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IMPACT OF DEFORESTATION AND AGRICULTURAL DEVELOPMENT ON ANOPHELINE ECOLOGY AND MALARIA EPIDEMIOLOGY

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  • 1 Department of Population and International Health, Harvard School of Public Health, Boston, Massachusetts

To clarify mechanisms linking deforestation, anopheline ecology, and malaria epidemiology, this study draws together 60 examples of changes in anopheline ecology and malaria incidence as a consequence of deforestation and agricultural development. The deforestation projects were classified based on subsequent land use and were reviewed in terms of their impact on anopheline density and malaria incidence. To further examine different anopheline responses to land transformation, two major ecological characteristics of 31 anopheline species were tested for their associations with changes in their densities and malaria incidence. Although niche width of anopheline species was not associated with density changes, sun preference was significantly associated with an increase in density. This study suggests the possibility of predicting potential impacts of future deforestation on vector density by using information on types of planned agricultural development and the ecology of local anopheline species.

INTRODUCTION

Deforestation is one of the most potent factors at work in emerging and re-emerging infectious diseases.1,2 Through the process of clearing forests and subsequent agricultural development, deforestation alters every element of local ecosystems such as microclimate, soil, and aquatic conditions, and most significantly, the ecology of local flora and fauna, including human disease vectors. The effects of deforestation on ecosystems and human health are diverse and have taken place for many decades, though both the rate and geographic range have increased markedly over the last 30 years.3 Deforestation is driven by a wide variety of human activities, including agricultural development, logging, transmigration programs, road construction, mining, and hydropower development.3,4

Of all the forest species that transmit diseases to humans, mosquitoes are among the most sensitive to environmental changes because of deforestation: their survival, density, and distribution are dramatically influenced by small changes in environmental conditions, such as temperature, humidity, and the availability of suitable breeding sites.57 Changes in mosquito ecology and human behavior patterns in deforested regions influence the transmission of mosquito-borne diseases such as malaria, Japanese encephalitis, and filariasis.3,8 In particular, each incident of deforestation and land transformation has a different influence on the prevalence, incidence, and distribution of malaria directly and indirectly.3

Numerous country and area studies have described the influence of deforestation and subsequent land use on the density of local mosquito vectors. Some of the studies were able to follow-up further and observe changes in local malaria incidence and prevalence due to the mosquito density change.3,4,8 However, few studies have conducted cross-regional research to systematically review deforestation cases from different countries, and no studies statistically analyzed the associations between each mosquito species’ ecological characteristics and its reaction to different kinds of agricultural developments.

This study, therefore, seeks to clarify the mechanisms linking deforestation and agricultural development with anopheline ecology and malaria epidemiology and to contribute to improve health impact assessments for future forest development projects. Combining a literature review and statistical analyses, we aimed at examining the possibility of predicting potential impacts of future deforestation on vector density and malaria epidemiology using information on types of planned land use and the ecology of local anopheline species. Sixty examples of changes in anopheline ecology as a consequence of deforestation were collected, classified based on types of subsequent land use, and descriptively compared in terms of their impacts on mosquito density and malaria incidence. In addition, statistical analyses were conducted to test whether niche width and sun preference, two of the major anopheline characteristics, were associated with species’ reactions to deforestation and their influence on malaria incidence.

MATERIALS AND METHODS

Literature review on the impact of deforestation on anopheline ecology and malaria epidemiology.

The main source of information for deforestation and agricultural development cases, as well as the ecological characteristics of anopheline species, was the peer-reviewed scientific literature obtained through Pubmed. We obtained literature, using combinations of keywords deforestation, agriculture, mosquito, and malaria. All deforestation projects reported to have changed anopheline density or malaria epidemiology were incorporated to our data sets. Relevant references of each literature were also reviewed. Although information from primary sources was preferred, more general reviews and texts were also consulted. Land use and land cover changes were classified into 14 categories based on the difference in subsequent land use type, especially agriculture (crops): 1) general deforestation, 2) land exploitation and pollution, 3) cacao plantation, 4) cassava, 5) sugarcane, 6) coffee plantation, 7) tea plantation, 8) rubber plantation, 9) rice cultivation, 10) irrigation system, 11) hydropower dam, 12) clearing of mangroves or swamps for fish pond or charcoal, 13) mining, and 14) new settlements and urbanization, including highway construction. These land use types were descriptively compared in terms of their impacts on mosquito density and malaria incidence.

