.29–1.90)*  0.94 (0.77–1.14)  0.43 (0.32–0.56)*  0.95 (0.69–1.32)  Shady  1.10 (0.69–1.76)  1.12 (0.67–1.90)  0.23 (0.06–0.85)*  0.56 (0.33–0.94)*  1.08 (0.67–1.77)  Habitat characteristics  An. barbirostris  An. peditaeniatus  An. nivipes  An. vagus  An. varuna  Correlation  pH  −0.10  −0.15*  −0.06  0.15*  0.04  Habitat temperature  0.05  0.06  0.23*  0.13  −0.18*  Habitat characteristics RR (95% CI)  Muddy  1.19 (0.89–1.58)  0.76 (0.60–0.95)*  0.54 (0.43–0.69)*  0.93 (0.79–1.10)  1.71 (1.27–2.30)*  Fresh  0.70 (0.51–0.97)*  1.48 (1.21–1.80)*  1.39 (1.14–1.69)*  0.80 (0.66–0.99)*  0.94 (0.60–1.47)  Polluted with organic waste  0.92 (0.66–1.28)  0.89 (0.67–1.19)  1.18 (0.92–1.50)  0.68 (0.49–0.93)*  0.80 (0.50–1.29)  Polluted with mineral waste  0a  0a  0.64 (0.20–1.98)  0.40 (0.10–1.62)  0a  Polluted with both organic and mineral waste  0a  0a  0a  1.12 (0.62–2.04)  0a  Floating debris/trash  0.98 (0.63–1.53)  0.35 (0.12–1.01)*  0.33 (0.15–0.74)*  0.66 (0.47–0.94)*  1.02 (0.63–1.68)  Algae turbid  0.46 (0.20–1.04)  0.92 (0.57–1.48)  0.75 (0.40–1.40)  1.30 (0.97–1.74)  0.30 (0.08–1.10)  Algae crusted  0.29 (0.12–0.73)*  0.57 (0.30–1.07)  0.40 (0.22–0.73)*  0.67 (0.43–1.02)  0.02 (0.002–0.12)*  Algae floating  0.57 (0.32–0.99)*  0.60 (0.34–1.06)  0.63 (0.38–1.02)  0.41 (0.24–0.71)*  0.01(0.002–0.11)*  Aquatic emergent grass  1.25 (0.90–1.74)  0.64 (0.46–0.89)*  1.50 (1.18–1.90)*  0.61 (0.41–0.89)*  0.81 (0.57–1.14)  Aquatic floating vegetation  1.50 (1.12–2.01)*  0.64 (0.45–0.89)*  1.04 (0.79–1.37)  0.69 (0.49–0.99)*  0.89 (0.63–1.27)  Live grass—choked  0.56 (0.09–3.36)  0a  0a  0.79 (0.29–2.22)  0a  Dead grass—choked  0.55 (0.09–3.36)  0a  0.33 (0.09–1.19)  0.05 (0.007–0.36)*  0a  Tree leaf  0a  0a  0a  0.32 (0.08–1.15)  0.27 (0.08–0.84)*  Stagnant water  16.78 (5.36–52.50)*  2.83 (1.41–5.67)*  1.20 (0.67–2.19)  2.25 (1.24–4.10)*  2.29 (0.38–13.96)  Running water  0.06 (0.02–0.19)*  0.39 (0.20–0.78)*  0.82 (0.45–1.50)  0.47 (0.25–0.88)*  0a  Sunlit  1.01 (0.74–1.35)  0.65 (0.53–0.79)*  1.13 (0.94–1.37)  2.25 (1.74–2.91)*  0.95 (0.70–1.28)  Half shady  0.95 (0.68–1.32)  1.56 (1.29–1.90)*  0.94 (0.77–1.14)  0.43 (0.32–0.56)*  0.95 (0.69–1.32)  Shady  1.10 (0.69–1.76)  1.12 (0.67–1.90)  0.23 (0.06–0.85)*  0.56 (0.33–0.94)*  1.08 (0.67–1.77)  aNo specimen of corresponding species was found. *P value < 0.05. View Large The zero-inflated Poisson regression revealed that An. barbirostris were significantly less abundant in fresh water (RR 0.70; CI 0.51, 0.97, P < 0.05), in the presence of algae encrusting a site (RR 0.29; CI 0.12, 0.73, P < 0.05), floating algae (RR 0.57; CI 0.32, 0.99, P < 0.05), and running water (RR0.06; CI 0.02, 0.19, P < 0.05) and significantly high in floating vegetation (RR 1.50; CI 1.12, 2.01, P < 0.05) and stagnant water (RR 16.78; CI 5.36, 52.50, P < 0.05). Significantly less abundance of An. peditaeniatus was observed from muddy water (RR 0.76; CI 0.60, 0.95, P < 0.05), sites with floating debris (RR 0.35; CI 0.12, 1.01, P < 0.05), aquatic emergent grass (RR 0.64; CI 0.46, 0.89, P < 0.05), aquatic floating vegetation (RR 0.64; CI 0.45, 0.89, P < 0.05), running water (RR 0.39; CI 0.20, 0.78, P < 0.05), and if sunlit (RR 0.65; CI 0.53, 0.79, P < 0.05), and significant high abundance was observed when the habitats were characterized with fresh water (RR 1.48; CI 1.21, 1.80, P < 0.05), stagnant water (RR 2.83; CI 1.41, 5.67, P < 0.05), and half shaded (RR 1.56; CI 1.29, 1.90, P < 0.05). An. nivipes was significantly less abundant in muddy water (RR 0.54; CI 0.43, 0.69, P < 0.05), with floating debris (RR 0.33; CI 0.15, 0.74, P < 0.05), encrusted with algae (RR 0.40; CI 0.22, 0.73, P < 0.05), and shade (RR 0.23; CI 0.06, 0.85, P < 0.05), and were highly abundant in fresh water (RR 1.39; CI 1.14, 1.69, P < 0.05) and with aquatic emergent grass (RR 1.50; CI 1.18, 1.90, P < 0.05). Abundance of An. vagus was negatively associated with fresh water (RR 0.80; CI 0.66, 0.99, P < 0.05), polluted organic waste (RR 0.68; CI 0.49, 0.93, P < 0.05), floating debris (RR 0.66; CI 0.47, 0.94, P < 0.05), floating algae (RR 0.41; CI 0.24, 0.71, P < 0.05), aquatic emergent grass (RR 0.61; CI 0.41, 0.89, P < 0.05), aquatic floating vegetation (RR 0.69; CI 0.49, 0.99, P < 0.05), dead choked grass (RR 0.05; CI 0.007, 0.36, P < 0.05), running water (RR 0.47; CI 0.25, 0.88, P < 0.05) and both half shaded (RR 0.43; CI 0.32, 0.56, P < 0.05) and shaded (RR 0.56; CI 0.33, 0.94, P < 0.05). On the other hand, it was observed that abundance of An. vagus was significantly higher in stagnant water (RR 2.25; CI 1.24, 4.10, P < 0.05) and if sunlit (RR 2.25; CI 1.74, 2.91, P < 0.05). Larval abundance of An. varuna was significantly low in habitats encrusted with algae (RR 0.02; CI 0.002, 0.12, P < 0.05), with floating algae (RR 0.01; CI 0.002, 0.11, P < 0.05), and tree leaves (RR 0.27; CI 0.08, 0.84, P < 0.05), but in contrast, high in muddy water (RR 1.71; CI 1.27, 2.30, P < 0.05) (Table 7). Discussion A total of 16 Anopheles species were identified in the current study, fewer species than have been identified in recent adult mosquito surveys by CDC light trap in Bandarban (20 species) and adjacent ecologically similar regions (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). Twelve Anopheles species collected in the current study as larvae have been reported positive for Plasmodium infection in recent studies focused on adult malaria vectors (Maheswary et al. 1992, Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). These reports include three of the five most common species collected as larvae in this study; An. vagus, An. nivipes, and An. barbirostris, all reported as infected adults from Bangladesh. Although Plasmodium-positive An. varuna and An. peditaeniatus adults have not been reported from Bangladesh, these species are documented as parasite-positive in neighboring India (Nagpal and Sharma 1995) and Indonesia (Sugiarto et al. 2016), respectively. An. vagus, the dominant anopheline species in the larval collections reported in the present study, was also among the dominant species in the adult collections conducted in CHT, and is similarly considered an important vector of malaria in Bangladesh and India (Prakash et al. 2004, Alam et al. 2010, Alam et al. 2012b, Al-Amin et al. 2015). Renshaw et al. (1996) reported collecting large numbers of An. vagus where most of the collections were conducted in ponds. Gunathilaka et al. (2013) similarly reported ponds to be the most important source of An. vagus, as was also observed in the current study. However a number of other larval habitats have been reported for An. vagus including; rice fields, irrigation canals, drainage ditches, temporary pools, cement-lined and unlined wells used for domestic purposes, animal hoof prints, borrow pits, rock pools, lake margins, and slow moving water (Rahman et al. 2002, Rueda et al. 2011, Gunathilaka et al. 2013). In the current study, all sampled habitats were positive for An. vagus except for field flood pool, reinforcing the opportunistic range of habitats An. vagus will utilize. In the Philippines, An. vagus larvae were reported to be abundant from rice fields and irrigation drainage ditches with slow flowing water, having pH ranging from 6.79 to 7.62 (Rueda et al. 2011). In the present study, An. vagus abundance was significantly associated with pH in this relatively broad range. In Bangladesh, work focused on the flood plains had illustrated that An. philippinensis, one of the dominant vectors of malaria in this region, favored larval habitats that included tanks, rice fields, submerged low lands, and ponds with vegetation (Elias 1996). Although An. philippinensis was not collected from the CHT in this study, a closely related species, An. nivipes, was collected primarily from ponds, rice fields, and field flood pools. Previous studies have documented the moderately high relative abundance of An. barbirostris, An. peditaeniatus, and An. varuna as adult mosquitoes in the CHT (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). All three species were collected in relatively high abundance in this study, primarily from ponds and rice fields, although An. peditaeniatus was common in all habitats except animal hoof prints. Understanding Anopheles larval ecology and critical habitat characteristics for malaria vectors is key to predicting species presence and abundance and therefore, important to design of malaria control program strategies. Unfortunately, this data has been lacking or has not been current for the CHT. This is the first comprehensive report on anopheline larval habitat characteristics in Bangladesh. Ponds, rice fields, irrigation canals, puddles, animal hoof prints, and wells were the most utilized larval habitats for Anopheles mosquitoes as revealed in the current study. These habitats were largely either human-generated or associated with anthropogenic variables. In CHT, ponds are generally used for culturing fish, domestic activities (e.g., dish and clothes washing and bathing), and agricultural irrigation. Abandoned ponds are also common and although mostly stagnant, provide significant habitat for larval Anopheles mosquitoes in addition to abundant small larval habitats such as hoof prints. A growing human population in the catchment area has resulted in increased human activity and environmental impact including deforestation, agricultural expansion, livestock and fish rearing, all of which create suitable larval habitats for mosquitoes (Norris 2004, Derraik and Slaney 2007). Agricultural expansion alone can create favorable larval habitats for Anopheles mosquitoes, resulting in high larval counts as observed in the rice fields and irrigation canals in this study. Moreover, agriculture can cause increased sedimentation due to erosion which can slow or block streams and decrease water depth creating shallow waters ideal for mosquito larvae (Norris 2004, Kamdem et al. 2012). Presence of multiple Anopheles species in each habitat type sampled provides evidence of the ecological overlap of different species and potential malaria vectors within the same site (Mwangangi et al. 2007). Although most larvae were collected from ponds, the relative abundance was higher in the field flood pool. However, field flood pools were uncommon in our study sites and further study and sampling across a broader geographic area is required to draw definitive conclusions. Historically, An. baimaii, An. minimus, and An. philippinensis were the most prevalent and established malaria vector mosquitoes in Bangladesh (Rosenberg 1982, Renshaw et al. 1996). However, An. baimaii and An. philippinensis were completely absent in this larval survey and only three specimens of An. minimus were collected from ponds. Recent entomological surveys using CDC light traps also demonstrate the low abundance of these species as adult host-seeking vectors (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). Large-scale conversion of forests to agricultural lands (Anonymous 2017) could be a driver of the apparent absence of these historic vector species. Such findings also suggest that other anophelines may have taken advantage of these habitat changes and vacant ecological niches, and currently play a new and/or larger role in maintaining malaria transmission. In this study five Anopheles species; An. barbirostris, An. jamesii, An. karwari, An. peditaeniatus, and An. vagus, were collected from small artificial containers, a habitat not commonly associated or reported for Anopheles mosquitoes, and mentioned as special oviposition sites in the WHO guidelines (WHO 2003). A small pilot study conducted earlier in Bandarban also reported An. vagus and Anopheles (C.) kochi Dӧnitz from artificial containers (Alam et al. 2012a). With increasing human populations and urbanization, small artificial containers will become more abundant and perhaps associated with increased anopheline abundance. Anopheles larvae were significantly more abundant in habitats with floating debris, trash, and algae despite reports of larval inhibition by water pollution (Barbazan et al. 1998). Theoretically, the presence of debris could be beneficial for Anopheles larvae by increasing food and space for larvae while hindering predation (Keating et al. 2004). In addition, studies have documented the importance of floating algae on the presence of Anopheles larvae by promoting thermal stability during low night-time temperatures (Rejmankova 1993, Pinault and Hunter 2012). Abundance of larvae was observed irrespective of water current in the present study. However, most of the larvae were collected from ponds without water flow, which might serve as an explanation for the significant correlation between larval presence and stagnant water that provides abundant algal and bacterial growth, as these are unlikely to be independent variables. The presence of grass or vegetation in running water may also protect Anopheles larvae from being washed out of habitats (Imbahale et al. 2011). This study has revealed that anopheline larvae utilize multiple habitat types in the CHT region of Bangladesh. Diverse species of vector mosquitoes are present in malaria-endemic Bandarban, a fact which is crucial for understanding the transmission of malaria. Policy makers and malaria control programs need to consider options for the management of Anopheles populations that target both natural and human-created larval habitats. Regular surveys of all available oviposition habitats need to be performed frequently to design a comprehensive and effective malaria control strategy and to determine and better understand the anthropogenic impact on malaria vector composition. Acknowledgments We are grateful to Jacob Khyang, Chai Shwai Prue, Abu Naser Mohon, Mohammad Sharif Hossain, Humayun Kabir, and all Bandarban field staffs for their contributions and participation to this study. This research study was funded by the Johns Hopkins Malaria Research Institute (JHMRI) of Johns Hopkins Bloomberg School of Public Health. icddr,b acknowledges with gratitude the commitment of Johns Hopkins University, Baltimore, MD, United States, to its research efforts. icddr,b is also grateful to the Government of Bangladesh, Canada, Sweden, and the United Kingdom for providing core/unrestricted support. References Cited Ahmed, S., Galagan S., Scobie H., Khyang J., Prue C. S., Khan W. A., Ram M., Alam M. S., Haq M. Z., Akter J.,et al.   2013. Malaria hotspots drive hypoendemic transmission in the Chittagong Hill Districts of Bangladesh. PLoS One  8: e69713. Google Scholar CrossRef Search ADS PubMed  Al-Amin, H. M., Elahi R., Mohon A. N., Kafi M. A. H., Chakma S., Lord J. S., Khan W. A., Haque R., Norris D. E., and Alam M. S.. 2015. Role of underappreciated vectors in malaria transmission in an endemic region of Bangladesh-India border. Parasit. Vectors  8: 195. Google Scholar CrossRef Search ADS PubMed  Alam, M. S., Khan M. G., Chaudhury N., Deloer S., Nazib F., Bangali A. M., and Haque R.. 2010. Prevalence of anopheline species and their Plasmodium infection status in epidemic-prone border areas of Bangladesh. Malar. J . 9: 15. Google Scholar CrossRef Search ADS PubMed  Alam, M. S., Chakma S., Al-Amin H. M., Elahi R., Mohon A. N., Khan W. A., Haque R., Glass G. E., Sack D. A., Sullivan D. J.,et al.   2012a. Role of artificial containers as breeding sites for anopheline mosquitoes in malaria hypoendemic areas of rural Bandarban, Bangladesh: evidence from a baseline survey. The 61st Annual Meeting of the American Society of Tropical Medicine and Hygiene, Atlanta Marriott Marquis Atlanta, GA. Alam, M. S., Chakma S., Khan W. A., Glass G. E., Mohon A. N., Elahi R., Norris L. C., Podder M. P., Ahmed S., Haque R.,et al.   2012b. Diversity of anopheline species and their Plasmodium infection status in rural Bandarban, Bangladesh. Parasit. Vectors  5: 150. Google Scholar CrossRef Search ADS   Amerasinghe, F. P., Indrajith N. G., and Ariyasena T. G.. 1995. Physico-chemical characteristics of mosquito breeding habitats in an irrigation development area in Sri Lanka. Cey. J. Sci. (Biol Sci)  24: 13– 29. Anonymous. Land Cover Dynamics in Greater Chittagong. http://geoapps.icimod.org/BDLandcover/ (accessed 15 March 2017). Barbazan, P., Baldet T., Darriet F., Escaffre H., Djoda D. H., and Hougard J. M.. 1998. Impact of treatments with Bacillus sphaericus on Anopheles populations and the transmission of malaria in Maroua, a large city in a savannah region of Cameroon. J. Am. Mosq. Control. Assoc . 14: 33– 39. Google Scholar PubMed  Bashar, K., and Tuno N.. 2014. Seasonal abundance of Anopheles mosquitoes and their association with meteorological factors and malaria incidence in Bangladesh. Parasit. Vectors  7: 442. Google Scholar CrossRef Search ADS PubMed  Bayoh, M. N., and Lindsay S. W.. 2003. Effect of temperature on the development of the aquatic stages of Anopheles gambiae sensu stricto (Diptera: Culicidae). Bull. Entomol. Res . 93: 375– 381. Google Scholar CrossRef Search ADS PubMed  Derraik, J. G. B., and Slaney D.. 2007. Anthropogenic environmental change, mosquito-borne diseases and human health in New Zealand. EcoHealth  4: 72– 81. Google Scholar CrossRef Search ADS   Elias, M. 1996. Larval habitat of Anopheles philippinensis: a vector of malaria in Bangladesh. Bull. World Health Organ . 74: 447– 450. Google Scholar PubMed  Ghebreyesus, T. A., Haile M., Witten K. H., Getachew A., Yohannes A. M., Yohannes M., Teklehaimanot H. D., Lindsay S. W., and Byass P.. 1999. Incidence of malaria among children living near dams in northern Ethiopia: community based incidence survey. BMJ  319: 663– 666. Google Scholar CrossRef Search ADS PubMed  Gunathilaka, N., Fernando T., Hapugoda M., Wickremasinghe R., Wijeyerathne P., and Abeyewickreme W.. 2013. Anopheles culicifacies breeding in polluted water bodies in Trincomalee District of Sri Lanka. Malar. J . 12: 285. Google Scholar CrossRef Search ADS PubMed  Imbahale, S. S., Paaijmans K. P., Mukabana W. R., van Lammeren R., Githeko A. K., and Takken W.. 2011. A longitudinal study on Anopheles mosquito larval abundance in distinct geographical and environmental settings in western Kenya. Malar. J . 10: 81. Google Scholar CrossRef Search ADS PubMed  Irish, S. R., Al-Amin H. M., Alam M. S., and Harbach R. E.. 2016. A review of the mosquito species (Diptera: Culicidae) of Bangladesh. Parasit. Vectors  9: 559. Google Scholar CrossRef Search ADS PubMed  Kamdem, C., Tene Fossog B., Simard F., Etouna J., Ndo C., Kengne P., Bousses P., Etoa F. X., Awono-Ambene P., Fontenille D.,et al.   2012. Anthropogenic habitat disturbance and ecological divergence between incipient species of the malaria mosquito Anopheles gambiae. PLoS One  7: e39453. Google Scholar CrossRef Search ADS PubMed  Keating, J., Macintyre K., Mbogo C. M., Githure J. I., and Beier J. C.. 2004. Characterization of potential larval habitats for Anopheles mosquitoes in relation to urban land-use in Malindi, Kenya. Int. J. Health. Geogr . 3: 9. Google Scholar CrossRef Search ADS PubMed  Khan, W. A., Sack D. A., Ahmed S., Prue C. S., Alam M. S., Haque R., Khyang J., Ram M., Akter J., Nyunt M. M.,et al.   2011. Mapping hypoendemic, seasonal malaria in rural Bandarban, Bangladesh: a prospective surveillance. Malar. J . 10: 124. Google Scholar CrossRef Search ADS PubMed  MacGregor, M. E. 1929. The Significance of the pH in the development of mosquito larvae. Parasitology  21: 132– 157. Google Scholar CrossRef Search ADS   Maheswary, N. P., Habib M. A., and Elias M.. 1992. Incrimination of Anopheles aconitus Donitz as a vector of epidemic malaria in Bangladesh. Southeast Asian J. Trop. Med. Public Health  23: 798– 801. Google Scholar PubMed  MOHFW. 2016. Health Bulletin 2016, pp. 101–105. In M. I. System (ed.). Directorate General of Health Services, Dhaka, Bangladesh. Mwangangi, J. M., Mbogo C. M., Muturi E. J., Nzovu J. G., Githure J. I., Yan G., Minakawa N., Novak R., and Beier J. C.. 2007. Spatial distribution and habitat characterisation of Anopheles larvae along the Kenyan coast. J. Vector Borne Dis . 44: 44– 51. Google Scholar PubMed  Nagpal, B. N., and Sharma V. P.. 1995. Indian Anophelines . Mohan Primlani for Oxford & IBH Publishing Co. Pvt. Ltd, New Delhi, India. NMCP. 2015. Malaria National Strategic Plan (2015–2020), pp. 1–39. In C. D. C. Division (ed.). Directorate General of Health Services, Ministry of Health & Family Welfare, Dhaka, Bangladesh. Norris, D. E. 2004. Mosquito-borne diseases as a consequence of land use change. Eco. Health  1: 19– 24. Paul, B. K. 1984. Malaria in Bangladesh. Geogr. Rev . 74: 63– 75. Google Scholar CrossRef Search ADS PubMed  Peyton, E. L., Scanlon J. E., Malikul V., and Imvitaya S.. 1966. Illustrated key to the female Anopheles mosquitoes of Thailiand . DTIC Document. Applied Scientific Research Corporation of Thailand, 196 Phahonyothin Road, Bangkehn, Bangkok, Thailand. Pinault, L. L., and Hunter F. F.. 2012. Characterization of larval habitats of Anopheles albimanus, Anopheles pseudopunctipennis, Anopheles punctimacula, and Anopheles oswaldoi s.l. populations in lowland and highland Ecuador. J. Vector Ecol . 37: 124– 136. Google Scholar CrossRef Search ADS PubMed  Prakash, A., Bhattacharyya D. R., Mohapatra P. K., and Mahanta J.. 2004. Role of the prevalent Anopheles species in the transmission of Plasmodium falciparum and P. vivax in Assam state, north-eastern India. Ann. Trop. Med. Parasitol . 98: 559– 568. Google Scholar CrossRef Search ADS PubMed  Rahman, W. A., Adanan C. R., and Abu Hassan A.. 2002. Species composition of adult Anopheles populations and their breeding habitats in Hulu Perak district, Peninsular Malaysia. Southeast Asian J. Trop. Med. Public Health  33: 547– 550. Google Scholar PubMed  Rattanarithikul, R., Harrison B. A., Harbach R. E., Panthusiri P., and Coleman R. E.. 2006. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian J Trop Med Public Health  37 ( Suppl 2): 1– 128. Rejmankova, E., Roberts D. R., Harbach R. E., Pecor J., Peyton E. L., Manguin S., Krieg R., Polanco J., Legters L.. 1993. Environmental and regional determinants of Anopheles (Diptera: Culicidae) larval distribution in Belize, Central America. Community and Ecosystem Ecology  22: 978– 992. Renshaw, M., Elias M., Maheswary N. P., Hassan M. M., Silver J. B., and Birley M. H.. 1996. A survey of larval and adult mosquitoes on the flood plains of Bangladesh, in relation to flood-control activities. Ann. Trop. Med. Parasitol . 90: 621– 634. Google Scholar CrossRef Search ADS PubMed  Rosenberg, R. 1982. Forest malaria in Bangladesh. III. Breeding habits of Anopheles dirus. Am. J. Trop. Med. Hyg . 31: 192– 201. Google Scholar CrossRef Search ADS PubMed  Rueda, L. M., Pecor J. E., and Harrison B. A.. 2011. Updated distribution records for Anopheles vagus (Diptera: Culicidae) in the Republic of Philippines, and considerations regarding its secondary vector roles in Southeast Asia. Trop. Biomed . 28: 181– 187. Google Scholar PubMed  Sanchez-Ribas, J., Parra-Henao G., and Guimaraes A. E.. 2012. Impact of dams and irrigation schemes in Anopheline (Diptera: Culicidae) bionomics and malaria epidemiology. Rev. Inst. Med. Trop. Sao Paulo  54: 179– 191. Google Scholar CrossRef Search ADS PubMed  Stojanovich, C. J., and Scott H. G.. 1966. Illustrated key to Anopheles mosquitoes of Thailand . US Public Health Serv., Atlanta, GA. Sugiarto, U. Kesumawati Hadi, S. Soviana, and Hakim L.. 2016. Confirmation of Anopheles peditaeniatus and Anopheles sundaicus as Malaria Vectors (Diptera: Culicidae) in Sungai Nyamuk Village, Sebatik Island North Kalimantan, Indonesia Using an Enzyme-Linked Immunosorbent Assay. J. Med. Entomol . 53: 1422– 1424. Google Scholar CrossRef Search ADS PubMed  Uddin, K., and Gurung D. R.. 2010. Land cover change in Bangladesh-A knowledge based classification approach, pp. 41– 46, 10th International Center for Integrated Mountain Development, Kathmandu (ICIMOD), Khumaltar, Lalitpur, Nepal. WHO. 2003. Malaria entomology and vector control (learner’s guide) . WHO, Geneva, Switzerland. © The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Entomology Oxford University Press

Loading next page...