Statistical analysis.

Statistical analyses were conducted to test whether anopheline niche width and sun preference are associated with changes in anopheline density and subsequent malaria incidence. A niche-width index and a sun-preference index were created as follows.

Niche-width index.

The niche-width index was created based on information from the literature. First, the peer-reviewed scientific literature on breeding habitats of 31 anopheline species, whose density was reported to have changed due to deforestation, was obtained through Pubmed, using each specie’s name. If necessary, more sources were obtained through search engines. The niche width for each species was established based on data from a minimum of five sources. Ten kinds of mutually exclusive breeding habitats were identified: 1) forests, including forest fringes, foothills, plains, and tree holes; 2) streams or rivers; 3) fresh water swamps or marshes; 4) brackish water, including brackish water swamps, marshes, lagoons, and mangrove forests; 5) tree plantations; 6) small pools, including small water collections and animal footprints; 7) mining areas, including burrow pits; 8) artificial containers and reservoirs in towns or villages; 9) rice fields; and 10) irrigation canals for agriculture, including irrigation channels, ditches, furrows, and drainages. Since the descriptions of habitat were made by different authors, and some such as mining sites were macro-environmental while others such as tree holes were more micro-environmental, we aggregated habitats to reduce any overlap. Each species was entered into a matrix that indicated its breeding habitats. The niche-width index of each species was created by dividing the number of habitat types a species breeds in by the total number of habitat types (10).

Sun-preference index.

Records of the sun or shade preference of the same 31 anopheline species were collected from the literature. The sun-preference index for each species was established based on data from a minimum of 3 sources except for An. pulcherrimus and An. sacharovi, for which only 2 and 1 sources were available. It was calculated by dividing the number of sources that reported a specie’s sun preference by the total number of sources that reported its sun or shade preference. For example, in case of An. minimus, three sources described its sun-preference, and two reported its shade-preference. Therefore, its sun-preference index was calculated as 3/5.

Correlations between species’ niche-width and sun-preference indices and changes in density and malaria incidence due to deforestation and agricultural development were examined using Spearman’s rank correlation. Using a two-sample t-test, both indices were then compared between species that increased versus those that decreased, and between species that led to a malaria increase versus a decrease.

RESULTS

Review of each deforestation and agricultural development case.

Sixty examples of changes in anopheline ecology as a consequence of deforestation and agricultural development were collected from the literature. The changes in anopheline density and malaria incidence varied by types of agricultural development and locality (Table 1). Anophleline density decreased in 24 examples and in 8 of the cases, malaria incidence also decreased; in 36 examples density increased and in 25 of these cases malaria also increased. The following information presents some of the results by category of deforestation.

General deforestation.

The massive clearing of forests has enormous impacts on local ecosystems. It alters microclimates by reducing shade, altering rainfall patterns, augmenting air movement, and changing the humidity regimen.9 It also reduces biodiversity and increases surface water availability through the loss of topsoil and vegetation root systems that absorb rain water.10 For anopheline species that breed in shaded water bodies, deforestation can reduce their breeding habitats, thus affecting their propagation. Conversely, some environmental and climatic changes due to deforestation can facilitate the survival of other anopheline species, resulting in prolonged seasonal malaria transmission.8

Changes in anopheline density due to deforestation diverged significantly by species and locality.1,1115 For example, in Kanchanaburi, Thailand, large-scale deforestation from 1986 to 1995 eliminated breeding sites of An. dirus, and thus decreased malaria incidence.1 Conversely, in northeast India, where An. minimus was historically the primary malaria vector, invasion and increase of An. fluviatilis due to deforestation has increased malaria transmission period.12 In other deforested areas, some anophelines increased while others decreased.1315 In Sri Lanka, while uninhabited forests were cleared in the late 1980s, habitat simplification and niche reduction decreased species richness but increased the abundance of a few species that preferred the environment created by deforestation. Consequently, the completion of the development project brought huge malaria epidemics.14,15

Land exploitation and pollution.