 
/lp/ou_press/abundance-and-dynamics-of-anopheles-diptera-culicidae-larvae-in-a-7DzyJ4Nx1I
Publisher
Entomological Society of America
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
0022-2585
eISSN
1938-2928
D.O.I.
10.1093/jme/tjx196
Publisher site
See Article on Publisher Site

Abstract

Abstract Malaria is a major health burden in the border-belt areas of Bangladesh. There are recent data from adult mosquito collections that a number of vectors are involved in the transmission cycle. However, little information regarding the larval habitats of Anopheles mosquitoes are available in Bangladesh. To start filling this gap, a cross-sectional larval survey was conducted in Bandarban, Bangladesh from October 2011 to September 2012. Descriptive analysis, Poisson regression, spearman correlations and zero-inflated Poisson regression were used to calculate the degree of association between the abundance of larval Anopheles species and environmental factors. From the 300 larval habitats sampled, 5,568 Anopheles larvae were collected and of these, 2,263 (40.6%) were identified to species. Collections represented 16 Anopheles species with Anopheles vagus (26.4%, n = 598) as the dominant species. A total of 16 Anopheles larval habitat types were identified. Larval abundance was significantly different (P < 0.05) among habitats with pond (40%, n = 914) and rice field (34%, n = 779) implicated to be the most utilized. Larval abundance varied significantly (P < 0.05) with habitat characteristics. Most of the larvae were collected from sites with a range of pH from 7.0 to 8.0. Data obtained from this study revealed both natural and human-created larval habitats were favorable for anopheline larval survival and development. Such information elucidates plausible drivers of high anopheline diversity, high vector abundance, changes in relative species abundance from historic data, and sustained transmission of malaria in endemic areas of Bangladesh. Anopheles, larval production site, abundance, factor, Bangladesh At least 13 million people remain at risk for malaria in Bangladesh (NMCP 2015). Although once widespread throughout the country and accounting for 15% of annual deaths prior to the malaria eradication programme (MEP) in the 1960’s (Paul 1984), malaria is now restricted to 13 districts of Bangladesh bordering India and Myanmar. Of these districts, three in the Chittagong Hill Tracts (CHT); Rangamati, Bandarban, and Khagrachari, contribute 90% of the country’s total malaria cases of which 90% are due to Plasmodium falciparum Welch (Haemosporida: Plasmodidae) (NMCP 2015). During 2015, Bangladesh reported 39,719 malaria cases with nine deaths, where more than 90% (n = 35,968) of cases occurred in the CHT and nearly 50% (n = 18,262) of those cases were reported from Bandarban (MOHFW 2016). The CHT differs from all other regions of Bangladesh. Most of the CHT are difficult to reach both for surveillance and control which has resulted in refugia for malaria parasite transmission by the vectors indigenous to these areas. The topography of the CHT includes elongated ranges of hills, most of which are covered by forest, and intervening valleys with rivers and small lakes (NMCP 2015). During the last two decades, damming of hilly streams and rivers has allowed for high elevation irrigation from the resulting impoundments. These water bodies are suitable larval habitat for mosquitoes and might be one of the reasons for the persistence of malaria transmission in the CHT. Of the 36 Anopheles Meigen (Diptera: Culicidae) species reported in Bangladesh, only seven were recognized as important vectors of malaria (Irish et al. 2016). Historically, four of these species, Anopheles (Cellia) baimaii Sallum & Peyton, Anopheles (C.) philippinensis Ludlow, Anopheles (C.) sundaicus s.l. (Rodenwaldt) and Anopheles (C.) minimus Theobald were targeted for control by the MEP (Renshaw et al. 1996). Recent studies, however, have implicated previously unsuspected species such as Anopheles (C.) nivipes Theobald and Anopheles (C.) jeyporiensis James as playing key roles in malaria transmission (Alam et al. 2012b, Al-Amin et al. 2015). In addition to a decrease in the abundance of recognized malaria vectors, spatial and temporal changes in the distribution and densities of these vectors at both local and larger endemic scales has recently been observed (Al-Amin et al. 2015). Intact forest in the CHT had historically provided abundant larval habitat for principal vectors like An. baimaii (Rosenberg 1982). However, over the decades, land use and land cover have changed with deforestation for teak/rubber monoculture and agricultural expansion, urbanization, and expansion of human settlements (Uddin and Gurung 2010). These events have led to habitat change that benefits additional or alternative anopheline species (Al-Amin et al. 2015). Development projects such as dams and irrigation schemes can result in a net increase of suitable mosquito habitat (Sanchez-Ribas et al. 2012). In fact, small dams built in many parts of the world similar to those constructed in CHT for irrigation, have been correlated with increases in malaria transmission (Ghebreyesus et al. 1999, Norris 2004). Moreover, climate change could also favor the growth and survival of Anopheles immatures. Increased water temperatures associated with small shallow pools have been shown to enhance maturation of egg to larvae and subsequently adult, increasing the abundance of adult vectors and ultimately leading to increased pathogen transmission (Bayoh and Lindsay 2003). Anopheles larval ecology and identification of favorable aquatic habitat for malaria vectors is critical for understanding Plasmodium transmission and planning effective control measures (Amerasinghe et al. 1995). Unfortunately, there are few data available on the larval habitat of Anopheles mosquitoes from the CHT eco-zone. Notable reports focused either on An. baimaii (Rosenberg 1982) in the Sylhet region or An. philippinensis in the flood plains (Elias 1996). A single report by Renshaw et al. (1996) describes generalized Anopheles larval habitat in the flood plain with regards to implementation of the flood action plan (Renshaw et al. 1996). Despite these efforts in the 1990s, the larval biology of Anopheles mosquitoes has never been studied or well documented in the CHT. Therefore, the goal of this study was to identify and quantify potential oviposition habitats of Anopheles mosquitoes and the factors associated with larval presence and abundance in a malaria endemic area of the CHT. Materials and Methods An entomological survey of Anopheles larval habitats was conducted in Rajbila (22°20′42.48″N, 92°8′55.56″E) and Kuhalong (22°12′48.90″N, 92°12′11.46″E) unions of Bandarban District, from October 2011 to September 2012. Each of these two unions was further divided into 12 clusters for the purpose of the study. Description of the study areas with associated demography have been described elsewhere (Khan et al. 2011, Ahmed et al. 2013). Collection of Mosquito Larvae In each month larval surveys were undertaken in one cluster from each union. Once mosquitoes were collected from a cluster, no repeated visit was made for that cluster in the subsequent months. A team consisting of three to four persons walked along the longest possible transect of a cluster after obtaining information of cluster area from the Bandarban field office and inspected all artificial or natural habitats within line of sight where water could stand for few days. Collections were conducted if mosquito larvae or pupae were observed, irrespective to the size of the water bodies and habitat characteristics (type), and mosquito counts were recorded. Although many more mosquito-producing sites undoubtedly exist, collections were limited by available resources and no repeated visit or sampling at any one container was conducted. Mosquito larvae and/or pupae were sampled using standard dipping techniques (WHO 2003) where 5 to 7 dips in each larval habitat were made using a standard dipper (350 ml). In large water bodies, dipping was carried out over a 50 m walk. For small water reservoirs (e.g., animals hoof print, puddle, small artificial container and wheel tracts), a maximum amount of water was sampled using a serological pipette. All larvae and/or pupae were placed into screw cap plastic cups or Whirl-Paks (Nasco, Fort Atkinson, WI) with source water, labeled with collection date, and transported to the Bandarban field laboratory for further processing. Anopheles larvae were heat-killed by transferring them into hot water (50 to 70°C) after separating from other mosquito larvae, pupae, and insects, and morphologically identified using standard taxonomic keys (Stojanovich and Scott 1966, Rattanarithikul et al. 2006). Following larval identification, pupae were counted and reared to adults in cages labeled with collection site and date. After eclosion, adults were morphologically identified (Peyton et al. 1966, Nagpal and Sharma 1995) to maximize the accuracy of larval identifications. Data Collection of Habitat Characteristics and Climate Data Data on abiotic and biotic factors including pH, turbidity, presence of organic or inorganic waste materials, emergent grass, algae, floating vegetation, movement of water and exposure to sunlit was recorded on-site at the time of collection. pH was measured using a HANNA HI-98106 Champ Pocket pH Tester (Hanna Instruments Inc., Woonsocket, RI). Visual descriptions were made on the flow of water in the habitat as either stagnant or running; exposure to sunlight was recorded as shady, half shady and sunlit based on the presence or absence of trees, branches, or human constructions over the habitat; any floating vegetation were considered as aquatic floating and any emergent plants including aquatic and terrestrial immersed vegetation was considered as aquatic emergent plants. Muddy was qualified by taking sample water in a glass test tube and holding it against white background. If the water disrupts the visibility of the background by creating cloudiness and sediment precipitated to the bottom of the test tube, the sample was considered as muddy. Presence of algae was classified into three categories; algal turbidity (detected by visualizing water sample against a white background), algae encrusted and algae floating. Algae encrusted and floating were differentiated by having the habitat completely covered by algae or algae free floating. Visual observation was also conducted to describe if the water body contained any organic (i.e., animal feces) or inorganic (i.e., trash) waste materials. Data on rainfall, temperature, and humidity were collected from Soil Resource Development Institute, Bandarban. Data Analysis Temporal abundance of anopheline larvae across habitat types was explored by descriptive analysis. The relative abundance of the five most common mosquito species are presented in a stacked bar diagram (Fig. 1). The relative risk (RR) was calculated to identify associations between larval habitat types and mosquito abundance using Poisson regression. Since pond was observed to be the most common larval habitat type, it was compared with other habitat types in the regression analysis. Influence of climate factors including temperature, rainfall, and humidity on larval abundance were analyzed using Poisson regression. Only positive collections, i.e., any collection where at least one Anopheles species was present and identified, were included in the data analysis. Therefore, for each collection the total number of mosquito was calculated by aggregating the total number of species, which was never zero. The effect of habitat characteristics on overall mosquito abundance was explored by RR using Poisson regression. pH indicates the association of chemical and biological factors in the mosquito habitats upon which larval development depends (MacGregor 1929). Therefore, mosquito larval abundance with pH and habitat temperature was explored using spearman correlation. Most collections were dominated by a single species, with counts for the remaining species as zeros. Therefore, we applied zero-inflated Poisson regression instead of traditional regression to calculate RR of mosquito abundance on the basis of presence or absence of aforementioned habitat characteristics to explore the association of species presence and abundance with different habitat characteristics. All data were analyzed using Stata 13 (StataCorp LP, College Station, TX). Fig. 1. View largeDownload slide Abundance (%) of the five most common anopheline species in the most common larval habitats. Fig. 1. View largeDownload slide Abundance (%) of the five most common anopheline species in the most common larval habitats. Results A total of 300 mosquito-positive collections (158 from Rajbila and 142 from Kuhalong) were made over the study period. Mosquito larvae from these habitats represented five mosquito genera; Aedes Meigen (Diptera: Culicidae), Anopheles,Culex Linnaeus (Diptera: Culicidae), Mansonia Blanchard (Diptera: Culicidae), and Toxorhynchites Theobald (Diptera: Culicidae). Overall, 5,568 Anopheles larvae were collected from which a subset of 2,263 (40.6%) were morphologically identified to species (Table 1). The greatest number of larvae were collected in November (15.4%, n = 855) followed by July (14.3%, n = 799), January (13.1%, n = 728) and February (12.8%, n = 714) (Table 1). Most of the larvae (3,095, 55.6%) were collected during ‘dry’ months (November 2011 to April 2012) with less rainfall (Table 1). Table 1. Seasonal abundance of anophelines (October 2011 to September 2012) in Bandarban Season  Month  Number of anopheline larvae collected (%)  Number of morphologically identified specimens (%)  Wet  Oct.  602 (10.8)  305 (13.5)  Dry  Nov.  855 (15.4)  320 (14.1)  Dry  Dec.  339 (6.1)  115 (5.1)  Dry  Jan.  728 (13.1)  236 (10.4)  Dry  Feb.  714 (12.8)  124 (5.5)  Dry  Mar.  279 (5)  109 (4.8)  Dry  April  180 (3.2)  78 (3.4)  Wet  May  385 (6.9)  211 (9.3)  Wet  June  193 (3.5)  108 (4.8)  Wet  July  799 (14.3)  353 (15.6)  Wet  Aug.  82 (1.5)  74 (3.3)  Wet  Sept.  412 (7.4)  230 (10.2)  Total  5,568  2,263  Season  Month  Number of anopheline larvae collected (%)  Number of morphologically identified specimens (%)  Wet  Oct.  602 (10.8)  305 (13.5)  Dry  Nov.  855 (15.4)  320 (14.1)  Dry  Dec.  339 (6.1)  115 (5.1)  Dry  Jan.  728 (13.1)  236 (10.4)  Dry  Feb.  714 (12.8)  124 (5.5)  Dry  Mar.  279 (5)  109 (4.8)  Dry  April  180 (3.2)  78 (3.4)  Wet  May  385 (6.9)  211 (9.3)  Wet  June  193 (3.5)  108 (4.8)  Wet  July  799 (14.3)  353 (15.6)  Wet  Aug.  82 (1.5)  74 (3.3)  Wet  Sept.  412 (7.4)  230 (10.2)  Total  5,568  2,263  View Large A total of 16 Anopheles species were identified (Table 2). Overall, Anopheles (C.) vagus Dӧnitz (26.4%, n = 598) was the dominant Anopheles species followed by An. nivipes (20.8%, n = 471), Anopheles peditaeniatus Leicester (20.1%, n = 454), Anopheles (A.) barbirostris van der Wulp (10.3%, n = 234), and Anopheles (C.) varuna Iyengar (9.1%, n = 205) to round out the five most abundant species in the survey (Table 2). During the wet monsoon months (October 2011 and May to September 2012), An. vagus (27.8%, n = 357) was the dominant species, followed by An. nivipes (27.4%, n= 352), An. peditaeniatus (16.4%, n = 211), An. barbirostris (10.8%, n = 138), and An. varuna (5.2%, n = 67). In contrast, during the dry months, An. peditaeniatus (24.7%, n = 243) was dominant followed by An. vagus (24.5%, n = 241), An. varuna (14.1%, n = 138), An. nivipes (12.1%, n = 119), and An. barbirostris (9.8%, n = 96) (Tables 1 and 2). Table 2. Total numbers (%) of Anopheles larvae by month and species in Bandarban, October 2011 to September 2012   Number of Anopheles species (%)  Month  Anopheles (C.) aconitus Dӧnitz  An. barbirostris  Anopheles(C.) culicifacis Giles  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  Anopheles (A.) nigerrimus Giles  An. nivipes  An. peditaeniatus  Anopheles (C.) subpictus Grassi  Anopheles (A.) umbrosus (Theobald)  An. vagus  An. varuna  Anopheles (C.) willmori (James)  Oct.  0 (0)  6 (2.0)  2 (0.7)  0 (0)  12 (3.9)  4 (1.3)  2 (0.7)  0 (0)  14 (4.6)  105 (34.4)  89 (29.2)  0 (0)  0 (0)  71 (23.3)  0 (0)  0 (0)  Nov.  0 (0)  11 (3.4)  0 (0)  9 (2.8)  6 (1.9)  6 (1.9)  4 (1.3)  0 (0)  5 (1.6)  46 (14.4)  126 (39.4)  0 (0)  0 (0)  95 (29.7)  12 (3.8)  0 (0)  Dec.  0 (0)  16 (14.0)  0 (0)  6 (5.2)  2 (1.7)  5 (4.4)  3 (2.6)  2 (1.7)  2 (1.7)  12 (10.4)  36 (31.3)  0 (0)  0 (0)  15 (13.4)  16 (13.9)  0 (0)  Jan.  0 (0)  32 (13.6)  1 (0.4)  17 (7.2)  5 (2.1)  3 (1.3)  11 (4.7)  0 (0)  1 (0.4)  54 (22.9)  51 (21.6)  3 (1.3)  0 (0)  23 (9.8)  34 (14.4)  1 (0.4)  Feb.  0 (0)  3 (2.4)  0 (0)  1 (0.8)  0 (0)  1 (0.8)  8 (6.5)  0 (0)  0 (0)  3 (2.4)  10 (8.1)  0 (0)  0 (0)  54 (43.6)  44 (35.5)  0 (0)  Mar.  0 (0)  12 (11.0)  0 (0)  9 (8.3)  3 (2.8)  0 (0)  1 (0.9)  1 (0.9)  0 (0)  0 (0)  6 (5.5)  4 (3.7)  0 (0)  50 (47.9)  23 (21.1)  0 (0)  April  0 (0)  22 (28.2)  0 (0)  8 (10.3)  2 (2.6)  4 (5.1)  10 (12.8)  0 (0)  1 (1.3)  4 (5.1)  14 (18.0)  0 (0)  0 (0)  4 (5.1)  9 (11.5)  0 (0)  May  0 (0)  38 (18.0)  0 (0)  6 (2.8)  0 (0)  1 (0.5)  0 (0)  0 (0)  22 (10.4)  43 (20.4)  71 (33.7)  9 (4.3)  0 (0)  17 (8.1)  4 (1.9)  0 (0)  June  0 (0)  9 (8.3)  0 (0)  7 (6.5)  0 (0)  2 (1.9)  0 (0)  0 (0)  0 (0)  59 (54.6)  18 (16.7)  0 (0)  0 (0)  13 (12.0)  0 (0)  0 (0)  July  1 (0.3)  75 (21.3)  0 (0)  13 (3.5)  2 (0.6)  4 (1.1)  18 (5.1)  0 (0)  3 (0.9)  47 (13.3)  11 (3.1)  19 (5.4)  0 (0)  110 (31.2)  49 (13.9)  1 (0.3)  Aug.  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  1 (1.4)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  71 (96.0)  2 (2.7)  0 (0)  Sept.  0 (0)  10 (4.4)  3 (1.3)  0 (0)  1 (0.4)  1 (0.4)  0 (0)  0 (0)  0 (0)  98 (42.6)  22 (9.6)  6 (2.6)  2 (0.9)  75 (32.6)  12 (5.2)  0 (0)  Total  1 (0.0)  234 (10.3)  6 (0.3)  76 (3.4)  33 (1.5)  31 (1.4)  58 (2.6)  3 (0.1)  48 (2.1)  471 (20.8)  454 (20.1)  41 (1.8)  2 (0.1)  598 (26.4)  205 (9.1)  2 (0.1)    Number of Anopheles species (%)  Month  Anopheles (C.) aconitus Dӧnitz  An. barbirostris  Anopheles(C.) culicifacis Giles  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  Anopheles (A.) nigerrimus Giles  An. nivipes  An. peditaeniatus  Anopheles (C.) subpictus Grassi  Anopheles (A.) umbrosus (Theobald)  An. vagus  An. varuna  Anopheles (C.) willmori (James)  Oct.  0 (0)  6 (2.0)  2 (0.7)  0 (0)  12 (3.9)  4 (1.3)  2 (0.7)  0 (0)  14 (4.6)  105 (34.4)  89 (29.2)  0 (0)  0 (0)  71 (23.3)  0 (0)  0 (0)  Nov.  0 (0)  11 (3.4)  0 (0)  9 (2.8)  6 (1.9)  6 (1.9)  4 (1.3)  0 (0)  5 (1.6)  46 (14.4)  126 (39.4)  0 (0)  0 (0)  95 (29.7)  12 (3.8)  0 (0)  Dec.  0 (0)  16 (14.0)  0 (0)  6 (5.2)  2 (1.7)  5 (4.4)  3 (2.6)  2 (1.7)  2 (1.7)  12 (10.4)  36 (31.3)  0 (0)  0 (0)  15 (13.4)  16 (13.9)  0 (0)  Jan.  0 (0)  32 (13.6)  1 (0.4)  17 (7.2)  5 (2.1)  3 (1.3)  11 (4.7)  0 (0)  1 (0.4)  54 (22.9)  51 (21.6)  3 (1.3)  0 (0)  23 (9.8)  34 (14.4)  1 (0.4)  Feb.  0 (0)  3 (2.4)  0 (0)  1 (0.8)  0 (0)  1 (0.8)  8 (6.5)  0 (0)  0 (0)  3 (2.4)  10 (8.1)  0 (0)  0 (0)  54 (43.6)  44 (35.5)  0 (0)  Mar.  0 (0)  12 (11.0)  0 (0)  9 (8.3)  3 (2.8)  0 (0)  1 (0.9)  1 (0.9)  0 (0)  0 (0)  6 (5.5)  4 (3.7)  0 (0)  50 (47.9)  23 (21.1)  0 (0)  April  0 (0)  22 (28.2)  0 (0)  8 (10.3)  2 (2.6)  4 (5.1)  10 (12.8)  0 (0)  1 (1.3)  4 (5.1)  14 (18.0)  0 (0)  0 (0)  4 (5.1)  9 (11.5)  0 (0)  May  0 (0)  38 (18.0)  0 (0)  6 (2.8)  0 (0)  1 (0.5)  0 (0)  0 (0)  22 (10.4)  43 (20.4)  71 (33.7)  9 (4.3)  0 (0)  17 (8.1)  4 (1.9)  0 (0)  June  0 (0)  9 (8.3)  0 (0)  7 (6.5)  0 (0)  2 (1.9)  0 (0)  0 (0)  0 (0)  59 (54.6)  18 (16.7)  0 (0)  0 (0)  13 (12.0)  0 (0)  0 (0)  July  1 (0.3)  75 (21.3)  0 (0)  13 (3.5)  2 (0.6)  4 (1.1)  18 (5.1)  0 (0)  3 (0.9)  47 (13.3)  11 (3.1)  19 (5.4)  0 (0)  110 (31.2)  49 (13.9)  1 (0.3)  Aug.  