In the Mediterranean area, anopheline species with complex larval habitats vulnerable to pollution and/or land exploitation decreased in response to development projects, which significantly reduced malaria transmission.16

Cacao plantations.

During the 1940s, malaria became a serious problem in Trinidad, when forests were cleared and cacao was planted under nurse trees (Erythrina). These trees are ideal sites for epiphytic bromeliads, which provide breeding sites of An. bellator, the local principal malaria vector. As a result, a malaria epidemic occurred and was not controlled until the nurse trees were reduced and plantation techniques were modified.17,18

Cassava plantations.

Cassava plantations, which need little water and provide little shade, often create unfavorable environment for anophelines, especially those that require shade.1922 In Cholburin, Thailand, malaria was hyperendemic in the early 1960s due to An. dirus. By 1985, nearly 90% of the forest was cut down mainly for cassava plantations, which decreased the density of An. dirus and malaria prevalence. Soon after, however, the area was occupied by An. minimus, and malaria returned in the resettled population.2022

Sugarcane cultivation.

A similar tendency was observed in resettled villages in Kanchanaburi, Thailand, where the forest was cleared for sugarcane cultivation. Although the development eliminated shady breeding habitats for An. dirus, it created widespread breeding grounds for An. minimus, which had greater sun preference and was the predominant species throughout the year. Consequently, malaria transmission among resettled cultivators became very high.20,23,24

Coffee plantations.

Coffee plantations also favored the propagation of An. minimus. In southeast Thailand, deforestation, followed by development of coffee and rubber plantations, favored the breeding of An. minimus. Accordingly, these plantation areas, which were almost free from transmission, became hyperendemic.25 Conversely, in Karnataka, India, large-scale deforestation for coffee plantations reduced seepages, which were the principal breeding sites for An. fliviatilis, a vector responsible for hyperendemic malaria. As a result, this vector population completely collapsed, and malaria disappeared from the area.12

Tea plantations.

In Sri Lanka, natural forest was felled for tea plantations in 1875, which caused serious soil erosion. Loss of the absorptive quality of the soil resulted in scoured riverbeds, and erratic flow in dry years replaced perennial flow. This favored An. culicifacies breeding and led to the apparent anomaly of the occurrence of severe epidemic malaria among non-immune populations in drought years.26

Rubber plantations.

Local major malaria vectors increased in all four examples of rubber plantations in Malaysia and Thailand.1,3,4,8,19,2730 In Malaysian hilly areas, forest clearance for rubber plantations, which started in the early 1900s, exposed the land and streams to the sun and created breeding places for An. maculatus, which led to an increase in this species and a marked increase in the incidence and severity of malaria.27 Cyclic malaria epidemics in Malaysia over 50 years are correlated with rubber replanting in response to market fluctuations.3,4,28

Rice cultivation.

Impact of deforestation for rice cultivation varied by vectors and locality. Some cases demonstrated an increase in major malaria vectors.9,11,3134 In Java-Bali, Indonesia, rice cultivation and its irrigation systems increased breeding places and prolonged the breeding season of rice field mosquitoes such as An. aconitus, resulting in an increase of malaria incidence.34 Species replacement after deforestation for rice cultivation was reported in three sources.3,8,30,3539 In Thailand, where deforestation for cereal cultures took place, An. dirus retreated with the forest and was replaced by An. minimus.8,30 Likewise, different anopheline species responded differently to deforestation for rice cultivation in Sri Lanka.15,40

Irrigation systems.