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  1 (1.4)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  0 (0)  71 (96.0)  2 (2.7)  0 (0)  Sept.  0 (0)  10 (4.4)  3 (1.3)  0 (0)  1 (0.4)  1 (0.4)  0 (0)  0 (0)  0 (0)  98 (42.6)  22 (9.6)  6 (2.6)  2 (0.9)  75 (32.6)  12 (5.2)  0 (0)  Total  1 (0.0)  234 (10.3)  6 (0.3)  76 (3.4)  33 (1.5)  31 (1.4)  58 (2.6)  3 (0.1)  48 (2.1)  471 (20.8)  454 (20.1)  41 (1.8)  2 (0.1)  598 (26.4)  205 (9.1)  2 (0.1)  View Large Anopheline larvae were collected from 16 different habitat types. The most common mosquito-positive habitat type in the survey was ‘pond’, which was also the habitat type to account for most of the Anopheles larvae collected in this survey (40.4%, n = 914). The next most productive habitat types in the context of this survey were rice field (34%, n = 779), irrigation canal (4%, n = 90), puddle (3.7%, n = 83), animal hoof print (2.5%, n = 56) and well (2.2%, n = 50) (Table 3). Notably, 30 Anopheles larvae (1.3%) of An. barbirostris, Anopheles (C.) jamesii Theobald, Anopheles (C.) karwari James, An. peditaeniatus and An. vagus were identified from small artificial containers (plastic buckets) (Table 3). Anopheles larvae were also collected once from a bamboo hole but the specimen could not be identified, hence, it was not included in the analysis. Table 3. Total numbers (%) of Anopheles larvae identified by larval habitat type in Bandarban, October 2011 to September 2012 Habitat  Number of mosquito- positive observations  An. aconitus  An. barbirostris  An. culicifacis  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  An. nigerrimus  An. nivipes  An. peditaeniatus  An. subpictus  An. umbrosus  An. vagus  An. varuna  An. willmori  Total  Pond  111  0  153 (16.7)  3 (0.3)  32 (3.5)  5 (0.6)  8 (0.9)  27 (3.0)  3 (0.3)  13 (1.4)  140 (15.3)  111 (12.1)  19 (2.1)  0  216 (23.6)  184 (20.1)  0  914 (40.4)  Rice field  108  1 (0.1)  41 (5.3)  0  27 (3.5)  17 (2.2)  11 (1.4)  20 (2.6)  0  23 (3.0)  238 (30.6)  230 (29.5)  3 (0.4)  0  157 (20.2)  10 (1.3)  1 (0.1)  779 (34.4)  Permanent wetland  6  0  4 (11.1)  0  1 (2.8)  0  1 (2.8)  0  0  1 (2.8)  6 (16.7)  19 (52.8)  0  0  4 (11.1)  0  0  36 (1.6)  Temporary pond  4  0  0  0  1 (2.9)  3 (8.8)  2 (5.9)  4 (11.7)  0  0  4 (11.7)  2 (5.8)  8 (23.5)  0  10 (29.4)  0  0  34 (1.5)  Field flood pool  1  0  0  0  1 (2.8)  0  0  0  0  1 (2.8)  25 (69.4)  9 (25.0)  0  0  0  0  0  36 (1.6)  Puddle  10  0  0  3 (3.6)  0  2 (2.4)  0  1 (1.2)  0  0  12 (14.5)  7 (8.4)  1 (1.2)  0  57 (68.7)  0  0  83 (3.7)  Receding river  9  0  1 (2.3)  0  0  1 (2.3)  1 (2.3)  0  0  1 (2.3)  4 (9.3)  1 (2.3)  1 (2.3)  0  33 (76.7)  0  0  43 (1.9)  Irrigation canal  17  0  3 (3.3)  0  8 (8.9)  4 (4.4)  3 (3.3)  1 (1.1)  0  5 (5.6)  16 (17.8)  27 (30.0)  0  0  23 (25.6)  0  0  90 (4.0)  Irrigation pond  4  0  7 (18.9)  0  0  0  0  0  0  2 (5.4)  10 (27.0)  16 (43.2)  0  0  2 (5.4)  0  0  37 (1.6)  Stream  4  0  1 (11.1)  0  0  0  0  0  0  0  0  7 (77.8)  0  0  1 (11.1)  0  0  9 (0.4)  Well  7  0  5 (10. 0)  0  3 (6.0)  1 (2.0)  1 (2.0)  3 (6.0)  0  0  6 (12.0)  4 (8.0)  2 (4.0)  2 (4.0)  21 (42.0)  2 (4.0)  0  50 (2.2)  Animal hoof print  4  0  0  0  0  0  0  0  0  0  5 (8.9)  0  4 (7.1)  0  47 (83.9)  0  0  56 (2.5)  Small artificial container  2  0  5 (16.7)  0  1 (3.3)  0  4 (13.3)  0  0  0  0  12 (40.0)  0  0  8 (26.7)  0  0  30 (1.3)  Channel  5  0  8 (32.0)  0  1 (4.0)  0  0  1 (4.0)  0  0  2 (8.0)  2 (8.0)  1 (4.0)  0  7 (28.0)  3 (12.0)  0  25 (1.1)  Drain  7  0  6 (20.0)  0  1 (3.3)  0  0  1 (3.3)  0  2 (6.7)  3 (10.0)  7 (23.3)  1 (3.3)  0  5 (16.7)  3 (10.0)  1 (3.3)  30 (1.3)  Wheel track  1  0  0  0  0  0  0  0  0  0  0  0  1 (9.1)  0  7 (63.6)  3 (27.3)  0  11 (0.5)  Habitat  Number of mosquito- positive observations  An. aconitus  An. barbirostris  An. culicifacis  An. jamesii  An. jeyporiensis  An. karwari  An. kochi  An. minimus  An. nigerrimus  An. nivipes  An. peditaeniatus  An. subpictus  An. umbrosus  An. vagus  An. varuna  An. willmori  Total  Pond  111  0  153 (16.7)  3 (0.3)  32 (3.5)  5 (0.6)  8 (0.9)  27 (3.0)  3 (0.3)  13 (1.4)  140 (15.3)  111 (12.1)  19 (2.1)  0  216 (23.6)  184 (20.1)  0  914 (40.4)  Rice field  108  1 (0.1)  41 (5.3)  0  27 (3.5)  17 (2.2)  11 (1.4)  20 (2.6)  0  23 (3.0)  238 (30.6)  230 (29.5)  3 (0.4)  0  157 (20.2)  10 (1.3)  1 (0.1)  779 (34.4)  Permanent wetland  6  0  4 (11.1)  0  1 (2.8)  0  1 (2.8)  0  0  1 (2.8)  6 (16.7)  19 (52.8)  0  0  4 (11.1)  0  0  36 (1.6)  Temporary pond  4  0  0  0  1 (2.9)  3 (8.8)  2 (5.9)  4 (11.7)  0  0  4 (11.7)  2 (5.8)  8 (23.5)  0  10 (29.4)  0  0  34 (1.5)  Field flood pool  1  0  0  0  1 (2.8)  0  0  0  0  1 (2.8)  25 (69.4)  9 (25.0)  0  0  0  0  0  36 (1.6)  Puddle  10  0  0  3 (3.6)  0  2 (2.4)  0  1 (1.2)  0  0  12 (14.5)  7 (8.4)  1 (1.2)  0  57 (68.7)  0  0  83 (3.7)  Receding river  9  0  1 (2.3)  0  0  1 (2.3)  1 (2.3)  0  0  1 (2.3)  4 (9.3)  1 (2.3)  1 (2.3)  0  33 (76.7)  0  0  43 (1.9)  Irrigation canal  17  0  3 (3.3)  0  8 (8.9)  4 (4.4)  3 (3.3)  1 (1.1)  0  5 (5.6)  16 (17.8)  27 (30.0)  0  0  23 (25.6)  0  0  90 (4.0)  Irrigation pond  4  0  7 (18.9)  0  0  0  0  0  0  2 (5.4)  10 (27.0)  16 (43.2)  0  0  2 (5.4)  0  0  37 (1.6)  Stream  4  0  1 (11.1)  0  0  0  0  0  0  0  0  7 (77.8)  0  0  1 (11.1)  0  0  9 (0.4)  Well  7  0  5 (10. 0)  0  3 (6.0)  1 (2.0)  1 (2.0)  3 (6.0)  0  0  6 (12.0)  4 (8.0)  2 (4.0)  2 (4.0)  21 (42.0)  2 (4.0)  0  50 (2.2)  Animal hoof print  4  0  0  0  0  0  0  0  0  0  5 (8.9)  0  4 (7.1)  0  47 (83.9)  0  0  56 (2.5)  Small artificial container  2  0  5 (16.7)  0  1 (3.3)  0  4 (13.3)  0  0  0  0  12 (40.0)  0  0  8 (26.7)  0  0  30 (1.3)  Channel  5  0  8 (32.0)  0  1 (4.0)  0  0  1 (4.0)  0  0  2 (8.0)  2 (8.0)  1 (4.0)  0  7 (28.0)  3 (12.0)  0  25 (1.1)  Drain  7  0  6 (20.0)  0  1 (3.3)  0  0  1 (3.3)  0  2 (6.7)  3 (10.0)  7 (23.3)  1 (3.3)  0  5 (16.7)  3 (10.0)  1 (3.3)  30 (1.3)  Wheel track  1  0  0  0  0  0  0  0  0  0  0  0  1 (9.1)  0  7 (63.6)  3 (27.3)  0  11 (0.5)  View Large When data of the five most common mosquito species were analyzed, more than 90% of larvae collected from animal hoof prints were An. vagus. In fact, this species was common in most of the larval habitats identified. An. vagus was the most abundant species in puddles (75.0%), wells (55.3%), and ponds (26.9%). An. nivipes was the most common (35.2%) anopheline collected the rice fields. An. peditaeniatus was the dominant species observed in both small artificial containers (48.0%) and irrigation canals (39.1%). The remaining two most common species across all collections, An. barbirostris and An. varuna, did not dominate in any single habitat, but were very abundant in ponds (Fig. 1). Although, most anopheline larvae were collected from ponds, the RR for the abundance of mosquito larvae was 2.25 times higher for flood pools than ponds (RR 2.25; 95% CI 2.009, 2.523; P = 0.001) (Table 4). In contrast, significantly lower abundance of anopheline larvae was associated with permanent wetlands (RR 0.66; 95% CI 0.484, 0.913; P = 0.012), temporary ponds (RR 0.63; 95% CI 0.434, 0.942; P = 0.024), puddles (RR 0.58; 95% CI 0.424, 0.789; P = 0.001), and streams (RR 0.60; 95% CI 0.378, 0.958; P = 0.032) (Table 4). Table 4. Association of habitat types with anopheline larval abundance Factors  IRR  95% CI  P value  Permanent wetland  0.6652  0.4846–0.9133  0.012  Bamboo hole  1.0235  0.9133–1.1469  0.689  Rice field  1.0050  0.8610–1.1731  0.949  Temporary pond  0.6397  0.4342–0.9425  0.024  Field flood pool  2.2517  2.0094–2.5233  0.001  Puddle  0.5782  0.4240–0.7887  0.001  Receding River  0.8927  0.5397–1.4765  0.658  Irrigation canal  0.8429  0.6011–1.1819  0.322  Irrigation pond  0. 8060  0.4301–1.5103  0.501  Stream  0.6013  0.3775–0.9578  0.032  Well  0.8188  0.6226–1.0768  0.153  Animal hoof print  0.7293  0.2808–1.8938  0.517  Small artificial container  0.9979  0.6196–1.6072  0.993  Othersa  0.8582  0.5428–1.3567  0.513  Factors  IRR  95% CI  P value  Permanent wetland  0.6652  0.4846–0.9133  0.012  Bamboo hole  1.0235  0.9133–1.1469  0.689  Rice field  1.0050  0.8610–1.1731  0.949  Temporary pond  0.6397  0.4342–0.9425  0.024  Field flood pool  2.2517  2.0094–2.5233  0.001  Puddle  0.5782  0.4240–0.7887  0.001  Receding River  0.8927  0.5397–1.4765  0.658  Irrigation canal  0.8429  0.6011–1.1819  0.322  Irrigation pond  0. 8060  0.4301–1.5103  0.501  Stream  0.6013  0.3775–0.9578  0.032  Well  0.8188  0.6226–1.0768  0.153  Animal hoof print  0.7293  0.2808–1.8938  0.517  Small artificial container  0.9979  0.6196–1.6072  0.993  Othersa  0.8582  0.5428–1.3567  0.513  Reference variable: Pond. RR determined by Poisson regression. aInclude channel—drain- and wheel track. View Large The abundance of Anopheles larvae were significantly associated with climate factors. Each millimeter increase of monthly rainfall was associated with an increase in larval abundance (0.437; 95% CI 0.366, 0.508; P = 0.001). However, increases in temperature (−0.019; 95% CI −0.037, −0.001; P = 0.037) and humidity (−0.006; 95% CI −0.011, −0.002, P = 0.003) were significantly associated with limits in anopheline larval abundance (Table 5, Fig. 2). Monthly mosquito larval counts peaked in November 2011 and July 2012, both observations followed high rainfall events by 1 to 2 mo suggesting a lag effect of rain on mosquito production in Bandarban (Fig. 2). Table 5. Association of abiotic climate factors on monthly abundance of anopheline larvae Factors  Regression coefficient  95% CI  P value  Rainfall  0.4375  0.366, 0.508  0.001  Temperature  −0.0192  −0.037, −0.001  0.037  Humidity  −0.0068  −0.011, −0.002  0.003  Factors  Regression coefficient  95% CI  P value  Rainfall  0.4375  0.366, 0.508  0.001  Temperature  −0.0192  −0.037, −0.001  0.037  Humidity  −0.0068  −0.011, −0.002  0.003  Value < 0.05. View Large Fig. 2. View largeDownload slide Abiotic climate factors and monthly abundance of anopheline larvae. Fig. 2. View largeDownload slide Abiotic climate factors and monthly abundance of anopheline larvae. Mosquito larvae were collected from habitats ranging in pH from <4.0 to >9.0, although anopheline larvae from these pH extremes were relatively uncommon. Water pH ranging from 7.0 to 8.0 was observed to be the most suitable for the five most