All the literature on the development of irrigation systems reported an increase in malaria incidence caused by increased vector density. When irrigation was introduced to Kunduz Valley in northern Afghanistan in the early 1960s for cultivation of rice, cotton, and watermelons, overflows from irrigation ditches created ideal new habitats for the An. pulcherrimus and An. hyrcanus group, which led to a four-fold increase in malaria in some villages in the region.32,40 Many irrigation schemes in Africa resulted in increases in established malaria vectors such as An. gambiae and An. arabensis and thus in increased transmission.16,32,40,41 Keizer and others stressed that integrated malaria control measures with effective water management are mandatory to mitigate the burden of malaria in unstable malaria endemic areas near irrigation or dam sites.42

Hydropower dams.

Construction of hydropower dams could increase breeding sites. In Sri Lanka, the development of hydropower dams on Mahaweli River created pools with sandy and rocky nature, which are suitable for the breeding of An. culicifacies, the primary malaria vector of the country. Consequently, malaria epidemics occurred along the river in 1987.15,43

Clearing of mangroves or swamps for fish pond or mining.

Clearing of mangroves or swamps along the coastline in Indonesia increased numbers of An. sundaicus, a coastal mosquito that breeds in sunlit brackish water.3436 In Malaysia, shade providing mangroves in the brackish water zone were cut down for charcoal and mine props in the early 20th century, which exposed the contact zone between tidal seawater and freshwater to direct sunlight. Because of the increase in suitable breeding sites, An. sundaicus increased, and malaria outbreaks occurred.3,35

Mining.

Mining often increases breeding sites for anophelines and human contacts with vectors. In Kanchanaburi, Thailand, the primary forest malaria vector, An. dirus, increased mainly because breeding places were created by excavation work. In addition, ore digging during the rainy season resulted in increased human exposure to An. dirus.8 Where settlement and mining activities took place in the Amazon, An. darlingi increased because of the increase in breeding sites, including borrow pits after road or settlement constructions, drains, and opencast mine workings. As a result, malaria, which was present in the indigenous population of the Amazon region, was transmitted to setters and miners.44,45

New settlements and urbanization.

Impact of urbanization on vector density varied by how deforestation was carried out.12,34,4548 Clear felling and burning of the forest during transmigration programs in Kalimantan, Indonesia, attracted immigrants to search for firewood, food, and medical plants in deeper forest, which put them at high risk for malaria. Conversely, another transmigration program in east Kalimantan completely cleared the forest before immigrants arrived. Local malaria vectors were eliminated by the lack of shade, and malaria outbreaks were prevented.34

Associations between niche width and sun preference of species and changes in density and malaria incidence.

Table 2 summarizes the changes in the density of 31 anopheline species caused by different types of deforestation and agricultural development. Each species responded differently, depending on the type of agriculture and the locality. Because most of the species had only one or at most only a few examples of development projects, it was not possible to find patterns or tendencies in density change.

Some species with several examples, however, demonstrated an interesting complexity of the associations between development projects and mosquito density change. The density of 7 of 31 species changed in both directions. Even among forest malaria vectors, responses to development differed. For example, An. dirus decreased due to deforestation, rice cultivation, and cassava and sugarcane plantations, and increased only when rubber plantations were introduced to the area. Anopheles minimus also decreased by deforestation but increased in all subsequent agricultural development cases (i.e., rice cultivation and cassava, sugarcane, and coffee plantations). However, An. fluviatilis increased with deforestation and decreased with rice and coffee cultivations.

In addition, the same type of agriculture influenced the same species differently. Rice cultivation decreased An. culicifacies in Sri Lanka, but increased it in Nepal. This could in part be because An. culicifacies is a complex of five species, of which An. culicifacies B is the only one in Sri Lanka while species A and B are found in Terai, Nepal.49

Breeding places of anophelines were categorized into 10 habitat types, and the presence of larvae of each species in each habitat was marked based on information collected from the literature (Table 3). The niche width differed by species, ranging from 1 to 10 habitat types, and was quantified as an index as described above. The sun-preference index was also calculated based on the literature, ranging from 0 (complete shade preference) to 1.0 (complete sun preference).

As expected, anopheline species, whose density increased because of development had significantly higher sun preference than those with decreased density (P = 0.020) (Table 4). However, no associations were found between sun preference and the change in malaria incidence. Niche width had no associations with changes in either species’ density or malaria incidence.

A specie’s niche-width index was not correlated with the percentage of total cases in which the density of the species increased (Spearman’s r = 0.073, P = 0.698). The result was the same for the sun-preference index (r = 0.239, P = 0.195). In addition, no correlations were found between the niche-width index or sun-preference index of a species and the percentage of total cases in which malaria incidence increased. (r = 0.310, P = 0.150 and r = −0.164, P = 0.456; respectively).

DISCUSSION

This study demonstrates that mechanisms linking deforestation and agricultural development with mosquito ecology and malaria epidemiology are extremely complex. The impacts of deforestation on mosquito density and malaria incidence are influenced by both the nature of the agricultural development and the ecological characteristics of the local vector mosquitoes. Some anopheline species were directly affected by deforestation and/or subsequent agricultural development, some favored or could adapt to the different environmental conditions that were created, and some invaded and/or replaced other species in the process of development and cultivation. Malaria incidence fluctuated according to different stages of development, changes in vector density, and altered human contact patterns with vectors.

The results of the statistical analyses showed that deforestation and agricultural development are favorable for sun-loving species, allowing them to increase in or invade deforested areas where water bodies became exposed to sunlight. Contrary to expectations, niche width was not associated with density change of a species. This could be because some of the specialist species, including An. aquasalis, An. bellator, and An. sundaicus, preferred environmental conditions created by development and could propagate as much as species with a wide niche. Another explanation to this complexity could be natural enemies of mosquitoes. A change that increased mosquito breeding sites might also have increased the number of enemies even more. A field study conducted in Sri Lanka showed associations, mostly positive correlations, between mosquitoes and predatory aquatic insects.50 Also, some land use types such as dams and irrigation canals would increase habitat stability, which results in increased number of predators. Therefore, we should keep in mind that an environmental change could affect more species than the one we are worried about.

Recent field studies have examined potential mechanisms of how land use and land cover changes could affect vectors. Deforestation and land transformation influence vector anophelines, especially larval survivorship, adult survivorship, reproduction and vectorial capacity, through changing environmental and microclimatic conditions such as temperature (average, variability), sunlight (amount, duration), humidity, water condition (distribution, temperature, quality, turbidity, current), soil condition, and vegetation. In the western Kenyan highlands, deforestation and cultivation of natural swamps increased larval survivorship and adult productivity of An. gambiae through their effects on water temperature and nutrients.51 Significant increases in net reproductive fitness and intrinsic growth rate were also recorded in deforested area, which was due mainly to deforestation-induced increase in indoor temperature.52 Increased ambient temperature caused by deforestation also shortened mosquito gonotrophic cycle, which implies increased daily biting frequency, thus increased vectorial capacity.53 Further understanding of the mechanisms will enable us to predict possible impacts of planned land transformations on local vectors more precisely.

Neither niche width nor sun preference was associated with changes in malaria incidence. This indicates the complexity of the link from deforestation to anopheline density change to malaria incidence. Although sun-loving species were more likely to increase due to deforestation, the change in malaria incidence varied. This is consistent with previous findings in Africa and Asia that malaria-agriculture linkages are complex and situation-specific.54 In some agricultural regions in Africa, inverse relationships between the mosquito abundance and malaria incidence have been observed.55,56 Although it is counter-intuitive that an increase in mosquitoes does not always result in an increase in malaria, this could come about in two ways. First, if warmer temperatures increase mosquito populations by shortening the generation, individual size is reduced and perhaps also life expectancy. Since a mosquito has to take two blood meals to acquire and then transmit malaria, this could reduce transmission. Thus, the opposing effects of temperature increase might account for the unexpected result. A second possibility is that people’s behavior could change in such as way as to reduce exposure when mosquito abundance increases. People’s self-protection could add an extra complexity to the link between anopheline density change and malaria incidence.