abundant anopheline species although small numbers were collected outside this pH range (Fig. 3). An. vagus was recorded to be most abundant in the range of pH 7.6 to 8.0, with nearly 50% of An. vagus larvae collected at this pH and more than 70% of this species collected from a wider pH range, pH 7.0 to 8.0 (Fig. 3a). Likewise, nearly 70% of the An. nivipes larvae were collected from pH 7.0 to 8.0. However, unlike An. vagus, nearly 40% of the An. nivipes and An. peditaeniatus larvae were collected from the low end of this pH range, pH 7.0 to 7.5 (Fig. 3b and 3c). In contrast, more than 40% of An. varuna larvae were collected from larval habitats where the pH ranged from 7.6 to 8.0 (Fig. 3d) and nearly 60% of An. barbirostris larvae collected at pH 7.0 to 8.0 (Fig. 3e). Fig. 3. View largeDownload slide Association of pH and abundance of the five most common anopheline species. (a) An. vagus (b) An. nivipes (c) An. peditaeniatus (d) An. varuna (e) An. barbirostris. Fig. 3. View largeDownload slide Association of pH and abundance of the five most common anopheline species. (a) An. vagus (b) An. nivipes (c) An. peditaeniatus (d) An. varuna (e) An. barbirostris. Larval abundance did not significantly vary between habitats with and without organic waste, inorganic waste or both (Table 6). However, Anopheles larval abundance varied significantly (RR 1.14; CI 1.03, 1.26; P < 0.05) with the presence of floating debris and trash materials. Interestingly, presence of algae including sites encrusted with algae (RR 0.85; CI 0.76, 0.95; P < 0.05) and floating algae (RR 0.81; CI 0.72, 0.89; P < 0.05) and movement of water in the habitat had significant positive association with Anopheles larval abundance (P < 0.05) whereas emergent grass, floating vegetation or amount of sunlight did not (Table 6). Table 6. Association of habitat characteristics on anopheline larval abundance Habitat characteristics  RR  95% CI  P value  Polluted organic waste  1.0084  0.9376, 1.0845  0.821  Polluted mineral waste  0.8103  0.48821, 1.3451  0.416  Polluted mineral and organic waste  1.1732  0.9187, 1.4983  0.200  Floating debris and trash  1.1462  1.0377, 1.2661  0.007  Algae turbid  1.1756  1.0469, 1.3202  0.006  Algae crusted  0.8544  0.7635, 0.9562  0.006  Algae floating  0.8058  0.7265, 0.8937  0.000  Aquatic emergent grass  1.0720  0.9910, 1.1596  0.083  Aquatic floating vegetation  1.0498  0.9718, 1.1340  0.217  Water stagnant  0.5357  0.4604, 0.6232  0.000  Water running  1.8975  1.6214, 2.2206  0.000  Sunlit  1.0335  0.9785, 1.0917  0.237  Half shady  0.9647  0.9113, 1.0213  0.217  Shady  1.0049  0.8966, 1.1263  0.932  Habitat characteristics  RR  95% CI  P value  Polluted organic waste  1.0084  0.9376, 1.0845  0.821  Polluted mineral waste  0.8103  0.48821, 1.3451  0.416  Polluted mineral and organic waste  1.1732  0.9187, 1.4983  0.200  Floating debris and trash  1.1462  1.0377, 1.2661  0.007  Algae turbid  1.1756  1.0469, 1.3202  0.006  Algae crusted  0.8544  0.7635, 0.9562  0.006  Algae floating  0.8058  0.7265, 0.8937  0.000  Aquatic emergent grass  1.0720  0.9910, 1.1596  0.083  Aquatic floating vegetation  1.0498  0.9718, 1.1340  0.217  Water stagnant  0.5357  0.4604, 0.6232  0.000  Water running  1.8975  1.6214, 2.2206  0.000  Sunlit  1.0335  0.9785, 1.0917  0.237  Half shady  0.9647  0.9113, 1.0213  0.217  Shady  1.0049  0.8966, 1.1263  0.932  View Large The abundance of An. peditaeniatus decreased significantly with increased pH (r = −0.15, P value < 0.05) as revealed by a Spearman correlation analysis, but in contrast, the abundance of An. vagus increased with a rise in pH (r = 0.15, P value < 0.05) (Table 7). An. nivipes abundance was observed to increase (r = 0.23, P value < 0.05) significantly with increased habitat temperature whereas, An. varuna larval abundance increased (r = −0.18, P value < 0.05) with lower habitat temperature (Table 7). Table 7. Association of habitat characteristics with the five most abundant anopheline species Habitat characteristics  An. barbirostris  An. peditaeniatus  An. nivipes  An. vagus  An. varuna  Correlation  pH  −0.10  −0.15*  −0.06  0.15*  0.04  Habitat temperature  0.05  0.06  0.23*  0.13  −0.18*  Habitat characteristics RR (95% CI)  Muddy  1.19 (0.89–1.58)  0.76 (0.60–0.95)*  0.54 (0.43–0.69)*  0.93 (0.79–1.10)  1.71 (1.27–2.30)*  Fresh  0.70 (0.51–0.97)*  1.48 (1.21–1.80)*  1.39 (1.14–1.69)*  0.80 (0.66–0.99)*  0.94 (0.60–1.47)  Polluted with organic waste  0.92 (0.66–1.28)  0.89 (0.67–1.19)  1.18 (0.92–1.50)  0.68 (0.49–0.93)*  0.80 (0.50–1.29)  Polluted with mineral waste  0a  0a  0.64 (0.20–1.98)  0.40 (0.10–1.62)  0a  Polluted with both organic and mineral waste  0a  0a  0a  1.12 (0.62–2.04)  0a  Floating debris/trash  0.98 (0.63–1.53)  0.35 (0.12–1.01)*  0.33 (0.15–0.74)*  0.66 (0.47–0.94)*  1.02 (0.63–1.68)  Algae turbid  0.46 (0.20–1.04)  0.92 (0.57–1.48)  0.75 (0.40–1.40)  1.30 (0.97–1.74)  0.30 (0.08–1.10)  Algae crusted  0.29 (0.12–0.73)*  0.57 (0.30–1.07)  0.40 (0.22–0.73)*  0.67 (0.43–1.02)  0.02 (0.002–0.12)*  Algae floating  0.57 (0.32–0.99)*  0.60 (0.34–1.06)  0.63 (0.38–1.02)  0.41 (0.24–0.71)*  0.01(0.002–0.11)*  Aquatic emergent grass  1.25 (0.90–1.74)  0.64 (0.46–0.89)*  1.50 (1.18–1.90)*  0.61 (0.41–0.89)*  0.81 (0.57–1.14)  Aquatic floating vegetation  1.50 (1.12–2.01)*  0.64 (0.45–0.89)*  1.04 (0.79–1.37)  0.69 (0.49–0.99)*  0.89 (0.63–1.27)  Live grass—choked  0.56 (0.09–3.36)  0a  0a  0.79 (0.29–2.22)  0a  Dead grass—choked  0.55 (0.09–3.36)  0a  0.33 (0.09–1.19)  0.05 (0.007–0.36)*  0a  Tree leaf  0a  0a  0a  0.32 (0.08–1.15)  0.27 (0.08–0.84)*  Stagnant water  16.78 (5.36–52.50)*  2.83 (1.41–5.67)*  1.20 (0.67–2.19)  2.25 (1.24–4.10)*  2.29 (0.38–13.96)  Running water  0.06 (0.02–0.19)*  0.39 (0.20–0.78)*  0.82 (0.45–1.50)  0.47 (0.25–0.88)*  0a  Sunlit  1.01 (0.74–1.35)  0.65 (0.53–0.79)*  1.13 (0.94–1.37)  2.25 (1.74–2.91)*  0.95 (0.70–1.28)  Half shady  0.95 (0.68–1.32)  1.56 (1.29–1.90)*  0.94 (0.77–1.14)  0.43 (0.32–0.56)*  0.95 (0.69–1.32)  Shady  1.10 (0.69–1.76)  1.12 (0.67–1.90)  0.23 (0.06–0.85)*  0.56 (0.33–0.94)*  1.08 (0.67–1.77)  Habitat characteristics  An. barbirostris  An. peditaeniatus  An. nivipes  An. vagus  An. varuna  Correlation  pH  −0.10  −0.15*  −0.06  0.15*  0.04  Habitat temperature  0.05  0.06  0.23*  0.13  −0.18*  Habitat characteristics RR (95% CI)  Muddy  1.19 (0.89–1.58)  0.76 (0.60–0.95)*  0.54 (0.43–0.69)*  0.93 (0.79–1.10)  1.71 (1.27–2.30)*  Fresh  0.70 (0.51–0.97)*  1.48 (1.21–1.80)*  1.39 (1.14–1.69)*  0.80 (0.66–0.99)*  0.94 (0.60–1.47)  Polluted with organic waste  0.92 (0.66–1.28)  0.89 (0.67–1.19)  1.18 (0.92–1.50)  0.68 (0.49–0.93)*  0.80 (0.50–1.29)  Polluted with mineral waste  0a  0a  0.64 (0.20–1.98)  0.40 (0.10–1.62)  0a  Polluted with both organic and mineral waste  0a  0a  0a  1.12 (0.62–2.04)  0a  Floating debris/trash  0.98 (0.63–1.53)  0.35 (0.12–1.01)*  0.33 (0.15–0.74)*  0.66 (0.47–0.94)*  1.02 (0.63–1.68)  Algae turbid  0.46 (0.20–1.04)  0.92 (0.57–1.48)  0.75 (0.40–1.40)  1.30 (0.97–1.74)  0.30 (0.08–1.10)  Algae crusted  0.29 (0.12–0.73)*  0.57 (0.30–1.07)  0.40 (0.22–0.73)*  0.67 (0.43–1.02)  0.02 (0.002–0.12)*  Algae floating  0.57 (0.32–0.99)*  0.60 (0.34–1.06)  0.63 (0.38–1.02)  0.41 (0.24–0.71)*  0.01(0.002–0.11)*  Aquatic emergent grass  1.25 (0.90–1.74)  0.64 (0.46–0.89)*  1.50 (1.18–1.90)*  0.61 (0.41–0.89)*  0.81 (0.57–1.14)  Aquatic floating vegetation  1.50 (1.12–2.01)*  0.64 (0.45–0.89)*  1.04 (0.79–1.37)  0.69 (0.49–0.99)*  0.89 (0.63–1.27)  Live grass—choked  0.56 (0.09–3.36)  0a  0a  0.79 (0.29–2.22)  0a  Dead grass—choked  0.55 (0.09–3.36)  0a  0.33 (0.09–1.19)  0.05 (0.007–0.36)*  0a  Tree leaf  0a  0a  0a  0.32 (0.08–1.15)  0.27 (0.08–0.84)*  Stagnant water  16.78 (5.36–52.50)*  2.83 (1.41–5.67)*  1.20 (0.67–2.19)  2.25 (1.24–4.10)*  2.29 (0.38–13.96)  Running water  0.06 (0.02–0.19)*  0.39 (0.20–0.78)*  0.82 (0.45–1.50)  0.47 (0.25–0.88)*  0a  Sunlit  1.01 (0.74–1.35)  0.65 (0.53–0.79)*  1.13 (0.94–1.37)  2.25 (1.74–2.91)*  0.95 (0.70–1.28)  Half shady  0.95 (0.68–1.32)  1.56 (1.29–1.90)*  0.94 (0.77–1.14)  0.43 (0.32–0.56)*  0.95 (0.69–1.32)  Shady  1.10 (0.69–1.76)  1.12 (0.67–1.90)  0.23 (0.06–0.85)*  0.56 (0.33–0.94)*  1.08 (0.67–1.77)  aNo specimen of corresponding species was found. *P value < 0.05. View Large The zero-inflated Poisson regression revealed that An. barbirostris were significantly less abundant in fresh water (RR 0.70; CI 0.51, 0.97, P < 0.05), in the presence of algae encrusting a site (RR 0.29; CI 0.12, 0.73, P < 0.05), floating algae (RR 0.57; CI 0.32, 0.99, P < 0.05), and running water (RR0.06; CI 0.02, 0.19, P < 0.05) and significantly high in floating vegetation (RR 1.50; CI 1.12, 2.01, P < 0.05) and stagnant water (RR 16.78; CI 5.36, 52.50, P < 0.05). Significantly less abundance of An. peditaeniatus was observed from muddy water (RR 0.76; CI 0.60, 0.95, P < 0.05), sites with floating debris (RR 0.35; CI 0.12, 1.01, P < 0.05), aquatic emergent grass (RR 0.64; CI 0.46, 0.89, P < 0.05), aquatic floating vegetation (RR 0.64; CI 0.45, 0.89, P < 0.05), running water (RR 0.39; CI 0.20, 0.78, P < 0.05), and if sunlit (RR 0.65; CI 0.53, 0.79, P < 0.05), and significant high abundance was observed when the habitats were characterized with fresh water (RR 1.48; CI 1.21, 1.80, P < 0.05), stagnant water (RR 2.83; CI 1.41, 5.67, P < 0.05), and half shaded (RR 1.56; CI 1.29, 1.90, P < 0.05). An. nivipes was significantly less abundant in muddy water (RR 0.54; CI 0.43, 0.69, P < 0.05), with floating debris (RR 0.33; CI 0.15, 0.74, P < 0.05), encrusted with algae (RR 0.40; CI 0.22, 0.73, P < 0.05), and shade (RR 0.23; CI 0.06, 0.85, P < 0.05), and were highly abundant in fresh water (RR 1.39; CI 1.14, 1.69, P < 0.05) and with aquatic emergent grass (RR 1.50; CI 1.18, 1.90, P < 0.05). Abundance of An. vagus was negatively associated with fresh water (RR 0.80; CI 0.66, 0.99, P < 0.05), polluted organic waste (RR 0.68; CI 0.49, 0.93, P < 0.05), floating debris (RR 0.66; CI 0.47, 0.94, P < 0.05), floating algae (RR 0.41; CI 0.24, 0.71, P < 0.05), aquatic emergent grass (RR 0.61; CI 0.41, 0.89, P < 0.05), aquatic floating vegetation (RR 0.69; CI 0.49, 0.99, P < 0.05), dead choked grass (RR 0.05; CI 0.007, 0.36, P < 0.05), running water (RR 0.47; CI 0.25, 0.88, P < 0.05) and both half shaded (RR 0.43; CI 0.32, 0.56, P < 0.05) and shaded (RR 0.56; CI 0.33, 0.94, P < 0.05). On the other hand, it was observed that abundance of An. vagus was significantly higher in stagnant water (RR 2.25; CI 1.24, 4.10, P < 0.05) and if sunlit (RR 2.25; CI 1.74, 2.91, P < 0.05). Larval abundance of An. varuna was significantly low in habitats encrusted with algae (RR 0.02; CI 0.002, 0.12, P < 0.05), with floating algae (RR 0.01; CI 0.002, 0.11, P < 0.05), and tree leaves (RR 0.27; CI 0.08, 0.84, P < 0.05), but in contrast, high in muddy water (RR 1.71; CI 1.27, 2.30, P < 0.05) (Table 7). Discussion A total of 16 Anopheles species were identified in the current study, fewer species than have been identified in recent adult mosquito surveys by CDC light trap in Bandarban (20 species) and adjacent ecologically similar regions (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). Twelve Anopheles species collected in the current study as larvae have been reported positive for Plasmodium infection in recent studies focused on adult malaria vectors (Maheswary et al. 1992, Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). These reports include three of the five most common species collected as larvae in this study; An. vagus, An. nivipes, and An. barbirostris, all reported as infected adults from Bangladesh. Although Plasmodium-positive An. varuna and An. peditaeniatus adults have not been reported from Bangladesh, these species are documented as parasite-positive in neighboring India (Nagpal and Sharma 1995) and Indonesia (Sugiarto et al. 2016), respectively. An. vagus, the dominant anopheline species in the larval collections reported in the present study, was also among the dominant species in the adult collections conducted in CHT, and is similarly considered an important vector of malaria in Bangladesh and India (Prakash et al. 2004, Alam et al. 2010, Alam et al. 2012b, Al-Amin et al. 2015). Renshaw et al. (1996) reported collecting large numbers of An. vagus where most of the collections were conducted in ponds. Gunathilaka et al. (2013) similarly reported ponds to be the most important source of An. vagus, as was also observed in the current study. However a number of other larval habitats have been reported for An. vagus including; rice fields, irrigation canals, drainage ditches, temporary pools, cement-lined and unlined wells used for domestic purposes, animal hoof prints, borrow pits, rock pools, lake margins, and slow moving water (Rahman et al. 2002, Rueda et al. 2011, Gunathilaka et al. 2013). In the current study, all sampled habitats were positive for An. vagus except for field flood pool, reinforcing the opportunistic range of habitats An. vagus will utilize. In the Philippines, An. vagus larvae were reported to be abundant from rice fields and irrigation drainage ditches with slow flowing water, having pH ranging from 6.79 to 7.62 (Rueda et al. 2011). In the present study, An. vagus abundance was significantly associated with pH in this relatively broad range. In Bangladesh, work focused on the flood plains had illustrated that An. philippinensis, one of the dominant vectors of malaria in this region, favored larval habitats that included tanks, rice fields, submerged low lands, and ponds with vegetation (Elias 1996). Although An. philippinensis was not collected from the CHT in this study, a closely related species, An. nivipes, was collected primarily from ponds, rice fields, and field flood pools. Previous studies have documented the moderately high relative abundance of An. barbirostris, An. peditaeniatus, and An. varuna as adult mosquitoes in the CHT (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). All three species were collected in relatively high abundance in this study, primarily from ponds and rice fields, although An. peditaeniatus was common in all habitats except animal hoof prints. Understanding Anopheles larval ecology and critical habitat characteristics for malaria vectors is key to predicting species presence and abundance and therefore, important to design of malaria control program strategies. Unfortunately, this data has been lacking or has not been current for the CHT. This is the first comprehensive report on anopheline larval habitat characteristics in Bangladesh. Ponds, rice fields, irrigation canals, puddles, animal hoof prints, and wells were the most utilized larval habitats for Anopheles mosquitoes as revealed in the current study. These habitats were largely either human-generated or associated with anthropogenic variables. In CHT, ponds are generally used for culturing fish, domestic activities (e.g., dish and clothes washing and bathing), and agricultural irrigation. Abandoned ponds are also common and although mostly stagnant, provide significant habitat for larval Anopheles mosquitoes in addition to abundant small larval habitats such as hoof prints. A growing human population in the catchment area has resulted in increased human activity and environmental impact including deforestation, agricultural expansion, livestock and fish rearing, all of which create suitable larval habitats for mosquitoes (Norris 2004, Derraik and Slaney 2007). Agricultural expansion alone can create favorable larval habitats for Anopheles mosquitoes, resulting in high larval counts as observed in the rice fields and irrigation canals in this study. Moreover, agriculture can cause increased sedimentation due to erosion which can slow or block streams and decrease water depth creating shallow waters ideal for mosquito larvae (Norris 2004, Kamdem et al. 2012). Presence of multiple Anopheles species in each habitat type sampled provides evidence of the ecological overlap of different species and potential malaria vectors within the same site (Mwangangi et al. 2007). Although most larvae were collected from ponds, the relative abundance was higher in the field flood pool. However, field flood pools were uncommon in our study sites and further study and sampling across a broader geographic area is required to draw definitive conclusions. Historically, An. baimaii, An. minimus, and An. philippinensis were the most prevalent and established malaria vector mosquitoes in Bangladesh (Rosenberg 1982, Renshaw et al. 1996). However, An. baimaii and An. philippinensis were completely absent in this larval survey and only three specimens of An. minimus were collected from ponds. Recent entomological surveys using CDC light traps also demonstrate the low abundance of these species as adult host-seeking vectors (Alam et al. 2010, Alam et al. 2012b, Bashar and Tuno 2014, Al-Amin et al. 2015). Large-scale conversion of forests to agricultural lands (Anonymous 2017) could be a driver of the apparent absence of these historic vector species. Such findings also suggest that other anophelines may have taken advantage of these habitat changes and vacant ecological niches, and currently play a new and/or larger role in maintaining malaria transmission. In this study five Anopheles species; An. barbirostris, An. jamesii, An. karwari, An. peditaeniatus, and An. vagus, were collected from small artificial containers, a habitat not commonly associated or reported for Anopheles mosquitoes, and mentioned as special oviposition sites in the WHO guidelines (WHO 2003). A small pilot study conducted earlier in Bandarban also reported An. vagus and Anopheles (C.) kochi Dӧnitz from artificial containers (Alam et al. 2012a). With increasing human populations and urbanization, small artificial containers will become more abundant and perhaps associated with increased anopheline abundance. Anopheles larvae were significantly more abundant in habitats with floating debris, trash, and algae despite reports of larval inhibition by water pollution (Barbazan et al. 1998). Theoretically, the presence of debris could be beneficial for Anopheles larvae by increasing food and space for larvae while hindering predation (Keating et al. 2004). In addition, studies have documented the importance of floating algae on the presence of Anopheles larvae by promoting thermal stability during low night-time temperatures (Rejmankova 1993, Pinault and Hunter 2012). Abundance of larvae was observed irrespective of water current in the present study. However, most of the larvae were collected from ponds without water flow, which might serve as an explanation for the significant correlation between larval presence and stagnant water that provides abundant algal and bacterial growth, as these are unlikely to be independent variables. The presence of grass or vegetation in running water may also protect Anopheles larvae from being washed out of habitats (Imbahale et al. 2011). This study has revealed that anopheline larvae utilize multiple habitat types in the CHT region of Bangladesh. Diverse species of vector mosquitoes are present in malaria-endemic Bandarban, a fact which is crucial for understanding the transmission of malaria. Policy makers and malaria control programs need to consider options for the management of Anopheles populations that target both natural and human-created larval habitats. Regular surveys of all available oviposition habitats need to be performed frequently to design a comprehensive and effective malaria control strategy and to determine and better understand the anthropogenic impact on malaria vector composition. Acknowledgments We are grateful to Jacob Khyang, Chai Shwai Prue, Abu Naser Mohon, Mohammad Sharif Hossain, Humayun Kabir, and all Bandarban field staffs for their contributions and participation to this study. This research study was funded by the Johns Hopkins Malaria Research Institute (JHMRI) of Johns Hopkins Bloomberg School of Public Health. icddr,b acknowledges with gratitude the commitment of Johns Hopkins University, Baltimore, MD, United States, to its research efforts. icddr,b is also grateful to the Government of Bangladesh, Canada, Sweden, and the United Kingdom for providing core/unrestricted support. References Cited Ahmed, S., Galagan S., Scobie H., Khyang J., Prue C. S., Khan W. A., Ram M., Alam M. S., Haq M. Z., Akter J.,et al.   2013. Malaria hotspots drive hypoendemic transmission in the Chittagong Hill Districts of Bangladesh. PLoS One  8: e69713. Google Scholar CrossRef Search ADS PubMed  Al-Amin, H. M., Elahi R., Mohon A. N., Kafi M. A. H., Chakma S., Lord J. S., Khan W. A., Haque R., Norris D. E., and Alam M. S.. 2015. Role of underappreciated vectors in malaria transmission in an endemic region of Bangladesh-India border. Parasit. Vectors  8: 195. Google Scholar CrossRef Search ADS PubMed  Alam, M. S., Khan M. G., Chaudhury N., Deloer S., Nazib F., Bangali A. M., and Haque R.. 2010. Prevalence of anopheline species and their Plasmodium infection status in epidemic-prone border areas of Bangladesh. Malar. J . 