One limitation of this study is that much of the collected literature regarding the changes in mosquito density and malaria incidence only provided descriptive information (without statistics showing the extent of changes). Therefore, this study had to focus on the direction of changes in mosquito density, such as increase, decrease, and stable. Publication bias (more articles might deal with the increase in anopheline density and malaria incidence rather than the decrease) should be minimal in this study because the density of different anopheline species that reacted to a single deforestation and agricultural development in different directions (increased or decreased), and many of the sources report changes in plural anopheline species that show both directions of density change.

One strength is that this study collected as many as 60 examples of changes in anopheline ecology and malaria epidemiology as a consequence of deforestation and agricultural development, reviewed them systematically, and classified them according to species and different types of agriculture. A thorough literature review was also done to identify the breeding habitats and to measure the niche width and sun preference for each anopheline species. Statistical analyses carried out based on these data and indices are also original.

Further accumulation of data on the impacts of deforestation and different land use on mosquito ecology and malaria epidemiology will be crucial to develop predictive models to prevent possible malaria epidemics. More detailed data on breeding sites, including water temperature, pH, and kinds of vegetation (e.g., algae) that could influence mosquito propagation and species composition, should also be collected. In the process of deforestation and subsequent land transformations, improved surveillance and monitoring are necessary to detect changes in the environment, vector density, human migration and behavior, and malaria incidence to prevent further deterioration of malaria status in the region. Because deforestation is a process that cannot be readily controlled for a variety of political and economic reasons, investigations and assessments of possible impacts of future deforestation will be crucial to minimize the ecological degradation caused by human activities and to prevent epidemics of malaria and other vector-borne diseases.

Table 1

Deforestation and agricultural development that changed anopheline densities

Density decreaseDensity increaseIncreased human contacts
Deforestation/agricultural developmentCountry/regionSpeciesMalariaSpeciesMalariaSpeciesMalariaReferences
DeforestationThailandAn. dirus1
NepalAn. minimusAn. fluviatilis13
IndiaAn. fluviatilis12
Sri LankaAn. barbirostrisAn. annularis+
An. jamesii+
An. nigerrimus+14,15
An. subpictus+
An. peditaeniatus?
Sahel, AfricaAn. funestus11
Land exploitation/pollutionMediterraneanAn. labranchiae16
An. sacharovi
An. superpictus
Cacao plantationTrinidadAn. bellator17,18
CassavaThailandAn. dirusAn. minimus+2022
ThailandAn. dirus19
SugarcaneThailandAn. dirusAn. minimus+20,23,24
Coffee plantation
    plus irrigation damsIndiaAn. fluviatilis12
    plus tree cropsThailandAn. minimus+25
Tea plantationSri LankaAn. culicifacies (Only in drought years)26
RubberMalaysiaAn. maculatus+3,27,28
    plus fruitsThailandAn. dirus+19,29,30
ThailandAn. dirus30
    plus orchardsThailandAn. dirus+1
RiceChinaAn. sinensis3133
MalaysiaAn. umbrosusAn. campestris+35,36
IndonesiaAn. aconitus+34
Southeast AsiaAn. dirus8
NepalAn. fluviatilisAn. culicifacies+3,3739
Sri LankaAn. annularisAn. jamesii15,40
An. barbirostrisAn. subpictus
An. culicifacies
An. varuna
AfricaAn. funestus9,11
An. gambiae
Rice plus maizeThailandAn. dirusAn. minimus8,30
Irrigation systemIndiaAn. culicifacies+18,40
AfghanistanAn. superpictusAn. pulcherrimus+32,40
AfricaAn. arabiensis+32,40
An. gambiae+
SaharaAn. gambiae+16,41
GuyanaAn. darlingiAn. aquasalis+18,40
Hydropower damSri LankaAn. culicifacies+15,43
Clearing of mangroves/swamps for fish pond or miningMalaysiaAn. sundaicus+3,35
IndonesiaAn. sundaicus+34
IndonesiaAn. sundaicus35,36
MiningThailandAn. dirus8
    plus settlementAmazonAn. darlingi+44,45
Settlements plus urbanization or highway constructionAmazonAn. darlingi4547
IndonesiaAn. balabacensis+34
IndonesiaAn. balabacensis34
An. leucosphyrus
IndiaAn. stephensi+12
Table 2