9: 15. Google Scholar CrossRef Search ADS PubMed  Alam, M. S., Chakma S., Al-Amin H. M., Elahi R., Mohon A. N., Khan W. A., Haque R., Glass G. E., Sack D. A., Sullivan D. J.,et al.   2012a. Role of artificial containers as breeding sites for anopheline mosquitoes in malaria hypoendemic areas of rural Bandarban, Bangladesh: evidence from a baseline survey. The 61st Annual Meeting of the American Society of Tropical Medicine and Hygiene, Atlanta Marriott Marquis Atlanta, GA. Alam, M. S., Chakma S., Khan W. A., Glass G. E., Mohon A. N., Elahi R., Norris L. C., Podder M. P., Ahmed S., Haque R.,et al.   2012b. Diversity of anopheline species and their Plasmodium infection status in rural Bandarban, Bangladesh. Parasit. Vectors  5: 150. Google Scholar CrossRef Search ADS   Amerasinghe, F. P., Indrajith N. G., and Ariyasena T. G.. 1995. Physico-chemical characteristics of mosquito breeding habitats in an irrigation development area in Sri Lanka. Cey. J. Sci. (Biol Sci)  24: 13– 29. Anonymous. Land Cover Dynamics in Greater Chittagong. http://geoapps.icimod.org/BDLandcover/ (accessed 15 March 2017). Barbazan, P., Baldet T., Darriet F., Escaffre H., Djoda D. H., and Hougard J. M.. 1998. Impact of treatments with Bacillus sphaericus on Anopheles populations and the transmission of malaria in Maroua, a large city in a savannah region of Cameroon. J. Am. Mosq. Control. Assoc . 14: 33– 39. Google Scholar PubMed  Bashar, K., and Tuno N.. 2014. Seasonal abundance of Anopheles mosquitoes and their association with meteorological factors and malaria incidence in Bangladesh. Parasit. Vectors  7: 442. Google Scholar CrossRef Search ADS PubMed  Bayoh, M. N., and Lindsay S. W.. 2003. Effect of temperature on the development of the aquatic stages of Anopheles gambiae sensu stricto (Diptera: Culicidae). Bull. Entomol. Res . 93: 375– 381. Google Scholar CrossRef Search ADS PubMed  Derraik, J. G. B., and Slaney D.. 2007. Anthropogenic environmental change, mosquito-borne diseases and human health in New Zealand. EcoHealth  4: 72– 81. Google Scholar CrossRef Search ADS   Elias, M. 1996. Larval habitat of Anopheles philippinensis: a vector of malaria in Bangladesh. Bull. World Health Organ . 74: 447– 450. Google Scholar PubMed  Ghebreyesus, T. A., Haile M., Witten K. H., Getachew A., Yohannes A. M., Yohannes M., Teklehaimanot H. D., Lindsay S. W., and Byass P.. 1999. Incidence of malaria among children living near dams in northern Ethiopia: community based incidence survey. BMJ  319: 663– 666. Google Scholar CrossRef Search ADS PubMed  Gunathilaka, N., Fernando T., Hapugoda M., Wickremasinghe R., Wijeyerathne P., and Abeyewickreme W.. 2013. Anopheles culicifacies breeding in polluted water bodies in Trincomalee District of Sri Lanka. Malar. J . 12: 285. Google Scholar CrossRef Search ADS PubMed  Imbahale, S. S., Paaijmans K. P., Mukabana W. R., van Lammeren R., Githeko A. K., and Takken W.. 2011. A longitudinal study on Anopheles mosquito larval abundance in distinct geographical and environmental settings in western Kenya. Malar. J . 10: 81. Google Scholar CrossRef Search ADS PubMed  Irish, S. R., Al-Amin H. M., Alam M. S., and Harbach R. E.. 2016. A review of the mosquito species (Diptera: Culicidae) of Bangladesh. Parasit. Vectors  9: 559. Google Scholar CrossRef Search ADS PubMed  Kamdem, C., Tene Fossog B., Simard F., Etouna J., Ndo C., Kengne P., Bousses P., Etoa F. X., Awono-Ambene P., Fontenille D.,et al.   2012. Anthropogenic habitat disturbance and ecological divergence between incipient species of the malaria mosquito Anopheles gambiae. PLoS One  7: e39453. Google Scholar CrossRef Search ADS PubMed  Keating, J., Macintyre K., Mbogo C. M., Githure J. I., and Beier J. C.. 2004. Characterization of potential larval habitats for Anopheles mosquitoes in relation to urban land-use in Malindi, Kenya. Int. J. Health. Geogr . 3: 9. Google Scholar CrossRef Search ADS PubMed  Khan, W. A., Sack D. A., Ahmed S., Prue C. S., Alam M. S., Haque R., Khyang J., Ram M., Akter J., Nyunt M. M.,et al.   2011. Mapping hypoendemic, seasonal malaria in rural Bandarban, Bangladesh: a prospective surveillance. Malar. J . 10: 124. Google Scholar CrossRef Search ADS PubMed  MacGregor, M. E. 1929. The Significance of the pH in the development of mosquito larvae. Parasitology  21: 132– 157. Google Scholar CrossRef Search ADS   Maheswary, N. P., Habib M. A., and Elias M.. 1992. Incrimination of Anopheles aconitus Donitz as a vector of epidemic malaria in Bangladesh. Southeast Asian J. Trop. Med. Public Health  23: 798– 801. Google Scholar PubMed  MOHFW. 2016. Health Bulletin 2016, pp. 101–105. In M. I. System (ed.). Directorate General of Health Services, Dhaka, Bangladesh. Mwangangi, J. M., Mbogo C. M., Muturi E. J., Nzovu J. G., Githure J. I., Yan G., Minakawa N., Novak R., and Beier J. C.. 2007. Spatial distribution and habitat characterisation of Anopheles larvae along the Kenyan coast. J. Vector Borne Dis . 44: 44– 51. Google Scholar PubMed  Nagpal, B. N., and Sharma V. P.. 1995. Indian Anophelines . Mohan Primlani for Oxford & IBH Publishing Co. Pvt. Ltd, New Delhi, India. NMCP. 2015. Malaria National Strategic Plan (2015–2020), pp. 1–39. In C. D. C. Division (ed.). Directorate General of Health Services, Ministry of Health & Family Welfare, Dhaka, Bangladesh. Norris, D. E. 2004. Mosquito-borne diseases as a consequence of land use change. Eco. Health  1: 19– 24. Paul, B. K. 1984. Malaria in Bangladesh. Geogr. Rev . 74: 63– 75. Google Scholar CrossRef Search ADS PubMed  Peyton, E. L., Scanlon J. E., Malikul V., and Imvitaya S.. 1966. Illustrated key to the female Anopheles mosquitoes of Thailiand . DTIC Document. Applied Scientific Research Corporation of Thailand, 196 Phahonyothin Road, Bangkehn, Bangkok, Thailand. Pinault, L. L., and Hunter F. F.. 2012. Characterization of larval habitats of Anopheles albimanus, Anopheles pseudopunctipennis, Anopheles punctimacula, and Anopheles oswaldoi s.l. populations in lowland and highland Ecuador. J. Vector Ecol . 37: 124– 136. Google Scholar CrossRef Search ADS PubMed  Prakash, A., Bhattacharyya D. R., Mohapatra P. K., and Mahanta J.. 2004. Role of the prevalent Anopheles species in the transmission of Plasmodium falciparum and P. vivax in Assam state, north-eastern India. Ann. Trop. Med. Parasitol . 98: 559– 568. Google Scholar CrossRef Search ADS PubMed  Rahman, W. A., Adanan C. R., and Abu Hassan A.. 2002. Species composition of adult Anopheles populations and their breeding habitats in Hulu Perak district, Peninsular Malaysia. Southeast Asian J. Trop. Med. Public Health  33: 547– 550. Google Scholar PubMed  Rattanarithikul, R., Harrison B. A., Harbach R. E., Panthusiri P., and Coleman R. E.. 2006. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian J Trop Med Public Health  37 ( Suppl 2): 1– 128. Rejmankova, E., Roberts D. R., Harbach R. E., Pecor J., Peyton E. L., Manguin S., Krieg R., Polanco J., Legters L.. 1993. Environmental and regional determinants of Anopheles (Diptera: Culicidae) larval distribution in Belize, Central America. Community and Ecosystem Ecology  22: 978– 992. Renshaw, M., Elias M., Maheswary N. P., Hassan M. M., Silver J. B., and Birley M. H.. 1996. A survey of larval and adult mosquitoes on the flood plains of Bangladesh, in relation to flood-control activities. Ann. Trop. Med. Parasitol . 90: 621– 634. Google Scholar CrossRef Search ADS PubMed  Rosenberg, R. 1982. Forest malaria in Bangladesh. III. Breeding habits of Anopheles dirus. Am. J. Trop. Med. Hyg . 31: 192– 201. Google Scholar CrossRef Search ADS PubMed  Rueda, L. M., Pecor J. E., and Harrison B. A.. 2011. Updated distribution records for Anopheles vagus (Diptera: Culicidae) in the Republic of Philippines, and considerations regarding its secondary vector roles in Southeast Asia. Trop. Biomed . 28: 181– 187. Google Scholar PubMed  Sanchez-Ribas, J., Parra-Henao G., and Guimaraes A. E.. 2012. Impact of dams and irrigation schemes in Anopheline (Diptera: Culicidae) bionomics and malaria epidemiology. Rev. Inst. Med. Trop. Sao Paulo  54: 179– 191. Google Scholar CrossRef Search ADS PubMed  Stojanovich, C. J., and Scott H. G.. 1966. Illustrated key to Anopheles mosquitoes of Thailand . US Public Health Serv., Atlanta, GA. Sugiarto, U. Kesumawati Hadi, S. Soviana, and Hakim L.. 2016. Confirmation of Anopheles peditaeniatus and Anopheles sundaicus as Malaria Vectors (Diptera: Culicidae) in Sungai Nyamuk Village, Sebatik Island North Kalimantan, Indonesia Using an Enzyme-Linked Immunosorbent Assay. J. Med. Entomol . 53: 1422– 1424. Google Scholar CrossRef Search ADS PubMed  Uddin, K., and Gurung D. R.. 2010. Land cover change in Bangladesh-A knowledge based classification approach, pp. 41– 46, 10th International Center for Integrated Mountain Development, Kathmandu (ICIMOD), Khumaltar, Lalitpur, Nepal. WHO. 2003. Malaria entomology and vector control (learner’s guide) . WHO, Geneva, Switzerland. © The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Journal

Journal of Medical EntomologyOxford University Press

Published: Mar 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off