Changes in the density of each anopheline species due to deforestation and agricultural development

Density change due to deforestation/agricultural developmentDensity change due to deforestation/agricultural development
SpeciesDecreasedIncreasedNo changeReferencesSpeciesDecreasedIncreasedNo changeReferences
An. aconitusRice34An. jamesiiDeforestation14,15,40
An. annularisRiceDeforestation14,15,40Rice
An. aquasalisIrrigation18,40An. labranchiaeLand exploitation plus pollution16
An. arabiensisIrrigation32,40An. leucosphyrusNew settlements plus urbanization34
An. balabacensisUrbanization34An. maculatusRubber3,27,28
An. barbirostrisDeforestation14,15,40An. minimusDeforestationCassava8,13,2025,30
RiceSugarcane
An. bellatorCacao plantation17,18Coffee plantation plus tree crops
An. campestrisRice35,36Rice plus maize
An. culicifaciesRiceRice3,15,18,26, 3740,43An. nigerrimusDeforestationRice15,40
Tea (Only in drought years)An. peditaeniatusDeforestation14,15
IrrigationAn. pulcherrimusIrrigation32,40
Hydropower damAn. sacharoviLand exploitation plus pollution16
An. darlingiIrrigationSettlement plus mining18,40,4447An. sinensisRice3133
HighwayAn. stephensiUrbanization12
An. dirusDeforestationRubber plus fruits (2)1,8,1924,An. subpictusDeforestation14,15,40
Cassava (2)Rubber plus orchards29,30Rice
SugarcaneAn. sundaicusClearing of mangrove/swamp for fish pond or charcoal (3)3,34,35
RiceAn. superpictusLand exploitation plus pollution16,32,40
Rice plus maizeIrrigation
An. fluviatilisRiceDeforestation (2)3,12,13,An. umbrosusRice35,36
Coffee plantation plus irrigation3739An. vagusRice14,15,40
An. funestusDeforestationRice9,11An. varunaRice14,15,40
An. gambiaeRice9,11,16,32, 40,41
Irrigation (2)
Table 3

Breeding habitats and the niche-width and sun-preference indices of each anopheline species

SpeciesForestStream/riverFresh water swamp/marshBrackish waterTree plantationSmall poolsMining areasArtificial containersRice fieldIrrigation canalNiche-width indexSun-preference indexDensity increased/total casesReferences for nicheReferences showing shade preferenceReferences showing sun preference
An. aconitus+++++++0.80.6671/115, 57635762,63
An. annularis+++++++0.71.0001/214, 15, 6467NA63, 67, 68
An. aquasalis+++0.30.7501/132, 58, 63, 69715858, 63, 72
An. arabiensis+++++0.51.0001/132, 58, 63, 73, 74NA32, 58, 63, 73
An. balabacensis+++++0.50.0000/160, 63, 69, 70, 7560, 63, 69, 70NA
An. barbirostris++++++++0.80.2860/214, 15, 62, 64, 76, 7715, 62, 68, 76, 7776,77
An. bellator+0.10.0001/14, 17, 58, 78834, 17, 79NA
An. campestris++++++0.60.3331/127, 57, 58, 63, 8427,5863
An. culicifacies++++++++0.80.6154/515, 32, 60, 64, 69, 70, 7577, 8515, 32, 60, 76, 7715, 32, 60, 69, 76, 77, 85, 86
An. darlingi+++++0.50.0001/345, 46, 70, 878945, 70, 87, 88NA
An. dirus+++++++0.70.0003/932, 58, 60, 69, 909258, 60, 69NA
An. fluviatilis+++++0.50.8002/432, 58, 60, 65, 69, 759358, 60, 86, 93
An. funestus+++++++0.70.2501/232, 58, 70, 73, 75, 9432, 58, 8670
An. gambiae++++++0.61.0003/316, 32, 63, 64, 70, 73, 75NA16, 32, 64, 70, 73, 86
An. jamesii+++++++0.70.5002/214, 15, 64, 68, 7614,7668,76
An. labranchiae+++++++0.71.0000/163, 70, 90, 9598NA63, 90, 95
An. leucosphyrus+++++0.50.0000/132, 63, 69, 84, 9910132, 63, 69, 86NA
An. maculatus+++++++0.70.6671/159, 60, 6365, 69, 75, 916560, 63, 69
An. minimus++++++0.60.6004/532, 59, 60, 63, 65, 69, 9165,8660, 63, 69
An. nigerrimus++++++0.60.6001/215, 32, 58, 59, 64, 7632,6858, 59, 68
An. peditaeniatus++++++0.60.5001/115, 32, 59, 64, 9932,6859,68
An. pulcherrimus++++++0.60.5001/163, 86, 102107102104
An. sacharovi++++0.41.0000/160, 63, 69, 70, 108111NA63
An. sinensis++++++0.60.6671/132, 59, 63, 69, 703259,63
An. stephensi+++++++++0.90.6671/132, 60, 63, 64, 69, 70, 76, 112104,113104, 109, 113
An. subpictus++++++++++1.00.8572/214, 15, 32, 63, 64, 76, 77, 85, 1146814, 63, 68, 85, 104, 114
An. sundaicus+++0.31.0003/332, 60, 63, 69, 70, 75, 91NA58, 62, 63, 70
An. superpictus++++0.41.0000/163, 69, 70, 75, 103, 107NA63, 69, 70
An. umbrosus++++0.40.0000/032, 57, 60, 63, 6932, 60, 63, 69, 86NA
An. vagus+++++++0.70.4290/114, 15, 64, 76, 7768, 76, 77, 11568, 76, 77
An. varuna+++++++0.70.6250/114, 15, 63, 64, 76, 7768, 76, 7763, 64, 68, 76, 77
Table 4

Associations between niche-width and sun-preference indices of anopheline species and changes in anopheline density or malaria incidence due to deforestation or agricultural development

Changes in density or malaria incidence (No. of cases)Type of indexMean indexSDt-test P value
Anopheline density change (60)
Increased (36)Niche-width index0.6170.1920.855
Decreased (24)0.6080.138
Increased (36)Sun-preference index0.6140.3190.020
Decreased (24)0.3870.420
Malaria incidence change (33)
Increased (25)Niche-width index0.6320.1750.321
Decreased (8)0.5630.151
Increased (25)Sun-preference index0.6060.3290.402
Decreased (8)0.4750.512

*

Address correspondence to Junko Yasuoka, Department of Population and International Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. E-mail: jyasuoka@post.harvard.edu

Authors’ address: Junko Yasuoka and Richard Levins, Department of Population and International Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, Telephone: 617-432-1484, Fax: 617-566-0365, E-mails: jyasuoka@post.harvard.edu and humaneco@hsph.harvard.edu.

Acknowledgments: We are grateful to Andrew Spielman, Burton. H. Singer, and Thomas W. Mangione for their helpful advice on the manuscript.

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Author Notes

Reprint requests: Junko Yasuoka, Department of Population and International Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115.
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